Descriptive
Petr Yarkin
8/15/2021
rm(list = ls())
date()## [1] "Tue May 21 00:07:14 2024"sessionInfo()## R version 4.3.2 (2023-10-31)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.6.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Moscow
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.34 R6_2.5.1 fastmap_1.1.1 xfun_0.41
## [5] cachem_1.0.8 knitr_1.45 htmltools_0.5.7 rmarkdown_2.25
## [9] lifecycle_1.0.4 cli_3.6.2 sass_0.4.8 jquerylib_0.1.4
## [13] compiler_4.3.2 rstudioapi_0.16.0 tools_4.3.2 evaluate_0.23
## [17] bslib_0.6.1 yaml_2.3.8 rlang_1.1.3 jsonlite_1.8.8options(scipen = 999) # Убирает научную запись чиселБиблиотеки
library(ggplot2)
library(tidyr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphaИмпорт таблицы
Список переменных
Nationality - Страна
Sex - Пол
Age - Возраст
ResidenceChild - Место жительства в детстве
ResidenceAdult - Текущее место жительства
Психологическое благополучие
W_PositiveRel
W_Autonomy
W_EnvMastery
W_PersGrowth
W_PurposeLife
W_SelfAcceptance WellBeing
EnvIdentity
Ценности:
V_Conformity
V_tradition V_benevolence
V_universalism
V_Selfdirection V_Stimulation
V_Hedonism
V_Achievement
V_Power V_Security
Data <- read.delim2("../thes_2.tsv")
Data$Nationality <- factor(Data$Nationality, levels = c(0, 1), labels = c("France", "Russia"), ordered = FALSE)
Data$Sex <- factor(Data$Sex, levels = c(0, 1), labels = c("Female", "Male"), ordered = FALSE)
Data$ResidenceChild <- factor(Data$ResidenceChild, levels = c(1:4), ordered = TRUE, labels = c("BigCity" , "SmallTown"
, "Village"
, "SmallVillage"
)
)
Data$ResidenceAdult <- factor(Data$ResidenceAdult, levels = c(1:4), ordered = TRUE, labels = c("BigCity"
, "SmallTown"
, "Village"
, "SmallVillage"
)
) Проверка таблицы
head(Data) # Начало таблицы## Nationality Sex Age ResidenceChild ResidenceAdult W_PositiveRel W_Autonomy
## 1 Russia Female 23 BigCity BigCity 51 45
## 2 Russia Male 22 BigCity BigCity 47 60
## 3 Russia Female 21 BigCity BigCity 64 57
## 4 Russia Male 34 BigCity BigCity 51 62
## 5 Russia Male 25 BigCity BigCity 66 54
## 6 Russia Female 23 BigCity BigCity 54 55
## W_EnvMastery W_PersGrowth W_PurposeLife W_SelfAcceptance WellBeing
## 1 54 60 53 45 308
## 2 55 59 54 64 339
## 3 56 62 65 62 366
## 4 56 59 43 50 321
## 5 67 64 66 66 383
## 6 61 65 60 57 352
## EnvIdentity V_Conformity V_tradition V_benevolence V_universalism
## 1 41 2.4 1.6 2.0 2.63
## 2 59 2.0 1.4 3.2 2.75
## 3 64 2.0 2.6 4.2 4.50
## 4 47 3.0 3.0 4.0 4.13
## 5 53 1.6 1.0 4.0 3.38
## 6 64 2.4 3.2 4.4 5.88
## V_Selfdirection V_Stimulation V_Hedonism V_Achievement V_Power V_Security
## 1 3.0 2.67 3.00 2.75 2.50 3.0
## 2 3.6 1.67 1.33 2.75 1.25 2.6
## 3 4.8 3.00 5.67 4.00 2.50 4.6
## 4 4.8 4.67 5.00 4.25 3.50 4.0
## 5 4.6 2.00 4.67 4.25 2.75 3.2
## 6 6.0 1.33 3.33 3.50 0.25 3.6str(Data) # Структура данных## 'data.frame': 109 obs. of 23 variables:
## $ Nationality : Factor w/ 2 levels "France","Russia": 2 2 2 2 2 2 2 2 2 2 ...
## $ Sex : Factor w/ 2 levels "Female","Male": 1 2 1 2 2 1 2 1 2 1 ...
## $ Age : int 23 22 21 34 25 23 33 30 25 23 ...
## $ ResidenceChild : Ord.factor w/ 4 levels "BigCity"<"SmallTown"<..: 1 1 1 1 1 1 1 2 1 1 ...
## $ ResidenceAdult : Ord.factor w/ 4 levels "BigCity"<"SmallTown"<..: 1 1 1 1 1 1 1 1 1 1 ...
## $ W_PositiveRel : int 51 47 64 51 66 54 63 65 59 43 ...
## $ W_Autonomy : int 45 60 57 62 54 55 60 45 58 47 ...
## $ W_EnvMastery : int 54 55 56 56 67 61 61 56 48 41 ...
## $ W_PersGrowth : int 60 59 62 59 64 65 69 62 58 56 ...
## $ W_PurposeLife : int 53 54 65 43 66 60 63 57 49 45 ...
## $ W_SelfAcceptance: int 45 64 62 50 66 57 56 56 55 34 ...
## $ WellBeing : int 308 339 366 321 383 352 372 341 327 266 ...
## $ EnvIdentity : int 41 59 64 47 53 64 62 39 49 60 ...
## $ V_Conformity : num 2.4 2 2 3 1.6 2.4 2 3.6 3.2 1 ...
## $ V_tradition : num 1.6 1.4 2.6 3 1 3.2 2.8 3.6 2.8 0.4 ...
## $ V_benevolence : num 2 3.2 4.2 4 4 4.4 5 4.6 5 4.2 ...
## $ V_universalism : num 2.63 2.75 4.5 4.13 3.38 5.88 4.38 5.25 3.25 5.13 ...
## $ V_Selfdirection : num 3 3.6 4.8 4.8 4.6 6 4.2 5.8 5.4 5.4 ...
## $ V_Stimulation : num 2.67 1.67 3 4.67 2 1.33 3.33 5.33 3.33 3.67 ...
## $ V_Hedonism : num 3 1.33 5.67 5 4.67 3.33 5.33 6 5.33 6.67 ...
## $ V_Achievement : num 2.75 2.75 4 4.25 4.25 3.5 3.25 5.75 2.25 2 ...
## $ V_Power : num 2.5 1.25 2.5 3.5 2.75 0.25 1.5 4.5 1 2 ...
## $ V_Security : num 3 2.6 4.6 4 3.2 3.6 2.8 5 3.8 3.6 ...summary(Data) # Общая статистика по колонкам## Nationality Sex Age ResidenceChild
## France:62 Female:64 Min. :19.00 BigCity :49
## Russia:47 Male :45 1st Qu.:22.00 SmallTown :26
## Median :24.00 Village :20
## Mean :24.81 SmallVillage:14
## 3rd Qu.:27.00
## Max. :34.00
## ResidenceAdult W_PositiveRel W_Autonomy W_EnvMastery
## BigCity :85 Min. :41.00 Min. :33.00 Min. :34.00
## SmallTown : 7 1st Qu.:52.00 1st Qu.:45.00 1st Qu.:43.00
## Village :13 Median :56.00 Median :50.00 Median :49.00
## SmallVillage: 4 Mean :56.29 Mean :51.75 Mean :49.54
## 3rd Qu.:60.00 3rd Qu.:55.00 3rd Qu.:55.00
## Max. :81.00 Max. :82.00 Max. :71.00
## W_PersGrowth W_PurposeLife W_SelfAcceptance WellBeing
## Min. :40.00 Min. :41.00 Min. :33.00 Min. :258.0
## 1st Qu.:48.00 1st Qu.:50.00 1st Qu.:48.00 1st Qu.:303.0
## Median :53.00 Median :58.00 Median :57.00 Median :326.0
## Mean :55.88 Mean :58.61 Mean :56.64 Mean :328.7
## 3rd Qu.:64.00 3rd Qu.:66.00 3rd Qu.:65.00 3rd Qu.:343.0
## Max. :80.00 Max. :81.00 Max. :83.00 Max. :447.0
## EnvIdentity V_Conformity V_tradition V_benevolence
## Min. :26.00 Min. :0.800 Min. :0.000 Min. :2.000
## 1st Qu.:49.00 1st Qu.:2.400 1st Qu.:2.000 1st Qu.:4.400
## Median :61.00 Median :3.200 Median :3.000 Median :5.000
## Mean :59.85 Mean :3.167 Mean :3.095 Mean :4.985
## 3rd Qu.:70.00 3rd Qu.:3.800 3rd Qu.:4.000 3rd Qu.:5.600
## Max. :85.00 Max. :5.600 Max. :7.000 Max. :7.000
## V_universalism V_Selfdirection V_Stimulation V_Hedonism
## Min. :2.500 Min. :2.800 Min. :0.330 Min. :0.330
## 1st Qu.:4.130 1st Qu.:4.800 1st Qu.:3.330 1st Qu.:4.330
## Median :5.000 Median :5.400 Median :4.330 Median :5.000
## Mean :4.962 Mean :5.345 Mean :4.339 Mean :4.975
## 3rd Qu.:5.880 3rd Qu.:6.000 3rd Qu.:5.330 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## V_Achievement V_Power V_Security
## Min. :1.250 Min. :-1.000 Min. :1.200
## 1st Qu.:3.250 1st Qu.: 1.500 1st Qu.:3.600
## Median :4.250 Median : 2.500 Median :4.400
## Mean :4.266 Mean : 2.711 Mean :4.461
## 3rd Qu.:5.250 3rd Qu.: 3.750 3rd Qu.:5.400
## Max. :7.000 Max. : 6.750 Max. :7.000table(Data$Nationality, Data$ResidenceChild)##
## BigCity SmallTown Village SmallVillage
## France 22 16 14 10
## Russia 27 10 6 4table(Data$Nationality, Data$ResidenceAdult)##
## BigCity SmallTown Village SmallVillage
## France 41 7 12 2
## Russia 44 0 1 2#Гистограммы
ggplot(Data, aes(x = W_Autonomy)) +
geom_histogram(binwidth = 5) +
facet_grid(rows = vars(Sex), cols = vars(Nationality)) +
labs(title = "Histogram of W_Autonomy by Nationality and Sex", x = "W_Autonomy", y = "Count") +
theme_light()
Data %>%
select(1:11) %>% #Берем только первые 11 колонок
pivot_longer(cols = starts_with("W_"), names_to = "Variable", values_to = "Value") %>% # Колонки, которые начинаются с "W_"
ggplot(., aes(x = Variable, y = Value, fill = Nationality)) +
geom_boxplot() +
labs(title = "Well-being", x = "Variable", y = "Value") +
theme_light() +
# theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(Data, aes(x = Nationality, y = WellBeing)) +
geom_boxplot() +
labs(title = "Well-being", x = "Nationality", y = "Value") +
theme_light() +
# theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Тест Вилкоксона
W_variables <- c("W_PositiveRel", "W_Autonomy", "W_EnvMastery", "W_PersGrowth", "W_PurposeLife", "W_SelfAcceptance", "WellBeing")
# Проведение теста Манна-Уитни для каждой пары переменных
for (var in W_variables) {
result <- wilcox.test(get(var) ~ Nationality, data = Data, alternative = "two.sided")
print(paste("Test for", var))
print(result)
}## [1] "Test for W_PositiveRel"
##
## Wilcoxon rank sum test with continuity correction
##
## data: get(var) by Nationality
## W = 1111.5, p-value = 0.03455
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Test for W_Autonomy"
##
## Wilcoxon rank sum test with continuity correction
##
## data: get(var) by Nationality
## W = 425, p-value = 0.0000000002584
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Test for W_EnvMastery"
##
## Wilcoxon rank sum test with continuity correction
##
## data: get(var) by Nationality
## W = 322, p-value = 0.000000000003701
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Test for W_PersGrowth"
##
## Wilcoxon rank sum test with continuity correction
##
## data: get(var) by Nationality
## W = 80, p-value < 0.00000000000000022
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Test for W_PurposeLife"
##
## Wilcoxon rank sum test with continuity correction
##
## data: get(var) by Nationality
## W = 1405.5, p-value = 0.7548
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Test for W_SelfAcceptance"
##
## Wilcoxon rank sum test with continuity correction
##
## data: get(var) by Nationality
## W = 1381, p-value = 0.6439
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Test for WellBeing"
##
## Wilcoxon rank sum test with continuity correction
##
## data: get(var) by Nationality
## W = 511.5, p-value = 0.000000007344
## alternative hypothesis: true location shift is not equal to 0Регрессия - графики
Data %>%
select(1:13) %>% #Берем только первые 13 колонок
pivot_longer(cols = starts_with("W_"), names_to = "Variable", values_to = "Value") %>% # Колонки, которые начинаются с "W_"
ggplot(., aes(x = Value, y = EnvIdentity, col = Nationality)) +
geom_point() +
geom_smooth(method = 'lm', se = FALSE) + # se - убирает стандартную ошибку st. error
facet_grid(cols = vars(Variable)) +
labs(title = "Well-being", x = "Well-Being", y = "EnvIdentity") +
theme_light() ## `geom_smooth()` using formula = 'y ~ x'
Data %>%
select(1:13) %>% #Берем только первые 13 колонок
pivot_longer(cols = starts_with("W_"), names_to = "Variable", values_to = "Value") %>% # Колонки, которые начинаются с "W_"
ggplot(., aes(x = EnvIdentity
, y = Value
, col = Nationality)) +
geom_point() +
geom_smooth(method = 'lm') +
facet_grid(cols = vars(Variable)) +
labs(title = "Well-being"
, x = "EnvIdentity"
, y = "Well-Being") +
theme_light() ## `geom_smooth()` using formula = 'y ~ x'
ggplot(Data, aes(x = EnvIdentity, y = WellBeing , col = Nationality)) +
geom_point() +
geom_smooth(method = 'lm') +
labs(title = "Well-being", x = "EnvIdentity", y = "Well-being" ) +
theme_light()## `geom_smooth()` using formula = 'y ~ x'
Корреляционная матрица - график
Встроенная функция
pairs(Data[, 6:11])
pairs(Data[, 13:23])
С использованием пакета psych (хороший пакет для анализа именно психологических данных).
pairs.panels(Data[, 6:11],
method = "pearson", # correlation method
lm = TRUE,
stars = TRUE
)
Но если нужно взять достаточно много переменных, то можно убать точки и оставить только элипсы, которые показывают разброс.
pairs.panels(Data[, 6:23]
, method = "pearson" # correlation method
, ellipses = TRUE # draws correlation ellipses
, lm = TRUE
, stars = TRUE
, show.points = FALSE
, pch = "."
, gap = 0
)
Корреляция Psych
corPlot(Data[,6:23], stars = TRUE)
# Регрессионная модель
lmCor(EnvIdentity ~ V_Conformity + V_tradition + V_benevolence + V_universalism + V_Selfdirection + V_Stimulation + V_Hedonism + V_Achievement + V_Power + V_Security, data = Data)
## Call: lmCor(y = EnvIdentity ~ V_Conformity + V_tradition + V_benevolence +
## V_universalism + V_Selfdirection + V_Stimulation + V_Hedonism +
## V_Achievement + V_Power + V_Security, data = Data)
##
## Multiple Regression from raw data
##
## DV = EnvIdentity
## slope se t p lower.ci upper.ci VIF Vy.x
## (Intercept) 0.00 0.09 0.00 1.0000 -0.18 0.18 1.00 0.00
## V_Conformity -0.03 0.16 -0.20 0.8400 -0.35 0.28 3.26 -0.01
## V_tradition 0.08 0.13 0.57 0.5700 -0.19 0.34 2.30 0.02
## V_benevolence 0.13 0.14 0.91 0.3700 -0.15 0.40 2.50 0.04
## V_universalism 0.38 0.13 2.88 0.0049 0.12 0.64 2.23 0.17
## V_Selfdirection -0.13 0.13 -1.05 0.2900 -0.39 0.12 2.10 -0.03
## V_Stimulation -0.08 0.12 -0.73 0.4700 -0.31 0.14 1.71 -0.01
## V_Hedonism 0.05 0.11 0.43 0.6700 -0.17 0.26 1.49 0.01
## V_Achievement 0.04 0.14 0.26 0.7900 -0.24 0.31 2.39 0.00
## V_Power -0.12 0.12 -0.95 0.3400 -0.37 0.13 1.98 0.01
## V_Security 0.13 0.13 1.05 0.3000 -0.12 0.38 2.01 0.03
##
## Residual Standard Error = 0.92 with 98 degrees of freedom
##
## Multiple Regression
## R R2 Ruw R2uw Shrunken R2 SE of R2 overall F df1 df2 p
## EnvIdentity 0.49 0.24 0.36 0.13 0.16 0.06 3.02 10 98 0.00228Модель по полу
lm(EnvIdentity ~ Sex, Data) %>%
summary()##
## Call:
## lm(formula = EnvIdentity ~ Sex, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.984 -7.822 2.016 9.016 23.178
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 61.984 1.553 39.906 <0.0000000000000002 ***
## SexMale -5.162 2.417 -2.135 0.035 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.43 on 107 degrees of freedom
## Multiple R-squared: 0.04087, Adjusted R-squared: 0.03191
## F-statistic: 4.56 on 1 and 107 DF, p-value: 0.03501Страна
lm(EnvIdentity ~ Nationality, Data) %>%
summary()##
## Call:
## lm(formula = EnvIdentity ~ Nationality, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.839 -10.839 1.161 10.128 25.128
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.83871 1.61139 37.135 <0.0000000000000002 ***
## NationalityRussia 0.03363 2.45394 0.014 0.989
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.69 on 107 degrees of freedom
## Multiple R-squared: 1.755e-06, Adjusted R-squared: -0.009344
## F-statistic: 0.0001878 on 1 and 107 DF, p-value: 0.9891lm(EnvIdentity ~ Sex + Nationality, Data) %>%
summary()##
## Call:
## lm(formula = EnvIdentity ~ Sex + Nationality, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.070 -7.905 1.930 8.930 23.244
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 61.9216 1.8637 33.225 <0.0000000000000002 ***
## SexMale -5.1655 2.4294 -2.126 0.0358 *
## NationalityRussia 0.1489 2.4151 0.062 0.9510
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.48 on 106 degrees of freedom
## Multiple R-squared: 0.04091, Adjusted R-squared: 0.02281
## F-statistic: 2.261 on 2 and 106 DF, p-value: 0.1093Взаимодействие пола и страны
lm(EnvIdentity ~ Sex*Nationality, Data) %>%
summary()##
## Call:
## lm(formula = EnvIdentity ~ Sex * Nationality, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.11 -7.72 1.28 8.80 24.28
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.622 2.056 30.462 <0.0000000000000002 ***
## SexMale -6.902 3.237 -2.132 0.0353 *
## NationalityRussia -1.511 3.165 -0.477 0.6342
## SexMale:NationalityRussia 3.991 4.908 0.813 0.4180
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.5 on 105 degrees of freedom
## Multiple R-squared: 0.04691, Adjusted R-squared: 0.01968
## F-statistic: 1.723 on 3 and 105 DF, p-value: 0.1668lm(EnvIdentity ~ WellBeing, Data) %>%
summary()##
## Call:
## lm(formula = EnvIdentity ~ WellBeing, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.909 -10.546 1.380 9.957 25.091
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.64420 10.65629 5.409 0.000000389 ***
## WellBeing 0.00672 0.03221 0.209 0.835
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.69 on 107 degrees of freedom
## Multiple R-squared: 0.0004067, Adjusted R-squared: -0.008935
## F-statistic: 0.04354 on 1 and 107 DF, p-value: 0.8351lm(EnvIdentity ~ WellBeing*Nationality, Data) %>%
summary()##
## Call:
## lm(formula = EnvIdentity ~ WellBeing * Nationality, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.653 -10.436 1.584 9.813 25.396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 61.98896 22.04007 2.813 0.00587 **
## WellBeing -0.00693 0.07084 -0.098 0.92226
## NationalityRussia -8.02263 27.69339 -0.290 0.77262
## WellBeing:NationalityRussia 0.02366 0.08512 0.278 0.78161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.8 on 105 degrees of freedom
## Multiple R-squared: 0.001288, Adjusted R-squared: -0.02725
## F-statistic: 0.04512 on 3 and 105 DF, p-value: 0.9872lm(EnvIdentity ~ Nationality*WellBeing, Data) %>%
summary()##
## Call:
## lm(formula = EnvIdentity ~ Nationality * WellBeing, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.653 -10.436 1.584 9.813 25.396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 61.98896 22.04007 2.813 0.00587 **
## NationalityRussia -8.02263 27.69339 -0.290 0.77262
## WellBeing -0.00693 0.07084 -0.098 0.92226
## NationalityRussia:WellBeing 0.02366 0.08512 0.278 0.78161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.8 on 105 degrees of freedom
## Multiple R-squared: 0.001288, Adjusted R-squared: -0.02725
## F-statistic: 0.04512 on 3 and 105 DF, p-value: 0.9872lm(EnvIdentity ~ ResidenceChild, Data) %>%
summary()##
## Call:
## lm(formula = EnvIdentity ~ ResidenceChild, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.939 -8.231 2.061 8.769 27.769
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 61.317 1.306 46.943 < 0.0000000000000002 ***
## ResidenceChild.L 7.614 2.632 2.893 0.00464 **
## ResidenceChild.Q 2.804 2.612 1.073 0.28558
## ResidenceChild.C -1.464 2.593 -0.565 0.57356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.3 on 105 degrees of freedom
## Multiple R-squared: 0.07709, Adjusted R-squared: 0.05072
## F-statistic: 2.924 on 3 and 105 DF, p-value: 0.03735Подбор моделей
lm(EnvIdentity ~ ., Data) %>%
step(direction = "both", steps = 1000000, trace = 0) %>%
summary()##
## Call:
## lm(formula = EnvIdentity ~ Nationality + ResidenceChild + W_Autonomy +
## V_Conformity + V_universalism + V_Power, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.0406 -6.4827 0.7083 7.0603 22.0603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.7618 8.9102 5.248 0.000000864 ***
## NationalityRussia 8.5836 2.8800 2.980 0.00361 **
## ResidenceChild.L 6.1477 2.4138 2.547 0.01239 *
## ResidenceChild.Q 2.1924 2.3193 0.945 0.34678
## ResidenceChild.C -1.3469 2.2798 -0.591 0.55598
## W_Autonomy -0.3045 0.1617 -1.883 0.06258 .
## V_Conformity 2.1332 1.2659 1.685 0.09509 .
## V_universalism 4.5640 1.0805 4.224 0.000053066 ***
## V_Power -1.1326 0.7356 -1.540 0.12676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.81 on 100 degrees of freedom
## Multiple R-squared: 0.3222, Adjusted R-squared: 0.2679
## F-statistic: 5.941 on 8 and 100 DF, p-value: 0.000003199lm(EnvIdentity ~ Nationality *(W_PositiveRel
+ W_Autonomy
+ W_EnvMastery
+ W_PersGrowth
+ W_PurposeLife
+ W_SelfAcceptance
+ WellBeing
+ V_Conformity
+ V_tradition
+ V_benevolence
+ V_universalism
+ V_Selfdirection
+ V_Stimulation
+ V_Hedonism
+ V_Achievement
+ V_Power
+ V_Security)
, Data
) %>%
summary()##
## Call:
## lm(formula = EnvIdentity ~ Nationality * (W_PositiveRel + W_Autonomy +
## W_EnvMastery + W_PersGrowth + W_PurposeLife + W_SelfAcceptance +
## WellBeing + V_Conformity + V_tradition + V_benevolence +
## V_universalism + V_Selfdirection + V_Stimulation + V_Hedonism +
## V_Achievement + V_Power + V_Security), data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.5814 -4.7201 -0.6123 6.7881 25.5461
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 71.7972008 43.6441866 1.645 0.1041
## NationalityRussia -43.9016091 49.5474665 -0.886 0.3784
## W_PositiveRel -0.1679453 0.4335805 -0.387 0.6996
## W_Autonomy -0.1525111 0.3641382 -0.419 0.6765
## W_EnvMastery -0.5278506 0.3963133 -1.332 0.1869
## W_PersGrowth 0.0887066 0.4250689 0.209 0.8353
## W_PurposeLife -0.1881714 0.3511546 -0.536 0.5936
## W_SelfAcceptance 0.1021562 0.2774401 0.368 0.7138
## WellBeing NA NA NA NA
## V_Conformity 2.5642611 2.7825053 0.922 0.3597
## V_tradition 0.9675598 1.6353731 0.592 0.5559
## V_benevolence 3.5858447 3.0313175 1.183 0.2406
## V_universalism 4.6240959 2.1753168 2.126 0.0368 *
## V_Selfdirection -3.6379652 2.4729953 -1.471 0.1455
## V_Stimulation -1.1542413 1.5969794 -0.723 0.4721
## V_Hedonism 0.1264883 2.0513366 0.062 0.9510
## V_Achievement 0.5634595 2.1040248 0.268 0.7896
## V_Power -2.9560107 1.5931742 -1.855 0.0675 .
## V_Security 0.7181213 1.7274560 0.416 0.6788
## NationalityRussia:W_PositiveRel 0.0302011 0.5135918 0.059 0.9533
## NationalityRussia:W_Autonomy -0.2025224 0.4356237 -0.465 0.6433
## NationalityRussia:W_EnvMastery -0.0023517 0.5439472 -0.004 0.9966
## NationalityRussia:W_PersGrowth 0.5514822 0.5866536 0.940 0.3502
## NationalityRussia:W_PurposeLife 0.6089816 0.4894496 1.244 0.2173
## NationalityRussia:W_SelfAcceptance -0.0230141 0.4034051 -0.057 0.9547
## NationalityRussia:WellBeing NA NA NA NA
## NationalityRussia:V_Conformity 0.0001367 4.6352586 0.000 1.0000
## NationalityRussia:V_tradition 0.6693923 2.5956868 0.258 0.7972
## NationalityRussia:V_benevolence -5.5402175 4.0381042 -1.372 0.1742
## NationalityRussia:V_universalism -1.8579844 3.4960955 -0.531 0.5967
## NationalityRussia:V_Selfdirection 0.9272871 3.9028306 0.238 0.8128
## NationalityRussia:V_Stimulation 0.6789967 2.2877665 0.297 0.7674
## NationalityRussia:V_Hedonism 2.4747061 2.6979726 0.917 0.3620
## NationalityRussia:V_Achievement -0.6292525 3.2232991 -0.195 0.8457
## NationalityRussia:V_Power 2.3245429 2.3080444 1.007 0.3171
## NationalityRussia:V_Security 1.7634870 3.3561817 0.525 0.6008
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.29 on 75 degrees of freedom
## Multiple R-squared: 0.4453, Adjusted R-squared: 0.2013
## F-statistic: 1.825 on 33 and 75 DF, p-value: 0.01652Полная плюс страна
lm(EnvIdentity ~ Nationality *(W_PositiveRel
+ W_Autonomy
+ W_EnvMastery
+ W_PersGrowth
+ W_PurposeLife
+ W_SelfAcceptance
+ WellBeing
+ V_Conformity
+ V_tradition
+ V_benevolence
+ V_universalism
+ V_Selfdirection
+ V_Stimulation
+ V_Hedonism
+ V_Achievement
+ V_Power
+ V_Security)
, Data
) %>%
step(direction = "both", steps = 1000000, trace = 0) %>%
summary()##
## Call:
## lm(formula = EnvIdentity ~ Nationality + W_Autonomy + W_EnvMastery +
## W_PurposeLife + V_Conformity + V_benevolence + V_universalism +
## V_Selfdirection + V_Power + Nationality:W_PurposeLife + Nationality:V_benevolence +
## Nationality:V_Power, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.4666 -5.3423 -0.8143 8.5880 24.8622
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 74.2128 16.5068 4.496 0.0000194 ***
## NationalityRussia -14.5153 16.3143 -0.890 0.375837
## W_Autonomy -0.3645 0.1678 -2.172 0.032340 *
## W_EnvMastery -0.3816 0.2097 -1.820 0.071856 .
## W_PurposeLife -0.2244 0.1491 -1.505 0.135503
## V_Conformity 3.7794 1.5075 2.507 0.013852 *
## V_benevolence 3.3021 1.8372 1.797 0.075419 .
## V_universalism 4.4931 1.2926 3.476 0.000765 ***
## V_Selfdirection -2.5211 1.5695 -1.606 0.111500
## V_Power -2.7308 1.0325 -2.645 0.009544 **
## NationalityRussia:W_PurposeLife 0.6906 0.2554 2.704 0.008104 **
## NationalityRussia:V_benevolence -4.0136 2.1365 -1.879 0.063334 .
## NationalityRussia:V_Power 2.8793 1.3900 2.071 0.040996 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.51 on 96 degrees of freedom
## Multiple R-squared: 0.384, Adjusted R-squared: 0.307
## F-statistic: 4.987 on 12 and 96 DF, p-value: 0.00000226Полная плюс пол
lm(EnvIdentity ~ Sex *(W_PositiveRel
+ W_Autonomy
+ W_EnvMastery
+ W_PersGrowth
+ W_PurposeLife
+ W_SelfAcceptance
+ WellBeing
+ V_Conformity
+ V_tradition
+ V_benevolence
+ V_universalism
+ V_Selfdirection
+ V_Stimulation
+ V_Hedonism
+ V_Achievement
+ V_Power
+ V_Security)
, Data
) %>%
step(direction = "both", steps = 1000000, trace = 0) %>%
summary()##
## Call:
## lm(formula = EnvIdentity ~ Sex + W_PositiveRel + W_PersGrowth +
## W_PurposeLife + V_Conformity + V_universalism + V_Selfdirection +
## V_Hedonism + V_Power + V_Security + Sex:W_PositiveRel + Sex:W_PurposeLife +
## Sex:V_Conformity + Sex:V_Power + Sex:V_Security, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.2273 -5.7729 0.2438 6.5063 22.3331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.16749 12.84793 2.037 0.04452 *
## SexMale -5.88066 19.98629 -0.294 0.76923
## W_PositiveRel -0.01276 0.21483 -0.059 0.95275
## W_PersGrowth 0.46648 0.14407 3.238 0.00167 **
## W_PurposeLife -0.24815 0.18085 -1.372 0.17332
## V_Conformity 5.25385 1.91906 2.738 0.00741 **
## V_universalism 4.18801 1.36409 3.070 0.00280 **
## V_Selfdirection -2.45399 1.64014 -1.496 0.13798
## V_Hedonism 2.03501 1.10101 1.848 0.06774 .
## V_Power -3.69006 1.21771 -3.030 0.00316 **
## V_Security -0.11671 1.49768 -0.078 0.93806
## SexMale:W_PositiveRel -0.54353 0.37801 -1.438 0.15382
## SexMale:W_PurposeLife 0.45606 0.26923 1.694 0.09363 .
## SexMale:V_Conformity -4.64094 2.76229 -1.680 0.09629 .
## SexMale:V_Power 2.71642 1.65262 1.644 0.10361
## SexMale:V_Security 3.23613 2.25785 1.433 0.15513
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.82 on 93 degrees of freedom
## Multiple R-squared: 0.3683, Adjusted R-squared: 0.2664
## F-statistic: 3.615 on 15 and 93 DF, p-value: 0.00006278Сохранение объекта
save(Data, file = "Data.RData")
#load(file = "Data.RData")