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Re: st: corr_svy & covariance matrix for survey data
I used 1000, as the weight is in US pounds, but 100 will work too.
On Oct 3, 2008, at 8:19 AM, Ishtar Govia wrote:
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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