SPSS uses a different calculation. As far as I understood, they test main effects without the covariate. Regarding the difference between my and your results, did you use sum contrasts? options(contrasts=c("contr.sum","contr.poly"))
On Fri, Jun 4, 2010 at 2:19 AM, Anita Narwani <anitanarw...@gmail.com>wrote: > Hi Joris, > That seems to have worked and the contrasts look correct. > I have tried comparing the results to what SPSS produces for the same > model. The two programs produce very different results, although the model F > statistics, R squared and adjusted R squared values are identical. The > results are so different that I don't know what to trust. > > For the same model you coded I got: > > test <- lm(C.Mean~ Mean.richness + Diversity + Zoop + Diversity/Phyto + > + Zoop*Diversity/Phyto) > > Anova(test,type="III") > Anova Table (Type III tests) > > Response: C.Mean > Sum Sq Df F value Pr(>F) > (Intercept) 28223311 1 11.8056 0.001701 ** > Mean.richness 49790403 1 20.8269 7.471e-05 *** > Diversity 31055477 1 12.9903 0.001082 ** > Zoop 2736238 1 1.1445 0.292953 > Diversity:Phyto 27943313 6 1.9481 0.104103 > Diversity:Zoop 168184 1 0.0703 0.792584 > Diversity:Zoop:Phyto 61710145 6 4.3021 0.002879 ** > Residuals 74110911 31 > --- > Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 > > (Also sightly different from your result) > > and > > > summary(test) > > Call: > lm(formula = C.Mean ~ Mean.richness + Diversity + Zoop + Diversity/Phyto + > +Zoop * Diversity/Phyto) > > Residuals: > Min 1Q Median 3Q Max > -3555.26 -479.53 49.94 423.49 4073.20 > > Coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) -8562.9 2492.2 -3.436 0.00170 ** > Mean.richness 4605.7 1009.2 4.564 7.47e-05 *** > DiversityL 6576.9 1824.8 3.604 0.00108 ** > ZoopD -1414.4 1322.1 -1.070 0.29295 > DiversityH:PhytoP2 -4307.5 1824.8 -2.361 0.02472 * > DiversityL:PhytoP2 -268.4 1262.5 -0.213 0.83300 > DiversityH:PhytoP3 -2233.4 1393.0 -1.603 0.11900 > DiversityL:PhytoP3 -1571.4 1262.5 -1.245 0.22257 > DiversityH:PhytoP4 -7914.8 2647.2 -2.990 0.00543 ** > DiversityL:PhytoP4 -1612.8 1262.5 -1.277 0.21092 > DiversityL:ZoopD 484.9 1828.0 0.265 0.79258 > DiversityH:ZoopD:PhytoP2 683.9 1855.3 0.369 0.71493 > DiversityL:ZoopD:PhytoP2 6346.4 1785.4 3.555 0.00124 ** > DiversityH:ZoopD:PhytoP3 4922.8 1786.3 2.756 0.00971 ** > DiversityL:ZoopD:PhytoP3 1085.4 1785.4 0.608 0.54766 > DiversityH:ZoopD:PhytoP4 3261.8 1985.6 1.643 0.11055 > DiversityL:ZoopD:PhytoP4 681.9 1785.4 0.382 0.70513 > --- > Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 > > Residual standard error: 1546 on 31 degrees of freedom > Multiple R-squared: 0.7858, Adjusted R-squared: 0.6753 > F-statistic: 7.109 on 16 and 31 DF, p-value: 1.810e-06 > > From SPSS I got > Tests of Between-Subjects Effects > > > > > Dependent Variable:C Mean > > > > > Source Type III Sum of Squares df Mean Square F Sig. Corrected Model > 2.719E+08 16 1.700E+07 7.109 .000 Intercept 2.394E+07 1 2.394E+07 10.012 > .003 Meanrichness 4.979E+07 1 4.979E+07 20.827 .000 Diversity 3.581E+07 > 1 3.581E+07 14.978 .001 Zoop 1.079E+07 1 1.079E+07 4.515 .042 Diversity > * Zoop 261789.172 1 261789.172 .110 .743 Phyto(Diversity) 1.186E+08 6 > 1.976E+07 8.265 .000 Phyto * Zoop(Diversity) 6.171E+07 6 1.029E+07 4.302 > .003 Error 7.411E+07 31 2.391E+06 > > Total 7.959E+08 48 > > > Corrected Total 3.460E+08 47 > > > > > Which, gives some similar results, but a completely different F statistic > and P-value for the main effect of Zoop and the nested effect of Phyto. > Obviously SPSS is not necessarily the perfect reference, but when using the > Type I SS, the results did agree. Any thoughts on why this might be? Could > the two programs be calculating the Type III SS differently? Might it be > wise to stick to Type I SS? > > Thanks very much for your time and effort. It has been very helpful. > Anita. > > > On Thu, Jun 3, 2010 at 4:25 PM, Joris Meys <jorism...@gmail.com> wrote: > >> I see where my confusion comes from. I counted 4 levels of Phyto, but >> you have 8, being 4 in every level of Diversity. There's your >> aliasing. >> >> > table(Diversity,Phyto) >> Phyto >> Diversity M1 M2 M3 M4 P1 P2 P3 P4 >> H 0 0 0 0 6 6 6 6 >> L 6 6 6 6 0 0 0 0 >> >> There's no need to code them differently for every level of Diversity. >> If you don't, all is fine : >> >> > Phyto <- gsub("M","P",as.character(Phyto)) >> > Phyto <- as.factor(Phyto) >> > >> > test <- lm(C.Mean~ Mean.richness + Diversity + Zoop + Diversity/Phyto + >> + Zoop*Diversity/Phyto) >> > >> > Anova(test,type="III") >> Anova Table (Type III tests) >> >> Response: C.Mean >> Sum Sq Df F value Pr(>F) >> (Intercept) 23935609 1 10.0121 0.0034729 ** >> Mean.richness 49790385 1 20.8269 7.471e-05 *** >> Diversity 35807205 1 14.9779 0.0005234 *** >> Zoop 10794614 1 4.5153 0.0416688 * >> Diversity:Phyto 118553464 6 8.2650 2.184e-05 *** >> Diversity:Zoop 261789 1 0.1095 0.7429356 >> Diversity:Zoop:Phyto 61710162 6 4.3021 0.0028790 ** >> Residuals 74110938 31 >> --- >> Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 >> > >> >> You can check with summary(test) that the model is fitted correctly. >> >> On Fri, Jun 4, 2010 at 12:48 AM, Anita Narwani <anitanarw...@gmail.com> >> wrote: >> > >> > You have everything right except that there are only 2 zooplankton >> species (C & D, which stand for Ceriodaphnia and Daphnia). >> > >> > > -- Ghent University Faculty of Bioscience Engineering Department of Applied mathematics, biometrics and process control tel : +32 9 264 59 87 joris.m...@ugent.be ------------------------------- Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php [[alternative HTML version deleted]]
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