date()
## [1] "Sat May 3 10:54:21 2025"
options(scipen = 999)
libraries
library(kableExtra)
library(reshape2)
library(ggplot2)
library(dplyr)
##
## Присоединяю пакет: 'dplyr'
## Следующий объект скрыт от 'package:kableExtra':
##
## group_rows
## Следующие объекты скрыты от 'package:stats':
##
## filter, lag
## Следующие объекты скрыты от 'package:base':
##
## intersect, setdiff, setequal, union
library(stats)
library(tidyr)
##
## Присоединяю пакет: 'tidyr'
## Следующий объект скрыт от 'package:reshape2':
##
## smiths
library(readxl)
library(psych)
## Warning: пакет 'psych' был собран под R версии 4.3.3
##
## Присоединяю пакет: 'psych'
## Следующие объекты скрыты от 'package:ggplot2':
##
## %+%, alpha
library(corrplot)
## Warning: пакет 'corrplot' был собран под R версии 4.3.3
## corrplot 0.95 loaded
margo <- read_excel("Margo_full.xlsx")
margo$Sex <- factor(margo$Sex, levels = c(1, 2), labels = c("Male", 'Female'), ordered = FALSE)
margo$Range <- factor(margo$Range, levels = c(1:3), ordered = TRUE, labels = c("1,2 razryad",
'kms',
'ms'))
margo$Discipline <- factor(margo$Discipline, levels = c('Пистолет','Винтовка'), ordered = TRUE, labels = c("Pistol",'Vint'))
#margo$Samoobl_Personal_N <- factor(margo$Samoobl_Personal_N, levels = c('высокий','средний','низкий'), ordered = FALSE, labels = c('Large', 'Medium', 'Small'))
female <- subset(margo, Sex == 'Female')
male <- subset(margo, Sex == 'Male')
razryad <- subset(margo, Range == '1,2 razryad')
kms <- subset(margo, Range == 'kms')
ms <- subset(margo, Range == 'ms')
summary(margo)
## Sex Age Experience Discipline Range
## Male : 52 Min. :12.00 Min. : 1.000 Pistol:73 1,2 razryad:56
## Female:118 1st Qu.:15.25 1st Qu.: 3.250 Vint :97 kms :43
## Median :18.00 Median : 6.000 ms :71
## Mean :19.99 Mean : 7.294
## 3rd Qu.:22.00 3rd Qu.: 9.750
## Max. :50.00 Max. :37.000
## S_Moral S_Coping S_Values S_StressGrowth
## Min. :13.00 Min. :12.00 Min. :13.00 Min. :17.00
## 1st Qu.:30.00 1st Qu.:29.00 1st Qu.:33.00 1st Qu.:35.00
## Median :35.00 Median :35.00 Median :38.00 Median :41.00
## Mean :34.99 Mean :34.34 Mean :37.37 Mean :39.03
## 3rd Qu.:41.00 3rd Qu.:41.00 3rd Qu.:42.00 3rd Qu.:45.00
## Max. :49.00 Max. :49.00 Max. :49.00 Max. :49.00
## S_Index Samoobl_Personal_Q Samoobl_Personal_N Samoobl_Event_Q
## Min. : 56.0 Min. : 3.00 Length:170 Min. : 2.000
## 1st Qu.:130.0 1st Qu.:13.25 Class :character 1st Qu.: 8.000
## Median :146.5 Median :16.00 Mode :character Median :10.000
## Mean :145.7 Mean :15.69 Mean : 9.941
## 3rd Qu.:165.0 3rd Qu.:18.00 3rd Qu.:12.000
## Max. :192.0 Max. :21.00 Max. :14.000
## Samoobl_Event_N Samoobl_Exist_Q Samoobl_Exist_N Samoobl_Index_Q
## Length:170 Min. : 2.00 Length:170 Min. : 7.00
## Class :character 1st Qu.: 8.00 Class :character 1st Qu.:31.25
## Mode :character Median :10.00 Mode :character Median :36.00
## Mean :10.09 Mean :35.72
## 3rd Qu.:12.00 3rd Qu.:41.00
## Max. :14.00 Max. :49.00
## Samoobl_Index_N Anxiety_Q Anxiety_N Prochnost_Q
## Length:170 Min. :10.00 Length:170 Min. :12.00
## Class :character 1st Qu.:16.00 Class :character 1st Qu.:41.00
## Mode :character Median :18.00 Mode :character Median :44.00
## Mean :18.84 Mean :43.32
## 3rd Qu.:22.00 3rd Qu.:48.00
## Max. :30.00 Max. :55.00
## Prochnost_N Zh_Vovlech_Q Zh_Vovlech_N Zh_Control_Q
## Length:170 Min. : 6.00 Length:170 Min. : 5.00
## Class :character 1st Qu.:10.00 Class :character 1st Qu.:11.00
## Mode :character Median :11.00 Mode :character Median :12.00
## Mean :11.44 Mean :12.15
## 3rd Qu.:13.00 3rd Qu.:13.00
## Max. :16.00 Max. :16.00
## Zh_Control_N Zh_Acceptance_Q Zh_Acceptance_N Zh_Index_Q
## Length:170 Min. : 6.00 Length:170 Min. :22.00
## Class :character 1st Qu.: 9.00 Class :character 1st Qu.:31.00
## Mode :character Median :10.00 Mode :character Median :34.00
## Mean :10.04 Mean :33.64
## 3rd Qu.:11.00 3rd Qu.:37.00
## Max. :14.00 Max. :46.00
## Zh_Index_N Resilience
## Length:170 Min. : 6.00
## Class :character 1st Qu.:18.00
## Mode :character Median :20.00
## Mean :20.21
## 3rd Qu.:23.00
## Max. :30.00
корреляция
numeric_data <- margo[sapply(margo, is.numeric)]
correlation_matrix <- cor(numeric_data)
p_values <- matrix(NA, nrow = ncol(numeric_data), ncol = ncol(numeric_data))
rownames(p_values) <- colnames(p_values) <- colnames(numeric_data)
for (i in 1:(ncol(numeric_data) - 1)) {
for (j in (i + 1):ncol(numeric_data)) {
cor_test_result <- cor.test(numeric_data[[i]], numeric_data[[j]])
p_values[i, j] <- p_values[j, i] <- cor_test_result$p.value
}
}
significant_level <- 0.05
significant <- ifelse(p_values < significant_level, "*", "")
cor_matrix_with_significance <- round(correlation_matrix, 2)
cor_matrix_with_significance[lower.tri(cor_matrix_with_significance)] <- paste(cor_matrix_with_significance[lower.tri(cor_matrix_with_significance)], significant[lower.tri(significant)], sep = "")
cor_thes <- as.data.frame(cor_matrix_with_significance)
cor_thes <- cor_thes[-1, -1]
kable(cor_thes, format = "html") %>%
kable_styling()
|
|
Experience
|
S_Moral
|
S_Coping
|
S_Values
|
S_StressGrowth
|
S_Index
|
Samoobl_Personal_Q
|
Samoobl_Event_Q
|
Samoobl_Exist_Q
|
Samoobl_Index_Q
|
Anxiety_Q
|
Prochnost_Q
|
Zh_Vovlech_Q
|
Zh_Control_Q
|
Zh_Acceptance_Q
|
Zh_Index_Q
|
Resilience
|
|
Experience
|
1
|
0.16
|
0.12
|
0.04
|
0.07
|
0.11
|
0.1
|
0.13
|
0.19
|
0.16
|
-0.12
|
-0.03
|
0.05
|
-0.02
|
-0.08
|
-0.01
|
0.05
|
|
S_Moral
|
0.16*
|
1
|
0.64
|
0.7
|
0.67
|
0.86
|
0.37
|
0.4
|
0.55
|
0.51
|
-0.29
|
0.09
|
0.23
|
0.17
|
0.12
|
0.24
|
0.32
|
|
S_Coping
|
0.12
|
0.64*
|
1
|
0.71
|
0.68
|
0.88
|
0.47
|
0.61
|
0.47
|
0.62
|
-0.54
|
0.27
|
0.36
|
0.22
|
0.24
|
0.38
|
0.55
|
|
S_Values
|
0.04
|
0.7*
|
0.71*
|
1
|
0.68
|
0.88
|
0.36
|
0.44
|
0.52
|
0.51
|
-0.3
|
0.16
|
0.36
|
0.25
|
0.18
|
0.37
|
0.34
|
|
S_StressGrowth
|
0.07
|
0.67*
|
0.68*
|
0.68*
|
1
|
0.87
|
0.35
|
0.47
|
0.44
|
0.49
|
-0.44
|
0.2
|
0.28
|
0.27
|
0.11
|
0.31
|
0.43
|
|
S_Index
|
0.11
|
0.86*
|
0.88*
|
0.88*
|
0.87*
|
1
|
0.45
|
0.56
|
0.56
|
0.62
|
-0.46
|
0.21
|
0.35
|
0.26
|
0.19
|
0.37
|
0.48
|
|
Samoobl_Personal_Q
|
0.1
|
0.37*
|
0.47*
|
0.36*
|
0.35*
|
0.45*
|
1
|
0.58
|
0.51
|
0.89
|
-0.34
|
0.29
|
0.18
|
0.14
|
0.2
|
0.23
|
0.5
|
|
Samoobl_Event_Q
|
0.13
|
0.4*
|
0.61*
|
0.44*
|
0.47*
|
0.56*
|
0.58*
|
1
|
0.48
|
0.81
|
-0.42
|
0.35
|
0.21
|
0.17
|
0.2
|
0.26
|
0.46
|
|
Samoobl_Exist_Q
|
0.19*
|
0.55*
|
0.47*
|
0.52*
|
0.44*
|
0.56*
|
0.51*
|
0.48*
|
1
|
0.77
|
-0.33
|
0.22
|
0.26
|
0.23
|
0.06
|
0.26
|
0.33
|
|
Samoobl_Index_Q
|
0.16*
|
0.51*
|
0.62*
|
0.51*
|
0.49*
|
0.62*
|
0.89*
|
0.81*
|
0.77*
|
1
|
-0.43
|
0.35
|
0.25
|
0.21
|
0.19
|
0.3
|
0.53
|
|
Anxiety_Q
|
-0.12
|
-0.29*
|
-0.54*
|
-0.3*
|
-0.44*
|
-0.46*
|
-0.34*
|
-0.42*
|
-0.33*
|
-0.43*
|
1
|
-0.17
|
-0.3
|
-0.27
|
-0.14
|
-0.33
|
-0.55
|
|
Prochnost_Q
|
-0.03
|
0.09
|
0.27*
|
0.16*
|
0.2*
|
0.21*
|
0.29*
|
0.35*
|
0.22*
|
0.35*
|
-0.17*
|
1
|
0.24
|
0.28
|
0.09
|
0.29
|
0.3
|
|
Zh_Vovlech_Q
|
0.05
|
0.23*
|
0.36*
|
0.36*
|
0.28*
|
0.35*
|
0.18*
|
0.21*
|
0.26*
|
0.25*
|
-0.3*
|
0.24*
|
1
|
0.46
|
0.29
|
0.84
|
0.22
|
|
Zh_Control_Q
|
-0.02
|
0.17*
|
0.22*
|
0.25*
|
0.27*
|
0.26*
|
0.14
|
0.17*
|
0.23*
|
0.21*
|
-0.27*
|
0.28*
|
0.46*
|
1
|
0.16
|
0.75
|
0.23
|
|
Zh_Acceptance_Q
|
-0.08
|
0.12
|
0.24*
|
0.18*
|
0.11
|
0.19*
|
0.2*
|
0.2*
|
0.06
|
0.19*
|
-0.14
|
0.09
|
0.29*
|
0.16*
|
1
|
0.6
|
0.14
|
|
Zh_Index_Q
|
-0.01
|
0.24*
|
0.38*
|
0.37*
|
0.31*
|
0.37*
|
0.23*
|
0.26*
|
0.26*
|
0.3*
|
-0.33*
|
0.29*
|
0.84*
|
0.75*
|
0.6*
|
1
|
0.27
|
|
Resilience
|
0.05
|
0.32*
|
0.55*
|
0.34*
|
0.43*
|
0.48*
|
0.5*
|
0.46*
|
0.33*
|
0.53*
|
-0.55*
|
0.3*
|
0.22*
|
0.23*
|
0.14
|
0.27*
|
1
|
корреляция красивая!
selected_vars <- margo[, c(2, 3, 6:10, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31)]
numeric_data <- selected_vars[sapply(selected_vars, is.numeric)]
corPlot(
numeric_data,
stars = TRUE,
MAR = 4,
xlas = 2,
cex = 0.6, # уменьшение размера шрифта подписей
labels = c(
"Взр", "Ст", "МВУ", "СтрС", "ЦСм", "ПТР", "ПБЛ",
"ЛС", "СС", "ЭС", "ОС", "Тр", "МП", "В", "К",
"Пр", "Ж", "Р"
)
)
дисперсии
data_list <- list(razryad$Samoobl_Index_Q, kms$Samoobl_Exist_Q, ms$Samoobl_Index_Q)
boxplot(data_list, names = c("12", "кмс", "мс"), main = "", xlab = "Разряд", ylab = "Тревожность")
регрессия
model <- lm(S_Index ~ Age + Experience + Samoobl_Index_Q + Range + Anxiety_Q + Prochnost_Q + Zh_Index_Q + Resilience +Discipline , data = margo)
final_model <- step(model)
## Start: AIC=1023.27
## S_Index ~ Age + Experience + Samoobl_Index_Q + Range + Anxiety_Q +
## Prochnost_Q + Zh_Index_Q + Resilience + Discipline
##
## Df Sum of Sq RSS AIC
## - Discipline 1 41.3 61471 1021.4
## - Experience 1 82.6 61512 1021.5
## - Prochnost_Q 1 435.1 61865 1022.5
## <none> 61430 1023.3
## - Resilience 1 782.0 62212 1023.4
## - Age 1 994.3 62424 1024.0
## - Range 2 2099.6 63529 1025.0
## - Zh_Index_Q 1 1978.8 63409 1026.7
## - Anxiety_Q 1 2476.6 63906 1028.0
## - Samoobl_Index_Q 1 15440.5 76870 1059.4
##
## Step: AIC=1021.39
## S_Index ~ Age + Experience + Samoobl_Index_Q + Range + Anxiety_Q +
## Prochnost_Q + Zh_Index_Q + Resilience
##
## Df Sum of Sq RSS AIC
## - Experience 1 75.6 61547 1019.6
## - Prochnost_Q 1 451.2 61922 1020.6
## <none> 61471 1021.4
## - Resilience 1 835.6 62307 1021.7
## - Age 1 1015.2 62486 1022.2
## - Range 2 2160.4 63632 1023.3
## - Zh_Index_Q 1 2096.3 63567 1025.1
## - Anxiety_Q 1 2435.7 63907 1026.0
## - Samoobl_Index_Q 1 15404.3 76875 1057.4
##
## Step: AIC=1019.6
## S_Index ~ Age + Samoobl_Index_Q + Range + Anxiety_Q + Prochnost_Q +
## Zh_Index_Q + Resilience
##
## Df Sum of Sq RSS AIC
## - Prochnost_Q 1 435.2 61982 1018.8
## <none> 61547 1019.6
## - Resilience 1 868.0 62415 1020.0
## - Age 1 1571.5 63118 1021.9
## - Range 2 2506.0 64053 1022.4
## - Zh_Index_Q 1 2228.7 63775 1023.6
## - Anxiety_Q 1 2368.9 63916 1024.0
## - Samoobl_Index_Q 1 15328.8 76876 1055.4
##
## Step: AIC=1018.8
## S_Index ~ Age + Samoobl_Index_Q + Range + Anxiety_Q + Zh_Index_Q +
## Resilience
##
## Df Sum of Sq RSS AIC
## - Resilience 1 722.7 62705 1018.8
## <none> 61982 1018.8
## - Range 2 2347.9 64330 1021.1
## - Age 1 1647.3 63629 1021.3
## - Zh_Index_Q 1 1943.1 63925 1022.0
## - Anxiety_Q 1 2499.0 64481 1023.5
## - Samoobl_Index_Q 1 14948.5 76931 1053.5
##
## Step: AIC=1018.77
## S_Index ~ Age + Samoobl_Index_Q + Range + Anxiety_Q + Zh_Index_Q
##
## Df Sum of Sq RSS AIC
## <none> 62705 1018.8
## - Age 1 1739.4 64444 1021.4
## - Range 2 2693.4 65398 1021.9
## - Zh_Index_Q 1 2030.3 64735 1022.2
## - Anxiety_Q 1 4444.1 67149 1028.4
## - Samoobl_Index_Q 1 20360.7 83065 1064.6
summary(final_model)
##
## Call:
## lm(formula = S_Index ~ Age + Samoobl_Index_Q + Range + Anxiety_Q +
## Zh_Index_Q, data = margo)
##
## Residuals:
## Min 1Q Median 3Q Max
## -61.955 -9.255 0.828 10.913 67.591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 73.6369 19.1897 3.837 0.000178 ***
## Age 0.5123 0.2409 2.126 0.034976 *
## Samoobl_Index_Q 1.6428 0.2258 7.275 0.0000000000138 ***
## Range.L -6.1680 2.7945 -2.207 0.028697 *
## Range.Q -4.3361 2.9054 -1.492 0.137524
## Anxiety_Q -1.4042 0.4131 -3.399 0.000851 ***
## Zh_Index_Q 0.9050 0.3940 2.297 0.022872 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.61 on 163 degrees of freedom
## Multiple R-squared: 0.4757, Adjusted R-squared: 0.4564
## F-statistic: 24.65 on 6 and 163 DF, p-value: < 0.00000000000000022