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Экологические установки и ситуативные стимулы в условиях трансформирующейся экологической культуры России: эмпирическое исследование |
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Оцениваются 4 поведенческих блока:
- Переработка (Recycling Behavior, RB),
- Эко-покупки (Eco-Shopping Behavior, ESB),
- Ресурсосбережение (Resource-Saving Behavior, RSB),
- Эко-мобильность (Eco-Mobility Behavior, EMB).
По каждому из блоков считается аддитивный индекс, представляющий собой оценку склонности респондента к определённому виду эко-поведения. Дополнительно считается индекс склонности к комфорту против склонности к экономии (tendency to be comfortable, TC).
Гипотеза H1. Эко-поведение не однородно - склонность к одному из видов эко-поведения не предопределяет склонность к другому.
Гипотеза H2. Люди обычно переоценивают собственную склонность к эко-поведению и недооценивают склонность окружающих.
Гипотеза H3. Склонность к тому или иному виду эко-поведения ситуативно подвержена влиянию стимулов, как эмоционального, так и рационального характера.
Гипотеза H3а. Склонность к более затратным видам поведения менее подвержена ситуативному влиянию стимулов (low-cost hypothesis).
Ниже представленны параметры окружения R. Производится загрузка данных, логарифмирование некоторых признаков (возраст, размер города, время ответа). Вычисляются аддитивные индексы, затем производится их унификация в диапазоне [0...1] для удобства графического отображения и интерпретации.
library(knitr)
library(MatchIt)
library(dplyr)
library(ggplot2)
library(ggthemes)
library(gplots)
library(gtable)
library(egg)
library(corrplot)
library(scales)#environment info
sessionInfo()## R version 3.6.3 (2020-02-29)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18363)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Russian_Russia.1251 LC_CTYPE=Russian_Russia.1251
## [3] LC_MONETARY=Russian_Russia.1251 LC_NUMERIC=C
## [5] LC_TIME=Russian_Russia.1251
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] scales_1.1.1 corrplot_0.84 egg_0.4.5 gridExtra_2.3 gtable_0.3.0
## [6] gplots_3.0.3 ggthemes_4.2.0 ggplot2_3.3.1 dplyr_0.8.5 MatchIt_3.0.2
## [11] knitr_1.28
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.4.6 pillar_1.4.4 compiler_3.6.3 bitops_1.0-6
## [5] tools_3.6.3 digest_0.6.25 evaluate_0.14 lifecycle_0.2.0
## [9] tibble_3.0.1 pkgconfig_2.0.3 rlang_0.4.6 yaml_2.2.1
## [13] xfun_0.14 withr_2.2.0 stringr_1.4.0 vctrs_0.3.0
## [17] gtools_3.8.2 caTools_1.18.0 grid_3.6.3 tidyselect_1.1.0
## [21] glue_1.4.1 R6_2.4.1 rmarkdown_2.2 gdata_2.18.0
## [25] purrr_0.3.4 magrittr_1.5 ellipsis_0.3.1 htmltools_0.4.0
## [29] MASS_7.3-51.6 assertthat_0.2.1 colorspace_1.4-1 KernSmooth_2.23-16
## [33] stringi_1.4.6 munsell_0.5.0 crayon_1.3.4
#loading raw data
data<-read.csv("ecodata.csv", sep=";", dec=",")
data<-data[!is.na(data$age),]
data<-data[!is.na(data$income),]
data$group<-factor(data$group, labels = c("Control","Rational","Emotional"))
#log data
data$log_age <- log10(data$age)
data$log_city_size <- log10(data$city_size)
data$log_time_RB <- log10(data$time_12)
data$log_time_ESB <- log10(data$time_34)
data$log_time_RSB <- log10(data$time_56)
data$log_time_EMB <- log10(data$time_78)
data$log_time_TC <- log10(data$time_9)
#make an additive indexes
data$RB <- (data$case_1_1 + data$case_1_2 + data$case_2_1 + data$case_2_2) #1..24
data$ESB <- (data$case_3_1 + data$case_3_2 + data$case_4_1 + data$case_4_2) #1..24
data$RSB <- (data$case_5_1 + data$case_5_2 + data$case_6_1 + data$case_6_2) #1..24
data$EMB <- (data$case_7_1 + data$case_7_2 + data$case_8_1 + data$case_8_2) #1..24
data<-data[!is.na(data$ESB),]
data$TC <- (data$case_9_1 + data$case_9_2) #1..12
data$RBo <- (data$case_1_3 + data$case_2_3) #1..12
data$ESBo <- (data$case_3_3 + data$case_4_3) #1..12
data$RSBo <- (data$case_5_3 + data$case_6_3) #1..12
data$EMBo <- (data$case_7_3 + data$case_8_3) #1..12
#rescale <- function(x){(x-min(x))/(max(x)-min(x))}
data$RB <- rescale(data$RB)
data$ESB <- rescale(data$ESB)
data$RSB <- rescale(data$RSB)
data$EMB <- rescale(data$EMB)
data$TC <- rescale(data$TC)
data$RBo <- rescale(data$RBo)
data$ESBo <- rescale(data$ESBo)
data$RSBo <- rescale(data$RSBo)
data$EMBo <- rescale(data$EMBo)summary(data[,c(29,30,32:37,39, 54:62)])## emo rat sex age
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :14.00
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:20.00
## Median :0.0000 Median :0.0000 Median :0.0000 Median :26.00
## Mean :0.4977 Mean :0.4859 Mean :0.3354 Mean :28.88
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:35.00
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :67.00
## NA's :103 NA's :108 NA's :1
## income isworking edu car
## Min. :1.000 Min. :0.0000 Min. :1.000 Min. :0.0000
## 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:0.0000
## Median :2.000 Median :1.0000 Median :3.000 Median :1.0000
## Mean :2.366 Mean :0.6509 Mean :3.065 Mean :0.7339
## 3rd Qu.:3.000 3rd Qu.:1.0000 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :5.000 Max. :1.0000 Max. :4.000 Max. :1.0000
## NA's :2 NA's :196 NA's :196
## city_size RB ESB RSB
## Min. : 2552 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.: 686430 1st Qu.:0.4500 1st Qu.:0.5000 1st Qu.:0.4000
## Median : 1195446 Median :0.5500 Median :0.6500 Median :0.5000
## Mean : 2694228 Mean :0.5858 Mean :0.6252 Mean :0.4959
## 3rd Qu.: 1483119 3rd Qu.:0.7500 3rd Qu.:0.7500 3rd Qu.:0.5500
## Max. :12692466 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :17
## EMB TC RBo ESBo
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.4875 1st Qu.:0.5000 1st Qu.:0.2000 1st Qu.:0.5000
## Median :0.6000 Median :0.8000 Median :0.3000 Median :0.5000
## Mean :0.5837 Mean :0.6937 Mean :0.3197 Mean :0.5197
## 3rd Qu.:0.7500 3rd Qu.:1.0000 3rd Qu.:0.5000 3rd Qu.:0.6000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## RSBo EMBo
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.4000 1st Qu.:0.3000
## Median :0.5000 Median :0.5000
## Mean :0.4928 Mean :0.4259
## 3rd Qu.:0.6000 3rd Qu.:0.5000
## Max. :1.0000 Max. :1.0000
##
par(mfrow = c(3,4), mar=c(2,1,1,1))
hist(data$RB, breaks = "FD", main = "RB")
hist(data$ESB, breaks = "FD", main = "ESB")
hist(data$RSB, breaks = "FD", main = "RSB")
hist(data$EMB, breaks = "FD", main = "EMB")
hist(data$TC, breaks = "FD", main = "TC")
hist(data$RBo, breaks = "FD", main = "RBo")
hist(data$ESBo, breaks = "FD", main = "ESBo")
hist(data$RSBo, breaks = "FD", main = "RSBo")
hist(data$EMBo, breaks = "FD", main = "EMBo")
hist(data$log_age, breaks = "FD", main = "log_age")
hist(data$log_city_size, breaks = "FD", main = "log_city_size")
hist(data$income, breaks = "FD", main = "income")
par(mfrow = c(2,2), mar=c(2,2,2,1))
boxplot(RB ~ group, data=data, main="RB", horizontal = T)
boxplot(ESB ~ group, data=data, main="ESB", horizontal = T)
boxplot(RSB ~ group, data=data, main="RSB", horizontal = T)
boxplot(EMB ~ group, data=data, main="EMB", horizontal = T)
boxplot(RBo ~ group, data=data, main="RBo", horizontal = T)
boxplot(ESBo ~ group, data=data, main="ESBo", horizontal = T)
boxplot(RSBo ~ group, data=data, main="RSBo", horizontal = T)
boxplot(EMBo ~ group, data=data, main="EMBo", horizontal = T)
boxplot(RBo ~ group, data=data, main="RBo", horizontal = T)
boxplot(log_age ~ group, data=data, main="log age", horizontal = T)
boxplot(log_city_size ~ group, data=data, main="log city size", horizontal = T)
boxplot(income ~ group, data=data, main="income", horizontal = T)
boxplot(TC ~ group, data=data, main="TC", horizontal = T)
summary(data[data$group=="Control",c(29,30,32:37,39, 54:62)])## emo rat sex age income
## Min. :0 Min. :0 Min. :0.0000 Min. :15.00 Min. :1.00
## 1st Qu.:0 1st Qu.:0 1st Qu.:0.0000 1st Qu.:21.00 1st Qu.:1.00
## Median :0 Median :0 Median :0.0000 Median :24.00 Median :2.00
## Mean :0 Mean :0 Mean :0.3945 Mean :28.35 Mean :2.33
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:1.0000 3rd Qu.:35.00 3rd Qu.:3.00
## Max. :0 Max. :0 Max. :1.0000 Max. :67.00 Max. :5.00
##
## isworking edu car city_size
## Min. :0.0000 Min. :1.000 Min. :0.0000 Min. : 10293
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:1.0000 1st Qu.: 952136
## Median :1.0000 Median :3.000 Median :1.0000 Median : 1195446
## Mean :0.6389 Mean :2.955 Mean :0.8182 Mean : 2904776
## 3rd Qu.:1.0000 3rd Qu.:3.000 3rd Qu.:1.0000 3rd Qu.: 1483119
## Max. :1.0000 Max. :4.000 Max. :1.0000 Max. :12692466
## NA's :1 NA's :65 NA's :65 NA's :3
## RB ESB RSB EMB
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.3500 1st Qu.:0.5000 1st Qu.:0.4000 1st Qu.:0.4500
## Median :0.5000 Median :0.5500 Median :0.5000 Median :0.5500
## Mean :0.5459 Mean :0.5766 Mean :0.4968 Mean :0.5642
## 3rd Qu.:0.8000 3rd Qu.:0.7500 3rd Qu.:0.5500 3rd Qu.:0.7500
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## TC RBo ESBo RSBo
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.5000 1st Qu.:0.2000 1st Qu.:0.4000 1st Qu.:0.4000
## Median :0.8000 Median :0.3000 Median :0.5000 Median :0.5000
## Mean :0.6982 Mean :0.3431 Mean :0.4927 Mean :0.4991
## 3rd Qu.:1.0000 3rd Qu.:0.5000 3rd Qu.:0.6000 3rd Qu.:0.6000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :0.9000
##
## EMBo
## Min. :0.0000
## 1st Qu.:0.3000
## Median :0.5000
## Mean :0.4147
## 3rd Qu.:0.5000
## Max. :1.0000
##
head(data[data$group=="Control",c(29,30,32:37,39, 54:62)])## emo rat sex age income isworking edu car city_size RB ESB RSB EMB TC
## 2 0 0 1 18 1 0 NA NA 1195446 0.00 0.00 0.00 0.00 0.0
## 3 0 0 0 22 2 1 NA NA 1195446 0.80 0.50 0.55 0.55 1.0
## 4 0 0 0 30 3 1 NA NA 1195446 0.55 0.95 0.60 0.45 0.8
## 7 0 0 1 35 5 1 NA NA 1195446 0.40 0.85 0.85 0.75 0.4
## 13 0 0 1 22 2 0 NA NA 1195446 0.45 0.35 0.30 0.20 0.2
## 22 0 0 0 19 1 0 NA NA 1195446 0.75 0.50 0.70 1.00 0.6
## RBo ESBo RSBo EMBo
## 2 0.0 0.0 0.0 0.0
## 3 0.5 0.5 0.5 0.5
## 4 0.4 0.6 0.5 0.4
## 7 0.5 0.6 0.8 0.3
## 13 0.7 0.9 0.4 0.3
## 22 0.3 0.5 0.4 0.8
nrow(data[data$group=="Control",c(29,30,32:37,39, 54:62)])## [1] 109
summary(data[data$group=="Emotional",c(29,30,32:37,39, 54:62)])## emo rat sex age income
## Min. :1 Min. : NA Min. :0.0000 Min. :15.00 Min. :1.000
## 1st Qu.:1 1st Qu.: NA 1st Qu.:0.0000 1st Qu.:20.00 1st Qu.:1.000
## Median :1 Median : NA Median :0.0000 Median :26.50 Median :2.000
## Mean :1 Mean :NaN Mean :0.2685 Mean :29.29 Mean :2.546
## 3rd Qu.:1 3rd Qu.: NA 3rd Qu.:1.0000 3rd Qu.:36.25 3rd Qu.:3.000
## Max. :1 Max. : NA Max. :1.0000 Max. :62.00 Max. :5.000
## NA's :108
## isworking edu car city_size
## Min. :0.0000 Min. :1.000 Min. :0.0000 Min. : 2552
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:0.0000 1st Qu.: 1013468
## Median :1.0000 Median :3.000 Median :1.0000 Median : 1195446
## Mean :0.6822 Mean :3.171 Mean :0.7073 Mean : 2746519
## 3rd Qu.:1.0000 3rd Qu.:3.000 3rd Qu.:1.0000 3rd Qu.: 1618039
## Max. :1.0000 Max. :4.000 Max. :1.0000 Max. :12692466
## NA's :1 NA's :67 NA's :67 NA's :3
## RB ESB RSB EMB
## Min. :0.1000 Min. :0.1500 Min. :0.0000 Min. :0.0500
## 1st Qu.:0.5000 1st Qu.:0.5000 1st Qu.:0.4000 1st Qu.:0.4500
## Median :0.6500 Median :0.6500 Median :0.5000 Median :0.6000
## Mean :0.6356 Mean :0.6343 Mean :0.4921 Mean :0.5769
## 3rd Qu.:0.8000 3rd Qu.:0.7500 3rd Qu.:0.5500 3rd Qu.:0.7500
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## TC RBo ESBo RSBo
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.5000 1st Qu.:0.1000 1st Qu.:0.500 1st Qu.:0.4000
## Median :0.8000 Median :0.3000 Median :0.500 Median :0.5000
## Mean :0.6963 Mean :0.3009 Mean :0.538 Mean :0.4639
## 3rd Qu.:0.9250 3rd Qu.:0.5000 3rd Qu.:0.600 3rd Qu.:0.6000
## Max. :1.0000 Max. :0.7000 Max. :1.000 Max. :0.9000
##
## EMBo
## Min. :0.000
## 1st Qu.:0.300
## Median :0.450
## Mean :0.437
## 3rd Qu.:0.500
## Max. :1.000
##
head(data[data$group=="Emotional",c(29,30,32:37,39, 54:62)])## emo rat sex age income isworking edu car city_size RB ESB RSB EMB TC
## 1 1 NA 0 34 3 1 NA NA 1195446 0.75 0.50 0.45 0.65 0.7
## 5 1 NA 0 34 3 1 NA NA 1195446 0.75 0.55 0.45 0.75 0.9
## 6 1 NA 0 25 2 1 NA NA 12692466 1.00 0.15 0.45 0.70 1.0
## 12 1 NA 0 19 1 0 NA NA 1195446 0.90 0.45 0.35 0.95 0.8
## 14 1 NA 0 24 1 0 NA NA 1195446 0.80 0.15 0.50 0.10 0.0
## 15 1 NA 0 17 1 0 NA NA 151275 0.90 0.65 0.50 0.80 1.0
## RBo ESBo RSBo EMBo
## 1 0.6 0.5 0.4 0.5
## 5 0.5 0.6 0.5 0.4
## 6 0.1 0.9 0.5 0.3
## 12 0.0 1.0 0.1 0.2
## 14 0.3 0.4 0.5 0.6
## 15 0.0 0.5 0.5 0.0
nrow(data[data$group=="Emotional",c(29,30,32:37,39, 54:62)])## [1] 108
summary(data[data$group=="Rational",c(29,30,32:37,39, 54:62)])## emo rat sex age income
## Min. : NA Min. :1 Min. :0.0000 Min. :14.00 Min. :1.000
## 1st Qu.: NA 1st Qu.:1 1st Qu.:0.0000 1st Qu.:20.00 1st Qu.:1.000
## Median : NA Median :1 Median :0.0000 Median :27.00 Median :2.000
## Mean :NaN Mean :1 Mean :0.3431 Mean :29.02 Mean :2.214
## 3rd Qu.: NA 3rd Qu.:1 3rd Qu.:1.0000 3rd Qu.:34.00 3rd Qu.:3.000
## Max. : NA Max. :1 Max. :1.0000 Max. :64.00 Max. :5.000
## NA's :103 NA's :1
## isworking edu car city_size
## Min. :0.0000 Min. :1.000 Min. :0.0000 Min. : 10854
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:0.0000 1st Qu.: 614367
## Median :1.0000 Median :3.000 Median :1.0000 Median : 1195446
## Mean :0.6311 Mean :3.077 Mean :0.6667 Mean : 2391960
## 3rd Qu.:1.0000 3rd Qu.:3.000 3rd Qu.:1.0000 3rd Qu.: 1483119
## Max. :1.0000 Max. :4.000 Max. :1.0000 Max. :12692466
## NA's :64 NA's :64 NA's :11
## RB ESB RSB EMB
## Min. :0.0000 Min. :0.050 Min. :0.000 Min. :0.0000
## 1st Qu.:0.4500 1st Qu.:0.550 1st Qu.:0.450 1st Qu.:0.5000
## Median :0.5500 Median :0.700 Median :0.500 Median :0.6000
## Mean :0.5757 Mean :0.667 Mean :0.499 Mean :0.6117
## 3rd Qu.:0.7000 3rd Qu.:0.750 3rd Qu.:0.550 3rd Qu.:0.7500
## Max. :1.0000 Max. :1.000 Max. :1.000 Max. :1.0000
##
## TC RBo ESBo RSBo
## Min. :0.0000 Min. :0.0000 Min. :0.1000 Min. :0.0000
## 1st Qu.:0.5000 1st Qu.:0.2000 1st Qu.:0.5000 1st Qu.:0.4000
## Median :0.8000 Median :0.3000 Median :0.5000 Median :0.5000
## Mean :0.6864 Mean :0.3146 Mean :0.5291 Mean :0.5165
## 3rd Qu.:1.0000 3rd Qu.:0.4500 3rd Qu.:0.6000 3rd Qu.:0.6000
## Max. :1.0000 Max. :0.9000 Max. :1.0000 Max. :1.0000
##
## EMBo
## Min. :0.0000
## 1st Qu.:0.3000
## Median :0.4000
## Mean :0.4262
## 3rd Qu.:0.5000
## Max. :1.0000
##
head(data[data$group=="Rational",c(29,30,32:37,39, 54:62)])## emo rat sex age income isworking edu car city_size RB ESB RSB EMB TC RBo
## 9 NA 1 0 59 5 0 NA NA NA 0.50 0.50 0.5 0.5 0.0 0.0
## 18 NA 1 0 22 1 0 NA NA 1195446 0.45 0.55 0.5 0.4 0.6 0.5
## 19 NA 1 0 20 1 0 NA NA 1195446 0.15 0.70 0.2 0.5 0.2 0.2
## 25 NA 1 0 23 2 1 NA NA 1195446 0.60 0.60 0.3 0.7 0.0 0.1
## 30 NA 1 0 27 3 1 NA NA 5392992 0.70 0.50 0.5 0.5 0.9 0.2
## 38 NA 1 1 32 3 1 NA NA 1195446 0.45 0.65 0.1 0.4 0.2 0.5
## ESBo RSBo EMBo
## 9 0.5 0.5 0.5
## 18 0.6 0.6 0.5
## 19 0.5 0.4 0.7
## 25 0.5 0.5 0.4
## 30 0.5 0.5 0.5
## 38 0.4 0.1 0.1
nrow(data[data$group=="Rational",c(29,30,32:37,39, 54:62)])## [1] 103
library(psych)
describeBy(data[,c(32:37,39,54:62)], data$group, na.rm = T)$Control[,c(2:5,8,9)]## n mean sd median min max
## sex 109 0.39 0.49 0.00 0 1.0
## age 109 28.35 10.37 24.00 15 67.0
## income 109 2.33 1.24 2.00 1 5.0
## isworking 108 0.64 0.48 1.00 0 1.0
## edu 44 2.95 0.68 3.00 1 4.0
## car 44 0.82 0.39 1.00 0 1.0
## city_size 106 2904776.10 4255889.26 1195446.00 10293 12692466.0
## RB 109 0.55 0.28 0.50 0 1.0
## ESB 109 0.58 0.23 0.55 0 1.0
## RSB 109 0.50 0.19 0.50 0 1.0
## EMB 109 0.56 0.24 0.55 0 1.0
## TC 109 0.70 0.32 0.80 0 1.0
## RBo 109 0.34 0.21 0.30 0 1.0
## ESBo 109 0.49 0.19 0.50 0 1.0
## RSBo 109 0.50 0.18 0.50 0 0.9
## EMBo 109 0.41 0.21 0.50 0 1.0
describeBy(data[,c(32:37,39,54:62)], data$group, na.rm = T)$Rational[,c(2:5,8,9)]## n mean sd median min max
## sex 102 0.34 0.48 0.00 0.00 1.0
## age 103 29.02 11.42 27.00 14.00 64.0
## income 103 2.21 1.17 2.00 1.00 5.0
## isworking 103 0.63 0.48 1.00 0.00 1.0
## edu 39 3.08 0.66 3.00 1.00 4.0
## car 39 0.67 0.48 1.00 0.00 1.0
## city_size 92 2391959.93 3488321.68 1195446.00 10854.00 12692466.0
## RB 103 0.58 0.22 0.55 0.00 1.0
## ESB 103 0.67 0.17 0.70 0.05 1.0
## RSB 103 0.50 0.20 0.50 0.00 1.0
## EMB 103 0.61 0.20 0.60 0.00 1.0
## TC 103 0.69 0.33 0.80 0.00 1.0
## RBo 103 0.31 0.21 0.30 0.00 0.9
## ESBo 103 0.53 0.15 0.50 0.10 1.0
## RSBo 103 0.52 0.18 0.50 0.00 1.0
## EMBo 103 0.43 0.19 0.40 0.00 1.0
describeBy(data[,c(32:37,39,54:62)], data$group, na.rm = T)$Emotional[,c(2:5,8,9)]## n mean sd median min max
## sex 108 0.27 0.45 0.00 0.00 1.0
## age 108 29.29 10.77 26.50 15.00 62.0
## income 108 2.55 1.34 2.00 1.00 5.0
## isworking 107 0.68 0.47 1.00 0.00 1.0
## edu 41 3.17 0.59 3.00 1.00 4.0
## car 41 0.71 0.46 1.00 0.00 1.0
## city_size 105 2746518.50 3836439.26 1195446.00 2552.00 12692466.0
## RB 108 0.64 0.21 0.65 0.10 1.0
## ESB 108 0.63 0.19 0.65 0.15 1.0
## RSB 108 0.49 0.20 0.50 0.00 1.0
## EMB 108 0.58 0.21 0.60 0.05 1.0
## TC 108 0.70 0.29 0.80 0.00 1.0
## RBo 108 0.30 0.19 0.30 0.00 0.7
## ESBo 108 0.54 0.17 0.50 0.00 1.0
## RSBo 108 0.46 0.20 0.50 0.00 0.9
## EMBo 108 0.44 0.17 0.45 0.00 1.0
Балансирование групп производится на основе метода PSM по основным характеристикам респондентов (пол, доход, занятость, возраст, размер города, склонность к комфорту).
Ниже представлены результаты балансировки и основные характеристики сбалансированных групп.
emo_data <- data %>% # MatchIt does not allow missing values
select(id, emo, sex, income, isworking, log_age, log_city_size, TC) %>%
na.omit()
match.emo <- matchit(emo ~ sex + income + isworking + log_age + log_city_size + TC, data = emo_data, method="nearest")
a <- summary(match.emo)
emo_data <- match.data(match.emo) %>%
select(id, distance, weights) %>%
left_join(data, by = c("id"))
plot(match.emo, type = 'jitter', interactive = F)
kable(a$nn, digits = 2, align = 'c', caption = 'Table: Sample sizes')Table: Table: Sample sizes
Control Treated
All 105 104
Matched 104 104
Unmatched 1 0
Discarded 0 0
t <- c("sex","income","isworking","log_age","log_city_size","TC")
t <- lapply(t, function(v) {
#t.test(emo_data[, v] ~ emo_data[, "rat"])
w<-wilcox.test(emo_data[, v] ~ emo_data[, "emo"], paired = F, alternative = "two.sided")
w$p.value
})
t<-unlist(t)
#Wilcoxon rank sum test with continuity correction
#W = 4459, p-value = 0.2775
#alternative hypothesis: true location shift is not equal to 0
a <- cbind(a$sum.matched[-1,c(1,2,4)], t)
names(a)[4]<-"p-value"
kable(a, digits = 3, align = 'c',
caption = 'Table: Summary of balance for matched data')Table: Table: Summary of balance for matched data
Means Treated Means Control Mean Diff p-value
sex 0.279 0.385 -0.106 0.106
income 2.567 2.308 0.260 0.172
isworking 0.683 0.625 0.058 0.384
log_age 1.441 1.428 0.013 0.480
log_city_size 6.095 6.081 0.013 0.532
TC 0.700 0.693 0.007 0.793
by<-factor(emo_data$emo, labels = c("Control","Emotional"))
par(mfrow = c(1,4), mar=c(2,2,2,1))
boxplot(RB ~ by, data=emo_data, main="RB", horizontal = F)
boxplot(ESB ~ by, data=emo_data, main="ESB", horizontal = F)
boxplot(RSB ~ by, data=emo_data, main="RSB", horizontal = F)
boxplot(EMB ~ by, data=emo_data, main="EMB", horizontal = F)
boxplot(RBo ~ by, data=emo_data, main="RBo", horizontal = F)
boxplot(ESBo ~ by, data=emo_data, main="ESBo", horizontal = F)
boxplot(RSBo ~ by, data=emo_data, main="RSBo", horizontal = F)
boxplot(EMBo ~ by, data=emo_data, main="EMBo", horizontal = F)
boxplot(RBo ~ by, data=emo_data, main="RBo", horizontal = F)
boxplot(log_age ~ by, data=emo_data, main="log age", horizontal = F)
boxplot(log_city_size ~ by, data=emo_data, main="log city size", horizontal = F)
boxplot(income ~ by, data=emo_data, main="income", horizontal = F)
boxplot(TC ~ by, data=emo_data, main="TC", horizontal = F)
summary(emo_data[emo_data$emo==1,c(34:37,41)])## sex age income isworking
## Min. :0.0000 Min. :15.00 Min. :1.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:20.00 1st Qu.:1.000 1st Qu.:0.0000
## Median :0.0000 Median :26.50 Median :2.000 Median :1.0000
## Mean :0.2788 Mean :29.38 Mean :2.567 Mean :0.6827
## 3rd Qu.:1.0000 3rd Qu.:36.25 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :62.00 Max. :5.000 Max. :1.0000
## city_size
## Min. : 2552
## 1st Qu.: 944783
## Median : 1195446
## Mean : 2761433
## 3rd Qu.: 1618039
## Max. :12692466
summary(emo_data[emo_data$emo==0,c(34:37,41)])## sex age income isworking
## Min. :0.0000 Min. :15.00 Min. :1.000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:21.00 1st Qu.:1.000 1st Qu.:0.000
## Median :0.0000 Median :24.50 Median :2.000 Median :1.000
## Mean :0.3846 Mean :28.39 Mean :2.308 Mean :0.625
## 3rd Qu.:1.0000 3rd Qu.:35.00 3rd Qu.:3.000 3rd Qu.:1.000
## Max. :1.0000 Max. :67.00 Max. :5.000 Max. :1.000
## city_size
## Min. : 10293
## 1st Qu.: 1020120
## Median : 1195446
## Mean : 2947686
## 3rd Qu.: 1483119
## Max. :12692466
Анализ значимости различий в средних между независимыми группами (u-тест Манна-Уитни). p-value > 0.05 означает отсутствие значимых различий.
rat_data <- data %>% # MatchIt does not allow missing values
select(id, rat, sex, income, isworking, log_age, log_city_size, TC) %>%
na.omit()
match.rat <- matchit(rat ~ sex + income + isworking + log_age + log_city_size + TC, data = rat_data, method="nearest")
a <- summary(match.rat)
rat_data <- match.data(match.rat) %>%
select(id, distance, weights) %>%
left_join(data, by = c("id"))
plot(match.rat, type = 'jitter', interactive = F)
kable(a$nn, digits = 2, align = 'c', caption = 'Table: Sample sizes')Table: Table: Sample sizes
Control Treated
All 105 91
Matched 91 91
Unmatched 14 0
Discarded 0 0
t <- c("sex","income","isworking","log_age","log_city_size","TC")
t <- lapply(t, function(v) {
#t.test(rat_data[, v] ~ rat_data[, "rat"])
w<-wilcox.test(rat_data[, v] ~ rat_data[, "rat"], paired = F, alternative = "two.sided")
w$p.value
})
t<-unlist(t)
#Wilcoxon rank sum test with continuity correction
#W = 4459, p-value = 0.2775
#alternative hypothesis: true location shift is not equal to 0
a <- cbind(a$sum.matched[-1,c(1,2,4)], t)
names(a)[4]<-"p-value"
kable(a, digits = 3, align = 'c',
caption = 'Table: Summary of balance for matched data')Table: Table: Summary of balance for matched data
Means Treated Means Control Mean Diff p-value
sex 0.308 0.385 -0.077 0.277
income 2.165 2.264 -0.099 0.541
isworking 0.615 0.615 0.000 1.000
log_age 1.430 1.420 0.010 0.806
log_city_size 6.041 6.072 -0.031 0.717
TC 0.697 0.682 0.014 0.689
by<-factor(rat_data$rat, labels = c("Control","Rational"))
par(mfrow = c(1,4), mar=c(2,2,2,1))
boxplot(RB ~ by, data=rat_data, main="RB", horizontal = F)
boxplot(ESB ~ by, data=rat_data, main="ESB", horizontal = F)
boxplot(RSB ~ by, data=rat_data, main="RSB", horizontal = F)
boxplot(EMB ~ by, data=rat_data, main="EMB", horizontal = F)
boxplot(RBo ~ by, data=rat_data, main="RBo", horizontal = F)
boxplot(ESBo ~ by, data=rat_data, main="ESBo", horizontal = F)
boxplot(RSBo ~ by, data=rat_data, main="RSBo", horizontal = F)
boxplot(EMBo ~ by, data=rat_data, main="EMBo", horizontal = F)
boxplot(RBo ~ by, data=rat_data, main="RBo", horizontal = F)
boxplot(log_age ~ by, data=rat_data, main="log age", horizontal = F)
boxplot(log_city_size ~ by, data=rat_data, main="log city size", horizontal = F)
boxplot(income ~ by, data=rat_data, main="income", horizontal = F)
boxplot(TC ~ by, data=rat_data, main="TC", horizontal = F)
summary(rat_data[rat_data$rat==1,c(34:37,41)])## sex age income isworking
## Min. :0.0000 Min. :14.00 Min. :1.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:20.00 1st Qu.:1.000 1st Qu.:0.0000
## Median :0.0000 Median :27.00 Median :2.000 Median :1.0000
## Mean :0.3077 Mean :28.87 Mean :2.165 Mean :0.6154
## 3rd Qu.:1.0000 3rd Qu.:34.00 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :64.00 Max. :5.000 Max. :1.0000
## city_size
## Min. : 10854
## 1st Qu.: 611261
## Median : 1195446
## Mean : 2405108
## 3rd Qu.: 1483119
## Max. :12692466
summary(rat_data[rat_data$rat==0,c(34:37,41)])## sex age income isworking
## Min. :0.0000 Min. :15.00 Min. :1.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:21.00 1st Qu.:1.000 1st Qu.:0.0000
## Median :0.0000 Median :23.00 Median :2.000 Median :1.0000
## Mean :0.3846 Mean :27.88 Mean :2.264 Mean :0.6154
## 3rd Qu.:1.0000 3rd Qu.:35.00 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :67.00 Max. :5.000 Max. :1.0000
## city_size
## Min. : 10293
## 1st Qu.: 986128
## Median : 1195446
## Mean : 2827260
## 3rd Qu.: 1483119
## Max. :12692466
Анализ значимости различий в средних между независимыми группами (u-тест Манна-Уитни). p-value > 0.05 означает отсутствие значимых различий.
library(lavaan)
library(semPlot)
library(semPower)
library(lavaanPlot)
library(psych)
library(pwr)
library(MBESS)
library(GPArotation)#power analysis
pwr.anova.test(k = 2, n = , sig.level = 0.05, power = 0.9, f = 0.25)##
## Balanced one-way analysis of variance power calculation
##
## k = 2
## n = 85.03128
## f = 0.25
## sig.level = 0.05
## power = 0.9
##
## NOTE: n is number in each group
pwr.anova.test(k = 3, n = , sig.level = 0.05, power = 0.9, f = 0.25)##
## Balanced one-way analysis of variance power calculation
##
## k = 3
## n = 68.49707
## f = 0.25
## sig.level = 0.05
## power = 0.9
##
## NOTE: n is number in each group
#f - 0.25 is medium size effect
#k - number of groups#Cronbach's alpha and McDonald's omega
#0.7 - 0.79 - acceptable
#0.8 - 0.89 - good
#0.6 - 0.69 - questionable
#alpha(data[,c("case_1_1","case_1_2","case_2_1","case_2_2")], check.keys = TRUE)
#alpha(data[,c("case_3_1","case_3_2","case_4_1","case_4_2")], check.keys = TRUE)
#alpha(data[,c("case_5_1","case_5_2","case_6_1","case_6_2")], check.keys = TRUE)
#alpha(data[,c("case_7_1","case_7_2","case_8_1","case_8_2")], check.keys = TRUE)
omega(data[,c("case_1_1","case_1_2","case_2_1","case_2_2")], check.keys = TRUE) #RB omg_t 0.85
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar, check.keys = TRUE)
## Alpha: 0.63
## G.6: 0.71
## Omega Hierarchical: 0.27
## Omega H asymptotic: 0.32
## Omega Total 0.85
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## case_1_1 0.27 0.71 0.61 0.39 0.12
## case_1_2 0.31 0.72 0.64 0.36 0.15
## case_2_1 0.41 0.76 0.78 0.22 0.22
## case_2_2 0.45 0.76 0.81 0.19 0.25
##
## With eigenvalues of:
## g F1* F2* F3*
## 0.54 1.15 1.02 0.12
##
## general/max 0.47 max/min = 9.22
## mean percent general = 0.18 with sd = 0.06 and cv of 0.33
## Explained Common Variance of the general factor = 0.19
##
## The degrees of freedom are -3 and the fit is 0
## The number of observations was 320 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 2 and the fit is 0.86
## The number of observations was 320 with Chi Square = 271.54 with prob < 1.1e-59
## The root mean square of the residuals is 0.3
## The df corrected root mean square of the residuals is 0.52
##
## RMSEA index = 0.649 and the 10 % confidence intervals are 0.586 0.716
## BIC = 260
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.53 0.83 0.82 0.56
## Multiple R square of scores with factors 0.28 0.68 0.67 0.32
## Minimum correlation of factor score estimates -0.44 0.36 0.34 -0.36
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.85 0.88 0.76 NA
## Omega general for total scores and subscales 0.27 0.21 0.11 NA
## Omega group for total scores and subscales 0.57 0.67 0.65 NA
omega(data[,c("case_3_1","case_3_2","case_4_1","case_4_2")], check.keys = TRUE) #ESB omg_t 0.84
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar, check.keys = TRUE)
## Alpha: 0.61
## G.6: 0.68
## Omega Hierarchical: 0.45
## Omega H asymptotic: 0.54
## Omega Total 0.84
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## case_3_1- -0.83 0.76 0.24 0.00
## case_3_2- 0.20 -0.81 0.22 0.72 0.28 0.06
## case_4_1 0.75 -0.23 0.62 0.38 0.91
## case_4_2 0.83 0.72 0.28 0.95
##
## With eigenvalues of:
## g F1* F2* F3*
## 1.29 1.35 0.00 0.17
##
## general/max 0.95 max/min = 387.47
## mean percent general = 0.48 with sd = 0.52 and cv of 1.08
## Explained Common Variance of the general factor = 0.46
##
## The degrees of freedom are -3 and the fit is 0
## The number of observations was 320 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 2 and the fit is 0.6
## The number of observations was 320 with Chi Square = 189.58 with prob < 6.8e-42
## The root mean square of the residuals is 0.26
## The df corrected root mean square of the residuals is 0.46
##
## RMSEA index = 0.541 and the 10 % confidence intervals are 0.478 0.609
## BIC = 178.04
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.89 0.91 0.05 0.60
## Multiple R square of scores with factors 0.79 0.84 0.00 0.35
## Minimum correlation of factor score estimates 0.59 0.67 -1.00 -0.29
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.84 0.84 NA 0.79
## Omega general for total scores and subscales 0.45 0.02 NA 0.79
## Omega group for total scores and subscales 0.37 0.82 NA 0.00
omega(data[,c("case_5_1","case_5_2","case_6_1","case_6_2")], check.keys = TRUE) #RSB omg_t 0.90## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully
## Warning in cov2cor(t(w) %*% r %*% w): diag(.) had 0 or NA entries; non-finite
## result is doubtful
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar, check.keys = TRUE)
## Alpha: 0.74
## G.6: 0.82
## Omega Hierarchical: 0.59
## Omega H asymptotic: 0.66
## Omega Total 0.9
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## case_5_1- 0.29 -0.83 0.76 0.24 0.11
## case_5_2- 0.22 -0.82 0.74 0.26 0.07
## case_6_1 0.89 0.80 0.20 0.99
## case_6_2 0.90 0.81 0.19 1.00
##
## With eigenvalues of:
## g F1* F2* F3*
## 1.73 0.00 1.35 0.03
##
## general/max 1.28 max/min = Inf
## mean percent general = 0.54 with sd = 0.52 and cv of 0.96
## Explained Common Variance of the general factor = 0.56
##
## The degrees of freedom are -3 and the fit is 0
## The number of observations was 320 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 2 and the fit is 0.73
## The number of observations was 320 with Chi Square = 229.35 with prob < 1.6e-50
## The root mean square of the residuals is 0.27
## The df corrected root mean square of the residuals is 0.47
##
## RMSEA index = 0.596 and the 10 % confidence intervals are 0.533 0.663
## BIC = 217.82
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.94 0 0.92 0.35
## Multiple R square of scores with factors 0.89 0 0.84 0.12
## Minimum correlation of factor score estimates 0.79 -1 0.69 -0.76
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.90 NA 0.86 0.89
## Omega general for total scores and subscales 0.59 NA 0.08 0.89
## Omega group for total scores and subscales 0.30 NA 0.78 0.00
omega(data[,c("case_7_1","case_7_2","case_8_1","case_8_2")], check.keys = TRUE) #EMB omg_t 0.89
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar, check.keys = TRUE)
## Alpha: 0.68
## G.6: 0.77
## Omega Hierarchical: 0.51
## Omega H asymptotic: 0.58
## Omega Total 0.89
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## case_7_1 0.83 0.72 0.28 0.96
## case_7_2 0.82 0.70 0.30 0.96
## case_8_1- 0.22 -0.87 0.82 0.18 0.06
## case_8_2- -0.87 0.82 0.18 0.04
##
## With eigenvalues of:
## g F1* F2* F3*
## 1.45 1.50 0.01 0.10
##
## general/max 0.96 max/min = 248.29
## mean percent general = 0.5 with sd = 0.53 and cv of 1.04
## Explained Common Variance of the general factor = 0.47
##
## The degrees of freedom are -3 and the fit is 0
## The number of observations was 320 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 2 and the fit is 0.88
## The number of observations was 320 with Chi Square = 277.59 with prob < 5.3e-61
## The root mean square of the residuals is 0.3
## The df corrected root mean square of the residuals is 0.51
##
## RMSEA index = 0.656 and the 10 % confidence intervals are 0.593 0.724
## BIC = 266.05
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.91 0.94 0.06 0.56
## Multiple R square of scores with factors 0.82 0.89 0.00 0.32
## Minimum correlation of factor score estimates 0.65 0.77 -0.99 -0.36
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.89 0.90 NA 0.82
## Omega general for total scores and subscales 0.51 0.04 NA 0.82
## Omega group for total scores and subscales 0.37 0.85 NA 0.00
Гипотеза H1. Эко-поведение не однородно - склонность к одному из видов эко-поведения не предопределяет склонность к другому.
Для исследования этой гипотезы прибегнем к методу Smallest Space Analyse (SSA). В рамках данного метода в едином пространстве размещаются признаки объекта на основе некоторой метрики близости. Относительно близкие друг к другу признаки считаются однородными. Для построения пространства будем использовать метрику d=1-r, где r - коэффициент корреляции Спирмена. Двумерная диаграмма рассеяния (рис.) на основе d показывает, что чем более скоррелированы индексы склонности к некоторым видам эко-поведения, тем ближе их метки на диаграмме и, соответственно, данные виды поведения относительно более однородны.
#cmdscale(d, k = 2, eig = FALSE, add = FALSE, x.ret = FALSE) #Multidimensional Scaling
cols <- 56:59
ctrl_emo <- cbind(
na.omit(emo_data[emo_data$emo==0,cols]), #na.omit
na.omit(emo_data[emo_data$emo==1,cols])
)
names(ctrl_emo)[1:4] <- sapply(names(ctrl_emo)[1:4],function(x) paste0(x,"c"))
names(ctrl_emo)[5:8] <- sapply(names(ctrl_emo)[5:8],function(x) paste0(x,"e"))
#ctrl_emo
mds_emo <- cmdscale(as.dist(apply(cor(ctrl_emo, method = "spearman"),1:2,function(x) 1-x)), k = 2)
p0<-ggplot()+labs(x="DIM 1", y="DIM 2")+xlim(-1,1)+ylim(-1,1)+theme_bw()+
geom_text(data=as.data.frame(mds_emo), aes(x=mds_emo[,1], y=mds_emo[,2], label=rownames(mds_emo), col="Emotional"))+
scale_colour_manual(name="",values=c("1","2"))
d<-na.omit(emo_data[emo_data$emo==1,cols])
mds_emo <- cmdscale(as.dist(apply(cor(d, method = "spearman"),1:2,function(x) 1-x)), k = 2)
d<-na.omit(emo_data[emo_data$emo==0,cols])
mds_emo_ctrl <- cmdscale(as.dist(apply(cor(d, method = "spearman"),1:2,function(x) 1-x)), k = 2)
d<-na.omit(rat_data[rat_data$rat==1,cols])
mds_rat <- cmdscale(as.dist(apply(cor(d, method = "spearman"),1:2,function(x) 1-x)), k = 2)
d<-na.omit(rat_data[rat_data$rat==0,cols])
mds_rat_ctrl <- cmdscale(as.dist(apply(cor(d, method = "spearman"),1:2,function(x) 1-x)), k = 2)
p1<-ggplot()+labs(x="DIM 1", y="DIM 2")+xlim(-1,1)+ylim(-1,1)+theme_bw()+
geom_text(data=as.data.frame(mds_emo), aes(x=mds_emo[,1], y=mds_emo[,2], label=rownames(mds_emo), col="Emotional"))+
geom_text(data=as.data.frame(mds_emo_ctrl), aes(x=mds_emo_ctrl[,1], y=mds_emo_ctrl[,2], label=rownames(mds_emo_ctrl), col="Control"))+
scale_colour_manual(name="",values=c("1","2"))
p2<-ggplot()+labs(x="DIM 1", y="DIM 2")+xlim(-1,1)+ylim(-1,1)+theme_bw()+
geom_text(data=as.data.frame(mds_rat), aes(x=mds_rat[,1], y=mds_rat[,2], label=rownames(mds_rat), col="Rational"))+
geom_text(data=as.data.frame(mds_rat_ctrl), aes(x=mds_rat_ctrl[,1], y=mds_rat_ctrl[,2], label=rownames(mds_rat_ctrl), col="Control"))+
scale_colour_manual(name="",values=c("1","2"))
p0 <- ggplot()+labs(x=NULL, y=NULL)+xlim(-0.8,0.8)+ylim(-0.8,0.8)+theme_bw()
p1 <- p0 + geom_text(data=as.data.frame(mds_emo), aes(x=mds_emo[,1], y=mds_emo[,2],
label=rownames(mds_emo)))+
#labs(title = "Emotional / Control")+
geom_text(aes(x=0.55, y=0.65,label="Emotional"), fontface = "bold")
p2 <- p0 + geom_text(data=as.data.frame(mds_emo_ctrl), aes(x=mds_emo_ctrl[,1], y=mds_emo_ctrl[,2],
label=rownames(mds_emo_ctrl)))+
#labs(title = "")
geom_text(aes(x=0.6, y=0.65,label="Control"), fontface = "bold")
p3 <- p0 + geom_text(data=as.data.frame(mds_rat), aes(x=mds_rat[,1], y=mds_rat[,2],
label=rownames(mds_rat)))+
#labs(title = "Rational / Control")
geom_text(aes(x=0.6, y=0.65,label="Rational"), fontface = "bold")
p4 <- p0 + geom_text(data=as.data.frame(mds_rat_ctrl), aes(x=mds_rat_ctrl[,1], y=mds_rat_ctrl[,2],
label=rownames(mds_rat_ctrl)))+
#labs(title = "")
geom_text(aes(x=0.6, y=0.65,label="Control"), fontface = "bold")
g <- grid.arrange(p2, p1, p4, p3, ncol = 2, nrow = 2)
#g<-arrangeGrob(p2, p1, p4, p3, ncol = 2, nrow = 2)
ggsave('H1.pdf', g)На графиках можно заметить, что рассматриваемые виды эко-поведения достаточно далеки друг от друга, как в контрольной так и в экспериментальной группах, т.е. неоднородны. Эта неоднородность достаточно устойчива, воздействие стимулов не приводит к радикальным изменениям. Визуально относительно близкими можно было бы признать ресурсосбережение (RSB) и эко-мобильность (EMB) в контрольных группах, но корреляция между ними не высока ~ 0.33.
sem1 <- '
#measurement model
RB ~~ ESB
RB ~~ RSB
RB ~~ EMB
ESB ~~ RSB
ESB ~~ EMB
RSB ~~ EMB
#residual correlations
'
fit_sem1 <- sem(sem1, data = data)#, group = "group")summary(fit_sem1, standardized=TRUE, rsquare=TRUE, fit.measure=TRUE)## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 10
##
## Number of observations 320
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 13.083
## Degrees of freedom 6
## P-value 0.042
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) 168.533
## Loglikelihood unrestricted model (H1) 168.533
##
## Akaike (AIC) -317.065
## Bayesian (BIC) -279.382
## Sample-size adjusted Bayesian (BIC) -311.100
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
## Warning in parameterEstimates(object, ci = ci, standardized = standardized, :
## lavaan WARNING: rsquare = TRUE, but there are no dependent variables
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RB ~~
## ESB 0.002 0.003 0.801 0.423 0.002 0.045
## RSB -0.001 0.003 -0.228 0.820 -0.001 -0.013
## EMB 0.002 0.003 0.761 0.446 0.002 0.043
## ESB ~~
## RSB 0.002 0.002 0.937 0.349 0.002 0.052
## EMB 0.006 0.003 2.497 0.013 0.006 0.141
## RSB ~~
## EMB 0.005 0.002 2.223 0.026 0.005 0.125
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RB 0.058 0.005 12.649 0.000 0.058 1.000
## ESB 0.041 0.003 12.649 0.000 0.041 1.000
## RSB 0.037 0.003 12.649 0.000 0.037 1.000
## EMB 0.048 0.004 12.649 0.000 0.048 1.000
fitMeasures(fit_sem1, c("cfi","rmsea","srmr"))## cfi rmsea srmr
## 1 0 0
#Specify which of the 3 you want (“regress”, “latent”, “covs”).
lavaanPlot(model = fit_sem1, node_options = list(shape = "box", fontname =
"Helvetica"), edge_options = list(color = "grey"), digits = 2, coefs = TRUE, covs = TRUE, stand = TRUE, stars = c("covs"))dopdf('sem_1.pdf')## png
## 2
Гипотеза H2. Люди обычно переоценивают собственную склонность к эко-поведению и недооценивают склонность окружающих.
Посмотрим в разрезе групп на графики плотности распределения самооцененной склонности (self) к различным видам эко-поведения и оцененной склонности окружающих (other). При построении графиков для наглядности применено сглаживание.
dplot <- function (d, s, o, t){
plot(density(d[,s], bw = 0.15), type = "l", col = "1", lwd = 1, bty="n", main = "",#main = paste(s,t),
yaxt="n", ylim=c(0,2.5), xaxt="n")
axis(side=1,at=c(0.0,0.5,1.0))
lines(density(d[,o], bw = 0.15), lty = 2, lwd = 1, col = "2")
#legend("topright",legend = c("self", "other"),
# col = c("1","2"), lwd = c(1,1),
# lty = c(1,2), bty = "n")
#print(paste0(s," ", o, " ", t)) #print label for wilcox data
#t.test(d[,s], d[,o], paired = TRUE, alternative = "two.sided") #Критерий Стьюдента
w<-wilcox.test(d[,s], d[,o], paired = TRUE, alternative = "greater") #Критерий Уилкоксона
legend("topright",legend = c(s, t, ifelse(w$p.value<0.01,"p < 0.01", "p > 0.1")), bty = "n")
#w # print wilcox output
}
#RB
cols <- c(56:59,61:64)
par(mfrow = c(4,4), mar=c(1.8,1.1,1.5,1))
d<-na.omit(rat_data[rat_data$rat==0,cols])
dplot(d,"RB","RBo","Control")
d<-na.omit(rat_data[rat_data$rat==1,cols])
dplot(d,"RB","RBo","Rational")
d<-na.omit(emo_data[emo_data$emo==0,cols])
dplot(d,"RB","RBo","Control")
d<-na.omit(emo_data[emo_data$emo==1,cols])
dplot(d,"RB","RBo","Emotional")
#ESB
#par(mfrow = c(4,4), mar=c(2,2,2,1))
d<-na.omit(rat_data[rat_data$rat==0,cols])
dplot(d,"ESB","ESBo","Control")
d<-na.omit(rat_data[rat_data$rat==1,cols])
dplot(d,"ESB","ESBo","Rational")
d<-na.omit(emo_data[emo_data$emo==0,cols])
dplot(d,"ESB","ESBo","Control")
d<-na.omit(emo_data[emo_data$emo==1,cols])
dplot(d,"ESB","ESBo","Emotional")
#RSB
#par(mfrow = c(2,2), mar=c(2,2,2,1))
d<-na.omit(rat_data[rat_data$rat==0,cols])
dplot(d,"RSB","RSBo","Control")
d<-na.omit(rat_data[rat_data$rat==1,cols])
dplot(d,"RSB","RSBo","Rational")
d<-na.omit(emo_data[emo_data$emo==0,cols])
dplot(d,"RSB","RSBo","Control")
d<-na.omit(emo_data[emo_data$emo==1,cols])
dplot(d,"RSB","RSBo","Emotional")
#EMB
#par(mfrow = c(2,2), mar=c(2,2,2,1))
d<-na.omit(rat_data[rat_data$rat==0,cols])
dplot(d,"EMB","EMBo","Control")
d<-na.omit(rat_data[rat_data$rat==1,cols])
dplot(d,"EMB","EMBo","Rational")
d<-na.omit(emo_data[emo_data$emo==0,cols])
dplot(d,"EMB","EMBo","Control")
d<-na.omit(emo_data[emo_data$emo==1,cols])
dplot(d,"EMB","EMBo","Emotional")
dopdf('H2.pdf')## png
## 2
Пунктирной линией показана оценка окружающих, сплошной - самооценка.
Гипотеза H3. Склонность к тому или иному виду эко-поведения ситуативно подвержена влиянию стимулов, как эмоционального, так и рационального характера.
Для проверки этой гипотезы определим различия между средними значениями индекса склонности в контрольной и исследуемой группах по каждому виду эко-поведения. Рассмотрим различия, как в исходных (unmatched), так и в сбалансированных (matched) выборках (рис.).
По левой шкале показан индекс склонности к соответствующему виду эко-поведения, на графике среднее значение по группам. Показано p-value для одностороннего критерия Манна-Уитни.
getmeans <- function (c) {
emo_all<-data[!is.na(data$emo),]
rat_all<-data[!is.na(data$rat),]
d<-rbind(
cbind( mean(emo_data[emo_data$emo==0,c], na.rm = T),
nrow(emo_data[emo_data$emo==0 & !is.na(emo_data[,c]),]),
mean(emo_data[emo_data$emo==1,c], na.rm = T),
nrow(emo_data[emo_data$emo==1 & !is.na(emo_data[,c]),]),
with(emo_data, wilcox.test(as.formula(paste(c,"~ emo")),
paired = F, alternative = "less"))$p.value),
cbind(mean(rat_data[rat_data$rat==0,c], na.rm = T),
nrow(rat_data[rat_data$rat==0 & !is.na(rat_data[,c]),]),
mean(rat_data[rat_data$rat==1,c], na.rm = T),
nrow(rat_data[rat_data$rat==1 & !is.na(rat_data[,c]),]),
with(rat_data, wilcox.test(as.formula(paste(c,"~ rat")),
paired = F, alternative = "less"))$p.value),
cbind(mean(emo_all[emo_all$emo==0,c], na.rm = T),
nrow(emo_all[emo_all$emo==0 & !is.na(emo_all[,c]),]),
mean(emo_all[emo_all$emo==1,c], na.rm = T),
nrow(emo_all[emo_all$emo==1 & !is.na(emo_all[,c]),]),
with(emo_all, wilcox.test(as.formula(paste(c,"~ emo")),
paired = F, alternative = "less"))$p.value),
cbind(mean(rat_all[rat_all$rat==0,c], na.rm = T),
nrow(rat_all[rat_all$rat==0 & !is.na(rat_all[,c]),]),
mean(rat_all[rat_all$rat==1,c], na.rm = T),
nrow(rat_all[rat_all$rat==1 & !is.na(rat_all[,c]),]),
with(rat_all, wilcox.test(as.formula(paste(c,"~ rat")),
paired = F, alternative = "less"))$p.value)
)
d<-cbind(d[,-5],d[,3]-d[,1],d[,5]) #reorder and add diff
d<-data.frame(d)
d<-cbind(c("Emotional (matched)","Rational (matched)",
"Emotional (unmatched)", "Rational (unmatched)"),d)
names(d) <- c("","Means Control","Obs. Control","Means Treated","Obs. Treated",
"Means Diff","p-value")
table <- kable(d, digits = 3, align = 'l', caption = paste('Table:',c,'u-тест Манна-Уитни $H_{0}: \\mu_{C} < \\mu_{T}$'))
par(mfrow = c(2,2), mar=c(2,2,1.5,1))
by<-factor(emo_data$emo, labels = c("Control","Emotional"))
with(emo_data, plotmeans(as.formula(paste(c,"~ by")), main = "",#paste(c,"means, matched groups"),
frame = F, mean.labels = F, connect = T, xlab = "", ylim=c(0.4,0.8)))
par(new=F)
text(cex=1, font=2, x=2.1, y=0.8, xpd=T, adj=0, paste0(c,"\nmatched\np = ",round(d[1,7],3)))
by<-factor(rat_data$rat, labels = c("Control","Rational"))
with(rat_data, plotmeans(as.formula(paste(c,"~ by")), main = "",#paste(c,"means, matched groups"),
frame = F, mean.labels = F, connect = T, xlab = "", ylim=c(0.4,0.8)))
par(new=F)
text(cex=1, font=2, x=2.1, y=0.8, xpd=T, adj=0, paste0(c,"\nmatched\np = ",round(d[2,7],3)))
by<-factor(emo_all$emo, labels = c("Control","Emotional"))
with(emo_all, plotmeans(as.formula(paste(c,"~ by")), main = "",#paste(c,"means, matched groups"),
frame = F, mean.labels = F, connect = T, xlab = "", ylim=c(0.4,0.8)))
par(new=F)
text(cex=1, font=2, x=2.1, y=0.8, xpd=T, adj=0, paste0(c,"\nunmatched\np = ",round(d[3,7],3)))
by<-factor(rat_all$rat, labels = c("Control","Rational"))
with(rat_all, plotmeans(as.formula(paste(c,"~ by")), main = "",#paste(c,"means, matched groups"),
frame = F, mean.labels = F, connect = T, xlab = "", ylim=c(0.4,0.8)))
par(new=F)
text(cex=1, font=2, x=2.1, y=0.8, xpd=T, adj=0, paste0(c,"\nunmatched\np = ",round(d[4,7],3)))
#with(data, plotmeans(as.formula(paste(c,"~ group")), main = "",#paste(c,"means, unmatched groups"),
# frame = F, mean.labels = F, connect=F, xlab = "", ylim=c(0.4,0.8)))
#par(new=F)
#text(cex=1, font=2, x=2.8, y=0.8, xpd=T, adj=0, paste0(c,"\nunmatched\np = 0.00"))
table
}
getmeans("RB")
Table: Table: RB u-тест Манна-Уитни
Means Control Obs. Control Means Treated Obs. Treated Means Diff p-value
Emotional (matched) 0.544 104 0.636 104 0.092 0.005
Rational (matched) 0.541 91 0.590 91 0.049 0.063
Emotional (unmatched) 0.546 109 0.636 108 0.090 0.007
Rational (unmatched) 0.546 109 0.576 103 0.030 0.162
dopdf('H3_1.pdf')## png
## 2
getmeans("ESB")
Table: Table: ESB u-тест Манна-Уитни
Means Control Obs. Control Means Treated Obs. Treated Means Diff p-value
Emotional (matched) 0.572 104 0.628 104 0.056 0.033
Rational (matched) 0.566 91 0.668 91 0.102 0.001
Emotional (unmatched) 0.577 109 0.634 108 0.058 0.027
Rational (unmatched) 0.577 109 0.667 103 0.090 0.002
dopdf('H3_2.pdf')## png
## 2
getmeans("RSB")
Table: Table: RSB u-тест Манна-Уитни
Means Control Obs. Control Means Treated Obs. Treated Means Diff p-value
Emotional (matched) 0.493 104 0.498 104 0.005 0.601
Rational (matched) 0.492 91 0.508 91 0.016 0.193
Emotional (unmatched) 0.497 109 0.492 108 -0.005 0.751
Rational (unmatched) 0.497 109 0.499 103 0.002 0.381
dopdf('H3_3.pdf')## png
## 2
getmeans("EMB")
Table: Table: EMB u-тест Манна-Уитни
Means Control Obs. Control Means Treated Obs. Treated Means Diff p-value
Emotional (matched) 0.560 104 0.575 104 0.015 0.274
Rational (matched) 0.575 91 0.602 91 0.027 0.271
Emotional (unmatched) 0.564 109 0.577 108 0.013 0.287
Rational (unmatched) 0.564 109 0.612 103 0.047 0.096
dopdf('H3_4.pdf')## png
## 2
Гипотеза H3а. Склонность к более затратным видам поведения менее подвержена ситуативному влиянию стимулов (low-cost hypothesis).
Для этой цели построим регрессионную модель, учитывающую все интересующие признаки и контрольные переменные. Наша базовая спецификация модели выглядит следующим образом:
где
Далее представлены сводные результаты на основе простой линейной регрессии для каждого стимула. Показано, что в рамках склонности к тому или иному виду поведения ситуативные стимулы влияют по разному, детерминируя вклад тех или иных индивидуальных характеристик респондентов.
library(lmtest)
library(sandwich)
library(DescTools)
library(car)
library(caret)#Функции агрегации информации о модели
fit.summary <- function (x) { #Сводная информация и базовые тесты для lm + графики
print(summary(x)) #F-stat p < 0.05 -> good model
#qqnorm(x$residuals)
#qqline(x$residuals, col = 2)
print(shapiro.test(x$residuals)) #p > 0.05 -> normal distrib
print(bptest(x)) # studentized Breusch-Pagan test if p<0.05 -> heteroskedacity is present
#coeftest(x) #test significance
print(coeftest(x, vcov = vcovHC(x, "HC1"))) #robust like in Stata
print(PseudoR2(x, which = "McFaddenAdj"))
plot(x)
avPlots(x)
}
fit.plots <- function (x, v) {
avPlots(x, as.formula(v), id=F)
plot(x)
}
fit.bootstrap <- function (x, r, ...) { #бутстрап
set.seed(1313)
mboot <- Boot(x, R = r, ...) # bootstrap samples--too small to be useful
cbind(summary(mboot),confint(mboot))
}
fit.coeff <- function (x, boot = F, r = 1000) { #вывод коэффициентов + бутстрап
df<-cbind(summary(x)$coefficients[,-3],
coeftest(x, vcov = vcovHC(x, "HC1"))[,c(-1,-3)]) #robust like in Stata
df<-data.frame(df)
names(df) <- c("Estimate","SE","pvalue","Robust.SE","Robust.pvalue")
if (boot) {
df<-cbind(df,
fit.bootstrap(x, r)[,c(-1,-2)])
}
write.table(df, file = paste0('models\\',deparse(substitute(x)),'_coef.csv'), sep=";", dec=",")
df}
fit.params <- function (x) { #вывод базовых свойств и некоторых тестов
rname <-deparse(substitute(x))
s<-summary(x) #F-stat p < 0.05 -> good model
t<-shapiro.test(x$residuals) #p > 0.05 -> normal distrib
l<-bptest(x) # studentized Breusch-Pagan test if p<0.05 -> heteroskedacity is present
f<-PseudoR2(x, which = "McFaddenAdj")
c<-coeftest(x, vcov = vcovHC(x, "HC1")) #robust like in Stata
df<-data.frame(type = as.character(s[["call"]][[1]]),
src = as.character(s[["call"]][["data"]]),
group = strsplit(as.character(s[["terms"]][1])," ~ ")[[3]],
r.squared = ifelse(!is.null(s$r.squared),s$r.squared,NA),
adj.r.squared = ifelse(!is.null(s$adj.r.squared),s$adj.r.squared,NA),
#adj.r.pseudo = f,
shapiro.p.value = t$p.value,
bp.p.value = l$p.value,
f.p.value = ifelse(!is.null(s[["fstatistic"]]),
pf(s[["fstatistic"]][["value"]],
s[["fstatistic"]][["numdf"]],
s[["fstatistic"]][["dendf"]], lower.tail = F), NA))#,
#aic = ifelse(!is.null(s[["aic"]]),s[["aic"]],NA))
if (is.null(rname))
row.names(df)<-s[["terms"]][[2]]
else row.names(df)<-rname
df}emo_data$month <- factor(emo_data$month)
dummy <- dummyVars( ~ month, data = emo_data)
df<-predict(dummy, newdata=emo_data)
emo_data<-cbind(emo_data,df)
rat_data$month <- factor(rat_data$month)
dummy <- dummyVars( ~ month, data = rat_data)
df<-predict(dummy, newdata=rat_data)
rat_data<-cbind(rat_data,df)#models RB
emo_all<-data[!is.na(data$emo),]
emo_all<-na.omit(emo_all[,c(29,32:35,45:58)])
emo_all$month <- factor(emo_all$month)
dummy <- dummyVars( ~ month, data = emo_all)
df<-predict(dummy, newdata=emo_all)
emo_all<-cbind(emo_all,df)
#whole data lm
RB_emo_m0 <- lm(RB ~ emo+sex+income+isworking+log_age+log_city_size+TC+log_time_RB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = emo_all, na.action=na.exclude)
#matched data lm
RB_emo_m1 <- lm(RB ~ emo+sex+income+isworking+log_age+log_city_size+TC+log_time_RB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = emo_data, na.action=na.exclude)
#matched data lm no controls
RB_emo_m2 <- lm(RB ~ emo, data = emo_data, na.action=na.exclude)
rat_all<-data[!is.na(data$rat),]
rat_all<-na.omit(rat_all[,c(30,32:35,45:58)])
rat_all$month <- factor(rat_all$month)
dummy <- dummyVars( ~ month, data = rat_all)
df<-predict(dummy, newdata=rat_all)
rat_all<-cbind(rat_all,df)
#whole data lm
RB_rat_m0 <- lm(RB ~ rat+sex+income+isworking+log_age+log_city_size+TC+log_time_RB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = rat_all, na.action=na.exclude)
#matched data lm
RB_rat_m1 <- lm(RB ~ rat+sex+income+isworking+log_age+log_city_size+TC+log_time_RB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = rat_data, na.action=na.exclude)
#matched data lm no controls
RB_rat_m2 <- lm(RB ~ rat, data = rat_data, na.action=na.exclude)#models ESB
emo_all<-data[!is.na(data$emo),]
emo_all<-na.omit(emo_all[,c(29,32:35,45:58)])
emo_all$month <- factor(emo_all$month)
dummy <- dummyVars( ~ month, data = emo_all)
df<-predict(dummy, newdata=emo_all)
emo_all<-cbind(emo_all,df)
ESB_emo_m0 <- lm(ESB ~ emo+sex+income+isworking+log_age+log_city_size+TC+log_time_ESB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = emo_all, na.action=na.exclude)
#matched data lm
ESB_emo_m1 <- lm(ESB ~ emo+sex+income+isworking+log_age+log_city_size+TC+log_time_ESB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = emo_data, na.action=na.exclude)
ESB_emo_m2 <- lm(ESB ~ emo, data = emo_data, na.action=na.exclude)
rat_all<-data[!is.na(data$rat),]
rat_all<-na.omit(rat_all[,c(30,32:35,45:58)])
rat_all$month <- factor(rat_all$month)
dummy <- dummyVars( ~ month, data = rat_all)
df<-predict(dummy, newdata=rat_all)
rat_all<-cbind(rat_all,df)
ESB_rat_m0 <- lm(ESB ~ rat+sex+income+isworking+log_age+log_city_size+TC+log_time_ESB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = rat_all, na.action=na.exclude)
#matched data lm
ESB_rat_m1 <- lm(ESB ~ rat+sex+income+isworking+log_age+log_city_size+TC+log_time_ESB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = rat_data, na.action=na.exclude)
ESB_rat_m2 <- lm(ESB ~ rat, data = rat_data, na.action=na.exclude)#models RSB
emo_all<-data[!is.na(data$emo),]
emo_all<-na.omit(emo_all[,c(29,32:35,45:58)])
emo_all$month <- factor(emo_all$month)
dummy <- dummyVars( ~ month, data = emo_all)
df<-predict(dummy, newdata=emo_all)
emo_all<-cbind(emo_all,df)
#whole data lm
RSB_emo_m0 <- lm(RSB ~ emo+sex+income+isworking+log_age+log_city_size+TC+log_time_RSB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = emo_all, na.action=na.exclude)
#matched data lm
RSB_emo_m1 <- lm(RSB ~ emo+sex+income+isworking+log_age+log_city_size+TC+log_time_RSB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = emo_data, na.action=na.exclude)
RSB_emo_m2 <- lm(RSB ~ emo, data = emo_data, na.action=na.exclude)
rat_all<-data[!is.na(data$rat),]
rat_all<-na.omit(rat_all[,c(30,32:35,45:58)])
rat_all$month <- factor(rat_all$month)
dummy <- dummyVars( ~ month, data = rat_all)
df<-predict(dummy, newdata=rat_all)
rat_all<-cbind(rat_all,df)
#whole data lm
RSB_rat_m0 <- lm(RSB ~ rat+sex+income+isworking+log_age+log_city_size+TC+log_time_RSB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = rat_all, na.action=na.exclude)
#matched data lm
RSB_rat_m1 <- lm(RSB ~ rat+sex+income+isworking+log_age+log_city_size+TC+log_time_RSB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = rat_data, na.action=na.exclude)
RSB_rat_m2 <- lm(RSB ~ rat, data = rat_data, na.action=na.exclude)#models EMB
emo_all<-data[!is.na(data$emo),]
emo_all<-na.omit(emo_all[,c(29,32:35,45:58)])
emo_all$month <- factor(emo_all$month)
dummy <- dummyVars( ~ month, data = emo_all)
df<-predict(dummy, newdata=emo_all)
emo_all<-cbind(emo_all,df)
#whole data lm
EMB_emo_m0 <- lm(EMB ~ emo+sex+income+isworking+log_age+log_city_size+TC+log_time_EMB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = emo_all, na.action=na.exclude)
#matched data lm
EMB_emo_m1 <- lm(EMB ~ emo+sex+income+isworking+log_age+log_city_size+TC+log_time_EMB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = emo_data, na.action=na.exclude)
EMB_emo_m2 <- lm(EMB ~ emo, data = emo_data, na.action=na.exclude)
rat_all<-data[!is.na(data$rat),]
rat_all<-na.omit(rat_all[,c(30,32:35,45:58)])
rat_all$month <- factor(rat_all$month)
dummy <- dummyVars( ~ month, data = rat_all)
df<-predict(dummy, newdata=rat_all)
rat_all<-cbind(rat_all,df)
#whole data lm
EMB_rat_m0 <- lm(EMB ~ rat+sex+income+isworking+log_age+log_city_size+TC+log_time_EMB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = rat_all, na.action=na.exclude)
#matched data lm
EMB_rat_m1 <- lm(EMB ~ rat+sex+income+isworking+log_age+log_city_size+TC+log_time_EMB+log_time_TC+
+month.1 +month.2 +month.3 +month.4 +month.6 +month.7 +month.8 +month.9 +month.10 +month.11+month.12,
data = rat_data, na.action=na.exclude)
EMB_rat_m2 <- lm(EMB ~ rat, data = rat_data, na.action=na.exclude)#RB
fit.coeff(RB_emo_m0, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.497066325 0.53235206 0.351641080 0.52051547 3.408227e-01
## emo 0.087720637 0.03344211 0.009425562 0.03380393 1.020176e-02
## sex -0.030540502 0.03764273 0.418199374 0.03886556 4.329695e-01
## income -0.047704007 0.01749416 0.006996065 0.01811843 9.166349e-03
## isworking 0.056256932 0.04500055 0.212794564 0.04395180 2.021249e-01
## log_age -0.130356818 0.15504974 0.401554912 0.17530814 4.580499e-01
## log_city_size 0.057135414 0.02836163 0.045371086 0.02991349 5.764461e-02
## TC 0.145799949 0.05636090 0.010435670 0.06497692 2.600077e-02
## log_time_RB -0.034335467 0.06094702 0.573853558 0.05306985 5.184258e-01
## log_time_TC 0.018898193 0.06174084 0.759873789 0.07324354 7.966733e-01
## month.1 -0.071052053 0.24025201 0.767753556 0.06074702 2.436194e-01
## month.2 -0.442218381 0.33333909 0.186231056 0.04649958 9.002827e-18
## month.3 -0.057031836 0.24027423 0.812633502 0.05349139 2.876991e-01
## month.4 0.006694217 0.27620825 0.980689834 0.12060463 9.557944e-01
## month.6 0.066139582 0.24609585 0.788411076 0.07756373 3.948979e-01
## month.7 0.072251596 0.24318865 0.766715664 0.05704280 2.068496e-01
## month.8 0.064632949 0.24541419 0.792557918 0.05757866 2.630672e-01
## month.9 -0.195945786 0.24256555 0.420218081 0.06888420 4.937232e-03
## month.10 -0.069062236 0.24120226 0.774942824 0.06164763 2.640186e-01
## month.11 -0.172426750 0.26316328 0.513130368 0.13139271 1.910102e-01
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 3.649447e-02 0.55625162 0.5539807969 -0.77128407 1.51989147
## emo -1.492403e-03 0.03470395 0.0860343411 0.01934960 0.15580525
## sex 6.778089e-03 0.03953841 -0.0272704899 -0.10231230 0.05139121
## income -2.958719e-05 0.01820281 -0.0472241679 -0.08612921 -0.01623385
## isworking -5.014627e-03 0.04593885 0.0527850472 -0.04160157 0.14186982
## log_age -6.739320e-04 0.17529107 -0.1311960757 -0.45774399 0.20964896
## log_city_size -1.111876e-03 0.03068682 0.0576587709 -0.00387525 0.11301691
## TC 2.047331e-03 0.06515511 0.1485368890 0.02151457 0.28042922
## log_time_RB 3.691607e-03 0.06128512 -0.0329864994 -0.14196831 0.10821435
## log_time_TC -9.018098e-03 0.07945436 0.0094334605 -0.12213583 0.18816408
## month.1 -5.396975e-03 0.06407719 -0.0752897865 -0.19519057 0.05617239
## month.2 3.653397e-03 0.04941133 -0.4363613648 -0.56225289 -0.35846411
## month.3 -5.041641e-03 0.05736288 -0.0653905153 -0.15541074 0.07234285
## month.4 -1.109464e-02 0.13983100 0.0003680697 -0.29742443 0.26878923
## month.6 -2.303735e-05 0.07804256 0.0672952512 -0.09459953 0.21758199
## month.7 -4.134783e-03 0.06408520 0.0677928094 -0.05828051 0.20177854
## month.8 -2.570478e-03 0.05820089 0.0625191057 -0.05491502 0.19041337
## month.9 -8.394356e-03 0.07200573 -0.2049685632 -0.32641513 -0.01556124
## month.10 -4.499996e-03 0.06405231 -0.0756305424 -0.19330655 0.05826317
## month.11 2.913518e-03 0.14418348 -0.1725443754 -0.43367504 0.15506633
fit.plots(RB_emo_m0, "~ emo")
fit.coeff(RB_rat_m0, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.569886142 0.58771407 0.33354385 0.48880453 0.245241540
## rat 0.038742024 0.03698480 0.29629989 0.03656070 0.290750034
## sex -0.076339826 0.04090638 0.06367597 0.04343728 0.080575904
## income -0.046783587 0.01929257 0.01632118 0.01930354 0.016381205
## isworking 0.055989642 0.04775468 0.24260376 0.04361001 0.200874558
## log_age 0.012342330 0.16400196 0.94009554 0.17815035 0.944845064
## log_city_size 0.058198942 0.03136318 0.06517633 0.02976111 0.052103048
## TC 0.099564836 0.05855969 0.09085439 0.06334315 0.117785339
## log_time_RB -0.079882964 0.06664674 0.23229383 0.06071333 0.189972540
## log_time_TC 0.036107417 0.07202574 0.61677753 0.07758459 0.642224561
## month.1 -0.155667093 0.25854596 0.54789126 0.05788132 0.007846153
## month.2 -0.008103131 0.36222571 0.98217784 0.06477385 0.900588111
## month.3 -0.105748097 0.25847202 0.68294329 0.06466033 0.103744002
## month.4 -0.011027701 0.29440153 0.97016226 0.07598934 0.884780860
## month.6 -0.103181232 0.26447953 0.69691263 0.09262108 0.266791979
## month.7 -0.111854156 0.26431142 0.67267273 0.08317184 0.180402180
## month.8 -0.215498611 0.27016553 0.42614617 0.08894763 0.016416878
## month.9 -0.268979140 0.26401166 0.30968906 0.09218418 0.003984621
## month.10 -0.125659964 0.25811846 0.62698411 0.06066993 0.039798607
## month.11 -0.191560044 0.27196478 0.48214200 0.09122674 0.037170051
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 0.0248026950 0.54456789 0.573187004 -0.40399357 1.788659815
## rat -0.0024271220 0.03747732 0.039084751 -0.04617172 0.105459253
## sex -0.0005282826 0.04504543 -0.078557340 -0.16816856 0.009487591
## income -0.0002996443 0.01973050 -0.047013180 -0.08634268 -0.007655166
## isworking 0.0034123835 0.04640053 0.063948279 -0.04015638 0.134993008
## log_age 0.0053070717 0.18295429 0.024111088 -0.40429911 0.352347279
## log_city_size -0.0035502366 0.03183898 0.053798856 -0.00183382 0.124765341
## TC -0.0025788777 0.06142340 0.099706226 -0.03153885 0.220990835
## log_time_RB 0.0068447985 0.06840767 -0.069890863 -0.26227418 0.038517049
## log_time_TC -0.0088835680 0.08024840 0.033438737 -0.14008804 0.183772564
## month.1 -0.0029893799 0.05868805 -0.157567839 -0.25582530 -0.034022269
## month.2 0.0061015087 0.06923279 -0.004205301 -0.13966333 0.130727564
## month.3 0.0004154134 0.06532554 -0.105752016 -0.22976185 0.025888142
## month.4 -0.0005319742 0.09047037 -0.021174670 -0.14601174 0.264602937
## month.6 0.0052240083 0.09918443 -0.101454239 -0.33365641 0.076492331
## month.7 -0.0069283551 0.08367696 -0.112858464 -0.28951046 0.035671942
## month.8 0.0020774703 0.09108302 -0.210211052 -0.40529946 -0.047746296
## month.9 -0.0108367553 0.10307213 -0.279367010 -0.46363007 -0.070866688
## month.10 -0.0032239630 0.06043017 -0.124640970 -0.24747715 -0.020787096
## month.11 -0.0041916547 0.10478750 -0.191092700 -0.40820475 0.018962726
fit.plots(RB_rat_m0, "~ rat")
fit.coeff(RB_emo_m1, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.504698239 0.53115241 0.343233212 0.51920212 3.322672e-01
## emo 0.084509365 0.03344720 0.012340125 0.03392347 1.359831e-02
## sex -0.034999835 0.03769672 0.354360407 0.03882735 3.685173e-01
## income -0.043630768 0.01770537 0.014627664 0.01817259 1.732916e-02
## isworking 0.053558733 0.04493988 0.234847348 0.04397497 2.247743e-01
## log_age -0.139531910 0.15483683 0.368659142 0.17593214 4.287189e-01
## log_city_size 0.056385396 0.02830146 0.047783645 0.02979476 5.996643e-02
## TC 0.148106098 0.05625601 0.009174917 0.06479549 2.338466e-02
## log_time_RB -0.029466640 0.06091017 0.629110613 0.05290047 5.781761e-01
## log_time_TC 0.014974102 0.06166493 0.808401728 0.07325667 8.382574e-01
## month.1 -0.073783401 0.23970571 0.758568965 0.06049398 2.241135e-01
## month.2 -0.442080124 0.33256963 0.185364497 0.04656842 1.040924e-17
## month.3 -0.051425377 0.23975453 0.830396048 0.05360888 3.386549e-01
## month.4 0.002255376 0.27558971 0.993479010 0.12346416 9.854449e-01
## month.6 0.062733746 0.24554035 0.798621958 0.07740710 4.187125e-01
## month.7 0.068387871 0.24264368 0.778372485 0.05715794 2.330192e-01
## month.8 0.062509351 0.24485258 0.798775250 0.05763761 2.795215e-01
## month.9 -0.196481607 0.24200593 0.417882919 0.06877107 4.757771e-03
## month.10 -0.070359605 0.24064733 0.770321253 0.06172385 2.557742e-01
## month.11 -0.173311537 0.26255659 0.510002616 0.13175943 1.899897e-01
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 0.0008136587 0.54921507 0.49673932 -0.544842913 1.597943816
## emo -0.0020470621 0.03667278 0.08021199 0.016328021 0.169342461
## sex 0.0039061579 0.03899332 -0.03443692 -0.107665229 0.040563826
## income 0.0002095497 0.01819404 -0.04307878 -0.078909071 -0.006452717
## isworking -0.0038443071 0.04502153 0.05182808 -0.040310798 0.136724540
## log_age 0.0144656205 0.17306392 -0.12220992 -0.509670880 0.174338928
## log_city_size -0.0005853347 0.03100371 0.05626824 -0.004097303 0.114382555
## TC 0.0044328378 0.06121475 0.15746871 -0.009999976 0.242646378
## log_time_RB 0.0049288881 0.05777382 -0.02538247 -0.144280833 0.089151832
## log_time_TC -0.0089642832 0.07923968 0.01656477 -0.160849640 0.139770086
## month.1 -0.0007257869 0.05712054 -0.07434811 -0.188445824 0.039707059
## month.2 0.0050456164 0.04768491 -0.43857805 -0.526820872 -0.351914474
## month.3 -0.0037289347 0.05082529 -0.05212212 -0.156857016 0.041845481
## month.4 -0.0062536978 0.14597766 -0.01110641 -0.252947819 0.317406399
## month.6 0.0047547046 0.08047900 0.07042881 -0.119429794 0.190857491
## month.7 -0.0051951969 0.05910282 0.06505835 -0.049287245 0.197010535
## month.8 -0.0023999286 0.05951984 0.05645713 -0.051851533 0.186015478
## month.9 -0.0002448515 0.07201880 -0.20075912 -0.327355671 -0.036948562
## month.10 0.0003524571 0.06011898 -0.06692520 -0.210708943 0.040081050
## month.11 0.0036826505 0.15007253 -0.17142592 -0.441346779 0.146668896
fit.plots(RB_emo_m1, "~ emo")
fit.coeff(RB_rat_m1, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.76817640 0.60524132 0.20618773 0.51467739 0.137502648
## rat 0.03958633 0.03806259 0.29987459 0.03816201 0.301131236
## sex -0.06567463 0.04194895 0.11939723 0.04415858 0.138893894
## income -0.03915399 0.02189257 0.07557061 0.02136898 0.068745612
## isworking 0.05593282 0.05049239 0.26961286 0.04672421 0.233022424
## log_age -0.05970395 0.17179635 0.72864621 0.17933991 0.739633083
## log_city_size 0.05374509 0.03341016 0.10964099 0.03263627 0.101539643
## TC 0.09129415 0.06023920 0.13158771 0.06681884 0.173740322
## log_time_RB -0.08289121 0.06796808 0.22440402 0.06330757 0.192272822
## log_time_TC 0.02142333 0.07662130 0.78014098 0.08428547 0.799682085
## month.1 -0.16256718 0.25917923 0.53138598 0.05933470 0.006836502
## month.2 -0.01658501 0.36274436 0.96358898 0.06642950 0.803163961
## month.3 -0.11351959 0.25919207 0.66198797 0.06721597 0.093166593
## month.4 -0.01113649 0.29460321 0.96989237 0.07755930 0.886005274
## month.6 -0.09931323 0.26502547 0.70835072 0.09594496 0.302161981
## month.7 -0.11837668 0.26550452 0.65629686 0.08729717 0.176980007
## month.8 -0.22339018 0.27037290 0.40988887 0.09036838 0.014469830
## month.9 -0.26933018 0.26571048 0.31227431 0.10078230 0.008301272
## month.10 -0.10979693 0.25844407 0.67151829 0.05877907 0.063573806
## month.11 -0.18596921 0.27241639 0.49579340 0.09153909 0.043830137
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 3.024398e-02 0.53033528 0.77911156 -0.255242313 1.75110218
## rat -1.960154e-03 0.03606270 0.03860716 -0.031376043 0.10810851
## sex 5.470743e-03 0.04550204 -0.06043757 -0.172997869 0.01277135
## income -2.045642e-03 0.02227768 -0.04127930 -0.079257025 0.01198778
## isworking 3.006444e-03 0.04909686 0.05966057 -0.063342034 0.14755527
## log_age 1.372648e-02 0.17261856 -0.05218481 -0.381240354 0.29408061
## log_city_size -5.141236e-03 0.03204407 0.05026570 -0.004020441 0.12281718
## TC 6.562242e-03 0.06732662 0.10096161 -0.073571361 0.20775122
## log_time_RB 6.129427e-05 0.07173980 -0.08361709 -0.228562508 0.05291870
## log_time_TC -4.064432e-03 0.08600482 0.01498128 -0.141440605 0.18132024
## month.1 -2.809836e-03 0.06129911 -0.16757128 -0.273470755 -0.02462674
## month.2 1.665508e-03 0.06853420 -0.01869660 -0.143378973 0.11695446
## month.3 -2.563859e-03 0.07041575 -0.11682848 -0.243572705 0.02212843
## month.4 -8.261670e-04 0.08827482 -0.01788576 -0.154496538 0.22941782
## month.6 1.799732e-05 0.10420415 -0.10004156 -0.297932126 0.10194883
## month.7 -3.985268e-03 0.08370164 -0.12162708 -0.278550111 0.05565766
## month.8 5.415984e-03 0.09541371 -0.21310769 -0.422217667 -0.05623821
## month.9 -5.692161e-03 0.10595158 -0.27185950 -0.485681586 -0.06144654
## month.10 -3.457850e-03 0.06479684 -0.11353680 -0.228087496 0.02187533
## month.11 -4.224199e-03 0.10775918 -0.19192213 -0.374751562 0.05779791
fit.plots(RB_rat_m1, "~ rat")
fit.coeff(RB_emo_m2, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.54375000 0.02370311 1.213711e-58 0.02655443 1.450389e-51
## emo 0.09182692 0.03352126 6.695645e-03 0.03352126 6.695645e-03
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) -0.0004480696 0.02673077 0.5441175 0.48958098 0.5949260
## emo 0.0011145043 0.03428475 0.0940604 0.01987816 0.1557074
fit.plots(RB_emo_m2, "~ emo")
## hat values (leverages) are all = 0.009615385
## and there are no factor predictors; no plot no. 5
fit.coeff(RB_rat_m2, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.5406593 0.02619797 2.555018e-49 0.02808404 1.433468e-45
## rat 0.0489011 0.03704952 1.885496e-01 0.03704952 1.885496e-01
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 8.115758e-04 0.02808542 0.54070004 0.48577714 0.5964788
## rat -2.422702e-05 0.03611234 0.04811704 -0.01932489 0.1198964
fit.plots(RB_rat_m2, "~ rat")
## hat values (leverages) are all = 0.01098901
## and there are no factor predictors; no plot no. 5
#ESB
fit.coeff(ESB_emo_m0, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.29697563 0.47737313 0.53462338 0.51459486 5.645548e-01
## emo 0.06861565 0.03056519 0.02593381 0.03101581 2.814524e-02
## sex -0.02162330 0.03404759 0.52613741 0.03581899 5.467784e-01
## income 0.01266986 0.01586614 0.42555528 0.01612736 4.330783e-01
## isworking -0.03600956 0.04088207 0.37953638 0.03829159 3.482107e-01
## log_age 0.08969994 0.14330903 0.53212278 0.12459022 4.724392e-01
## log_city_size -0.03925533 0.02568794 0.12814413 0.02796928 1.621039e-01
## TC 0.09328203 0.05103309 0.06914526 0.05610761 9.805894e-02
## log_time_ESB -0.05568663 0.06373407 0.38337189 0.07491348 4.581953e-01
## log_time_TC 0.08727121 0.05676336 0.12585394 0.05187949 9.418322e-02
## month.1 0.22350440 0.21755633 0.30557321 0.04752092 4.929491e-06
## month.2 0.30705357 0.30188458 0.31039547 0.03751318 3.938637e-14
## month.3 0.22941792 0.21749350 0.29285122 0.04803764 3.580576e-06
## month.4 0.22004158 0.24999436 0.37987628 0.15235218 1.503115e-01
## month.6 0.15486328 0.22257053 0.48741242 0.05675361 6.958617e-03
## month.7 0.12294302 0.22010091 0.57711250 0.06159631 4.737545e-02
## month.8 0.12162158 0.22224949 0.58486634 0.07440392 1.037948e-01
## month.9 0.12650122 0.21920490 0.56456461 0.05996501 3.621062e-02
## month.10 0.19478225 0.21842801 0.37366309 0.05447695 4.438595e-04
## month.11 0.01335538 0.23911408 0.95551745 0.11737456 9.095296e-01
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 0.1031941910 0.52700513 0.38181349 -0.791284943 1.30012017
## emo 0.0041363575 0.03195694 0.07262604 0.001713258 0.12694462
## sex 0.0015689823 0.03612187 -0.02334842 -0.085936418 0.05188540
## income 0.0007373683 0.01573914 0.01352331 -0.021737247 0.04315791
## isworking -0.0013833394 0.03870627 -0.03766256 -0.115402196 0.03534106
## log_age -0.0147369299 0.12901638 0.07690115 -0.137219618 0.33813860
## log_city_size -0.0025002416 0.02953014 -0.04043778 -0.095504031 0.01500056
## TC -0.0021669773 0.05208511 0.08790783 0.011743851 0.20502793
## log_time_ESB -0.0088139539 0.07529924 -0.06680127 -0.186298184 0.11134759
## log_time_TC -0.0050408011 0.05668716 0.08306569 -0.019686600 0.20987733
## month.1 -0.0044593877 0.04710552 0.22062625 0.120166360 0.31924248
## month.2 0.0011112924 0.03997141 0.31039440 0.225126451 0.38375217
## month.3 -0.0007743204 0.04503061 0.22736022 0.138910082 0.33003898
## month.4 -0.0072810413 0.17456777 0.22321047 -0.172061689 0.50511043
## month.6 0.0014212831 0.05866949 0.16141039 0.022314652 0.24941665
## month.7 -0.0032787322 0.06319777 0.11806124 0.005680676 0.25858958
## month.8 -0.0087977286 0.07342721 0.11017378 -0.006983540 0.28156838
## month.9 -0.0051072222 0.06107283 0.12276577 0.010036887 0.25776083
## month.10 -0.0026637947 0.05410815 0.18931488 0.095500699 0.32377149
## month.11 0.0057488855 0.14300114 0.01096669 -0.268558444 0.27949240
fit.plots(ESB_emo_m0, "~ emo")
fit.coeff(ESB_rat_m0, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) -0.4101183164 0.48609113 0.3999786103 0.52253526 4.335894e-01
## rat 0.0990273611 0.03113444 0.0017369595 0.03023618 1.271875e-03
## sex -0.0208408080 0.03431659 0.5444279085 0.03592118 5.625342e-01
## income 0.0074928449 0.01616186 0.6434980550 0.01806846 6.788724e-01
## isworking -0.0163988544 0.04028419 0.6844445177 0.03582144 6.476648e-01
## log_age -0.0756721472 0.13803438 0.5842407834 0.12972132 5.604086e-01
## log_city_size -0.0009416787 0.02648941 0.9716820610 0.02906537 9.741908e-01
## TC 0.0889930051 0.04873029 0.0695091405 0.05051256 7.983937e-02
## log_time_ESB 0.0654885180 0.05577246 0.2418977225 0.06013172 2.776057e-01
## log_time_TC 0.0030018075 0.06346485 0.9623287593 0.06558948 9.635482e-01
## month.1 0.6932670842 0.21945023 0.0018629858 0.06228358 3.971356e-22
## month.2 0.7799689978 0.30460897 0.0112900619 0.06884183 1.069092e-22
## month.3 0.7196494579 0.21876194 0.0012116679 0.05967855 8.537620e-25
## month.4 0.7427661488 0.24868974 0.0032221651 0.17423290 3.282633e-05
## month.6 0.7832477064 0.22394840 0.0005947211 0.07385469 1.245744e-20
## month.7 0.7262880629 0.22393265 0.0014135788 0.07640387 1.486607e-17
## month.8 0.6760050584 0.22887694 0.0035705724 0.09611488 4.299446e-11
## month.9 0.7013532195 0.22311343 0.0019597031 0.06904782 2.283800e-19
## month.10 0.6859756563 0.21829308 0.0019662399 0.06091588 1.683765e-22
## month.11 0.5655021357 0.22972005 0.0147915666 0.06427749 1.260628e-15
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 0.045372610 0.50441373 -0.3701238099 -1.343628227 0.61313332
## rat 0.002425763 0.02858378 0.1019982729 0.038567256 0.15157501
## sex -0.002889340 0.03474631 -0.0249605220 -0.074842176 0.05687480
## income 0.001399341 0.01827486 0.0085778217 -0.029123824 0.03965059
## isworking -0.001916675 0.03557204 -0.0208531430 -0.081085628 0.05580827
## log_age -0.021124144 0.13236264 -0.0941404343 -0.333734083 0.16836670
## log_city_size 0.001578902 0.02821602 0.0002664029 -0.054109933 0.05308418
## TC -0.004551114 0.04902409 0.0830344507 -0.001779735 0.18706107
## log_time_ESB 0.004103792 0.06173672 0.0705378802 -0.066761982 0.17798500
## log_time_TC -0.010812842 0.06625355 -0.0083628275 -0.111869316 0.14589621
## month.1 0.003557648 0.06292332 0.6949013773 0.580947007 0.81169141
## month.2 0.002089186 0.07028923 0.7788801911 0.640359821 0.93369901
## month.3 0.008205962 0.05948804 0.7312103368 0.587050766 0.82465404
## month.4 0.009956444 0.19687654 0.7727244183 0.297815018 1.01994962
## month.6 0.008550703 0.07335051 0.7968221381 0.614027361 0.89621972
## month.7 0.008856022 0.07201578 0.7317994414 0.585496341 0.87586757
## month.8 0.004183623 0.10106101 0.6788782733 0.475249165 0.89075793
## month.9 0.004002611 0.06908258 0.7002307044 0.573784900 0.86047321
## month.10 0.004256125 0.06401305 0.6893116184 0.556845755 0.80752818
## month.11 0.005696393 0.07163804 0.5718257301 0.409181585 0.69696949
fit.plots(ESB_rat_m0, "~ rat")
fit.coeff(ESB_emo_m1, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.28767622 0.47834527 0.54829895 0.51531123 5.773330e-01
## emo 0.07006561 0.03069740 0.02358174 0.03116209 2.571003e-02
## sex -0.01981310 0.03421883 0.56327456 0.03604224 5.831647e-01
## income 0.01096761 0.01611333 0.49692767 0.01653537 5.079627e-01
## isworking -0.03480965 0.04098945 0.39683093 0.03832226 3.648614e-01
## log_age 0.09320964 0.14363953 0.51718619 0.12477141 4.559708e-01
## log_city_size -0.03888366 0.02573489 0.13248492 0.02795817 1.659361e-01
## TC 0.09217346 0.05114279 0.07310374 0.05613723 1.022760e-01
## log_time_ESB -0.05633942 0.06384242 0.37864629 0.07500266 4.534922e-01
## log_time_TC 0.08871033 0.05689726 0.12064698 0.05182725 8.860955e-02
## month.1 0.22503729 0.21791145 0.30307068 0.04762514 4.492286e-06
## month.2 0.30657563 0.30235993 0.31191326 0.03750556 4.307113e-14
## month.3 0.22745627 0.21785698 0.29779718 0.04813069 4.479928e-06
## month.4 0.22239632 0.25041441 0.37561475 0.15233239 1.459753e-01
## month.6 0.15682479 0.22294149 0.48265512 0.05681041 6.343071e-03
## month.7 0.12499467 0.22047024 0.57142731 0.06156214 4.372649e-02
## month.8 0.12284468 0.22260701 0.58170958 0.07462719 1.014115e-01
## month.9 0.12734812 0.21955339 0.56258715 0.06000578 3.512472e-02
## month.10 0.19565849 0.21877558 0.37228659 0.05451482 4.232835e-04
## month.11 0.01380546 0.23949089 0.95409261 0.11747122 9.065718e-01
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 5.924815e-02 0.54200531 0.291388257 -0.782095830 1.40749729
## emo 4.658696e-03 0.03036373 0.073483459 0.012987898 0.12481564
## sex -2.079631e-03 0.03602321 -0.021365086 -0.089481549 0.05558904
## income 7.896936e-04 0.01709899 0.010723404 -0.020633709 0.04640666
## isworking -4.612378e-04 0.04224013 -0.033588135 -0.120002733 0.04610167
## log_age -1.280756e-02 0.13031922 0.082110442 -0.140175909 0.37065964
## log_city_size -4.130468e-03 0.03017699 -0.042774067 -0.093681778 0.02097547
## TC 1.729800e-03 0.05442796 0.094327971 -0.025583147 0.19063835
## log_time_ESB 2.188224e-03 0.07566576 -0.051872158 -0.233105898 0.08483804
## log_time_TC -6.617782e-03 0.05978908 0.085101210 -0.032322456 0.19960903
## month.1 2.860424e-05 0.04716340 0.227234684 0.124771479 0.31276125
## month.2 2.711531e-04 0.03929598 0.306712080 0.230228604 0.38292709
## month.3 -8.524775e-04 0.04815344 0.230658873 0.127244992 0.31837564
## month.4 -5.646091e-03 0.17509115 0.218272885 -0.163980969 0.50832682
## month.6 -1.044294e-03 0.06587655 0.156836840 0.007431757 0.28006017
## month.7 -1.901749e-03 0.06476391 0.125132213 -0.007835457 0.24543931
## month.8 -1.321211e-03 0.07806367 0.116678855 -0.018338222 0.31062720
## month.9 1.200610e-03 0.06042939 0.131399295 0.003632139 0.22779976
## month.10 -5.010571e-03 0.05660877 0.189811432 0.091220366 0.31791665
## month.11 -5.980675e-03 0.13827139 0.005971785 -0.242447926 0.31081887
fit.plots(ESB_emo_m1, "~ emo")
fit.coeff(ESB_rat_m1, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) -0.261465641 0.49423364 0.5975070548 0.56014117 6.412807e-01
## rat 0.109662608 0.03136867 0.0006096211 0.03077678 4.815646e-04
## sex -0.026203880 0.03436359 0.4468420530 0.03683040 4.778136e-01
## income 0.019229049 0.01793661 0.2852892628 0.01992479 3.359425e-01
## isworking -0.030327042 0.04158940 0.4669315228 0.03719480 4.160652e-01
## log_age -0.137439251 0.14079238 0.3304288046 0.13942490 3.257217e-01
## log_city_size 0.008678853 0.02748207 0.7525603319 0.02942611 7.684195e-01
## TC 0.060468081 0.04891027 0.2181341577 0.05092885 2.368453e-01
## log_time_ESB 0.026337931 0.05817954 0.6513697682 0.06498715 6.858071e-01
## log_time_TC 0.020637621 0.06717795 0.7590794049 0.06977173 7.677710e-01
## month.1 0.674686038 0.21499077 0.0020199782 0.06576046 2.418755e-19
## month.2 0.760757469 0.29798807 0.0116040796 0.07041669 7.837168e-21
## month.3 0.702330635 0.21433063 0.0012842440 0.06086362 7.285729e-23
## month.4 0.735968185 0.24301614 0.0028609900 0.17171385 3.112496e-05
## month.6 0.777748404 0.21910491 0.0005050444 0.07686757 5.879750e-19
## month.7 0.744245037 0.21922968 0.0008636487 0.06935634 1.243215e-20
## month.8 0.653711206 0.22376253 0.0039817436 0.09792462 3.729228e-10
## month.9 0.679554433 0.21961226 0.0023241546 0.06842889 1.893375e-18
## month.10 0.670413831 0.21350553 0.0020081876 0.06470818 1.283885e-19
## month.11 0.560689528 0.22456748 0.0135332096 0.06163305 3.234111e-16
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 0.118124123 0.54995161 -0.115466756 -1.34109717 0.68370920
## rat 0.003914520 0.03160971 0.114125036 0.03675127 0.16240657
## sex 0.001528525 0.03873513 -0.024161339 -0.10489364 0.04655950
## income -0.001704485 0.02074938 0.017625053 -0.02061245 0.05974936
## isworking 0.003083471 0.04019221 -0.027389459 -0.11434078 0.04005457
## log_age -0.025726737 0.14849034 -0.174593495 -0.37679948 0.24721165
## log_city_size -0.001256876 0.03282016 0.008632136 -0.06273221 0.07535930
## TC -0.007711389 0.05150303 0.051683992 -0.02993568 0.18331783
## log_time_ESB -0.002023577 0.06431603 0.028228845 -0.11667325 0.14412939
## log_time_TC -0.011856380 0.07005557 0.010066577 -0.10083169 0.17715004
## month.1 -0.006768419 0.06549346 0.668615127 0.54795571 0.82944781
## month.2 -0.008586870 0.07951863 0.753284025 0.60441723 0.92370134
## month.3 -0.002545620 0.06525728 0.701496378 0.57787708 0.83844620
## month.4 -0.005089333 0.18791156 0.733864353 0.34294793 1.03171185
## month.6 -0.004158030 0.08195586 0.777057452 0.59349581 0.92801060
## month.7 0.002929773 0.07048542 0.745595401 0.61677366 0.88833740
## month.8 -0.007049286 0.10514936 0.648349434 0.47207525 0.87125783
## month.9 -0.005258615 0.06817752 0.672198330 0.54640249 0.83134725
## month.10 -0.007052514 0.06441708 0.665106414 0.54857189 0.80659097
## month.11 -0.002189000 0.06891701 0.557006149 0.43912461 0.71143717
fit.plots(ESB_rat_m1, "~ rat")
fit.coeff(ESB_emo_m2, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.5716346 0.02093318 2.185229e-70 0.02310564 1.311251e-63
## emo 0.0562500 0.02960399 5.881904e-02 0.02960399 5.881904e-02
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) -0.001199995 0.02259470 0.5706545 0.528224407 0.6173243
## emo 0.001385872 0.02952333 0.0572667 -0.001998598 0.1136650
fit.plots(ESB_emo_m2, "~ emo")
## hat values (leverages) are all = 0.009615385
## and there are no factor predictors; no plot no. 5
fit.coeff(ESB_rat_m2, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.5659341 0.02165506 3.613932e-63 0.02408115 9.694983e-57
## rat 0.1016484 0.03062488 1.092872e-03 0.03062488 1.092872e-03
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) -0.0004687915 0.02410911 0.5661977 0.51485518 0.6103261
## rat 0.0008355673 0.03101501 0.1029111 0.03572546 0.1601923
fit.plots(ESB_rat_m2, "~ rat")
## hat values (leverages) are all = 0.01098901
## and there are no factor predictors; no plot no. 5
#RSB
fit.coeff(RSB_emo_m0, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.111739749 0.41814580 0.78958594 0.46226747 0.809258429
## emo 0.010328796 0.02719540 0.70452077 0.02778059 0.710458954
## sex 0.039628545 0.03046361 0.19489406 0.03071661 0.198580337
## income -0.010080647 0.01423847 0.47982588 0.01372881 0.463694543
## isworking -0.026330842 0.03641592 0.47053771 0.03628279 0.468913184
## log_age 0.279737867 0.12529807 0.02675043 0.12603610 0.027641640
## log_city_size 0.028970645 0.02295190 0.20842026 0.02074449 0.164187908
## TC 0.110563744 0.04600713 0.01721977 0.04508897 0.015109575
## log_time_RSB -0.035954441 0.04955500 0.46901441 0.04628050 0.438200166
## log_time_TC -0.001609401 0.05134342 0.97502684 0.07651909 0.983241784
## month.1 -0.077532017 0.19475909 0.69101175 0.04411323 0.080439949
## month.2 -0.144830513 0.27037622 0.59282133 0.04756426 0.002659302
## month.3 -0.032051627 0.19460038 0.86935232 0.04304469 0.457430703
## month.4 -0.205891137 0.22370639 0.35855620 0.10708516 0.056022898
## month.6 -0.087758944 0.19914029 0.65994177 0.05593363 0.118324468
## month.7 -0.007316007 0.19706175 0.97042416 0.05887302 0.901235490
## month.8 -0.081341936 0.19890919 0.68304666 0.04842897 0.094684850
## month.9 -0.052711926 0.19616514 0.78844441 0.05533853 0.342041980
## month.10 -0.105645710 0.19562074 0.58979575 0.05373181 0.050743667
## month.11 -0.116623275 0.21344506 0.58544544 0.05721900 0.042924039
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 0.0125744623 0.48835486 0.120275912 -0.890114500 1.065768689
## emo 0.0029735953 0.02877254 0.011773863 -0.056888997 0.066423552
## sex -0.0001466025 0.02897972 0.039855739 -0.022019395 0.095979248
## income 0.0005621169 0.01390543 -0.009283218 -0.037499426 0.014685691
## isworking -0.0032726457 0.03627539 -0.030651967 -0.096505773 0.049292785
## log_age 0.0022323644 0.12192480 0.284403544 0.036050453 0.502509778
## log_city_size -0.0014956167 0.02049532 0.026419880 -0.006856053 0.072664896
## TC -0.0008648594 0.04211740 0.110237760 0.026948603 0.200764063
## log_time_RSB -0.0087355311 0.05167687 -0.047161261 -0.114846774 0.105123377
## log_time_TC 0.0082628439 0.07993602 0.004374286 -0.155857640 0.146191900
## month.1 -0.0022532523 0.04637946 -0.081939946 -0.158114570 0.019335557
## month.2 -0.0068878626 0.04974964 -0.148059588 -0.236583856 -0.051528310
## month.3 -0.0027518708 0.04207065 -0.035494000 -0.108958402 0.064643056
## month.4 -0.0060518421 0.12193641 -0.211896007 -0.409217822 0.081301973
## month.6 -0.0007870990 0.05564473 -0.088459351 -0.200572247 0.020097126
## month.7 -0.0035900433 0.05494136 -0.011891997 -0.118500116 0.109710935
## month.8 -0.0032081353 0.04662274 -0.084592406 -0.161311070 0.031656948
## month.9 -0.0002829689 0.05878379 -0.056236431 -0.149978504 0.079179784
## month.10 -0.0006492028 0.05297696 -0.106660059 -0.211479019 -0.002478603
## month.11 0.0028983426 0.06794858 -0.108458567 -0.337402904 -0.021038713
fit.plots(RSB_emo_m0, "~ emo")
fit.coeff(RSB_rat_m0, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) -0.408473847 0.43982642 0.35430823 0.41107902 0.32175132
## rat 0.019778345 0.02839546 0.48701470 0.02775025 0.47695878
## sex 0.049297010 0.03153500 0.11979065 0.03343765 0.14218826
## income -0.018125130 0.01486590 0.22438402 0.01629364 0.26748101
## isworking -0.019530697 0.03675468 0.59582555 0.03823261 0.61010422
## log_age 0.297845778 0.12624589 0.01940817 0.12612194 0.01929103
## log_city_size 0.016292224 0.02412973 0.50043924 0.01840140 0.37716010
## TC 0.098612616 0.04502243 0.02981729 0.05024205 0.05125127
## log_time_RSB -0.029313862 0.05700330 0.60772333 0.05358850 0.58505944
## log_time_TC 0.100846914 0.05847247 0.08633882 0.05832271 0.08554123
## month.1 -0.006310103 0.19882928 0.97471832 0.05237640 0.90424345
## month.2 -0.031905304 0.27858562 0.90895137 0.06016458 0.59657209
## month.3 0.034796897 0.19896634 0.86136838 0.06111688 0.56984455
## month.4 -0.085802100 0.22661534 0.70542312 0.07611262 0.26114828
## month.6 0.030151742 0.20346166 0.88235953 0.08388566 0.71969790
## month.7 0.043431936 0.20323689 0.83102755 0.06670308 0.51581603
## month.8 0.012453250 0.20781432 0.95228345 0.08906980 0.88896643
## month.9 0.037374144 0.20324269 0.85431204 0.06494808 0.56572436
## month.10 0.029906001 0.19857864 0.88046349 0.05598173 0.59387037
## month.11 -0.009027340 0.20913148 0.96561825 0.08822428 0.91861708
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 0.0588856632 0.45369417 -0.357261105 -1.284442667 0.51720369
## rat 0.0007619124 0.02590789 0.020291902 -0.028806422 0.07196145
## sex 0.0002294013 0.03348549 0.049512157 -0.016057720 0.12080961
## income -0.0006909493 0.01653782 -0.019000714 -0.049716455 0.01602531
## isworking -0.0015321516 0.04041074 -0.021401163 -0.095205438 0.06761613
## log_age 0.0052499474 0.14290998 0.297108418 0.041776583 0.59496963
## log_city_size 0.0001215200 0.02115551 0.016629062 -0.024626927 0.05867764
## TC -0.0030826334 0.04954470 0.094063895 0.009273425 0.21144898
## log_time_RSB -0.0070830081 0.06275485 -0.036014082 -0.141716525 0.08965023
## log_time_TC -0.0064438358 0.05986319 0.096885391 -0.011773822 0.23561917
## month.1 -0.0006694180 0.05292201 -0.007610865 -0.118261609 0.09743766
## month.2 0.0040235262 0.06263895 -0.028777154 -0.164240815 0.08237194
## month.3 0.0021618391 0.06125854 0.035488428 -0.100251994 0.15576534
## month.4 0.0037331778 0.08807696 -0.093849360 -0.215641472 0.15537509
## month.6 -0.0037701507 0.07986212 0.020768551 -0.111879909 0.19056907
## month.7 0.0007200817 0.07057431 0.045513445 -0.088582824 0.19507109
## month.8 0.0013210745 0.08555632 0.012296034 -0.150610082 0.17291333
## month.9 0.0080281832 0.06713701 0.042440427 -0.087258905 0.16959243
## month.10 0.0024849936 0.05702384 0.030529193 -0.099490031 0.13152172
## month.11 0.0015813348 0.09584477 -0.001204909 -0.229066276 0.15091919
fit.plots(RSB_rat_m0, "~ rat")
fit.coeff(RSB_emo_m1, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.109586231 0.41912165 0.79401908 0.46147496 0.812551159
## emo 0.009426636 0.02735373 0.73076545 0.02797076 0.736479585
## sex 0.038593064 0.03064639 0.20948251 0.03088641 0.213029771
## income -0.009068206 0.01450284 0.53255132 0.01418112 0.523304439
## isworking -0.026992056 0.03653680 0.46097195 0.03637386 0.458969290
## log_age 0.277265723 0.12573829 0.02865982 0.12603901 0.029037320
## log_city_size 0.028804252 0.02300741 0.21214140 0.02078881 0.167521337
## TC 0.110852647 0.04611643 0.01719901 0.04516809 0.015029225
## log_time_RSB -0.033405187 0.05009128 0.50566240 0.04677486 0.476007998
## log_time_TC -0.003066276 0.05159316 0.95267129 0.07643767 0.968044233
## month.1 -0.077982477 0.19520019 0.68997830 0.04408810 0.078550922
## month.2 -0.143868174 0.27099503 0.59612257 0.04756946 0.002839704
## month.3 -0.030791521 0.19506428 0.87474190 0.04323140 0.477193622
## month.4 -0.206935136 0.22422502 0.35724719 0.10794254 0.056743225
## month.6 -0.088604841 0.19959954 0.65761596 0.05588868 0.114560454
## month.7 -0.008039407 0.19751328 0.96757577 0.05908459 0.891914643
## month.8 -0.081728056 0.19935867 0.68230582 0.04840505 0.092988653
## month.9 -0.052930409 0.19660680 0.78805592 0.05529806 0.339703723
## month.10 -0.105663746 0.19606040 0.59056955 0.05373592 0.050729871
## month.11 -0.116465365 0.21392515 0.58679688 0.05736332 0.043733831
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 0.033333702 0.42727056 0.150989874 -0.743901583 0.896054589
## emo 0.006327046 0.02768117 0.015769334 -0.046902060 0.057577543
## sex 0.001440065 0.03171344 0.038250798 -0.016609647 0.101801357
## income 0.001990272 0.01566993 -0.007933324 -0.039838245 0.021952538
## isworking -0.005916280 0.03838241 -0.034025096 -0.093330754 0.060413521
## log_age -0.013853245 0.13091930 0.265007134 0.014035208 0.545034045
## log_city_size -0.004745518 0.02284811 0.024597639 -0.015203678 0.082031397
## TC 0.006734720 0.04548408 0.118087443 -0.003468923 0.186707179
## log_time_RSB -0.006928723 0.05031524 -0.040884630 -0.120748052 0.086163264
## log_time_TC 0.009180540 0.07935347 -0.002567774 -0.137963372 0.163494702
## month.1 -0.001624928 0.04205391 -0.082370740 -0.152811140 0.019986392
## month.2 -0.008809980 0.05223027 -0.145416001 -0.254506464 -0.046335687
## month.3 -0.004498988 0.04024085 -0.036532416 -0.095485332 0.065420095
## month.4 -0.008777843 0.12898307 -0.219946375 -0.419020639 0.092260889
## month.6 -0.002104278 0.05877932 -0.088674645 -0.224353298 0.020365925
## month.7 -0.004705457 0.06079679 -0.013883830 -0.125127883 0.122419233
## month.8 -0.004350246 0.05309495 -0.085956208 -0.188433872 0.020722842
## month.9 -0.005220834 0.05488930 -0.058007103 -0.147437400 0.075910067
## month.10 0.001794088 0.05373166 -0.101800636 -0.220559369 -0.006207430
## month.11 0.003278384 0.06568942 -0.110365506 -0.276685313 -0.006693031
fit.plots(RSB_emo_m1, "~ emo")
fit.coeff(RSB_rat_m1, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) -0.4370041843 0.47258194 0.356488922 0.48745000 0.37131031
## rat 0.0131185651 0.02966033 0.658866867 0.02840698 0.64483870
## sex 0.0419332000 0.03245854 0.198231665 0.03488135 0.23105316
## income -0.0329703922 0.01684395 0.052018495 0.01887660 0.08259733
## isworking -0.0069944606 0.03902362 0.857975902 0.04060253 0.86344295
## log_age 0.3625822967 0.13250278 0.006904746 0.13110625 0.00634249
## log_city_size 0.0198833962 0.02576231 0.441356221 0.02084950 0.34167518
## TC 0.1073713204 0.04669001 0.022743730 0.05298432 0.04435656
## log_time_RSB -0.0416248476 0.07355416 0.572239705 0.08615556 0.62965150
## log_time_TC 0.1038347335 0.06610713 0.118202524 0.07104148 0.14578592
## month.1 -0.0210840335 0.19991362 0.916136746 0.05548876 0.70446579
## month.2 -0.0344915442 0.27993443 0.902091210 0.06465194 0.59442155
## month.3 0.0131414273 0.20020255 0.947744851 0.06522140 0.84056860
## month.4 -0.0998177239 0.22763735 0.661612062 0.07486589 0.18430945
## month.6 0.0099753491 0.20446800 0.961149260 0.08426877 0.90591688
## month.7 0.0276261009 0.20484623 0.892887902 0.07132968 0.69904128
## month.8 0.0009674009 0.20859539 0.996305377 0.09123738 0.99155314
## month.9 0.0488469852 0.20505672 0.812017655 0.06717611 0.46818469
## month.10 0.0316492174 0.19945291 0.874118518 0.05943027 0.59507918
## month.11 -0.0324983347 0.21007091 0.877249030 0.09116404 0.72194354
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 0.0585078684 0.51610245 -0.405964666 -1.43972937 0.517308159
## rat 0.0004921827 0.02970515 0.013077012 -0.04154617 0.073353386
## sex -0.0029358094 0.03417463 0.038888246 -0.02675173 0.112385799
## income -0.0002786967 0.01881172 -0.032437913 -0.07318287 0.003103423
## isworking -0.0023080395 0.04134510 -0.009813261 -0.08950462 0.072165422
## log_age 0.0048323130 0.13935244 0.355245946 0.11002920 0.658444067
## log_city_size -0.0006071906 0.02146286 0.019496706 -0.02017282 0.063981669
## TC -0.0065696597 0.05221575 0.098696255 0.02350431 0.247344133
## log_time_RSB -0.0059767261 0.08947770 -0.043018564 -0.22723697 0.133882328
## log_time_TC -0.0058639975 0.06822301 0.097416389 -0.02713319 0.248290065
## month.1 0.0008081330 0.05453648 -0.021976817 -0.11727542 0.086461312
## month.2 0.0081446389 0.06324746 -0.028883203 -0.16793543 0.083865092
## month.3 0.0009848572 0.06661499 0.018292507 -0.12461337 0.141160815
## month.4 0.0081413969 0.09031717 -0.107129047 -0.22241734 0.140205085
## month.6 -0.0003259708 0.08703587 0.005880736 -0.15423437 0.186593709
## month.7 0.0042847080 0.07279309 0.034712931 -0.12415456 0.159349633
## month.8 0.0040990748 0.09769998 0.003543233 -0.19782416 0.189159197
## month.9 0.0037978002 0.07226587 0.048977467 -0.08364820 0.212397649
## month.10 -0.0002891505 0.05897065 0.027532090 -0.07941686 0.151906042
## month.11 0.0063974372 0.09311498 -0.025926934 -0.26778179 0.127507387
fit.plots(RSB_rat_m1, "~ rat")
fit.coeff(RSB_emo_m2, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.493269231 0.01898874 6.421266e-67 0.01863601 3.273360e-68
## emo 0.004807692 0.02685414 8.580902e-01 0.02685414 8.580902e-01
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) -5.761101e-04 0.01881011 0.492727273 0.45575901 0.53219864
## emo -2.127436e-06 0.02649260 0.004707074 -0.04561947 0.05740483
fit.plots(RSB_emo_m2, "~ emo")
## hat values (leverages) are all = 0.009615385
## and there are no factor predictors; no plot no. 5
fit.coeff(RSB_rat_m2, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.49230769 0.02048099 4.434911e-58 0.02010235 3.371877e-59
## rat 0.01593407 0.02896449 5.829163e-01 0.02896449 5.829163e-01
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) -0.0002652897 0.02025909 0.49232732 0.45324077 0.53260409
## rat -0.0001409882 0.02906404 0.01671099 -0.04417238 0.06812455
fit.plots(RSB_rat_m2, "~ rat")
## hat values (leverages) are all = 0.01098901
## and there are no factor predictors; no plot no. 5
#EMB
fit.coeff(EMB_emo_m0, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) -0.56860096 0.50392582 0.26060553 0.48648109 0.2439547612
## emo 0.03674972 0.03248805 0.25941407 0.03193332 0.2512573387
## sex 0.07166720 0.03649402 0.05101903 0.03298784 0.0310600481
## income -0.03961513 0.01697265 0.02064367 0.01768744 0.0262734082
## isworking 0.02521660 0.04376810 0.56520535 0.04503625 0.5761985452
## log_age 0.26916782 0.15222571 0.07863823 0.14627006 0.0673053828
## log_city_size 0.02196186 0.02756247 0.42656532 0.02692152 0.4156568035
## TC 0.08682960 0.05462036 0.11357536 0.05308759 0.1035894307
## log_time_EMB -0.02390833 0.06242382 0.70215008 0.05882393 0.6848799806
## log_time_TC 0.12285745 0.06100713 0.04544641 0.05501943 0.0267232884
## month.1 0.15063048 0.23316703 0.51905027 0.05823641 0.0104463303
## month.2 -0.05171273 0.32630962 0.87424977 0.06577967 0.4327638180
## month.3 0.19616409 0.23311093 0.40112837 0.05008332 0.0001252858
## month.4 0.16790667 0.26795301 0.53165972 0.12938327 0.1959561196
## month.6 0.02644980 0.23852166 0.91182081 0.08166140 0.7463749485
## month.7 0.17107563 0.23590915 0.46924104 0.06055018 0.0052302434
## month.8 0.17417704 0.23820810 0.46556439 0.07653770 0.0239856294
## month.9 0.12450413 0.23508196 0.59699692 0.06611167 0.0612038762
## month.10 0.09841803 0.23420456 0.67480025 0.06550337 0.1346400933
## month.11 0.08890196 0.25620750 0.72898337 0.08792743 0.3132692081
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 0.0729434380 0.50852561 -0.53288157 -1.5413811858 0.449969087
## emo -0.0010747513 0.03328592 0.03215076 -0.0256901246 0.111306179
## sex -0.0002272931 0.03299513 0.07259233 0.0001448952 0.131459367
## income 0.0003071473 0.01740887 -0.03920926 -0.0752339657 -0.007315677
## isworking -0.0017588431 0.04656319 0.02127219 -0.0701818652 0.116664317
## log_age -0.0065088033 0.14892813 0.27321843 -0.0474738899 0.551135448
## log_city_size -0.0029260974 0.02804169 0.01953820 -0.0309080731 0.083961759
## TC 0.0065602708 0.05572676 0.09629365 -0.0424761112 0.188470850
## log_time_EMB -0.0025913836 0.06150287 -0.02737652 -0.1431562400 0.108930115
## log_time_TC -0.0075552981 0.05707138 0.11364440 0.0181738919 0.239334863
## month.1 0.0002983471 0.05890325 0.15083136 0.0404249306 0.260294442
## month.2 0.0053510900 0.06723994 -0.05104155 -0.1795191098 0.081357756
## month.3 -0.0014612837 0.05487286 0.19200974 0.0864185488 0.306637851
## month.4 -0.0038975882 0.14618153 0.15975411 -0.1330143437 0.463532022
## month.6 -0.0040594997 0.08224958 0.02685523 -0.1399516614 0.198303251
## month.7 0.0042394241 0.06005283 0.17691560 0.0375697395 0.281990195
## month.8 -0.0014311314 0.07578486 0.17335509 0.0072542329 0.326411865
## month.9 -0.0024501770 0.06731587 0.12109273 -0.0185815563 0.270310119
## month.10 -0.0061001530 0.06783845 0.09483063 -0.0347265642 0.228690043
## month.11 0.0073414200 0.10580478 0.09967131 -0.1882983747 0.259871724
fit.plots(EMB_emo_m0, "~ emo")
fit.coeff(EMB_rat_m0, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) -0.554270773 0.52285564 0.29055911 0.49124367 0.26072847
## rat 0.055102425 0.03352866 0.10207664 0.03365807 0.10339136
## sex 0.025448929 0.03716314 0.49437762 0.03769650 0.50049781
## income -0.022525518 0.01746862 0.19892154 0.01869066 0.22975367
## isworking -0.025063841 0.04336180 0.56399171 0.04563758 0.58356913
## log_age 0.176770252 0.14987164 0.23979993 0.16334137 0.28063812
## log_city_size 0.034041503 0.02862669 0.23598156 0.02909624 0.24359795
## TC 0.037701595 0.05275967 0.47580840 0.05041080 0.45552631
## log_time_EMB 0.008225685 0.06535790 0.89998954 0.06138950 0.89356220
## log_time_TC 0.117379327 0.06765921 0.08451605 0.06526064 0.07379177
## month.1 0.095828403 0.23472151 0.68357594 0.05890146 0.10554152
## month.2 0.007858685 0.33046484 0.98105448 0.07822399 0.92009041
## month.3 0.157593792 0.23476532 0.50292082 0.06581869 0.01769984
## month.4 0.015289185 0.26738287 0.95446598 0.16870294 0.92789136
## month.6 0.024260375 0.24026648 0.91968700 0.07876943 0.75845242
## month.7 0.131520576 0.23991227 0.58424731 0.08118484 0.10701965
## month.8 0.056704495 0.24529952 0.81745567 0.10120268 0.57598306
## month.9 0.153753925 0.23974241 0.52214279 0.06883737 0.02677026
## month.10 0.052124114 0.23442303 0.82429873 0.06057233 0.39066942
## month.11 -0.001737250 0.24697744 0.99439566 0.10249986 0.98649665
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 7.586375e-03 0.52822300 -0.542533015 -1.599360958 0.49591000
## rat 3.499253e-03 0.03154677 0.057020768 -0.008184813 0.11667995
## sex -1.479922e-03 0.03800319 0.025698824 -0.053063252 0.09898035
## income 5.757785e-05 0.01867728 -0.023158311 -0.055814640 0.01788995
## isworking -2.089865e-03 0.04352716 -0.026678196 -0.114734425 0.05665405
## log_age -3.847393e-03 0.16547722 0.164009628 -0.115375130 0.54377838
## log_city_size 1.594475e-04 0.03021990 0.035029837 -0.037552901 0.09170369
## TC -5.209172e-03 0.04817992 0.029975607 -0.052548950 0.13066547
## log_time_EMB 6.626652e-03 0.06268849 0.010086980 -0.121554145 0.13781398
## log_time_TC -7.538150e-03 0.07090162 0.115959001 -0.037944752 0.24020509
## month.1 -5.357678e-04 0.05614086 0.096244720 -0.027478709 0.20374355
## month.2 -3.466825e-04 0.07698511 0.010360553 -0.172200895 0.14221856
## month.3 2.245044e-03 0.05916118 0.160096112 0.033616809 0.26161329
## month.4 1.354379e-02 0.20129268 0.002485018 -0.243012943 0.53007531
## month.6 3.418884e-03 0.07280572 0.022928025 -0.104776645 0.19852626
## month.7 3.716445e-03 0.08439043 0.135069293 -0.043483682 0.28741044
## month.8 -2.832362e-03 0.10199511 0.059999503 -0.142307632 0.24210550
## month.9 9.172340e-03 0.06848632 0.163398993 -0.006357444 0.26983417
## month.10 5.652174e-04 0.06193869 0.055643901 -0.083521761 0.16462542
## month.11 -5.002980e-03 0.10571517 -0.008610991 -0.187427148 0.20415856
fit.plots(EMB_rat_m0, "~ rat")
fit.coeff(EMB_emo_m1, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) -0.58660058 0.50495632 0.24683586 0.48729669 0.2301861033
## emo 0.03858543 0.03260477 0.23813349 0.03201729 0.2296627330
## sex 0.07405726 0.03665740 0.04477622 0.03317842 0.0267895387
## income -0.04183965 0.01722408 0.01607541 0.01796867 0.0209501110
## isworking 0.02690141 0.04386489 0.54043318 0.04504886 0.5511185347
## log_age 0.27290286 0.15245410 0.07505268 0.14653346 0.0641080568
## log_city_size 0.02250605 0.02759909 0.41583908 0.02685073 0.4029877486
## TC 0.08526431 0.05471199 0.12081525 0.05312936 0.1102067579
## log_time_EMB -0.02301931 0.06249733 0.71304548 0.05866692 0.6952271325
## log_time_TC 0.12435466 0.06109867 0.04322350 0.05512644 0.0252346413
## month.1 0.15259188 0.23341668 0.51408349 0.05834155 0.0096332511
## month.2 -0.05299229 0.32664438 0.87129711 0.06572746 0.4211215410
## month.3 0.19355093 0.23337087 0.40794658 0.05000419 0.0001496047
## month.4 0.17106492 0.26825468 0.52444753 0.12752519 0.1814020836
## month.6 0.02905234 0.23878636 0.90329295 0.08166316 0.7224204776
## month.7 0.17388300 0.23617526 0.46249889 0.06050116 0.0045192797
## month.8 0.17581162 0.23845858 0.46186901 0.07676878 0.0231231532
## month.9 0.12574152 0.23532547 0.59374436 0.06630356 0.0594338387
## month.10 0.09967100 0.23444734 0.67122686 0.06550919 0.1298192926
## month.11 0.08901540 0.25646719 0.72891720 0.08779581 0.3119377574
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 0.0792854543 0.46084450 -0.52117476 -1.6068367895 0.305567930
## emo 0.0033436942 0.03149126 0.04073899 -0.0211035306 0.106550597
## sex -0.0002888063 0.03347990 0.07365564 0.0070412686 0.139994230
## income 0.0016989889 0.01873848 -0.03841363 -0.0820643987 -0.009214026
## isworking -0.0039179182 0.04447388 0.02325897 -0.0564875022 0.115325174
## log_age -0.0213709210 0.15135450 0.25102796 -0.0104878663 0.569489311
## log_city_size -0.0033059898 0.02915632 0.02161110 -0.0329973099 0.076642282
## TC 0.0056360940 0.05436786 0.08530482 -0.0088452799 0.205134556
## log_time_EMB -0.0048161414 0.06151541 -0.02644173 -0.1398770729 0.099849377
## log_time_TC -0.0022665344 0.05727779 0.12480696 -0.0022789483 0.241299497
## month.1 -0.0036070614 0.05485245 0.14968700 0.0445415139 0.276370821
## month.2 0.0027293523 0.06656698 -0.05422644 -0.1781416562 0.091763249
## month.3 -0.0007049132 0.04890190 0.19434883 0.0986176034 0.284543048
## month.4 -0.0028871633 0.14685066 0.16669007 -0.0964633375 0.495064157
## month.6 -0.0017756003 0.08568507 0.03697953 -0.1604812485 0.167981556
## month.7 0.0046317690 0.06224717 0.17736166 0.0481446061 0.291107971
## month.8 -0.0041743366 0.08296952 0.17354015 -0.0005989996 0.332686869
## month.9 -0.0074680212 0.06463092 0.11652719 0.0041965359 0.260817862
## month.10 -0.0058378374 0.06505110 0.09520181 -0.0292933578 0.219171472
## month.11 -0.0005644666 0.10498154 0.08742287 -0.1621812315 0.274369114
fit.plots(EMB_emo_m1, "~ emo")
fit.coeff(EMB_rat_m1, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) -0.722167963 0.53422333 0.17832079 0.49229477 0.144330393
## rat 0.034647302 0.03416835 0.31208656 0.03437096 0.314938509
## sex 0.026844614 0.03768243 0.47724763 0.03918270 0.494251147
## income -0.023180415 0.01959343 0.23851436 0.02029761 0.255128050
## isworking -0.033385343 0.04527367 0.46193864 0.04789981 0.486812493
## log_age 0.269378982 0.15469358 0.08351679 0.15692343 0.087958695
## log_city_size 0.032413413 0.03004061 0.28219759 0.03146089 0.304416077
## TC 0.027897854 0.05374741 0.60443085 0.05334939 0.601739266
## log_time_EMB 0.026340247 0.06784718 0.69835621 0.06636937 0.691983057
## log_time_TC 0.114086655 0.07225261 0.11628734 0.07186706 0.114356567
## month.1 0.115971896 0.23253718 0.61865026 0.06194136 0.062969435
## month.2 0.030004370 0.32693341 0.92699006 0.08206627 0.715131974
## month.3 0.150440569 0.23261380 0.51871571 0.06787286 0.028049922
## month.4 0.001532389 0.26446989 0.99538406 0.16451330 0.992579527
## month.6 0.014570040 0.23789951 0.95123997 0.07931535 0.854480414
## month.7 0.114086640 0.23824302 0.63267921 0.08590359 0.186020524
## month.8 0.052389167 0.24262736 0.82931831 0.10195319 0.608053468
## month.9 0.193171017 0.23854449 0.41924856 0.06677192 0.004341007
## month.10 0.060442945 0.23200045 0.79478628 0.06236552 0.333903843
## month.11 -0.011674929 0.24448732 0.96197216 0.10241282 0.909379976
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) 0.0935619504 0.49208448 -0.634965066 -1.784566687 0.14151822
## rat 0.0025794842 0.03434822 0.037971145 -0.034471507 0.10127078
## sex -0.0012703885 0.04017142 0.025425289 -0.053582452 0.09972216
## income 0.0001063334 0.02101354 -0.023134437 -0.063813796 0.01728130
## isworking -0.0022688936 0.04811565 -0.034556027 -0.123919021 0.05168558
## log_age -0.0138374669 0.15991210 0.262359604 -0.059766349 0.58679774
## log_city_size -0.0011126394 0.03170871 0.030144369 -0.025639818 0.10010619
## TC -0.0078736517 0.05303676 0.021372078 -0.069975543 0.15751729
## log_time_EMB -0.0017663112 0.07438292 0.026022385 -0.112178424 0.17069088
## log_time_TC -0.0118965639 0.07209124 0.105547692 -0.033430507 0.24530129
## month.1 -0.0001943762 0.06033498 0.119192927 -0.008222683 0.22163174
## month.2 0.0030724050 0.08152798 0.028616657 -0.125750038 0.20875648
## month.3 0.0031339519 0.06859920 0.153093384 0.011135681 0.27719039
## month.4 0.0096568134 0.20123263 -0.019421808 -0.250009456 0.56153577
## month.6 0.0025229295 0.08067284 0.018438944 -0.157776684 0.15808871
## month.7 0.0060745193 0.08913198 0.115286821 -0.047177314 0.30465696
## month.8 0.0046022502 0.10056643 0.056546432 -0.147687286 0.26578051
## month.9 0.0002011243 0.07195330 0.195360161 0.041122482 0.32514229
## month.10 -0.0000840130 0.06326455 0.061039824 -0.072996945 0.18455921
## month.11 0.0067764736 0.11724399 0.001547804 -0.242237782 0.20286934
fit.plots(EMB_rat_m1, "~ rat")
fit.coeff(EMB_emo_m2, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.56009615 0.02258903 9.304637e-64 0.02390605 5.216551e-60
## emo 0.01490385 0.03194571 6.413244e-01 0.03194571 6.413244e-01
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) -0.001343322 0.02373836 0.5587549 0.5149849 0.60666318
## emo 0.001623848 0.03176194 0.0170218 -0.0485328 0.07541806
fit.plots(EMB_emo_m2, "~ emo")
## hat values (leverages) are all = 0.009615385
## and there are no factor predictors; no plot no. 5
fit.coeff(EMB_rat_m2, boot = T)## Estimate SE pvalue Robust.SE Robust.pvalue
## (Intercept) 0.57472527 0.02364965 9.821768e-59 0.02529887 9.396213e-55
## rat 0.02747253 0.03344565 4.125005e-01 0.03344565 4.125005e-01
## bootBias bootSE bootMed 2.5 % 97.5 %
## (Intercept) -0.0005534032 0.02525750 0.57473401 0.52355215 0.6273632
## rat 0.0003472735 0.03376152 0.02837064 -0.04024144 0.0916640
fit.plots(EMB_rat_m2, "~ rat")
## hat values (leverages) are all = 0.01098901
## and there are no factor predictors; no plot no. 5
#make summary
df <- rbind(
fit.params(RB_emo_m0),
fit.params(RB_emo_m1),
fit.params(RB_rat_m0),
fit.params(RB_rat_m1),
fit.params(ESB_emo_m0),
fit.params(ESB_emo_m1),
fit.params(ESB_rat_m0),
fit.params(ESB_rat_m1),
fit.params(RSB_emo_m0),
fit.params(RSB_emo_m1),
fit.params(RSB_rat_m0),
fit.params(RSB_rat_m1),
fit.params(EMB_emo_m0),
fit.params(EMB_emo_m1),
fit.params(EMB_rat_m0),
fit.params(EMB_rat_m1)
)
write.table(df, file = paste0('models\\models_pars.csv'), sep=";", dec=",")
df## type src group r.squared adj.r.squared shapiro.p.value
## RB_emo_m0 lm emo_all emo 0.1854741 0.103590541 5.933183e-01
## RB_emo_m1 lm emo_data emo 0.1810835 0.098320622 5.209019e-01
## RB_rat_m0 lm rat_all rat 0.1277595 0.033597123 1.266375e-01
## RB_rat_m1 lm rat_data rat 0.1089132 0.004402973 1.478884e-01
## ESB_emo_m0 lm emo_all emo 0.1164439 0.027620785 1.897174e-01
## ESB_emo_m1 lm emo_data emo 0.1146713 0.025196591 1.966178e-01
## ESB_rat_m0 lm rat_all rat 0.1496946 0.057900272 1.205193e-01
## ESB_rat_m1 lm rat_data rat 0.1656386 0.067781431 8.001263e-02
## RSB_emo_m0 lm emo_all emo 0.1222804 0.034044014 3.224553e-05
## RSB_emo_m1 lm emo_data emo 0.1227599 0.034102684 3.836915e-05
## RSB_rat_m0 lm rat_all rat 0.1075883 0.011248445 1.360207e-05
## RSB_rat_m1 lm rat_data rat 0.1246319 0.021965282 3.727486e-05
## EMB_emo_m0 lm emo_all emo 0.1117455 0.022450038 1.139032e-01
## EMB_emo_m1 lm emo_data emo 0.1132409 0.023621655 1.232945e-01
## EMB_rat_m0 lm rat_all rat 0.1055124 0.008948425 3.392518e-01
## EMB_rat_m1 lm rat_data rat 0.1135954 0.009634384 3.013467e-01
## bp.p.value f.p.value
## RB_emo_m0 0.04575478 0.002836128
## RB_emo_m1 0.05614150 0.004146493
## RB_rat_m0 0.55106162 0.154221474
## RB_rat_m1 0.43334585 0.416436439
## ESB_emo_m0 0.12327216 0.180156956
## ESB_emo_m1 0.11830000 0.199501419
## ESB_rat_m0 0.24963046 0.053257379
## ESB_rat_m1 0.18287578 0.042070734
## RSB_emo_m0 0.88509428 0.137666064
## RSB_emo_m1 0.89952954 0.138344527
## RSB_rat_m0 0.33492134 0.337748689
## RSB_rat_m1 0.28540558 0.252133574
## EMB_emo_m0 0.29931493 0.220839892
## EMB_emo_m1 0.29793699 0.212091204
## EMB_rat_m0 0.91919484 0.361916850
## EMB_rat_m1 0.85347883 0.362859156
#make summary
df <- rbind(
fit.params(RB_emo_m2),
fit.params(RB_rat_m2),
fit.params(ESB_emo_m2),
fit.params(ESB_rat_m2),
fit.params(RSB_emo_m2),
fit.params(RSB_rat_m2),
fit.params(EMB_emo_m2),
fit.params(EMB_rat_m2)
)
write.table(df, file = paste0('models\\models_pars_nocontrols.csv'), sep=";", dec=",")
df## type src group r.squared adj.r.squared shapiro.p.value
## RB_emo_m2 lm emo_data emo 0.0351474021 0.030463652 7.021981e-03
## RB_rat_m2 lm rat_data rat 0.0095855383 0.004083236 1.815040e-03
## ESB_emo_m2 lm emo_data emo 0.0172239159 0.012453158 2.733900e-03
## ESB_rat_m2 lm rat_data rat 0.0576740628 0.052438919 8.040261e-03
## RSB_emo_m2 lm emo_data emo 0.0001555665 -0.004698047 3.912548e-07
## RSB_rat_m2 lm rat_data rat 0.0016784920 -0.003867739 1.048777e-07
## EMB_emo_m2 lm emo_data emo 0.0010554698 -0.003793775 1.752698e-02
## EMB_rat_m2 lm rat_data rat 0.0037343977 -0.001800411 1.716637e-02
## bp.p.value f.p.value
## RB_emo_m2 0.001516882 0.006695645
## RB_rat_m2 0.097530432 0.188549619
## ESB_emo_m2 0.026710838 0.058819040
## ESB_rat_m2 0.037629672 0.001092872
## RSB_emo_m2 0.752093750 0.858090235
## RSB_rat_m2 0.781909540 0.582916254
## EMB_emo_m2 0.169012654 0.641324362
## EMB_rat_m2 0.147869707 0.412500479
.