[R] dropterm in MANOVA for MLM objects

[John Fox]

Dear Vickie,

I'm afraid that the test problem that you've constructed makes no sense, and
doesn't correspond to the problem that you initially described, in which a
matrix of presumably 5 responses for presumably 140 observations is
regressed on 6 predictors. You regressed your randomly generated matrix of 5
responses and 140 observations on a factor constructed from the distinct 140
observation names. That factor has 140 levels, and so the model uses 140 df,
all the df in the data. It's therefore not surprising that the error SSP
matrix has 0 df, which is exactly what Anova.mlm (actually,
linearHypothesis.mlm, which it calls) tells you.

The remark that you found about univariate tests that you apparently found
on-line concerns repeated-measures designs and is not relevant to your data.
And you can't do a univariate ANOVA when there's 0 df for error in any
event.

Here's a proper simulation of the kind of data that I think you have:

> set.seed(12345)
> E <- matrix(rnorm(140*5), ncol=5)
> X <- matrix(rnorm(140*6), ncol=6)
> Beta <- matrix(runif(6*5), ncol=5)
> Y <- X %*% Beta + E
> colnames(Y) <- c("A", "B", "C", "D", "E")
> colnames(X) <- c("syct", "mmin", "mmax", "cach", "chmin", "chmax")
> Data <- as.data.frame(cbind(Y, X))
> mod <- lm(cbind(A, B, C, D, E) ~ syct + mmin + mmax + cach + chmin +
chmax, data=Data)
> Anova(mod)

Type II MANOVA Tests: Pillai test statistic
      Df test stat approx F num Df den Df    Pr(>F)    
syct   1   0.41622   18.395      5    129  9.31e-14 ***
mmin   1   0.48288   24.091      5    129 < 2.2e-16 ***
mmax   1   0.62100   42.273      5    129 < 2.2e-16 ***
cach   1   0.61711   41.583      5    129 < 2.2e-16 ***
chmin  1   0.72547   68.180      5    129 < 2.2e-16 ***
chmax  1   0.54825   31.311      5    129 < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Best,
 John

> -----Original Message-----
> From: Vickie S [mailto:isvik at live.com]
> Sent: February-08-12 5:53 PM
> To: jfox at mcmaster.ca
> Cc: r-help at r-project.org
> Subject: RE: [R] dropterm in MANOVA for MLM objects
> 
> 
> Dear Prof Fox,
> I tried anova but got the following error message:
> 
> mat <- matrix(rnorm(700), ncol=5, dimnames=list( paste("f", c(1:140),
> sep="_"), c("A", "B", "C", "D", "E"))) summary(Anova(lm(cbind(A, B, C,
> D, E) ~ factor(rownames(mat)), data=as.data.frame(mat))))
> 
> Error in summary(Anova(lm(cbind(A, B, C, D, E) ~
> factor(rownames(mat)),  :
>   error in evaluating the argument 'object' in selecting a method for
> function 'summary': Error in linearHypothesis.mlm(mod, hyp.matrix.2,
> SSPE = SSPE, V = V, ...) :
>   The error SSP matrix is apparently of deficient rank = 0 < 5
> 
> I looked in previous forum and it seems like i have only option of
> performing the univariate test here.
> 
> Therefore I used the following, but it still results in an error
> message:
> Anova(lm(cbind(A, B, C, D, E) ~ factor(rownames(mat)),
> data=as.data.frame(mat)), univariate=TRUE, multivariate=F) Error in
> linearHypothesis.mlm(mod, hyp.matrix.2, SSPE = SSPE, V = V, ...) :
>   The error SSP matrix is apparently of deficient rank = 0 < 5
> 
> Any suggestions ?
> 
> Thanks
> Vickie
> 
> 
> 
> I think I am still missing some important clues here. Is it because
> the feww
> 
> > From: jfox at mcmaster.ca
> > To: isvik at live.com
> > CC: r-help at r-project.org
> > Subject: RE: [R] dropterm in MANOVA for MLM objects
> > Date: Wed, 8 Feb 2012 17:01:34 -0500
> >
> > Dear Vicki,
> >
> > I think that the Anova() function in the car package will do what
> you
> > want (and will also properly handle models with more structure, such
> > as interactions).
> >
> > Best,
> >  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-bounces at r-project.org [mailto:r-help-bounces at r-
> > > project.org] On Behalf Of Vickie S
> > > Sent: February-08-12 3:57 PM
> > > To: r-help at r-project.org
> > > Subject: [R] dropterm in MANOVA for MLM objects
> > >
> > >
> > > Dear R fans,
> > > I have got a difficult sounding problem.
> > >
> > > For fitting a linear model using continuous response and then for
> > > re- fitting the model after excluding every single variable, the
> > > following functions can be used.
> > > library(MASS)
> > > model = lm(perf ~ syct + mmin + mmax + cach + chmin + chmax, data
> =
> > > cpus) dropterm(model, test = "F")
> > >
> > > But I am not sure whether any similar functions is available in R
> > > for multivariate data with categorical response.
> > > My data looks like the following:
> > > mat <- matrix(rnorm(700), ncol=5, dimnames=list( paste("f",
> > > c(1:140), sep="_"), c("A", "B", "C", "D", "E")))
> > >
> > > There are 140 features describing 5 different plant species. I
> want
> > > to retain only those features that show good performance in model
> > > (by using a function similar to dropterm, which can not be used
> for
> > > mlm objects).
> > >
> > > I wud appreciate some hints n suggestions.
> > >
> > > Thx
> > > - Vickie
> > >
> > >
> > >
> > >
> > >
> > > 	[[alternative HTML version deleted]]
> > >
> > > ______________________________________________
> > > R-help at 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.
> >
>  		 	   		  =