Using finalfit conventions, produces a multivariable linear regression model for a set of explanatory variables against a continuous dependent.

lmmulti(.data, dependent, explanatory, weights = "", ...)

Arguments

.data

Dataframe.

dependent

Character vector of length 1: name of depdendent variable (must a continuous vector).

explanatory

Character vector of any length: name(s) of explanatory variables.

weights

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.

Value

A multivariable lm fitted model.

Details

Uses lm with finalfit modelling conventions. Output can be passed to fit2df.

See also

fit2df

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

Examples

library(finalfit)
library(dplyr)

explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "nodes"

colon_s %>%
  lmmulti(dependent, explanatory) %>%
  fit2df()
#>             explanatory                     Coefficient
#> 1 age.factor40-59 years -1.21 (-2.16 to -0.26, p=0.012)
#> 2   age.factor60+ years -1.25 (-2.18 to -0.33, p=0.008)
#> 3        sex.factorMale  -0.07 (-0.54 to 0.40, p=0.779)
#> 4    obstruct.factorYes  -0.31 (-0.91 to 0.29, p=0.313)
#> 5      perfor.factorYes   0.28 (-1.09 to 1.66, p=0.686)