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Question

我对R很新，这是我在第一步中遇到的事情。

在一些论文中，需要提出一个相关变量的主要对角线相关变量的可靠性（Cronbach Alpha）的相关图（其中相关性为1）

一个例子可能是5个相关的心理测量指标

Job_ins（平均4项）

就业能力（平均4项）

INT_to_quit（平均4项）

Mobility_pref（平均5项）

Career_self_mgmt（平均8项）

请注意，在原生cor（）R函数中，主要诊断（度量与自身的相关性）显示为1.

我想做的是在主对角线中呈现内部可靠性（cronbach alphas）。

任何想法？

萨尔

Answer 1

如果我理解正确，这是我的（长期）解决方案。

#Loading pkgs
require(tidyverse)
require(Hmisc)
require(psych)

#Creating example data
set.seed(123) #making the random data reproducible
#Creating the items for each subject
job <- sim.congeneric(N=200, short = FALSE, low = 1, high=10,categorical=TRUE)
Employability <- sim.congeneric(N=200, short = FALSE, low = 1, high=10,categorical=TRUE)
Career <- sim.congeneric(N=200, short = FALSE, low = 1, high=10,categorical=TRUE)
#Arranging the data to one data.frame
df <- data.frame(as.data.frame(job$observed) %>% 
                   set_names(c("job1", "job2", "job3", "job4")), 
                 as.data.frame(Employability$observed) %>% 
                   set_names(c("Employability1", "Employability2",
                               "Employability3", "Employability4")), 
                 as.data.frame(job$observed) %>% 
                   set_names(c("Career1", "Career2", "Career3", "Career4")))


#Creating a vector with the Cronbach's alpha for each subject
CronAlpha <- c(
  alpha(df %>% 
          select(job1, job2, job3, job4))$total$std.alpha,
  alpha(df %>% 
          select(Employability1, Employability2,
                 Employability3, Employability4))$total$std.alpha,
  alpha(df %>% 
          select(Career1, Career2,
                 Career3, Career4))$total$std.alpha)

#Calculating the mean for each subject, than the correlations
Correlation <- df %>% 
  #Calculating the means
  mutate(job = rowMeans(data.frame(job1, job2, job3, job4), na.rm = TRUE), 
         Employability =rowMeans(data.frame(Employability1, Employability2,
                                            Employability3, Employability4), na.rm = TRUE),
         Career =rowMeans(data.frame(Career1, Career2,
                                     Career3, Career4), na.rm = TRUE)) %>% 
  #Selecting only the vars that I want for the correlation matrix
  select(job, Employability, Career) %>%
  as.matrix() %>% 
  rcorr()

#Extracting the Pearson's r
CorrelationRs <- Correlation$r 

#Looping through the correlation data.frame and replacing with
# Cronbach's alpha
i <- 1
for (i in 1:nrow(CorrelationRs)) {
  CorrelationRs[i, i] <- CronAlpha[i]

}

CorrelationRs

修改

我应该使用diag()而不是使用循环。

diag(CorrelationRs) <- CronAlpha

在主对角线上具有可靠性的相关图

1 个答案:

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