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

[David L Carlson]

You do not need to change it to numeric if it is integer. But if you want to change it, you need to follow the example I included and use sapply(). 

Here is my effort to replicate your error. Since I'm using random data, it fails to converge, but I do not get the error message you are getting. This is the best I can do since you have not given us reproducible data. 

> set.seed(42)
> mortdata <- data.frame(matrix(sample(0:1, 28*27, replace=TRUE), 28, 27))
> dim(mortdata)
[1] 28 27
> library(vegan)
Loading required package: permute
Loading required package: lattice
This is vegan 2.4-1
> sapply(mortdata, type)
Error in match.fun(FUN) : object 'type' not found
> sapply(mortdata, class)  # Is everything numeric (which includes integer)?
       X1        X2        X3        X4        X5        X6        X7        X8        X9 
"integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer" 
      X10       X11       X12       X13       X14       X15       X16       X17       X18 
"integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer" 
      X19       X20       X21       X22       X23       X24       X25       X26       X27 
"integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer" 
> mortdata.mds <- metaMDS(mortdata)
. . . Many messages 
... New best solution
... Procrustes: rmse 0.07861438  max resid 0.2210256 
Run 18 stress 0.277969 
Run 19 stress 0.2633298 
Run 20 stress 0.2838487 
*** No convergence -- monoMDS stopping criteria:
    20: stress ratio > sratmax

-------------------------------------
David L Carlson
Department of Anthropology
Texas A&M University
College Station, TX 77840-4352




From: Kirsten Green [mailto:kagbones at gmail.com] 
Sent: Tuesday, October 18, 2016 2:27 PM
To: David L Carlson
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 change it to numeric using:
str(mortdata)
class(mortdata)
is.numeric(mortdata)
mortdata.num <- as.numeric(data.frame(mortdata))

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
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-- 
*Kirsten Green*
kagbones at gmail.com
916-712-5193