Hi, You can use the following code for calculating the KS statistic. Proc npar1way gives us the KS statistic. proc npar1way data= "proc logistic output dataset " edf; class "response variable"; var "predicted variable in proc logistic"; run;
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Assuming that you mean all the program code you want is saved in separate program code files (with .sas suffix), the easiest way would be to open a new project and import all the program code files you want. Richard
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SAS enumerated variable lists can be incremented by 1 or -1 only as far as I know. You seem to want an arbitrary increment in this case MONTH. That's find but it does not work with enumerated variable lists. If it were me I would keep the months/dates/ whatever as rows as long as possible. Then when you need to have the months/dates/whatever become variables transpose to variables and LABEL the variables in a way that communicates the details of the data that you have placed in the meta data. PROC TRANSPOSE statement IDLABEL will allow you to assign labels to the new variables using data. Using a name prefix list you don't even have to know how many.
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Here is code to input an arbitrary number of columns named with numbers. I still have no idea why you would want to do this. options validvarname=any; %let first=1; %let last=3; data net_revenues; infile cards dlm=',' truncover ; input type $ "&first"n - "&last"n; put (_all_) (=); cards; a,18,12,100 b,20,23,50 c,22,1,75 ; type=a 1=18 2=12 3=100 type=b 1=20 2=23 3=50 type=c 1=22 2=1 3=75
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You can use variable lists in sum you name variables with a common stem such as: total = sum (of var:); to get the total of all variables whose names start with VAR. If using a numeric suffix as in your example you specify a range: total = sum (of var1-var100); You can also mix lists and variable names.
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Just google on that for the sas site. Chaid analyses is relative old. Most of it, sorry is Miner (google exclude use -miner) How to get the statistics you need from SAS Enterprise Guide - The SAS Dummy
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The number of selected rows will be binomially distributed. The expected number of selected rows (the mean) will be N*p. The standard deviation will be sqrt(N*p*(1-p)). When N is large, most of the sample sizes will be within three standard deviations of the mean.
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