Could someone help me on this please?
-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Dwaipayan Dasgupta
Sent: Thursday, May 03, 2012 1:43 PM
To: r-help@r-project.org
Subject: [R] Validation of logistic models in R 2.12
Hi everyone,
I am trying to validate a logistic model built in R. Not my version of R is
2.12 and I cannot install ROCR.
I have gone to a point where I have the predicted values using the code:
pred1 = predict(trainlogit1,testdata_1, type = "response")
How do I proceed from here? Is there another way in which I can plot lift
charts?
My model output is:
Call:
glm(formula = Attrition_ind ~ Time.in.com + UV_LTIA_Base +
as.factor(new_hire_ind) +
as.factor(promotion_ind) + as.factor(Time.in.comp...5.years) +
as.factor(Change.in.Job.Code) + Positioning_num +
as.factor(Below.Guideline) +
as.factor(Above.Guideline) + as.factor(Drop.in.Rating.in.2010) +
as.factor(Increase.in.Rating.in.2010) + as.factor(job_band),
family = binomial(link = "logit"), data = traindata_1)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.9604 -0.4888 -0.4221 -0.3514 2.9813
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.425494 0.115476 -12.345 < 2e-16
Time.in.comp -0.022690 0.007753 -2.927 0.003425
UV_LTIA_Base -0.960578 0.210247 -4.569 4.91e-06
as.factor(new_hire_ind)Y -0.446913 0.145942 -3.062 0.002197
as.factor(promotion_ind)Y -0.739080 0.195584 -3.779 0.000158
as.factor(Time.in.comp...5.years)Y -0.248776 0.118014 -2.108 0.035029
as.factor(Change.in.Job.Code)Y -0.244962 0.118475 -2.068 0.038675
Positioning_num -1.987576 0.529700 -3.752 0.000175
as.factor(Below.Guideline)Y -0.622856 0.171635 -3.629 0.000285
as.factor(Above.Guideline)Y -0.446067 0.252602 -1.766 0.077414
as.factor(Drop.in.Rating.in.2010)Y 0.292605 0.174543 1.676 0.093659
as.factor(Increase.in.Rating.in.2010)Y -0.315004 0.101213 -3.112 0.001856
as.factor(job_band)40 0.391219 0.108038 3.621 0.000293
as.factor(job_band)45 1.228778 0.213261 5.762 8.32e-09
as.factor(job_band)50 1.603452 0.434487 3.690 0.000224
as.factor(job_band)60 2.578905 0.559087 4.613 3.97e-06
(Intercept) ***
Time.in.comp **
UV_LTIA_Base ***
as.factor(new_hire_ind)Y **
as.factor(promotion_ind)Y ***
as.factor(Time.in.comp...5.years)Y *
as.factor(Change.in.Job.Code)Y *
Positioning_num ***
as.factor(Below.Guideline)Y ***
as.factor(Above.Guideline)Y .
as.factor(Drop.in.Rating.in.2010)Y .
as.factor(Increase.in.Rating.in.2010)Y **
as.factor(job_band)40 ***
as.factor(job_band)45 ***
as.factor(job_band)50 ***
as.factor(job_band)60 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 4737.8 on 7473 degrees of freedom Residual deviance:
4607.0 on 7458 degrees of freedom
(250 observations deleted due to missingness)
AIC: 4639
Number of Fisher Scoring iterations: 5
Could you please help? How can I understand if my model is a good fit or not?
Regards,
Doy
American Express made the following annotations on Fri May 04 2012 01:37:47
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American Express a ajouté le commentaire suivant le Fri May 04 2012 01:37:47
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