Using finalfit conventions, produces multiple univariable linear
regression models for a set of explanatory variables against a continuous dependent.
lmuni(.data, dependent, explanatory, weights = "", ...)Dataframe.
Character vector of length 1, name of depdendent variable (must be continuous vector).
Character vector of any length: name(s) of explanatory variables.
Character vector of length 1: name of variabe for weighting. 'Prior weights' to be used in the fitting process.
Other arguments to pass to lm.
A list of multivariable lm fitted model outputs.
Output is of class lmlist.
Other finalfit model wrappers:
coxphmulti(),
coxphuni(),
crrmulti(),
crruni(),
glmmixed(),
glmmulti_boot(),
glmmulti(),
glmuni(),
lmmixed(),
lmmulti(),
svyglmmulti(),
svyglmuni()
library(finalfit)
library(dplyr)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "nodes"
colon_s %>%
lmuni(dependent, explanatory) %>%
fit2df()
#> explanatory Coefficient
#> 1 age.factor40-59 years -1.14 (-2.08 to -0.21, p=0.016)
#> 2 age.factor60+ years -1.19 (-2.10 to -0.28, p=0.010)
#> 3 sex.factorMale -0.14 (-0.60 to 0.33, p=0.565)
#> 4 obstruct.factorYes -0.24 (-0.83 to 0.36, p=0.435)
#> 5 perfor.factorYes 0.24 (-1.13 to 1.61, p=0.735)