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, ...)

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)
#>                 Dependent: API in 2000 (api00)                  unit
#> 1     English language learners (percent)(ell)  [0.0,84.0] Mean (sd)
#> 2              Meals eligible (percent)(meals) [0.0,100.0] Mean (sd)
#> 3 First year at the school (percent)(mobility)  [1.0,99.0] Mean (sd)
#>           value    Coefficient (univariable)  Coefficient (multivariable)
#> 1 652.8 (121.0) -3.73 (-4.36--3.10, p<0.001)  -0.48 (-1.25-0.29, p=0.222)
#> 2 652.8 (121.0) -3.38 (-3.71--3.05, p<0.001) -3.14 (-3.70--2.58, p<0.001)
#> 3 652.8 (121.0)  -1.43 (-3.31-0.46, p=0.137)   0.23 (-0.55-1.00, p=0.567)

# 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)
#>   Dependent: School-wide target met (sch.wide)                    No
#> 1     English language learners (percent)(ell) Mean (SD) 22.5 (19.3)
#> 2              Meals eligible (percent)(meals) Mean (SD) 46.0 (29.1)
#> 3 First year at the school (percent)(mobility) Mean (SD)  13.9 (8.6)
#>           Yes          OR (univariable)        OR (multivariable)
#> 1 20.5 (20.0) 1.00 (0.98-1.01, p=0.715) 1.00 (0.97-1.02, p=0.851)
#> 2 44.7 (29.0) 1.00 (0.99-1.01, p=0.968) 1.00 (0.98-1.02, p=0.732)
#> 3 17.2 (13.0) 1.06 (1.00-1.12, p=0.049) 1.06 (1.00-1.13, p=0.058)