CODEWITHSUNDEEP
rregressionspss
Kunio
Kunio

Reputation: 149

Differences between stepAIC in R and stepwise in SPSS

I am trying to learn R after learning SPSS and using SPSS for my statistics on a couple papers. I have been using my data to help me learn and understand R as well. In my data, i had to find some Linear Regressions in SPSS using a stepwise comparison to eliminate variables that do not "fit" the model. I tried using stepAIC with the MASS package, because i thought it was the equivalent, and got some completely different output, as well as stuff i did not understand and had to look up. My question is, what are the differences between stepwise in SPSS and stepAIC? (is stepwise more conservative than stepAIC?) Is there a way to write stepAIC code that would be equivalent to stepwise? Or is there a different package that could help me out?

Here is my code:

mydata <- read.csv("Eric.csv")
AveSBP <- mydata[, 3]
MaxVi <- mydata[, 7]
PeakForce <- mydata[, 8]
MaxPO <- mydata[, 9]
Height <- mydata[, 10]
BMI <- mydata[, 11]
NeckCirc <- mydata[, 12]
ArmLength <- mydata[, 13]
ArmSpan <- mydata[, 14]
WaistCircum <- mydata[, 15]
LegLength <- mydata[, 16]
FatAth <- mydata[, 17]
Diff <- mydata[, 18]
Ratio <- mydata[, 19]
lm1 <- lm(AveSBP ~ MaxVi + PeakForce + MaxPO + Height + BMI + NeckCirc + ArmLength + ArmSpan + WaistCircum + LegLength + FatAth + Diff + Ratio)
summary(lm1)
stepAIC(lm1, directions="both")

I am running them on Windows 7 Pro x64, R x64 3.1.0, and SPSS x64 v21.

Upvotes: 2

Views: 3082

Answers (1)

Andy W
Andy W

Reputation: 5089

SPSS does not use the AIC criteria for stepwise (either forward or backward) in linear regression, so it is not guaranteed that they will converge to the same solution. See the SPSS help files on regression and the F-value criteria it uses.

Some quick googling provides this answer by Joris Meys for an R function to replicate this type of selection criteria.

Obligatory note, do you really want to use stepwise regression to select the model?

Upvotes: 3

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