Standarized residuals in SPSS not maching R rstandard(lm())

问题

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.

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

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

Have a look at this example

#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")

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.

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

回答1:

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

Here are my codes:

#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 syntax:

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).

回答2:

Not very familiar with SPSS, but I ran the model R and Stata, getting the same residuals. So the problem is on the SPSS end. My guess is that it looks like you have called a stepwise regression command in SPSS. Could you try simply:

REGRESSION
  /DEPENDENT y  
  /METHOD=ENTER x
  /SAVE ZRESID SRESID.

And see if that works?

回答3:

Following @JKP suggestion I went though SPSS Algorithm manual and on page 853 (Regression Algorithm chapter) we can find, that Standardized Residuals saved via simple regression analysis are computed as follows:

来源：https://stackoverflow.com/questions/40062482/standarized-residuals-in-spss-not-maching-r-rstandardlm

标签

linear-regression

spss