Using `finalfit`

conventions, produces multiple multivariable linear
regression models for a set of explanatory variables against a continuous dependent.

lmmulti(.data, dependent, explanatory, ...)

.data | Dataframe. |
---|---|

dependent | Character vector usually of length 1, but can take more than 1 dependent: name of depdendent variable (must a continuous vector). |

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

... | Other arguments to pass to |

A list of multivariable `lm`

fitted model outputs.
Output is of class `lmlist`

.

Uses `lm`

with `finalfit`

modelling conventions. Output can be
passed to `fit2df`

. Note that this function can take multiple `dependent`

variables as well, but performs multiple individual models, not a multivariate analysis.

Other finalfit model wrappers: `coxphmulti`

,
`coxphuni`

, `crrmulti`

,
`crruni`

, `glmmixed`

,
`glmmulti_boot`

, `glmmulti`

,
`glmuni`

, `lmmixed`

,
`lmuni`

, `svyglmmulti`

,
`svyglmuni`

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)