svyglmuni {finalfit} | R Documentation |
Wrapper for svyglm
. Fit a generalised linear model to
data from a complex survey design, with inverse-probability weighting and
design-based standard errors.
svyglmuni(design, dependent, explanatory, ...)
design |
Survey design. |
dependent |
Character vector of length 1: name of depdendent variable (must have 2 levels). |
explanatory |
Character vector of any length: name(s) of explanatory variables. |
... |
Other arguments to be passed to |
A list of univariable fitted model outputs. Output is of class
svyglmlist
.
Other finalfit model wrappers:
coxphmulti()
,
coxphuni()
,
crrmulti()
,
crruni()
,
glmmixed()
,
glmmulti_boot()
,
glmmulti()
,
glmuni()
,
lmmixed()
,
lmmulti()
,
lmuni()
,
svyglmmulti()
# Examples taken from survey::svyglm() help page. library(survey) library(dplyr) data(api) dependent = "api00" explanatory = c("ell", "meals", "mobility") library(survey) library(dplyr) data(api) apistrat = apistrat %>% mutate( api00 = ff_label(api00, "API in 2000 (api00)"), ell = ff_label(ell, "English language learners (percent)(ell)"), meals = ff_label(meals, "Meals eligible (percent)(meals)"), mobility = ff_label(mobility, "First year at the school (percent)(mobility)"), sch.wide = ff_label(sch.wide, "School-wide target met (sch.wide)") ) # Linear example dependent = "api00" explanatory = c("ell", "meals", "mobility") # Stratified design dstrat = svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) # Univariable fit fit_uni = dstrat %>% svyglmuni(dependent, explanatory) %>% fit2df(estimate_suffix = " (univariable)") # Multivariable fit fit_multi = dstrat %>% svyglmmulti(dependent, explanatory) %>% fit2df(estimate_suffix = " (multivariable)") # Pipe together apistrat %>% summary_factorlist(dependent, explanatory, fit_id = TRUE) %>% ff_merge(fit_uni) %>% ff_merge(fit_multi) %>% select(-fit_id, -index) %>% dependent_label(apistrat, dependent) # Binomial example ## Note model family needs specified and exponentiation if desired dependent = "sch.wide" explanatory = c("ell", "meals", "mobility") # Univariable fit fit_uni = dstrat %>% svyglmuni(dependent, explanatory, family = "quasibinomial") %>% fit2df(exp = TRUE, estimate_name = "OR", estimate_suffix = " (univariable)") # Multivariable fit fit_multi = dstrat %>% svyglmmulti(dependent, explanatory, family = "quasibinomial") %>% fit2df(exp = TRUE, estimate_name = "OR", estimate_suffix = " (multivariable)") # Pipe together apistrat %>% summary_factorlist(dependent, explanatory, fit_id = TRUE) %>% ff_merge(fit_uni) %>% ff_merge(fit_multi) %>% select(-fit_id, -index) %>% dependent_label(apistrat, dependent)