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Andreas Gammelgaard Damsbo 2025-02-19 13:17:16 +01:00
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man/regression_model.Rd
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@ -2,41 +2,85 @@
% Please edit documentation in R/regression_model.R
\name{regression_model}
\alias{regression_model}
\alias{regression_model_uv}
\alias{regression_model_list}
\alias{regression_model_uv_list}
\title{Create a regression model programatically}
\usage{
regression_model(
data,
outcome.str,
auto.mode = TRUE,
auto.mode = FALSE,
formula.str = NULL,
args.list = NULL,
fun = NULL,
vars = NULL,
...
)
regression_model_uv(
data,
outcome.str,
args.list = NULL,
fun = NULL,
vars = NULL,
...
)
regression_model_list(
data,
outcome.str,
fun.descr,
fun = NULL,
formula.str = NULL,
args.list = NULL,
vars = NULL,
...
)
regression_model_uv_list(
data,
outcome.str,
fun.descr,
fun = NULL,
formula.str = NULL,
args.list = NULL,
vars = NULL,
...
)
}
\arguments{
\item{data}{data set}
\item{data}{data}
\item{outcome.str}{Name of outcome variable. Character vector.}
\item{outcome.str}{name of outcome variable}
\item{auto.mode}{Make assumptions on function dependent on outcome data format. Overwrites other arguments.}
\item{formula.str}{Formula as string. Passed through 'glue::glue'. If given, 'outcome.str' and 'vars' are ignored. Optional.}
\item{formula.str}{custom formula glue string. Default is NULL.}
\item{args.list}{List of arguments passed to 'fun' with 'do.call'.}
\item{args.list}{custom character string to be converted using
argsstring2list() or list of arguments. Default is NULL.}
\item{fun}{Name of function as character vector or function to use for model creation.}
\item{fun}{name of custom function. Default is NULL.}
\item{vars}{character vector of variables to include}
\item{...}{ignored for now}
\item{...}{ignored}
\item{fun.descr}{Description of chosen function matching description in
"supported_functions()"}
}
\value{
object of standard class for fun
object of standard class for fun
list
list
}
\description{
Create a regression model programatically
Output is a concatenated list of model information and model
}
\examples{
gtsummary::trial |>
@ -49,10 +93,73 @@ gtsummary::trial |>
formula.str = "{outcome.str}~.",
args.list = NULL
)
gtsummary::trial |> regression_model(
gtsummary::trial |>
default_parsing() |>
regression_model(
outcome.str = "trt",
auto.mode = FALSE,
fun = "stats::glm",
args.list = list(family = binomial(link = "logit"))
)
m <- mtcars |>
default_parsing() |>
regression_model(
outcome.str = "mpg",
auto.mode = FALSE,
fun = "stats::lm",
formula.str = "{outcome.str}~{paste(vars,collapse='+')}",
args.list = NULL,
vars = c("mpg", "cyl")
)
broom::tidy(m)
\dontrun{
gtsummary::trial |>
regression_model_uv(outcome.str = "age")
gtsummary::trial |>
regression_model_uv(
outcome.str = "age",
fun = "stats::lm",
args.list = NULL
)
m <- gtsummary::trial |> regression_model_uv(
outcome.str = "trt",
auto.mode = FALSE,
fun = "stats::glm",
args.list = list(family = binomial(link = "logit"))
args.list = list(family = stats::binomial(link = "logit"))
)
lapply(m,broom::tidy) |> dplyr::bind_rows()
}
\dontrun{
gtsummary::trial |>
regression_model(
outcome.str = "age",
fun = "stats::lm",
formula.str = "{outcome.str}~.",
args.list = NULL
)
ls <- regression_model_list(data = default_parsing(mtcars), outcome.str = "cyl", fun.descr = "Ordinal logistic regression model")
summary(ls$model)
ls <- regression_model_list(data = default_parsing(gtsummary::trial), outcome.str = "trt", fun.descr = "Logistic regression model")
tbl <- gtsummary::tbl_regression(ls$model, exponentiate = TRUE)
m <- gtsummary::trial |>
default_parsing() |>
regression_model(
outcome.str = "trt",
fun = "stats::glm",
formula.str = "{outcome.str}~.",
args.list = list(family = stats::binomial(link = "logit"))
)
tbl2 <- gtsummary::tbl_regression(m, exponentiate = TRUE)
broom::tidy(ls$model)
broom::tidy(m)
}
\dontrun{
gtsummary::trial |> regression_model_uv(
outcome.str = "trt",
fun = "stats::glm",
args.list = list(family = stats::binomial(link = "logit"))
) |> lapply(broom::tidy) |> dplyr::bind_rows()
ms <- regression_model_uv_list(data = default_parsing(mtcars), outcome.str = "mpg", fun.descr = "Linear regression model")
lapply(ms$model,broom::tidy) |> dplyr::bind_rows()
}
}