Efeito do autocontrole e confiança do julgamento sobre decisões orcamentárias de investimento de capital
Ana Claudia Afra Neitzke & Walmes Zeviani
February 8, 2019
Importação dos pacotes
#-----------------------------------------------------------------------
# Carrega os pacotes.
# library(nFactors)
library(gridExtra)
library(tidyverse)Experimento 1
Leitura do arquivo de dados
#-----------------------------------------------------------------------
# Informações sobre os arquivos de dados.
# BASE_EXPERIMENTO 1.txt -----------------------------------------------
# TM: acertou ou errou a tarefa de esgotamento.
# D_*: são as decisões (0 ou 1).
# C_*: nível de confiança das decisões.
# BI_*: respostas para questões de business impusiviness.
# EAC_*: respostas para questões de escala de autocontrole.
# OE_*: orçamento empresarial.
# VI_*: verificação das intruções.
#-----------------------------------------------------------------------
# Exibe o conteúdo do diretório de trabalho.
dir()## [1] "analise.html" "analise.R"
## [3] "analise.Rmd" "BASE_EXPERIMENTO 1.txt"
## [5] "BASE_EXPERIMENTO 1.xlsx" "BASE_EXPERIMENTO 2.txt"
## [7] "BASE_EXPERIMENTO 2.xlsx"# Importa a base de dados.
da <- read_tsv("BASE_EXPERIMENTO 1.txt")## Parsed with column specification:
## cols(
## .default = col_integer(),
## Participantes = col_character()
## )## See spec(...) for full column specifications.attr(da, "spec") <- NULL
str(da)## Classes 'tbl_df', 'tbl' and 'data.frame': 94 obs. of 152 variables:
## $ Participantes: chr "A01" "A02" "A03" "A04" ...
## $ TM : int 1 0 1 0 1 1 1 1 1 0 ...
## $ D_1 : int 0 0 1 0 0 1 1 0 0 0 ...
## $ D_2 : int 1 0 1 1 1 1 1 0 1 1 ...
## $ D_3 : int 1 1 0 1 0 1 1 0 1 1 ...
## $ D_4 : int 1 1 1 1 1 1 1 0 0 1 ...
## $ D_5 : int 1 1 1 0 1 1 1 0 1 1 ...
## $ D_6 : int 0 1 1 1 0 1 1 0 0 1 ...
## $ D_7 : int 1 1 1 1 0 0 1 0 0 1 ...
## $ D_8 : int 1 1 0 1 0 0 1 0 1 1 ...
## $ D_9 : int 1 0 0 1 1 1 1 0 1 1 ...
## $ D_10 : int 1 1 1 1 0 1 1 0 1 1 ...
## $ D_11 : int 1 1 0 1 0 1 1 0 1 1 ...
## $ D_12 : int 1 1 1 1 1 1 1 0 1 1 ...
## $ D_13 : int 0 0 1 1 0 0 1 0 0 1 ...
## $ D_14 : int 0 1 0 1 0 0 1 0 1 0 ...
## $ D_15 : int 1 1 0 1 0 1 1 0 1 1 ...
## $ D_16 : int 1 0 0 1 1 1 1 0 1 0 ...
## $ D_17 : int 1 1 0 1 0 1 1 0 1 1 ...
## $ D_18 : int 1 1 0 1 0 1 1 0 1 1 ...
## $ D_19 : int 1 1 0 1 0 1 1 0 1 1 ...
## $ D_20 : int 1 1 0 1 0 1 1 0 0 1 ...
## $ D_21 : int 1 1 0 1 0 0 1 0 1 1 ...
## $ D_22 : int 1 1 0 1 0 1 1 0 1 0 ...
## $ D_23 : int 1 1 0 1 1 1 1 0 1 0 ...
## $ D_24 : int 1 1 0 1 1 1 1 0 1 1 ...
## $ D_25 : int 1 1 0 0 1 1 1 0 1 1 ...
## $ D_26 : int 1 1 0 1 0 0 1 0 1 1 ...
## $ D_27 : int 1 1 0 1 0 1 1 0 1 0 ...
## $ D_28 : int 0 0 0 0 0 0 1 0 1 1 ...
## $ D_29 : int 1 1 0 0 1 1 1 0 1 1 ...
## $ D_30 : int 1 1 0 1 0 1 1 0 1 1 ...
## $ D_31 : int 1 1 1 1 0 1 1 0 1 1 ...
## $ D_32 : int 1 1 0 1 1 1 1 0 1 1 ...
## $ D_33 : int 0 0 0 1 0 0 1 0 1 1 ...
## $ D_34 : int 0 1 1 1 0 0 1 0 1 1 ...
## $ D_35 : int 1 1 0 1 0 1 1 0 1 1 ...
## $ D_36 : int 1 1 0 1 1 1 1 0 0 1 ...
## $ D_37 : int 1 1 1 1 0 1 1 0 1 0 ...
## $ D_38 : int 1 1 1 1 0 1 1 0 1 0 ...
## $ D_39 : int 1 1 1 1 0 1 1 0 1 0 ...
## $ D_40 : int 1 1 0 1 0 1 1 0 0 0 ...
## $ C_1 : int 8 8 10 10 10 6 8 9 8 8 ...
## $ C_2 : int 7 9 9 10 8 8 9 8 7 8 ...
## $ C_3 : int 6 9 10 9 8 8 8 7 7 8 ...
## $ C_4 : int 6 7 10 7 8 8 7 7 7 9 ...
## $ C_5 : int 8 7 9 8 10 7 8 8 8 9 ...
## $ C_6 : int 6 7 10 9 9 7 8 8 8 8 ...
## $ C_7 : int 8 7 9 10 9 7 8 8 7 8 ...
## $ C_8 : int 9 7 10 10 10 6 8 8 8 8 ...
## $ C_9 : int 7 7 8 6 8 8 8 8 8 9 ...
## $ C_10 : int 8 8 9 10 10 6 8 8 8 9 ...
## $ C_11 : int 7 7 9 8 8 6 8 8 7 8 ...
## $ C_12 : int 6 7 10 9 9 9 9 8 7 8 ...
## $ C_13 : int 9 8 9 10 9 7 8 8 8 9 ...
## $ C_14 : int 7 8 10 10 8 6 8 8 8 8 ...
## $ C_15 : int 7 8 10 8 8 7 8 8 8 9 ...
## $ C_16 : int 9 7 8 10 7 9 8 8 7 9 ...
## $ C_17 : int 7 7 9 10 8 6 8 8 7 9 ...
## $ C_18 : int 6 6 8 10 8 7 8 8 7 9 ...
## $ C_19 : int 6 6 9 10 7 7 8 8 7 9 ...
## $ C_20 : int 7 10 8 10 6 9 8 8 7 9 ...
## $ C_21 : int 7 8 8 7 7 6 8 8 6 8 ...
## $ C_22 : int 9 7 9 10 9 7 8 8 8 9 ...
## $ C_23 : int 7 8 9 10 6 8 8 8 7 8 ...
## $ C_24 : int 7 7 8 8 8 7 8 8 7 7 ...
## $ C_25 : int 6 9 10 10 8 7 8 8 7 9 ...
## $ C_26 : int 7 9 8 7 7 6 8 8 7 9 ...
## $ C_27 : int 8 8 10 7 7 8 8 8 7 7 ...
## $ C_28 : int 9 7 9 10 8 7 8 8 8 8 ...
## $ C_29 : int 6 6 9 10 6 8 8 8 8 8 ...
## $ C_30 : int 8 7 8 8 6 7 8 8 8 8 ...
## $ C_31 : int 7 8 9 1 7 7 8 8 8 8 ...
## $ C_32 : int 8 7 10 9 8 8 8 8 8 8 ...
## $ C_33 : int 7 6 8 8 8 7 8 8 7 9 ...
## $ C_34 : int 7 8 9 10 6 7 8 8 7 9 ...
## $ C_35 : int 9 8 10 8 7 7 8 8 7 8 ...
## $ C_36 : int 7 7 8 10 8 8 8 8 6 9 ...
## $ C_37 : int 8 7 9 10 7 6 8 8 7 8 ...
## $ C_38 : int 7 6 10 10 7 7 8 8 7 8 ...
## $ C_39 : int 8 8 9 7 8 7 8 8 7 9 ...
## $ C_40 : int 9 9 8 9 9 8 8 8 7 7 ...
## $ BI_1 : int 3 3 4 3 4 4 4 3 3 3 ...
## $ BI_2 : int 2 2 2 2 2 1 1 1 1 4 ...
## $ BI_3 : int 1 2 3 2 4 1 2 2 1 2 ...
## $ BI_4 : int 2 1 1 1 3 1 1 1 1 2 ...
## $ BI_5 : int 4 2 2 2 2 1 1 1 2 2 ...
## $ BI_6 : int 4 2 3 4 4 4 2 2 4 1 ...
## $ BI_7 : int 3 3 4 3 2 3 4 4 4 1 ...
## $ BI_8 : int 4 3 3 4 4 1 3 3 3 1 ...
## $ BI_9 : int 1 2 3 4 4 2 2 4 1 2 ...
## $ BI_10 : int 3 4 3 1 4 4 3 3 4 2 ...
## $ BI_11 : int 4 1 1 4 2 3 1 1 1 2 ...
## $ BI_12 : int 3 3 4 4 3 3 4 4 4 2 ...
## $ BI_13 : int 4 4 4 4 4 4 4 4 3 3 ...
## $ BI_14 : int 3 1 1 1 4 2 1 1 2 2 ...
## $ BI_15 : int 1 3 4 4 4 1 2 3 3 3 ...
## $ BI_16 : int 1 1 2 1 1 1 1 1 2 1 ...
## $ BI_17 : int 2 2 4 1 1 2 1 1 1 3 ...
## [list output truncated]# Criar o tratamento de autocontrole.
da$autocon <- da$Participantes %>%
substr(start = 0, stop = 1) %>%
as.factor()
#-----------------------------------------------------------------------
# Tabela que associa os nomes das questões que são as mesmas.
# Correspondência entre as decisões.
decis <- matrix(data = sprintf("%02d", 1:40), ncol = 2)
# Renomeia os números para ter dois digitos, então 1 fica 01.
names(da) <- names(da) %>%
str_replace(pattern = "(.*)(_)(\\d)$",
replacement = "\\1\\20\\3")Análise de componentes principais
As variáveis de BI foram medidas para quantificar as diferenças sobre a impulsividade entre os participantes. Imagina-se que as respostas para as questões de BI possam ser explicadas por um conjunto pequeno de fatores latentes. O mesmo para OE e EAC. Para determinar o índice de impulsividade individual, será feita a análise de componentes principais com as respostas do questionário de BI. O número de componentes ideal a ser usado na análise de regressão será determinado depois.
Business impulsiviness
#-----------------------------------------------------------------------
# Análise de componentes principais nas variáveis BI_*.
# Extrai e cria uma matriz com as variáveis de BI_*.
X <- da %>%
select(contains("BI"))
dim(X)## [1] 94 30bi_basica <- X %>%
gather(key = "BI", value = "valor") %>%
group_by(BI) %>%
summarise(n = n(),
média = mean(valor),
mediana = median(valor),
desvpad = sd(valor),
mínimo = min(valor),
máximo = max(valor)) %>%
mutate(BI = str_replace(BI, "BI_", ""))
bi_basica %>%
print(n = Inf)## # A tibble: 30 x 7
## BI n média mediana desvpad mínimo máximo
## <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 01 94 3.07 3 0.779 1 4
## 2 02 94 1.86 2 0.798 1 4
## 3 03 94 2.27 2 0.845 1 4
## 4 04 94 1.60 1 0.780 1 4
## 5 05 94 1.79 2 0.731 1 4
## 6 06 94 2.45 2 1.03 1 4
## 7 07 94 2.80 3 1.06 1 4
## 8 08 94 3.07 3 0.765 1 4
## 9 09 94 2.53 2 0.799 1 4
## 10 10 94 2.79 3 1.13 1 4
## 11 11 94 1.94 2 0.959 1 4
## 12 12 94 3.10 3 0.734 1 4
## 13 13 94 3.44 4 0.862 1 4
## 14 14 94 1.94 2 0.759 1 4
## 15 15 94 2.87 3 0.964 1 4
## 16 16 94 1.56 1 0.770 1 4
## 17 17 94 1.85 2 0.775 1 4
## 18 18 94 2.05 2 0.860 1 4
## 19 19 94 1.89 2 0.823 1 4
## 20 20 94 2.76 3 0.758 1 4
## 21 21 94 1.35 1 0.813 1 4
## 22 22 94 1.97 2 0.989 1 4
## 23 23 94 2.09 2 0.969 1 4
## 24 24 94 1.91 2 0.851 1 4
## 25 25 94 1.32 1 0.707 1 4
## 26 26 94 2.90 3 0.928 1 4
## 27 27 94 2.24 2 0.838 1 4
## 28 28 94 2.28 2 0.966 1 4
## 29 29 94 3.04 3 0.961 1 4
## 30 30 94 3.22 3 0.750 1 4# Gráfico com média e segmentos de erro para 1 desvio-padrão.
ggplot(bi_basica, aes(x = BI, y = média)) +
geom_point() +
geom_errorbar(aes(ymin = média - desvpad,
ymax = média + desvpad),
width = 0.5) +
xlab("Business impulsiviness") +
ylab(expression("Média" %+-% "desvio padrão")) +
coord_flip()
# Cria a matriz para fazer a análise de componentes principais.
X <- X %>%
as.matrix()
dim(X)## [1] 94 30# Renomeia para uma melhor exibição no biplot.
colnames(X) <- colnames(X) %>%
str_replace("BI_", "")
# Aplica componentes principais.
acp <- princomp(X, cor = TRUE)
# summary(acp)
# Exibe o resultado da ACP.
print(acp$loadings, digits = 2, cut = 0.2, sort = TRUE)##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## 29 -0.25 -0.20 0.57 0.33
## 21 -0.24 -0.41
## 01 0.28 0.30
## 02 -0.29
## 03 -0.27 -0.49 -0.41
## 04 -0.26 -0.20
## 05 -0.24 0.25 0.21
## 06 -0.26 -0.33
## 07 -0.25
## 08 -0.44 -0.23 -0.20
## 09 -0.28 -0.23 -0.29
## 10 0.23 -0.25 -0.32
## 11 -0.25 -0.37 0.33 0.31
## 12 0.25 0.25 0.23
## 13 -0.23 -0.21 -0.27 0.31
## 14 -0.27 -0.25
## 15 -0.32 0.20 0.29 0.25 -0.24
## 16 0.49
## 17 -0.24 0.29
## 18 -0.20 0.36 -0.30
## 19 -0.28 0.39
## 20 -0.26 -0.25 -0.30 0.29
## 22 0.36 0.22
## 23 0.46 -0.27
## 24 0.37 0.28 -0.36
## 25 0.45 -0.28 0.25
## 26 0.21 -0.23 -0.21 0.37 -0.29
## 27 -0.26 -0.22 -0.38
## 28 0.24 -0.36 0.25
## 30 0.25 0.27 0.20
## Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Comp.16 Comp.17 Comp.18 Comp.19
## 29 -0.35
## 21 0.37 0.62 0.22
## 01 -0.41
## 02 -0.27 0.25 0.21 -0.25
## 03 0.27 -0.28
## 04 0.31 -0.24 -0.34
## 05 -0.27 0.22 -0.34
## 06 -0.30 0.29 0.24
## 07 0.34 -0.27 0.40 -0.30
## 08 -0.27 0.38 -0.28
## 09 -0.26 -0.26 -0.21
## 10 -0.26 0.20 0.21
## 11
## 12 0.24 -0.37
## 13 -0.22 0.24 -0.24 -0.30
## 14 -0.33 -0.21 -0.47
## 15 0.20 -0.39 0.35
## 16 0.23 0.43 -0.31 0.21 -0.21
## 17 0.35
## 18 -0.39 -0.28
## 19 0.22 -0.22
## 20 -0.37 -0.26
## 22 0.26 -0.25 0.24
## 23 -0.30 -0.33
## 24 -0.37
## 25
## 26 0.24 -0.20 0.27 0.31
## 27 0.50 0.33
## 28 -0.30
## 30 -0.21 0.24 0.36
## Comp.20 Comp.21 Comp.22 Comp.23 Comp.24 Comp.25 Comp.26 Comp.27 Comp.28
## 29 -0.26 -0.25
## 21
## 01 0.23 -0.33 -0.25
## 02 0.39 0.36 -0.34
## 03 0.21 0.35
## 04 -0.21 -0.33 0.38 0.24
## 05 0.40 0.20 -0.36 0.23
## 06 -0.22 0.35 -0.22
## 07 0.23 0.27 0.22
## 08 -0.36
## 09 -0.32 0.40 -0.22 0.21
## 10 -0.33 0.44 -0.24
## 11 0.23 -0.47
## 12 -0.29 0.45 -0.21 0.23
## 13 0.23 0.22
## 14 -0.20 0.22 0.23
## 15 0.25 0.22
## 16 -0.21
## 17 -0.40 -0.33 -0.35
## 18 -0.22 -0.25 0.31
## 19 -0.24 -0.27 0.41 -0.27
## 20 0.35 0.33
## 22 -0.20 0.35 0.29 -0.30
## 23 0.33 0.28 -0.23
## 24 -0.27 -0.34
## 25 0.48 -0.25 -0.26
## 26 0.27
## 27
## 28 -0.28 0.27 -0.25 0.36
## 30 0.32 0.39
## Comp.29 Comp.30
## 29
## 21
## 01 0.41
## 02
## 03
## 04
## 05
## 06 0.29
## 07
## 08
## 09
## 10 -0.22
## 11 0.27
## 12 -0.20
## 13 0.38
## 14 -0.33 0.21
## 15 -0.27
## 16
## 17 0.28
## 18 0.31
## 19 -0.26
## 20
## 22 -0.31
## 23
## 24 -0.29
## 25
## 26 -0.24
## 27 0.27 0.23
## 28 -0.23
## 30 0.27
##
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
## Cumulative Var 0.03 0.07 0.10 0.13 0.17 0.20 0.23 0.27
## Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.03 0.03 0.03 0.03 0.03 0.03 0.03
## Cumulative Var 0.30 0.33 0.37 0.40 0.43 0.47 0.50
## Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 Comp.21 Comp.22
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.03 0.03 0.03 0.03 0.03 0.03 0.03
## Cumulative Var 0.53 0.57 0.60 0.63 0.67 0.70 0.73
## Comp.23 Comp.24 Comp.25 Comp.26 Comp.27 Comp.28 Comp.29
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.03 0.03 0.03 0.03 0.03 0.03 0.03
## Cumulative Var 0.77 0.80 0.83 0.87 0.90 0.93 0.97
## Comp.30
## SS loadings 1.00
## Proportion Var 0.03
## Cumulative Var 1.00# Proporção de variância acumulada.
plot(cbind(x = 1:length(acp$sdev),
y = c(cumsum(acp$sdev^2))/sum(acp$sdev^2)),
type = "o",
ylim = c(0, 1),
xlab = "Componente",
ylab = "Proporção de variância acumulada")
abline(h = c(0.5, 0.75, 0.9), lty = 2)
# Biplot.
biplot(acp, choices = c(1, 2))
# Escores para serem usados na regressão.
S <- acp$scores
colnames(S) <- str_replace(colnames(S), "Comp\\.", "BI_S")
# pairs(S[, 1:6])
# Concatena os escores com as demais variáveis.
da <- cbind(da, S)Orçamento empresarial
#-----------------------------------------------------------------------
# Análise de componentes principais nas variáveis OE_*.
# Extrai e cria uma matriz com as variáveis de OE_*.
X <- da %>%
select(starts_with("OE"))
oe_basica <- X %>%
gather(key = "OE", value = "valor") %>%
group_by(OE) %>%
summarise(n = n(),
média = mean(valor),
mediana = median(valor),
desvpad = sd(valor),
mínimo = min(valor),
máximo = max(valor)) %>%
mutate(OE = str_replace(OE, "OE_", ""))
oe_basica %>%
print(n = Inf)## # A tibble: 9 x 7
## OE n média mediana desvpad mínimo máximo
## <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 01 94 6.09 7 1.42 1 7
## 2 02 94 3.13 3 1.84 1 7
## 3 03 94 2.87 3 1.86 1 7
## 4 04 94 2.49 2 1.90 1 7
## 5 05 94 2.31 1 1.75 1 7
## 6 06 94 2.45 1.5 1.77 1 7
## 7 07 94 6.29 7 1.13 1 7
## 8 08 94 6.45 7 0.980 1 7
## 9 09 94 5.69 6 1.32 1 7# Gráfico com média e segmentos de erro para 1 desvio-padrão.
ggplot(oe_basica, aes(x = OE, y = média)) +
geom_point() +
geom_errorbar(aes(ymin = média - desvpad,
ymax = média + desvpad),
width = 0.5) +
xlab("Orçamento empresarial") +
ylab(expression("Média" %+-% "desvio padrão")) +
coord_flip()
# Cria a matriz para fazer a análise de componentes principais.
X <- X %>%
as.matrix()
dim(X)## [1] 94 9# Renomeia para uma melhor exibição no biplot.
colnames(X) <- colnames(X) %>%
str_replace("OE_", "")
# Aplica componentes principais.
acp <- princomp(X, cor = TRUE)
# summary(acp)
# Exibe o resultado da ACP.
print(acp$loadings, digits = 2, cut = 0.2, sort = TRUE)##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9
## 07 0.56 -0.43 -0.53 -0.38
## 08 0.54 -0.26 0.37 0.21 0.30 0.49 0.35
## 02 -0.25 0.66 0.63 0.27
## 01 0.30 0.56 -0.59 0.39 0.23
## 03 -0.43 -0.60 -0.42 0.40 0.25
## 09 0.49 -0.29 -0.59 0.49 -0.26
## 06 -0.45 -0.31 0.24 0.53 0.28 -0.52
## 04 -0.49 -0.20 -0.72 0.39
## 05 -0.51 -0.50 0.65
##
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11
## Cumulative Var 0.11 0.22 0.33 0.44 0.56 0.67 0.78 0.89
## Comp.9
## SS loadings 1.00
## Proportion Var 0.11
## Cumulative Var 1.00# Proporção de variância acumulada.
plot(cbind(x = 1:length(acp$sdev),
y = c(cumsum(acp$sdev^2))/sum(acp$sdev^2)),
type = "o",
ylim = c(0, 1),
xlab = "Componente",
ylab = "Proporção de variância acumulada")
abline(h = c(0.5, 0.75, 0.9), lty = 2)
# Biplot.
biplot(acp, choices = c(1, 2))
# Escores para serem usados na regressão.
S <- acp$scores
colnames(S) <- str_replace(colnames(S), "Comp\\.", "OE_S")
# pairs(S[, 1:3])
# Concatena os escores com as demais variáveis.
da <- cbind(da, S)Escala de autocontrole
#-----------------------------------------------------------------------
# Análise de componentes principais nas variáveis EAC_*.
# Extrai e cria uma matriz com as variáveis de EAC_*.
X <- da %>%
select(starts_with("EAC"))
eac_basica <- X %>%
gather(key = "EAC", value = "valor") %>%
group_by(EAC) %>%
summarise(n = n(),
média = mean(valor),
mediana = median(valor),
desvpad = sd(valor),
mínimo = min(valor),
máximo = max(valor)) %>%
mutate(EAC = str_replace(EAC, "EAC_", ""))
eac_basica %>%
print(n = Inf)## # A tibble: 24 x 7
## EAC n média mediana desvpad mínimo máximo
## <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 01 94 1.62 1 0.869 1 4
## 2 02 94 1.39 1 0.736 1 4
## 3 03 94 1.59 1 0.822 1 4
## 4 04 94 1.80 2 0.934 1 4
## 5 05 94 2.62 3 1.06 1 4
## 6 06 94 2.38 2 1.14 1 4
## 7 07 94 1.44 1 0.784 1 4
## 8 08 94 2.14 2 1.12 1 4
## 9 09 94 1.79 2 0.902 1 4
## 10 10 94 2.77 3 1.04 1 4
## 11 11 94 2.18 2 1.07 1 4
## 12 12 94 2.54 3 1.14 1 4
## 13 13 94 2.30 2 1.09 1 4
## 14 14 94 2.15 2 0.972 1 4
## 15 15 94 1.51 1 0.786 1 4
## 16 16 94 1.65 1 0.901 1 4
## 17 17 94 1.61 1 0.819 1 4
## 18 18 94 1.65 1 0.758 1 4
## 19 19 94 2.03 2 0.848 1 4
## 20 20 94 1.41 1 0.646 1 4
## 21 21 94 2.12 2 0.971 1 4
## 22 22 94 1.82 2 0.867 1 4
## 23 23 94 1.64 1 0.801 1 4
## 24 24 94 2.13 2 1.06 1 4# Gráfico com média e segmentos de erro para 1 desvio-padrão.
ggplot(eac_basica, aes(x = EAC, y = média)) +
geom_point() +
geom_errorbar(aes(ymin = média - desvpad,
ymax = média + desvpad),
width = 0.5) +
xlab("Orçamento empresarial") +
ylab(expression("Média" %+-% "desvio padrão")) +
coord_flip()
# Cria a matriz para fazer a análise de componentes principais.
X <- X %>%
as.matrix()
dim(X)## [1] 94 24# Renomeia para uma melhor exibição no biplot.
colnames(X) <- colnames(X) %>%
str_replace("EAC_", "")
# Aplica componentes principais.
acp <- princomp(X, cor = TRUE)
# summary(acp)
# Exibe o resultado da ACP.
print(acp$loadings, digits = 2, cut = 0.2, sort = TRUE)##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## 14 -0.21 0.22 -0.38 0.54 0.24
## 16 -0.24 -0.21 -0.22 -0.30 0.52
## 24 -0.37 -0.22
## 15 -0.26 -0.24 0.22 -0.26
## 03 -0.31 -0.44
## 01 -0.28 -0.27 -0.26 0.30 -0.34
## 02 -0.33 -0.22 -0.35
## 04 -0.28 0.21 -0.24 0.21 -0.25
## 05 -0.26 0.23 0.28 0.22 0.38 -0.36
## 06 -0.28 0.23 0.33
## 07 -0.26 0.31 -0.29 0.40 0.30
## 08 -0.21 0.30 0.25
## 09 -0.26 0.24 0.22 -0.28 0.26 -0.28
## 10 -0.26 0.35 0.30
## 11 -0.22 0.22 0.29 -0.23
## 12 0.32 -0.41 -0.34 -0.27
## 13 0.40 -0.28
## 17 -0.21 -0.20 -0.23 -0.41 -0.27 0.25
## 18 -0.24 -0.20 -0.21 0.29 0.34
## 19 -0.22 -0.32 0.30 0.20 0.21
## 20 -0.33 0.44 0.21 0.29 -0.22
## 21 -0.28 -0.26 -0.34 0.21
## 22 -0.36 -0.21 -0.32 -0.30
## 23 -0.32 -0.31 -0.33
## Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Comp.16 Comp.17 Comp.18 Comp.19
## 14 -0.20 0.23 0.21
## 16 0.20 0.27 -0.22
## 24 -0.53 -0.29 -0.31 -0.38
## 15 -0.25 -0.25
## 03 0.33
## 01 -0.23 0.37
## 02 -0.21 0.33 -0.24 -0.33
## 04 0.46 -0.23
## 05 -0.22 -0.22 -0.29
## 06 -0.36 0.43 0.21 -0.39
## 07 -0.33 0.48
## 08 0.37 -0.32 0.22 -0.35
## 09 0.29 -0.20 0.26
## 10 -0.31 0.32 0.44 -0.21
## 11 -0.34 -0.42 -0.31 -0.27 -0.29
## 12 0.23
## 13 -0.40
## 17 0.37 -0.32
## 18 -0.23 0.45 -0.35
## 19 -0.49 0.20 -0.39 0.26
## 20 0.30 0.21
## 21 0.21 0.44 -0.28 -0.43
## 22 0.21 0.34 0.46
## 23 -0.31 -0.29 0.21 0.48 -0.27
## Comp.20 Comp.21 Comp.22 Comp.23 Comp.24
## 14 -0.29 0.22
## 16 -0.30 -0.20
## 24
## 15 0.51 -0.44
## 03 -0.28 -0.22 -0.28 0.51 -0.23
## 01 0.28 -0.29 -0.28
## 02 -0.40 -0.38
## 04 0.23 0.28 0.35
## 05 0.25 -0.31
## 06 0.32
## 07
## 08 -0.22 -0.26
## 09 0.22 -0.23 0.25 -0.24
## 10 0.22 -0.21
## 11 -0.21 0.21
## 12 -0.34 0.39
## 13 0.29 -0.45
## 17 0.33 0.28
## 18 0.34
## 19 -0.26 -0.20
## 20 0.40 0.30
## 21
## 22 0.37
## 23
##
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
## Cumulative Var 0.04 0.08 0.12 0.17 0.21 0.25 0.29 0.33
## Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.04 0.04 0.04 0.04 0.04 0.04 0.04
## Cumulative Var 0.37 0.42 0.46 0.50 0.54 0.58 0.62
## Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 Comp.21 Comp.22
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.04 0.04 0.04 0.04 0.04 0.04 0.04
## Cumulative Var 0.67 0.71 0.75 0.79 0.83 0.88 0.92
## Comp.23 Comp.24
## SS loadings 1.00 1.00
## Proportion Var 0.04 0.04
## Cumulative Var 0.96 1.00# Proporção de variância acumulada.
plot(cbind(x = 1:length(acp$sdev),
y = c(cumsum(acp$sdev^2))/sum(acp$sdev^2)),
type = "o",
ylim = c(0, 1),
xlab = "Componente",
ylab = "Proporção de variância acumulada")
abline(h = c(0.5, 0.75, 0.9), lty = 2)
# Biplot.
biplot(acp, choices = c(1, 2))
# Escores para serem usados na regressão.
S <- acp$scores
colnames(S) <- str_replace(colnames(S), "Comp\\.", "EAC_S")
# pairs(S[, 1:3])
# Concatena os escores com as demais variáveis.
da <- cbind(da, S)Análise exploratória das decisões
#-----------------------------------------------------------------------
# Gráficos.
# Proporção de acertos da tarefa das matrizes por grupo de autocontrole.
da %>%
group_by(autocon) %>%
summarise(prop = mean(TM))## # A tibble: 2 x 2
## autocon prop
## <fct> <dbl>
## 1 A 0.579
## 2 C 0.811db <- list()
# Empilha as decisões.
db[[1]] <- da %>%
select(Participantes, TM, autocon, starts_with("D_")) %>%
gather(key = "decisoes", value = "acerto", contains("D_"))
# Empilha as confianças nas decisões.
db[[2]] <- da %>%
select(Participantes, starts_with("C_")) %>%
gather(key = "decisoes", value = "nivel", contains("C_")) %>%
mutate(nivel = 10 * nivel)
# str(db[[1]])
# str(db[[2]])
# Remove os prefixos `D_` e `C_`.
db[[1]]$decisoes <- db[[1]]$decisoes %>% str_replace("D_", "")
db[[2]]$decisoes <- db[[2]]$decisoes %>% str_replace("C_", "")
# Junção da parte da decisões com as confianças.
db <- full_join(db[[1]], db[[2]])## Joining, by = c("Participantes", "decisoes")str(db)## 'data.frame': 3760 obs. of 6 variables:
## $ Participantes: chr "A01" "A02" "A03" "A04" ...
## $ TM : int 1 0 1 0 1 1 1 1 1 0 ...
## $ autocon : Factor w/ 2 levels "A","C": 1 1 1 1 1 1 1 1 1 1 ...
## $ decisoes : chr "01" "01" "01" "01" ...
## $ acerto : int 0 0 1 0 0 1 1 0 0 0 ...
## $ nivel : num 80 80 100 100 100 60 80 90 80 80 ...# Renomeia para que D_21 seja D_01 e assim por diante.
u <- decis[match(x = db$decisoes,
table = decis[, 2],), 1]
db$decisoes[!is.na(u)] <- u[!is.na(u)]
# Passa para inteiro.
db$decisoes <- db$decisoes %>%
as.integer()
# Obtém a estatística descritiva.
db_prop <- db %>%
group_by(autocon, decisoes) %>%
summarise(acerto_prop = mean(acerto),
conf_média = mean(nivel),
conf_sd = sd(nivel))
db_prop %>%
print(n = Inf)## # A tibble: 40 x 5
## # Groups: autocon [?]
## autocon decisoes acerto_prop conf_média conf_sd
## <fct> <int> <dbl> <dbl> <dbl>
## 1 A 1 0.395 74.5 14.6
## 2 A 2 0.737 76.3 14.3
## 3 A 3 0.719 78.9 12.7
## 4 A 4 0.675 76.6 11.7
## 5 A 5 0.711 78.1 12.6
## 6 A 6 0.561 78.4 11.6
## 7 A 7 0.588 78.0 12.2
## 8 A 8 0.482 79.9 13.1
## 9 A 9 0.746 77.0 14.1
## 10 A 10 0.579 76.8 13.3
## 11 A 11 0.596 74.6 13.4
## 12 A 12 0.816 79.9 13.3
## 13 A 13 0.439 78.9 12.9
## 14 A 14 0.456 78.1 13.9
## 15 A 15 0.570 76.9 12.7
## 16 A 16 0.763 79.1 13.7
## 17 A 17 0.553 75.6 13.0
## 18 A 18 0.553 75 12.9
## 19 A 19 0.535 75 12.6
## 20 A 20 0.649 82.4 14.3
## 21 C 1 0.446 73.1 15.6
## 22 C 2 0.743 75 15.5
## 23 C 3 0.770 76.1 13.8
## 24 C 4 0.770 76.1 14.2
## 25 C 5 0.743 88.0 85.0
## 26 C 6 0.581 75.5 14.5
## 27 C 7 0.581 76.4 12.6
## 28 C 8 0.581 79.3 13.9
## 29 C 9 0.797 78.1 14.3
## 30 C 10 0.608 74.5 13.8
## 31 C 11 0.595 74.2 13.7
## 32 C 12 0.824 80.3 14.6
## 33 C 13 0.568 74.6 14.5
## 34 C 14 0.486 76.6 13.3
## 35 C 15 0.622 75.3 14.6
## 36 C 16 0.824 79.2 14.5
## 37 C 17 0.554 74.6 13.8
## 38 C 18 0.649 75.7 14.5
## 39 C 19 0.635 75.8 14.6
## 40 C 20 0.784 80.5 15.5# Versão lado a lado para acerto.
db_prop %>%
select(autocon, decisoes, acerto_prop) %>%
spread(key = autocon, value = acerto_prop)## # A tibble: 20 x 3
## decisoes A C
## <int> <dbl> <dbl>
## 1 1 0.395 0.446
## 2 2 0.737 0.743
## 3 3 0.719 0.770
## 4 4 0.675 0.770
## 5 5 0.711 0.743
## 6 6 0.561 0.581
## 7 7 0.588 0.581
## 8 8 0.482 0.581
## 9 9 0.746 0.797
## 10 10 0.579 0.608
## 11 11 0.596 0.595
## 12 12 0.816 0.824
## 13 13 0.439 0.568
## 14 14 0.456 0.486
## 15 15 0.570 0.622
## 16 16 0.763 0.824
## 17 17 0.553 0.554
## 18 18 0.553 0.649
## 19 19 0.535 0.635
## 20 20 0.649 0.784# Versão lado a lado para confiança média.
db_prop %>%
select(autocon, decisoes, conf_média) %>%
spread(key = autocon, value = conf_média)## # A tibble: 20 x 3
## decisoes A C
## <int> <dbl> <dbl>
## 1 1 74.5 73.1
## 2 2 76.3 75
## 3 3 78.9 76.1
## 4 4 76.6 76.1
## 5 5 78.1 88.0
## 6 6 78.4 75.5
## 7 7 78.0 76.4
## 8 8 79.9 79.3
## 9 9 77.0 78.1
## 10 10 76.8 74.5
## 11 11 74.6 74.2
## 12 12 79.9 80.3
## 13 13 78.9 74.6
## 14 14 78.1 76.6
## 15 15 76.9 75.3
## 16 16 79.1 79.2
## 17 17 75.6 74.6
## 18 18 75 75.7
## 19 19 75 75.8
## 20 20 82.4 80.5gg1 <-
ggplot(db_prop,
aes(x = autocon,
y = acerto_prop,
group = decisoes)) +
geom_point() +
geom_line() +
geom_text(aes(x = (as.integer(autocon) +
0.075 * scale(as.integer(autocon))),
label = decisoes)) +
ylab("Proporção de acerto") +
xlab("Autocontrole")
gg2 <-
ggplot(db_prop,
aes(x = autocon,
y = conf_média,
group = decisoes)) +
geom_point() +
# geom_point(aes(size = conf_sd)) +
geom_line() +
geom_text(aes(x = (as.integer(autocon) +
0.075 * scale(as.integer(autocon))),
label = decisoes)) +
ylab("Confiança média") +
xlab("Autocontrole")
grid.arrange(gg1, gg2, nrow = 1)
Regressão logística para cada decisão
#-----------------------------------------------------------------------
# Análise de regressão logística.
# Cria correspondência entre decisões.
decis <- "D_" %>%
str_c(decis) %>%
matrix(ncol = 2)
# Porção apenas com os escores da ACP.
da_scores <- da %>%
select(Participantes,
starts_with("BI_S"),
starts_with("OE_S"),
starts_with("EAC_S"))
# Coloca os escores ao lado das variáveis filtrando para a decisão.
dd <- db %>%
filter(decisoes == 1) %>%
full_join(da_scores)## Joining, by = "Participantes"# Ajuste do modelo.
fit <- glm(acerto ~
EAC_S1 + EAC_S2 + EAC_S3 + EAC_S4 + EAC_S5 + EAC_S6 +
OE_S1 + OE_S2 + OE_S3 +
BI_S1 + BI_S2 + BI_S3 + BI_S4 + BI_S5 +
autocon * nivel +
autocon * TM,
data = dd,
family = quasibinomial)
# anova(fit, test = "F")
drop1(fit, test = "F", scope = . ~ .)## Single term deletions
##
## Model:
## acerto ~ EAC_S1 + EAC_S2 + EAC_S3 + EAC_S4 + EAC_S5 + EAC_S6 +
## OE_S1 + OE_S2 + OE_S3 + BI_S1 + BI_S2 + BI_S3 + BI_S4 + BI_S5 +
## autocon * nivel + autocon * TM
## Df Deviance F value Pr(>F)
## <none> 240.92
## EAC_S1 1 241.22 0.2037 0.6524
## EAC_S2 1 241.73 0.5606 0.4551
## EAC_S3 1 241.17 0.1705 0.6802
## EAC_S4 1 240.93 0.0033 0.9540
## EAC_S5 1 241.23 0.2164 0.6424
## EAC_S6 1 242.50 1.1028 0.2952
## OE_S1 1 241.01 0.0603 0.8063
## OE_S2 1 242.91 1.3851 0.2409
## OE_S3 1 241.12 0.1398 0.7090
## BI_S1 1 241.28 0.2470 0.6198
## BI_S2 1 241.82 0.6253 0.4302
## BI_S3 1 241.93 0.6985 0.4045
## BI_S4 1 240.92 0.0001 0.9936
## BI_S5 1 241.55 0.4387 0.5087
## autocon 1 242.45 1.0654 0.3035
## nivel 1 241.31 0.2714 0.6031
## TM 1 242.36 1.0046 0.3176
## autocon:nivel 1 241.88 0.6673 0.4151
## autocon:TM 1 241.31 0.2705 0.6037summary(fit)##
## Call:
## glm(formula = acerto ~ EAC_S1 + EAC_S2 + EAC_S3 + EAC_S4 + EAC_S5 +
## EAC_S6 + OE_S1 + OE_S2 + OE_S3 + BI_S1 + BI_S2 + BI_S3 +
## BI_S4 + BI_S5 + autocon * nivel + autocon * TM, family = quasibinomial,
## data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5767 -1.0189 -0.7352 1.1771 1.9489
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.156741 1.274005 -0.123 0.902
## EAC_S1 -0.050532 0.099073 -0.510 0.611
## EAC_S2 0.094227 0.111554 0.845 0.399
## EAC_S3 0.055877 0.119744 0.467 0.641
## EAC_S4 0.008862 0.135745 0.065 0.948
## EAC_S5 -0.079540 0.151565 -0.525 0.600
## EAC_S6 -0.209219 0.177156 -1.181 0.239
## OE_S1 0.029197 0.105350 0.277 0.782
## OE_S2 0.168343 0.130638 1.289 0.199
## OE_S3 -0.069552 0.164748 -0.422 0.673
## BI_S1 -0.053820 0.095959 -0.561 0.576
## BI_S2 -0.119966 0.134807 -0.890 0.375
## BI_S3 0.119881 0.127486 0.940 0.348
## BI_S4 0.001184 0.130863 0.009 0.993
## BI_S5 -0.106694 0.142603 -0.748 0.455
## autoconC 2.353940 2.030829 1.159 0.248
## nivel -0.009312 0.015839 -0.588 0.557
## TM 0.527786 0.468477 1.127 0.262
## autoconC:nivel -0.022424 0.024422 -0.918 0.360
## autoconC:TM -0.502397 0.852919 -0.589 0.557
##
## (Dispersion parameter for quasibinomial family taken to be 1.122522)
##
## Null deviance: 255.15 on 187 degrees of freedom
## Residual deviance: 240.92 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4# Fórmula do preditor linear que será usado em todas as decisões.
formula <- acerto ~
EAC_S1 + EAC_S2 + EAC_S3 + EAC_S4 + EAC_S5 + EAC_S6 +
OE_S1 + OE_S2 + OE_S3 +
BI_S1 + BI_S2 + BI_S3 + BI_S4 + BI_S5 +
autocon * nivel +
autocon * TM
# Ajustes em lote para cada decisão.
all_fits <- lapply(1:20,
FUN = function(i) {
dd <- db %>%
filter(decisoes == i) %>%
full_join(da_scores)
fit <- glm(formula = formula,
data = dd,
family = quasibinomial)
return(fit)
})## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"# lapply(all_fits, anova, test = "F")
# lapply(all_fits, drop1, test = "F", scope = . ~ .)
lapply(all_fits, summary)## [[1]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5767 -1.0189 -0.7352 1.1771 1.9489
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.156741 1.274005 -0.123 0.902
## EAC_S1 -0.050532 0.099073 -0.510 0.611
## EAC_S2 0.094227 0.111554 0.845 0.399
## EAC_S3 0.055877 0.119744 0.467 0.641
## EAC_S4 0.008862 0.135745 0.065 0.948
## EAC_S5 -0.079540 0.151565 -0.525 0.600
## EAC_S6 -0.209219 0.177156 -1.181 0.239
## OE_S1 0.029197 0.105350 0.277 0.782
## OE_S2 0.168343 0.130638 1.289 0.199
## OE_S3 -0.069552 0.164748 -0.422 0.673
## BI_S1 -0.053820 0.095959 -0.561 0.576
## BI_S2 -0.119966 0.134807 -0.890 0.375
## BI_S3 0.119881 0.127486 0.940 0.348
## BI_S4 0.001184 0.130863 0.009 0.993
## BI_S5 -0.106694 0.142603 -0.748 0.455
## autoconC 2.353940 2.030829 1.159 0.248
## nivel -0.009312 0.015839 -0.588 0.557
## TM 0.527786 0.468477 1.127 0.262
## autoconC:nivel -0.022424 0.024422 -0.918 0.360
## autoconC:TM -0.502397 0.852919 -0.589 0.557
##
## (Dispersion parameter for quasibinomial family taken to be 1.122522)
##
## Null deviance: 255.15 on 187 degrees of freedom
## Residual deviance: 240.92 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
##
## [[2]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2166 -0.8513 0.5639 0.7783 1.3009
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.398333 1.526724 1.571 0.1181
## EAC_S1 0.099680 0.120805 0.825 0.4105
## EAC_S2 0.054978 0.128915 0.426 0.6703
## EAC_S3 -0.050467 0.129789 -0.389 0.6979
## EAC_S4 0.004471 0.161294 0.028 0.9779
## EAC_S5 -0.238441 0.171859 -1.387 0.1672
## EAC_S6 0.298175 0.203454 1.466 0.1446
## OE_S1 0.141180 0.120680 1.170 0.2437
## OE_S2 -0.098029 0.159285 -0.615 0.5391
## OE_S3 -0.317600 0.193521 -1.641 0.1026
## BI_S1 -0.082259 0.115662 -0.711 0.4779
## BI_S2 0.056164 0.169710 0.331 0.7411
## BI_S3 0.260932 0.162241 1.608 0.1096
## BI_S4 -0.208926 0.151823 -1.376 0.1706
## BI_S5 -0.115159 0.174588 -0.660 0.5104
## autoconC -1.653095 2.410248 -0.686 0.4937
## nivel -0.023794 0.019033 -1.250 0.2130
## TM 0.995970 0.516487 1.928 0.0555 .
## autoconC:nivel 0.036387 0.028139 1.293 0.1978
## autoconC:TM -1.558347 0.998782 -1.560 0.1206
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.116303)
##
## Null deviance: 215.72 on 187 degrees of freedom
## Residual deviance: 195.11 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
##
## [[3]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6335 -0.7806 0.4922 0.7038 1.5463
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.986252 1.806462 -0.546 0.5858
## EAC_S1 0.154704 0.134822 1.147 0.2528
## EAC_S2 0.295849 0.146023 2.026 0.0443 *
## EAC_S3 -0.139684 0.143664 -0.972 0.3323
## EAC_S4 0.095085 0.173013 0.550 0.5833
## EAC_S5 -0.520872 0.207391 -2.512 0.0130 *
## EAC_S6 -0.081818 0.215192 -0.380 0.7043
## OE_S1 0.296856 0.135896 2.184 0.0303 *
## OE_S2 0.268151 0.148270 1.809 0.0723 .
## OE_S3 -0.445962 0.218599 -2.040 0.0429 *
## BI_S1 -0.308909 0.125066 -2.470 0.0145 *
## BI_S2 -0.111162 0.190396 -0.584 0.5601
## BI_S3 0.156103 0.181458 0.860 0.3909
## BI_S4 0.112472 0.165059 0.681 0.4966
## BI_S5 0.121465 0.188594 0.644 0.5204
## autoconC 1.384593 2.734258 0.506 0.6132
## nivel 0.020334 0.022804 0.892 0.3738
## TM 0.915140 0.566729 1.615 0.1082
## autoconC:nivel 0.004258 0.033132 0.129 0.8979
## autoconC:TM -1.722530 1.179567 -1.460 0.1461
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.196757)
##
## Null deviance: 215.72 on 187 degrees of freedom
## Residual deviance: 178.53 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
##
##
## [[4]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3744 -0.9876 0.5370 0.8023 1.6533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.649228 1.596520 -1.659 0.0989 .
## EAC_S1 0.078423 0.120375 0.651 0.5156
## EAC_S2 0.170748 0.126774 1.347 0.1798
## EAC_S3 0.085483 0.135136 0.633 0.5279
## EAC_S4 0.094748 0.156043 0.607 0.5445
## EAC_S5 -0.319593 0.186329 -1.715 0.0882 .
## EAC_S6 0.053320 0.193981 0.275 0.7838
## OE_S1 0.207812 0.121442 1.711 0.0889 .
## OE_S2 0.111921 0.134736 0.831 0.4073
## OE_S3 -0.242281 0.191239 -1.267 0.2069
## BI_S1 -0.154223 0.112369 -1.372 0.1717
## BI_S2 -0.132586 0.171743 -0.772 0.4412
## BI_S3 0.255181 0.161635 1.579 0.1163
## BI_S4 -0.083451 0.145197 -0.575 0.5662
## BI_S5 -0.000552 0.169142 -0.003 0.9974
## autoconC 1.824532 2.555143 0.714 0.4762
## nivel 0.038474 0.020423 1.884 0.0613 .
## TM 0.891678 0.493447 1.807 0.0725 .
## autoconC:nivel 0.005741 0.031213 0.184 0.8543
## autoconC:TM -2.044020 1.125541 -1.816 0.0711 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.095919)
##
## Null deviance: 225.47 on 187 degrees of freedom
## Residual deviance: 193.64 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
##
##
## [[5]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2420 -1.0047 0.5911 0.7704 1.5413
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.673085 1.690457 0.398 0.69101
## EAC_S1 0.127848 0.118394 1.080 0.28176
## EAC_S2 0.243008 0.128100 1.897 0.05954 .
## EAC_S3 -0.283024 0.130902 -2.162 0.03202 *
## EAC_S4 0.190390 0.157634 1.208 0.22882
## EAC_S5 -0.062907 0.171866 -0.366 0.71481
## EAC_S6 0.006826 0.197272 0.035 0.97244
## OE_S1 0.024450 0.124103 0.197 0.84406
## OE_S2 0.021417 0.131842 0.162 0.87115
## OE_S3 -0.198848 0.191437 -1.039 0.30043
## BI_S1 -0.325147 0.117619 -2.764 0.00634 **
## BI_S2 0.092165 0.158806 0.580 0.56245
## BI_S3 0.027660 0.146015 0.189 0.84998
## BI_S4 0.124898 0.149840 0.834 0.40572
## BI_S5 0.141524 0.165083 0.857 0.39250
## autoconC -3.849244 2.702767 -1.424 0.15625
## nivel -0.001017 0.020867 -0.049 0.96121
## TM 0.718186 0.504933 1.422 0.15678
## autoconC:nivel 0.047653 0.031958 1.491 0.13781
## autoconC:TM 0.219533 0.937407 0.234 0.81512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.119993)
##
## Null deviance: 221.73 on 187 degrees of freedom
## Residual deviance: 197.82 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 6
##
##
## [[6]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5477 -1.0547 0.5010 0.9068 1.9205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.837e-02 1.737e+00 0.057 0.95491
## EAC_S1 1.511e-01 1.091e-01 1.385 0.16792
## EAC_S2 4.473e-01 1.383e-01 3.234 0.00147 **
## EAC_S3 1.904e-01 1.372e-01 1.387 0.16722
## EAC_S4 -2.612e-01 1.568e-01 -1.666 0.09763 .
## EAC_S5 2.101e-03 1.700e-01 0.012 0.99015
## EAC_S6 6.612e-02 2.080e-01 0.318 0.75097
## OE_S1 1.608e-01 1.191e-01 1.350 0.17899
## OE_S2 -6.217e-02 1.713e-01 -0.363 0.71717
## OE_S3 -1.533e-01 1.840e-01 -0.833 0.40587
## BI_S1 -2.076e-01 1.082e-01 -1.919 0.05674 .
## BI_S2 1.582e-01 1.665e-01 0.950 0.34333
## BI_S3 4.024e-01 1.557e-01 2.584 0.01063 *
## BI_S4 -1.374e-01 1.521e-01 -0.904 0.36746
## BI_S5 -2.953e-02 1.558e-01 -0.190 0.84991
## autoconC 2.330e+00 2.535e+00 0.919 0.35948
## nivel -8.760e-06 2.163e-02 0.000 0.99968
## TM 1.561e-01 4.822e-01 0.324 0.74663
## autoconC:nivel 2.817e-03 3.002e-02 0.094 0.92535
## autoconC:TM -2.668e+00 1.115e+00 -2.392 0.01785 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.161787)
##
## Null deviance: 257.02 on 187 degrees of freedom
## Residual deviance: 214.95 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
##
##
## [[7]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9021 -1.0740 0.6416 0.9612 1.7670
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.43608 1.58625 0.905 0.3666
## EAC_S1 0.02922 0.10250 0.285 0.7759
## EAC_S2 0.26489 0.11762 2.252 0.0256 *
## EAC_S3 0.05940 0.11779 0.504 0.6148
## EAC_S4 -0.15964 0.14522 -1.099 0.2732
## EAC_S5 0.01371 0.15917 0.086 0.9315
## EAC_S6 0.05898 0.18632 0.317 0.7520
## OE_S1 0.10211 0.10828 0.943 0.3470
## OE_S2 0.13847 0.12496 1.108 0.2694
## OE_S3 -0.27181 0.17718 -1.534 0.1269
## BI_S1 -0.21329 0.10267 -2.077 0.0393 *
## BI_S2 0.02254 0.14384 0.157 0.8756
## BI_S3 0.32288 0.14202 2.273 0.0243 *
## BI_S4 -0.10398 0.14407 -0.722 0.4715
## BI_S5 -0.19443 0.15304 -1.270 0.2057
## autoconC 2.11231 2.40794 0.877 0.3816
## nivel -0.01866 0.02025 -0.922 0.3581
## TM 0.59504 0.47057 1.265 0.2078
## autoconC:nivel -0.01469 0.02966 -0.495 0.6210
## autoconC:TM -1.17877 0.86349 -1.365 0.1740
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.094101)
##
## Null deviance: 255.15 on 187 degrees of freedom
## Residual deviance: 225.88 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
##
## [[8]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9932 -1.0282 0.3268 1.0173 2.0585
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.82756 1.52883 0.541 0.58902
## EAC_S1 0.28698 0.11568 2.481 0.01409 *
## EAC_S2 0.20328 0.11601 1.752 0.08157 .
## EAC_S3 0.11348 0.12306 0.922 0.35779
## EAC_S4 -0.08927 0.14104 -0.633 0.52762
## EAC_S5 -0.15762 0.17469 -0.902 0.36819
## EAC_S6 -0.51959 0.20419 -2.545 0.01184 *
## OE_S1 -0.02750 0.11304 -0.243 0.80811
## OE_S2 0.06959 0.12354 0.563 0.57400
## OE_S3 -0.29236 0.17788 -1.644 0.10214
## BI_S1 -0.22077 0.10241 -2.156 0.03252 *
## BI_S2 -0.12912 0.14654 -0.881 0.37950
## BI_S3 0.15523 0.13538 1.147 0.25318
## BI_S4 0.08214 0.14014 0.586 0.55859
## BI_S5 -0.16421 0.14809 -1.109 0.26908
## autoconC 4.50225 2.55172 1.764 0.07948 .
## nivel -0.01710 0.01884 -0.908 0.36527
## TM 0.68799 0.45380 1.516 0.13138
## autoconC:nivel -0.01774 0.02807 -0.632 0.52829
## autoconC:TM -3.19895 1.09045 -2.934 0.00382 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.090418)
##
## Null deviance: 260.28 on 187 degrees of freedom
## Residual deviance: 220.35 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
##
## [[9]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2788 0.2198 0.4491 0.7393 1.4462
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.518075 1.453664 -0.356 0.7220
## EAC_S1 0.205918 0.124356 1.656 0.0996 .
## EAC_S2 0.057231 0.132556 0.432 0.6665
## EAC_S3 0.124386 0.145864 0.853 0.3950
## EAC_S4 0.054406 0.168057 0.324 0.7465
## EAC_S5 -0.298396 0.183825 -1.623 0.1064
## EAC_S6 0.212838 0.209712 1.015 0.3116
## OE_S1 0.189066 0.126748 1.492 0.1377
## OE_S2 -0.077921 0.152024 -0.513 0.6089
## OE_S3 -0.500484 0.210537 -2.377 0.0186 *
## BI_S1 -0.150841 0.117694 -1.282 0.2017
## BI_S2 0.111102 0.172948 0.642 0.5215
## BI_S3 0.007576 0.154816 0.049 0.9610
## BI_S4 -0.093263 0.154678 -0.603 0.5474
## BI_S5 -0.080302 0.184622 -0.435 0.6642
## autoconC -3.942957 2.623937 -1.503 0.1348
## nivel 0.015302 0.018695 0.819 0.4142
## TM 1.105094 0.519476 2.127 0.0349 *
## autoconC:nivel 0.053005 0.031882 1.663 0.0983 .
## autoconC:TM -0.007857 0.954739 -0.008 0.9934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.047164)
##
## Null deviance: 204.59 on 187 degrees of freedom
## Residual deviance: 172.00 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
##
##
## [[10]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1233 -1.0320 0.6477 0.9225 1.7113
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.05477 1.48826 -0.037 0.97069
## EAC_S1 0.09057 0.10225 0.886 0.37705
## EAC_S2 0.34871 0.12298 2.836 0.00514 **
## EAC_S3 0.18466 0.12623 1.463 0.14536
## EAC_S4 -0.07581 0.14555 -0.521 0.60315
## EAC_S5 0.15813 0.15843 0.998 0.31967
## EAC_S6 0.10233 0.19520 0.524 0.60081
## OE_S1 0.18163 0.11284 1.610 0.10936
## OE_S2 0.03210 0.14672 0.219 0.82706
## OE_S3 -0.12915 0.17804 -0.725 0.46922
## BI_S1 -0.19724 0.10354 -1.905 0.05848 .
## BI_S2 0.20881 0.14901 1.401 0.16297
## BI_S3 0.17987 0.13894 1.295 0.19725
## BI_S4 -0.15776 0.14418 -1.094 0.27542
## BI_S5 -0.17592 0.14997 -1.173 0.24244
## autoconC 1.75130 2.21378 0.791 0.43001
## nivel 0.00168 0.01858 0.090 0.92809
## TM 0.34024 0.46572 0.731 0.46606
## autoconC:nivel -0.01001 0.02772 -0.361 0.71850
## autoconC:TM -0.90028 0.88865 -1.013 0.31248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.126793)
##
## Null deviance: 254.44 on 187 degrees of freedom
## Residual deviance: 224.78 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
##
## [[11]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2956 -0.9045 0.5315 0.8845 1.9272
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.235306 1.493254 0.827 0.40926
## EAC_S1 0.226547 0.107959 2.098 0.03736 *
## EAC_S2 0.383032 0.132731 2.886 0.00442 **
## EAC_S3 0.118694 0.134840 0.880 0.37998
## EAC_S4 -0.149297 0.148857 -1.003 0.31733
## EAC_S5 0.079969 0.170296 0.470 0.63926
## EAC_S6 0.035278 0.203506 0.173 0.86258
## OE_S1 0.136638 0.116376 1.174 0.24201
## OE_S2 0.035701 0.145723 0.245 0.80676
## OE_S3 0.019710 0.179582 0.110 0.91273
## BI_S1 -0.348188 0.110648 -3.147 0.00195 **
## BI_S2 0.148565 0.160327 0.927 0.35544
## BI_S3 0.314336 0.145358 2.162 0.03199 *
## BI_S4 -0.044236 0.145463 -0.304 0.76143
## BI_S5 0.004615 0.153235 0.030 0.97601
## autoconC 1.902246 2.487370 0.765 0.44549
## nivel -0.014997 0.019552 -0.767 0.44416
## TM 0.468991 0.487065 0.963 0.33699
## autoconC:nivel 0.009014 0.029678 0.304 0.76172
## autoconC:TM -3.082144 1.120043 -2.752 0.00658 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.113315)
##
## Null deviance: 253.69 on 187 degrees of freedom
## Residual deviance: 209.75 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
##
## [[12]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2498 0.1540 0.3395 0.6053 1.6623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.488143 1.969587 -1.263 0.20824
## EAC_S1 0.402431 0.150196 2.679 0.00811 **
## EAC_S2 0.072598 0.132981 0.546 0.58584
## EAC_S3 -0.229156 0.142769 -1.605 0.11035
## EAC_S4 -0.034968 0.193414 -0.181 0.85675
## EAC_S5 -0.236965 0.184816 -1.282 0.20155
## EAC_S6 0.187586 0.223364 0.840 0.40220
## OE_S1 -0.003383 0.125934 -0.027 0.97860
## OE_S2 0.140561 0.133055 1.056 0.29230
## OE_S3 -0.106554 0.219204 -0.486 0.62753
## BI_S1 -0.348650 0.124370 -2.803 0.00565 **
## BI_S2 0.164650 0.194207 0.848 0.39775
## BI_S3 0.318626 0.180810 1.762 0.07985 .
## BI_S4 -0.022720 0.184688 -0.123 0.90224
## BI_S5 -0.157260 0.215058 -0.731 0.46565
## autoconC -0.421151 2.610330 -0.161 0.87202
## nivel 0.045895 0.025162 1.824 0.06994 .
## TM 1.460319 0.594414 2.457 0.01504 *
## autoconC:nivel 0.003077 0.032194 0.096 0.92398
## autoconC:TM 0.022838 0.913740 0.025 0.98009
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.8679743)
##
## Null deviance: 177.73 on 187 degrees of freedom
## Residual deviance: 138.26 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 6
##
##
## [[13]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8626 -1.0407 -0.3737 1.1101 1.7542
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.19382 1.51574 0.788 0.4320
## EAC_S1 0.19540 0.10565 1.849 0.0662 .
## EAC_S2 0.25403 0.12120 2.096 0.0376 *
## EAC_S3 0.08537 0.12439 0.686 0.4935
## EAC_S4 -0.08743 0.14258 -0.613 0.5406
## EAC_S5 0.12847 0.15809 0.813 0.4176
## EAC_S6 -0.02218 0.19646 -0.113 0.9102
## OE_S1 0.08369 0.11261 0.743 0.4584
## OE_S2 -0.31846 0.16374 -1.945 0.0535 .
## OE_S3 -0.22430 0.17567 -1.277 0.2034
## BI_S1 -0.08213 0.10077 -0.815 0.4162
## BI_S2 0.05816 0.14798 0.393 0.6948
## BI_S3 0.13587 0.13390 1.015 0.3117
## BI_S4 -0.10210 0.14153 -0.721 0.4716
## BI_S5 -0.16956 0.14246 -1.190 0.2356
## autoconC 1.65191 2.20237 0.750 0.4543
## nivel -0.02032 0.01876 -1.083 0.2802
## TM 0.26230 0.44966 0.583 0.5604
## autoconC:nivel 0.01096 0.02567 0.427 0.6700
## autoconC:TM -2.47581 1.01028 -2.451 0.0153 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.070658)
##
## Null deviance: 260.54 on 187 degrees of freedom
## Residual deviance: 226.12 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
##
## [[14]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0707 -0.9727 -0.4058 1.0002 1.9958
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.05163 1.74314 2.324 0.02130 *
## EAC_S1 0.08895 0.10788 0.825 0.41080
## EAC_S2 0.40609 0.13052 3.111 0.00219 **
## EAC_S3 -0.17319 0.13148 -1.317 0.18956
## EAC_S4 -0.22550 0.15470 -1.458 0.14680
## EAC_S5 0.22626 0.16106 1.405 0.16192
## EAC_S6 0.21629 0.21406 1.010 0.31374
## OE_S1 0.16504 0.11895 1.387 0.16713
## OE_S2 0.05437 0.12791 0.425 0.67135
## OE_S3 -0.12073 0.18191 -0.664 0.50779
## BI_S1 -0.21088 0.10676 -1.975 0.04987 *
## BI_S2 0.12971 0.14766 0.878 0.38097
## BI_S3 0.21564 0.13143 1.641 0.10274
## BI_S4 -0.06094 0.15506 -0.393 0.69481
## BI_S5 -0.24657 0.15235 -1.618 0.10745
## autoconC -0.55684 2.39773 -0.232 0.81664
## nivel -0.05478 0.02145 -2.554 0.01154 *
## TM -0.13067 0.48616 -0.269 0.78842
## autoconC:nivel 0.01346 0.02892 0.465 0.64231
## autoconC:TM -0.28391 0.89299 -0.318 0.75093
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.132724)
##
## Null deviance: 259.86 on 187 degrees of freedom
## Residual deviance: 221.81 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
##
## [[15]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6068 -0.9352 0.4430 0.9419 1.9821
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.055059 1.672712 -0.033 0.97378
## EAC_S1 0.237436 0.117364 2.023 0.04465 *
## EAC_S2 0.254901 0.130118 1.959 0.05177 .
## EAC_S3 0.192491 0.136271 1.413 0.15963
## EAC_S4 -0.135716 0.155594 -0.872 0.38432
## EAC_S5 -0.042354 0.173581 -0.244 0.80753
## EAC_S6 0.021405 0.203847 0.105 0.91650
## OE_S1 -0.047793 0.121515 -0.393 0.69459
## OE_S2 -0.211628 0.173852 -1.217 0.22520
## OE_S3 -0.279432 0.192732 -1.450 0.14897
## BI_S1 -0.214551 0.113997 -1.882 0.06156 .
## BI_S2 0.181481 0.164553 1.103 0.27166
## BI_S3 0.497596 0.167436 2.972 0.00339 **
## BI_S4 -0.154550 0.154463 -1.001 0.31848
## BI_S5 -0.239747 0.155680 -1.540 0.12544
## autoconC 2.934453 2.565084 1.144 0.25425
## nivel -0.002608 0.021388 -0.122 0.90309
## TM 0.933220 0.491843 1.897 0.05949 .
## autoconC:nivel 0.011614 0.028511 0.407 0.68428
## autoconC:TM -4.212394 1.358996 -3.100 0.00227 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.166102)
##
## Null deviance: 254.44 on 187 degrees of freedom
## Residual deviance: 209.94 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
##
##
## [[16]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5986 0.2299 0.4326 0.6623 1.8459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.063474 1.759215 -1.741 0.0834 .
## EAC_S1 0.217742 0.138650 1.570 0.1182
## EAC_S2 0.009189 0.133008 0.069 0.9450
## EAC_S3 -0.367682 0.150121 -2.449 0.0153 *
## EAC_S4 0.120645 0.189317 0.637 0.5248
## EAC_S5 -0.414695 0.194048 -2.137 0.0340 *
## EAC_S6 -0.309431 0.237681 -1.302 0.1947
## OE_S1 0.049709 0.131808 0.377 0.7066
## OE_S2 0.224112 0.150384 1.490 0.1380
## OE_S3 -0.026128 0.210329 -0.124 0.9013
## BI_S1 -0.240249 0.121138 -1.983 0.0490 *
## BI_S2 -0.147828 0.181128 -0.816 0.4156
## BI_S3 -0.029854 0.156111 -0.191 0.8486
## BI_S4 -0.043953 0.182692 -0.241 0.8102
## BI_S5 -0.142638 0.215252 -0.663 0.5085
## autoconC 2.153725 2.617706 0.823 0.4118
## nivel 0.050343 0.023190 2.171 0.0313 *
## TM 1.141248 0.580313 1.967 0.0509 .
## autoconC:nivel -0.025405 0.033248 -0.764 0.4459
## autoconC:TM -0.254747 1.016274 -0.251 0.8024
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.017723)
##
## Null deviance: 194.62 on 187 degrees of freedom
## Residual deviance: 158.07 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
##
##
## [[17]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2909 -1.0611 0.5461 0.9679 1.8353
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.52733 1.52358 0.346 0.7297
## EAC_S1 -0.07621 0.10102 -0.754 0.4517
## EAC_S2 0.31328 0.12389 2.529 0.0124 *
## EAC_S3 0.18003 0.12386 1.453 0.1480
## EAC_S4 -0.20001 0.14987 -1.335 0.1838
## EAC_S5 -0.01855 0.15685 -0.118 0.9060
## EAC_S6 0.22208 0.19150 1.160 0.2478
## OE_S1 0.15714 0.11170 1.407 0.1613
## OE_S2 -0.06050 0.14950 -0.405 0.6862
## OE_S3 -0.24962 0.17820 -1.401 0.1631
## BI_S1 -0.07319 0.10131 -0.722 0.4711
## BI_S2 0.33042 0.14666 2.253 0.0256 *
## BI_S3 0.24799 0.14263 1.739 0.0839 .
## BI_S4 -0.20393 0.14936 -1.365 0.1739
## BI_S5 -0.15357 0.14961 -1.026 0.3062
## autoconC 0.34867 2.23532 0.156 0.8762
## nivel -0.01187 0.01976 -0.601 0.5488
## TM 0.86772 0.46907 1.850 0.0661 .
## autoconC:nivel 0.02297 0.02765 0.831 0.4073
## autoconC:TM -2.38681 0.95374 -2.503 0.0133 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.11922)
##
## Null deviance: 258.49 on 187 degrees of freedom
## Residual deviance: 225.74 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
##
## [[18]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8583 -1.0234 0.6160 0.9516 1.9649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.748943 1.535378 0.488 0.6263
## EAC_S1 -0.002316 0.103204 -0.022 0.9821
## EAC_S2 0.352510 0.123028 2.865 0.0047 **
## EAC_S3 0.083258 0.120756 0.689 0.4915
## EAC_S4 -0.088046 0.146051 -0.603 0.5474
## EAC_S5 0.115441 0.158164 0.730 0.4665
## EAC_S6 0.099240 0.191235 0.519 0.6045
## OE_S1 0.162381 0.108256 1.500 0.1355
## OE_S2 0.176211 0.126386 1.394 0.1651
## OE_S3 -0.094199 0.173123 -0.544 0.5871
## BI_S1 -0.214260 0.099859 -2.146 0.0333 *
## BI_S2 0.252106 0.141786 1.778 0.0772 .
## BI_S3 0.119133 0.132792 0.897 0.3709
## BI_S4 -0.167874 0.143393 -1.171 0.2434
## BI_S5 -0.180359 0.149697 -1.205 0.2300
## autoconC 1.707100 2.282693 0.748 0.4556
## nivel -0.010936 0.019814 -0.552 0.5817
## TM 0.343575 0.455726 0.754 0.4520
## autoconC:nivel 0.002351 0.027121 0.087 0.9310
## autoconC:TM -1.498878 0.958181 -1.564 0.1196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.098885)
##
## Null deviance: 254.44 on 187 degrees of freedom
## Residual deviance: 224.08 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
##
## [[19]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9230 -1.0253 0.4724 0.9241 2.0034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.745408 1.562545 -1.117 0.26558
## EAC_S1 0.022993 0.102110 0.225 0.82212
## EAC_S2 0.356933 0.127764 2.794 0.00582 **
## EAC_S3 0.181854 0.125133 1.453 0.14801
## EAC_S4 -0.165606 0.145366 -1.139 0.25623
## EAC_S5 -0.149786 0.160445 -0.934 0.35187
## EAC_S6 0.097857 0.186104 0.526 0.59971
## OE_S1 0.245405 0.115496 2.125 0.03507 *
## OE_S2 -0.149742 0.161873 -0.925 0.35626
## OE_S3 -0.129956 0.181566 -0.716 0.47514
## BI_S1 -0.170307 0.102079 -1.668 0.09710 .
## BI_S2 0.282760 0.151289 1.869 0.06336 .
## BI_S3 0.437964 0.156228 2.803 0.00565 **
## BI_S4 -0.047090 0.147875 -0.318 0.75054
## BI_S5 -0.168258 0.150435 -1.118 0.26496
## autoconC 3.106024 2.332846 1.331 0.18485
## nivel 0.013600 0.020364 0.668 0.50516
## TM 1.329495 0.490460 2.711 0.00741 **
## autoconC:nivel -0.004333 0.027920 -0.155 0.87685
## autoconC:TM -2.681261 0.995948 -2.692 0.00782 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.050221)
##
## Null deviance: 256.44 on 187 degrees of freedom
## Residual deviance: 213.99 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
##
##
## [[20]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8715 -0.8552 0.4509 0.8145 1.5974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.780337 1.622610 -1.097 0.27412
## EAC_S1 0.369038 0.140805 2.621 0.00957 **
## EAC_S2 0.144506 0.134736 1.073 0.28503
## EAC_S3 -0.209269 0.148958 -1.405 0.16190
## EAC_S4 -0.087756 0.178865 -0.491 0.62433
## EAC_S5 -0.215299 0.186373 -1.155 0.24965
## EAC_S6 0.078173 0.212901 0.367 0.71395
## OE_S1 -0.001989 0.131228 -0.015 0.98793
## OE_S2 -0.093522 0.147022 -0.636 0.52557
## OE_S3 -0.397646 0.216561 -1.836 0.06810 .
## BI_S1 -0.219286 0.130386 -1.682 0.09446 .
## BI_S2 0.078742 0.178410 0.441 0.65953
## BI_S3 0.523701 0.197990 2.645 0.00894 **
## BI_S4 -0.050225 0.166775 -0.301 0.76367
## BI_S5 -0.230884 0.185477 -1.245 0.21493
## autoconC -1.821015 2.510170 -0.725 0.46918
## nivel 0.026960 0.019436 1.387 0.16725
## TM 0.524501 0.543528 0.965 0.33593
## autoconC:nivel 0.031899 0.029439 1.084 0.28011
## autoconC:TM 0.317939 0.998593 0.318 0.75059
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.27284)
##
## Null deviance: 229.00 on 187 degrees of freedom
## Residual deviance: 187.86 on 168 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5Regressão linear com a soma das decisões
#-----------------------------------------------------------------------
# Agrega com a soma das decisões e média da confiança por
# indivíduo:aucoton.
dd <- db %>%
group_by(Participantes, autocon) %>%
summarise(acerto = sum(acerto),
nivel = mean(nivel)) %>%
ungroup() %>%
full_join(da_scores)## Joining, by = "Participantes"# Ajuste com resultados agregados por unidade experimental.
fit <- lm(acerto ~
EAC_S1 + EAC_S2 + EAC_S3 + EAC_S4 + EAC_S5 + EAC_S6 +
OE_S1 + OE_S2 + OE_S3 +
BI_S1 + BI_S2 + BI_S3 + BI_S4 + BI_S5 +
autocon * nivel,
data = dd)
# Quadros para os testes dos efeitos de cada termo.
anova(fit)## Analysis of Variance Table
##
## Response: acerto
## Df Sum Sq Mean Sq F value Pr(>F)
## EAC_S1 1 5.7 5.72 0.0425 0.83731
## EAC_S2 1 164.1 164.14 1.2187 0.27309
## EAC_S3 1 75.4 75.36 0.5595 0.45677
## EAC_S4 1 16.8 16.81 0.1248 0.72481
## EAC_S5 1 13.3 13.26 0.0985 0.75455
## EAC_S6 1 11.6 11.64 0.0864 0.76960
## OE_S1 1 218.9 218.85 1.6250 0.20628
## OE_S2 1 19.3 19.28 0.1431 0.70623
## OE_S3 1 147.1 147.12 1.0923 0.29927
## BI_S1 1 758.5 758.51 5.6319 0.02016 *
## BI_S2 1 36.2 36.16 0.2684 0.60588
## BI_S3 1 227.8 227.80 1.6914 0.19735
## BI_S4 1 56.2 56.21 0.4174 0.52019
## BI_S5 1 41.8 41.79 0.3103 0.57914
## autocon 1 220.2 220.22 1.6351 0.20489
## nivel 1 0.0 0.00 0.0000 0.99974
## autocon:nivel 1 3.0 2.96 0.0220 0.88257
## Residuals 76 10235.8 134.68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1drop1(fit, test = "F", scope = . ~ .)## Single term deletions
##
## Model:
## acerto ~ EAC_S1 + EAC_S2 + EAC_S3 + EAC_S4 + EAC_S5 + EAC_S6 +
## OE_S1 + OE_S2 + OE_S3 + BI_S1 + BI_S2 + BI_S3 + BI_S4 + BI_S5 +
## autocon * nivel
## Df Sum of Sq RSS AIC F value Pr(>F)
## <none> 10236 476.89
## EAC_S1 1 119.98 10356 475.99 0.8908 0.34825
## EAC_S2 1 708.10 10944 481.18 5.2576 0.02462 *
## EAC_S3 1 3.18 10239 474.92 0.0236 0.87830
## EAC_S4 1 13.11 10249 475.01 0.0974 0.75587
## EAC_S5 1 11.09 10247 474.99 0.0824 0.77490
## EAC_S6 1 6.54 10242 474.95 0.0486 0.82614
## OE_S1 1 153.16 10389 476.29 1.1372 0.28962
## OE_S2 1 40.46 10276 475.26 0.3004 0.58525
## OE_S3 1 134.95 10371 476.12 1.0020 0.32000
## BI_S1 1 636.76 10873 480.57 4.7279 0.03279 *
## BI_S2 1 32.56 10268 475.19 0.2418 0.62434
## BI_S3 1 300.22 10536 477.61 2.2291 0.13957
## BI_S4 1 41.27 10277 475.27 0.3065 0.58149
## BI_S5 1 60.81 10297 475.45 0.4515 0.50364
## autocon 1 0.03 10236 474.89 0.0002 0.98895
## nivel 1 1.69 10238 474.91 0.0126 0.91110
## autocon:nivel 1 2.96 10239 474.92 0.0220 0.88257
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Estimativas dos parâmetros.
summary(fit)##
## Call:
## lm(formula = acerto ~ EAC_S1 + EAC_S2 + EAC_S3 + EAC_S4 + EAC_S5 +
## EAC_S6 + OE_S1 + OE_S2 + OE_S3 + BI_S1 + BI_S2 + BI_S3 +
## BI_S4 + BI_S5 + autocon * nivel, data = dd)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.474 -8.682 2.634 7.515 20.026
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.71494 17.37758 1.480 0.1431
## EAC_S1 0.68867 0.72966 0.944 0.3482
## EAC_S2 1.84049 0.80268 2.293 0.0246 *
## EAC_S3 -0.13137 0.85507 -0.154 0.8783
## EAC_S4 -0.31982 1.02497 -0.312 0.7559
## EAC_S5 -0.31478 1.09684 -0.287 0.7749
## EAC_S6 -0.28884 1.31040 -0.220 0.8261
## OE_S1 0.81713 0.76625 1.066 0.2896
## OE_S2 0.48331 0.88185 0.548 0.5853
## OE_S3 -1.23040 1.22917 -1.001 0.3200
## BI_S1 -1.54712 0.71152 -2.174 0.0328 *
## BI_S2 0.49204 1.00067 0.492 0.6243
## BI_S3 1.35652 0.90857 1.493 0.1396
## BI_S4 -0.56079 1.01300 -0.554 0.5815
## BI_S5 -0.70583 1.05038 -0.672 0.5036
## autoconC 0.28313 20.37068 0.014 0.9889
## nivel -0.02502 0.22337 -0.112 0.9111
## autoconC:nivel 0.03868 0.26094 0.148 0.8826
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.61 on 76 degrees of freedom
## Multiple R-squared: 0.1645, Adjusted R-squared: -0.02234
## F-statistic: 0.8804 on 17 and 76 DF, p-value: 0.598# Avaliação dos pressupostos.
par(mfrow = c(2, 2))
plot(fit)
layout(1)
#-----------------------------------------------------------------------Experimento 2
Leitura do arquivo de dados
#-----------------------------------------------------------------------
# Informações sobre os arquivos de dados.
rm(list = objects())
# BASE_EXPERIMENTO 2.txt -----------------------------------------------
# TM: acertou ou errou a tarefa de esgotamento.
# TLME: tarefa de livre manifestação escrita.
# D_*: são as decisões (0 ou 1).
# C_*: nível de confiança das decisões.
# BI_*: respostas para questões de business impusiviness.
# EAC_*: respostas para questões de escala de autocontrole.
# OE_*: orçamento empresarial.
# VI_*: verificação das intruções.
#-----------------------------------------------------------------------
# Exibe o conteúdo do diretório de trabalho.
dir()## [1] "analise_files" "analise.html"
## [3] "analise.R" "analise.Rmd"
## [5] "BASE_EXPERIMENTO 1.txt" "BASE_EXPERIMENTO 1.xlsx"
## [7] "BASE_EXPERIMENTO 2.txt" "BASE_EXPERIMENTO 2.xlsx"# Importa a base de dados.
da <- read_tsv("BASE_EXPERIMENTO 2.txt")## Parsed with column specification:
## cols(
## .default = col_integer(),
## Participantes = col_character(),
## Curso_F = col_character()
## )## See spec(...) for full column specifications.attr(da, "spec") <- NULL
str(da)## Classes 'tbl_df', 'tbl' and 'data.frame': 60 obs. of 154 variables:
## $ Participantes: chr "S_A1" "S_A2" "S_A3" "S_A4" ...
## $ TM : int 0 0 0 0 0 1 0 0 0 0 ...
## $ TLME : int 1 1 1 0 1 0 0 0 1 1 ...
## $ D_1 : int 0 0 0 0 0 1 1 0 0 1 ...
## $ D_2 : int 0 1 1 0 1 1 1 1 1 0 ...
## $ D_3 : int 0 0 1 0 1 1 1 1 1 0 ...
## $ D_4 : int 1 1 1 1 1 1 1 1 1 0 ...
## $ D_5 : int 0 1 1 1 1 1 1 0 1 1 ...
## $ D_6 : int 0 1 1 1 0 1 1 0 0 0 ...
## $ D_7 : int 0 1 1 1 1 1 1 0 0 0 ...
## $ D_8 : int 0 0 0 0 1 1 1 0 1 0 ...
## $ D_9 : int 1 0 1 1 1 1 1 1 1 1 ...
## $ D_10 : int 0 0 1 1 1 1 1 0 1 0 ...
## $ D_11 : int 0 1 1 1 1 1 1 0 0 1 ...
## $ D_12 : int 1 0 1 1 1 0 1 1 1 1 ...
## $ D_13 : int 0 0 1 1 1 1 1 0 1 1 ...
## $ D_14 : int 1 0 1 1 0 1 1 0 1 0 ...
## $ D_15 : int 0 0 1 1 0 1 1 0 1 1 ...
## $ D_16 : int 0 0 1 1 1 1 1 1 1 1 ...
## $ D_17 : int 1 1 1 1 0 0 1 0 1 0 ...
## $ D_18 : int 0 1 1 1 1 0 1 0 1 0 ...
## $ D_19 : int 0 1 1 1 0 1 1 0 1 0 ...
## $ D_20 : int 1 0 1 1 1 1 1 0 1 1 ...
## $ D_21 : int 0 1 1 1 0 1 1 0 1 0 ...
## $ D_22 : int 0 1 1 1 1 0 1 0 1 0 ...
## $ D_23 : int 1 0 1 1 1 0 1 0 1 0 ...
## $ D_24 : int 1 0 1 1 0 1 1 0 1 0 ...
## $ D_25 : int 1 0 1 1 1 0 1 1 1 1 ...
## $ D_26 : int 0 1 1 1 0 1 1 1 1 0 ...
## $ D_27 : int 0 0 1 1 0 0 1 1 1 0 ...
## $ D_28 : int 0 0 1 1 0 1 1 1 1 0 ...
## $ D_29 : int 1 0 1 1 1 1 1 1 1 1 ...
## $ D_30 : int 0 1 1 0 1 1 1 1 1 0 ...
## $ D_31 : int 0 1 1 1 1 1 1 0 1 0 ...
## $ D_32 : int 1 0 1 1 0 1 1 0 1 1 ...
## $ D_33 : int 0 0 1 1 0 1 1 0 1 0 ...
## $ D_34 : int 0 1 1 1 0 0 1 0 1 0 ...
## $ D_35 : int 0 1 1 1 0 1 1 0 1 0 ...
## $ D_36 : int 1 0 1 1 0 1 1 1 1 1 ...
## $ D_37 : int 0 0 1 1 0 1 1 0 1 0 ...
## $ D_38 : int 0 1 1 1 1 1 1 0 1 0 ...
## $ D_39 : int 0 0 1 1 0 1 1 0 1 0 ...
## $ D_40 : int 1 1 0 0 1 1 1 1 1 1 ...
## $ C_1 : int 10 6 7 8 10 9 9 9 10 7 ...
## $ C_2 : int 9 8 7 6 9 9 10 7 9 8 ...
## $ C_3 : int 9 8 7 9 10 9 10 5 9 8 ...
## $ C_4 : int 9 8 8 9 8 9 10 5 8 6 ...
## $ C_5 : int 9 8 8 7 8 9 10 5 9 6 ...
## $ C_6 : int 9 8 7 8 8 9 10 9 9 8 ...
## $ C_7 : int 9 8 7 9 8 9 10 9 9 7 ...
## $ C_8 : int 9 9 7 6 8 9 10 10 8 8 ...
## $ C_9 : int 9 7 8 7 8 9 10 6 9 6 ...
## $ C_10 : int 9 7 7 7 7 9 10 8 8 7 ...
## $ C_11 : int 9 6 8 7 7 9 10 7 9 7 ...
## $ C_12 : int 9 7 8 8 7 9 10 5 9 7 ...
## $ C_13 : int 9 6 8 7 7 9 10 8 9 6 ...
## $ C_14 : int 9 6 8 7 9 9 10 7 9 7 ...
## $ C_15 : int 9 6 8 7 9 9 10 8 8 7 ...
## $ C_16 : int 9 8 8 8 7 9 10 5 9 8 ...
## $ C_17 : int 9 6 8 8 7 9 10 7 8 8 ...
## $ C_18 : int 9 6 8 8 7 9 10 7 9 7 ...
## $ C_19 : int 9 6 8 8 8 9 10 8 9 6 ...
## $ C_20 : int 9 6 8 8 8 9 10 6 9 8 ...
## $ C_21 : int 9 6 7 7 7 9 10 7 8 7 ...
## $ C_22 : int 9 5 8 7 8 9 10 8 7 7 ...
## $ C_23 : int 9 7 8 7 7 9 10 6 8 7 ...
## $ C_24 : int 9 6 8 7 7 9 10 6 8 8 ...
## $ C_25 : int 9 7 8 7 6 9 10 5 8 7 ...
## $ C_26 : int 9 7 8 7 8 9 10 8 8 8 ...
## $ C_27 : int 9 8 8 7 8 9 10 8 8 7 ...
## $ C_28 : int 9 6 7 7 9 9 10 8 8 6 ...
## $ C_29 : int 9 6 8 7 6 9 10 8 8 7 ...
## $ C_30 : int 9 7 8 7 6 9 10 8 8 7 ...
## $ C_31 : int 9 7 6 8 6 9 10 7 8 7 ...
## $ C_32 : int 9 7 7 8 7 9 10 6 8 8 ...
## $ C_33 : int 9 6 7 9 8 9 10 8 8 7 ...
## $ C_34 : int 9 5 7 7 7 9 10 8 8 7 ...
## $ C_35 : int 9 6 7 7 7 9 10 7 8 8 ...
## $ C_36 : int 9 6 7 8 8 9 10 5 8 7 ...
## $ C_37 : int 9 7 7 8 7 9 10 7 8 7 ...
## $ C_38 : int 9 6 7 8 7 9 10 7 8 8 ...
## $ C_39 : int 9 6 7 8 7 9 10 7 8 7 ...
## $ C_40 : int 9 6 7 5 7 9 10 5 8 8 ...
## $ BI_1 : int 4 2 4 3 4 3 3 2 1 3 ...
## $ BI_2 : int 1 3 1 1 2 1 2 3 4 2 ...
## $ BI_3 : int 2 2 2 3 2 3 1 2 2 2 ...
## $ BI_4 : int 2 1 1 2 1 1 1 1 1 2 ...
## $ BI_5 : int 1 3 1 2 2 3 1 4 2 1 ...
## $ BI_6 : int 1 2 2 3 3 1 1 2 4 2 ...
## $ BI_7 : int 4 1 3 2 4 4 4 4 4 2 ...
## $ BI_8 : int 3 3 3 3 2 3 3 2 4 3 ...
## $ BI_9 : int 3 3 3 2 3 3 4 3 3 3 ...
## $ BI_10 : int 2 1 3 3 3 3 3 3 2 3 ...
## $ BI_11 : int 2 2 1 3 3 3 1 4 4 3 ...
## $ BI_12 : int 4 2 4 3 3 1 4 1 2 3 ...
## $ BI_13 : int 4 4 3 3 3 3 4 4 4 3 ...
## $ BI_14 : int 1 3 1 1 2 3 1 4 4 2 ...
## $ BI_15 : int 2 3 3 3 2 1 4 3 4 2 ...
## $ BI_16 : int 1 1 2 1 1 1 2 1 1 1 ...
## [list output truncated]# Indivíduo com tuplas preenchimento errado.
da <- da %>%
filter(Participantes != "S_C18")
# Criar o tratamento de autocontrole.
da$autocon <- da$Participantes %>%
substr(start = 3, stop = 3) %>%
as.factor()
#-----------------------------------------------------------------------
# Tabela que associa os nomes das questões que são as mesmas.
# Correspondência entre as decisões.
decis <- matrix(data = sprintf("%02d", 1:40), ncol = 2)
# Renomeia os números para ter dois digitos, então 1 fica 01.
names(da) <- names(da) %>%
str_replace(pattern = "(.*)(_)(\\d)$",
replacement = "\\1\\20\\3")Análise de componentes principais
As variáveis de BI foram medidas para quantificar as diferenças sobre a impulsividade entre os participantes. Imagina-se que as respostas para as questões de BI possam ser explicadas por um conjunto pequeno de fatores latentes. O mesmo para OE e EAC. Para determinar o índice de impulsividade individual, será feita a análise de componentes principais com as respostas do questionário de BI. O número de componentes ideal a ser usado na análise de regressão será determinado depois.
Business impulsiviness
#-----------------------------------------------------------------------
# Análise de componentes principais nas variáveis BI_*.
# Extrai e cria uma matriz com as variáveis de BI_*.
X <- da %>%
select(contains("BI"))
dim(X)## [1] 59 30bi_basica <- X %>%
gather(key = "BI", value = "valor") %>%
group_by(BI) %>%
summarise(n = n(),
média = mean(valor),
mediana = median(valor),
desvpad = sd(valor),
mínimo = min(valor),
máximo = max(valor)) %>%
mutate(BI = str_replace(BI, "BI_", ""))
bi_basica %>%
print(n = Inf)## # A tibble: 30 x 7
## BI n média mediana desvpad mínimo máximo
## <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 01 59 2.90 3 0.803 1 4
## 2 02 59 1.80 2 0.783 1 4
## 3 03 59 2.36 2 0.737 1 4
## 4 04 59 1.25 1 0.477 1 3
## 5 05 59 1.71 2 0.811 1 4
## 6 06 59 2.03 2 0.909 1 4
## 7 07 59 3.02 3 1.06 1 4
## 8 08 59 3.03 3 0.694 2 4
## 9 09 59 2.78 3 0.696 1 4
## 10 10 59 2.69 3 0.933 1 4
## 11 11 59 2.05 2 1.01 1 4
## 12 12 59 3.08 3 0.896 1 4
## 13 13 59 3.46 4 0.678 1 4
## 14 14 59 1.90 2 0.923 1 4
## 15 15 59 2.51 3 0.954 1 4
## 16 16 59 1.47 1 0.728 1 4
## 17 17 59 1.64 2 0.609 1 3
## 18 18 59 2.19 2 0.840 1 4
## 19 19 59 1.75 2 0.709 1 4
## 20 20 59 2.76 3 0.773 1 4
## 21 21 59 1.29 1 0.671 1 4
## 22 22 59 1.90 2 0.736 1 4
## 23 23 59 2.07 2 1.10 1 4
## 24 24 59 1.85 2 0.827 1 4
## 25 25 59 1.56 1 0.815 1 4
## 26 26 59 2.88 3 0.930 1 4
## 27 27 59 2.17 2 0.834 1 4
## 28 28 59 2.19 2 0.900 1 4
## 29 29 59 2.59 3 1.00 1 4
## 30 30 59 3.19 3 0.861 1 4# Gráfico com média e segmentos de erro para 1 desvio-padrão.
ggplot(bi_basica, aes(x = BI, y = média)) +
geom_point() +
geom_errorbar(aes(ymin = média - desvpad,
ymax = média + desvpad),
width = 0.5) +
xlab("Business impulsiviness") +
ylab(expression("Média" %+-% "desvio padrão")) +
coord_flip()
# Cria a matriz para fazer a análise de componentes principais.
X <- X %>%
as.matrix()
dim(X)## [1] 59 30# Renomeia para uma melhor exibição no biplot.
colnames(X) <- colnames(X) %>%
str_replace("BI_", "")
# Aplica componentes principais.
acp <- princomp(X, cor = TRUE)
# summary(acp)
# Exibe o resultado da ACP.
print(acp$loadings, digits = 2, cut = 0.2, sort = TRUE)##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## 30 0.24 -0.46
## 09 0.28 -0.24 -0.32
## 01 0.25 0.22 -0.23
## 02 -0.24 -0.23 -0.33 -0.25
## 03 0.24 -0.22 0.33 -0.20 -0.32
## 04 -0.38 -0.28
## 05 -0.35 0.31 -0.26
## 06 -0.29 0.25 0.22
## 07 0.24 -0.31 -0.22
## 08 0.36
## 10 0.22 0.29
## 11 0.34 -0.30
## 12 0.22 0.22 -0.27
## 13 0.23 -0.21 -0.20 -0.29
## 14 -0.28 0.22 -0.32
## 15 0.27 0.26 -0.27 -0.23 -0.27
## 16 0.27 0.36 -0.25 -0.24
## 17 -0.30 0.21
## 18 0.30 -0.25 -0.21 -0.35
## 19 -0.29
## 20 0.23 -0.29 -0.25
## 21 0.30 -0.25 0.30 -0.32 -0.29
## 22 -0.24 -0.24 -0.21 -0.23
## 23 0.28 0.25 0.22 0.31 0.25
## 24 0.21 0.32 0.33 0.41
## 25 -0.23 0.27 -0.36
## 26 0.30 0.28
## 27 0.25 -0.37 0.25
## 28 0.28 -0.29
## 29 -0.23 0.45 -0.41
## Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Comp.16 Comp.17 Comp.18 Comp.19
## 30 0.20 0.24 -0.26
## 09
## 01 0.28 0.26 -0.26
## 02 0.37
## 03 -0.21 -0.33 -0.21
## 04 -0.31 -0.21 0.34 0.29
## 05 -0.35 -0.22 0.33
## 06
## 07 0.31 -0.24
## 08 -0.24 -0.34 -0.28
## 10 0.26 0.33 0.25 -0.24 0.28
## 11 -0.22 0.27
## 12 -0.33 0.39 0.23 0.33
## 13 -0.37 0.23 -0.38 -0.34
## 14
## 15 0.34 -0.38
## 16 -0.31 0.29 0.22 -0.37
## 17 -0.27 -0.23
## 18 -0.26 -0.26 -0.34
## 19 0.21
## 20 0.39 -0.24 0.29
## 21 -0.22 -0.22 -0.22 0.33
## 22 0.30 -0.34 0.30
## 23 -0.44 -0.35
## 24 -0.33 -0.21 0.24 -0.22
## 25 0.45 -0.21 0.24
## 26 -0.36 0.36
## 27 -0.26 -0.22 -0.22
## 28 -0.24 0.31 0.45
## 29 -0.27 0.33
## Comp.20 Comp.21 Comp.22 Comp.23 Comp.24 Comp.25 Comp.26 Comp.27 Comp.28
## 30 -0.51
## 09 0.30 -0.23 -0.37 -0.52
## 01 0.44 0.23 -0.22 0.27
## 02 0.22 0.45
## 03 0.22 0.32 0.24 -0.20
## 04 0.25
## 05 0.22 -0.26
## 06 -0.36 0.37 -0.29 0.22 0.26 -0.34
## 07 -0.21 0.22 0.29 0.34 0.21 0.24
## 08 0.24 -0.22 0.24 0.23
## 10 -0.25 -0.21
## 11 -0.26 0.38
## 12 -0.21 0.30
## 13
## 14 -0.20 -0.27 0.26 -0.26
## 15 -0.30 -0.25
## 16 -0.27
## 17 -0.25 -0.21 -0.32 -0.35 0.24
## 18 -0.30 0.25 -0.22
## 19 0.30 0.43 0.49 0.34
## 20 -0.40 0.29 0.30
## 21 0.32
## 22 -0.43 -0.27
## 23 -0.27
## 24 -0.24 0.20
## 25 0.24
## 26 0.31 -0.29 -0.33
## 27 -0.29 -0.21 0.23
## 28 0.38
## 29 0.25 0.24
## Comp.29 Comp.30
## 30
## 09 0.20
## 01
## 02 0.35
## 03
## 04 -0.21
## 05 0.25
## 06
## 07
## 08 -0.45
## 10 -0.26 0.23
## 11 0.44
## 12
## 13 0.28
## 14 -0.47
## 15
## 16
## 17 0.25 -0.37
## 18
## 19
## 20
## 21 0.21
## 22
## 23
## 24
## 25 -0.33
## 26
## 27 0.33
## 28 -0.22
## 29
##
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
## Cumulative Var 0.03 0.07 0.10 0.13 0.17 0.20 0.23 0.27
## Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.03 0.03 0.03 0.03 0.03 0.03 0.03
## Cumulative Var 0.30 0.33 0.37 0.40 0.43 0.47 0.50
## Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 Comp.21 Comp.22
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.03 0.03 0.03 0.03 0.03 0.03 0.03
## Cumulative Var 0.53 0.57 0.60 0.63 0.67 0.70 0.73
## Comp.23 Comp.24 Comp.25 Comp.26 Comp.27 Comp.28 Comp.29
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.03 0.03 0.03 0.03 0.03 0.03 0.03
## Cumulative Var 0.77 0.80 0.83 0.87 0.90 0.93 0.97
## Comp.30
## SS loadings 1.00
## Proportion Var 0.03
## Cumulative Var 1.00# Proporção de variância acumulada.
plot(cbind(x = 1:length(acp$sdev),
y = c(cumsum(acp$sdev^2))/sum(acp$sdev^2)),
type = "o",
ylim = c(0, 1),
xlab = "Componente",
ylab = "Proporção de variância acumulada")
abline(h = c(0.5, 0.75, 0.9), lty = 2)
# Biplot.
biplot(acp, choices = c(1, 2))
# Escores para serem usados na regressão.
S <- acp$scores
colnames(S) <- str_replace(colnames(S), "Comp\\.", "BI_S")
# pairs(S[, 1:6])
# Concatena os escores com as demais variáveis.
da <- cbind(da, S)Orçamento empresarial
#-----------------------------------------------------------------------
# Análise de componentes principais nas variáveis OE_*.
# Extrai e cria uma matriz com as variáveis de OE_*.
X <- da %>%
select(starts_with("OE"))
oe_basica <- X %>%
gather(key = "OE", value = "valor") %>%
group_by(OE) %>%
summarise(n = n(),
média = mean(valor),
mediana = median(valor),
desvpad = sd(valor),
mínimo = min(valor),
máximo = max(valor)) %>%
mutate(OE = str_replace(OE, "OE_", ""))
oe_basica %>%
print(n = Inf)## # A tibble: 9 x 7
## OE n média mediana desvpad mínimo máximo
## <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 01 59 5.90 7 1.59 1 7
## 2 02 59 5.61 6 1.68 1 7
## 3 03 59 3.68 3 2.18 1 7
## 4 04 59 3.47 3 2.20 1 7
## 5 05 59 3.64 3 2.30 1 7
## 6 06 59 3.25 3 2.23 1 7
## 7 07 59 6.25 7 1.17 1 7
## 8 08 59 6.49 7 0.728 4 7
## 9 09 59 5.95 7 1.51 1 7# Gráfico com média e segmentos de erro para 1 desvio-padrão.
ggplot(oe_basica, aes(x = OE, y = média)) +
geom_point() +
geom_errorbar(aes(ymin = média - desvpad,
ymax = média + desvpad),
width = 0.5) +
xlab("Orçamento empresarial") +
ylab(expression("Média" %+-% "desvio padrão")) +
coord_flip()
# Cria a matriz para fazer a análise de componentes principais.
X <- X %>%
as.matrix()
dim(X)## [1] 59 9# Renomeia para uma melhor exibição no biplot.
colnames(X) <- colnames(X) %>%
str_replace("OE_", "")
# Aplica componentes principais.
acp <- princomp(X, cor = TRUE)
# summary(acp)
# Exibe o resultado da ACP.
print(acp$loadings, digits = 2, cut = 0.2, sort = TRUE)##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9
## 01 -0.58 0.35 0.47 0.42 -0.31
## 02 -0.57 0.39 0.21 -0.47 -0.35 -0.24 0.27
## 09 -0.46 0.84
## 07 -0.39 -0.44 -0.28 0.70
## 08 -0.35 -0.54 -0.32 -0.61 -0.27
## 06 -0.46 0.25 -0.46 0.52 0.40
## 03 -0.47 -0.26 0.25 -0.54 0.55
## 05 -0.48 0.33 0.20 -0.58 0.48
## 04 -0.48 -0.22 -0.32 -0.74
##
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11
## Cumulative Var 0.11 0.22 0.33 0.44 0.56 0.67 0.78 0.89
## Comp.9
## SS loadings 1.00
## Proportion Var 0.11
## Cumulative Var 1.00# Proporção de variância acumulada.
plot(cbind(x = 1:length(acp$sdev),
y = c(cumsum(acp$sdev^2))/sum(acp$sdev^2)),
type = "o",
ylim = c(0, 1),
xlab = "Componente",
ylab = "Proporção de variância acumulada")
abline(h = c(0.5, 0.75, 0.9), lty = 2)
# Biplot.
biplot(acp, choices = c(1, 2))
# Escores para serem usados na regressão.
S <- acp$scores
colnames(S) <- str_replace(colnames(S), "Comp\\.", "OE_S")
# pairs(S[, 1:3])
# Concatena os escores com as demais variáveis.
da <- cbind(da, S)Escala de autocontrole
#-----------------------------------------------------------------------
# Análise de componentes principais nas variáveis EAC_*.
# Extrai e cria uma matriz com as variáveis de EAC_*.
X <- da %>%
select(starts_with("EAC"))
eac_basica <- X %>%
gather(key = "EAC", value = "valor") %>%
group_by(EAC) %>%
summarise(n = n(),
média = mean(valor),
mediana = median(valor),
desvpad = sd(valor),
mínimo = min(valor),
máximo = max(valor)) %>%
mutate(EAC = str_replace(EAC, "EAC_", ""))
eac_basica %>%
print(n = Inf)## # A tibble: 24 x 7
## EAC n média mediana desvpad mínimo máximo
## <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 01 59 1.32 1 0.681 1 4
## 2 02 59 1.14 1 0.434 1 3
## 3 03 59 1.36 1 0.637 1 3
## 4 04 59 1.59 1 0.912 1 4
## 5 05 59 2.59 3 1.15 1 4
## 6 06 59 2.37 2 1.17 1 4
## 7 07 59 1.41 1 0.790 1 4
## 8 08 59 1.98 2 1.11 1 4
## 9 09 59 1.63 1 0.908 1 4
## 10 10 59 2.42 2 1.10 1 4
## 11 11 59 2.19 2 1.07 1 4
## 12 12 59 2.68 3 1.17 1 4
## 13 13 59 2.42 2 1.15 1 4
## 14 14 59 2.17 2 1.12 1 4
## 15 15 59 1.51 1 0.935 1 4
## 16 16 59 1.51 1 0.878 1 4
## 17 17 59 1.56 1 0.915 1 4
## 18 18 59 1.61 1 0.891 1 4
## 19 19 59 2.07 2 1.08 1 4
## 20 20 59 1.49 1 0.838 1 4
## 21 21 59 2.14 2 1.15 1 4
## 22 22 59 1.76 1 0.989 1 4
## 23 23 59 1.41 1 0.619 1 3
## 24 24 59 1.76 1 0.935 1 4# Gráfico com média e segmentos de erro para 1 desvio-padrão.
ggplot(eac_basica, aes(x = EAC, y = média)) +
geom_point() +
geom_errorbar(aes(ymin = média - desvpad,
ymax = média + desvpad),
width = 0.5) +
xlab("Orçamento empresarial") +
ylab(expression("Média" %+-% "desvio padrão")) +
coord_flip()
# Cria a matriz para fazer a análise de componentes principais.
X <- X %>%
as.matrix()
dim(X)## [1] 59 24# Renomeia para uma melhor exibição no biplot.
colnames(X) <- colnames(X) %>%
str_replace("EAC_", "")
# Aplica componentes principais.
acp <- princomp(X, cor = TRUE)
# summary(acp)
# Exibe o resultado da ACP.
print(acp$loadings, digits = 2, cut = 0.2, sort = TRUE)##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## 13 -0.21 -0.52 0.27
## 23 0.22 0.25 0.33 0.57
## 10 -0.38 -0.25
## 04 -0.23 0.39 0.22 -0.34 -0.27 -0.24
## 06 -0.30 0.22
## 01 0.23 -0.40 -0.24
## 02 0.24 -0.40 0.30 0.23 -0.26 0.28 -0.20
## 03 0.26 -0.41 -0.44
## 05 -0.23 -0.27 0.22 0.27 -0.42
## 07 -0.27 -0.28 -0.21 0.22 0.28
## 08 -0.25 -0.46 -0.24
## 09 -0.29 0.22 -0.25
## 11 0.33 -0.20 0.25 0.34 0.32
## 12 -0.21 -0.28 -0.25 -0.23
## 14 -0.20 0.35 0.37
## 15 -0.28 0.20 0.34
## 16 -0.28 -0.22 0.22 0.25
## 17 -0.24 0.30 -0.36 -0.25
## 18 -0.27 0.21 0.33 -0.30
## 19 -0.21 -0.31 0.25 0.24 0.22 0.37
## 20 -0.37 0.40
## 21 -0.34 -0.20 -0.35
## 22 -0.34 0.22 0.26
## 24 0.46 0.27 -0.30
## Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Comp.16 Comp.17 Comp.18 Comp.19
## 13 -0.20 0.22
## 23 -0.25 -0.37
## 10 0.30 0.52 0.26 0.25
## 04 -0.24 0.55 0.20
## 06 -0.27 -0.21 0.30 0.31
## 01 -0.23 -0.29 -0.24 0.41
## 02 0.26
## 03 -0.28 0.30 0.36
## 05 -0.26 -0.22 -0.27
## 07 -0.34 0.39 -0.22 0.26
## 08 0.30 0.24 0.29 -0.40
## 09 0.36 -0.40 -0.21 0.43
## 11 0.28 -0.23
## 12 -0.47 -0.37
## 14 -0.23 0.31 -0.46
## 15 -0.24 0.47 0.21
## 16 0.26 -0.25 0.41
## 17 -0.38
## 18 -0.23 -0.29 -0.23
## 19 -0.22 -0.25 0.23 0.35 -0.26
## 20 0.21 0.24 0.36 -0.24 0.35 0.23 0.26
## 21 -0.35 0.20 0.23 0.27 -0.27 -0.26
## 22 -0.38 0.25 0.36
## 24 -0.22 -0.25 0.29
## Comp.20 Comp.21 Comp.22 Comp.23 Comp.24
## 13 -0.24 -0.45
## 23 0.21
## 10 0.27
## 04
## 06 0.51 0.21
## 01 0.39
## 02 -0.33 -0.37
## 03 -0.26
## 05 -0.21 -0.22 -0.35
## 07 -0.42 0.20
## 08 0.29
## 09 0.26
## 11 -0.23 -0.38 0.32
## 12 -0.27 0.37
## 14 0.27
## 15 -0.30 -0.24 -0.26 0.24
## 16 0.47
## 17 -0.46 -0.25
## 18 0.45
## 19 -0.22
## 20
## 21 0.29
## 22 -0.39
## 24 0.23 0.35 -0.26
##
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
## Cumulative Var 0.04 0.08 0.12 0.17 0.21 0.25 0.29 0.33
## Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.04 0.04 0.04 0.04 0.04 0.04 0.04
## Cumulative Var 0.38 0.42 0.46 0.50 0.54 0.58 0.62
## Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 Comp.21 Comp.22
## SS loadings 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## Proportion Var 0.04 0.04 0.04 0.04 0.04 0.04 0.04
## Cumulative Var 0.67 0.71 0.75 0.79 0.83 0.88 0.92
## Comp.23 Comp.24
## SS loadings 1.00 1.00
## Proportion Var 0.04 0.04
## Cumulative Var 0.96 1.00# Proporção de variância acumulada.
plot(cbind(x = 1:length(acp$sdev),
y = c(cumsum(acp$sdev^2))/sum(acp$sdev^2)),
type = "o",
ylim = c(0, 1),
xlab = "Componente",
ylab = "Proporção de variância acumulada")
abline(h = c(0.5, 0.75, 0.9), lty = 2)
# Biplot.
biplot(acp, choices = c(1, 2))
# Escores para serem usados na regressão.
S <- acp$scores
colnames(S) <- str_replace(colnames(S), "Comp\\.", "EAC_S")
# pairs(S[, 1:3])
# Concatena os escores com as demais variáveis.
da <- cbind(da, S)Análise exploratória das decisões
#-----------------------------------------------------------------------
# Gráficos.
# Proporção de acertos da tarefa das matrizes por grupo de autocontrole.
names(da)## [1] "Participantes" "TM" "TLME" "D_01"
## [5] "D_02" "D_03" "D_04" "D_05"
## [9] "D_06" "D_07" "D_08" "D_09"
## [13] "D_10" "D_11" "D_12" "D_13"
## [17] "D_14" "D_15" "D_16" "D_17"
## [21] "D_18" "D_19" "D_20" "D_21"
## [25] "D_22" "D_23" "D_24" "D_25"
## [29] "D_26" "D_27" "D_28" "D_29"
## [33] "D_30" "D_31" "D_32" "D_33"
## [37] "D_34" "D_35" "D_36" "D_37"
## [41] "D_38" "D_39" "D_40" "C_01"
## [45] "C_02" "C_03" "C_04" "C_05"
## [49] "C_06" "C_07" "C_08" "C_09"
## [53] "C_10" "C_11" "C_12" "C_13"
## [57] "C_14" "C_15" "C_16" "C_17"
## [61] "C_18" "C_19" "C_20" "C_21"
## [65] "C_22" "C_23" "C_24" "C_25"
## [69] "C_26" "C_27" "C_28" "C_29"
## [73] "C_30" "C_31" "C_32" "C_33"
## [77] "C_34" "C_35" "C_36" "C_37"
## [81] "C_38" "C_39" "C_40" "BI_01"
## [85] "BI_02" "BI_03" "BI_04" "BI_05"
## [89] "BI_06" "BI_07" "BI_08" "BI_09"
## [93] "BI_10" "BI_11" "BI_12" "BI_13"
## [97] "BI_14" "BI_15" "BI_16" "BI_17"
## [101] "BI_18" "BI_19" "BI_20" "BI_21"
## [105] "BI_22" "BI_23" "BI_24" "BI_25"
## [109] "BI_26" "BI_27" "BI_28" "BI_29"
## [113] "BI_30" "EAC_01" "EAC_02" "EAC_03"
## [117] "EAC_04" "EAC_05" "EAC_06" "EAC_07"
## [121] "EAC_08" "EAC_09" "EAC_10" "EAC_11"
## [125] "EAC_12" "EAC_13" "EAC_14" "EAC_15"
## [129] "EAC_16" "EAC_17" "EAC_18" "EAC_19"
## [133] "EAC_20" "EAC_21" "EAC_22" "EAC_23"
## [137] "EAC_24" "VI_01" "VI_02" "OE_01"
## [141] "OE_02" "OE_03" "OE_04" "OE_05"
## [145] "OE_06" "OE_07" "OE_08" "OE_09"
## [149] "Gênero" "Idade" "Curso_F" "Curso_Atual"
## [153] "P_Curso" "Turma" "autocon" "BI_S1"
## [157] "BI_S2" "BI_S3" "BI_S4" "BI_S5"
## [161] "BI_S6" "BI_S7" "BI_S8" "BI_S9"
## [165] "BI_S10" "BI_S11" "BI_S12" "BI_S13"
## [169] "BI_S14" "BI_S15" "BI_S16" "BI_S17"
## [173] "BI_S18" "BI_S19" "BI_S20" "BI_S21"
## [177] "BI_S22" "BI_S23" "BI_S24" "BI_S25"
## [181] "BI_S26" "BI_S27" "BI_S28" "BI_S29"
## [185] "BI_S30" "OE_S1" "OE_S2" "OE_S3"
## [189] "OE_S4" "OE_S5" "OE_S6" "OE_S7"
## [193] "OE_S8" "OE_S9" "EAC_S1" "EAC_S2"
## [197] "EAC_S3" "EAC_S4" "EAC_S5" "EAC_S6"
## [201] "EAC_S7" "EAC_S8" "EAC_S9" "EAC_S10"
## [205] "EAC_S11" "EAC_S12" "EAC_S13" "EAC_S14"
## [209] "EAC_S15" "EAC_S16" "EAC_S17" "EAC_S18"
## [213] "EAC_S19" "EAC_S20" "EAC_S21" "EAC_S22"
## [217] "EAC_S23" "EAC_S24"da %>%
group_by(autocon) %>%
summarise(prop_TM = mean(TM),
prop_TLME = mean(TLME))## # A tibble: 2 x 3
## autocon prop_TM prop_TLME
## <fct> <dbl> <dbl>
## 1 A 0.0606 0.697
## 2 C 0.962 1db <- list()
# Empilha as decisões.
db[[1]] <- da %>%
select(Participantes, TM, TLME, autocon, starts_with("D_")) %>%
gather(key = "decisoes", value = "acerto", contains("D_"))
# Empilha as confianças nas decisões.
db[[2]] <- da %>%
select(Participantes, starts_with("C_")) %>%
gather(key = "decisoes", value = "nivel", contains("C_")) %>%
mutate(nivel = 10 * nivel)
# str(db[[1]])
# str(db[[2]])
# Remove os prefixos `D_` e `C_`.
db[[1]]$decisoes <- db[[1]]$decisoes %>% str_replace("D_", "")
db[[2]]$decisoes <- db[[2]]$decisoes %>% str_replace("C_", "")
# Junção da parte da decisões com as confianças.
db <- full_join(db[[1]], db[[2]])## Joining, by = c("Participantes", "decisoes")str(db)## 'data.frame': 2360 obs. of 7 variables:
## $ Participantes: chr "S_A1" "S_A2" "S_A3" "S_A4" ...
## $ TM : int 0 0 0 0 0 1 0 0 0 0 ...
## $ TLME : int 1 1 1 0 1 0 0 0 1 1 ...
## $ autocon : Factor w/ 2 levels "A","C": 1 1 1 1 1 1 1 1 1 1 ...
## $ decisoes : chr "01" "01" "01" "01" ...
## $ acerto : int 0 0 0 0 0 1 1 0 0 1 ...
## $ nivel : num 100 60 70 80 100 90 90 90 100 70 ...# Renomeia para que D_21 seja D_01 e assim por diante.
u <- decis[match(x = db$decisoes,
table = decis[, 2],), 1]
db$decisoes[!is.na(u)] <- u[!is.na(u)]
# Passa para inteiro.
db$decisoes <- db$decisoes %>%
as.integer()
# Obtém a estatística descritiva.
db_prop <- db %>%
group_by(autocon, decisoes) %>%
summarise(acerto_prop = mean(acerto),
conf_média = mean(nivel),
conf_sd = sd(nivel))
db_prop %>%
print(n = Inf)## # A tibble: 40 x 5
## # Groups: autocon [?]
## autocon decisoes acerto_prop conf_média conf_sd
## <fct> <int> <dbl> <dbl> <dbl>
## 1 A 1 0.515 79.4 12.3
## 2 A 2 0.606 77.7 12.7
## 3 A 3 0.697 79.5 14.0
## 4 A 4 0.697 77.6 13.4
## 5 A 5 0.758 76.4 13.7
## 6 A 6 0.576 76.8 13.4
## 7 A 7 0.545 79.7 12.1
## 8 A 8 0.530 78.6 12.3
## 9 A 9 0.788 78.5 13.0
## 10 A 10 0.667 77.1 12.5
## 11 A 11 0.606 76.4 13.9
## 12 A 12 0.712 78.0 13.2
## 13 A 13 0.591 77.6 13.0
## 14 A 14 0.485 76.8 13.6
## 15 A 15 0.606 77.7 12.7
## 16 A 16 0.727 77.6 14.7
## 17 A 17 0.576 77.3 12.8
## 18 A 18 0.636 75.6 12.5
## 19 A 19 0.561 76.7 12.7
## 20 A 20 0.742 80.3 15.4
## 21 C 1 0.519 74.8 12.9
## 22 C 2 0.673 73.5 12.8
## 23 C 3 0.615 76.2 11.9
## 24 C 4 0.673 75.2 13.1
## 25 C 5 0.75 74.6 13.1
## 26 C 6 0.654 75.6 13.2
## 27 C 7 0.615 73.3 13.8
## 28 C 8 0.423 75.6 14.6
## 29 C 9 0.712 74.8 13.1
## 30 C 10 0.654 73.5 14.1
## 31 C 11 0.442 70.6 11.6
## 32 C 12 0.769 75.6 12.9
## 33 C 13 0.481 74.4 14.3
## 34 C 14 0.423 72.5 15.3
## 35 C 15 0.538 71.3 13.7
## 36 C 16 0.75 74.4 13.2
## 37 C 17 0.635 74.0 13.3
## 38 C 18 0.596 72.3 12.1
## 39 C 19 0.538 73.7 13.1
## 40 C 20 0.654 76.0 14.6# Versão lado a lado para acerto.
db_prop %>%
select(autocon, decisoes, acerto_prop) %>%
spread(key = autocon, value = acerto_prop)## # A tibble: 20 x 3
## decisoes A C
## <int> <dbl> <dbl>
## 1 1 0.515 0.519
## 2 2 0.606 0.673
## 3 3 0.697 0.615
## 4 4 0.697 0.673
## 5 5 0.758 0.75
## 6 6 0.576 0.654
## 7 7 0.545 0.615
## 8 8 0.530 0.423
## 9 9 0.788 0.712
## 10 10 0.667 0.654
## 11 11 0.606 0.442
## 12 12 0.712 0.769
## 13 13 0.591 0.481
## 14 14 0.485 0.423
## 15 15 0.606 0.538
## 16 16 0.727 0.75
## 17 17 0.576 0.635
## 18 18 0.636 0.596
## 19 19 0.561 0.538
## 20 20 0.742 0.654# Versão lado a lado para confiança média.
db_prop %>%
select(autocon, decisoes, conf_média) %>%
spread(key = autocon, value = conf_média)## # A tibble: 20 x 3
## decisoes A C
## <int> <dbl> <dbl>
## 1 1 79.4 74.8
## 2 2 77.7 73.5
## 3 3 79.5 76.2
## 4 4 77.6 75.2
## 5 5 76.4 74.6
## 6 6 76.8 75.6
## 7 7 79.7 73.3
## 8 8 78.6 75.6
## 9 9 78.5 74.8
## 10 10 77.1 73.5
## 11 11 76.4 70.6
## 12 12 78.0 75.6
## 13 13 77.6 74.4
## 14 14 76.8 72.5
## 15 15 77.7 71.3
## 16 16 77.6 74.4
## 17 17 77.3 74.0
## 18 18 75.6 72.3
## 19 19 76.7 73.7
## 20 20 80.3 76.0gg1 <-
ggplot(db_prop,
aes(x = autocon,
y = acerto_prop,
group = decisoes)) +
geom_point() +
geom_line() +
geom_text(aes(x = (as.integer(autocon) +
0.075 * scale(as.integer(autocon))),
label = decisoes)) +
ylab("Proporção de acerto") +
xlab("Autocontrole")
gg2 <-
ggplot(db_prop,
aes(x = autocon,
y = conf_média,
group = decisoes)) +
geom_point() +
# geom_point(aes(size = conf_sd)) +
geom_line() +
geom_text(aes(x = (as.integer(autocon) +
0.075 * scale(as.integer(autocon))),
label = decisoes)) +
ylab("Confiança média") +
xlab("Autocontrole")
grid.arrange(gg1, gg2, nrow = 1)
Regressão logística para cada decisão
#-----------------------------------------------------------------------
# Análise de regressão logística.
# Cria correspondência entre decisões.
decis <- "D_" %>%
str_c(decis) %>%
matrix(ncol = 2)
# Porção apenas com os escores da ACP.
da_scores <- da %>%
select(Participantes,
starts_with("BI_S"),
starts_with("OE_S"),
starts_with("EAC_S"))
# Coloca os escores ao lado das variáveis filtrando para a decisão.
dd <- db %>%
filter(decisoes == 1) %>%
full_join(da_scores)## Joining, by = "Participantes"# Ajuste do modelo.
fit <- glm(acerto ~
EAC_S1 + EAC_S2 + EAC_S3 + EAC_S4 + EAC_S5 + EAC_S6 +
OE_S1 + OE_S2 + OE_S3 +
BI_S1 + BI_S2 + BI_S3 + BI_S4 + BI_S5 +
autocon * nivel +
autocon * TM +
TLME,
data = dd,
family = quasibinomial)
# anova(fit, test = "F")
drop1(fit, test = "F", scope = . ~ .)## Single term deletions
##
## Model:
## acerto ~ EAC_S1 + EAC_S2 + EAC_S3 + EAC_S4 + EAC_S5 + EAC_S6 +
## OE_S1 + OE_S2 + OE_S3 + BI_S1 + BI_S2 + BI_S3 + BI_S4 + BI_S5 +
## autocon * nivel + autocon * TM + TLME
## Df Deviance F value Pr(>F)
## <none> 137.96
## EAC_S1 1 138.47 0.3596 0.55013
## EAC_S2 1 140.29 1.6440 0.20283
## EAC_S3 1 138.08 0.0898 0.76511
## EAC_S4 1 138.58 0.4360 0.51061
## EAC_S5 1 139.82 1.3102 0.25518
## EAC_S6 1 140.81 2.0105 0.15942
## OE_S1 1 138.19 0.1654 0.68509
## OE_S2 1 138.59 0.4429 0.50732
## OE_S3 1 139.10 0.8029 0.37244
## BI_S1 1 142.06 2.8881 0.09244 .
## BI_S2 1 138.27 0.2228 0.63800
## BI_S3 1 138.64 0.4798 0.49016
## BI_S4 1 138.02 0.0469 0.82894
## BI_S5 1 138.58 0.4366 0.51036
## autocon 1 138.04 0.0595 0.80776
## nivel 1 138.63 0.4762 0.49179
## TM 1 146.01 5.6618 0.01930 *
## TLME 1 138.05 0.0642 0.80053
## autocon:nivel 1 138.00 0.0319 0.85858
## autocon:TM 1 143.19 3.6830 0.05791 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(fit)##
## Call:
## glm(formula = acerto ~ EAC_S1 + EAC_S2 + EAC_S3 + EAC_S4 + EAC_S5 +
## EAC_S6 + OE_S1 + OE_S2 + OE_S3 + BI_S1 + BI_S2 + BI_S3 +
## BI_S4 + BI_S5 + autocon * nivel + autocon * TM + TLME, family = quasibinomial,
## data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.84072 -1.05174 0.00036 0.95191 1.95251
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.502e+00 2.213e+00 0.679 0.4989
## EAC_S1 -8.868e-02 1.348e-01 -0.658 0.5121
## EAC_S2 2.674e-01 1.937e-01 1.380 0.1706
## EAC_S3 -6.072e-02 1.832e-01 -0.331 0.7410
## EAC_S4 1.428e-01 1.947e-01 0.734 0.4650
## EAC_S5 -2.646e-01 2.116e-01 -1.251 0.2141
## EAC_S6 3.690e-01 2.416e-01 1.527 0.1300
## OE_S1 -6.834e-02 1.522e-01 -0.449 0.6544
## OE_S2 1.351e-01 1.840e-01 0.734 0.4646
## OE_S3 -2.043e-01 2.080e-01 -0.982 0.3284
## BI_S1 2.381e-01 1.301e-01 1.831 0.0702 .
## BI_S2 -8.497e-02 1.619e-01 -0.525 0.6010
## BI_S3 -1.436e-01 1.887e-01 -0.761 0.4487
## BI_S4 -4.619e-02 1.926e-01 -0.240 0.8109
## BI_S5 -1.461e-01 2.018e-01 -0.724 0.4710
## autoconC 9.520e-01 3.529e+00 0.270 0.7879
## nivel -2.111e-02 2.791e-02 -0.756 0.4512
## TM 1.782e+01 1.273e+03 0.014 0.9889
## TLME -2.241e-01 8.006e-01 -0.280 0.7802
## autoconC:nivel 7.688e-03 3.893e-02 0.197 0.8439
## autoconC:TM -1.880e+01 1.273e+03 -0.015 0.9882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.162731)
##
## Null deviance: 163.45 on 117 degrees of freedom
## Residual deviance: 137.96 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 15# Fórmula do preditor linear que será usado em todas as decisões.
formula <- acerto ~
EAC_S1 + EAC_S2 + EAC_S3 + EAC_S4 + EAC_S5 + EAC_S6 +
OE_S1 + OE_S2 + OE_S3 +
BI_S1 + BI_S2 + BI_S3 + BI_S4 + BI_S5 +
autocon * nivel +
autocon * TM +
TLME
# Ajustes em lote para cada decisão.
all_fits <- lapply(1:20,
FUN = function(i) {
dd <- db %>%
filter(decisoes == i) %>%
full_join(da_scores)
fit <- glm(formula = formula,
data = dd,
family = quasibinomial)
return(fit)
})## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"
## Joining, by = "Participantes"# lapply(all_fits, anova, test = "F")
# lapply(all_fits, drop1, test = "F", scope = . ~ .)
lapply(all_fits, summary)## [[1]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.84072 -1.05174 0.00036 0.95191 1.95251
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.502e+00 2.213e+00 0.679 0.4989
## EAC_S1 -8.868e-02 1.348e-01 -0.658 0.5121
## EAC_S2 2.674e-01 1.937e-01 1.380 0.1706
## EAC_S3 -6.072e-02 1.832e-01 -0.331 0.7410
## EAC_S4 1.428e-01 1.947e-01 0.734 0.4650
## EAC_S5 -2.646e-01 2.116e-01 -1.251 0.2141
## EAC_S6 3.690e-01 2.416e-01 1.527 0.1300
## OE_S1 -6.834e-02 1.522e-01 -0.449 0.6544
## OE_S2 1.351e-01 1.840e-01 0.734 0.4646
## OE_S3 -2.043e-01 2.080e-01 -0.982 0.3284
## BI_S1 2.381e-01 1.301e-01 1.831 0.0702 .
## BI_S2 -8.497e-02 1.619e-01 -0.525 0.6010
## BI_S3 -1.436e-01 1.887e-01 -0.761 0.4487
## BI_S4 -4.619e-02 1.926e-01 -0.240 0.8109
## BI_S5 -1.461e-01 2.018e-01 -0.724 0.4710
## autoconC 9.520e-01 3.529e+00 0.270 0.7879
## nivel -2.111e-02 2.791e-02 -0.756 0.4512
## TM 1.782e+01 1.273e+03 0.014 0.9889
## TLME -2.241e-01 8.006e-01 -0.280 0.7802
## autoconC:nivel 7.688e-03 3.893e-02 0.197 0.8439
## autoconC:TM -1.880e+01 1.273e+03 -0.015 0.9882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.162731)
##
## Null deviance: 163.45 on 117 degrees of freedom
## Residual deviance: 137.96 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 15
##
##
## [[2]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2209 -1.1874 0.6980 0.9445 1.5400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.637e+00 2.136e+00 0.767 0.445
## EAC_S1 -1.767e-01 1.446e-01 -1.222 0.225
## EAC_S2 2.208e-01 1.885e-01 1.172 0.244
## EAC_S3 -1.436e-01 1.851e-01 -0.776 0.440
## EAC_S4 1.054e-01 2.022e-01 0.521 0.603
## EAC_S5 1.846e-02 2.055e-01 0.090 0.929
## EAC_S6 1.066e-01 2.316e-01 0.461 0.646
## OE_S1 -5.580e-02 1.511e-01 -0.369 0.713
## OE_S2 2.741e-01 1.961e-01 1.398 0.165
## OE_S3 -1.453e-01 2.180e-01 -0.667 0.507
## BI_S1 5.087e-02 1.310e-01 0.388 0.699
## BI_S2 -1.357e-01 1.631e-01 -0.832 0.407
## BI_S3 -1.846e-02 1.988e-01 -0.093 0.926
## BI_S4 6.667e-02 1.804e-01 0.370 0.712
## BI_S5 -1.352e-01 2.121e-01 -0.637 0.525
## autoconC 1.449e+01 1.127e+03 0.013 0.990
## nivel -5.263e-03 2.747e-02 -0.192 0.848
## TM 1.472e-01 1.384e+00 0.106 0.916
## TLME -9.079e-01 7.995e-01 -1.136 0.259
## autoconC:nivel 1.632e-02 3.859e-02 0.423 0.673
## autoconC:TM -1.559e+01 1.127e+03 -0.014 0.989
##
## (Dispersion parameter for quasibinomial family taken to be 1.200088)
##
## Null deviance: 154.80 on 117 degrees of freedom
## Residual deviance: 140.82 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 14
##
##
## [[3]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0611 -0.9919 0.6019 0.8705 1.5114
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.39710 2.01046 -0.695 0.489
## EAC_S1 0.12978 0.14273 0.909 0.365
## EAC_S2 0.11501 0.19362 0.594 0.554
## EAC_S3 0.08069 0.17988 0.449 0.655
## EAC_S4 0.27427 0.22065 1.243 0.217
## EAC_S5 -0.32213 0.21034 -1.531 0.129
## EAC_S6 -0.17304 0.24115 -0.718 0.475
## OE_S1 0.06192 0.16183 0.383 0.703
## OE_S2 0.14463 0.20384 0.710 0.480
## OE_S3 0.28420 0.22371 1.270 0.207
## BI_S1 0.06948 0.13100 0.530 0.597
## BI_S2 -0.14239 0.16082 -0.885 0.378
## BI_S3 0.16713 0.20369 0.821 0.414
## BI_S4 0.09506 0.18717 0.508 0.613
## BI_S5 -0.22643 0.21297 -1.063 0.290
## autoconC 0.15086 3.72216 0.041 0.968
## nivel 0.02740 0.02635 1.040 0.301
## TM 1.14009 1.61503 0.706 0.482
## TLME 0.07595 0.80217 0.095 0.925
## autoconC:nivel -0.01328 0.04077 -0.326 0.745
## autoconC:TM -0.32752 2.37939 -0.138 0.891
##
## (Dispersion parameter for quasibinomial family taken to be 1.194377)
##
## Null deviance: 151.12 on 117 degrees of freedom
## Residual deviance: 131.05 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
##
## [[4]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4491 -0.9531 0.4566 0.8258 1.7800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.381e+00 2.122e+00 -0.651 0.517
## EAC_S1 -1.629e-01 1.524e-01 -1.069 0.288
## EAC_S2 2.025e-01 2.044e-01 0.991 0.324
## EAC_S3 1.079e-01 1.821e-01 0.592 0.555
## EAC_S4 1.162e-01 2.119e-01 0.549 0.585
## EAC_S5 -2.534e-01 2.185e-01 -1.160 0.249
## EAC_S6 1.436e-01 2.487e-01 0.577 0.565
## OE_S1 -2.261e-02 1.823e-01 -0.124 0.902
## OE_S2 3.472e-01 2.152e-01 1.613 0.110
## OE_S3 2.378e-01 2.152e-01 1.105 0.272
## BI_S1 -1.292e-03 1.381e-01 -0.009 0.993
## BI_S2 -2.030e-01 1.870e-01 -1.086 0.280
## BI_S3 -4.816e-02 2.191e-01 -0.220 0.827
## BI_S4 2.320e-01 1.958e-01 1.185 0.239
## BI_S5 -1.024e-01 2.297e-01 -0.446 0.657
## autoconC -2.068e+01 3.031e+03 -0.007 0.995
## nivel 4.493e-02 2.942e-02 1.527 0.130
## TM 1.710e+01 2.113e+03 0.008 0.994
## TLME -1.469e+00 1.026e+00 -1.431 0.156
## autoconC:nivel 9.745e-03 4.531e-02 0.215 0.830
## autoconC:TM 3.264e+00 3.695e+03 0.001 0.999
##
## (Dispersion parameter for quasibinomial family taken to be 1.173704)
##
## Null deviance: 146.77 on 117 degrees of freedom
## Residual deviance: 118.61 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 16
##
##
## [[5]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.49282 0.06603 0.42076 0.70741 1.60878
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.41970 2.33831 -1.035 0.3033
## EAC_S1 0.08013 0.17654 0.454 0.6509
## EAC_S2 0.16772 0.23070 0.727 0.4690
## EAC_S3 -0.07358 0.19998 -0.368 0.7137
## EAC_S4 0.09653 0.23132 0.417 0.6774
## EAC_S5 -0.48461 0.25753 -1.882 0.0629 .
## EAC_S6 0.28240 0.27817 1.015 0.3125
## OE_S1 -0.26640 0.21303 -1.251 0.2141
## OE_S2 0.29153 0.26815 1.087 0.2797
## OE_S3 -0.28705 0.25857 -1.110 0.2697
## BI_S1 -0.04113 0.16692 -0.246 0.8059
## BI_S2 -0.32424 0.19575 -1.656 0.1009
## BI_S3 0.50631 0.26859 1.885 0.0624 .
## BI_S4 0.01293 0.22209 0.058 0.9537
## BI_S5 -0.03540 0.28777 -0.123 0.9023
## autoconC 0.15316 3.79253 0.040 0.9679
## nivel 0.06654 0.03324 2.001 0.0481 *
## TM -1.74280 1.69298 -1.029 0.3058
## TLME -1.48658 1.10353 -1.347 0.1811
## autoconC:nivel -0.00486 0.04446 -0.109 0.9132
## autoconC:TM 2.49909 2.49925 1.000 0.3198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.178895)
##
## Null deviance: 131.60 on 117 degrees of freedom
## Residual deviance: 104.31 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
##
##
## [[6]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2585 -0.8239 0.2492 0.6980 1.9082
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.495e-01 2.108e+00 0.403 0.68783
## EAC_S1 1.541e-03 1.535e-01 0.010 0.99201
## EAC_S2 4.825e-01 2.311e-01 2.088 0.03941 *
## EAC_S3 -1.832e-02 1.997e-01 -0.092 0.92711
## EAC_S4 5.531e-01 2.112e-01 2.618 0.01026 *
## EAC_S5 -1.203e-01 2.161e-01 -0.557 0.57900
## EAC_S6 5.644e-01 2.927e-01 1.928 0.05675 .
## OE_S1 1.196e-01 1.705e-01 0.702 0.48466
## OE_S2 5.281e-01 2.164e-01 2.440 0.01648 *
## OE_S3 1.815e-02 2.243e-01 0.081 0.93567
## BI_S1 1.901e-01 1.576e-01 1.206 0.23083
## BI_S2 -4.409e-01 1.837e-01 -2.400 0.01829 *
## BI_S3 8.198e-02 2.115e-01 0.388 0.69915
## BI_S4 -4.357e-01 2.094e-01 -2.081 0.04011 *
## BI_S5 -2.068e-01 2.452e-01 -0.843 0.40113
## autoconC -7.477e+00 2.841e+03 -0.003 0.99791
## nivel 2.644e-02 2.739e-02 0.965 0.33689
## TM 1.943e+01 1.616e+03 0.012 0.99043
## TLME -3.929e+00 1.179e+00 -3.331 0.00122 **
## autoconC:nivel -7.950e-02 4.048e-02 -1.964 0.05241 .
## autoconC:TM -3.664e+00 3.268e+03 -0.001 0.99911
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.031138)
##
## Null deviance: 157.81 on 117 degrees of freedom
## Residual deviance: 108.94 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 16
##
##
## [[7]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0604 -1.0495 0.5525 0.9265 1.7826
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.155e-01 2.369e+00 -0.133 0.8943
## EAC_S1 4.269e-02 1.325e-01 0.322 0.7480
## EAC_S2 2.790e-01 1.873e-01 1.489 0.1397
## EAC_S3 -1.071e-02 1.751e-01 -0.061 0.9514
## EAC_S4 1.682e-01 1.902e-01 0.884 0.3787
## EAC_S5 -2.576e-01 2.072e-01 -1.243 0.2168
## EAC_S6 3.484e-01 2.389e-01 1.458 0.1480
## OE_S1 -2.582e-04 1.583e-01 -0.002 0.9987
## OE_S2 4.726e-01 2.019e-01 2.341 0.0213 *
## OE_S3 2.118e-02 2.060e-01 0.103 0.9183
## BI_S1 1.287e-01 1.281e-01 1.004 0.3177
## BI_S2 -2.066e-01 1.587e-01 -1.302 0.1961
## BI_S3 7.091e-02 1.934e-01 0.367 0.7148
## BI_S4 6.215e-03 1.827e-01 0.034 0.9729
## BI_S5 -1.348e-01 2.162e-01 -0.624 0.5343
## autoconC 1.875e+01 1.112e+03 0.017 0.9866
## nivel 2.222e-02 2.832e-02 0.785 0.4346
## TM 1.804e+00 1.549e+00 1.165 0.2470
## TLME -2.041e+00 8.816e-01 -2.315 0.0227 *
## autoconC:nivel -1.828e-02 3.852e-02 -0.474 0.6362
## autoconC:TM -1.795e+01 1.112e+03 -0.016 0.9872
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.166749)
##
## Null deviance: 160.83 on 117 degrees of freedom
## Residual deviance: 136.22 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 14
##
##
## [[8]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7100 -0.9232 -0.1568 1.0339 2.4228
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.995e+00 2.372e+00 -0.841 0.4026
## EAC_S1 7.103e-02 1.333e-01 0.533 0.5952
## EAC_S2 5.178e-01 2.229e-01 2.323 0.0222 *
## EAC_S3 -8.186e-02 1.819e-01 -0.450 0.6537
## EAC_S4 4.087e-01 2.228e-01 1.835 0.0696 .
## EAC_S5 -8.694e-02 2.186e-01 -0.398 0.6917
## EAC_S6 3.845e-01 2.619e-01 1.468 0.1453
## OE_S1 4.908e-03 1.513e-01 0.032 0.9742
## OE_S2 2.743e-01 1.999e-01 1.372 0.1732
## OE_S3 -7.544e-02 2.064e-01 -0.366 0.7155
## BI_S1 -3.683e-02 1.302e-01 -0.283 0.7780
## BI_S2 -2.284e-01 1.669e-01 -1.368 0.1743
## BI_S3 4.092e-01 2.126e-01 1.925 0.0571 .
## BI_S4 -7.564e-02 1.911e-01 -0.396 0.6931
## BI_S5 -2.988e-02 2.123e-01 -0.141 0.8883
## autoconC -1.032e+01 3.018e+03 -0.003 0.9973
## nivel 4.051e-02 3.048e-02 1.329 0.1870
## TM 1.863e+01 1.974e+03 0.009 0.9925
## TLME -1.877e+00 8.545e-01 -2.196 0.0305 *
## autoconC:nivel -6.270e-02 4.065e-02 -1.542 0.1263
## autoconC:TM -2.981e+00 3.606e+03 -0.001 0.9993
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.163615)
##
## Null deviance: 163.45 on 117 degrees of freedom
## Residual deviance: 128.30 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 16
##
##
## [[9]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.16246 0.00014 0.38840 0.72609 1.52235
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.996e-01 2.302e+00 -0.130 0.8967
## EAC_S1 7.156e-02 1.435e-01 0.499 0.6190
## EAC_S2 4.489e-01 2.003e-01 2.241 0.0273 *
## EAC_S3 3.046e-01 1.801e-01 1.691 0.0940 .
## EAC_S4 1.836e-01 2.307e-01 0.796 0.4280
## EAC_S5 -4.448e-01 2.487e-01 -1.789 0.0768 .
## EAC_S6 -2.927e-01 2.542e-01 -1.152 0.2523
## OE_S1 2.228e-01 1.743e-01 1.278 0.2042
## OE_S2 6.332e-02 2.079e-01 0.305 0.7613
## OE_S3 2.767e-01 2.358e-01 1.173 0.2436
## BI_S1 -2.578e-01 1.472e-01 -1.752 0.0830 .
## BI_S2 -3.843e-01 1.664e-01 -2.309 0.0231 *
## BI_S3 -4.275e-03 2.462e-01 -0.017 0.9862
## BI_S4 4.942e-01 2.186e-01 2.261 0.0260 *
## BI_S5 -1.530e-01 2.202e-01 -0.695 0.4887
## autoconC 1.498e+01 2.749e+03 0.005 0.9957
## nivel 3.197e-02 3.003e-02 1.064 0.2898
## TM 1.801e+01 1.883e+03 0.010 0.9924
## TLME -6.245e-01 9.513e-01 -0.656 0.5131
## autoconC:nivel -1.838e-02 3.903e-02 -0.471 0.6387
## autoconC:TM -3.215e+01 3.332e+03 -0.010 0.9923
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9653502)
##
## Null deviance: 131.60 on 117 degrees of freedom
## Residual deviance: 104.32 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 16
##
##
## [[10]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9387 -1.0747 0.6083 0.8942 1.7294
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.86686 2.09526 -0.414 0.6800
## EAC_S1 -0.06157 0.13284 -0.463 0.6441
## EAC_S2 0.18882 0.19020 0.993 0.3233
## EAC_S3 0.08743 0.17897 0.488 0.6263
## EAC_S4 0.11644 0.18377 0.634 0.5278
## EAC_S5 0.13036 0.21041 0.620 0.5370
## EAC_S6 0.28115 0.25579 1.099 0.2744
## OE_S1 -0.03776 0.15432 -0.245 0.8072
## OE_S2 0.32446 0.19610 1.655 0.1012
## OE_S3 -0.08016 0.19912 -0.403 0.6881
## BI_S1 0.01484 0.13055 0.114 0.9097
## BI_S2 -0.26728 0.16117 -1.658 0.1005
## BI_S3 0.12543 0.19550 0.642 0.5227
## BI_S4 -0.22263 0.17862 -1.246 0.2156
## BI_S5 0.00853 0.21677 0.039 0.9687
## autoconC 21.12922 2923.73670 0.007 0.9942
## nivel 0.04219 0.02747 1.536 0.1279
## TM 16.96438 1684.17036 0.010 0.9920
## TLME -2.21583 0.96234 -2.303 0.0234 *
## autoconC:nivel -0.03229 0.03613 -0.894 0.3737
## autoconC:TM -35.13376 3374.11593 -0.010 0.9917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.09233)
##
## Null deviance: 151.12 on 117 degrees of freedom
## Residual deviance: 129.27 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 16
##
##
## [[11]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7658 -1.0600 0.3249 0.9183 2.0397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.049e-01 1.776e+00 -0.397 0.69234
## EAC_S1 3.079e-01 1.815e-01 1.696 0.09308 .
## EAC_S2 1.660e-01 2.170e-01 0.765 0.44604
## EAC_S3 -2.934e-01 1.821e-01 -1.611 0.11045
## EAC_S4 2.238e-01 2.210e-01 1.013 0.31381
## EAC_S5 -1.907e-01 2.069e-01 -0.922 0.35895
## EAC_S6 1.977e-01 2.374e-01 0.833 0.40704
## OE_S1 -2.357e-01 1.812e-01 -1.300 0.19660
## OE_S2 2.257e-01 1.932e-01 1.168 0.24569
## OE_S3 -8.648e-02 2.071e-01 -0.418 0.67719
## BI_S1 -1.427e-01 1.386e-01 -1.030 0.30576
## BI_S2 -5.782e-01 2.133e-01 -2.711 0.00794 **
## BI_S3 3.078e-01 1.957e-01 1.573 0.11894
## BI_S4 -1.173e-01 1.898e-01 -0.618 0.53811
## BI_S5 -5.702e-02 2.136e-01 -0.267 0.79002
## autoconC -1.446e+01 1.074e+03 -0.013 0.98929
## nivel 2.642e-02 2.493e-02 1.060 0.29186
## TM 1.367e+00 1.622e+00 0.843 0.40133
## TLME -1.342e+00 8.630e-01 -1.555 0.12327
## autoconC:nivel -9.269e-06 3.868e-02 0.000 0.99981
## autoconC:TM 1.310e+01 1.074e+03 0.012 0.99029
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.089808)
##
## Null deviance: 163.04 on 117 degrees of freedom
## Residual deviance: 130.06 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 14
##
##
## [[12]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5028 -0.4450 0.2681 0.6651 1.7788
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.35196 2.86645 -2.216 0.02903 *
## EAC_S1 0.06541 0.16622 0.393 0.69482
## EAC_S2 0.25615 0.20561 1.246 0.21583
## EAC_S3 0.27381 0.17848 1.534 0.12826
## EAC_S4 0.20205 0.28219 0.716 0.47570
## EAC_S5 -0.75537 0.28127 -2.686 0.00852 **
## EAC_S6 -0.46197 0.27135 -1.702 0.09187 .
## OE_S1 -0.02286 0.19297 -0.118 0.90596
## OE_S2 0.32120 0.25938 1.238 0.21858
## OE_S3 0.09356 0.23400 0.400 0.69018
## BI_S1 -0.35781 0.16705 -2.142 0.03471 *
## BI_S2 -0.42776 0.17991 -2.378 0.01938 *
## BI_S3 0.29117 0.28173 1.034 0.30392
## BI_S4 0.66788 0.26507 2.520 0.01338 *
## BI_S5 0.07767 0.23887 0.325 0.74578
## autoconC 16.39723 1673.13858 0.010 0.99220
## nivel 0.10038 0.04025 2.494 0.01432 *
## TM 0.99427 2.05469 0.484 0.62955
## TLME 0.63376 0.94727 0.669 0.50506
## autoconC:nivel -0.05995 0.04752 -1.261 0.21015
## autoconC:TM -13.24678 1673.13651 -0.008 0.99370
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.972376)
##
## Null deviance: 135.906 on 117 degrees of freedom
## Residual deviance: 95.942 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 15
##
##
## [[13]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.04485 -0.97851 0.00025 0.86180 2.09643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.199e+00 2.391e+00 1.338 0.1841
## EAC_S1 3.524e-02 1.312e-01 0.269 0.7887
## EAC_S2 2.816e-01 1.962e-01 1.435 0.1544
## EAC_S3 2.415e-02 1.807e-01 0.134 0.8940
## EAC_S4 9.632e-03 1.932e-01 0.050 0.9603
## EAC_S5 -4.696e-01 2.317e-01 -2.027 0.0454 *
## EAC_S6 1.851e-01 2.373e-01 0.780 0.4374
## OE_S1 -4.603e-03 1.615e-01 -0.028 0.9773
## OE_S2 3.168e-01 2.007e-01 1.578 0.1177
## OE_S3 5.869e-02 2.184e-01 0.269 0.7887
## BI_S1 6.592e-02 1.305e-01 0.505 0.6146
## BI_S2 -2.647e-01 1.665e-01 -1.590 0.1151
## BI_S3 1.361e-01 2.103e-01 0.647 0.5190
## BI_S4 -3.522e-02 1.972e-01 -0.179 0.8586
## BI_S5 4.969e-02 2.075e-01 0.239 0.8113
## autoconC 1.896e+01 3.057e+03 0.006 0.9951
## nivel -2.554e-02 2.949e-02 -0.866 0.3886
## TM 1.807e+01 2.130e+03 0.008 0.9932
## TLME -1.355e+00 8.718e-01 -1.555 0.1233
## autoconC:nivel -7.481e-03 4.040e-02 -0.185 0.8535
## autoconC:TM -3.666e+01 3.726e+03 -0.010 0.9922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.194115)
##
## Null deviance: 162.73 on 117 degrees of freedom
## Residual deviance: 128.57 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 16
##
##
## [[14]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6914 -0.8312 -0.4875 0.8090 2.0309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.40478 2.17452 -1.106 0.271509
## EAC_S1 -0.06382 0.15440 -0.413 0.680285
## EAC_S2 0.01761 0.20119 0.088 0.930430
## EAC_S3 0.17986 0.20432 0.880 0.380881
## EAC_S4 0.18972 0.20808 0.912 0.364162
## EAC_S5 -0.35217 0.23979 -1.469 0.145164
## EAC_S6 0.32045 0.27042 1.185 0.238922
## OE_S1 -0.03130 0.17142 -0.183 0.855477
## OE_S2 0.81845 0.23397 3.498 0.000709 ***
## OE_S3 -0.22825 0.23195 -0.984 0.327543
## BI_S1 0.04659 0.14544 0.320 0.749413
## BI_S2 -0.10974 0.16872 -0.650 0.516958
## BI_S3 -0.02782 0.21905 -0.127 0.899215
## BI_S4 0.02199 0.19010 0.116 0.908167
## BI_S5 0.27168 0.21658 1.254 0.212724
## autoconC 7.24058 3.29443 2.198 0.030341 *
## nivel 0.04536 0.02885 1.572 0.119153
## TM 0.93457 1.57703 0.593 0.554818
## TLME -1.62703 0.86385 -1.883 0.062634 .
## autoconC:nivel -0.08552 0.04013 -2.131 0.035604 *
## autoconC:TM -1.61709 2.39603 -0.675 0.501343
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.164237)
##
## Null deviance: 162.73 on 117 degrees of freedom
## Residual deviance: 123.88 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
##
##
## [[15]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2062 -0.9152 0.3225 0.8804 2.7738
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.877e-01 2.923e+00 0.304 0.7620
## EAC_S1 8.085e-02 1.604e-01 0.504 0.6153
## EAC_S2 3.893e-01 2.449e-01 1.590 0.1151
## EAC_S3 2.702e-01 2.521e-01 1.072 0.2864
## EAC_S4 1.497e-01 2.226e-01 0.673 0.5027
## EAC_S5 -4.415e-01 2.815e-01 -1.568 0.1200
## EAC_S6 8.949e-01 3.611e-01 2.479 0.0149 *
## OE_S1 -1.514e-01 1.826e-01 -0.829 0.4092
## OE_S2 2.492e-01 2.266e-01 1.100 0.2741
## OE_S3 2.056e-01 2.753e-01 0.747 0.4570
## BI_S1 -7.890e-02 1.676e-01 -0.471 0.6389
## BI_S2 -3.607e-01 1.919e-01 -1.879 0.0632 .
## BI_S3 -1.757e-01 2.459e-01 -0.714 0.4767
## BI_S4 2.151e-01 2.431e-01 0.885 0.3784
## BI_S5 -1.145e-03 2.483e-01 -0.005 0.9963
## autoconC 1.891e+01 3.377e+03 0.006 0.9955
## nivel -7.171e-03 3.744e-02 -0.192 0.8485
## TM 1.903e+01 2.389e+03 0.008 0.9937
## TLME -3.069e-01 9.956e-01 -0.308 0.7585
## autoconC:nivel -3.259e-02 4.775e-02 -0.682 0.4966
## autoconC:TM -3.526e+01 4.137e+03 -0.009 0.9932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.468892)
##
## Null deviance: 160.83 on 117 degrees of freedom
## Residual deviance: 122.64 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 16
##
##
## [[16]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4335 -0.5545 0.4067 0.7360 1.4507
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.769e+00 2.274e+00 0.778 0.4385
## EAC_S1 8.291e-02 1.649e-01 0.503 0.6162
## EAC_S2 1.963e-01 2.087e-01 0.941 0.3493
## EAC_S3 8.591e-02 1.797e-01 0.478 0.6336
## EAC_S4 2.646e-01 2.428e-01 1.090 0.2785
## EAC_S5 -6.566e-01 2.757e-01 -2.382 0.0192 *
## EAC_S6 -2.355e-01 2.617e-01 -0.900 0.3704
## OE_S1 -1.319e-01 2.176e-01 -0.606 0.5457
## OE_S2 5.960e-01 2.791e-01 2.135 0.0353 *
## OE_S3 1.456e-01 2.464e-01 0.591 0.5560
## BI_S1 -1.806e-01 1.498e-01 -1.206 0.2307
## BI_S2 -2.942e-01 1.771e-01 -1.661 0.1000 .
## BI_S3 1.646e-01 2.542e-01 0.648 0.5188
## BI_S4 3.086e-01 2.166e-01 1.425 0.1574
## BI_S5 2.147e-01 2.404e-01 0.893 0.3739
## autoconC 1.235e+01 2.948e+03 0.004 0.9967
## nivel 5.061e-03 2.928e-02 0.173 0.8631
## TM 1.753e+01 2.079e+03 0.008 0.9933
## TLME -1.252e+00 1.002e+00 -1.249 0.2146
## autoconC:nivel 5.225e-02 4.664e-02 1.120 0.2653
## autoconC:TM -3.333e+01 3.608e+03 -0.009 0.9926
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.126166)
##
## Null deviance: 135.91 on 117 degrees of freedom
## Residual deviance: 103.84 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 16
##
##
## [[17]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0744 -1.0535 0.4907 0.9555 2.1472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.67329 2.14342 -0.314 0.7541
## EAC_S1 -0.06936 0.14678 -0.473 0.6376
## EAC_S2 0.22951 0.20635 1.112 0.2688
## EAC_S3 0.22234 0.20350 1.093 0.2773
## EAC_S4 0.17500 0.19118 0.915 0.3623
## EAC_S5 -0.16813 0.21396 -0.786 0.4339
## EAC_S6 0.62434 0.28382 2.200 0.0302 *
## OE_S1 -0.18798 0.16406 -1.146 0.2547
## OE_S2 0.44956 0.21340 2.107 0.0377 *
## OE_S3 0.08278 0.21827 0.379 0.7053
## BI_S1 0.03311 0.14804 0.224 0.8235
## BI_S2 -0.36375 0.16610 -2.190 0.0309 *
## BI_S3 0.09273 0.20611 0.450 0.6538
## BI_S4 0.04677 0.18826 0.248 0.8043
## BI_S5 0.08605 0.22948 0.375 0.7085
## autoconC 5.05975 3.55032 1.425 0.1573
## nivel 0.02233 0.02714 0.823 0.4127
## TM 1.40815 1.55224 0.907 0.3666
## TLME -1.10994 0.87863 -1.263 0.2095
## autoconC:nivel -0.05781 0.04020 -1.438 0.1536
## autoconC:TM -1.21666 2.38162 -0.511 0.6106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.178309)
##
## Null deviance: 158.67 on 117 degrees of freedom
## Residual deviance: 131.41 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
##
##
## [[18]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0707 -1.0346 0.4356 0.9421 1.8085
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.802e-01 2.058e+00 0.185 0.8538
## EAC_S1 -9.818e-03 1.424e-01 -0.069 0.9452
## EAC_S2 2.769e-01 1.902e-01 1.456 0.1487
## EAC_S3 5.887e-02 1.796e-01 0.328 0.7438
## EAC_S4 2.179e-01 1.885e-01 1.156 0.2506
## EAC_S5 -4.042e-01 2.187e-01 -1.848 0.0676 .
## EAC_S6 3.569e-01 2.461e-01 1.450 0.1502
## OE_S1 -2.329e-01 1.661e-01 -1.402 0.1640
## OE_S2 4.815e-01 2.158e-01 2.232 0.0279 *
## OE_S3 -1.946e-01 2.150e-01 -0.905 0.3676
## BI_S1 -7.051e-03 1.384e-01 -0.051 0.9595
## BI_S2 -2.479e-01 1.584e-01 -1.565 0.1208
## BI_S3 6.042e-02 2.023e-01 0.299 0.7659
## BI_S4 9.880e-02 1.794e-01 0.551 0.5830
## BI_S5 2.127e-01 2.234e-01 0.952 0.3435
## autoconC 1.617e+01 1.108e+03 0.015 0.9884
## nivel 1.429e-02 2.795e-02 0.512 0.6102
## TM 9.756e-01 1.490e+00 0.655 0.5141
## TLME -1.174e+00 8.752e-01 -1.341 0.1830
## autoconC:nivel 1.029e-04 4.278e-02 0.002 0.9981
## autoconC:TM -1.694e+01 1.108e+03 -0.015 0.9878
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.159268)
##
## Null deviance: 156.87 on 117 degrees of freedom
## Residual deviance: 132.92 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 14
##
##
## [[19]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7878 -0.8837 0.0896 0.8035 2.0781
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.297e+00 2.501e+00 -2.118 0.03673 *
## EAC_S1 5.431e-02 1.368e-01 0.397 0.69221
## EAC_S2 5.137e-01 2.072e-01 2.479 0.01489 *
## EAC_S3 -5.978e-02 1.833e-01 -0.326 0.74504
## EAC_S4 5.268e-01 2.135e-01 2.468 0.01534 *
## EAC_S5 -2.427e-01 2.235e-01 -1.086 0.28022
## EAC_S6 3.995e-01 2.573e-01 1.552 0.12381
## OE_S1 2.246e-02 1.616e-01 0.139 0.88971
## OE_S2 5.493e-01 2.012e-01 2.730 0.00753 **
## OE_S3 2.830e-02 2.087e-01 0.136 0.89242
## BI_S1 9.407e-03 1.372e-01 0.069 0.94548
## BI_S2 -4.753e-01 1.778e-01 -2.674 0.00881 **
## BI_S3 -1.999e-01 1.988e-01 -1.006 0.31715
## BI_S4 1.481e-01 1.913e-01 0.774 0.44091
## BI_S5 -5.310e-02 2.305e-01 -0.230 0.81828
## autoconC 2.711e+01 2.896e+03 0.009 0.99255
## nivel 8.636e-02 3.381e-02 2.554 0.01221 *
## TM 1.999e+01 1.881e+03 0.011 0.99155
## TLME -1.873e+00 9.233e-01 -2.029 0.04524 *
## autoconC:nivel -1.170e-01 4.444e-02 -2.633 0.00986 **
## autoconC:TM -3.747e+01 3.453e+03 -0.011 0.99137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.077767)
##
## Null deviance: 162.36 on 117 degrees of freedom
## Residual deviance: 121.10 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 16
##
##
## [[20]]
##
## Call:
## glm(formula = formula, family = quasibinomial, data = dd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1489 -0.9233 0.4696 0.8433 1.6914
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.38639 2.10839 -2.080 0.0401 *
## EAC_S1 -0.22213 0.16061 -1.383 0.1698
## EAC_S2 0.01212 0.18928 0.064 0.9491
## EAC_S3 0.13077 0.17714 0.738 0.4622
## EAC_S4 0.22225 0.23081 0.963 0.3380
## EAC_S5 -0.28520 0.21963 -1.299 0.1972
## EAC_S6 0.06824 0.24788 0.275 0.7837
## OE_S1 -0.32946 0.19115 -1.724 0.0880 .
## OE_S2 0.19143 0.21004 0.911 0.3643
## OE_S3 -0.04245 0.21288 -0.199 0.8423
## BI_S1 -0.05759 0.14882 -0.387 0.6996
## BI_S2 -0.15926 0.16651 -0.956 0.3412
## BI_S3 0.30924 0.25573 1.209 0.2295
## BI_S4 0.02213 0.20198 0.110 0.9130
## BI_S5 -0.35213 0.23802 -1.479 0.1423
## autoconC 2.42033 3.02829 0.799 0.4261
## nivel 0.06995 0.02815 2.485 0.0147 *
## TM 14.37177 1231.63471 0.012 0.9907
## TLME 0.36196 0.99365 0.364 0.7164
## autoconC:nivel -0.06247 0.03436 -1.818 0.0721 .
## autoconC:TM -12.43622 1231.63605 -0.010 0.9920
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.077699)
##
## Null deviance: 143.48 on 117 degrees of freedom
## Residual deviance: 116.03 on 97 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 15Regressão linear com a soma das decisões
#-----------------------------------------------------------------------
# Agrega com a soma das decisões e média da confiança por
# indivíduo:aucoton.
dd <- db %>%
group_by(Participantes, autocon) %>%
summarise(acerto = sum(acerto),
nivel = mean(nivel),
TM = mean(TM),
TLME = mean(TLME)) %>%
ungroup() %>%
full_join(da_scores)## Joining, by = "Participantes"# Ajuste com resultados agregados por unidade experimental.
fit <- lm(acerto ~
EAC_S1 + EAC_S2 + EAC_S3 + EAC_S4 + EAC_S5 + EAC_S6 +
OE_S1 + OE_S2 + OE_S3 +
BI_S1 + BI_S2 + BI_S3 + BI_S4 + BI_S5 +
autocon * nivel + TM + TLME,
data = dd)
# Quadros para os testes dos efeitos de cada termo.
anova(fit)## Analysis of Variance Table
##
## Response: acerto
## Df Sum Sq Mean Sq F value Pr(>F)
## EAC_S1 1 2.9 2.91 0.0254 0.87410
## EAC_S2 1 24.5 24.51 0.2139 0.64628
## EAC_S3 1 91.6 91.61 0.7996 0.37669
## EAC_S4 1 16.5 16.52 0.1442 0.70624
## EAC_S5 1 296.2 296.21 2.5856 0.11591
## EAC_S6 1 52.1 52.15 0.4552 0.50387
## OE_S1 1 142.3 142.26 1.2418 0.27195
## OE_S2 1 466.7 466.68 4.0735 0.05048 .
## OE_S3 1 15.9 15.90 0.1388 0.71153
## BI_S1 1 3.8 3.82 0.0333 0.85612
## BI_S2 1 266.8 266.77 2.3286 0.13509
## BI_S3 1 23.9 23.90 0.2087 0.65036
## BI_S4 1 7.7 7.69 0.0671 0.79693
## BI_S5 1 6.2 6.20 0.0542 0.81719
## autocon 1 2.5 2.46 0.0215 0.88419
## nivel 1 201.8 201.77 1.7612 0.19219
## TM 1 164.6 164.62 1.4369 0.23787
## TLME 1 240.2 240.23 2.0969 0.15559
## autocon:nivel 1 113.4 113.43 0.9901 0.32586
## Residuals 39 4467.9 114.56
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1drop1(fit, test = "F", scope = . ~ .)## Single term deletions
##
## Model:
## acerto ~ EAC_S1 + EAC_S2 + EAC_S3 + EAC_S4 + EAC_S5 + EAC_S6 +
## OE_S1 + OE_S2 + OE_S3 + BI_S1 + BI_S2 + BI_S3 + BI_S4 + BI_S5 +
## autocon * nivel + TM + TLME
## Df Sum of Sq RSS AIC F value Pr(>F)
## <none> 4467.9 295.30
## EAC_S1 1 0.85 4468.8 293.31 0.0074 0.93178
## EAC_S2 1 221.61 4689.6 296.16 1.9344 0.17216
## EAC_S3 1 1.12 4469.1 293.32 0.0098 0.92161
## EAC_S4 1 115.50 4583.4 294.81 1.0081 0.32154
## EAC_S5 1 378.23 4846.2 298.10 3.3015 0.07691 .
## EAC_S6 1 106.34 4574.3 294.69 0.9282 0.34126
## OE_S1 1 1.98 4469.9 293.33 0.0173 0.89618
## OE_S2 1 560.34 5028.3 300.27 4.8911 0.03292 *
## OE_S3 1 0.17 4468.1 293.30 0.0015 0.96905
## BI_S1 1 17.36 4485.3 293.53 0.1515 0.69923
## BI_S2 1 455.31 4923.2 299.03 3.9743 0.05323 .
## BI_S3 1 32.09 4500.0 293.72 0.2801 0.59962
## BI_S4 1 24.02 4492.0 293.62 0.2096 0.64960
## BI_S5 1 1.92 4469.9 293.33 0.0168 0.89754
## autocon 1 53.31 4521.2 294.00 0.4653 0.49918
## nivel 1 304.60 4772.5 297.19 2.6588 0.11103
## TM 1 153.46 4621.4 295.29 1.3396 0.25415
## TLME 1 267.17 4735.1 296.73 2.3321 0.13480
## autocon:nivel 1 113.43 4581.4 294.78 0.9901 0.32586
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Estimativas dos parâmetros.
summary(fit)##
## Call:
## lm(formula = acerto ~ EAC_S1 + EAC_S2 + EAC_S3 + EAC_S4 + EAC_S5 +
## EAC_S6 + OE_S1 + OE_S2 + OE_S3 + BI_S1 + BI_S2 + BI_S3 +
## BI_S4 + BI_S5 + autocon * nivel + TM + TLME, data = dd)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.711 -5.937 -0.727 5.863 19.071
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.12654 16.80869 0.127 0.9000
## EAC_S1 0.07077 0.82140 0.086 0.9318
## EAC_S2 1.57492 1.13235 1.391 0.1722
## EAC_S3 0.11019 1.11257 0.099 0.9216
## EAC_S4 1.17848 1.17371 1.004 0.3215
## EAC_S5 -2.37229 1.30560 -1.817 0.0769 .
## EAC_S6 1.39478 1.44771 0.963 0.3413
## OE_S1 -0.12693 0.96645 -0.131 0.8962
## OE_S2 2.57507 1.16436 2.212 0.0329 *
## OE_S3 -0.05023 1.28621 -0.039 0.9690
## BI_S1 -0.29949 0.76945 -0.389 0.6992
## BI_S2 -1.94072 0.97350 -1.994 0.0532 .
## BI_S3 0.64978 1.22771 0.529 0.5996
## BI_S4 0.51417 1.12300 0.458 0.6496
## BI_S5 -0.15855 1.22330 -0.130 0.8975
## autoconC 16.11843 23.62922 0.682 0.4992
## nivel 0.35846 0.21984 1.631 0.1110
## TM 8.86446 7.65900 1.157 0.2542
## TLME -7.27670 4.76500 -1.527 0.1348
## autoconC:nivel -0.29836 0.29985 -0.995 0.3259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.7 on 39 degrees of freedom
## Multiple R-squared: 0.3238, Adjusted R-squared: -0.00561
## F-statistic: 0.983 on 19 and 39 DF, p-value: 0.4989# Avaliação dos pressupostos.
par(mfrow = c(2, 2))
plot(fit)
layout(1)
#-----------------------------------------------------------------------