Dear R users/developers
I requested help to solve the problem of formulating Multivariate Sample
selection model by using Full Information Maximum Likelihood
(FIML)estimation method. I could not get any response. I formulated the
following code of FIML to analyse univariate sample selection problem.
Would you please advise me where is my problem
library (sem)
library(nrmlepln)
Selection equation
ws = c(w1, w2, w3)
# values of dependent variables in selection equations are binary (1 and 0)
zs = c(z1, z2, z3, z4, z5)
# z1, z2, z3 continuous and z4 and z5 dummies explanatory variables in
selection equation
Level equation (extent of particular option use)
ys = c(y1, y2, y3)
# values of dependent variables are percentage with some zero cases
xs = c(x1, x2, x3, x4, x5)
# x1, x2, x3 continuous and x4 and x5 dummies dependent variables.
#Note: The variables in both selection and level equations are mostly same.
#Selection model
models1 = 'w1 ~ 1 + zs'
# Level model
model1 = 'w1 ~ 1 + zs|y1 ~ 1 + xs'
fit.fiml = sem(model1, data=MyRdata, estimator="Fiml") # not sure
"ML" or
"Fiml"
summary(fit.fiml)
I greatly appreciate your help.
Advance thank you.
Regards
Champak Ishram
[[alternative HTML version deleted]]
This is dangerously close to a statistics theory question, which would be
off-topic on this list. In any event, your example is definitely not
reproducible (no sample data) [1]. Now might also be a good opportunity for you
to read the Posting Guide mentioned at the bottom of every message on this list.
[1]
http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example
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Champak Ishram <champak.ishram at gmail.com> wrote:
>Dear R users/developers
>I requested help to solve the problem of formulating Multivariate
>Sample
>selection model by using Full Information Maximum Likelihood
>(FIML)estimation method. I could not get any response. I formulated the
>following code of FIML to analyse univariate sample selection problem.
>Would you please advise me where is my problem
>
>library (sem)
>library(nrmlepln)
>
>Selection equation
>ws = c(w1, w2, w3)
>
># values of dependent variables in selection equations are binary (1
>and 0)
>zs = c(z1, z2, z3, z4, z5)
># z1, z2, z3 continuous and z4 and z5 dummies explanatory variables in
>selection equation
>
>Level equation (extent of particular option use)
>ys = c(y1, y2, y3)
># values of dependent variables are percentage with some zero cases
>xs = c(x1, x2, x3, x4, x5)
># x1, x2, x3 continuous and x4 and x5 dummies dependent variables.
>
>#Note: The variables in both selection and level equations are mostly
>same.
>
>
> #Selection model
>
>models1 = 'w1 ~ 1 + zs'
>
> # Level model
>model1 = 'w1 ~ 1 + zs|y1 ~ 1 + xs'
>
>fit.fiml = sem(model1, data=MyRdata, estimator="Fiml") # not sure
"ML"
>or
>"Fiml"
>summary(fit.fiml)
>
>I greatly appreciate your help.
>
>Advance thank you.
>Regards
>Champak Ishram
>
> [[alternative HTML version deleted]]
>
>______________________________________________
>R-help at r-project.org mailing list
>https://stat.ethz.ch/mailman/listinfo/r-help
>PLEASE do read the posting guide
>http://www.R-project.org/posting-guide.html
>and provide commented, minimal, self-contained, reproducible code.
Dear Champak Ishram, It's not surprising that what you did doesn't work because the model specification and the arguments that you employed bear no relationship that I can discern to the sem() function in the sem() package. I think that you're probably confusing the sem package with something else. I hope this helps, John ------------------------------------------------ John Fox Sen. William McMaster Prof. of Social Statistics Department of Sociology McMaster University Hamilton, Ontario, Canada http://socserv.mcmaster.ca/jfox/ On Tue, 23 Apr 2013 12:44:11 +1200 Champak Ishram <champak.ishram at gmail.com> wrote:> Dear R users/developers > I requested help to solve the problem of formulating Multivariate Sample > selection model by using Full Information Maximum Likelihood > (FIML)estimation method. I could not get any response. I formulated the > following code of FIML to analyse univariate sample selection problem. > Would you please advise me where is my problem > > library (sem) > library(nrmlepln) > > Selection equation > ws = c(w1, w2, w3) > > # values of dependent variables in selection equations are binary (1 and 0) > zs = c(z1, z2, z3, z4, z5) > # z1, z2, z3 continuous and z4 and z5 dummies explanatory variables in > selection equation > > Level equation (extent of particular option use) > ys = c(y1, y2, y3) > # values of dependent variables are percentage with some zero cases > xs = c(x1, x2, x3, x4, x5) > # x1, x2, x3 continuous and x4 and x5 dummies dependent variables. > > #Note: The variables in both selection and level equations are mostly same. > > > #Selection model > > models1 = 'w1 ~ 1 + zs' > > # Level model > model1 = 'w1 ~ 1 + zs|y1 ~ 1 + xs' > > fit.fiml = sem(model1, data=MyRdata, estimator="Fiml") # not sure "ML" or > "Fiml" > summary(fit.fiml) > > I greatly appreciate your help. > > Advance thank you. > Regards > Champak Ishram > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.