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().

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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:kagbo...@gmail.com] Sent: Tuesday, October 18, 2016 2:27 PM To: David L Carlson Cc: r-help@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 <dcarl...@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-boun...@r-project.org] On Behalf Of Kirsten Green Sent: Tuesday, October 18, 2016 12:28 PM To: r-help@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* kagbo...@gmail.com 916-712-5193 ______________________________________________ R-help@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* kagbo...@gmail.com 916-712-5193 ______________________________________________ R-help@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.