Displaying 5 results from an estimated 5 matches for "apistrat".
Did you mean:
apistat
2008 Aug 06
1
Warning when using survey:::svyglm
...stat.ethz.ch/pipermail/r-help/2006-April/103862.html
I am still getting the same warning ("non-integer #successes in a
binomial glm!") when using svyglm:::survey. Using the API data:
library(survey)
data(api)
#stratified sample
dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
svyglm(sch.wide~meals+acs.core+hsg,dstrat,family="binomial")
I get:
Stratified Independent Sampling design
...
Warning message:
In eval(expr, envir, enclos) : non-integer #successes in a binomial glm!
Is this still indicative of a problem? Thanks,
Ben
2005 Oct 04
1
"Survey" package and NAMCS data... unsure of specification
...e of standard errors for a
cross-tabulation.
PROC CROSSTAB DATA=COMB1 DESIGN=WR FILETYPE=SAS;
NEST CSTRATM CPSUM/MISSUNIT;
In R, the svydesign command is used to set the sampling scheme, but as
follows (example from the documentation):
dstrat <- svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
stratified on stype, with sampling weights pw. The fpc variable contains
the population size for the stratum. As the schools are sampled
independently, each record in the data frame is a separate PSU. This is
indicated by id=~1. Since the sampling weights could have been determined
fro...
2012 Feb 13
1
survey package svystat objects from predict()
Hello,
I'm running R 2.14.1 on OS X (x86_64-apple-darwin9.8.0/x86_64 (64-bit)), with version 3.28 of Thomas Lumley's survey package. I was using predict() from svyglm(). E.g.:
data(api)
dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
out <- svyglm(sch.wide~ell+mobility, design=dstrat,
family=quasibinomial())
pred.df <- expand.grid(ell=c(20,50,80), mobility=20)
out.pred <- predict(out, pred.df)
From the console out.pred looks like this:
> class(out.pred)
[1] "svystat"
> print(out.pr...
2009 Dec 07
0
zelig logit survey
...se the model is not using "quasibinomial" (see R-News
2003, Analyzing Survey Data in R, by Thomas Lumley). Is there a way to
change the model to use "quasibinomial"?
Reproducible example:
library(Zelig)
library(survey)
data(api)
z.out <- zelig(form = yr.rnd ~ api00, data = apistrat, model =
'logit.survey', id = ~1, strata = ~ stype, weight = ~pw, fpc = ~fpc)
summary(z.out)
Respectfully,
Frank Lawrence
2014 Oct 15
2
Advice on package design for handling of dots in a formula
...ationID=~ID,
strata=~site,
data=mydata)
myresults <- doanalysis(formula=y~X1+X2+X3, design=mystudy)
But it seems that the survey package is also not designed to handle the dot.
data(api)
dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
svyglm(api00~., design=dstrat)
Error in svyglm.survey.design(api00 ~ ., design = dstrat) :
all variables must be in design= argument
Does anyone have advice on how best to handle this?
1. Tell my tester "Tough, you can't use dots in a formula in my
package".essentially...