glmmulti_boot {finalfit} | R Documentation |
Binomial logistic regression multivariable models with bootstrapped
confidence intervals: finalfit
model wrapper
Description
Using finalfit
conventions, produces a multivariable binomial logistic
regression models for a set of explanatory variables against a binary
dependent.
Usage
glmmulti_boot(.data, dependent, explanatory, R = 1000)
Arguments
.data |
Dataframe. |
dependent |
Character vector length 1: name of depdendent variable (must have 2 levels). |
explanatory |
Character vector of any length: name(s) of explanatory variables. |
R |
Number of draws. |
Details
Uses glm
with finalfit
modelling conventions.
boot::boot
is used to draw bootstrapped confidence
intervals on fixed effect model coefficients. Output can be passed to
fit2df
.
Value
A multivariable glm
fitted model with
bootstrapped confidence intervals. Output is of class glmboot
.
See Also
Other finalfit model wrappers:
coxphmulti()
,
coxphuni()
,
crrmulti()
,
crruni()
,
glmmixed()
,
glmmulti()
,
glmuni()
,
lmmixed()
,
lmmulti()
,
lmuni()
,
svyglmmulti()
,
svyglmuni()
Examples
library(finalfit)
library(dplyr)
## Note number of draws set to 100 just for speed in this example
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
colon_s %>%
glmmulti_boot(dependent, explanatory, R=100) %>%
fit2df(estimate_suffix="(multivariable (BS CIs))")