df<-read.csv("df.csv")
df3<-df[,c(6,34:38)]
str(df3)
## 'data.frame': 115 obs. of 6 variables:
## $ Career : int 5 3 3 3 2 5 3 2 1 4 ...
## $ comply_1: int 1 2 1 1 1 1 1 3 3 1 ...
## $ comply_2: int 2 2 1 2 1 2 1 3 3 1 ...
## $ comply_3: int 1 2 1 1 1 1 1 3 3 1 ...
## $ comply_4: int 2 2 1 1 1 1 1 3 3 1 ...
## $ comply_5: int 1 2 1 1 1 1 1 3 3 1 ...
df3$Career<-as.factor(df3$Career)
table(df3$Career)
##
## 1 2 3 4 5 15
## 19 46 27 13 9 1
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.2 v dplyr 1.0.6
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1
## Warning: 패키지 'ggplot2'는 R 버전 4.1.3에서 작성되었습니다
## Warning: 패키지 'tidyr'는 R 버전 4.1.3에서 작성되었습니다
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
df4<-df3 %>% filter(Career!="15")
names(df4)
## [1] "Career" "comply_1" "comply_2" "comply_3" "comply_4" "comply_5"
ga.out2<-lm(comply_1~Career,data=df4)
anova(ga.out2)
## Analysis of Variance Table
##
## Response: comply_1
## Df Sum Sq Mean Sq F value Pr(>F)
## Career 4 4.241 1.06027 2.8364 0.02801 *
## Residuals 105 39.250 0.37381
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
library(multcomp)
## Warning: 패키지 'multcomp'는 R 버전 4.1.2에서 작성되었습니다
## 필요한 패키지를 로딩중입니다: mvtnorm
## 필요한 패키지를 로딩중입니다: survival
## 필요한 패키지를 로딩중입니다: TH.data
## Warning: 패키지 'TH.data'는 R 버전 4.1.2에서 작성되었습니다
## 필요한 패키지를 로딩중입니다: MASS
##
## 다음의 패키지를 부착합니다: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
##
## 다음의 패키지를 부착합니다: 'TH.data'
## The following object is masked from 'package:MASS':
##
## geyser
go.out3<-glht(ga.out2,linfct = mcp(Career="Tukey"))
summary(go.out3)
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lm(formula = comply_1 ~ Career, data = df4)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2 - 1 == 0 -0.42935 0.17745 -2.420 0.1133
## 3 - 1 == 0 -0.58654 0.19427 -3.019 0.0246 *
## 4 - 1 == 0 -0.50962 0.22829 -2.232 0.1694
## 5 - 1 == 0 -0.68056 0.25475 -2.671 0.0622 .
## 3 - 2 == 0 -0.15719 0.15001 -1.048 0.8262
## 4 - 2 == 0 -0.08027 0.19204 -0.418 0.9932
## 5 - 2 == 0 -0.25121 0.22285 -1.127 0.7846
## 4 - 3 == 0 0.07692 0.20768 0.370 0.9957
## 5 - 3 == 0 -0.09402 0.23646 -0.398 0.9944
## 5 - 4 == 0 -0.17094 0.26512 -0.645 0.9657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
ga.out3<-lm(comply_2~Career,data=df4)
anova(ga.out3)
## Analysis of Variance Table
##
## Response: comply_2
## Df Sum Sq Mean Sq F value Pr(>F)
## Career 4 3.583 0.89581 2.2537 0.06821 .
## Residuals 105 41.735 0.39748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
library(multcomp)
ga.out4<-lm(comply_3~Career,data=df4)
anova(ga.out4)
## Analysis of Variance Table
##
## Response: comply_3
## Df Sum Sq Mean Sq F value Pr(>F)
## Career 4 4.697 1.17426 2.717 0.03376 *
## Residuals 103 44.516 0.43219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ga.out5<-lm(comply_4~Career,data=df4)
anova(ga.out5)
## Analysis of Variance Table
##
## Response: comply_4
## Df Sum Sq Mean Sq F value Pr(>F)
## Career 4 3.251 0.81274 1.8448 0.126
## Residuals 102 44.936 0.44055
ga.out6<-lm(comply_5~Career,data=df4)
anova(ga.out6)
## Analysis of Variance Table
##
## Response: comply_5
## Df Sum Sq Mean Sq F value Pr(>F)
## Career 4 8.28 2.0712 0.5257 0.7171
## Residuals 103 405.82 3.9400