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 Thu May 03 2012 01:13:02 

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