On 05/26/2010 07:00 AM, Joris Meys wrote:
Hi,
first of all, you shouldn't backtransform your prediction, use the option
type=response instead :
salichpred<-predict(salic.lr, newdata=profilevalidation,type="response")
limit<- 0.5
salichpredcat<- ifelse(salichpred<limit,0,1) # prediction of categories.
Read in on sensitivity, specificity and ROC-curves. With changing the limit,
you can calculate sensitivity and specificity, and you can construct a ROC
curve that will tell you how well your predictions are. It all depends on
how much error you allow on the predictions.
Cheers
Joris
If you want to use split-sample validation, your validation sample is
perhaps 100 times too small.
There are more direct ways to validate predictions than using
sensitivity, specificity, and ROC, for example smooth calibration curves
and various indexes of predictive accuracy. These are implemented in
the rms package. See the validate.lrm and calibrate.lrm functions.
Frank
On Wed, May 26, 2010 at 10:04 AM, azam jaafari<azamjaaf...@yahoo.com>wrote:
Hi
I did validation for prediction by logistic regression according to
following:
validationsize<- 23
set.seed(1)
random<-runif(123)
order(random)
nrprofilesinsample<-sort(order(random)[1:100])
profilesample<- data[nrprofilesinsample,]
profilevalidation<- data[-nrprofilesinsample,]
salich<-profilesample$SALIC.H.1
salic.lr<-glm(salich~wetnessindex, profilesample,
family=binomial('logit'))
summary(salic.lr)
salichpred<-predict(salic.lr, newdata=profilevalidation)
expsalichpred<-exp(salichpred)
salichprediction<-(expsalichpred/(1+expsalichpred))
So,
table(salichprediction, profilevalidation$SALIC.H.1)
in result:
salichprediction 0 1
0.0408806327422231 1 0
0.094509645033899 1 0
0.118665480273383 1 0
0.129685441514168 1 0
0.13545295569511 1 0
0.137580612201769 1 0
0.197265822234215 1 0
0.199278585548248 0 1
0.202436276322278 1 0
0.211278767985746 1 0
0.261036846823867 1 0
0.283792703256058 1 0
0.362229486187581 0 1
0.362795636267779 1 0
0.409067386115694 1 0
0.410860613509484 0 1
0.423960962956254 1 0
0.428164288793652 1 0
0.448509687866763 0 1
0.538401659478058 0 1
0.557282539294224 1 0
0.603881788227797 0 1
0.63633478460736 0 1
So, I have salichprediction between 0 to 1 and binary variable(observed
values) 0 or 1. I want to compare these data together and I want to know is
ok this model(logistic regression) for prediction or no?
please help me?
Thanks alot
Azam
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