Please see code below:
%macro do_regression(dep);
proc logistic data = mydata;
class payinc0;
model &dep= payinc0;
output out= &dep predicted= pd_&dep;
run;quit; %mend;
%do_regression (paydec1)
%do_regression (payinc1)
%do_regression (joblost1)
%do_regression (jobstart1)
I have four outcomes that I am interested in modeling. For each of these I want to control for there levels at Time 0 in individual models. How can I modify the code for this?
Related
I have a response variable like Clinical indicator and will perform a multiple regression based on a series predictor variables, all of the predictor variables are numeric or categorical dummy variable, only except for race being the 4-level categorical variable. To perform this regression, I need dummy coding for the race variable. What's the exact coding or option for statement?
Many regression PROCs like proc glmselect do not require dummy coding. You can specify your categorical variables directly with the class statement.
proc glmselect data=sashelp.cars;
class make origin;
model horsepower = make origin msrp / showpvalues selection=stepwise(sle=0.1 sls=0.05);
run;
Following Rick Wicklin's dummy coding method, you can use proc glmselect to generate dummies for you. You can turn this into a macro variable to make generating dummies fast and simple.
%macro get_dummies(data=, vars=, out=dummies, drop=NO);
data _tmp_;
set &data.;
__y__ = 0;
run;
proc glmselect
data=_tmp_
noprint
outdesign(addinputvars) = &out.(drop=__y__
%if(%upcase(&drop.)=YES) %then %do;
&vars.
%end;
)
;
class &vars.;
model __y__ = &vars. / noint selection=none;
run;
proc datasets lib=work nolist;
delete _tmp_;
quit;
%mend;
Example:
%get_dummies(data=sashelp.cars, vars=make origin, drop=yes);
If drop=yes, then it drops the original variables and only leaves the dummy-encoded values.
When I run a proc glimmix in SAS, sometimes it drops observations.
How do I get the set of dropped/excluded observations or maybe the set of included observations so that I can identify the dropped set?
My current Proc GLIMMX code is as follows-
%LET EST=inputf.aarefestimates;
%LET MODEL_VAR3 = age Male Yearc2010 HOSPST
Hx_CTSURG Cardiogenic_Shock COPD MCANCER DIABETES;
data work.refmodel;
set inputf.readmref;
Yearc2010 = YEAR - 2010;
run;
PROC GLIMMIX DATA = work.refmodel NOCLPRINT MAXLMMUPDATE=100;
CLASS hospid HOSPST(ref="xx");
ODS OUTPUT PARAMETERESTIMATES = &est (KEEP=EFFECT ESTIMATE STDERR);
MODEL RADM30 = &MODEL_VAR3 /Dist=b LINK=LOGIT SOLUTION;
XBETA=_XBETA_;
LINP=_LINP_;
RANDOM INTERCEPT/SUBJECT= hospid SOLUTION;
OUTPUT OUT = inputf.aar
PRED(BLUP ILINK)=PREDPROB PRED(NOBLUP ILINK)=EXPPROB;
ID XBETA LINP hospst hospid Visitlink Key RADM30;
NLOPTIONS TECH=NRRIDG;
run;
Thank you in advance!
It drops records with missing values in any variable you're using in the model, in a CLASS, BY, MODEL, RANDOM statement. So you can check for missing among those variables to see what you get. Usually the output data set will also indicate this by not having predictions for the records that are not used.
You can run the code below.
*create fake data;
data heart;set sashelp.heart; ;run;
*Logistic Regression model, ageCHDdiag is missing ;
proc logistic data=heart;
class sex / param=ref;
model status(event='Dead') = ageCHDdiag height weight diastolic;
*generate output data;
output out=want p=pred;
run;
*explicitly flag records as included;
data included;
set want;
if missing(pred) then include='N'; else include='Y';
run;
*check that Y equals total obs included above;
proc freq data=included;
table include;
run;
The output will show:
The LOGISTIC Procedure
Model Information
Data Set WORK.HEART
Response Variable Status
Number of Response Levels 2
Model binary logit
Optimization Technique Fisher's scoring
Number of Observations Read 5209
Number of Observations Used 1446
And then the PROC FREQ will show:
The FREQ Procedure
Cumulative Cumulative
include Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
N 3763 72.24 3763 72.24
Y 1446 27.76 5209 100.00
And 1,446 records are included in both of the data sets.
I think I answered my question.
The code line -
OUTPUT OUT = inputf.aar
gives the output of the model. This table includes all the observations used in the proc statement. So I can match the data in this table to my input table and find the observations that get dropped.
#REEZA - I already looked for missing values for all the columns in the data. Was not able to identify the records there are getting dropped by only identifying the no. of records with missing values. Thanks for the suggestion though.
I have the following problem. I need to run PROC FREQ on multiple variables, but I want the output to all be on the same table. Currently, a PROC FREQ statement with something like TABLES ERstatus Age Race, InsuranceStatus; will calculate frequencies for each variable and print them all on separate tables. I just want the data on ONE table.
Any help would be appreciated. Thanks!
P.S. I tried using PROC TABULATE, but it didn't not calculate N correctly, so I'm not sure what I did wrong. Here is my code for PROC TABULATE. My variables are all categorical, so I just need to know N and percentages.
PROC TABULATE DATA = BCanalysis;
CLASS ERstatus PRstatus Race TumorStage InsuranceStatus;
TABLE (ERstatus PRstatus Race TumorStage) * (N COLPCTN), InsuranceStatus;
RUN;
The above code does not return the correct frequencies based on InsuranceStatus where 0 = insured and 1 = uninsured, but PROC FREQ does. Also doesn't calculate correctly with ROWPCTN. So any way that I can get PROC FREQ to calculate multiple variables on one table, or PROC TABULATE to return the correct frequencies, would be appreciated.
Here is a nice image of my output in a simplified analysis of only ERstatus and InsuranceStatus. You can see that PROC FREQ returns 204 people with an ERstatus of 1 and InsuranceStatus of 1. That's correct. The values in PROC TABULATE are not.
OUTPUT
I'll answer this separately as this is answering the other possible interpretation of the question; when it's clarified I'll delete one or the other.
If you want this in a single printed table, then you either need to use proc tabulate or you need to normalize your data - meaning put it in the form of variable | value. PROC FREQ is not capable of doing multiple one-way frequencies in a single table.
For PROC TABULATE, likely your issue is missing data. Any variable that is on the class statement will be checked for missingness, and if any rows are missing data for any of the class variables, those rows are entirely excluded from the tabulation for all variables.
You can override this by adding the missing option on the class statement, or in the table statement, or in the proc tabulate statement. So:
PROC TABULATE DATA = BCanalysis;
CLASS ERstatus PRstatus Race TumorStage InsuranceStatus/missing;
TABLE (ERstatus PRstatus Race TumorStage) * (N COLPCTN), InsuranceStatus;
RUN;
This will result in a slightly different appearance than on your table, though, as it will include the missing rows in places you probably do not want them, and they'll be factored against the colpctn when again you probably don't want them.
Typically some manipulation is then necessary; the easiest is to normalize your data and then run a tabulation (using PROC TABULATE or PROC FREQ, whichever is more appropriate; TABULATE has better percentaging options though) against that normalized dataset.
Let's say we have this:
data class;
set sashelp.class;
if _n_=5 then call missing(age);
if _n_=3 then call missing(sex);
run;
And we want these two tables in one table.
proc freq data=class;
tables age sex;
run;
If we do this:
proc tabulate data=class;
class age sex;
tables (age sex),(N colpctn);
run;
Then we get an N=17 total for both subtables - that's not what we want, we want N=18. Then we can do:
proc tabulate data=class;
class age sex/missing;
tables (age sex),(N colpctn);
run;
But that's not quite right either; I want F to have 8/18 = 44.44% and M 10/18 = 55.55%, not 42% and 53% with 5% allocated to the missing row.
The way I do this is to normalize the data. This means you get a dataset with 2 variables, varname and val, or whatever makes sense for your data, plus whatever identifier/demographic/whatnot variables you might have. val has to be character unless all of your values are numeric.
So for example here I normalize class with age and sex variables. I don't keep any identifiers, but you certainly could in your data, I imagine InsuranceStatus would be kept there if I understand what you're doing in that table. Once I have the normalized table, I just use those two variables, and carefully construct a denominator definition in proc tabulate to have the right basis for my pctn value. It's not quite the same as the single table before - the variable name is in its own column, not on top of the list of values - but honestly that looks better in my opinion.
data class_norm;
set class;
length val $2;
varname='age';
val=put(age,2. -l);
if not missing(age) then output;
varname='sex';
val=sex;
if not missing(sex) then output;
keep varname val;
run;
proc tabulate data=class_norm;
class varname val;
tables varname=' '*val=' ',n pctn<val>;
run;
If you want something better than this, you'll probably have to construct it in proc report. That gives you the most flexibility, but is the most onerous to program in also.
You can use ODS OUTPUT to get all of the PROC FREQ output to one dataset.
ods output onewayfreqs=class_freqs;
proc freq data=sashelp.class;
tables age sex;
run;
ods output close;
or
ods output crosstabfreqs=class_tabs;
proc freq data=sashelp.class;
tables sex*(height weight);
run;
ods output close;
Crosstabfreqs is the name of the cross-tab output, while one-way frequencies are onewayfreqs. You can use ods trace to find out the name if you forget it.
You may (probably will) still need to manipulate this dataset some to get the structure you want ultimately.
Is it possible to score a data set with a model created by PROC ARIMA in SAS?
This is the code I have that is not working:
proc arima data=work.data;
identify var=x crosscorr=(y(7) y(30));
estimate outest=work.arima;
run;
proc score data=work.data score=work.arima type=parms predict out=pred;
var x;
run;
When I run this code I get an error from the PROC SCORE portion that says "ERROR: Variable x not found." The x column is in the data set work.data.
proc score does not support autocorrelated variables. The simplest way to get an out-of-sample score is to combine both proc arima and a data step. Here's an example using sashelp.air.
Step 1: Generate historical data
We leave out the year 1960 as our score dataset.
data have;
set sashelp.air;
where year(date) < 1960;
run;
Step 2: Generate a model and forecast
The nooutall option tells proc arima to only produce the 12 future forecasts.
proc arima data=have;
identify var=air(12);
estimate p=1 q=(2) method=ml;
forecast lead=12 id=date interval=month out=forecast nooutall;
run;
Step 3: Score
Merge together your forecast and full historical dataset to see how well the model did. I personally like the update statement because it will not replace anything with missing values.
data want;
update forecast(in=fcst)
sashelp.air(in=historical);
by Date;
/* Generate fit statistics */
Error = Forecast-Air;
PctError = Error/Air;
AbsPctError = abs(PctError);
/* Helpful for bookkeeping */
if(fcst) then Type = 'Score';
else if(historical) then Type = 'Est';
format PctError AbsPctError percent8.2;
run;
You can take this code and convert it into a generalized macro for yourself. That way in the future, if you wanted to score something, you could simply call a macro program to get what you need.
I am running a proc logistic with selection =score , to get the best model based on chi-square value. Here is the code
options symbolgen;
%let input_var=ABC_DEF_CkkkkkedHojjjjjerRen101 dept_gert home_value
child_household ;
ods output bestsubsets=score;
proc logistic data=trail;
model response(event='Y')=&input_var
/ selection=score best=1;
run;
The output dataset named score has been generated through ods output. Below is the image of the data set.
score data set image
In the score dataset, in the "variables included in model" column, you can only see a part of variable name "ABC_DEF_CkkkkkedHojjjjjerRen101" and not the entire name. May I know why is this happening and how do I get the entire variable name. Please let me know
Add NAMELEN=32 to your proc logistic statement.