svyglmuni: Univariable survey-weighted generalised linear models

In finalfit: Quickly Create Elegant Regression Results Tables and Plots when Modelling

Univariable survey-weighted generalised linear models

Description

Wrapper for svyglm. Fit a generalised linear model to data from a complex survey design, with inverse-probability weighting and design-based standard errors.

Usage

svyglmuni(design, dependent, explanatory, ...)

Arguments

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 svyglm.

Value

A list of univariable fitted model outputs. Output is of class svyglmlist.

See Also

fit2df, finalfit_merge

Other finalfit model wrappers: coxphmulti(), coxphuni(), crrmulti(), crruni(), glmmixed(), glmmulti_boot(), glmmulti(), glmuni(), lmmixed(), lmmulti(), lmuni(), svyglmmulti()

Examples

# 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)

finalfit documentation built on Nov. 17, 2023, 1:09 a.m.