all.
I have already got output from logistic regression, those coefficients. I would like to plug those coefficients into new data set and use the variables in the new data set but the old coefficients to predict new "y". What should I do?I have already tried proc score, but not sure if it is the proper way.
Use can use PROC Logistic Inmodel statement, See the example from SAS documentation here:-
proc logistic inmodel = your_coefficient_file_from_logistic_run;
score data= new_dataset_to_score out=new_scored_dataset;
run;
Let me know if you have any questions
Related
I am analyzing a temporal trend(yr) of certain chemicals(a b & c).
I use proc sgplot and series statement to draw a plot and found there was a decreasing trend.
Becuase the data is right-skewed, I used the median concentration of each year to draw the plot.
Now I would like to conduct a statistical test on the trend. My data came from the NHANES and need to use the proc survey** to perform analysis. I know I can do an ANOVA test based on proc surveyreg and use ANOVA option in the MODELstatement.
proc suveyreg data=a;
stratum stra;
cluster clus;
weight wt;
model a=yr/anova;
run;
But since the original data is right-skewed, I think maybe it is better to use Kruskal-Wallis test on the original data. But I don't know how to write a code in SAS and I didn't find information in proc survey**-related document.
My plan B is to use the log-transformed data and ANOVA test. But I am not sure if that is an appropriate approach. Can somebody tell me how to get the normality test of the residual in ANOVA while using proc surveyreg? I would also like to know if I can test a b & c in one procedure or I should write multiple procedures with changes in MODEL statement.
Looking forward to your engagement.Thank you!
I am new to SAS, and I would like how easy/difficult it would be to try to do an iterative multiple imputation in SAS. In R, this is relatively easy.
The algorithm is as follows:
impute missing data using known distribution
fit model to complete data in 1
use model fit in 2 to impute missing data
repeat model fitting and imputation steps 50 times (e.g. 50 data sets total)
take every 10th dataset and pool the results
Based on my limited experience in SAS, I'm guessing I would have to write a MACRO. I am specifically interested in using proc nlmixed to fit my model. I am not using R because SAS's nlmixed is more flexible and gives more robust results.
proc mi NIMPUTE=n
proc sort; by _Imputation_
proc NLMIXED; by _Imputation_
proc mianalyze;
I want to put all confidence interval plot in one plot for all strata variable after logistic regression. For example, my SAS code is:
proc logistic data=data1;
model y = x;
strata cv1;
output out=out1 unknown1=x_beta1 unknown2=lowerbound unknown3=upperbound unknown4=strata_variable;
run;
I do not know what variable names(unknown1 unknown2 unknown3) I can use in the output statement. As in the sas support page, it said "If a STRATA statement is specified, only the PREDICTED=, DFBETAS=, and H= options are available",here is the link.
My plot statement will be:
proc sgplot data=out1;
scatter y=strata_variable x=x_beta1 / xerrorlower=lowerbound xerrorupper=upperbound
markerattrs=(symbol=circlefilled size=9);
run;
The first plot in this page shows exactly what I want. Sorry I cannot insert any plot as my reputation is not high enough.
I find an another way to finish this. I wrote a macro do loop to get every strata data. And then added
ods output OddsRatios=odds_temp;
to get the estimation and confidence interval and merger all the strata together to make the plot I need.
I'm not so familiar with SAS proc glm. All I have done using proc glm so far is to output parameter estimates and predicted values on training datasets. But I also need to use the fitted model to make prediction on testing dataset. (both point estimates and interval estimates)
Here is my code.
ods output ParameterEstimates=Pi_Parameters FitStatistics=Pi_Summary PredictedValues=Pi_Fitted;
proc glm data=Train_Pi;
class Area Fo5 Tye M0 M1 M2 M3;
model Pi = Dow Area Fo5 Tye M0|HC M1|HC M2|HC M3|HC/solution p ss3 /*tolerance*/;
run;
But how to proceed to next step? something like predict(Model_from_Train_Pi,Test_Pi)
If you're on SAS 9.4 see Jake's answer from this question:
How to predict probability in logistic regression in SAS?
If not on 9.4, my answer applies for adding the data in to the original data set.
A third option is PROC SCORE - documentation has an example for proc reg that's almost identical to your question:
http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_score_sect018.htm
I am estimating a model for firm bankruptcy that involves 11 factors. I have data from 1900 to 2000 and my goal is to estimate my model using proc logistic for the period 1900-1950 and then test its performance on the 1951 through 2000 data. Proc logistic runs fine but the problem I have is that the estimated coefficients have the same name as my factors that I was using in my model. Suppose the dataset that contains all my observations is called myData and the dataset that contains the estimated coefficients which I obtain using an outtest statement (in proc logistic) is called factorEstimates. Now both of these data sets have the variables factor1, factor2, ..., factorN. Now I want to form the dataset outOfSampleResults that does something like the following:
data outOfSampleResults;
set myData factorEstimates;
newVar=factor1*factor1;
run;
Where the first mention of factor1 refers to that contained in myData and the second refers to that contained in factorEstimates. How can I inform sas which dataset it should read for this variable that is common to both of the datasets in the set statement? Alternatively, how could I quickly rename factor1, factor2, ..., factorN as factor1Estimate, factor2Estimate, ..., factorNEstimate in the factorEstimates dataset so as to circumvent this common variable name issue altogether?
Two quick ways to get estimates for a model already developed:
1. Proc logistic score statement
http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_logistic_sect066.htm
Include the data in your original proc logistic but use a new variable and ensure that the dependent variable is missing for the observations you want to predict.
data stacked;
set all;
if year >1950 then predicted=.;
else predicted=y;
run;
proc logistic data=stacked;
model predicted = factor1 - factor12;
output out=out_predicted predicted=p;
run;