Score sample data set from output of a SAS logistic regression model
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Description
Experience Level: Intermediate
General information for the business: I need someone to carry out some regression for me to generate a model that takes the sample data below and comes up with the model output statements.
Num. of modules: 1
Description of every module: I would like someone to take the sample data below and carry out a logisitic regression for me. The important aspect is that the model generates the output model so that additional data can be scored using 'proc score' by using the scoring parameters from the sample data.
Description of requirements/functionality: data Remission;
input remiss cell smear infil li blast temp;
label remiss='Complete Remission';
datalines;
1 .8 .83 .66 1.9 1.1 .996
1 .9 .36 .32 1.4 .74 .992
0 .8 .88 .7 .8 .176 .982
0 1 .87 .87 .7 1.053 .986
1 .9 .75 .68 1.3 .519 .98
0 1 .65 .65 .6 .519 .982
1 .95 .97 .92 1 1.23 .992
0 .95 .87 .83 1.9 1.354 1.02
0 1 .45 .45 .8 .322 .999
0 .95 .36 .34 .5 0 1.038
0 .85 .39 .33 .7 .279 .988
0 .7 .76 .53 1.2 .146 .982
0 .8 .46 .37 .4 .38 1.006
0 .2 .39 .08 .8 .114 .99
0 1 .9 .9 1.1 1.037 .99
1 1 .84 .84 1.9 2.064 1.02
0 .65 .42 .27 .5 .114 1.014
0 1 .75 .75 1 1.322 1.004
0 .5 .44 .22 .6 .114 .99
1 1 .63 .63 1.1 1.072 .986
0 1 .33 .33 .4 .176 1.01
0 .9 .93 .84 .6 1.591 1.02
1 1 .58 .58 1 .531 1.002
0 .95 .32 .3 1.6 .886 .988
1 1 .6 .6 1.7 .964 .99
1 1 .69 .69 .9 .398 .986
0 1 .73 .73 .7 .398 .986
;
CMS and Admin requirements: Not applicable
Specific technologies required: SAS 9.1.3
OS requirements: Windows
Extra notes: I can do most of the analysis but need some direction for linking the results to applying them to other datasets. I can understand the output matrix and how to utilise this with the proc score funtion to reproduce the predicted values. This is where you come in....
Output matrix example:
Stepwise Regression on Cancer Remission Data
Parameter Estimates and Covariance Matrix
Obs _LINK_ _TYPE_ _STATUS_ _NAME_ Intercept cell smear infil li blast temp _LNLIKE_
1 LOGIT PARMS 0 Converged ESTIMATE 67.63 9.652 . . 3.8671 . -82.07 -10.9767
2 LOGIT COV 0 Converged Intercept 3236.19 157.097 . . 64.5726 . -3483.23 -10.9767
3 LOGIT COV 0 Converged cell 157.10 60.079 . . 6.9454 . -223.67 -10.9767
4 LOGIT COV 0 Converged smear . . . . . . . -10.9767
5 LOGIT COV 0 Converged infil . . . . . . . -10.9767
6 LOGIT COV 0 Converged li 64.57 6.945 . . 3.1623 . -75.35 -10.9767
7 LOGIT COV 0 Converged blast . . . . . . . -10.9767
8 LOGIT COV 0 Converged temp -3483.23 -223.669 . . -75.3513 . 3808.42 -10.9767
Num. of modules: 1
Description of every module: I would like someone to take the sample data below and carry out a logisitic regression for me. The important aspect is that the model generates the output model so that additional data can be scored using 'proc score' by using the scoring parameters from the sample data.
Description of requirements/functionality: data Remission;
input remiss cell smear infil li blast temp;
label remiss='Complete Remission';
datalines;
1 .8 .83 .66 1.9 1.1 .996
1 .9 .36 .32 1.4 .74 .992
0 .8 .88 .7 .8 .176 .982
0 1 .87 .87 .7 1.053 .986
1 .9 .75 .68 1.3 .519 .98
0 1 .65 .65 .6 .519 .982
1 .95 .97 .92 1 1.23 .992
0 .95 .87 .83 1.9 1.354 1.02
0 1 .45 .45 .8 .322 .999
0 .95 .36 .34 .5 0 1.038
0 .85 .39 .33 .7 .279 .988
0 .7 .76 .53 1.2 .146 .982
0 .8 .46 .37 .4 .38 1.006
0 .2 .39 .08 .8 .114 .99
0 1 .9 .9 1.1 1.037 .99
1 1 .84 .84 1.9 2.064 1.02
0 .65 .42 .27 .5 .114 1.014
0 1 .75 .75 1 1.322 1.004
0 .5 .44 .22 .6 .114 .99
1 1 .63 .63 1.1 1.072 .986
0 1 .33 .33 .4 .176 1.01
0 .9 .93 .84 .6 1.591 1.02
1 1 .58 .58 1 .531 1.002
0 .95 .32 .3 1.6 .886 .988
1 1 .6 .6 1.7 .964 .99
1 1 .69 .69 .9 .398 .986
0 1 .73 .73 .7 .398 .986
;
CMS and Admin requirements: Not applicable
Specific technologies required: SAS 9.1.3
OS requirements: Windows
Extra notes: I can do most of the analysis but need some direction for linking the results to applying them to other datasets. I can understand the output matrix and how to utilise this with the proc score funtion to reproduce the predicted values. This is where you come in....
Output matrix example:
Stepwise Regression on Cancer Remission Data
Parameter Estimates and Covariance Matrix
Obs _LINK_ _TYPE_ _STATUS_ _NAME_ Intercept cell smear infil li blast temp _LNLIKE_
1 LOGIT PARMS 0 Converged ESTIMATE 67.63 9.652 . . 3.8671 . -82.07 -10.9767
2 LOGIT COV 0 Converged Intercept 3236.19 157.097 . . 64.5726 . -3483.23 -10.9767
3 LOGIT COV 0 Converged cell 157.10 60.079 . . 6.9454 . -223.67 -10.9767
4 LOGIT COV 0 Converged smear . . . . . . . -10.9767
5 LOGIT COV 0 Converged infil . . . . . . . -10.9767
6 LOGIT COV 0 Converged li 64.57 6.945 . . 3.1623 . -75.35 -10.9767
7 LOGIT COV 0 Converged blast . . . . . . . -10.9767
8 LOGIT COV 0 Converged temp -3483.23 -223.669 . . -75.3513 . 3808.42 -10.9767
Peter V.
97% (6)Projects Completed
6
Freelancers worked with
6
Projects awarded
50%
Last project
10 Sep 2023
United Kingdom
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