Hi John, Your assumptions are correct and those examples were very helpful, thanks!
I think I'm almost there, but I'm screwing up something with the within-subjects factor (the example has two, I only have one). See below. Why am I not seeing my within-subjects factors in the ANOVA report? > Dat2 Subj Age Sex Time HippV 1 s01 32.9 1 0w 6.50098 2 s01 32.9 1 6w 6.91793 3 s02 35.1 0 0w 7.32480 4 s02 35.1 0 6w 7.56012 5 s03 34.4 0 0w 6.51385 6 s03 34.4 0 6w 6.56875 9 s05 39.9 1 0w 6.92855 10 s05 39.9 1 6w 6.94926 11 s06 29.5 1 0w 6.99383 12 s06 29.5 1 6w 7.10568 13 s07 45.9 1 0w 6.94380 14 s07 45.9 1 6w 7.08190 15 s08 20.3 1 0w 7.76881 16 s08 20.3 1 6w 7.72725 17 s09 26.9 0 0w 5.37566 18 s09 26.9 0 6w 5.74887 21 s11 22.0 0 0w 7.12992 22 s11 22.0 0 6w 7.16237 23 s12 31.0 1 0w 6.70629 24 s12 31.0 1 6w 6.80872 25 s13 50.1 1 0w 7.22649 26 s13 50.1 1 6w 7.58900 27 s14 22.2 0 0w 5.97577 28 s14 22.2 0 6w 5.80801 29 s16 20.4 1 0w 7.99554 30 s16 20.4 1 6w 8.09260 31 s20 24.0 0 0w 7.01014 32 s20 24.0 0 6w 6.87821 33 s21 32.4 0 0w 5.90883 34 s21 32.4 0 6w 5.95392 37 s24 22.2 0 0w 6.15474 38 s24 22.2 0 6w 6.00906 41 s27 22.2 1 0w 7.88765 42 s27 22.2 1 6w 7.76038 49 s35 24.3 0 0w 6.05998 50 s35 24.3 0 6w 6.07399 51 s36 23.5 0 0w 7.83182 52 s36 23.5 0 6w 7.60268 53 s38 59.7 1 0w 7.39672 54 s38 59.7 1 6w 6.98291 55 s39 40.5 0 0w 7.31330 56 s39 40.5 0 6w 7.50559 57 s40 24.2 1 0w 8.54958 58 s40 24.2 1 6w 8.65016 59 s41 23.6 1 0w 7.76049 60 s41 23.6 1 6w 7.58946 61 s42 53.3 0 0w 7.03388 62 s42 53.3 0 6w 7.48384 63 s43 34.4 0 0w 6.86967 64 s43 34.4 0 6w 6.81076 65 s44 44.8 0 0w 7.33779 66 s44 44.8 0 6w 7.86175 67 s45 40.2 1 0w 6.55963 68 s45 40.2 1 6w 6.52577 71 s47 26.5 0 0w 7.09418 72 s47 26.5 0 6w 6.92850 73 s48 22.7 0 0w 6.77078 74 s48 22.7 0 6w 6.67289 75 s50 36.1 1 0w 7.47208 76 s50 36.1 1 6w 7.55876 > library(car) Loading required package: MASS Loading required package: nnet Loading required package: survival Loading required package: splines > timepoints <- factor(c("0w", "6w"), + levels=c("0w","6w")) > idata=data.frame(timepoints) > mod.ok <- lm(HippV ~ Age*Sex,data=Dat2) > (av.ok <- Anova(mod.ok, idata=idata, idesign=~timepoints)) Anova Table (Type II tests) Response: HippV Sum Sq Df F value Pr(>F) Age 0.0234 1 0.071 0.7909202 Sex 5.3082 1 16.128 0.0001781 *** Age:Sex 4.7772 1 14.514 0.0003478 *** Residuals 18.4314 56 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > ________________________________ From: John Fox <j...@mcmaster.ca> Cc: r-help@r-project.org Sent: Saturday, November 26, 2011 7:19 PM Subject: RE: [R] SPSS -> R Dear Kristi, I assume that this is a repeated-measures ANOVA with one within-subjects factor (Time) and two between-subjects factors (Age and Sex, which are crossed). If Age is numeric, and not a factor, then the "type-III" tests that you requested don't test sensible hypotheses. In any event, if my guess is right about the design, then you can use the Anova() function in the car package for an equivalent analysis. See the repeated-measures example in ?Anova (for the O'Brien and Kaiser data). You've already had an answer to the more general question. I hope this helps, John -------------------------------- John Fox Senator William McMaster Professor of Social Statistics Department of Sociology McMaster University Hamilton, Ontario, Canada http://socserv.mcmaster.ca/jfox > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-bounces@r- > project.org] On Behalf Of Kristi Shoemaker > Sent: November-26-11 11:08 AM > To: r-help@r-project.org > Subject: [R] SPSS -> R > > I'm an SPSS user trying to make the transition to R. > > Can someone help me translate the following SPSS code into R?: > > > GLM Total_tp1 Total_tp2 WITH Age Sex > /WSFACTOR=Time 2 Repeated > /METHOD=SSTYPE(3) > /CRITERIA=ALPHA(.05) > /WSDESIGN= Time > /DESIGN= Age Sex Age*Sex. > > Also. can anyone recommend any resources to help SPSS users learn to > things in R? > > Thanks, > -kristi > > [[alternative HTML version deleted]] [[alternative HTML version deleted]]
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