I'm doing an analysis of the Current Population Survey. I have a wage variable (wage), a time-series variable (qtr), and an observational weight (pworwgt). Each quarter has thousands of observations.
I can easily make a table showing the weighted average wage in each quarter:
table qtr [iw=pworwgt], contents(mean wage)
What I want to do, however, is graph this easily within Stata. I tried to use egen to make a variable containing the mean by qtr, but egen mean() does not allow for weights.
One of the many ways this can be done is with a regression followed by two margins* commands:
webuse hanley
table rating [iw=pop], contents(mean disease)
reg disease i.rating [iw=pop]
margins rating
marginsplot, noci
This has the advantage of not altering your data in any way.
Related
I have a rather simple question regarding the output of tabstat command in Stata.
To be more specific, I have a large panel dataset containing several hundred thousands of observations, over a 9 year period.
The context:
bysort year industry: egen total_expenses=total(expenses)
This line should create total expenses by year and industry (or sum of all expenses by all id's in one particular year for one particular industry).
Then I'm using:
tabstat total_expenses, by(country)
As far as I understand, tabstat should show in a table format the means of expenses. Please do note that ids are different from countries.
In this case tabstat calculates the means for all 9 years for all industries for a particular country, or it just the mean of one year and one industry by each country from my panel data?
What would happen if this command is used in the following context:
bysort year industry: egen mean_expenses=mean(expenses)
tabstat mean_expenses, by(country)
Does tabstat creates means of means? This is a little bit confusing.
I don't know what is confusing you about what tabstat does, but you need to be clear about what calculating means implies. Your dataset is far too big to post here, but for your sake as well as ours creating a tiny sandbox dataset would help you see what is going on. You should experiment with examples where the correct answer (what you want) is obvious or at least easy to calculate.
As a detail, your explanation that ids are different from countries is itself confusing. My guess is that your data are on firms and the identifier concerned identifies the firm. Then you have aggregations by industry and by country and separately by year.
bysort year industry: egen total_expenses = total(expenses)
This does calculate totals and assigns them to every observation. Thus if there are 123 observations for industry A and 2013, there will be 123 identical values of the total in the new variable.
tabstat total_expenses, by(country)
The important detail is that tabstat by default calculates and shows a mean. It just works on all the observations available, unless you specify otherwise. Stata has no memory or understanding of how total_expenses was just calculated. The mean will take no account of different numbers in each (industry, year) combination. There is no selection of individual values for (industry, year) combinations.
Your final question really has the same flavour. What your command asks for is a brute force calculation using all available data. In effect your calculations are weighted by the numbers of observations in whatever combinations of industry, country and year are being aggregated.
I suspect that you need to learn about two commands (1) collapse and (2) egen, specifically its tag() function. If you are using Stata 16, frames may be useful to you. That should apply to any future reader of this using a later version.
I am implementing a logit model in a database of households using as dependent variable the classification of poor or not poor household (1 if it is poor, 0 if it is not):
proc logistic data=regression;
model poor(event="1") = variable1 variable2 variable3 variable4;
run;
Using the proc logistic in SAS, I obtained the table "Association of predicted probabilities and observed responses" that allows me to know the concordant percentage. However, I require detailed information of how many households are classified poor adequately, in this way:
I will appreciate your help with this issue.
Add the CTABLE option to your MODEL statement.
model poor(event="1") = variable1 variable2 variable3 variable4 / ctable;
CTABLE classifies the input binary response observations according to
whether the predicted event probabilities are above or below some
cutpoint value z in the range . An observation is predicted as an
event if the predicted event probability exceeds or equals z. You can
supply a list of cutpoints other than the default list by specifying
the PPROB= option. Also, you can compute positive and negative
predictive values as posterior probabilities by using Bayes’ theorem.
You can use the PEVENT= option to specify prior probabilities for
computing these statistics. The CTABLE option is ignored if the data
have more than two response levels. This option is not available with
the STRATA statement.
For more information, see the section Classification Table.
How do you take an average of the coefficients across all months?
Please refer to this question earlier
How do I perform regression by month on the same SAS data set?
The comments in the linked question provide the code to get the estimates in a data set. Then you would run a PROC MEANS on the saved data set to get the averages. But you could also run the model without which a variable to get the monthly estimates alone. In general, it isn't common to average parameter estimates this way, except in a bootstrapping process.
I am new to SAS and data analytics in general. So sorry if my question sound too dumb.
I have a dataset of brand medicine with three variables. Variable 1 contains the drug name, variable two contains whether that drug is BRANDED, Generic or Brand-Generic and variable 3 contains the total sale of that drug.
What I want is percent split the BRANDED, GENERIC AND BRANDED GENERIC drugs among total drug sale. The final output should look like
Branded : 35%
Generic : 25%
Branded-Generic : 40%
Any help with a sas code which would do that is greatly appreciated thank you.
So you want a % sale split! You can try using SQL (proc sql) to get your desired answer.
proc sql;
create table want as
select drug_type, sum(total_sale) as tot_sale
from have
group by drug_type;
create table want as
select *, tot_sale/sum(tot_sale) as percent_sale format=percent10.2
from want;
quit;
I created a table 'want' that will have total sale for each drug type. Using that table, I created a column that has the calculated sale percentage and formatted it to a percent (for easy view).
Of course, there are other ways of doing it, like using proc summary or proc freq or even a data step. But as a beginner, I guess starting out with SQL would be a good decision.
I have four variables Name, Date, MarketCap and Return. Name is the company name. Date is the time stamp. MarketCap shows the size of the company. Return is its return at day Date.
I want to create an additional variable MarketReturn which is the value weighted return of the market at each point time. For each day t, MarketCap weighted return = sum [ return(i)* (MarketCap(i)/Total(MarketCap) ] (return(i) is company i's return at day t).
The way I do this is very inefficient. I guess there must be some function can easily achieve this traget in SAS, So I want to ask if anyone can improve my code please.
step1: sort data by date
step2: calculate total market value at each day TotalMV = sum(MarketCap).
step3: calculate the weight for each company (weight = MarketCap/TotalMV)
step4: create a new variable 'Contribution' = Return * weight for each company
step5: sum up Contribution at each day. Sum(Contribution)
Weighted averages are supported in a number of SAS PROCs. One of the more common, all-around useful ones is PROC SUMMARY:
PROC SUMMARY NWAY DATA = my_data_set ;
CLASS Date ;
VAR Return / WEIGHT = MarketCap ;
OUTPUT
OUT = my_result_set
MEAN (Return) = MarketReturn
;
RUN;
The NWAY piece tells the PROC that the observations should be grouped only by what is stated in the CLASS statement - it shouldn't also provide an ungrouped grand total, etc.
The CLASS Date piece tells the PROC to group the observations by date. You do not need to pre-sort the data when you use CLASS. You do have to pre-sort if you say BY Date instead. The only rationale for using BY is if your dataset is very large and naturally ordered, you can gain some performance. Stick to CLASS in most cases.
VAR Return / WEIGHT = MarketCap tells the proc that any weighted calculations on Return should use MarketCap as the weight.
Lastly, the OUTPUT statement specifies the data set to write the results to (using the OUT option), and specifies the calculation of a mean on Return that will be written as MarketReturn.
There are many, many more things you can do with PROC SUMMARY. The documentation for PROC SUMMARY is sparse, but only because it is the nearly identical sibling of PROC MEANS, and SAS did not want to produce reams of mostly identical documentation for both. Here is the link to the SAS 9.4 PROC MEANS documentation. The main difference between the two PROCS is that SUMMARY only outputs to a dataset, while MEANS by default outputs to the screen. Try PROC MEANS if you want to see the result pop up on the screen right away.
The MEAN keyword in the OUTPUT statement comes from SAS's list of statistical keywords, a helpful reference for which is here.