Hello all,

I have generated a principal components regression model using the pcr()
function from the PLS package (R version 2.12.0).  I am getting a
"non-conformable arguments" error when I try to use the predict() function
on new data, but only when I try to read in the new data from a separate
file.

More specifically, when my data looks like this

#########training data #1#################

var1          var2           var3             response            train
1                2              type1            33                     TRUE
2                23            type2            44                     TRUE
.....
   .......
18              11            type1            45                      FALSE


and I use the predict() function from the PLS package as in the example
from http://rss.acs.unt.edu/Rdoc/library/pls/html/predict.mvr.html, e.g.

###################################
mydata <- read.csv("mydata.csv", header=TRUE)

mydata <- data.frame(mydata)

pcrmodel <- pcr(response ~ var1+var2+var3, data = mydata[mydata$train,])

predict(pcrmodel, type = "response", newdata = mydata[!mydata$train,])

###################################

the code works, and the model predicts new values for the "response"
variable rows where train=FALSE.

However, as soon as I put the rows where train = FALSE into a separate file
and remove the "train" column so that my training data looks like this:

#########training data #2 ################
var1          var2           var3             response
1                2              type1            33
2                23            type2            44
.....


and my new test data, saved in a separate file (say "newdata.csv") looks
like this

########test data in separate file, newdata.csv ###############
var1          var2           var3             response
3                5              type1            23
4                7              type2            30
.....
18              11            type1            45

if I train a PCR model using the training data #2 and try to predict with
the resulting model and the data from "newdata.csv", e.g.,

##################################
trainingdata <- read.csv("mydata_without_train_column.csv", header=TRUE)

trainingdata <- data.frame(trainingdata)

testingdata <- read.csv("newdata.csv", header=TRUE)

testingdata <- data.frame(testingdata)

pcrmodel2 <- pcr(response ~ var1+var2+var3, data = trainingdata)

predict(pcrmodel, type = "response", newdata = testingdata)
##############################

I get the following error:

"Error in newX %*% B : non-conformable arguments"

I don't understand why I get this error only when I put the non-training
data into a separate file from the training data and load it as a separate
object. Any help is appreciated,

Alison

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