I'm trying to fit a naive Bayes model and predict on a new data set using
the functions naivebayes and predict (package = e1071).
R version 2.5.1 on a Linux machine
My data set looks like this. class is the response and k1 - k3 are the
independent variables. All of them are factors. The response has 52 levels
and k1 - k3 have 2-6 levels. I have about 9,300 independent variables but
omit the long list here for simple demonstration. There are no missing
values in the observations.
class k1 k2 k3
1 0 0 1
8 0 0 0
# model fitting, I also tried setting laplace=0 but didn't help
nbmodel - naiveBayes(class~., data=train, laplace=1)
# predict
nb.fit - predict(nbmodel, x.test[,-1])
First I had no trouble fitting the model. R also returned the predictions
for some of my large data sets. But for some data sets, R can fit the model
(no error message, nb.model$tables look ok). When I invoked the predict
function, it kept giving me the following message:
# my data set has 1 response variable and 9318 independent variables
Error in FUN(1:9319[[4L]], ...) : subscript out of bounds
# Here's what traceback() returns
10: FUN(1:9319[[4L]], ...)
9: lapply(X, FUN, ...)
8: sapply(1:nattribs, function(v) {
nd - ndata[v]
if (is.na(nd))
rep(1, length(object$apriori))
else {
prob - if (isnumeric[v]) {
msd - object$tables[[v]]
dnorm(nd, msd[, 1], msd[, 2])
}
else object$tables[[v]][, nd]
prob[prob == 0] - threshold
prob
}
})
7: log(sapply(1:nattribs, function(v) {
nd - ndata[v]
if (is.na(nd))
rep(1, length(object$apriori))
else {
prob - if (isnumeric[v]) {
msd - object$tables[[v]]
dnorm(nd, msd[, 1], msd[, 2])
}
else object$tables[[v]][, nd]
prob[prob == 0] - threshold
prob
}
}))
6: apply(log(sapply(1:nattribs, function(v) {
nd - ndata[v]
if (is.na(nd))
rep(1, length(object$apriori))
else {
prob - if (isnumeric[v]) {
msd - object$tables[[v]]
dnorm(nd, msd[, 1], msd[, 2])
}
else object$tables[[v]][, nd]
prob[prob == 0] - threshold
prob
}
})), 1, sum)
5: FUN(1:30[[1L]], ...)
4: lapply(X, FUN, ...)
3: sapply(1:nrow(newdata), function(i) {
ndata - newdata[i, ]
L - log(object$apriori) + apply(log(sapply(1:nattribs, function(v) {
nd - ndata[v]
if (is.na(nd))
rep(1, length(object$apriori))
else {
prob - if (isnumeric[v]) {
msd - object$tables[[v]]
dnorm(nd, msd[, 1], msd[, 2])
}
else object$tables[[v]][, nd]
prob[prob == 0] - threshold
prob
}
})), 1, sum)
if (type == class)
L
else {
L - exp(L)
L/sum(L)
}
})
2: predict.naiveBayes(nbmodel, validf[1:30, ])
1: predict(nbmodel, validf[1:30, ])
Does anyone have an idea what went wrong? Thanks in advance.
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