R: Cross validation on a dataset with factors - r

Question

Often, I want to run a cross validation on a dataset which contains some factor variables and after running for a while, the cross validation routine fails with the error: factor x has new levels Y.
For example, using package boot:
library(boot)
d <- data.frame(x=c('A', 'A', 'B', 'B', 'C', 'C'), y=c(1, 2, 3, 4, 5, 6))
m <- glm(y ~ x, data=d)
m.cv <- cv.glm(d, m, K=2) # Sometimes succeeds
m.cv <- cv.glm(d, m, K=2)
# Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) :
# factor x has new levels B
Update: This is a toy example. The same problem occurs with larger datasets as well, where there are several occurrences of level C but none of them is present in the training partition.
The function createDataPartition function from the package caret does stratified sampling for the outcome variables and correctly warns:
Also, for ‘createDataPartition’, very small class sizes (<= 3) the classes may not show up in both the training and test data.
There are two solutions which spring to mind:
First, create a subset of the data by selecting one random sample of each factor level first, starting from the rarest class (by frequency) and then greedily satisfying the next rare class and so on. Then using createDataPartition on the rest of the dataset and merging the results to create a new train dataset which contains all levels.
Using createDataPartitions and and doing rejection sampling.
So far, option 2 has worked for me because of the data sizes, but I cannot help but think that there must be a better solution than a hand rolled out one.
Ideally, I would want a solution which just works for creating partitions and fails early if there is no way to create such partitions.
Is there a fundamental theoretical reason why packages do not offer this? Do they offer it and I just haven't been able to spot them because of a blind spot? Is there a better way of doing this stratified sampling?
Please leave a comment if I should ask this question on stats.stackoverflow.com.
Update:
This is what my hand rolled out solution (2) looks like:
get.cv.idx <- function(train.data, folds, factor.cols = NA) {
if (is.na(factor.cols)) {
all.cols <- colnames(train.data)
factor.cols <- all.cols[laply(llply(train.data[1, ], class), function (x) 'factor' %in% x)]
}
n <- nrow(train.data)
test.n <- floor(1 / folds * n)
cond.met <- FALSE
n.tries <- 0
while (!cond.met) {
n.tries <- n.tries + 1
test.idx <- sample(nrow(train.data), test.n)
train.idx <- setdiff(1:nrow(train.data), test.idx)
cond.met <- TRUE
for(factor.col in factor.cols) {
train.levels <- train.data[ train.idx, factor.col ]
test.levels <- train.data[ test.idx , factor.col ]
if (length(unique(train.levels)) < length(unique(test.levels))) {
cat('Factor level: ', factor.col, ' violated constraint, retrying.\n')
cond.met <- FALSE
}
}
}
cat('Done in ', n.tries, ' trie(s).\n')
list( train.idx = train.idx
, test.idx = test.idx
)
}

Answer 1

Everyone agrees that there sure is an optimal solution. But personally, I would just try the cv.glm call until it works usingwhile.
m.cv<- try(cv.glm(d, m, K=2)) #First try
class(m.cv) #Sometimes error, sometimes list
while ( inherits(m.cv, "try-error") ) {
m.cv<- try(cv.glm(d, m, K=2))
}
class(m.cv) #always list
I've tried it with 100,000 rows in the data.fame and it only takes a few seconds.
library(boot)
n <-100000
d <- data.frame(x=c(rep('A',n), rep('B', n), 'C', 'C'), y=1:(n*2+2))
m <- glm(y ~ x, data=d)
m.cv<- try(cv.glm(d, m, K=2))
class(m.cv) #Sometimes error, sometimes list
while ( inherits(m.cv, "try-error") ) {
m.cv<- try(cv.glm(d, m, K=2))
}
class(m.cv) #always list

Answer 2

When I call traceback I get this:
> traceback()
9: stop(sprintf(ngettext(length(m), "factor %s has new level %s",
"factor %s has new levels %s"), nm, paste(nxl[m], collapse = ", ")),
domain = NA)
8: model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels)
7: model.frame(Terms, newdata, na.action = na.action, xlev = object$xlevels)
6: predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type ==
"link", "response", type), terms = terms, na.action = na.action)
5: predict.glm(d.glm, data[j.out, , drop = FALSE], type = "response")
4: predict(d.glm, data[j.out, , drop = FALSE], type = "response")
3: mean((y - yhat)^2)
2: cost(glm.y[j.out], predict(d.glm, data[j.out, , drop = FALSE],
type = "response"))
1: cv.glm(d, m, K = 2)
And looking at the cv.glm function gives:
> cv.glm
function (data, glmfit, cost = function(y, yhat) mean((y - yhat)^2),
K = n)
{
call <- match.call()
if (!exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE))
runif(1)
seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE)
n <- nrow(data)
out <- NULL
if ((K > n) || (K <= 1))
stop("'K' outside allowable range")
K.o <- K
K <- round(K)
kvals <- unique(round(n/(1L:floor(n/2))))
temp <- abs(kvals - K)
if (!any(temp == 0))
K <- kvals[temp == min(temp)][1L]
if (K != K.o)
warning(gettextf("'K' has been set to %f", K), domain = NA)
f <- ceiling(n/K)
s <- sample0(rep(1L:K, f), n)
n.s <- table(s)
glm.y <- glmfit$y
cost.0 <- cost(glm.y, fitted(glmfit))
ms <- max(s)
CV <- 0
Call <- glmfit$call
for (i in seq_len(ms)) {
j.out <- seq_len(n)[(s == i)]
j.in <- seq_len(n)[(s != i)]
Call$data <- data[j.in, , drop = FALSE]
d.glm <- eval.parent(Call)
p.alpha <- n.s[i]/n
cost.i <- cost(glm.y[j.out], predict(d.glm, data[j.out,
, drop = FALSE], type = "response"))
CV <- CV + p.alpha * cost.i
cost.0 <- cost.0 - p.alpha * cost(glm.y, predict(d.glm,
data, type = "response"))
}
list(call = call, K = K, delta = as.numeric(c(CV, CV + cost.0)),
seed = seed)
}
It seems the problem has to do with your extremely small sample size and categorical effect (with values "A", "B", and "C"). You are fitting a glm with 2 effects: "B:A" and "C:A". In each CV iteration you bootstrap from the sample dataset and fit a new model d.glm. Given the size, the bootstrapped data are guaranteed to come up with 1 or more iteration in which the value "C" is not sampled, hence the error comes from calculating fitted probabilities from the bootstrap model from the training data in which validation data has a "C" level for x not observed in the training data.
Frank Harrell (often on stats.stackexchange.com) wrote in Regression Modelling Strategies that one ought to favor against split sample validation when sample size is small and/or some cell counts are small in categorical data analysis. Singularity (as you are seeing here) is one of many reasons why I think this is true.
Given the small sample size here, you should consider some split sample cross validation alternatives like a permutation test, or a parametric bootstrap. Another important consideration is exactly why you feel model based inference isn't correct. As Tukey said of the bootstrap, he'd like to call it a shotgun. It will blow the head off of any problem, as long as you're willing to reassemble the pieces.

Answer 3

There don't seem to be many simple solutions around the web so here's one I worked out that should be easy to generalize to as many factors as you need. It uses pre-installed packages and Caret but you could get away with just base R if you really wanted.
To use cross-validation when you have multiple factors follow a two-step process. Convert the factors to numerics and then multiply them together. Use this new variable as the target variable in a stratified sampling function. Be sure to remove it or keep it out of your training set after creating your folds.
If y is your DV and x is a factor then:
#Simulated factors (which are conveniently distributed for the example)
dataset <-data.frame(x=as.factor(rep(c(1,10),1000)),y=as.factor(rep(c(1,2,3,4),250)[sample(1000)]))
#Convert the factors to numerics and multiply together in new variable
dataset$cv.variable <-as.numeric(levels(dataset$x))[dataset$x]*as.numeric(levels(dataset$y))[dataset$y]
prop.table(table(dataset$y)) #One way to view distribution of levels
ftable(dataset$x,dataset$y) #A full table of all x and y combinations
folds <- caret::createFolds(dataset$cv.variable,k=10)
testIndexes <- folds[[k]]
testData <- as.data.frame(dataset[testIndexes, ])
trainData <- as.data.frame(dataset[-testIndexes, ])
prop.table(table(testData$y))
ftable(testData$x,testData$y) #evaluate distribution
which should produce a result that is close to balanced.
Note: In real life, if your sample lacks the requisite unique combinations of factors then your problem is harder overcome and might be impossible. You can either drop some levels from consideration before creating folds or employ some kind of over-sampling.