R predict single row

Question

I'm trying to predict some data from PCA using leave-one-out (LOO) cross validation.

The prcomp goes well, however when I come to predict the function gets upset

error: 'newdata' must be a matrix or data frame

because I'm supplying a vector (ie a single row) and not a matrix (ie multiple rows).

I've tried as.data.frame and as.matrix and various varieties thereof but I still get errors

error: 'newdata' does not have named columns matching one or more of the original columns`

In my example here loo is the LOO index and mydata and myinfo contain the data and metadata respectively.

tdata = mydata[-loo,]
tinfo = myinfo[-loo,]

vdata = mydata[loo,]
vinfo = myinfo[loo,]

p = prcomp( tdata )
predict(p, newdata = vdata )

Answer 1

没关系，找到了它：

predict(p, newdata = as.data.frame(t(vdata)) )