SPSS中的标准残差不进行R rstandard(lm())处理

问题描述

在寻找与R相关的解决方案时，我发现R和SPSS(版本24)在简单的线性模型中计算标准化残差时存在一些不一致之处.

While looking for a R related solution I found some inconsistency between R and SPSS (ver. 24) in computing standardized residuals in a simple linear model.

似乎SPSS所说的标准残差与R 学生残差

It appears that what SPSS calls standarized residuals matches R studentized residuals

我想假设某个地方存在软件错误，但显然这两个程序之间存在差异.

I'm far for assuming there is a software bug somewhere, but clearly things differ between those two programs.

看看这个例子

#generate data in R
set.seed(111)
y = rnorm(20, 0, 1) 
x = rnorm(20, 1, 1)

#calculate and standarized residuals
zresid<- rstandard(lm(y ~ x))
sresid<- rstudent(lm( y ~ x))

#make data frame
sampleData <- data.frame(y, x, zresid, sresid)

#save data for SPSS
library(foreign)
write.foreign(sampleData, "~/sampleData.sav",   package="SPSS") 

然后，在SPSS中，在所有窗口中单击以导入数据，并设置保存的线性回归ZRE和SRE残差.

Then, in SPSS click your way through all the windows to import data and set up a linear regression ZRE and SRE residuals saved.

#load data to spss via syntax 
GET DATA  /TYPE=TXT
  /FILE="~\sampleData.sav"
  /DELCASE=LINE
  /DELIMITERS=","
  /ARRANGEMENT=DELIMITED
  /FIRSTCASE=1
  /DATATYPEMIN PERCENTAGE=95.0
  /VARIABLES=
  y F8.0
  x F8.0
  zresid F8.0
  sresid F8.0
  /MAP.
RESTORE.

#run a simple regression with standarized residuals (ZRESID) and studentized residuals (SRESID)

REGRESSION
  /MISSING LISTWISE
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN 
  /DEPENDENT y
  /METHOD=ENTER x
  /SAVE ZRESID SRESID.

我生气(或愚蠢)还是这里确实出了什么问题?

Am I mad (or dumb) or indeed something is wrong here?

推荐答案

我做了更多的事情:结论如下:

I did a bit more:Here are the conclusions:

r stats::rstandard = MASS::stdres = SPSS studentized residual
r z score of resid or residuals = SPSS z score of unstandardized residual

这是我的代码:

#generate data in R
set.seed(111)
y = rnorm(20, 0, 1) 
x = rnorm(20, 1, 1)

#calculate and standarized residuals
stats_rstudent = stats::rstudent(lm( y ~ x))
stats_rstandard = stats::rstandard(lm(y ~ x))
MASS_stdres = MASS::stdres(lm( y ~ x))
scale_resid = as.vector(scale(resid(lm(y ~ x)),center=T,scale=T))
scale_residuals = as.vector(scale(residuals(lm(y ~ x)),center=T,scale=T))

#make data frame
sampleData <- data.frame(y, x, stats_rstudent, stats_rstandard, MASS_stdres, scale_resid, scale_residuals)

#save data for SPSS
library(foreign)
write.foreign(sampleData, "sampleData.sav",   package="SPSS")

SPSS语法:

REGRESSION
  /MISSING LISTWISE
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN 
  /DEPENDENT y
  /METHOD=ENTER x
  /SAVE RESID ZRESID SRESID.

* calc z score of resid.
descriptives RES_1_Unstandardized_Residual/save.

formats stats_rstudent(f11.6).
formats stats_rstandard(f11.6).
formats MASS_stdres(f11.6).
formats scale_resid(f11.6).
formats scale_residuals(f11.6).
formats ZRE_1_Standardized_Residual(f11.6).
formats SRE_1Studentized_Residual(f11.6).
formats RES_1_Unstandardized_Residual(f11.6).
formats Zscore_RES_1_Unstandardized_Residual(f11.6).

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