In SAS, may i know how to change alpha=0.02 in Proc Anova, as default alpha is 0.05
proc anova data=data1 alpha=0.02;
class cmg ;
model wmg=cmg ;
run;
If I give alpha=0.02 after data its not working.
I'm not familiar with proc anova but, from a quick look a the documentation, it seems like the alpha= option is available on the means statement:
proc anova data=data1 alpha=0.02;
class cmg ;
model wmg=cmg ;
means cmg / alpha=0.02;
run;
https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_anova_sect012.htm
Related
I'm trying to export quartile information on a grouped dataset as a dataset in SAS but when I run this code my output is a table with the correct information displayed but the dataset WORK.TOP_1O_PERC is only summary statistics of the set (no quartiles). Does anyone know how I can export this as the CLASS (PDX) and its 25th and 75th percentiles? Thanks!
PROC MEANS DATA=WORK.TOP_10_DX P25 P75;
CLASS PDX;
VAR AmtPaid;
OUTPUT OUT = WORK.TOP_10_PERC;
RUN;
I like the STACKODS output that is a data set which is like the default printed output.
proc means data=sashelp.class n p25 p75 stackods;
ods output summary=summary;
run;
proc print;
run;
You can use output statement with <statistics>= options.
PROC MEANS DATA=WORK.TOP_10_DX NOPRINT;
CLASS PDX;
VAR AmtPaid;
OUTPUT OUT = WORK.TOP_10_PERC P25=P25 P75=P75;
RUN;
Compared to ods output, output statement is much faster but less flexible with multiple analysis variables or by statement specified situation.
I am using proc glimmix and proc mixed to run a linear mixed model and am trying to look at the covariance matrix to see if it looks correct. How do I print it to the results?
Use the covb option in the model statement.
proc glimmix data=sashelp.cars;
model horsepower = msrp / covb;
run;
I am brand new to SAS . I'm trying to create dummy variables in SAS and run regression. After doing some research, I put the following codes together, but the results are showing 0 for a few Col. Does anyone know how to fix this? Thanks !
proc glmmod data = Work.Cost outdesign= GLMDesign outparm=GLMParm;
class sex age Membership; model amount=sex age ;
run;
proc print data=GLMDesign ; run; proc print data=GLMParm ; run;
proc reg data=GLMDesign; DummyVars: model amount = COL1-COL69; ods select ParameterEstimates;
quit;
I'm using SAS Proc GLM to make predictions for a dependent variable with some missing values. Note that all of the predictor variables are fully observed, i.e., the predictors contain no missing values. The standard syntax is:
proc glm data=test;
class a;
model dv=a b c/solution;
output out=testx p=pred;
run;
Since the predictors have no missing values the output data should contain predictions for the missing values wrt the dependent variable.
My output does not contain predictions for the missing values in the dependent variable.
What am I doing wrong?
You will need to show your work. This example contradicts your claim.
data class;
set sashelp.class;
x = ranuni(1);
if x gt .9 then weight=.;
run;
proc print;
run;
proc glm;
class sex;
model weight=sex height age / solution;
output out=testx p=pred r=r;
run;
proc print;
run;
I am trying to carry out a logistic regression with SAS. I have few settings for the model, and try to compare the difference.
What I want to archieve is to output the estimated coefficients to a file. I think ODS maybe a promising way, but don't know how to use it.
Can anyone write me a simple example?
Thank you very much.
To add a bit of additional color; ODS OUTPUT <NAME>=DATASET ... ; will save the output into the specified dataset.
Use ODS TRACE get the names of output tables. Information on the tables will be written to the log.
ods trace on;
ods output ParameterEstimates=estimates;
proc logistic data=test;
model y = i;
run;
ods trace off;
For Logistic:
proc logistic data = in descending outest = out;
class rank / param=ref ;
model admit = gre gpa rank;
run;
For proc reg:
proc reg data=a;
model y z=x1 x2;
output out=b
run;
for proc glm:
ods output Solution=parameters FitStatistics=fit;
proc glm data=hers;
model glucose = exercise ;
quit;
run;
for proc reg this doesn't work for me
Use proc reg OUTEST=b
proc reg data=a outest=b;
model y=x1;
run;
other reg can get other parameters added to OUTEST.