I'm having some problems with to categorize the characteristics of each variable using quartiles. Using the code below.
ods trace on;
/* Calculate Means and Standard Deviations */
PROC MEANS DATA=CoAnl.Combined_analysis MEAN STDDEV;
CLASS Quartiles;
VAR avg_Risk_Physical_Activity avg_dem_pctn4 Dem_Poverty_PC_Income avg_Out_Depres_Disor_CT;
OUTPUT OUT=Means_StdDevs MEAN=Mean_ STDDEV=StdDev_;
RUN;
/* Perform ANOVA with LSMEANS for p-values */
PROC GLM DATA=CoAnl.Combined_analysis;
CLASS Quartiles;
MODEL avg_Risk_Physical_Activity avg_dem_pctn4 Dem_Poverty_PC_Income avg_Out_Depres_Disor_CT = Quartiles;
LSMEANS Quartiles / adjust=tukey;
RUN;
ods trace off;
ods output OverallANOVA=table22;
PROC GLM DATA=CoAnl.Combined_analysis;
CLASS Quartiles;
MODEL avg_Risk_Physical_Activity avg_dem_pctn4 Dem_Poverty_PC_Income avg_Out_Depres_Disor_CT = Quartiles;
LSMEANS Quartiles / adjust=tukey;
RUN;
proc sql;
create table FINAL2 as
select a.Variable,a.MeanSD,
b.Probf label='p-Value'
from table_Mean a left join table22(where =(source="Model"))b
on a.variable =b.dependent;
quit;
Proc print data=FINAL2;
run;
Result
Obs Variable MeanSD ProbF12 4
| Income | 14.9(8.6) | 0.1919 |
| Depressive Disorder | 17.3(5.4) | <.0001 |
| physical activity | 19.7(8.5) | 0.2182 |
| Bachelors Degree or Higher | 33.9(14.7) | 0.4753 |
I tried to put the quartiles level in the Final table above when I used the proc print to make it seem like the image below, butI didn't know the appropriate code for that.
Result
Depression Quartiles N Obs Variable Mean Std Dev0 21
avg_Risk_Physical_Activity
avg_dem_pctn4
Dem_Poverty_PC_Income
avg_Out_Depres_Disor_CT
1 11
avg_Risk_Physical_Activity
avg_dem_pctn4
Dem_Poverty_PC_Income
avg_Out_Depres_Disor_CT
2 3
avg_Risk_Physical_Activity
avg_dem_pctn4
Dem_Poverty_PC_Income
avg_Out_Depres_Disor_CT
3 29
avg_Risk_Physical_Activity
avg_dem_pctn4
Dem_Poverty_PC_Income
avg_Out_Depres_Disor_CT
22.1945351 30.2583750 17.3667744 12.2072732 | 11.9459745 12.1870208 11.2971989 2.8700233 |
21.3504732 32.1421576 16.2652755 16.6428227 | 6.4469325 22.3623002 8.7059777 0.8111580 |
19.4700000 35.8216667 17.3083333 17.9250000 | 6.9890414 18.1325786 8.6883663 1.0158125 |
17.3202876 36.7880271 12.3661449 21.2076199 | 5.6867116 12.5118479 5.5296109 4.8850090 |
Your help is really appreciated.
