[R] MDS, line of best fit, and id of variables

[PIKAL Petr]

Hi

> -----Original Message-----
> From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Kirsten
> Green
> Sent: Tuesday, October 18, 2016 9:27 PM
> To: David L Carlson <dcarlson at tamu.edu>
> Cc: r-help at r-project.org
> Subject: Re: [R] MDS, line of best fit, and id of variables
>
> David,
>
> I have run the str() function and all my data is int (integer). So I am trying to

It is strange. Error from metaMDS(mortdata) indicates that you have some nonnumeric data in your data frame which definitely should be indicated by str.

e.g.
str(data.frame(a=factor(1:5), b=rnorm(5)))
'data.frame':   5 obs. of  2 variables:
 $ a: Factor w/ 5 levels "1","2","3","4",..: 1 2 3 4 5
 $ b: num  -0.201 -1.639 0.108 0.259 -1.33

> change it to numeric using:
> str(mortdata)
> class(mortdata)
> is.numeric(mortdata)
> mortdata.num <- as.numeric(data.frame(mortdata))

As Bert said you should spent some time to evaluate distinctions among various object types. For making data frame numeric you need to use sapply.

str(sapply(data.frame(a=factor(1:5), b=rnorm(5)), as.numeric))
 num [1:5, 1:2] 1 2 3 4 5 ...
 - attr(*, "dimnames")=List of 2
  ..$ : NULL
  ..$ : chr [1:2] "a" "b"

however if you have factors in your data frame you could get some nasty surprise with such simple approach and it also changes data.frame to matrix.

Cheers
Petr

>
> But I keep getting an error: > mortdata.num <- as.numeric(data.frame(
> mortdata))
> Error: (list) object cannot be coerced to type 'double
>
> On Tue, Oct 18, 2016 at 1:23 PM, David L Carlson <dcarlson at tamu.edu>
> wrote:
>
> > What do you get with str(mortdata)? The error message indicates that
> > at least one of the variables is not numeric and the second suggests
> > it is a factor. You said the values were coded binomially, but they
> > must be numeric, e.g. 0, 1 and not "Present" "Absent" or something
> > like that. If they are all factors, something like
> >
> > mortdata1 <- sapply(mortdata, as.numeric)-1
> >
> > would convert factor levels of 1 and 2 to 0 and 1.
> > -------------------------------------
> > David L Carlson
> > Department of Anthropology
> > Texas A&M University
> > College Station, TX 77840-4352
> >
> > -----Original Message-----
> > From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of
> > Kirsten Green
> > Sent: Tuesday, October 18, 2016 12:28 PM
> > To: r-help at r-project.org
> > Subject: [R] MDS, line of best fit, and id of variables
> >
> > Hi,
> >
> > I have created a dataset that includes 28 rows (burials) and 27
> > columns
> > (variables) that are coded binomially. I have gotten metaMDS to run in
> > the pst but now can't seem to get it run at all.
> > Error message:
> > > mortdata.mds <- metaMDS(mortdata)
> > Error in FUN(X[[i]], ...) :
> >   only defined on a data frame with all numeric variables In addition:
> > Warning message:
> > In Ops.factor(left, right) : ‘<’ not meaningful for factors
> >
> > I'd like to create a 3D MDS plot and add the line of best fit for the
> > 3 dimensions (3 variables). I am also trying to figure out, or
> > understand, which variables are causing the variation.
> >
> > I ran PCA and it told me that with 3 variables approximately 50% of
> > the data variation is explained. So I assumed that meant that running
> > MDS in 3 dimensions would show me 3 variables causing the variation
> > but I can't get that to work.
> >
> > Here is my code so far (i've also attached it to the email):
> >
> > mortdata<-read.csv("Table5.5.csv", header=TRUE) mortdata
> > row.names(mortdata) <- mortdata[,1]
> > mortdata <- mortdata[,-1]
> > mortdata
> >
> > mortdata.mds <- metaMDS(mortdata)
> > mortdata.mds.alt <- metaMDS(mortdata, distance="euclidean", k=3,
> trymax=50,
> >       autotransform=FALSE, noshare=FALSE)
> >
> >
> > *object = mortdata.mds.alt
> >
> > names(mortdata.mds.alt)
> >
> > mortdata.mds.alt
> > summary(mortdata.mds.alt)
> >
> > *stress plot
> > stressplot(mortdata.mds.alt)
> >
> > x <- mortdata.mds.alt$species
> > y <- mortdata.mds.alt$points
> > na.exclude(mortdata.mds.alt)
> > vScoresScale <- scale(, center = TRUE, scale = TRUE)
> >
> >
> > plot(mortdata.mds.alt)
> > plot(mortdata.mds.alt, type="t")
> >
> > *multiple linear regression model
> > lm(formula = x ~ y)
> > abline(lm(x ~ y), col="red")
> >
> >
> > *scatterplot3D
> > library(scatterplot3d)
> > attach(mortdata.mds.alt)
> > scatterplot3d(mortdata.mds.alt, x="sampleScores", y="variableScores",
> > main="3D Scatterplot")
> >
> > Any help would be greatly appreciated. I can also send the dataset if
> > that helps.
> >
> > --
> > *Kirsten Green*
> > kagbones at gmail.com
> > 916-712-5193
> > ______________________________________________
> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > 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.
> >
>
>
>
> --
> *Kirsten Green*
> kagbones at gmail.com
> 916-712-5193
>
>       [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> 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.

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