[R] dropterm in MANOVA for MLM objects

Thu Feb 9 15:59:23 CET 2012

Anywez, I have figured out a solution. Whether it is a magic wand or something else, that I don't know but I wish it would work.

Thx for the critical stand.

-- Vickie

----------------------------------------
> From: jfox at mcmaster.ca

> Subject: Re: [R] dropterm in MANOVA for MLM objects

> To: isvik at live.com

> CC: r-help at r-project.org

> Date: Thu, 9 Feb 2012 09:43:37 -0500

>

> 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/

>