datchi <- read.delim("/Users/riku/Documents/zxy/all.txt", row.names = 1)
chi_t <- rbind(datchi['atama_ts',], datchi['heiban_ts',],datchi['naka_ts',], datchi['o_ts',])
chi_p <- rbind(datchi['atama_ps',], datchi['heiban_ps',],datchi['naka_ps',], datchi['o_ps',])
chi_atama <- rbind(datchi['atama_ts',], datchi['atama_ps',])
chi_heiban <- rbind(datchi['heiban_ts',], datchi['heiban_ps',])
chi_naka <- rbind(datchi['naka_ts',], datchi['naka_ps',])
chi_o <- rbind(datchi['o_ts',], datchi['o_ps',])
chisq_test(chi_t)
## # A tibble: 1 × 6
## n statistic p df method p.signif
## * <int> <dbl> <dbl> <int> <chr> <chr>
## 1 184 24.6 0.000412 6 Chi-square test ***
chisq_test(chi_p)
## # A tibble: 1 × 6
## n statistic p df method p.signif
## * <int> <dbl> <dbl> <int> <chr> <chr>
## 1 184 13.8 0.0316 6 Chi-square test *
chisq_test(chi_atama)
## # A tibble: 1 × 6
## n statistic p df method p.signif
## * <int> <dbl> <dbl> <int> <chr> <chr>
## 1 92 1.18 0.554 2 Chi-square test ns
chisq_test(chi_heiban)
## # A tibble: 1 × 6
## n statistic p df method p.signif
## * <int> <dbl> <dbl> <int> <chr> <chr>
## 1 92 1.59 0.452 2 Chi-square test ns
chisq_test(chi_naka)
## # A tibble: 1 × 6
## n statistic p df method p.signif
## * <int> <dbl> <dbl> <int> <chr> <chr>
## 1 92 4.44 0.109 2 Chi-square test ns
chisq_test(chi_o)
## # A tibble: 1 × 6
## n statistic p df method p.signif
## * <int> <dbl> <dbl> <int> <chr> <chr>
## 1 92 2.77 0.251 2 Chi-square test ns
TS内
chisq <- chisq.test(chi_t)
chisq
##
## Pearson's Chi-squared test
##
## data: chi_t
## X-squared = 24.559, df = 6, p-value = 0.000412
chisq$observed # Observed counts
## down nil up
## atama_ts 4 13 29
## heiban_ts 8 26 12
## naka_ts 9 19 18
## o_ts 12 26 8
round(chisq$expected,2) # Expected counts
## down nil up
## atama_ts 8.25 21 16.75
## heiban_ts 8.25 21 16.75
## naka_ts 8.25 21 16.75
## o_ts 8.25 21 16.75
chisq$p.value
## [1] 0.0004119929
corrplot(chisq$residuals, is.cor = FALSE, title = "TS")
contrib <- 100*chisq$residuals^2/chisq$statistic
round(contrib, 3)
## down nil up
## atama_ts 8.915 12.409 36.479
## heiban_ts 0.031 4.847 5.485
## naka_ts 0.278 0.776 0.380
## o_ts 6.941 4.847 18.612
corrplot(contrib, is.cor = FALSE, title = "TS")
PS内
chisq <- chisq.test(chi_p)
chisq
##
## Pearson's Chi-squared test
##
## data: chi_p
## X-squared = 13.83, df = 6, p-value = 0.0316
chisq$observed # Observed counts
## down nil up
## atama_ps 6 16 24
## heiban_ps 11 20 15
## naka_ps 5 13 28
## o_ps 13 19 14
round(chisq$expected,2) # Expected counts
## down nil up
## atama_ps 8.75 17 20.25
## heiban_ps 8.75 17 20.25
## naka_ps 8.75 17 20.25
## o_ps 8.75 17 20.25
chisq$p.value
## [1] 0.03159853
corrplot(chisq$residuals, is.cor = FALSE, title = "PS")
contrib <- 100*chisq$residuals^2/chisq$statistic
round(contrib, 3)
## down nil up
## atama_ps 6.250 0.425 5.021
## heiban_ps 4.184 3.828 9.842
## naka_ps 11.621 6.806 21.447
## o_ps 14.927 1.701 13.948
corrplot(contrib, is.cor = FALSE, title = "PS")
Atama pstsé–“
chisq <- chisq.test(chi_atama)
chisq
##
## Pearson's Chi-squared test
##
## data: chi_atama
## X-squared = 1.182, df = 2, p-value = 0.5538
chisq$observed # Observed counts
## down nil up
## atama_ts 4 13 29
## atama_ps 6 16 24
round(chisq$expected,2) # Expected counts
## down nil up
## atama_ts 5 14.5 26.5
## atama_ps 5 14.5 26.5
chisq$p.value
## [1] 0.5537613
corrplot(chisq$residuals, is.cor = FALSE, title = "atama")
contrib <- 100*chisq$residuals^2/chisq$statistic
round(contrib, 3)
## down nil up
## atama_ts 16.92 13.127 19.953
## atama_ps 16.92 13.127 19.953
corrplot(contrib, is.cor = FALSE, title = "atama")
Heiban pstsé–“
chisq <- chisq.test(chi_heiban)
chisq
##
## Pearson's Chi-squared test
##
## data: chi_heiban
## X-squared = 1.5896, df = 2, p-value = 0.4517
chisq$observed # Observed counts
## down nil up
## heiban_ts 8 26 12
## heiban_ps 11 20 15
round(chisq$expected,2) # Expected counts
## down nil up
## heiban_ts 9.5 23 13.5
## heiban_ps 9.5 23 13.5
chisq$p.value
## [1] 0.4516656
corrplot(chisq$residuals, is.cor = FALSE, title = "heiban")
contrib <- 100*chisq$residuals^2/chisq$statistic
round(contrib, 3)
## down nil up
## heiban_ts 14.899 24.616 10.485
## heiban_ps 14.899 24.616 10.485
corrplot(contrib, is.cor = FALSE, title = "heiban")
Naka pstsé–“
chisq <- chisq.test(chi_naka)
chisq
##
## Pearson's Chi-squared test
##
## data: chi_naka
## X-squared = 4.4418, df = 2, p-value = 0.1085
chisq$observed # Observed counts
## down nil up
## naka_ts 9 19 18
## naka_ps 5 13 28
round(chisq$expected,2) # Expected counts
## down nil up
## naka_ts 7 16 23
## naka_ps 7 16 23
chisq$p.value
## [1] 0.108513
corrplot(chisq$residuals, is.cor = FALSE, title = "naka")
contrib <- 100*chisq$residuals^2/chisq$statistic
round(contrib, 3)
## down nil up
## naka_ts 12.865 12.664 24.471
## naka_ps 12.865 12.664 24.471
corrplot(contrib, is.cor = FALSE, title = "naka")
O pstsé–“
chisq <- chisq.test(chi_o)
chisq
##
## Pearson's Chi-squared test
##
## data: chi_o
## X-squared = 2.7653, df = 2, p-value = 0.2509
chisq$observed # Observed counts
## down nil up
## o_ts 12 26 8
## o_ps 13 19 14
round(chisq$expected,2) # Expected counts
## down nil up
## o_ts 12.5 22.5 11
## o_ps 12.5 22.5 11
chisq$p.value
## [1] 0.2509187
corrplot(chisq$residuals, is.cor = FALSE, title = "o")
contrib <- 100*chisq$residuals^2/chisq$statistic
round(contrib, 3)
## down nil up
## o_ts 0.723 19.689 29.588
## o_ps 0.723 19.689 29.588
corrplot(contrib, is.cor = FALSE, title = "o")
