On May 12, 2011, at 6:49 PM, John Dennison wrote:
It is little ugly i agree but it is acting as it should. I am trying
to capture the cases where the model produced a false positive but
only for one of the variables. ie where the model predicts "present"
but the case is "absent". I know this is only half of the
misclassifications,
23 cases out of 81
but the inverse is not interesting to me. I just imported the logic
from my own application to a general case, my apologies. Take that
part as correct. How would we save the rows it does returns.
It now will run. It just won't populate a dataframe because you
initialized it with on column. Try instead:
results<-data.frame(Kyphosis=NA, Age=NA, Number=NA, Start=NA)
You never reference 'x' so just leave it out.
The place where you use kyphosis[ c(i), ] is a bit ugly. You can just
use kyphosis[ i, ]
And don't put the row.names in results... put the whole row if that is
what you want.
#create output data.frame
results<-data.frame(Kyphosis=NA, Age=NA, Number=NA, Start=NA)
#misclassification index function
predict.function <- function(){
j<-0
for (i in 1:length(kyphosis$Kyphosis)) {
if (((kyphosis$Kyphosis[i]=="absent")==(prediction[i,1]==1)) == 0 ){
j<-j+1
results[j,]<-kyphosis[ i,]
print( kyphosis[i,])
} }
{
print(results)
save(results, file="results") } }
predict.function()
Thanks,
John
On Thu, May 12, 2011 at 6:37 PM, David Winsemius <dwinsem...@comcast.net
> wrote:
On May 12, 2011, at 6:26 PM, John Dennison wrote:
My apologies. I have transgressed the first law of posting, test
your code. here is an updated set this should run:
library(rpart)
# grow tree
fit <- rpart(Kyphosis ~ Age + Number + Start,
method="class", data=kyphosis)
#predict
prediction<-predict(fit, kyphosis)
#create output data.frame
results<-as.data.frame(1)
#misclassification index function
predict.function <- function(x){
j<-0
for (i in 1:length(kyphosis$Kyphosis)) {
if (((kyphosis$Kyphosis[i]=="absent")==(prediction[i,1]==1)) == 0 ){
I think your next task is figuring out if this expression ,,,, which
you have not explained at all ... is really doing what you intend:
(kyphosis$Kyphosis[i]=="absent")==(prediction[i,1]==1)) == 0
I would have guessed that you might be intending:
kyphosis$Kyphosis[i]=="absent" & prediction[i,1]==1
Since it will hold about half the time:
> sum(kyphosis$Kyphosis[1:81]=="absent" & prediction[1:81,1]==1)
[1] 41
j<-j+1
results[j,]<-row.names(kyphosis[c(i),])
print( row.names(kyphosis[c(i),]))
} }
{
print(results)
save(results, file="results") } }
predict.function(x)
results
output: results
1
1 1
load("results")
results
> results
1
1 1
2 2
3 4
4 13
5 18
6 24
7 27
8 28
9 32
10 33
11 35
12 43
13 44
14 48
15 50
16 51
17 60
18 63
19 68
20 71
21 72
22 74
23 79
why the two different 'results'??
Thanks
John Dennison
On Thu, May 12, 2011 at 6:06 PM, David Winsemius <dwinsem...@comcast.net
> wrote:
On May 12, 2011, at 5:41 PM, John Dennison wrote:
Having poked the problem a couple more times it appears my issue is
that the
object i save within the loop is not available after the function
ends. I
have no idea why it is acting in this manner.
library(rpart)
# grow tree
fit <- rpart(Kyphosis ~ Age + Number + Start,
method="class", data=kyphosis)
#predict
prediction<-predict(fit, kyphosis)
#misclassification index function
results<-as.data.frame(1)
predict.function <- function(x){
j<-0
for (i in 1:length(kyphosis$Kyphosis)) {
if (((kyphosis$Kyphosis[i]=="absent")==(prediction[i,1]==1)) == 0 ){
j<-j+1
results[j,]<-row.names(testing[c(i),])
Are we supposed to know where to find 'testing" (and if we cannot
find it, how is the R interpreter going to find it)?
print( row.names(kyphosis[c(i),]))
} }
{
print(results)
save(results, file="results") } }
i can load results from file and my out put is there. how ever if i
just
type results i get the original 1. what is in the lords name is
occurring.
Thanks
John
On Thu, May 12, 2011 at 1:50 PM, Phil Spector <spec...@stat.berkeley.edu
>wrote:
John -
In your example, the misclassified observations (as defined by
your predict.function) will be
kyphosis[kyphosis$Kyphosis == 'absent' & prediction[,1] != 1,]
so you could start from there.
- Phil Spector
Statistical Computing Facility
Department of Statistics
UC Berkeley
spec...@stat.berkeley.edu
On Thu, 12 May 2011, John Dennison wrote:
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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PLEASE do read the posting guide
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______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
David Winsemius, MD
West Hartford, CT
David Winsemius, MD
West Hartford, CT
David Winsemius, MD
West Hartford, CT
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
