I have a dataset pop_dm that contains individuals with some characteristics. I have another dataset lfp which contains parameters from a logit regression for the labor force participation. I want to use those parameters to calculate in pop_dm the individual probability of being in the labor force. I would like to know if there is a simple way to merge both datasets. The variable intercept from lfp should for instance match individuals by sex only. Similarly, the variable agegr_edu_p (parameters for the interaction agegr and edu) should match by sex, agegr and education. I thus cannot use a single merge statement, because the “by” are not the same for the different parameters.   My first idea was to do it in many data steps, but it looks over complicated.     ... View more

Hi, I have a macro "microsim" with 2 parameters, which are a starting year and an ending year. I want to loop this macro by increasing the year by 5 from 2010 until 2060. In other words, the loop should do: %microsim(2010,2015); %microsim(2015,2020); %microsim(2020,2025); ... %microsim(2055,2060);   I was thinking to do something like: %macro loop(start,end); %local year; %do year = &start %to &end; %microsim (&start, %eval(&year+5)) %end; %mend loop; %loop(2010, 2060); But the do statement increases by 1 while I want by 5; ... View more

Hi, In the following codes, the variable test must have the label of the corresponding variable in the array "prob" when the if conditions is met. For instance, if the first condition is met for i=1, then the value for the variable test should be "AN_urban". If it's the second  statement is true, then the value of test should be "AD_uban".   data work.pop_dm; merge work.pop_2010(in=in1) work.dom_mig; by region agegr edu sex year; if in1; a=rand('uniform'); array prob {*} AN_urban AD_urban AR_urban AS_urban BR_urban CH_urban CT_urban DN_urban DD_urban DL_urban GA_urban GJ_urban HR_urban HP_urban JK_urban JH_urban KA_urban KL_urban LD_urban MP_urban MH_urban MN_urban ML_urban MZ_urban NL_urban OR_urban PY_urban PB_urban RJ_urban SK_urban TN_urban TR_urban UT_urban UP_urban WB_urban AN_rural AD_rural AR_rural AS_rural BR_rural CH_rural CT_rural DN_rural DD_rural DL_rural GA_rural GJ_rural HR_rural HP_rural JK_rural JH_rural KA_rural KL_rural LD_rural MP_rural MH_rural MN_rural ML_rural MZ_rural NL_rural OR_rural PY_rural PB_rural RJ_rural SK_rural TN_rural TR_rural UT_rural UP_rural WB_rural; do i=1 to dim(prob); if i=1 and a<prob(i) then test="LABEL OF PROB"; if prob(i)<=a<prob(i+1) then test="LABEL OF PROB+1"; end; run; ... View more

I want to create a synthetic data that is representative of a population by age, sex, region and education. The original table looks like this (see attached for the full file): agegr edu SEX region Population 0 e1 0 AD_rural 2180000 0 e1 0 AD_urban 1084307 0 e1 0 AN_rural 9476 0 e1 0 AN_urban 5178 0 e1 0 AR_rural 58663 0 e1 0 AR_urban 13887 … … ... … … 100 E6 1 WB_rural 23   In the synthetic dataset, the weight of each individual should not be higher than 10,000. This means that I should create 218 individuals having the first set of variable (agegr=0, edu=e1, sex=0, AD_rural), each of them having a weight of 10,000.   Thank you ... View more

Hi, I'd like to duplicate rows in my dataset that have a weight over a certain threshold (1000 for instance), and then reduce the weight by 2. For instance, this: PERWT SEX agegr edu region 1100 0 65 e1 Andhra Prades 294.9 0 40 e3 Andhra Prades   Should be  like this:   PERWT SEX agegr edu region 550 0 30 e1 Andhra Prades 550 0 30 e1 Andhra Prades 294.9 0 40 e3 Andhra Prades   I know I can do it with the output statement, but I don't really get how this works.   Thank you ... View more

Hi, I produced a histogram with proc sgplot using this code:    proc sgplot data=work.test; histogram age_num; density age_num; run;   The plot produced is the followed: As you can see, there are some irregularities for some ages. To validate, I tried to create the same chart using prec freq instead:     proc freq data=work.test; table age_num /plots=freqplot; run;     The chart is the following, which, as you can see, is very different, since there are no more irregularities.   So I wonder: how is this possible? Maybe I do not interpret correctly the output produced by the  proc sgplot.     ... View more