Hello R users,
I am trying to run a three factor ANOVA on a data set with unequal sample sizes.
I fit the data to a 'lm' object and used the Anova function from the 'car' package with the 'type=III' option to get type III sums of squares. I also set the contrast coding option to 'options(contrasts = c("contr.sum", "contr.poly"))' as cautioned in Jon Fox's book "An R and S-plus Companion to Applied Regression'.
Is there anything else that I need to consider when using the type III option with the Anova function?
When I run the same data set in SPSS with General Linear Model and type III sums of squares, the sums of squares are different enough that one of the main effect terms is significant in the R table and not in the SPSS table. I found a similar discrepancy with a different data set, only SPSS showed a significant interaction effect while, while the 'Anova' function did not.
I also compared the results from SPSS those from the 'anova' function in the base package, and the results are nearly identical. I would expect the two methods with type III sums of squares to be more similar, does anyone have any ideas as to why that was not the case? I am hoping to not go back to SPSS at this point, so am trying to decide which of the two R functions is most appropriate for me (and defensible, considering the unequal sample sizes).
Thank you in advance for any ideas you may have!
Karla
Karla Sartor Montana State University - LRES [EMAIL PROTECTED]
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