This article explains two ways to score a validation dataset in PROC LOGISTIC.
1. SCORE Option in PROC LOGISTIC
Proc Logistic Data = training;
Model Sbp_flag = age_flag bmi_flag/ lackfit ctable pprob =0.5;
Output out= test p=ppred;
Score data=validation out = Logit_File;
Run;
2. OUTMODEL / INMODEL Option in PROC LOGISTIC
Proc Logistic Data = training outmodel= model;
Model Sbp_flag = age_flag bmi_flag/ lackfit ctable pprob =0.5;
Output out= test p=ppred;
Run;
proc logistic inmodel=model;
score data=validation out=valid;
run;
1. SCORE Option in PROC LOGISTIC
Proc Logistic Data = training;
Model Sbp_flag = age_flag bmi_flag/ lackfit ctable pprob =0.5;
Output out= test p=ppred;
Score data=validation out = Logit_File;
Run;
2. OUTMODEL / INMODEL Option in PROC LOGISTIC
Proc Logistic Data = training outmodel= model;
Model Sbp_flag = age_flag bmi_flag/ lackfit ctable pprob =0.5;
Output out= test p=ppred;
Run;
proc logistic inmodel=model;
score data=validation out=valid;
run;
Pls when is the best time to split a data set into training and validation - at the begining after forming the modeling data set or after cleaning the data (missing value imputation and outlier treatment)?
ReplyDeletePls when is the best time to split a data set into training and validation - at the begining after forming the modeling data set or after cleaning the data (missing value imputation and outlier treatment)?
ReplyDeletei split the data after cleaning the data , after missing value imputation but before outlier treatment. I do outlier treatment , during variable transformation, after initial run of proc logistic.
ReplyDeletesplit the data into training & modeling after cleaning,removing missing values and outlier, transformation. After that we run the proc logistic model.
ReplyDeletethe predicted value we get from that is that the odds ratio?
ReplyDeletemay I know where can I get your sample training data?
ReplyDelete