DesignSurvey {capm}  R Documentation 
A wraper for svydesign
function from the survey package, to define one of the following survey designs: twostage cluster, simple (systematic) or stratified. In the first case, weights are calculated considering a sample with probability proportional to size and with replacement for the first stage and a simple random sampling for the second stage. Finite population correction is specified as the population size for each level of sampling.
DesignSurvey(sample = NULL, psu.ssu = NULL, psu.col = NULL, ssu.col = NULL, cal.col = NULL, N = NULL, strata = NULL, cal.N = NULL, ...)
sample 

psu.ssu 

psu.col 
the column of 
ssu.col 
the column of 
cal.col 
the column of 
N 
for simple designs, a 
strata 
for stratified designs, a column of 
cal.N 
population total for the variable to calibrate the estimates. It must be used togheter with 
... 
further arguments passed to 
For twostage cluster designs, a PSU appearing in both psu.ssu
and in sample
must have the same identifier. SSU identifiers must be unique but can appear more than once if there is more than one observation per SSU. sample
argument must have just the varibles to be estimated plus the variables required to define the design (twostage cluster or stratified). cal.col
and cal.N
are needed only if estimates will be calibrated. The calibration is based on a population total.
An object of class survey.design.
Lumley, T. (2011). Complex surveys: A guide to analysis using R (Vol. 565). Wiley.
Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.
http://oswaldosantos.github.io/capm
data("cluster_sample") data("psu_ssu") ## Calibrated twostage cluster design design < DesignSurvey(na.omit(cluster_sample), psu.ssu = psu_ssu, psu.col = "census_tract_id", ssu.col = "interview_id", cal.col = "number_of_persons", cal.N = 129445) ## Simple design # If data in cluster_sample were from a simple design: design < DesignSurvey(na.omit(cluster_sample), N = sum(psu_ssu$hh), cal.N = 129445) ## Stratified design # Simulate strata and assume that the data in cluster_design came # from a stratified design cluster_sample$strat < sample(c("urban", "rural"), nrow(cluster_sample), prob = c(.95, .05), replace = TRUE) cluster_sample$strat_size < round(sum(psu_ssu$hh) * .95) cluster_sample$strat_size[cluster_sample$strat == "rural"] < round(sum(psu_ssu$hh) * .05) design < DesignSurvey(cluster_sample, N = "strat_size", strata = "strat", cal.N = 129445)