search for: svyciprop

...head(dta) str(dta) # declare design my.svy <- svydesign(ids=~1, weights=~wt, data=dta) # subset grp1 <- subset(my.svy, grp == "Group1") # set options and clear old warnings options(warn=0) assign("last.warning", NULL, envir = baseenv()) ## proportions and CIs p <- ((svyciprop(~grp, grp1, family=quasibinomial))[1]) # save warnings wrn1 <- warnings(p) ci_l <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[1]) ci_u <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[2]) ## sample counts n <- unwtd.count(~grp, grp1)[1]...

...gt; > > # subset > > grp1 <- subset(my.svy, grp == "Group1") > > > > # set options and clear old warnings > > options(warn=0) > > assign("last.warning", NULL, envir = baseenv()) > > > > ## proportions and CIs > > p <- ((svyciprop(~grp, grp1, family=quasibinomial))[1]) > > > > # save warnings > > wrn1 <- warnings(p) > > > > ci_l <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[1]) > > ci_u <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')...

...;- svydesign(ids=~1, weights=~wt, data=dta) > > # subset > grp1 <- subset(my.svy, grp == "Group1") > > # set options and clear old warnings > options(warn=0) > assign("last.warning", NULL, envir = baseenv()) > > ## proportions and CIs > p <- ((svyciprop(~grp, grp1, family=quasibinomial))[1]) > > # save warnings > wrn1 <- warnings(p) > > ci_l <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[1]) > ci_u <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[2]) > > ## sample count...

...;- svydesign(ids=~1, weights=~wt, data=dta) > > # subset > grp1 <- subset(my.svy, grp == "Group1") > > # set options and clear old warnings > options(warn=0) > assign("last.warning", NULL, envir = baseenv()) > > ## proportions and CIs > p <- ((svyciprop(~grp, grp1, family=quasibinomial))[1]) > > # save warnings > wrn1 <- warnings(p) > > ci_l <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[1]) > ci_u <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[2]) > > ## sample count...

...d count matches exactly svytotal( ~!is.na(ell), dclus1) # number of clusters match exactly dclus1 # none of the confidence interval options match exactly # the standard confint() will certainly be wrong, # since stata gives an asymmetric confidence interval confint(svymean(~I(ell==0), dclus1)) svyciprop(~I(ell==0), dclus1, method="me") # 2nd way to perform confint() # but none of the other methods match either.. svyciprop(~I(ell==0), dclus1, method="li") svyciprop(~I(ell==0), dclus1, method="lo") svyciprop(~I(ell==0), dclus1, method="as") svyciprop(~I(e...

Version 3.18 of the survey package is now percolating through CRAN. Since the last announcement on this list, in February, the main additions are - standard errors for survival curves (both Kaplan-Meier and Cox model) - svyciprop() for confidence intervals on proportions, especially in small samples or near 0 or 1. - predictive margins by direct standardization, with marginpred() - two-phase subsampling designs can now have arbitrary multistage designs at each phase - Rao-Scott-type likelihood ratio tests are now avai...

Version 3.18 of the survey package is now percolating through CRAN. Since the last announcement on this list, in February, the main additions are - standard errors for survival curves (both Kaplan-Meier and Cox model) - svyciprop() for confidence intervals on proportions, especially in small samples or near 0 or 1. - predictive margins by direct standardization, with marginpred() - two-phase subsampling designs can now have arbitrary multistage designs at each phase - Rao-Scott-type likelihood ratio tests are now avai...

...| | | Percent | 20.05 | 24.69 | 16.34 | -------------------------------------------------------------------------------- ------------------------- R Output: Estimate for the total is off. > svyciprop(~I(rfsmok=="At risk"), stdes, na.rm=TRUE, ci=TRUE, vartype="ci") 2.5% 97.5% I(rfsmok == "At risk") 0.187685 0.176309 0.19962 Estimates by sex are a match. > svyby(~I(rfsmok=="At risk"),...

...e 2= 5323 / 10874= .4895 candidate 3= 3419 / 10874= .3144 In this case, the unit of analysis is voters (or votes). If I specify the sampling design using the survey package in this way... design <-svydesign(id=~station + unit fpc=~probstation +probunit, data=sample, pps="brewer") svyciprop(~I(candidate1/totalVotes), design) ... I am assuming that the unit of analysis is the voting unit, right? and I am estimating an average among voting units? I should expand my database at individual level (voters), or I just have to include a unit weight according to the number of voters for voti...

Hi, I want to view the code for the function confint in the stats package. Typing just confint doesn't work. How can I view the code? Thanks, Walt ________________________ Walter R. Paczkowski, Ph.D. Data Analytics Corp. 44 Hamilton Lane Plainsboro, NJ 08536 ________________________ (V) 609-936-8999 (F) 609-936-3733 walt at dataanalyticscorp.com www.dataanalyticscorp.com

Hi, I'm trying to get standard errors for some of the variables in my data frame. One of the questions on my survey is whether faculty coordinate across curriculum to include Arts Education as subject matter. All the responses are coded in zeros and ones obviously. For some of the other variables I have a 2 for those that responded with "Don't Know". I'm getting NA for