化粧

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.0.5     ✓ dplyr   1.0.3
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(rstatix)

## 
## Attaching package: 'rstatix'

## The following object is masked from 'package:stats':
## 
##     filter

library(ggpubr)
library(MASS)

## 
## Attaching package: 'MASS'

## The following object is masked from 'package:rstatix':
## 
##     select

## The following object is masked from 'package:dplyr':
## 
##     select

library(yarrr)

## Loading required package: jpeg

## Loading required package: BayesFactor

## Loading required package: coda

## Loading required package: Matrix

## 
## Attaching package: 'Matrix'

## The following objects are masked from 'package:tidyr':
## 
##     expand, pack, unpack

## ************
## Welcome to BayesFactor 0.9.12-4.2. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
## 
## Type BFManual() to open the manual.
## ************

## Loading required package: circlize

## ========================================
## circlize version 0.4.13
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: https://jokergoo.github.io/circlize_book/book/
## 
## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
##   in R. Bioinformatics 2014.
## 
## This message can be suppressed by:
##   suppressPackageStartupMessages(library(circlize))
## ========================================

## yarrr v0.1.5. Citation info at citation('yarrr'). Package guide at yarrr.guide()

## Email me at Nathaniel.D.Phillips.is@gmail.com

## 
## Attaching package: 'yarrr'

## The following object is masked from 'package:ggplot2':
## 
##     diamonds

Data

dat1 %>%
  group_by(gender, country) %>%
  get_summary_stats(type = "mean_sd")

## # A tibble: 224 x 6
##    gender country variable     n  mean    sd
##    <chr>  <chr>   <chr>    <dbl> <dbl> <dbl>
##  1 female china   A1          29  4.17  1.07
##  2 female china   A10         29  3.31  1.69
##  3 female china   A11         29  2.31  1.54
##  4 female china   A12         29  3.41  1.64
##  5 female china   A13         29  3     1   
##  6 female china   A14         29  4.17  1.23
##  7 female china   A15         29  2.31  1.31
##  8 female china   A16         29  2.28  1.16
##  9 female china   A17         29  2.38  1.21
## 10 female china   A18         29  2.38  1.42
## # … with 214 more rows

2-WAY ANOVA

print("A1")

## [1] "A1"

res.aov <- dat1 %>% anova_test(A1 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd       F        p p<.05   ges
## 1         gender   1 115 129.065 1.64e-20     * 0.529
## 2        country   1 115   2.750 1.00e-01       0.023
## 3 gender:country   1 115   3.086 8.20e-02       0.026

print("A2")

## [1] "A2"

res.aov <- dat1 %>% anova_test(A2 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd          F        p p<.05      ges
## 1         gender   1 115 31.7890000 1.25e-07     * 2.17e-01
## 2        country   1 115  0.0000271 9.96e-01       2.36e-07
## 3 gender:country   1 115  3.3290000 7.10e-02       2.80e-02

print("A3")

## [1] "A3"

res.aov <- dat1 %>% anova_test(A3 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F     p p<.05      ges
## 1         gender   1 115  4.582 0.034     * 3.80e-02
## 2        country   1 115 11.373 0.001     * 9.00e-02
## 3 gender:country   1 115  0.001 0.972       1.08e-05

print("A4")

## [1] "A4"

res.aov <- dat1 %>% anova_test(A4 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd     F     p p<.05   ges
## 1         gender   1 115 3.691 0.057       0.031
## 2        country   1 115 0.268 0.606       0.002
## 3 gender:country   1 115 8.287 0.005     * 0.067

print("A5")

## [1] "A5"

res.aov <- dat1 %>% anova_test(A5 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05      ges
## 1         gender   1 115 60.344 3.65e-12     * 3.44e-01
## 2        country   1 115  0.008 9.29e-01       6.85e-05
## 3 gender:country   1 115  0.003 9.60e-01       2.20e-05

print("A6")

## [1] "A6"

res.aov <- dat1 %>% anova_test(A6 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05      ges
## 1         gender   1 115 77.328 1.66e-14     * 0.402000
## 2        country   1 115  0.083 7.74e-01       0.000718
## 3 gender:country   1 115  0.646 4.23e-01       0.006000

print("A7")

## [1] "A7"

res.aov <- dat1 %>% anova_test(A7 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F       p p<.05   ges
## 1         gender   1 115  4.322 0.04000     * 0.036
## 2        country   1 115  6.839 0.01000     * 0.056
## 3 gender:country   1 115 14.049 0.00028     * 0.109

print("A8")

## [1] "A8"

res.aov <- dat1 %>% anova_test(A8 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 23.314 4.27e-06     * 0.169
## 2        country   1 115  0.660 4.18e-01       0.006
## 3 gender:country   1 115  1.297 2.57e-01       0.011

print("A9")

## [1] "A9"

res.aov <- dat1 %>% anova_test(A9 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05     ges
## 1         gender   1 115 48.963 1.85e-10     * 0.29900
## 2        country   1 115  0.121 7.29e-01       0.00100
## 3 gender:country   1 115  0.063 8.02e-01       0.00055

print("A10")

## [1] "A10"

res.aov <- dat1 %>% anova_test(A10 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05      ges
## 1         gender   1 115 29.719 2.89e-07     * 0.205000
## 2        country   1 115  0.185 6.68e-01       0.002000
## 3 gender:country   1 115  0.083 7.74e-01       0.000719

print("A11")

## [1] "A11"

res.aov <- dat1 %>% anova_test(A11 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05      ges
## 1         gender   1 115 20.702 1.34e-05     * 0.153000
## 2        country   1 115  0.021 8.85e-01       0.000184
## 3 gender:country   1 115  1.124 2.91e-01       0.010000

print("A12")

## [1] "A12"

res.aov <- dat1 %>% anova_test(A12 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 25.608 1.60e-06     * 0.182
## 2        country   1 115  0.460 4.99e-01       0.004
## 3 gender:country   1 115  0.331 5.66e-01       0.003

print("A13")

## [1] "A13"

res.aov <- dat1 %>% anova_test(A13 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd     F     p p<.05   ges
## 1         gender   1 115 8.431 0.004     * 0.068
## 2        country   1 115 0.861 0.355       0.007
## 3 gender:country   1 115 1.002 0.319       0.009

print("A14")

## [1] "A14"

res.aov <- dat1 %>% anova_test(A14 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 53.505 3.73e-11     * 0.318
## 2        country   1 115  1.899 1.71e-01       0.016
## 3 gender:country   1 115  0.509 4.77e-01       0.004

print("A15")

## [1] "A15"

res.aov <- dat1 %>% anova_test(A15 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F     p p<.05   ges
## 1         gender   1 115 10.627 0.001     * 0.085
## 2        country   1 115  0.536 0.466       0.005
## 3 gender:country   1 115  6.342 0.013     * 0.052

print("A16")

## [1] "A16"

res.aov <- dat1 %>% anova_test(A16 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F     p p<.05   ges
## 1         gender   1 115 10.766 0.001     * 0.086
## 2        country   1 115  0.952 0.331       0.008
## 3 gender:country   1 115  2.730 0.101       0.023

print("A17")

## [1] "A17"

res.aov <- dat1 %>% anova_test(A17 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd     F     p p<.05   ges
## 1         gender   1 115 4.534 0.035     * 0.038
## 2        country   1 115 1.570 0.213       0.013
## 3 gender:country   1 115 0.743 0.391       0.006

print("A18")

## [1] "A18"

res.aov <- dat1 %>% anova_test(A18 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 13.162 0.000427     * 0.103
## 2        country   1 115  1.409 0.238000       0.012
## 3 gender:country   1 115  0.131 0.718000       0.001

print("A19")

## [1] "A19"

res.aov <- dat1 %>% anova_test(A19 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05      ges
## 1         gender   1 115 17.894 4.71e-05     * 0.135000
## 2        country   1 115  0.075 7.85e-01       0.000647
## 3 gender:country   1 115  4.708 3.20e-02     * 0.039000

print("A20")

## [1] "A20"

res.aov <- dat1 %>% anova_test(A20 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 99.340 3.06e-17     * 0.463
## 2        country   1 115  5.063 2.60e-02     * 0.042
## 3 gender:country   1 115  0.312 5.78e-01       0.003

print("A21")

## [1] "A21"

res.aov <- dat1 %>% anova_test(A21 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115  1.615 2.06e-01       0.014
## 2        country   1 115 31.763 1.26e-07     * 0.216
## 3 gender:country   1 115  5.761 1.80e-02     * 0.048

print("A22")

## [1] "A22"

res.aov <- dat1 %>% anova_test(A22 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 44.759 8.49e-10     * 0.280
## 2        country   1 115  1.253 2.65e-01       0.011
## 3 gender:country   1 115  1.282 2.60e-01       0.011

print("A23")

## [1] "A23"

res.aov <- dat1 %>% anova_test(A23 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd     F     p p<.05   ges
## 1         gender   1 115 0.137 0.712       0.001
## 2        country   1 115 3.438 0.066       0.029
## 3 gender:country   1 115 0.642 0.425       0.006

print("A24")

## [1] "A24"

res.aov <- dat1 %>% anova_test(A24 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 36.318 2.07e-08     * 0.240
## 2        country   1 115  0.480 4.90e-01       0.004
## 3 gender:country   1 115  1.022 3.14e-01       0.009

print("A25")

## [1] "A25"

res.aov <- dat1 %>% anova_test(A25 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd     F     p p<.05      ges
## 1         gender   1 115 0.913 0.341       0.008000
## 2        country   1 115 0.875 0.352       0.008000
## 3 gender:country   1 115 0.027 0.870       0.000234

print("A26")

## [1] "A26"

res.aov <- dat1 %>% anova_test(A26 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 17.267 6.27e-05     * 0.131
## 2        country   1 115 17.959 4.58e-05     * 0.135
## 3 gender:country   1 115  5.024 2.70e-02     * 0.042

print("B1")

## [1] "B1"

res.aov <- dat1 %>% anova_test(B1 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 26.178 1.26e-06     * 0.185
## 2        country   1 115  3.480 6.50e-02       0.029
## 3 gender:country   1 115 10.181 2.00e-03     * 0.081

print("B2")

## [1] "B2"

res.aov <- dat1 %>% anova_test(B2 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 42.989 1.63e-09     * 0.272
## 2        country   1 115  1.722 1.92e-01       0.015
## 3 gender:country   1 115  0.145 7.04e-01       0.001

print("B3")

## [1] "B3"

res.aov <- dat1 %>% anova_test(B3 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05      ges
## 1         gender   1 115  7.120 9.00e-03     * 0.058000
## 2        country   1 115 44.589 9.03e-10     * 0.279000
## 3 gender:country   1 115  0.029 8.64e-01       0.000254

print("B4")

## [1] "B4"

res.aov <- dat1 %>% anova_test(B4 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 45.396 6.72e-10     * 0.283
## 2        country   1 115  0.556 4.57e-01       0.005
## 3 gender:country   1 115  0.150 6.99e-01       0.001

print("B5")

## [1] "B5"

res.aov <- dat1 %>% anova_test(B5 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd     F     p p<.05   ges
## 1         gender   1 115 0.522 0.472       0.005
## 2        country   1 115 4.827 0.030     * 0.040
## 3 gender:country   1 115 1.782 0.185       0.015

print("B6")

## [1] "B6"

res.aov <- dat1 %>% anova_test(B6 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 52.104 6.09e-11     * 0.312
## 2        country   1 115 14.987 1.80e-04     * 0.115
## 3 gender:country   1 115  8.722 4.00e-03     * 0.070

print("B7")

## [1] "B7"

res.aov <- dat1 %>% anova_test(B7 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd     F     p p<.05   ges
## 1         gender   1 115 4.947 0.028     * 0.041
## 2        country   1 115 2.671 0.105       0.023
## 3 gender:country   1 115 2.498 0.117       0.021

print("B8")

## [1] "B8"

res.aov <- dat1 %>% anova_test(B8 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd     F     p p<.05   ges
## 1         gender   1 115 6.982 0.009     * 0.057
## 2        country   1 115 7.163 0.009     * 0.059
## 3 gender:country   1 115 2.064 0.154       0.018

print("B9")

## [1] "B9"

res.aov <- dat1 %>% anova_test(B9 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 59.678 4.55e-12     * 0.342
## 2        country   1 115 15.684 1.30e-04     * 0.120
## 3 gender:country   1 115 11.286 1.00e-03     * 0.089

print("B10")

## [1] "B10"

res.aov <- dat1 %>% anova_test(B10 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 94.735 1.08e-16     * 0.452
## 2        country   1 115 16.659 8.29e-05     * 0.127
## 3 gender:country   1 115  7.700 6.00e-03     * 0.063

print("B11")

## [1] "B11"

res.aov <- dat1 %>% anova_test(B11 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 68.917 2.25e-13     * 0.375
## 2        country   1 115 15.380 1.50e-04     * 0.118
## 3 gender:country   1 115  0.287 5.93e-01       0.002

print("B12")

## [1] "B12"

res.aov <- dat1 %>% anova_test(B12 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 48.478 2.20e-10     * 0.297
## 2        country   1 115  3.261 7.40e-02       0.028
## 3 gender:country   1 115 13.651 3.38e-04     * 0.106

print("B13")

## [1] "B13"

res.aov <- dat1 %>% anova_test(B13 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 66.933 4.23e-13     * 0.368
## 2        country   1 115 12.985 4.65e-04     * 0.101
## 3 gender:country   1 115 15.608 1.35e-04     * 0.120

print("B14")

## [1] "B14"

res.aov <- dat1 %>% anova_test(B14 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 74.087 4.47e-14     * 0.392
## 2        country   1 115  9.405 3.00e-03     * 0.076
## 3 gender:country   1 115  4.202 4.30e-02     * 0.035

print("B15")

## [1] "B15"

res.aov <- dat1 %>% anova_test(B15 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 43.496 1.35e-09     * 0.274
## 2        country   1 115 13.473 3.68e-04     * 0.105
## 3 gender:country   1 115  8.485 4.00e-03     * 0.069

print("B16")

## [1] "B16"

res.aov <- dat1 %>% anova_test(B16 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 30.401 2.19e-07     * 0.209
## 2        country   1 115 15.481 1.43e-04     * 0.119
## 3 gender:country   1 115  1.135 2.89e-01       0.010

print("C1")

## [1] "C1"

res.aov <- dat1 %>% anova_test(C1 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 42.698 1.82e-09     * 0.271
## 2        country   1 115 13.880 3.04e-04     * 0.108
## 3 gender:country   1 115  0.385 5.36e-01       0.003

print("C2")

## [1] "C2"

res.aov <- dat1 %>% anova_test(C2 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 11.556 0.000929     * 0.091
## 2        country   1 115  2.055 0.154000       0.018
## 3 gender:country   1 115  0.236 0.628000       0.002

print("C3")

## [1] "C3"

res.aov <- dat1 %>% anova_test(C3 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05      ges
## 1         gender   1 115 19.945 1.87e-05     * 0.148000
## 2        country   1 115  0.031 8.61e-01       0.000267
## 3 gender:country   1 115  1.337 2.50e-01       0.011000

print("C4")

## [1] "C4"

res.aov <- dat1 %>% anova_test(C4 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd     F     p p<.05      ges
## 1         gender   1 115 8.702 0.004     * 0.070000
## 2        country   1 115 4.030 0.047     * 0.034000
## 3 gender:country   1 115 0.026 0.873       0.000223

print("C5")

## [1] "C5"

res.aov <- dat1 %>% anova_test(C5 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd     F     p p<.05   ges
## 1         gender   1 115 2.227 0.138       0.019
## 2        country   1 115 7.857 0.006     * 0.064
## 3 gender:country   1 115 5.537 0.020     * 0.046

print("C6")

## [1] "C6"

res.aov <- dat1 %>% anova_test(C6 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd     F     p p<.05   ges
## 1         gender   1 115 9.583 0.002     * 0.077
## 2        country   1 115 5.435 0.021     * 0.045
## 3 gender:country   1 115 3.688 0.057       0.031

print("C7")

## [1] "C7"

res.aov <- dat1 %>% anova_test(C7 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd         F        p p<.05      ges
## 1         gender   1 115 20.627000 1.38e-05     * 1.52e-01
## 2        country   1 115  0.000261 9.87e-01       2.27e-06
## 3 gender:country   1 115  0.057000 8.11e-01       4.97e-04

print("C8")

## [1] "C8"

res.aov <- dat1 %>% anova_test(C8 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd     F     p p<.05      ges
## 1         gender   1 115 7.003 0.009     * 5.70e-02
## 2        country   1 115 8.107 0.005     * 6.60e-02
## 3 gender:country   1 115 0.008 0.928       7.17e-05

print("C9")

## [1] "C9"

res.aov <- dat1 %>% anova_test(C9 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 14.280 2.51e-04     * 0.110
## 2        country   1 115 19.321 2.48e-05     * 0.144
## 3 gender:country   1 115 13.670 3.35e-04     * 0.106

print("D1")

## [1] "D1"

res.aov <- dat1 %>% anova_test(D1 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd     F     p p<.05   ges
## 1         gender   1 115 8.042 0.005     * 0.065
## 2        country   1 115 0.586 0.446       0.005
## 3 gender:country   1 115 2.485 0.118       0.021

print("D2")

## [1] "D2"

res.aov <- dat1 %>% anova_test(D2 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05      ges
## 1         gender   1 115 14.882 1.89e-04     * 0.115000
## 2        country   1 115 40.613 3.98e-09     * 0.261000
## 3 gender:country   1 115  0.046 8.31e-01       0.000396

print("D3")

## [1] "D3"

res.aov <- dat1 %>% anova_test(D3 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 15.653 0.000132     * 0.120
## 2        country   1 115 11.709 0.000862     * 0.092
## 3 gender:country   1 115  6.125 0.015000     * 0.051

print("D4")

## [1] "D4"

res.aov <- dat1 %>% anova_test(D4 ~ gender * country)

## Coefficient covariances computed by hccm()

res.aov

## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1         gender   1 115 18.486 3.60e-05     * 0.138
## 2        country   1 115 42.856 1.72e-09     * 0.271
## 3 gender:country   1 115  1.428 2.34e-01       0.012

まとめ

交互作用あり： A4, A7, A15, A19, A21, A26 B1, B6, B9, B10, B12, B13, B14, B15 C5, C9 D3

figure

GROUP A

bxp <- ggboxplot(
  dat1, x = "gender", y = "A4",
  color = "country", palette = "jco", title = "A4"
)
bxp

bxp <- ggboxplot(
  dat1, x = "gender", y = "A7",
  color = "country", palette = "jco", title = "A7"
)
bxp

bxp <- ggboxplot(
  dat1, x = "gender", y = "A15",
  color = "country", palette = "jco", title = "A15"
)
bxp

bxp <- ggboxplot(
  dat1, x = "gender", y = "A19",
  color = "country", palette = "jco", title = "A19"
)
bxp

bxp <- ggboxplot(
  dat1, x = "gender", y = "A21",
  color = "country", palette = "jco", title = "A21"
)
bxp

bxp <- ggboxplot(
  dat1, x = "gender", y = "A26",
  color = "country", palette = "jco", title = "A26"
)
bxp

GROUP B

bxp <- ggboxplot(
  dat1, x = "gender", y = "B1",
  color = "country", palette = "jco", title = "B1"
)
bxp

bxp <- ggboxplot(
  dat1, x = "gender", y = "B6",
  color = "country", palette = "jco", title = "B6"
)
bxp

bxp <- ggboxplot(
  dat1, x = "gender", y = "B9",
  color = "country", palette = "jco", title = "B9"
)
bxp

bxp <- ggboxplot(
  dat1, x = "gender", y = "B10",
  color = "country", palette = "jco", title = "B10"
)
bxp

bxp <- ggboxplot(
  dat1, x = "gender", y = "B12",
  color = "country", palette = "jco", title = "B12"
)
bxp

bxp <- ggboxplot(
  dat1, x = "gender", y = "B13",
  color = "country", palette = "jco", title = "B13"
)
bxp

bxp <- ggboxplot(
  dat1, x = "gender", y = "B14",
  color = "country", palette = "jco", title = "B14"
)
bxp

bxp <- ggboxplot(
  dat1, x = "gender", y = "B15",
  color = "country", palette = "jco", title = "B15"
)
bxp

GROUP C

bxp <- ggboxplot(
  dat1, x = "gender", y = "C5",
  color = "country", palette = "jco", title = "C5"
)
bxp

bxp <- ggboxplot(
  dat1, x = "gender", y = "C9",
  color = "country", palette = "jco", title = "C9"
)
bxp

GROUP D

bxp <- ggboxplot(
  dat1, x = "gender", y = "D3",
  color = "country", palette = "jco", title = "D3"
)
bxp