Dear Johan and Dennis, I believe that the source of confusion is the difference between Anova.lm(), the Anova method for a linear-model object, which indeed has a summary method that returns an object from which you can extract p-values, and Anova.mlm(), which passes the multivariate-linear-model object through (as I explained in a previous response).
Best, John -------------------------------- John Fox Senator William McMaster Professor of Social Statistics Department of Sociology McMaster University Hamilton, Ontario, Canada web: socserv.mcmaster.ca/jfox > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On > Behalf Of Johan Steen > Sent: August-23-10 5:36 PM > To: Dennis Murphy > Cc: r-help@r-project.org > Subject: Re: [R] extracting p-values from Anova objects (from the car > library) > > Thanks for your replies, > > but unfortunately none of them seem to help. > I do get p-values in the output, but can't seem to locate them anywhere > in these objects via the str() function. I also get very different > output using str() than you obtained from the lm help page > > Here's my output: > > > A <- factor( rep(1:2,each=3) ) > > B <- factor( rep(1:3,times=2) ) > > idata <- data.frame(A,B) > > idata > A B > 1 1 1 > 2 1 2 > 3 1 3 > 4 2 1 > 5 2 2 > 6 2 3 > > > > fit <- lm( cbind(a1_b1,a1_b2,a1_b3,a2_b1,a2_b2,a2_b3) ~ sex, > data=Data.wide) > > result <- Anova(fit, type="III", test="Wilks", idata=idata, idesign=~A*B) > > result > > Type III Repeated Measures MANOVA Tests: Wilks test statistic > Df test stat approx F num Df den Df Pr(>F) > (Intercept) 1 0.02863 610.81 1 18 2.425e-15 > sex 1 0.76040 5.67 1 18 0.02849 > A 1 0.91390 1.70 1 18 0.20925 > sex:A 1 0.99998 0.00 1 18 0.98536 > B 1 0.26946 23.05 2 17 1.443e-05 > sex:B 1 0.98394 0.14 2 17 0.87140 > A:B 1 0.27478 22.43 2 17 1.704e-05 > sex:A:B 1 0.98428 0.14 2 17 0.87397 > > summary(result) > > Type III Repeated Measures MANOVA Tests: > > ------------------------------------------ > > Term: (Intercept) > > Response transformation matrix: > (Intercept) > a1_b1 1 > a1_b2 1 > a1_b3 1 > a2_b1 1 > a2_b2 1 > a2_b3 1 > > Sum of squares and products for the hypothesis: > (Intercept) > (Intercept) 1169345 > > Sum of squares and products for error: > (Intercept) > (Intercept) 34459.4 > > Multivariate Tests: (Intercept) > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.97137 610.8117 1 18 2.425e-15 > Wilks 1 0.02863 610.8117 1 18 2.425e-15 > Hotelling-Lawley 1 33.93399 610.8117 1 18 2.425e-15 > Roy 1 33.93399 610.8117 1 18 2.425e-15 > > ------------------------------------------ > > Term: sex > > Response transformation matrix: > (Intercept) > a1_b1 1 > a1_b2 1 > a1_b3 1 > a2_b1 1 > a2_b2 1 > a2_b3 1 > > Sum of squares and products for the hypothesis: > (Intercept) > (Intercept) 10857.8 > > Sum of squares and products for error: > (Intercept) > (Intercept) 34459.4 > > Multivariate Tests: sex > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.2395956 5.671614 1 18 0.028486 > Wilks 1 0.7604044 5.671614 1 18 0.028486 > Hotelling-Lawley 1 0.3150896 5.671614 1 18 0.028486 > Roy 1 0.3150896 5.671614 1 18 0.028486 > > ------------------------------------------ > > Term: A > > Response transformation matrix: > A1 > a1_b1 1 > a1_b2 1 > a1_b3 1 > a2_b1 -1 > a2_b2 -1 > a2_b3 -1 > > Sum of squares and products for the hypothesis: > A1 > A1 980 > > Sum of squares and products for error: > A1 > A1 10401.8 > > Multivariate Tests: A > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.0861024 1.695860 1 18 0.20925 > Wilks 1 0.9138976 1.695860 1 18 0.20925 > Hotelling-Lawley 1 0.0942145 1.695860 1 18 0.20925 > Roy 1 0.0942145 1.695860 1 18 0.20925 > > ------------------------------------------ > > Term: sex:A > > Response transformation matrix: > A1 > a1_b1 1 > a1_b2 1 > a1_b3 1 > a2_b1 -1 > a2_b2 -1 > a2_b3 -1 > > Sum of squares and products for the hypothesis: > A1 > A1 0.2 > > Sum of squares and products for error: > A1 > A1 10401.8 > > Multivariate Tests: sex:A > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.0000192 0.0003460939 1 18 0.98536 > Wilks 1 0.9999808 0.0003460939 1 18 0.98536 > Hotelling-Lawley 1 0.0000192 0.0003460939 1 18 0.98536 > Roy 1 0.0000192 0.0003460939 1 18 0.98536 > > ------------------------------------------ > > Term: B > > Response transformation matrix: > B1 B2 > a1_b1 1 0 > a1_b2 0 1 > a1_b3 -1 -1 > a2_b1 1 0 > a2_b2 0 1 > a2_b3 -1 -1 > > Sum of squares and products for the hypothesis: > B1 B2 > B1 3618.05 3443.2 > B2 3443.20 3276.8 > > Sum of squares and products for error: > B1 B2 > B1 2304.5 1396.8 > B2 1396.8 1225.2 > > Multivariate Tests: B > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.730544 23.04504 2 17 1.4426e-05 > Wilks 1 0.269456 23.04504 2 17 1.4426e-05 > Hotelling-Lawley 1 2.711181 23.04504 2 17 1.4426e-05 > Roy 1 2.711181 23.04504 2 17 1.4426e-05 > > ------------------------------------------ > > Term: sex:B > > Response transformation matrix: > B1 B2 > a1_b1 1 0 > a1_b2 0 1 > a1_b3 -1 -1 > a2_b1 1 0 > a2_b2 0 1 > a2_b3 -1 -1 > > Sum of squares and products for the hypothesis: > B1 B2 > B1 26.45 23 > B2 23.00 20 > > Sum of squares and products for error: > B1 B2 > B1 2304.5 1396.8 > B2 1396.8 1225.2 > > Multivariate Tests: sex:B > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.0160644 0.1387764 2 17 0.8714 > Wilks 1 0.9839356 0.1387764 2 17 0.8714 > Hotelling-Lawley 1 0.0163266 0.1387764 2 17 0.8714 > Roy 1 0.0163266 0.1387764 2 17 0.8714 > > ------------------------------------------ > > Term: A:B > > Response transformation matrix: > A1:B1 A1:B2 > a1_b1 1 0 > a1_b2 0 1 > a1_b3 -1 -1 > a2_b1 -1 0 > a2_b2 0 -1 > a2_b3 1 1 > > Sum of squares and products for the hypothesis: > A1:B1 A1:B2 > A1:B1 5152.05 738.3 > A1:B2 738.30 105.8 > > Sum of squares and products for error: > A1:B1 A1:B2 > A1:B1 3210.5 1334.4 > A1:B2 1334.4 924.0 > > Multivariate Tests: A:B > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.7252156 22.43334 2 17 1.7039e-05 > Wilks 1 0.2747844 22.43334 2 17 1.7039e-05 > Hotelling-Lawley 1 2.6392162 22.43334 2 17 1.7039e-05 > Roy 1 2.6392162 22.43334 2 17 1.7039e-05 > > ------------------------------------------ > > Term: sex:A:B > > Response transformation matrix: > A1:B1 A1:B2 > a1_b1 1 0 > a1_b2 0 1 > a1_b3 -1 -1 > a2_b1 -1 0 > a2_b2 0 -1 > a2_b3 1 1 > > Sum of squares and products for the hypothesis: > A1:B1 A1:B2 > A1:B1 26.45 2.3 > A1:B2 2.30 0.2 > > Sum of squares and products for error: > A1:B1 A1:B2 > A1:B1 3210.5 1334.4 > A1:B2 1334.4 924.0 > > Multivariate Tests: sex:A:B > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.0157232 0.1357821 2 17 0.87397 > Wilks 1 0.9842768 0.1357821 2 17 0.87397 > Hotelling-Lawley 1 0.0159744 0.1357821 2 17 0.87397 > Roy 1 0.0159744 0.1357821 2 17 0.87397 > > Univariate Type III Repeated-Measures ANOVA Assuming Sphericity > > SS num Df Error SS den Df F Pr(>F) > (Intercept) 194891 1 5743.2 18 610.8117 2.425e-15 > sex 1810 1 5743.2 18 5.6716 0.02849 > A 163 1 1733.6 18 1.6959 0.20925 > sex:A 0 1 1733.6 18 0.0003 0.98536 > B 1151 2 711.0 36 29.1292 2.990e-08 > sex:B 8 2 711.0 36 0.1979 0.82134 > A:B 1507 2 933.4 36 29.0532 3.078e-08 > sex:A:B 8 2 933.4 36 0.1565 0.85568 > > > Mauchly Tests for Sphericity > > Test statistic p-value > B 0.57532 0.0091036 > sex:B 0.57532 0.0091036 > A:B 0.45375 0.0012104 > sex:A:B 0.45375 0.0012104 > > > Greenhouse-Geisser and Huynh-Feldt Corrections > for Departure from Sphericity > > GG eps Pr(>F[GG]) > B 0.70191 2.143e-06 > sex:B 0.70191 0.7427 > A:B 0.64672 4.838e-06 > sex:A:B 0.64672 0.7599 > > HF eps Pr(>F[HF]) > B 0.74332 1.181e-06 > sex:B 0.74332 0.7560 > A:B 0.67565 3.191e-06 > sex:A:B 0.67565 0.7702 > > str(result) > List of 13 > $ SSP :List of 8 > ..$ (Intercept): num [1, 1] 1169345 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "(Intercept)" > .. .. ..$ : chr "(Intercept)" > ..$ sex : num [1, 1] 10858 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "(Intercept)" > .. .. ..$ : chr "(Intercept)" > ..$ A : num [1, 1] 980 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "A1" > .. .. ..$ : chr "A1" > ..$ sex:A : num [1, 1] 0.2 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "A1" > .. .. ..$ : chr "A1" > ..$ B : num [1:2, 1:2] 3618 3443 3443 3277 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "B1" "B2" > .. .. ..$ : chr [1:2] "B1" "B2" > ..$ sex:B : num [1:2, 1:2] 26.4 23 23 20 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "B1" "B2" > .. .. ..$ : chr [1:2] "B1" "B2" > ..$ A:B : num [1:2, 1:2] 5152 738 738 106 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > ..$ sex:A:B : num [1:2, 1:2] 26.4 2.3 2.3 0.2 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > $ SSPE :List of 8 > ..$ (Intercept): num [1, 1] 34459 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "(Intercept)" > .. .. ..$ : chr "(Intercept)" > ..$ sex : num [1, 1] 34459 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "(Intercept)" > .. .. ..$ : chr "(Intercept)" > ..$ A : num [1, 1] 10402 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "A1" > .. .. ..$ : chr "A1" > ..$ sex:A : num [1, 1] 10402 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "A1" > .. .. ..$ : chr "A1" > ..$ B : num [1:2, 1:2] 2304 1397 1397 1225 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "B1" "B2" > .. .. ..$ : chr [1:2] "B1" "B2" > ..$ sex:B : num [1:2, 1:2] 2304 1397 1397 1225 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "B1" "B2" > .. .. ..$ : chr [1:2] "B1" "B2" > ..$ A:B : num [1:2, 1:2] 3210 1334 1334 924 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > ..$ sex:A:B : num [1:2, 1:2] 3210 1334 1334 924 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > $ P :List of 8 > ..$ (Intercept): num [1:6, 1] 1 1 1 1 1 1 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr "(Intercept)" > ..$ sex : num [1:6, 1] 1 1 1 1 1 1 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr "(Intercept)" > ..$ A : num [1:6, 1] 1 1 1 -1 -1 -1 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr "A1" > ..$ sex:A : num [1:6, 1] 1 1 1 -1 -1 -1 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr "A1" > ..$ B : num [1:6, 1:2] 1 0 -1 1 0 -1 0 1 -1 0 ... > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr [1:2] "B1" "B2" > ..$ sex:B : num [1:6, 1:2] 1 0 -1 1 0 -1 0 1 -1 0 ... > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr [1:2] "B1" "B2" > ..$ A:B : num [1:6, 1:2] 1 0 -1 -1 0 1 0 1 -1 0 ... > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > ..$ sex:A:B : num [1:6, 1:2] 1 0 -1 -1 0 1 0 1 -1 0 ... > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > $ df : Named num [1:8] 1 1 1 1 1 1 1 1 > ..- attr(*, "names")= chr [1:8] "(Intercept)" "sex" "A" "sex:A" ... > $ error.df : int 18 > $ terms : chr [1:8] "(Intercept)" "sex" "A" "sex:A" ... > $ repeated : logi TRUE > $ type : chr "III" > $ test : chr "Wilks" > $ idata :'data.frame': 6 obs. of 2 variables: > ..$ A: Factor w/ 2 levels "1","2": 1 1 1 2 2 2 > .. ..- attr(*, "contrasts")= chr "contr.sum" > ..$ B: Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 > .. ..- attr(*, "contrasts")= chr "contr.sum" > $ idesign :Class 'formula' length 2 ~A * B > .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> > $ icontrasts: chr [1:2] "contr.sum" "contr.poly" > $ imatrix : NULL > - attr(*, "class")= chr "Anova.mlm" > > str(summary(result)) > > Type III Repeated Measures MANOVA Tests: > > ------------------------------------------ > > Term: (Intercept) > > Response transformation matrix: > (Intercept) > a1_b1 1 > a1_b2 1 > a1_b3 1 > a2_b1 1 > a2_b2 1 > a2_b3 1 > > Sum of squares and products for the hypothesis: > (Intercept) > (Intercept) 1169345 > > Sum of squares and products for error: > (Intercept) > (Intercept) 34459.4 > > Multivariate Tests: (Intercept) > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.97137 610.8117 1 18 2.425e-15 > Wilks 1 0.02863 610.8117 1 18 2.425e-15 > Hotelling-Lawley 1 33.93399 610.8117 1 18 2.425e-15 > Roy 1 33.93399 610.8117 1 18 2.425e-15 > > ------------------------------------------ > > Term: sex > > Response transformation matrix: > (Intercept) > a1_b1 1 > a1_b2 1 > a1_b3 1 > a2_b1 1 > a2_b2 1 > a2_b3 1 > > Sum of squares and products for the hypothesis: > (Intercept) > (Intercept) 10857.8 > > Sum of squares and products for error: > (Intercept) > (Intercept) 34459.4 > > Multivariate Tests: sex > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.2395956 5.671614 1 18 0.028486 > Wilks 1 0.7604044 5.671614 1 18 0.028486 > Hotelling-Lawley 1 0.3150896 5.671614 1 18 0.028486 > Roy 1 0.3150896 5.671614 1 18 0.028486 > > ------------------------------------------ > > Term: A > > Response transformation matrix: > A1 > a1_b1 1 > a1_b2 1 > a1_b3 1 > a2_b1 -1 > a2_b2 -1 > a2_b3 -1 > > Sum of squares and products for the hypothesis: > A1 > A1 980 > > Sum of squares and products for error: > A1 > A1 10401.8 > > Multivariate Tests: A > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.0861024 1.695860 1 18 0.20925 > Wilks 1 0.9138976 1.695860 1 18 0.20925 > Hotelling-Lawley 1 0.0942145 1.695860 1 18 0.20925 > Roy 1 0.0942145 1.695860 1 18 0.20925 > > ------------------------------------------ > > Term: sex:A > > Response transformation matrix: > A1 > a1_b1 1 > a1_b2 1 > a1_b3 1 > a2_b1 -1 > a2_b2 -1 > a2_b3 -1 > > Sum of squares and products for the hypothesis: > A1 > A1 0.2 > > Sum of squares and products for error: > A1 > A1 10401.8 > > Multivariate Tests: sex:A > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.0000192 0.0003460939 1 18 0.98536 > Wilks 1 0.9999808 0.0003460939 1 18 0.98536 > Hotelling-Lawley 1 0.0000192 0.0003460939 1 18 0.98536 > Roy 1 0.0000192 0.0003460939 1 18 0.98536 > > ------------------------------------------ > > Term: B > > Response transformation matrix: > B1 B2 > a1_b1 1 0 > a1_b2 0 1 > a1_b3 -1 -1 > a2_b1 1 0 > a2_b2 0 1 > a2_b3 -1 -1 > > Sum of squares and products for the hypothesis: > B1 B2 > B1 3618.05 3443.2 > B2 3443.20 3276.8 > > Sum of squares and products for error: > B1 B2 > B1 2304.5 1396.8 > B2 1396.8 1225.2 > > Multivariate Tests: B > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.730544 23.04504 2 17 1.4426e-05 > Wilks 1 0.269456 23.04504 2 17 1.4426e-05 > Hotelling-Lawley 1 2.711181 23.04504 2 17 1.4426e-05 > Roy 1 2.711181 23.04504 2 17 1.4426e-05 > > ------------------------------------------ > > Term: sex:B > > Response transformation matrix: > B1 B2 > a1_b1 1 0 > a1_b2 0 1 > a1_b3 -1 -1 > a2_b1 1 0 > a2_b2 0 1 > a2_b3 -1 -1 > > Sum of squares and products for the hypothesis: > B1 B2 > B1 26.45 23 > B2 23.00 20 > > Sum of squares and products for error: > B1 B2 > B1 2304.5 1396.8 > B2 1396.8 1225.2 > > Multivariate Tests: sex:B > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.0160644 0.1387764 2 17 0.8714 > Wilks 1 0.9839356 0.1387764 2 17 0.8714 > Hotelling-Lawley 1 0.0163266 0.1387764 2 17 0.8714 > Roy 1 0.0163266 0.1387764 2 17 0.8714 > > ------------------------------------------ > > Term: A:B > > Response transformation matrix: > A1:B1 A1:B2 > a1_b1 1 0 > a1_b2 0 1 > a1_b3 -1 -1 > a2_b1 -1 0 > a2_b2 0 -1 > a2_b3 1 1 > > Sum of squares and products for the hypothesis: > A1:B1 A1:B2 > A1:B1 5152.05 738.3 > A1:B2 738.30 105.8 > > Sum of squares and products for error: > A1:B1 A1:B2 > A1:B1 3210.5 1334.4 > A1:B2 1334.4 924.0 > > Multivariate Tests: A:B > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.7252156 22.43334 2 17 1.7039e-05 > Wilks 1 0.2747844 22.43334 2 17 1.7039e-05 > Hotelling-Lawley 1 2.6392162 22.43334 2 17 1.7039e-05 > Roy 1 2.6392162 22.43334 2 17 1.7039e-05 > > ------------------------------------------ > > Term: sex:A:B > > Response transformation matrix: > A1:B1 A1:B2 > a1_b1 1 0 > a1_b2 0 1 > a1_b3 -1 -1 > a2_b1 -1 0 > a2_b2 0 -1 > a2_b3 1 1 > > Sum of squares and products for the hypothesis: > A1:B1 A1:B2 > A1:B1 26.45 2.3 > A1:B2 2.30 0.2 > > Sum of squares and products for error: > A1:B1 A1:B2 > A1:B1 3210.5 1334.4 > A1:B2 1334.4 924.0 > > Multivariate Tests: sex:A:B > Df test stat approx F num Df den Df Pr(>F) > Pillai 1 0.0157232 0.1357821 2 17 0.87397 > Wilks 1 0.9842768 0.1357821 2 17 0.87397 > Hotelling-Lawley 1 0.0159744 0.1357821 2 17 0.87397 > Roy 1 0.0159744 0.1357821 2 17 0.87397 > > Univariate Type III Repeated-Measures ANOVA Assuming Sphericity > > SS num Df Error SS den Df F Pr(>F) > (Intercept) 194891 1 5743.2 18 610.8117 2.425e-15 > sex 1810 1 5743.2 18 5.6716 0.02849 > A 163 1 1733.6 18 1.6959 0.20925 > sex:A 0 1 1733.6 18 0.0003 0.98536 > B 1151 2 711.0 36 29.1292 2.990e-08 > sex:B 8 2 711.0 36 0.1979 0.82134 > A:B 1507 2 933.4 36 29.0532 3.078e-08 > sex:A:B 8 2 933.4 36 0.1565 0.85568 > > > Mauchly Tests for Sphericity > > Test statistic p-value > B 0.57532 0.0091036 > sex:B 0.57532 0.0091036 > A:B 0.45375 0.0012104 > sex:A:B 0.45375 0.0012104 > > > Greenhouse-Geisser and Huynh-Feldt Corrections > for Departure from Sphericity > > GG eps Pr(>F[GG]) > B 0.70191 2.143e-06 > sex:B 0.70191 0.7427 > A:B 0.64672 4.838e-06 > sex:A:B 0.64672 0.7599 > > HF eps Pr(>F[HF]) > B 0.74332 1.181e-06 > sex:B 0.74332 0.7560 > A:B 0.67565 3.191e-06 > sex:A:B 0.67565 0.7702 > List of 13 > $ SSP :List of 8 > ..$ (Intercept): num [1, 1] 1169345 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "(Intercept)" > .. .. ..$ : chr "(Intercept)" > ..$ sex : num [1, 1] 10858 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "(Intercept)" > .. .. ..$ : chr "(Intercept)" > ..$ A : num [1, 1] 980 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "A1" > .. .. ..$ : chr "A1" > ..$ sex:A : num [1, 1] 0.2 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "A1" > .. .. ..$ : chr "A1" > ..$ B : num [1:2, 1:2] 3618 3443 3443 3277 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "B1" "B2" > .. .. ..$ : chr [1:2] "B1" "B2" > ..$ sex:B : num [1:2, 1:2] 26.4 23 23 20 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "B1" "B2" > .. .. ..$ : chr [1:2] "B1" "B2" > ..$ A:B : num [1:2, 1:2] 5152 738 738 106 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > ..$ sex:A:B : num [1:2, 1:2] 26.4 2.3 2.3 0.2 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > $ SSPE :List of 8 > ..$ (Intercept): num [1, 1] 34459 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "(Intercept)" > .. .. ..$ : chr "(Intercept)" > ..$ sex : num [1, 1] 34459 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "(Intercept)" > .. .. ..$ : chr "(Intercept)" > ..$ A : num [1, 1] 10402 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "A1" > .. .. ..$ : chr "A1" > ..$ sex:A : num [1, 1] 10402 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr "A1" > .. .. ..$ : chr "A1" > ..$ B : num [1:2, 1:2] 2304 1397 1397 1225 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "B1" "B2" > .. .. ..$ : chr [1:2] "B1" "B2" > ..$ sex:B : num [1:2, 1:2] 2304 1397 1397 1225 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "B1" "B2" > .. .. ..$ : chr [1:2] "B1" "B2" > ..$ A:B : num [1:2, 1:2] 3210 1334 1334 924 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > ..$ sex:A:B : num [1:2, 1:2] 3210 1334 1334 924 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > $ P :List of 8 > ..$ (Intercept): num [1:6, 1] 1 1 1 1 1 1 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr "(Intercept)" > ..$ sex : num [1:6, 1] 1 1 1 1 1 1 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr "(Intercept)" > ..$ A : num [1:6, 1] 1 1 1 -1 -1 -1 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr "A1" > ..$ sex:A : num [1:6, 1] 1 1 1 -1 -1 -1 > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr "A1" > ..$ B : num [1:6, 1:2] 1 0 -1 1 0 -1 0 1 -1 0 ... > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr [1:2] "B1" "B2" > ..$ sex:B : num [1:6, 1:2] 1 0 -1 1 0 -1 0 1 -1 0 ... > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr [1:2] "B1" "B2" > ..$ A:B : num [1:6, 1:2] 1 0 -1 -1 0 1 0 1 -1 0 ... > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > ..$ sex:A:B : num [1:6, 1:2] 1 0 -1 -1 0 1 0 1 -1 0 ... > .. ..- attr(*, "dimnames")=List of 2 > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > $ df : Named num [1:8] 1 1 1 1 1 1 1 1 > ..- attr(*, "names")= chr [1:8] "(Intercept)" "sex" "A" "sex:A" ... > $ error.df : int 18 > $ terms : chr [1:8] "(Intercept)" "sex" "A" "sex:A" ... > $ repeated : logi TRUE > $ type : chr "III" > $ test : chr "Wilks" > $ idata :'data.frame': 6 obs. of 2 variables: > ..$ A: Factor w/ 2 levels "1","2": 1 1 1 2 2 2 > .. ..- attr(*, "contrasts")= chr "contr.sum" > ..$ B: Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 > .. ..- attr(*, "contrasts")= chr "contr.sum" > $ idesign :Class 'formula' length 2 ~A * B > .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> > $ icontrasts: chr [1:2] "contr.sum" "contr.poly" > $ imatrix : NULL > - attr(*, "class")= chr "Anova.mlm" > > result$`Pr(>F)` > NULL > > result[[4]] > (Intercept) sex A sex:A B sex:B > 1 1 1 1 1 1 > A:B sex:A:B > 1 1 > > > > Op 23/08/2010 22:23, Johan Steen schreef: > > Thanks for your replies, > > > > but unfortunately none of them seem to help. > > I do get p-values in the output, but can't seem to locate them anywhere > > in these objects via the str() function. I also get very different > > output using str() than you obtained from the lm help page > > > > Here's my output: > > > > > A <- factor( rep(1:2,each=3) ) > > > B <- factor( rep(1:3,times=2) ) > > > idata <- data.frame(A,B) > > > idata > > A B > > 1 1 1 > > 2 1 2 > > 3 1 3 > > 4 2 1 > > 5 2 2 > > 6 2 3 > > > > > > fit <- lm( cbind(a1_b1,a1_b2,a1_b3,a2_b1,a2_b2,a2_b3) ~ sex, > > data=Data.wide) > > > result <- Anova(fit, type="III", test="Wilks", idata=idata, > > idesign=~A*B) > > > result > > > > Type III Repeated Measures MANOVA Tests: Wilks test statistic > > Df test stat approx F num Df den Df Pr(>F) > > (Intercept) 1 0.02863 610.81 1 18 2.425e-15 > > sex 1 0.76040 5.67 1 18 0.02849 > > A 1 0.91390 1.70 1 18 0.20925 > > sex:A 1 0.99998 0.00 1 18 0.98536 > > B 1 0.26946 23.05 2 17 1.443e-05 > > sex:B 1 0.98394 0.14 2 17 0.87140 > > A:B 1 0.27478 22.43 2 17 1.704e-05 > > sex:A:B 1 0.98428 0.14 2 17 0.87397 > > > summary(result) > > > > Type III Repeated Measures MANOVA Tests: > > > > ------------------------------------------ > > > > Term: (Intercept) > > > > Response transformation matrix: > > (Intercept) > > a1_b1 1 > > a1_b2 1 > > a1_b3 1 > > a2_b1 1 > > a2_b2 1 > > a2_b3 1 > > > > Sum of squares and products for the hypothesis: > > (Intercept) > > (Intercept) 1169345 > > > > Sum of squares and products for error: > > (Intercept) > > (Intercept) 34459.4 > > > > Multivariate Tests: (Intercept) > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.97137 610.8117 1 18 2.425e-15 > > Wilks 1 0.02863 610.8117 1 18 2.425e-15 > > Hotelling-Lawley 1 33.93399 610.8117 1 18 2.425e-15 > > Roy 1 33.93399 610.8117 1 18 2.425e-15 > > > > ------------------------------------------ > > > > Term: sex > > > > Response transformation matrix: > > (Intercept) > > a1_b1 1 > > a1_b2 1 > > a1_b3 1 > > a2_b1 1 > > a2_b2 1 > > a2_b3 1 > > > > Sum of squares and products for the hypothesis: > > (Intercept) > > (Intercept) 10857.8 > > > > Sum of squares and products for error: > > (Intercept) > > (Intercept) 34459.4 > > > > Multivariate Tests: sex > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.2395956 5.671614 1 18 0.028486 > > Wilks 1 0.7604044 5.671614 1 18 0.028486 > > Hotelling-Lawley 1 0.3150896 5.671614 1 18 0.028486 > > Roy 1 0.3150896 5.671614 1 18 0.028486 > > > > ------------------------------------------ > > > > Term: A > > > > Response transformation matrix: > > A1 > > a1_b1 1 > > a1_b2 1 > > a1_b3 1 > > a2_b1 -1 > > a2_b2 -1 > > a2_b3 -1 > > > > Sum of squares and products for the hypothesis: > > A1 > > A1 980 > > > > Sum of squares and products for error: > > A1 > > A1 10401.8 > > > > Multivariate Tests: A > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.0861024 1.695860 1 18 0.20925 > > Wilks 1 0.9138976 1.695860 1 18 0.20925 > > Hotelling-Lawley 1 0.0942145 1.695860 1 18 0.20925 > > Roy 1 0.0942145 1.695860 1 18 0.20925 > > > > ------------------------------------------ > > > > Term: sex:A > > > > Response transformation matrix: > > A1 > > a1_b1 1 > > a1_b2 1 > > a1_b3 1 > > a2_b1 -1 > > a2_b2 -1 > > a2_b3 -1 > > > > Sum of squares and products for the hypothesis: > > A1 > > A1 0.2 > > > > Sum of squares and products for error: > > A1 > > A1 10401.8 > > > > Multivariate Tests: sex:A > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.0000192 0.0003460939 1 18 0.98536 > > Wilks 1 0.9999808 0.0003460939 1 18 0.98536 > > Hotelling-Lawley 1 0.0000192 0.0003460939 1 18 0.98536 > > Roy 1 0.0000192 0.0003460939 1 18 0.98536 > > > > ------------------------------------------ > > > > Term: B > > > > Response transformation matrix: > > B1 B2 > > a1_b1 1 0 > > a1_b2 0 1 > > a1_b3 -1 -1 > > a2_b1 1 0 > > a2_b2 0 1 > > a2_b3 -1 -1 > > > > Sum of squares and products for the hypothesis: > > B1 B2 > > B1 3618.05 3443.2 > > B2 3443.20 3276.8 > > > > Sum of squares and products for error: > > B1 B2 > > B1 2304.5 1396.8 > > B2 1396.8 1225.2 > > > > Multivariate Tests: B > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.730544 23.04504 2 17 1.4426e-05 > > Wilks 1 0.269456 23.04504 2 17 1.4426e-05 > > Hotelling-Lawley 1 2.711181 23.04504 2 17 1.4426e-05 > > Roy 1 2.711181 23.04504 2 17 1.4426e-05 > > > > ------------------------------------------ > > > > Term: sex:B > > > > Response transformation matrix: > > B1 B2 > > a1_b1 1 0 > > a1_b2 0 1 > > a1_b3 -1 -1 > > a2_b1 1 0 > > a2_b2 0 1 > > a2_b3 -1 -1 > > > > Sum of squares and products for the hypothesis: > > B1 B2 > > B1 26.45 23 > > B2 23.00 20 > > > > Sum of squares and products for error: > > B1 B2 > > B1 2304.5 1396.8 > > B2 1396.8 1225.2 > > > > Multivariate Tests: sex:B > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.0160644 0.1387764 2 17 0.8714 > > Wilks 1 0.9839356 0.1387764 2 17 0.8714 > > Hotelling-Lawley 1 0.0163266 0.1387764 2 17 0.8714 > > Roy 1 0.0163266 0.1387764 2 17 0.8714 > > > > ------------------------------------------ > > > > Term: A:B > > > > Response transformation matrix: > > A1:B1 A1:B2 > > a1_b1 1 0 > > a1_b2 0 1 > > a1_b3 -1 -1 > > a2_b1 -1 0 > > a2_b2 0 -1 > > a2_b3 1 1 > > > > Sum of squares and products for the hypothesis: > > A1:B1 A1:B2 > > A1:B1 5152.05 738.3 > > A1:B2 738.30 105.8 > > > > Sum of squares and products for error: > > A1:B1 A1:B2 > > A1:B1 3210.5 1334.4 > > A1:B2 1334.4 924.0 > > > > Multivariate Tests: A:B > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.7252156 22.43334 2 17 1.7039e-05 > > Wilks 1 0.2747844 22.43334 2 17 1.7039e-05 > > Hotelling-Lawley 1 2.6392162 22.43334 2 17 1.7039e-05 > > Roy 1 2.6392162 22.43334 2 17 1.7039e-05 > > > > ------------------------------------------ > > > > Term: sex:A:B > > > > Response transformation matrix: > > A1:B1 A1:B2 > > a1_b1 1 0 > > a1_b2 0 1 > > a1_b3 -1 -1 > > a2_b1 -1 0 > > a2_b2 0 -1 > > a2_b3 1 1 > > > > Sum of squares and products for the hypothesis: > > A1:B1 A1:B2 > > A1:B1 26.45 2.3 > > A1:B2 2.30 0.2 > > > > Sum of squares and products for error: > > A1:B1 A1:B2 > > A1:B1 3210.5 1334.4 > > A1:B2 1334.4 924.0 > > > > Multivariate Tests: sex:A:B > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.0157232 0.1357821 2 17 0.87397 > > Wilks 1 0.9842768 0.1357821 2 17 0.87397 > > Hotelling-Lawley 1 0.0159744 0.1357821 2 17 0.87397 > > Roy 1 0.0159744 0.1357821 2 17 0.87397 > > > > Univariate Type III Repeated-Measures ANOVA Assuming Sphericity > > > > SS num Df Error SS den Df F Pr(>F) > > (Intercept) 194891 1 5743.2 18 610.8117 2.425e-15 > > sex 1810 1 5743.2 18 5.6716 0.02849 > > A 163 1 1733.6 18 1.6959 0.20925 > > sex:A 0 1 1733.6 18 0.0003 0.98536 > > B 1151 2 711.0 36 29.1292 2.990e-08 > > sex:B 8 2 711.0 36 0.1979 0.82134 > > A:B 1507 2 933.4 36 29.0532 3.078e-08 > > sex:A:B 8 2 933.4 36 0.1565 0.85568 > > > > > > Mauchly Tests for Sphericity > > > > Test statistic p-value > > B 0.57532 0.0091036 > > sex:B 0.57532 0.0091036 > > A:B 0.45375 0.0012104 > > sex:A:B 0.45375 0.0012104 > > > > > > Greenhouse-Geisser and Huynh-Feldt Corrections > > for Departure from Sphericity > > > > GG eps Pr(>F[GG]) > > B 0.70191 2.143e-06 > > sex:B 0.70191 0.7427 > > A:B 0.64672 4.838e-06 > > sex:A:B 0.64672 0.7599 > > > > HF eps Pr(>F[HF]) > > B 0.74332 1.181e-06 > > sex:B 0.74332 0.7560 > > A:B 0.67565 3.191e-06 > > sex:A:B 0.67565 0.7702 > > > str(result) > > List of 13 > > $ SSP :List of 8 > > ..$ (Intercept): num [1, 1] 1169345 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "(Intercept)" > > .. .. ..$ : chr "(Intercept)" > > ..$ sex : num [1, 1] 10858 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "(Intercept)" > > .. .. ..$ : chr "(Intercept)" > > ..$ A : num [1, 1] 980 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "A1" > > .. .. ..$ : chr "A1" > > ..$ sex:A : num [1, 1] 0.2 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "A1" > > .. .. ..$ : chr "A1" > > ..$ B : num [1:2, 1:2] 3618 3443 3443 3277 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "B1" "B2" > > .. .. ..$ : chr [1:2] "B1" "B2" > > ..$ sex:B : num [1:2, 1:2] 26.4 23 23 20 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "B1" "B2" > > .. .. ..$ : chr [1:2] "B1" "B2" > > ..$ A:B : num [1:2, 1:2] 5152 738 738 106 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > ..$ sex:A:B : num [1:2, 1:2] 26.4 2.3 2.3 0.2 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > $ SSPE :List of 8 > > ..$ (Intercept): num [1, 1] 34459 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "(Intercept)" > > .. .. ..$ : chr "(Intercept)" > > ..$ sex : num [1, 1] 34459 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "(Intercept)" > > .. .. ..$ : chr "(Intercept)" > > ..$ A : num [1, 1] 10402 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "A1" > > .. .. ..$ : chr "A1" > > ..$ sex:A : num [1, 1] 10402 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "A1" > > .. .. ..$ : chr "A1" > > ..$ B : num [1:2, 1:2] 2304 1397 1397 1225 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "B1" "B2" > > .. .. ..$ : chr [1:2] "B1" "B2" > > ..$ sex:B : num [1:2, 1:2] 2304 1397 1397 1225 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "B1" "B2" > > .. .. ..$ : chr [1:2] "B1" "B2" > > ..$ A:B : num [1:2, 1:2] 3210 1334 1334 924 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > ..$ sex:A:B : num [1:2, 1:2] 3210 1334 1334 924 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > $ P :List of 8 > > ..$ (Intercept): num [1:6, 1] 1 1 1 1 1 1 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr "(Intercept)" > > ..$ sex : num [1:6, 1] 1 1 1 1 1 1 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr "(Intercept)" > > ..$ A : num [1:6, 1] 1 1 1 -1 -1 -1 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr "A1" > > ..$ sex:A : num [1:6, 1] 1 1 1 -1 -1 -1 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr "A1" > > ..$ B : num [1:6, 1:2] 1 0 -1 1 0 -1 0 1 -1 0 ... > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr [1:2] "B1" "B2" > > ..$ sex:B : num [1:6, 1:2] 1 0 -1 1 0 -1 0 1 -1 0 ... > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr [1:2] "B1" "B2" > > ..$ A:B : num [1:6, 1:2] 1 0 -1 -1 0 1 0 1 -1 0 ... > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > ..$ sex:A:B : num [1:6, 1:2] 1 0 -1 -1 0 1 0 1 -1 0 ... > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > $ df : Named num [1:8] 1 1 1 1 1 1 1 1 > > ..- attr(*, "names")= chr [1:8] "(Intercept)" "sex" "A" "sex:A" ... > > $ error.df : int 18 > > $ terms : chr [1:8] "(Intercept)" "sex" "A" "sex:A" ... > > $ repeated : logi TRUE > > $ type : chr "III" > > $ test : chr "Wilks" > > $ idata :'data.frame': 6 obs. of 2 variables: > > ..$ A: Factor w/ 2 levels "1","2": 1 1 1 2 2 2 > > .. ..- attr(*, "contrasts")= chr "contr.sum" > > ..$ B: Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 > > .. ..- attr(*, "contrasts")= chr "contr.sum" > > $ idesign :Class 'formula' length 2 ~A * B > > .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> > > $ icontrasts: chr [1:2] "contr.sum" "contr.poly" > > $ imatrix : NULL > > - attr(*, "class")= chr "Anova.mlm" > > > str(summary(result)) > > > > Type III Repeated Measures MANOVA Tests: > > > > ------------------------------------------ > > > > Term: (Intercept) > > > > Response transformation matrix: > > (Intercept) > > a1_b1 1 > > a1_b2 1 > > a1_b3 1 > > a2_b1 1 > > a2_b2 1 > > a2_b3 1 > > > > Sum of squares and products for the hypothesis: > > (Intercept) > > (Intercept) 1169345 > > > > Sum of squares and products for error: > > (Intercept) > > (Intercept) 34459.4 > > > > Multivariate Tests: (Intercept) > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.97137 610.8117 1 18 2.425e-15 > > Wilks 1 0.02863 610.8117 1 18 2.425e-15 > > Hotelling-Lawley 1 33.93399 610.8117 1 18 2.425e-15 > > Roy 1 33.93399 610.8117 1 18 2.425e-15 > > > > ------------------------------------------ > > > > Term: sex > > > > Response transformation matrix: > > (Intercept) > > a1_b1 1 > > a1_b2 1 > > a1_b3 1 > > a2_b1 1 > > a2_b2 1 > > a2_b3 1 > > > > Sum of squares and products for the hypothesis: > > (Intercept) > > (Intercept) 10857.8 > > > > Sum of squares and products for error: > > (Intercept) > > (Intercept) 34459.4 > > > > Multivariate Tests: sex > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.2395956 5.671614 1 18 0.028486 > > Wilks 1 0.7604044 5.671614 1 18 0.028486 > > Hotelling-Lawley 1 0.3150896 5.671614 1 18 0.028486 > > Roy 1 0.3150896 5.671614 1 18 0.028486 > > > > ------------------------------------------ > > > > Term: A > > > > Response transformation matrix: > > A1 > > a1_b1 1 > > a1_b2 1 > > a1_b3 1 > > a2_b1 -1 > > a2_b2 -1 > > a2_b3 -1 > > > > Sum of squares and products for the hypothesis: > > A1 > > A1 980 > > > > Sum of squares and products for error: > > A1 > > A1 10401.8 > > > > Multivariate Tests: A > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.0861024 1.695860 1 18 0.20925 > > Wilks 1 0.9138976 1.695860 1 18 0.20925 > > Hotelling-Lawley 1 0.0942145 1.695860 1 18 0.20925 > > Roy 1 0.0942145 1.695860 1 18 0.20925 > > > > ------------------------------------------ > > > > Term: sex:A > > > > Response transformation matrix: > > A1 > > a1_b1 1 > > a1_b2 1 > > a1_b3 1 > > a2_b1 -1 > > a2_b2 -1 > > a2_b3 -1 > > > > Sum of squares and products for the hypothesis: > > A1 > > A1 0.2 > > > > Sum of squares and products for error: > > A1 > > A1 10401.8 > > > > Multivariate Tests: sex:A > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.0000192 0.0003460939 1 18 0.98536 > > Wilks 1 0.9999808 0.0003460939 1 18 0.98536 > > Hotelling-Lawley 1 0.0000192 0.0003460939 1 18 0.98536 > > Roy 1 0.0000192 0.0003460939 1 18 0.98536 > > > > ------------------------------------------ > > > > Term: B > > > > Response transformation matrix: > > B1 B2 > > a1_b1 1 0 > > a1_b2 0 1 > > a1_b3 -1 -1 > > a2_b1 1 0 > > a2_b2 0 1 > > a2_b3 -1 -1 > > > > Sum of squares and products for the hypothesis: > > B1 B2 > > B1 3618.05 3443.2 > > B2 3443.20 3276.8 > > > > Sum of squares and products for error: > > B1 B2 > > B1 2304.5 1396.8 > > B2 1396.8 1225.2 > > > > Multivariate Tests: B > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.730544 23.04504 2 17 1.4426e-05 > > Wilks 1 0.269456 23.04504 2 17 1.4426e-05 > > Hotelling-Lawley 1 2.711181 23.04504 2 17 1.4426e-05 > > Roy 1 2.711181 23.04504 2 17 1.4426e-05 > > > > ------------------------------------------ > > > > Term: sex:B > > > > Response transformation matrix: > > B1 B2 > > a1_b1 1 0 > > a1_b2 0 1 > > a1_b3 -1 -1 > > a2_b1 1 0 > > a2_b2 0 1 > > a2_b3 -1 -1 > > > > Sum of squares and products for the hypothesis: > > B1 B2 > > B1 26.45 23 > > B2 23.00 20 > > > > Sum of squares and products for error: > > B1 B2 > > B1 2304.5 1396.8 > > B2 1396.8 1225.2 > > > > Multivariate Tests: sex:B > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.0160644 0.1387764 2 17 0.8714 > > Wilks 1 0.9839356 0.1387764 2 17 0.8714 > > Hotelling-Lawley 1 0.0163266 0.1387764 2 17 0.8714 > > Roy 1 0.0163266 0.1387764 2 17 0.8714 > > > > ------------------------------------------ > > > > Term: A:B > > > > Response transformation matrix: > > A1:B1 A1:B2 > > a1_b1 1 0 > > a1_b2 0 1 > > a1_b3 -1 -1 > > a2_b1 -1 0 > > a2_b2 0 -1 > > a2_b3 1 1 > > > > Sum of squares and products for the hypothesis: > > A1:B1 A1:B2 > > A1:B1 5152.05 738.3 > > A1:B2 738.30 105.8 > > > > Sum of squares and products for error: > > A1:B1 A1:B2 > > A1:B1 3210.5 1334.4 > > A1:B2 1334.4 924.0 > > > > Multivariate Tests: A:B > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.7252156 22.43334 2 17 1.7039e-05 > > Wilks 1 0.2747844 22.43334 2 17 1.7039e-05 > > Hotelling-Lawley 1 2.6392162 22.43334 2 17 1.7039e-05 > > Roy 1 2.6392162 22.43334 2 17 1.7039e-05 > > > > ------------------------------------------ > > > > Term: sex:A:B > > > > Response transformation matrix: > > A1:B1 A1:B2 > > a1_b1 1 0 > > a1_b2 0 1 > > a1_b3 -1 -1 > > a2_b1 -1 0 > > a2_b2 0 -1 > > a2_b3 1 1 > > > > Sum of squares and products for the hypothesis: > > A1:B1 A1:B2 > > A1:B1 26.45 2.3 > > A1:B2 2.30 0.2 > > > > Sum of squares and products for error: > > A1:B1 A1:B2 > > A1:B1 3210.5 1334.4 > > A1:B2 1334.4 924.0 > > > > Multivariate Tests: sex:A:B > > Df test stat approx F num Df den Df Pr(>F) > > Pillai 1 0.0157232 0.1357821 2 17 0.87397 > > Wilks 1 0.9842768 0.1357821 2 17 0.87397 > > Hotelling-Lawley 1 0.0159744 0.1357821 2 17 0.87397 > > Roy 1 0.0159744 0.1357821 2 17 0.87397 > > > > Univariate Type III Repeated-Measures ANOVA Assuming Sphericity > > > > SS num Df Error SS den Df F Pr(>F) > > (Intercept) 194891 1 5743.2 18 610.8117 2.425e-15 > > sex 1810 1 5743.2 18 5.6716 0.02849 > > A 163 1 1733.6 18 1.6959 0.20925 > > sex:A 0 1 1733.6 18 0.0003 0.98536 > > B 1151 2 711.0 36 29.1292 2.990e-08 > > sex:B 8 2 711.0 36 0.1979 0.82134 > > A:B 1507 2 933.4 36 29.0532 3.078e-08 > > sex:A:B 8 2 933.4 36 0.1565 0.85568 > > > > > > Mauchly Tests for Sphericity > > > > Test statistic p-value > > B 0.57532 0.0091036 > > sex:B 0.57532 0.0091036 > > A:B 0.45375 0.0012104 > > sex:A:B 0.45375 0.0012104 > > > > > > Greenhouse-Geisser and Huynh-Feldt Corrections > > for Departure from Sphericity > > > > GG eps Pr(>F[GG]) > > B 0.70191 2.143e-06 > > sex:B 0.70191 0.7427 > > A:B 0.64672 4.838e-06 > > sex:A:B 0.64672 0.7599 > > > > HF eps Pr(>F[HF]) > > B 0.74332 1.181e-06 > > sex:B 0.74332 0.7560 > > A:B 0.67565 3.191e-06 > > sex:A:B 0.67565 0.7702 > > List of 13 > > $ SSP :List of 8 > > ..$ (Intercept): num [1, 1] 1169345 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "(Intercept)" > > .. .. ..$ : chr "(Intercept)" > > ..$ sex : num [1, 1] 10858 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "(Intercept)" > > .. .. ..$ : chr "(Intercept)" > > ..$ A : num [1, 1] 980 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "A1" > > .. .. ..$ : chr "A1" > > ..$ sex:A : num [1, 1] 0.2 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "A1" > > .. .. ..$ : chr "A1" > > ..$ B : num [1:2, 1:2] 3618 3443 3443 3277 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "B1" "B2" > > .. .. ..$ : chr [1:2] "B1" "B2" > > ..$ sex:B : num [1:2, 1:2] 26.4 23 23 20 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "B1" "B2" > > .. .. ..$ : chr [1:2] "B1" "B2" > > ..$ A:B : num [1:2, 1:2] 5152 738 738 106 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > ..$ sex:A:B : num [1:2, 1:2] 26.4 2.3 2.3 0.2 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > $ SSPE :List of 8 > > ..$ (Intercept): num [1, 1] 34459 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "(Intercept)" > > .. .. ..$ : chr "(Intercept)" > > ..$ sex : num [1, 1] 34459 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "(Intercept)" > > .. .. ..$ : chr "(Intercept)" > > ..$ A : num [1, 1] 10402 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "A1" > > .. .. ..$ : chr "A1" > > ..$ sex:A : num [1, 1] 10402 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr "A1" > > .. .. ..$ : chr "A1" > > ..$ B : num [1:2, 1:2] 2304 1397 1397 1225 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "B1" "B2" > > .. .. ..$ : chr [1:2] "B1" "B2" > > ..$ sex:B : num [1:2, 1:2] 2304 1397 1397 1225 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "B1" "B2" > > .. .. ..$ : chr [1:2] "B1" "B2" > > ..$ A:B : num [1:2, 1:2] 3210 1334 1334 924 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > ..$ sex:A:B : num [1:2, 1:2] 3210 1334 1334 924 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > $ P :List of 8 > > ..$ (Intercept): num [1:6, 1] 1 1 1 1 1 1 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr "(Intercept)" > > ..$ sex : num [1:6, 1] 1 1 1 1 1 1 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr "(Intercept)" > > ..$ A : num [1:6, 1] 1 1 1 -1 -1 -1 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr "A1" > > ..$ sex:A : num [1:6, 1] 1 1 1 -1 -1 -1 > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr "A1" > > ..$ B : num [1:6, 1:2] 1 0 -1 1 0 -1 0 1 -1 0 ... > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr [1:2] "B1" "B2" > > ..$ sex:B : num [1:6, 1:2] 1 0 -1 1 0 -1 0 1 -1 0 ... > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr [1:2] "B1" "B2" > > ..$ A:B : num [1:6, 1:2] 1 0 -1 -1 0 1 0 1 -1 0 ... > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > ..$ sex:A:B : num [1:6, 1:2] 1 0 -1 -1 0 1 0 1 -1 0 ... > > .. ..- attr(*, "dimnames")=List of 2 > > .. .. ..$ : chr [1:6] "a1_b1" "a1_b2" "a1_b3" "a2_b1" ... > > .. .. ..$ : chr [1:2] "A1:B1" "A1:B2" > > $ df : Named num [1:8] 1 1 1 1 1 1 1 1 > > ..- attr(*, "names")= chr [1:8] "(Intercept)" "sex" "A" "sex:A" ... > > $ error.df : int 18 > > $ terms : chr [1:8] "(Intercept)" "sex" "A" "sex:A" ... > > $ repeated : logi TRUE > > $ type : chr "III" > > $ test : chr "Wilks" > > $ idata :'data.frame': 6 obs. of 2 variables: > > ..$ A: Factor w/ 2 levels "1","2": 1 1 1 2 2 2 > > .. ..- attr(*, "contrasts")= chr "contr.sum" > > ..$ B: Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 > > .. ..- attr(*, "contrasts")= chr "contr.sum" > > $ idesign :Class 'formula' length 2 ~A * B > > .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> > > $ icontrasts: chr [1:2] "contr.sum" "contr.poly" > > $ imatrix : NULL > > - attr(*, "class")= chr "Anova.mlm" > > > result$`Pr(>F)` > > NULL > > > result[[4]] > > (Intercept) sex A sex:A B sex:B > > 1 1 1 1 1 1 > > A:B sex:A:B > > 1 1 > > > > > > > > > > > > > > > > > > > Op 23/08/2010 21:56, Dennis Murphy schreef: > >> Hi: > >> > >> Look at > >> result$`Pr(>F)` > >> > >> (with backticks around Pr(>F) ), or more succinctly, result[[4]]. > >> > >> HTH, > >> Dennis > >> > >> On Mon, Aug 23, 2010 at 12:01 PM, Johan Steen <johan.st...@gmail.com > >> <mailto:johan.st...@gmail.com>> wrote: > >> > >> Dear all, > >> > >> is there anyone who can help me extracting p-values from an Anova > >> object from the car library? I can't seem to locate the p-values > >> using str(result) or str(summary(result)) in the example below > >> > >> > A <- factor( rep(1:2,each=3) ) > >> > B <- factor( rep(1:3,times=2) ) > >> > idata <- data.frame(A,B) > >> > fit <- lm( cbind(a1_b1,a1_b2,a1_b3,a2_b1,a2_b2,a2_b3) ~ sex, > >> data=Data.wide) > >> > result <- Anova(fit, type="III", test="Wilks", idata=idata, > >> idesign=~A*B) > >> > >> > >> Any help would be much appreciated! > >> > >> > >> Many thanks, > >> > >> Johan > >> > >> ______________________________________________ > >> R-help@r-project.org <mailto:R-help@r-project.org> mailing list > >> https://stat.ethz.ch/mailman/listinfo/r-help > >> PLEASE do read the posting guide > >> http://www.R-project.org/posting-guide.html > >> and provide commented, minimal, self-contained, reproducible code. > >> > >> > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
