Dear Nicholas, On Fri, 4 Apr 2014 04:59:09 +0000 <nicholas.suraw...@csiro.au> wrote: > Greetings, > > I'm interested in performing some post hoc tests after conducting a > multivariate analysis of covariance (MANCOVA) which I performed using the > Anova function in the car package. The covariate did not end up being > statistically significant, but the single factor's effect on the multivariate > response was statistically significant. From here, I would like to use the > linearHypothesis function in the car package to test for where significant > differences are occurring. > > Codewise, to fit the MANCOVA i've used: > Model_1 <- Anova(lm((Y)~X+covariate)) > I've then tried to perform multiple comparisons using: > linearHypothesis(Model_1,matchCoefs(Model_1,"X"),white.adjust=TRUE)
The argument white.adjust=TRUE isn't available for a multivariate linear model; see ?linearHypothesis. Also, the command (without the white.adjust argument) will give you the same test as Anova() did. Best, John ------------------------------------------------ John Fox, Professor McMaster University Hamilton, Ontario, Canada http://socserv.mcmaster.ca/jfox/ > > After I run the 2nd bit of code I get the error message: " Error in > vcov.default(model) : there is no vcov() method for models of class > Anova.mlm". > > Presumably, this means that I've stuffed up in trying to estimate the > variance-covariance matrix for the Anova function. > > Any suggestions/help to resolve this problem would be greatly appreciated. > > Cheers, > > Nic Surawski > > > > [[alternative HTML version deleted]] > > ______________________________________________ > 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.
