`finalfit`

model
wrapper`R/glmmulti.R`

`glmmulti.Rd`

Using `finalfit`

conventions, produces multiple multivariable binomial
logistic regression models for a set of explanatory variables against a
binary dependent.

glmmulti(.data, dependent, explanatory, family = "binomial", ...)

.data | Data frame. |
---|---|

dependent | Character vector usually of length 1, but can take more than 1 dependent: name of depdendent variable (must have 2 levels). |

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

family | Character vector quoted or unquoted of the error distribution
and link function to be used in the model, see |

... | Other arguments to pass to |

A list of multivariable `glm`

fitted model
outputs. Output is of class `glmlist`

.

Uses `glm`

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`

, `glmuni`

,
`lmmixed`

, `lmmulti`

,
`lmuni`

, `svyglmmulti`

,
`svyglmuni`

library(finalfit) library(dplyr) explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = "mort_5yr" colon_s %>% glmmulti(dependent, explanatory) %>% fit2df(estimate_suffix=" (univariable)")#>#> explanatory OR (univariable) #> 1 age.factor40-59 years 0.57 (0.34-0.98, p=0.041) #> 2 age.factor60+ years 0.81 (0.48-1.36, p=0.426) #> 3 sex.factorMale 0.98 (0.75-1.28, p=0.902) #> 4 obstruct.factorYes 1.25 (0.90-1.76, p=0.186) #> 5 perfor.factorYes 1.12 (0.51-2.44, p=0.770)