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Re: st: corr_svy & covariance matrix for survey data
I meant to ask if it was a typo when you wrote "Why divide by 1000?
Without dividing by 100,..." I'm supposed to be dividing by 100, right
(not 1000)?
On Oct 3, 2008, at 3:09 AM, Steven Samuels wrote:
You should run the code to see what it is does, Ishtar. You can put
-display- and -list- commands to see what it is happening. The
final result is a list of nine covariances. You may have to cut and
paste from the list of covariances by hand into your other
applications. I'm not too conversant with Stata's matrix language,
but instead of the "vec" function, I could have written: matrix
clist = e(b)' ('= transpose operator). Look up -help- matrix,
which may give you some ideas for converting the vector into a
matrix. If you are only transferring one covariance matrix,
creating it by hand and feeding it into the other applications may
be the easiest approach.
On Oct 3, 2008, at 12:16 AM, Ishtar Govia wrote:
Many thanks for sending along the code, Steve. Can you provide some
explanation for some lines of the code so that I can understand
better what exactly I would be doing to get the matrix?
Specifically, why am I only standardizing the weight variable?
"weight" is a variable in the system "auto" data set; it is not the
survey weight variable, set in the -pweight- option. After the -
sysuse- statement, -des- the data set and -sum- it to look at it. I
chose that data set because everybody has it. However it is not
really a survey data set, so it is necessary to pretend that it is,
and therefore I chose one of the variables to act as the survey -
pweight-. If you do not understand the -svyset- statement, consult
the -help-.
Why divide by 1000? Without dividing by 100, some of the covariance
terms and covariances involving the weight variable will be very
large; see what happens to the results if you run the code and
don't divide.
Should I also be standardizing the cluster and stratum variable?
No--there are no computations involving them.
What exactly do the "w" and the "v" in the code to generate the n^2
covariance terms mean/represent?
`v' and `w' are place-holders which take on the names of the
variables. There is no rule about how to name them, but short is
good; I could have used "x" and "y'. Look up -foreach-.
The number of covariance terms is not n^2, as I wrote, but p^2,
where "p" is the number of variables. In the example, 3^2=9 terms
are generated. You can -list- them and -summarize- them. It is
their means which are the estimated covariances themselves.
What would I be doing exactly in the format c_* %20.5fc line of the
code and why does that number change in the code to get the
covariance? (i.e. format c_* %15.5fc)
Some of the covariances are large, so it is necessary to give them
room to display without going to exponential notation.I included two
format statements by mistake. You can omit either one of them. Look
up "help format"
On Oct 2, 2008, at 1:50 PM, Steven Samuels wrote:
In addition, does anyone know how to use the matrix I am trying
to obtain above to obtain a covariance matrix that can be easily
transferred to Mplus or LISREL for CFA and SEM analyses?
In Stata V 10 Version 10.1 corr_svy does not return the proper
correlation matrix, at least for p>2 dimensions.
The following code should get you the covariance matrix in a
list. I don't know anything about the programs you want to feed
into, so you will have to manipulate the list yourself to suit
their requirements.
********************* CODE BEGINS**********************
* Create covariance matrix for survey data
*******************************************************
sysuse auto,clear
svyset _n [pweight=rep78]
gen wt000=weight/1000 //standardize to avoid large values
********************************************************
* put variables into a macro
*******************************************************
local cvars mpg wt000 trunk
*******************************************************
* Get means
*******************************************************
svy: mean `cvars' //outputs weighted means in _b[varname]
*******************************************************
* Get n^2 covariance terms
*******************************************************
foreach w of varlist `cvars' {
foreach v of varlist `cvars' {
gen c_`w'_`v' = ((`w' - _b[`w'])^2)*((`v' - _b[`v'])^2)
}
}
format c_* %20.5fc
*******************************************************
* Get Covariances
*******************************************************
svy: mean c_*
format c_* %15.5fc
matrix clist = vec(e(b))
matrix list clist
*****************CODE ENDS*************************
On Oct 1, 2008, at 9:57 PM, Ishtar Govia wrote:
Dear List,
I am using Stata 10/SE 10.1 for Macs. I have two questions. One
concerning the --corr_svy-- module and error messages I have been
getting, the second concerning how to obtain a covariance matrix
for complex survey sample data, within Stata.
I am working with a complex survey sample dataset and am using
Stata for some preliminary data analyses (univariate and
multivariate checks for normality, efas) before moving to Mplus
for SEM modeling. I installed the --corr_svy-- module from within
Stata by typing "ssc install corr_svy"
However, in my first attempt below, I got the following error
message:
svy, subpop (if race3cat==2 & sex==1 & (riwyes==1 & riwyes <.)):
corr_svy lesscurt lessresp poorserv notsmart actfraid thkdisho
actbettr callnmes trethara follstor, pw obs sig
corr_svy is not supported by svy with vce(linearized); see help
svy estimation for a list of Stata estimation commands that are
supported by svy
r(322);
I then attempted to reset the weight and design variables, and
run the --corr_svy--, following the example in the "help" for the
--corr_svy--, excluding my subpop specification.
. svyset clust [pweight=wgtcentriw], strata (stratum) ||_n
Note: stage 1 is sampled with replacement; all further stages
will be ignored
pweight: wgtcentriw
VCE: linearized
Single unit: missing
Strata 1: stratum
SU 1: clust
FPC 1: <zero>
. corr_svy lesscurt lessresp poorserv notsmart actfraid thkdisho
actbettr callnmes trethara follstor, pw obs sig
This worked--but it didn't include my subpop specification. I
then tried the same thing with my subpop specifcation and it
DIDN'T work. Any ideas on how to use the corr_svy module to
obtain the correlation matrix, accounting for the weight and
design variables and allowing me to restrict my analyses to my
subpop of interest?
corr_svy lesscurt lessresp poorserv notsmart actfraid thkdisho
actbettr callnmes trethara follstor [pweight=wgtcent],
strata(stratum) psu(clust) subpop(if race3cat==2 & sex==1 &
(riwyes==1 & riwyes <.)) pw obs sig print(10) star(5)
subpop() does not contain a valid varname
r(198);
. svyset clust [pweight=wgtcentriw], strata (stratum) ||_n
Note: stage 1 is sampled with replacement; all further stages
will be ignored
pweight: wgtcentriw
VCE: linearized
Single unit: missing
Strata 1: stratum
SU 1: clust
FPC 1: <zero>
. corr_svy lesscurt lessresp poorserv notsmart actfraid thkdisho
actbettr callnmes trethara follstor, pw obs sig
In addition, does anyone know how to use the matrix I am trying
to obtain above to obtain a covariance matrix that can be easily
transferred to Mplus or LISREL for CFA and SEM analyses?
Thanks for your consideration,
Ishtar Govia
[email protected]
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