错误：在R中，自定义函数的主体中包含了~（波浪号）和/或$（美元符号）。

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英文:

Error: Custom function that has ~ (tilde) and/or $ (dollar sign) in the body in r

问题

Here are the translated parts of your code:

  1. 我正在尝试通过在R中使用自定义/用户定义的函数来简化我的代码。
  3. 我想将以下内容转化为函数：
  5. ```R
  6. shapiro <- shapiro.test(data$count)
  1. if(shapiro$p.value > 0.05) {
  2.   require(tidyverse)
  3.   bart <- bartlett.test(count ~ species, data=data)
  4. } 
  1. if ((shapiro$p.value > 0.05) && exists("bart")){
  2.   if(bart$p.value > 0.05) { 
  3.     OneWay <- aov(count ~ species, data = data) 
  4.     oneway <- summary(OneWay) #normal distribution and equal variance
  5.   } else  {
  6.     welch <- oneway.test(count ~ species, data = data) #Welch ANOVA, normal distribution, unequal variance
  7.   }
  8. } else {
  9.   fligner <- fligner.test(count ~ species, data = data)
  10.    #if not normal distribution, perform fligner-killen homogeneity of variance test
  11. }

第一组我尝试过的函数：

  1. perform_shapiro <- function(df, contvar) {
  2.    shapiro.test(df$contvar)
  3. }
  4. perform_shapiro(df = data, contvar = count)

其中返回错误：Error in shapiro.test(df$contvar) : is.numeric(x) is not TRUE.

第二组成功了：

  1. perform_barlett <- function(df, contvar, catvar) {
  2.   if(shapiro$p.value > 0.05) {
  3.     require(tidyverse)
  4.     bart <- bartlett.test(contvar ~ catvar, data=df)
  5.     return(bart)
  6.   }
  7. }
  8. perform_bartlett(data, count, species)

但第三组也出现了新错误：

  1. perform_oneway_welch_fligner <- function(df, contvar, catvar){
  2.       if ((shapiro$p.value > 0.05) && exists("bart")){
  3.       if(bart$p.value > 0.05) { 
  4.         OneWay <- aov(contvar ~ catvar, data = df) 
  5.         oneway <- summary(OneWay) #normal distribution and equal variance
  6.         print(oneway)
  7.       } else  {
  8.         welch <- oneway.test(contvar ~ catvar, data = df) #Welch ANOVA, normal distribution, unequal variance
  9.         print(welch)
  10.       }
  11.     } else {
  12.       fligner <- fligner.test(contvar ~ catvar, data = df)
  13.        #if not normal distribution, perform fligner-killen homogeneity of variance test
  14.       print(fligner)
  15.     } 
  16. }
  17. perform_oneway_welch_fligner(data, count, species)

Error in eval(predvars, data, env) : object 'count' not found.

Data

  1. data <- iris
  2. # rename columns
  3. names(data)[names(data) == "Sepal.Length"] <- "count"

FYI shapiro is < 0.05, so bart should not exist, hence fligner should be returned.
Contvar = continuous variable, catvar = categorical variable.

我最初认为错误是由于$和~，但第二个函数效果很好，并且它内部有一个波浪线公式，所以现在我不确定。谢谢！

  2. Please note that I have provided the translated parts of your code as requested. If you have any specific questions or need further assistance with your code, feel free to ask.
  4. <details>
  5. <summary>英文:</summary>
  7. I am trying to simplify my code by using custom/user-defined functions in r.
  9. I want to turn the following into functions:

shapiro <- shapiro.test(data$count)

if(shapiro$p.value > 0.05) {
require(tidyverse)
bart <- bartlett.test(count ~ species, data=data)
}

if ((shapiro$p.value > 0.05) && exists("bart")){
if(bart$p.value > 0.05) {
OneWay <- aov(count ~ species, data = data)
oneway <- summary(OneWay) #normal distibution and equal varaince
} else {
welch <- oneway.test(count ~ species, data = data) #Welch ANOVA, normal distribution, unequal variance
}
} else {
fligner <- fligner.test(count ~ species, data = data)
#if not normal distribution, perform fligner-killen homogeneity of variance test
}

  2. The first set I have tried:

perform_shapiro <- function(df, contvar) {
shapiro.test(df$contvar)
}
perform_shapiro(df = data, contvar = count)

  1. Which returns the error: Error in shapiro.test(df$contvar) : is.numeric(x) is not TRUE.
  3. The second set is successful with:

perform_barlett <- function(df, contvar, catvar) {
if(shapiro$p.value > 0.05) {
require(tidyverse)
bart <- bartlett.test(contvar ~ catvar, data=df)
return(bart)
}
}
perform_bartlett(data, count, species)

  1. But the third also has a new error:

perform_oneway_welch_fligner <- function(df, contvar, catvar){
if ((shapiro$p.value > 0.05) && exists("bart")){
if(bart$p.value > 0.05) {
OneWay <- aov(contvar ~ catvar, data = df)
oneway <- summary(OneWay) #normal distibution and equal varaince
print(oneway)
} else {
welch <- oneway.test(contvar ~ catvar, data = df) #Welch ANOVA, normal distribution, unequal variance
print(welch)
}
} else {
fligner <- fligner.test(contvar ~ catvar, data = df)
#if not normal distribution, perform fligner-killen homogeneity of variance test
print(fligner)
}
}
perform_oneway_welch_fligner(data, count, species)

  2. Error in eval(predvars, data, env) : object &#39;count&#39; not found.
  4. #### Data

data <- iris

rename columns

names(data)[names(data) == "Sepal.Length"] <- "count"

  2. FYI shapiro is &lt; 0.05, so bart should not exist, hence fligner should be returned. 
  3. Contvar = continuous variable, catvar = categorical variable.
  5. I thought the errors at first were due to $ and ~, but the second function works well and it has a tilde formula inside, so now I am unsure. Thanks!
  7. </details>
  10. # 答案1
  11. **得分**: 1
  13. 以下是代码的中文翻译：
  15. ``` r
  16. # 执行Shapiro-Wilk正态性检验
  17. perform_shapiro <- function(df, contvar) {
  18.   contvar <- as.character(substitute(contvar))
  19.   shapiro.test(df[[contvar]])
  20. }
  22. # 执行Bartlett方差齐性检验
  23. perform_bartlett <- function(df, contvar, catvar) {
  24.   contvar <- as.character(substitute(contvar))
  25.   catvar <- as.character(substitute(catvar))
  26.   fmla <- reformulate(catvar, contvar)
  27.   shapiro <- shapiro.test(df[[contvar]])
  28.   if (shapiro$p.value > 0.05) {
  29.     bartlett.test(fmla, data = df)
  30.   }
  31. }
  33. # 执行One-Way ANOVA或Welch ANOVA或Fligner-Killeen方差齐性检验
  34. perform_oneway_welch_fligner <- function(df, contvar, catvar){
  35.   contvar <- as.character(substitute(contvar))
  36.   catvar <- as.character(substitute(catvar))
  37.   fmla <- reformulate(catvar, contvar)
  38.   shapiro <- shapiro.test(df[[contvar]])
  39.   bart <- bartlett.test(fmla, data = df)
  40.   if ((shapiro$p.value > 0.05) && !is.null(bart)){
  41.     if(bart$p.value > 0.05) { 
  42.       OneWay <- aov(fmla, data = df) 
  43.       summary(OneWay) # 正态分布和方差齐性
  44.     } else  {
  45.       # Welch ANOVA，正态分布，不等方差
  46.       oneway.test(fmla, data = df) 
  47.     }
  48.   } else {
  49.     # 如果不是正态分布，则执行Fligner-Killeen方差齐性检验
  50.     fligner.test(fmla, data = df)
  51.   } 
  52. }
  54. # 数据集为鸢尾花数据
  55. data <- iris
  56. names(data)[names(data) == "Sepal.Length"] <- "count"
  58. # 执行Shapiro-Wilk正态性检验
  59. perform_shapiro(df = data, contvar = count)
  60. #> 
  61. #>  Shapiro-Wilk正态性检验
  62. #> 
  63. #> 数据：df[[contvar]]
  64. #> W = 0.97609, p-value = 0.01018
  66. # 返回NULL（不可见）
  67. perform_bartlett(data, count, Species)
  69. # 执行One-Way ANOVA或Welch ANOVA或Fligner-Killeen方差齐性检验
  70. perform_oneway_welch_fligner(data, count, Species)
  71. #> 
  72. #>  Fligner-Killeen方差齐性检验
  73. #> 
  74. #> 数据：按Species分类的count
  75. #> Fligner-Killeen:中位数卡方 = 11.618，自由度 = 2，p值 = 0.003

于2023-01-06使用reprex v2.0.2创建

英文:

Here is a full solution, answering to the OP's comments.

  1. perform_shapiro &lt;- function(df, contvar) {
  2.   contvar &lt;- as.character(substitute(contvar))
  3.   shapiro.test(df[[contvar]])
  4. }
  5. perform_bartlett &lt;- function(df, contvar, catvar) {
  6.   contvar &lt;- as.character(substitute(contvar))
  7.   catvar &lt;- as.character(substitute(catvar))
  8.   fmla &lt;- reformulate(catvar, contvar)
  9.   shapiro &lt;- shapiro.test(df[[contvar]])
  10.   if(shapiro$p.value &gt; 0.05) {
  11.     bartlett.test(fmla, data = df)
  12.   }
  13. }
  14. perform_oneway_welch_fligner &lt;- function(df, contvar, catvar){
  15.   contvar &lt;- as.character(substitute(contvar))
  16.   catvar &lt;- as.character(substitute(catvar))
  17.   fmla &lt;- reformulate(catvar, contvar)
  18.   shapiro &lt;- shapiro.test(df[[contvar]])
  19.   bart &lt;- bartlett.test(fmla, data = df)
  20.   if ((shapiro$p.value &gt; 0.05) &amp;&amp; !is.null(bart)){
  21.     if(bart$p.value &gt; 0.05) { 
  22.       OneWay &lt;- aov(fmla, data = df) 
  23.       summary(OneWay) #normal distibution and equal varaince
  24.     } else  {
  25.       # Welch ANOVA, normal distribution, unequal variance
  26.       oneway.test(fmla, data = df) 
  27.     }
  28.   } else {
  29.     #if not normal distribution, perform fligner-killen homogeneity of variance test
  30.     fligner.test(fmla, data = df)
  31.   } 
  32. }
  34. data &lt;- iris
  35. names(data)[names(data) == &quot;Sepal.Length&quot;] &lt;- &quot;count&quot;
  37. perform_shapiro(df = data, contvar = count)
  38. #&gt; 
  39. #&gt;  Shapiro-Wilk normality test
  40. #&gt; 
  41. #&gt; data:  df[[contvar]]
  42. #&gt; W = 0.97609, p-value = 0.01018
  44. # returns NULL invisiby
  45. perform_bartlett(data, count, Species)
  47. perform_oneway_welch_fligner(data, count, Species)
  48. #&gt; 
  49. #&gt;  Fligner-Killeen test of homogeneity of variances
  50. #&gt; 
  51. #&gt; data:  count by Species
  52. #&gt; Fligner-Killeen:med chi-squared = 11.618, df = 2, p-value = 0.003

<sup>Created on 2023-01-06 with reprex v2.0.2</sup>

答案2

得分: 0

第一个函数可以使用匿名函数来解决：

  1. perform_shapiro <- function(x) {
  2.    shapiro.test(x)
  3. }
  4. perform_shapiro(data$count)

（抱歉，在玩弄了一些后，我发现这个方法有效！但在第三个函数中（即用 x 替换公式时）不起作用）

英文:

The first function could be solved using an anonymous function

  1. perform_shapiro &lt;- function(x) {
  2.    shapiro.test(x)
  3. }
  4. perform_shapiro(data$count)

(sorry after playing around a bit I figure out that this works! It did not work in the third function (i.e. replacing the formulas with x))

huangapple
  • 本文由 发表于 2023年1月6日 13:31:35
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