Re: st: predicted values in svy glm l(log) f(poisson)

[Steven Samuels <sjsamuels@gmail.com>]

--


In Stata 10, the easiest way will be to use -predictnl- after -svy:  
glm-.  Please read the FAQ next time. Section 3.3 states:

"The current version of Stata is 11.1. Please specify if you are using  
an earlier version; otherwise, the answer to your question is likely  
to refer to commands or features unavailable to you."


Steve

On Dec 28, 2010, at 2:08 PM, Douglas Levy wrote:

Is there a way to do this in Stata 10?

On Thu, Dec 23, 2010 at 4:59 PM, Steven Samuels <sjsamuels@gmail.com>  
wrote:
> Actually, the following code will work whether or not exposure was a  
> stratum
> variable at any stage.
>
> Steve
>
> Steven J. Samuels
> sjsamuels@gmail.com
> 18 Cantine's Island
> Saugerties NY 12477
> USA
> Voice: 845-246-0774
> Fax:    206-202-4783
>
> **************************CODE BEGINS**************************
> sysuse auto, clear
> svyset turn [pw= trunk]
>
> replace foreign = foreign +1  //convenient for -margins-
>
> // foreign =2 is the treated group
> svy: glm rep78 mpg weight i.foreign, link(log) family(poisson)
> margins, subpop(if foreign==2) at(foreign=(1,2)) post  
> vce(unconditional)
> // _at2 is foreign as foreign   _at1 is foreign as domestic
> lincom _b[2._at]- _b[1._at]  //ATT
> margins, coeflegend   //If you forget the coefficient names
> lincom _b[2._at] - _b[1bn._at]
>
> ***************************CODE ENDS***************************
>
>
>
>
>
>
> Use -margins-, but without knowing the survey design it's hard to  
> say more.
>  Were separate samples taken from the "exposed" and "unexposed" units
> (whatever they were)?  Were the PSUs stratified by exposure status?  
> Describe
> the design and your -svyset- statement.
>
>
> Steve
>
> On Dec 23, 2010, at 2:03 PM, Douglas Levy wrote:
>
> I am now revisiting this issue, having, with Steve's guidance, settled
> on option #2 from my original post. I.e., estimate glm model; predict
> daysmissed for exposed=1; predict daysmissed for the exposed group
> when exposed is set to 0; take difference of the [weighted] means of
> the predictions.
>
> Now my question is, how can I put confidence bounds on the difference
> in the mean predictions?
>
> I thank the group for any help it can offer.
> Best,
> Doug
>
>
> On Tue, Oct 26, 2010 at 1:34 PM, Steven Samuels  
> <sjsamuels@gmail.com> wrote:
> > --
> >
> > Your second suggestion would be an estimate of the average effect of
> > treatment (exposure, here) among the treated (ATT).  For an  
> > overview of
> > possibilities, see Austin Nichols's 2010 conference presentations;  
> > his 2007
> > Stata Journal Causal Inference article; and the 2008 Erratum, all  
> > linked at
> > http://ideas.repec.org/e/pni54.html.
> >
> > Holding covariates at the means in non-linear models can be  
> > dangerous.
> >  For an example, see
> > http://www.stata.com/statalist/archive/2010-07/msg01596.html and  
> > Michael N.
> > Mitchell's followup.
> >
> > Steve
> >
> > Steven J. Samuels
> > sjsamuels@gmail.com
> > 18 Cantine's Island
> > Saugerties NY 12477
> > USA
> > Voice: 845-246-0774
> > Fax:    206-202-4783
> >
> > On Oct 26, 2010, at 11:24 AM, Douglas Levy wrote:
> >
> > I have complex survey data on school days missed for an exposed and
> > unexposed group. I have modeled the effect of exposure on absenteeism
> > using svy: glm daysmissed exposure $covariates, l(log) f(poisson). I
> > would like to estimate adjusted mean days missed for the exposed and
> > control groups, but I'm not sure of the best way to deal with this in
> > a non-linear model. There are a couple of methods I've encountered,
> > and I would be grateful for some thoughts on the pros and cons of
> > each.
> >
> > 1. Estimate glm model. Reset all covariates to their [weighted]  
> > sample
> > means. Predict daysmissed when exposed=0 and when exposed=1.
> > 2. Estimate glm model. Predict daysmissed for exposed=1. Predict
> > daysmissed for the exposed group when exposed is set to 0. Take the
> > [weighted] means of the predictions.
> > 3. Other suggestions?
> >
> > Thanks.
> > -Doug
> > *
> > *   For searches and help try:
> > *   http://www.stata.com/help.cgi?search
> > *   http://www.stata.com/support/statalist/faq
> > *   http://www.ats.ucla.edu/stat/stata/
> >
> > *
> > *   For searches and help try:
> > *   http://www.stata.com/help.cgi?search
> > *   http://www.stata.com/support/statalist/faq
> > *   http://www.ats.ucla.edu/stat/stata/
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/