FreesearchR/man/regression_model.Rd

% Generated by roxygen2: do not edit by hand
% 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 = NULL,
  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}

\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}{custom formula glue string. Default is NULL.}

\item{args.list}{custom character string to be converted using
argsstring2list() or list of arguments. Default is NULL.}

\item{fun}{name of custom function. Default is NULL.}

\item{vars}{character vector of variables to include}

\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{
Output is a concatenated list of model information and model
}
\examples{
gtsummary::trial |>
  regression_model(outcome.str = "age")
gtsummary::trial |>
  regression_model(
    outcome.str = "age",
    auto.mode = FALSE,
    fun = "stats::lm",
    formula.str = "{outcome.str}~.",
    args.list = NULL
  )
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",
  fun = "stats::glm",
  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(mtcars), outcome.str = "mpg", fun.descr = "Linear regression model")

ls <- regression_model_list(data = default_parsing(gtsummary::trial), outcome.str = "trt", fun.descr = "Logistic regression model")
tbl <- gtsummary::tbl_regression(ls$model, exponentiate = TRUE)
m <- gtsummary::trial |>
  default_parsing() |>
  regression_model(
    outcome.str = "trt",
    fun = "stats::glm",
    formula.str = "{outcome.str}~.",
    args.list = list(family = "binomial")
  )
tbl2 <- gtsummary::tbl_regression(m, exponentiate = TRUE)
broom::tidy(ls$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")
ms$code
ls <- regression_model_uv_list(data = default_parsing(mtcars), outcome.str = "am", fun.descr = "Logistic regression model")
ls$code
lapply(ms$model, broom::tidy) |> dplyr::bind_rows()
}
}