Greetings R world,
I know some version of the this question has been asked before, but i need
to save the output of a loop into a data frame to eventually be written to a
postgres data base with dbWriteTable. Some background. I have developed
classifications models to help identify problem accounts. The logic is this,
if the model classifies the record as including variable X and it turns out
that record does not have X then it should be reviewed(ie i need the row
number/ID saved to a database). Generally i want to look at the
misclassified records. This is a little hack i know, anyone got a better
idea please let me know. Here is an example
library(rpart)
# grow tree
fit <- rpart(Kyphosis ~ Age + Number + Start,
method="class", data=kyphosis)
#predict
prediction<-predict(fit, kyphosis)
#misclassification index function
predict.function <- function(x){
for (i in 1:length(kyphosis$Kyphosis)) {
#the idea is that if the record is "absent" but the prediction is otherwise
then show me that record
if (((kyphosis$Kyphosis[i]=="absent")==(prediction[i,1]==1)) == 0 ){
#THIS WORKS
print( row.names(kyphosis[c(i),]))
}
} }
predict.function(x)
Now my issue is that i want to save these id to a data.frame so i can later
save them to a database. This this an incorrect approach. Can I save each id
to the postgres instance as it is found. i have a ignorant fear of lapply,
but it seems it may hold the key.
Ive tried
predict.function <- function(x){
results<-as.data.frame(1)
for (i in 1:length(kyphosis$Kyphosis)) {
#the idea is that if the record is "absent" but the prediction is otherwise
then show me that record
if (((kyphosis$Kyphosis[i]=="absent")==(prediction[i,1]==1)) == 0 ){
#THIS WORKS
results[i,]<- as.data.frame(row.names(kyphosis[c(i),]))
}
} }
this does not work. results object does not get saved. Any Help would be
greatly appreciated.
Thanks
John Dennison
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