[R] dropterm in MANOVA for MLM objects
John Fox
jfox at mcmaster.ca
Thu Feb 9 15:43:37 CET 2012
Dear Vickie,
No one will be able to wave a magic wand over your data to allow you to usefully estimate linear models with 0 df for error, and you certainly can't perform statistical tests. As Peter Dalgaard pointed out, the same confusion was reflected in your subsequent question about Hotelling's T^2. Hotelling T^2 is equivalent to MANOVA when there are two groups.
Best,
John
On Thu, 9 Feb 2012 09:38:51 +0100
Vickie S <isvik at live.com> wrote:
>
> Thanks for nice explanation.
> Unfortunately, matrix in my question is exactly similar to the one I posted earlier :
>
> mat <- matrix(rnorm(700), ncol=5, dimnames=list( paste("f", c(1:140),sep="_"), c("A", "B", "C", "D", "E")))
>
>
> Question here is which of the 140 characteristics (i.e. f_1...f_140) distinguish the most between the five plant
> species.
>
> Is it true that this matrix can't be regressed with factor responses (species) ? If so, what alternatives can be used ?
>
>
> - Vickie
>
>
> ----------------------------------------
> > 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 20:37:37 -0500
>
> >
>
> > 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.
>
> > > >
>
> > > =
>
> >
>
>
------------------------------------------------
John Fox
Sen. William McMaster Prof. of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/
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