Question

我的数据：

Subject Test1 Test2 Test3 Test4  
    1   8   7   1   6  
    2   9   5   2   5  
    3   6   2   3   8  
    4   5   3   1   9  
    5   8   4   5   8  
    6   7   5   6   7  
    7   10  2   7   2  
    8   12  6   8   1

mydata＆lt; - read.csv（＆＃34; myData.csv＆＃34;，header = TRUE）
mydataframe＆lt; - data.frame（mydata）

我将以下函数应用于我的数据框的每个列变量，其中包含4列：

qqfunc <- function(df,df_var) {    
          y <- quantile(df$df_var, c(0.25, 0.75))     
          x <- qnorm( c(0.25, 0.75))       
          slope <- diff(y) / diff(x)      
          int <- y[1] - slope * x[1]      
          ggplot() + aes(sample=df$df_var) + stat_qq(distribution=qnorm) +   
          geom_abline(intercept=int, slope=slope) + ylab("QQ")    
}

当我跑步时

qqfunc(mydataframe, Test1)

出现警告消息：

删除了包含缺失值的1行（geom_abline）。

结果，QQ图不会出现在pdf输出文件中。我不确定问题是在解析中还是在函数ggplot（）中。

PS：
1.奇怪的是，如果我在函数之外运行以下命令，它就可以工作：

y <- quantile(mydataframe$Test1, c(0.25, 0.75)) # Find the 1st and 3rd quartiles  
x <- qnorm( c(0.25, 0.75)) # Find the matching normal values on the x-axis
slope <- diff(y) / diff(x) # Compute the line slope
int <- y[1] - slope * x[1] # Compute the line intercept # Generate normal q-q plot   
ggplot() + aes(sample=mydataframe$Test1) + stat_qq(distribution=qnorm) + 
  geom_abline(intercept=int, slope=slope) + ylab("QQ Test1")

2.如果我运行这些命令：

qqfunc <- function(df, df_var) {   
  y <- quantile(df[[df_var]], c(0.25, 0.75))   
  x <- qnorm( c(0.25, 0.75))  
  slope <- diff(y) / diff(x)  
  int <- y[1] - slope * x[1]  
  ggplot() + aes(sample=df[[df_var]]) + stat_qq(distribution=qnorm) + 
    geom_abline(intercept=int, slope=slope) + ylab("QQ")   
}
qqfunc(mydataframe, Test1)

错误讯息：

（function（x，i，exact）if（is.matrix（i））as.matrix（x）[[i]] else .subset2（x，：对象＆＃39;测试1＆＃39;找不到

完整代码：

library(Hmisc)  
library(ggplot2)  
library(boot)  
library(polycor)  
library(ggm)  
library(gdata)  
library(readxl)  
library(csvread)  
library (plyr)  
library(psych)  
library(mice)  
library(VIM)  
library(ez)   
library(reshape)   
library(multcomp)  
library(nlme)  
library(pastecs)  
library(WRS2)  
library(dplyr)  

mydata <- read.csv("mydata.csv", header = TRUE) # CSV  
mydataframe <- data.frame(mydata)  

y <- quantile(mydataframe$Test1, c(0.25, 0.75)) # Find the 1st and 3rd quartiles   
x <- qnorm( c(0.25, 0.75)) # Find the matching normal values on the x-axis   
slope <- diff(y) / diff(x) # Compute the line slope   
int <- y[1] - slope * x[1] # Compute the line intercept # Generate normal q-q plot   
ggplot() + aes(sample=mydataframe$Test1) + stat_qq(distribution=qnorm) + geom_abline(intercept=int, slope=slope) + ylab("QQ Test 1") 

qqfunc <- function(df, df_var) {     
         y <- quantile(df[[df_var]], c(0.25, 0.75))   
         x <- qnorm( c(0.25, 0.75))   
         slope <- diff(y) / diff(x)   
         int <- y[1] - slope * x[1]   
         ggplot() + aes(sample=df[[df_var]]) + stat_qq(distribution=qnorm) + 
           geom_abline(intercept=int, slope=slope) + ylab("QQ")   
}
qqfunc(mydataframe, Test1)

Answer 1

与我合作。你应该听从我的建议并建议@Tung发布样本数据集。既然没有，这是完整的工作代码。

library(ggplot2)

qqfunc <- function(df, df_var) {    
          y <- quantile(df[[df_var]], c(0.25, 0.75))     
          x <- qnorm( c(0.25, 0.75))       
          slope <- diff(y) / diff(x)      
          int <- y[1] - slope * x[1]      
          ggplot() + aes(sample=df[[df_var]]) + stat_qq(distribution=qnorm) +   
              geom_abline(intercept=int, slope=slope) + ylab("QQ")    
}  

set.seed(3551)    # Make the results reproducible
n <- 100
mydataframe <- data.frame(X = rnorm(n))

column_variable <- "X"

qqfunc(mydataframe, column_variable)

用于计算R中QQ图的分位数和qnorm的函数

1 个答案: