I have a time series dataset with serious serial correlation problem, so I adopted Prais-Winsten estimator with iterated estimates to fix that. I did the regressions in Stata with the following command:
prais depvar indepvar indepvar2, vce(robust) rhotype(regress)
My colleague wanted to reproduce my results in SAS, so she used the following:
proc autoreg data=DATA;
model depvar = indepvar indepvar2/nlag=1 iter itprint method=YW;
run;
For the different specifications we ran, some of them roughly match, while others do not. Also I noticed that for each regression specification, Stata has many more iterations than SAS. I wonder if there is something wrong with my (or my colleague's) code.
Update
Inspired by Joe's comment, I modified my SAS code.
/*Iterated Estimation*/
proc autoreg data=DATA;
model depvar = indepvar indepvar2/nlag=1 itprint method=ITYW;
run;
/*Twostep Estimation*/
proc autoreg data=DATA;
model depvar = indepvar indepvar2/nlag=1 itprint method=YW;
run;
I have a few suggestions. Note that I'm not a real statistician and am not familiar with the specific estimators here, so this is just a quick read of the docs.
First off, the most likely issue is that it looks like SAS uses the OLS variance estimation method. That is, in your Stata code, you have vce(robust), which is in contrast to what I read SAS as using, the equivalent of vce(ols). See this page in the docs which explains how SAS does the Y-W method of autoregression, compared to this doc page that explains how Stata does it.
Second, you probably should not specify method=YW. SAS distinguishes between the simple Y-W estimation ("two-step" method) and iterated Y-W estimation. method=ITYW is what you want. You specify iter, so it may well be that you're getting this anyway as SAS tends to be smart about those sorts of things, but it's good to verify.
I would suggest actually turning the iterations off to begin with - have both do the two-step method (Stata option twostep, SAS by removing the iter request and specifying method=YW or no method specification). See how well they match there. Once you can get those to match, then move on to iterated; it's possible SAS has a different cutoff than Stata and may well not iterate past that.
I'd also suggest trying this with only one independent and dependent variable pair first, as it's possible the two programs handle things differently when you add in a second independent variable. Always start simple and then add complexity.
Related
Is there a default map for SAS that projects states like AK,HI, and PR in a more US-centric manner without the need for code workarounds as seen in this.
Alternatively, is there a simpler way to perform this than in the link provided above? Seems like a lot of code for what would seem to be a relatively common task...
The output I'm looking for would be something similar to this:
Ah I found one...
proc gmap data=mapsgfk.us map=mapsgfk.us ;
id statecode;
choro segment / levels=1 nolegend coutline=gray99 des='' name="blah";
run;
Produces the below image. It's missing PR but I can get by with this for the time being.
I have a SAS data set with missing data in multiple columns. I would like replace the missing data with a prediction based on the other data in the data set. Here a link that describes the method but doesn't show me how to do it. How do I replace the missing values with a prediction?
EDIT:
The method I had in mind was just using Proc Reg then apply the coefficents to the missing data to generate the estimate. Does this answer your question?
PROC STDIZE, PROC EXPAND, and PROC MI are all capable of performing different kinds of imputations on your data depending on exactly how you want do determine the 'prediction'.
For simple things like replacing with the mean, PROC STDIZE is the way to go. PROC MI is the most advanced - it performs multiple imputation. PROC EXPAND is appropriate if you have time-series data, as it will try to work out what the correct value is for that point in the time series.
If you have missing data in multiple columns you'll require multiple regressions. This probably isn't a good way to do this, but to answer the question - what you're requesting is called scoring a dataset and you can use PROC SCORE.
An alternative method is in your regression procedure request an OUTPUT data set that contains the predicted values for that regression.
output out=predicted1 p=pred_var_missing;
As a matter of methodology, I recommend #Joe's method instead.
Adding to #Joe 's answer, if you tell us why you want to do this imputation, we can provide better advice. I wrote a blog post called How to Ask a Statistics Question that may help.
However, often, single imputation is a bad method. More particularly, if you are going to do further analysis on this data (with the imputed values) then single imputation will underestimate the variability of the data and give wrong results.
PROC MI is usually a better approach.
is it possible to show the mathemetical formular / concept behind the analysis done with SAS Enterprise?
Assuming SAS would calculate a correlation between a list of numbers -- is it possible to see what exactly SAS did from a mathematical perspective?
It is not possible to ask SAS for the mathematical formula, no. You can check the documentation; for example, this page gives many of the 'elemantary statistics' formulas (like variance, UCLM, etc.)
If you need the formula behind something more complex that you can't find online, contact your SAS Support rep, and they may be able to put you in contact with the developer of that particular proc - like if you need to know some particular to how PROC GLM does something.
You can ask SAS to give you the SAS code that it ran if you executed a task (in most cases it's available by clicking on the task node), in many cases, but that would be something like proc freq; tables a*b; run;, not a mathematical formula per se.
I'm attempting to conduct a simple one-way random-effects ANOVA in SAS. I want to know if the population variance is significantly different than zero or not.
On UCLA's idre site, they state to use PROC MIXED as follows:
proc mixed data = in.hsb12 covtest noclprint;
class school;
model mathach = / solution;
random intercept / subject = school;
run;
This makes sense to me given my previous experience with using PROC MIXED.
However, in the text Biostatistical Design and Analysis Using R by Murray Logan, he says for a one-way ANOVA, fixed and random effects are not distinguished and conducts (in R) a "standard" one-way ANOVA even though he's testing the variance, not the means. I've found that in SAS, his R procedure is equivalent to using any of the following:
PROC ANOVA
PROC GLM (same as ANOVA, but with GLM in place of ANOVA)
PROC GLM with RANDOM statement
The p-values from the above three models are the same, but differ from the PROC MIXED model used by UCLA. For my data, it's a difference of p=0.2508 and p=0.3138. Although conclusions don't change in this instance, I'm not really comfortable with this difference.
Can anyone give advice on which one is more appropriate and also why there is this difference?
For your model, the difference between PROC ANOVA and PROC MIXED is only due to numerical noise(REML estimator of PROC MIXED). However, the p-values mentioned in your question correspond to the different tests. In order to get the F value using the output of COVTEST in PROC MIXED, you need to recalculate MS_groups taking into account the unequal sample sizes (either manually as explained on p.231 of http://bio.classes.ucsc.edu/bio286/MIcksBookPDFs/QK08.PDF, or just using PROC MIXED with the same fixed model spec as in PROC ANOVA). This paper (http://isites.harvard.edu/fs/docs/icb.topic1140782.files/S98.pdf) provides some examples of used of PROC MIXED in addition to SAS manual.
I have a large SAS dataset and I would like to make a series of tables and charts using by value processing. I am outputing these to a PDF.
Is there any way to get SAS to alternate between the table and the chart as it goes through the data? Right now, I have to print all of the tables first and then print the charts. If it were just 4 tables/charts, then I would be ok writing
Here is a simple example:
data sample;
input byval $ item $ amount;
datalines;
A X 15
A Y 16
A Z 12
B X 25
B Y 10
B Z 18
;
run;
symbol1 i=j;
proc print data=sample;
by byval;
var item amount;
run;
proc gplot uniform data=sample;
by byval;
plot amount*item;
run;
This prints 2 tables, followed by 2 charts.
I would like the Chart for "A" to come after the table for "A" so that the reader can flip through the pdf and always see the associated charts and tables together.
I could write separate procs for each one, but then the gplot won't have a uniform axis (and it gets messy if I have 100 different groups instead of 2).
I thought about pumping them into greplay but then you can't use titles with "#BYVAL1".
Is there any easy way to do this?
I've never used it, but it may be worth checking out ODS DOCUMENT. This allows you to store the output of all your procedures and then reference specific items from them using PROC DOCUMENT.
Below is a link to the SAS website with useful information about this, in particular the paper by Cynthia Zender for the SAS Global Forum 2009.
http://support.sas.com/rnd/base/ods/odsdocument/index.html
Cynthia also regularly contributes to the SAS Support Communities website (https://communities.sas.com/community/support-communities), so it may be worth asking on there if you are still stuck.
Good luck
I don't know of any way to do what you ask directly. GREPLAY is probably the closest you'll come; the primary problem is that SAS processes the PROCs linearly, first processing the entire PROC PRINT, then the entire PROC GPLOT. GREPLAY would allow you to redisplay the output, but if that doesn't work for your needs due to the #BYVAL issue, I'm not sure there's a better solution. Perhaps you can modify the title afterwards (not sure if GREPLAY allows this)?
You could try using ODS LAYOUT, but I don't think that would be any better. The one way it could be better is if you can work out having two columns on a 'page', one column being the PROC PRINT outputs, one the PROC GPLOT, and then print the columns one page than the other. I don't think this is possible, but it might be worth exploring.
You might also try setting up a macro to do each BYVAL separately, defining the axis in a uniform manner manually (ie, defining it based on your own calculation of the correct axis parameters, as an argument to the macro). That is probably the easiest solution that might still allow #BYVAL to work properly.
You might also try browsing about Richard DeVenezia's site (http://www.devenezia.com/downloads/sas/samples/ ) which has a lot of examples of SAS/GRAPH solutions. He also posts on SAS-L (sasl#listserv.uga.edu) sometimes, not sure if I've seen him on StackOverflow. He's probably the person most likely to be able to answer the question that I know of.