mvpaircomp.RdPerforms pairwise comparisons of multivariate mean vectors of factor levels, overall or nested.
The tests are run in the same spirt of summary.manova(), based on multivariate statistics such as Pillai's trace
and Wilks' lambda, which can be applied to test multivariate contrasts.
mvpaircomp(model, factor1, nesting.factor = NULL, test = "Pillai", adjust = "none", SSPerror = NULL, DFerror = NULL) # S3 method for mvpaircomp print(x, ...)
| model | a multivariate analysis of variance (MANOVA) model, fitted using |
|---|---|
| factor1 | a character string indicating a factor declared in the |
| nesting.factor | optional; a character string indicating a factor also declared in |
| test | a character string indicating the type of multivariate statistics to be calculated to perform the
F-test approximation. Default is |
| adjust | a character string indicating the p-value adjustment method for multiple comparisons. Default is |
| SSPerror | optional; a numeric matrix representing the residual sum of squares and cross-products, to be used to compute the multivariate statistics. |
| DFerror | optional; a numeric value representing the residual degrees of freedom, to be used to compute the multivariate statistics. |
| x | an object of class |
| ... | further arguments. |
An object of class mvpaircomp, a list of
an array containing the summary of the multivariate tests.
an array containing p-dimensional square matrices of sum of squares and cross-products of the contrasts.
a character string indicating the p-value adjustment method used.
a character string indicating the factor being tested.
a character string indicating the nesting factor.
Krzanowski, W. J. (1988) Principles of Multivariate Analysis. A User's Perspective. Oxford.
Anderson Rodrigo da Silva <anderson.agro@hotmail.com>
# Example 1 data(maize) M <- lm(cbind(NKPR, ED, CD, PH) ~ family + env, data = maize) anova(M) # MANOVA table#> Analysis of Variance Table #> #> Df Pillai approx F num Df den Df Pr(>F) #> (Intercept) 1 0.99948 4303.1 4 9 9.375e-15 *** #> family 4 2.26992 3.9 16 48 0.0001151 *** #> env 3 1.69631 3.6 12 33 0.0018017 ** #> Residuals 12 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mvpaircomp(M, factor1 = "family", adjust = "bonferroni")#> #> Multivariate Pairwise Comparisons #> #> Pillai approx F num DF den DF Pr(>F) #> 1 - 2 0.83217 11.1564 4 9 0.0154218 * #> 1 - 3 0.88819 17.8743 4 9 0.0026108 ** #> 1 - 4 0.56683 2.9442 4 9 0.8227952 #> 1 - 5 0.43243 1.7142 4 9 1.0000000 #> 2 - 3 0.81643 10.0070 4 9 0.0227391 * #> 2 - 4 0.78310 8.1233 4 9 0.0466348 * #> 2 - 5 0.86794 14.7877 4 9 0.0054222 ** #> 3 - 4 0.86242 14.1036 4 9 0.0064871 ** #> 3 - 5 0.91546 24.3634 4 9 0.0007605 *** #> 4 - 5 0.47649 2.0479 4 9 1.0000000 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> With bonferroni p-value adjustment for multiple comparisons# Example 2 (with nesting factor) # Data on producing plastic film from Krzanowski (1998, p. 381) tear <- c(6.5, 6.2, 5.8, 6.5, 6.5, 6.9, 7.2, 6.9, 6.1, 6.3, 6.7, 6.6, 7.2, 7.1, 6.8, 7.1, 7.0, 7.2, 7.5, 7.6) gloss <- c(9.5, 9.9, 9.6, 9.6, 9.2, 9.1, 10.0, 9.9, 9.5, 9.4, 9.1, 9.3, 8.3, 8.4, 8.5, 9.2, 8.8, 9.7, 10.1, 9.2) opacity <- c(4.4, 6.4, 3.0, 4.1, 0.8, 5.7, 2.0, 3.9, 1.9, 5.7, 2.8, 4.1, 3.8, 1.6, 3.4, 8.4, 5.2, 6.9, 2.7, 1.9) Y <- cbind(tear, gloss, opacity) rate <- gl(2, 10, labels = c("Low", "High")) additive <- gl(2, 5, length = 20, labels = c("Low", "High")) fit <- manova(Y ~ rate * additive) summary(fit, test = "Wilks") # MANOVA table#> Df Wilks approx F num Df den Df Pr(>F) #> rate 1 0.38186 7.5543 3 14 0.003034 ** #> additive 1 0.52303 4.2556 3 14 0.024745 * #> rate:additive 1 0.77711 1.3385 3 14 0.301782 #> Residuals 16 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mvpaircomp(fit, factor1 = "rate", nesting.factor = "additive", test = "Wilks")#> #> Multivariate Pairwise Comparisons #> #> --- #> Comparing levels of rate nested in additive #> #> $Low #> Wilks approx F num DF den DF Pr(>F) #> Low - High 0.46273 5.4184 3 14 0.011 * #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> $High #> Wilks approx F num DF den DF Pr(>F) #> Low - High 0.57322 3.4744 3 14 0.04503 * #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> With none p-value adjustment for multiple comparisonsmvpaircomp(fit, factor1 = "additive", nesting.factor = "rate", test = "Wilks")#> #> Multivariate Pairwise Comparisons #> #> --- #> Comparing levels of additive nested in rate #> #> $Low #> Wilks approx F num DF den DF Pr(>F) #> Low - High 0.81338 1.0707 3 14 0.3931 #> #> $High #> Wilks approx F num DF den DF Pr(>F) #> Low - High 0.50779 4.5234 3 14 0.02037 * #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> With none p-value adjustment for multiple comparisons# End (not run)