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414 lines
16 KiB
Markdown
414 lines
16 KiB
Markdown
# Create a regression model programatically
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Output is a concatenated list of model information and model
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## Usage
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``` r
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regression_model(
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data,
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outcome.str = NULL,
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auto.mode = FALSE,
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formula.str = NULL,
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args.list = NULL,
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fun = NULL,
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vars = NULL,
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...
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)
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regression_model_uv(
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data,
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outcome.str,
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args.list = NULL,
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fun = NULL,
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vars = NULL,
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...
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)
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regression_model_list(
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data,
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outcome.str,
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fun.descr,
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fun = NULL,
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formula.str = NULL,
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args.list = NULL,
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vars = NULL,
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...
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)
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regression_model_uv_list(
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data,
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outcome.str,
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fun.descr,
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fun = NULL,
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formula.str = NULL,
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args.list = NULL,
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vars = NULL,
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...
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)
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```
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## Arguments
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- data:
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data
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- outcome.str:
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name of outcome variable
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- auto.mode:
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Make assumptions on function dependent on outcome data format.
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Overwrites other arguments.
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- formula.str:
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custom formula glue string. Default is NULL.
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- args.list:
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custom character string to be converted using argsstring2list() or
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list of arguments. Default is NULL.
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- fun:
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name of custom function. Default is NULL.
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- vars:
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character vector of variables to include
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- ...:
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ignored
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- fun.descr:
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Description of chosen function matching description in
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"supported_functions()"
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## Value
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object of standard class for fun
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object of standard class for fun
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list
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list
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## Examples
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``` r
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gtsummary::trial |>
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regression_model(outcome.str = "age")
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#> Error in str2lang(x): <text>:2:0: unexpected end of input
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#> 1: age~
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#> ^
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gtsummary::trial |>
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regression_model(
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outcome.str = "age",
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auto.mode = FALSE,
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fun = "stats::lm",
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formula.str = "{outcome.str}~.",
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args.list = NULL
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)
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#>
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#> Call:
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#> (function (formula, data, subset, weights, na.action, method = "qr",
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#> model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
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#> contrasts = NULL, offset, ...)
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#> {
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#> ret.x <- x
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#> ret.y <- y
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#> cl <- match.call()
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#> mf <- match.call(expand.dots = FALSE)
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#> m <- match(c("formula", "data", "subset", "weights", "na.action",
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#> "offset"), names(mf), 0L)
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#> mf <- mf[c(1L, m)]
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#> mf$drop.unused.levels <- TRUE
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#> mf[[1L]] <- quote(stats::model.frame)
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#> mf <- eval(mf, parent.frame())
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#> if (method == "model.frame")
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#> return(mf)
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#> else if (method != "qr")
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#> warning(gettextf("method = '%s' is not supported. Using 'qr'",
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#> method), domain = NA)
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#> mt <- attr(mf, "terms")
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#> y <- model.response(mf, "numeric")
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#> w <- as.vector(model.weights(mf))
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#> if (!is.null(w) && !is.numeric(w))
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#> stop("'weights' must be a numeric vector")
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#> offset <- model.offset(mf)
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#> mlm <- is.matrix(y)
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#> ny <- if (mlm)
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#> nrow(y)
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#> else length(y)
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#> if (!is.null(offset)) {
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#> if (!mlm)
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#> offset <- as.vector(offset)
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#> if (NROW(offset) != ny)
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#> stop(gettextf("number of offsets is %d, should equal %d (number of observations)",
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#> NROW(offset), ny), domain = NA)
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#> }
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#> if (is.empty.model(mt)) {
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#> x <- NULL
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#> z <- list(coefficients = if (mlm) matrix(NA_real_, 0,
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#> ncol(y)) else numeric(), residuals = y, fitted.values = 0 *
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#> y, weights = w, rank = 0L, df.residual = if (!is.null(w)) sum(w !=
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#> 0) else ny)
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#> if (!is.null(offset)) {
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#> z$fitted.values <- offset
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#> z$residuals <- y - offset
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#> }
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#> }
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#> else {
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#> x <- model.matrix(mt, mf, contrasts)
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#> z <- if (is.null(w))
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#> lm.fit(x, y, offset = offset, singular.ok = singular.ok,
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#> ...)
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#> else lm.wfit(x, y, w, offset = offset, singular.ok = singular.ok,
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#> ...)
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#> }
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#> class(z) <- c(if (mlm) "mlm", "lm")
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#> z$na.action <- attr(mf, "na.action")
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#> z$offset <- offset
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#> z$contrasts <- attr(x, "contrasts")
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#> z$xlevels <- .getXlevels(mt, mf)
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#> z$call <- cl
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#> z$terms <- mt
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#> if (model)
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#> z$model <- mf
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#> if (ret.x)
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#> z$x <- x
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#> if (ret.y)
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#> z$y <- y
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#> if (!qr)
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#> z$qr <- NULL
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#> z
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#> })(formula = age ~ ., data = structure(list(trt = structure(c(1L,
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#> 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
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#> 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L,
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#> 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L,
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#> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
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#> 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
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#> 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
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#> 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
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#> 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
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#> 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L,
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#> 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L,
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#> 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
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#> 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
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#> 2L, 1L, 2L, 1L, 1L, 1L, 1L), levels = c("Drug A", "Drug B"), class = "factor", label = "Chemotherapy Treatment"),
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#> age = structure(c(23, 9, 31, NA, 51, 39, 37, 32, 31, 34,
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#> 42, 63, 54, 21, 48, 71, 38, 49, 57, 46, 47, 52, 61, 38, 34,
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#> 49, 63, 67, 68, 78, 36, 37, 53, 36, 51, 48, 57, 31, 37, 28,
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#> 40, 49, 61, 56, 54, 71, 38, 31, 48, NA, 83, 52, 32, 53, 69,
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#> 60, 45, 39, NA, 38, 36, 71, 31, 43, 57, 53, 25, 44, 25, 30,
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#> 51, 40, NA, 43, 21, 54, 67, 43, 54, 41, 34, 34, 6, 39, 36,
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#> 58, 27, 47, NA, 50, 61, 47, 52, 51, 68, 33, 65, 34, 38, 60,
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#> 10, 49, 56, 50, 60, 49, 54, 39, 48, 65, 47, 61, 34, NA, NA,
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#> 58, 26, 44, 17, 68, 57, 66, 44, NA, 67, 48, 62, 35, 53, 53,
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#> 66, 55, 57, 47, 58, 43, 45, 44, 63, 59, 44, 53, 51, 28, 65,
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#> 63, 76, 61, 33, 48, 42, 36, 55, 20, 26, 50, 47, 74, 50, 31,
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#> 45, 51, 66, 76, 47, 48, 56, 70, 46, 43, 41, 41, 19, 49, 43,
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#> 43, 75, 52, 42, 37, 45, 35, 67, 38, 44, 45, 39, 46, NA, 42,
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#> 60, 31, 45, 38, NA, 19, 69, 66, NA, 64), label = "Age"),
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#> marker = structure(c(0.16, 1.107, 0.277, 2.067, 2.767, 0.613,
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#> 0.354, 1.739, 0.144, 0.205, 0.513, 0.06, 0.831, 0.258, 0.128,
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#> 0.445, 2.083, 0.157, 0.066, 0.325, 0.266, 0.719, 1.713, 0.096,
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#> 0.105, 0.043, 0.981, 1.156, 0.105, 0.175, 0.309, 1.869, 2.008,
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#> 1.894, 0.16, 1.209, 0.108, 0.611, 0.222, 0.803, 0.37, NA,
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#> 0.177, 1.479, 0.161, 0.737, 0.124, 0.092, 0.385, 0.21, 0.475,
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#> 1.628, 0.583, NA, 0.702, 1.206, 2.213, 1.406, 0.101, 0.013,
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#> 2.032, 1.046, 0.408, 2.636, 1.263, NA, 2.447, 1.041, 0.531,
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#> 0.924, 1.087, 0.733, 2.157, 0.333, 1.527, 2.238, 0.153, 0.305,
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#> 0.131, 0.386, 1.645, 1.321, 0.229, 0.615, 1.976, 1.941, 0.22,
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#> 3.874, 0.982, 1.68, 1.091, 0.169, 0.511, 2.141, 0.599, NA,
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#> 0.389, 0.005, 0.075, 1.491, 0.358, 1.709, 0.056, 1.354, 2.522,
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#> 0.387, 0.592, 0.243, 0.215, 1.207, 0.29, 0.718, 0.589, 0.003,
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#> 1.328, 0.308, 0.691, 3.249, 0.039, 1.804, 0.238, 2.702, 1.441,
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#> 0.27, NA, NA, 0.062, 2.19, 0.976, 3.062, 0.124, 0.045, 1.892,
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#> 0.711, 1.079, 1.061, 0.239, 0.361, 0.033, 1.133, 1.225, 1.418,
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#> 3.751, 3.02, 0.086, 0.772, 1.882, 2.725, 2.41, 0.352, 0.895,
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#> 0.215, 0.141, 2.288, 1.658, 1.255, 1.306, 0.081, 0.667, 0.046,
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#> 0.662, 1.985, 1.063, 1.55, 0.015, 0.056, NA, 0.51, 0.929,
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#> 2.345, 0.25, 0.816, 0.022, 0.16, 0.547, 3.642, 0.092, 1.2,
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#> 1.512, 2.124, NA, 0.862, 0.182, 1.075, 0.021, 0.402, 0.063,
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#> 1.129, 0.61, NA, 0.717, 0.205, 0.946, 0.386, 0.37, 1.148,
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#> NA, 0.136, 0.439, 1.148), label = "Marker Level (ng/mL)"),
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#> stage = structure(c(1L, 2L, 1L, 3L, 4L, 4L, 1L, 1L, 1L, 3L,
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#> 1L, 3L, 4L, 4L, 1L, 4L, 4L, 2L, 1L, 1L, 2L, 2L, 4L, 4L, 4L,
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#> 2L, 4L, 1L, 4L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 4L, 4L,
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#> 3L, 1L, 4L, 3L, 4L, 1L, 1L, 2L, 1L, 4L, 1L, 2L, 2L, 3L, 3L,
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#> 2L, 4L, 1L, 4L, 2L, 4L, 1L, 4L, 1L, 4L, 1L, 1L, 1L, 4L, 1L,
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#> 2L, 2L, 2L, 1L, 4L, 4L, 2L, 2L, 4L, 4L, 3L, 2L, 4L, 3L, 2L,
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#> 4L, 1L, 2L, 1L, 4L, 3L, 3L, 1L, 3L, 2L, 3L, 2L, 2L, 3L, 4L,
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#> 4L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 1L, 3L, 4L, 1L, 4L,
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#> 3L, 3L, 2L, 4L, 2L, 1L, 2L, 1L, 4L, 3L, 3L, 3L, 2L, 1L, 2L,
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#> 1L, 2L, 2L, 1L, 3L, 2L, 1L, 1L, 1L, 3L, 3L, 4L, 1L, 2L, 2L,
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#> 1L, 3L, 4L, 2L, 4L, 2L, 1L, 2L, 3L, 1L, 3L, 4L, 2L, 2L, 1L,
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#> 2L, 2L, 3L, 1L, 2L, 2L, 1L, 1L, 3L, 2L, 3L, 1L, 1L, 2L, 4L,
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#> 2L, 4L, 4L, 2L, 3L, 4L, 3L, 4L, 4L, 1L, 1L, 4L, 4L, 4L, 1L,
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#> 2L, 2L, 2L, 4L, 3L, 3L, 2L, 2L, 4L, 3L), levels = c("T1",
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#> "T2", "T3", "T4"), class = "factor", label = "T Stage"),
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#> grade = structure(c(2L, 1L, 2L, 3L, 3L, 1L, 2L, 1L, 2L, 1L,
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#> 3L, 1L, 3L, 1L, 1L, 3L, 3L, 2L, 3L, 2L, 1L, 2L, 1L, 1L, 2L,
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#> 3L, 2L, 2L, 2L, 1L, 3L, 2L, 1L, 1L, 1L, 3L, 2L, 2L, 3L, 2L,
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#> 2L, 3L, 3L, 1L, 3L, 1L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 3L,
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#> 1L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 3L, 2L,
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#> 1L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 1L,
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#> 3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L,
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#> 3L, 1L, 2L, 2L, 1L, 2L, 2L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 2L,
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#> 3L, 3L, 2L, 1L, 3L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 3L, 2L, 3L,
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#> 1L, 1L, 1L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 3L, 2L,
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#> 2L, 2L, 2L, 2L, 1L, 1L, 3L, 2L, 2L, 1L, 2L, 1L, 2L, 3L, 2L,
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#> 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 3L, 3L, 1L, 3L, 2L, 1L,
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#> 3L, 1L, 2L, 1L, 1L, 3L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 2L, 2L,
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#> 3L, 2L, 3L, 3L, 2L, 1L, 2L, 3L, 3L, 1L), levels = c("I",
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#> "II", "III"), class = "factor", label = "Grade"), response = structure(c(0L,
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#> 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
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#> 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L,
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#> 0L, 0L, 1L, 0L, NA, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L,
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#> 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L,
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#> 0L, 0L, 0L, NA, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,
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#> 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L,
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#> 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, NA, 0L, 0L, 1L,
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#> 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
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#> 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,
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#> 1L, 1L, NA, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, NA, 1L, 0L,
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#> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L,
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#> 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, NA, 0L, 1L,
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#> 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, NA, 0L,
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#> 1L, 1L, 0L, 0L), label = "Tumor Response"), death = structure(c(0L,
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#> 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
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#> 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L,
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#> 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L,
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#> 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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#> 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
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#> 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L,
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#> 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L,
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#> 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
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#> 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L,
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#> 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L,
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#> 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L,
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#> 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L,
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#> 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L,
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#> 1L, 0L, 1L, 0L), label = "Patient Died"), ttdeath = structure(c(24,
|
||
#> 24, 24, 17.64, 16.43, 15.64, 24, 18.43, 24, 10.53, 24, 24,
|
||
#> 14.34, 12.89, 22.68, 8.71, 24, 15.21, 24, 24, 24, 24, 16.92,
|
||
#> 23.89, 6.32, 15.77, 24, 24, 15.45, 17.43, 24, 20.9, 24, 24,
|
||
#> 24, 21.19, 12.52, 24, 15.59, 18, 18.02, 12.43, 12.1, 24,
|
||
#> 17.42, 24, 24, 24, 12.19, 10.02, 18.23, 10.42, 24, 24, 19.34,
|
||
#> 12.21, 14.46, 19.34, 10.16, 13.15, 10.12, 24, 22.77, 24,
|
||
#> 24, 22.13, 24, 20.62, 23.23, 7.38, 24, 24, 24, 24, 24, 19.22,
|
||
#> 7.27, 23.88, 16.23, 24, 14.06, 24, 24, 24, 16.44, 23.81,
|
||
#> 24, 18.37, 11.44, 20.94, 5.33, 22.92, 10.33, 24, 24, 14.54,
|
||
#> 19.14, 24, 21.19, 16.07, 9.97, 24, 24, 24, 19.75, 16.67,
|
||
#> 11.18, 18.29, 24, 17.56, 17.45, 24, 22.86, 13.68, 24, 24,
|
||
#> 17.46, 24, 24, 24, 24, 24, 13, 9.73, 15.65, 24, 3.53, 20.35,
|
||
#> 23.41, 16.47, 24, 24, 14.65, 17.81, 24, 21.83, 24, 24, 21.49,
|
||
#> 12.68, 24, 24, 24, 24, 10.07, 24, 24, 24, 8.37, 20.33, 24,
|
||
#> 24, 21.33, 12.63, 13.08, 24, 15.1, 20.14, 10.55, 24, 24,
|
||
#> 24, 24, 23.6, 24, 19.98, 15.55, 23.72, 22.41, 19.54, 16.57,
|
||
#> 24, 24, 24, 21.91, 24, 12.53, 24, 18.63, 14.82, 16.46, 24,
|
||
#> 9.24, 17.77, 24, 24, 9.92, 16.16, 10.51, 20.81, 24, 16.44,
|
||
#> 24, 22.4, 11.76, 24, 21.6, 24, 19.81, 24), label = "Months to Death/Censor")), class = c("tbl_df",
|
||
#> "tbl", "data.frame"), row.names = c(NA, -200L)))
|
||
#>
|
||
#> Coefficients:
|
||
#> (Intercept) trtDrug B marker stageT2 stageT3 stageT4
|
||
#> 45.9247 -0.4052 -0.1435 2.0519 2.4437 -3.1573
|
||
#> gradeII gradeIII response death ttdeath
|
||
#> 0.1750 1.6855 4.9885 3.0775 -0.1394
|
||
#>
|
||
gtsummary::trial |>
|
||
default_parsing() |>
|
||
regression_model(
|
||
outcome.str = "trt",
|
||
auto.mode = FALSE,
|
||
fun = "stats::glm",
|
||
args.list = list(family = binomial(link = "logit"))
|
||
)
|
||
#> Error in str2lang(x): <text>:2:0: unexpected end of input
|
||
#> 1: trt~
|
||
#> ^
|
||
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")
|
||
)
|
||
#> Warning: the response appeared on the right-hand side and was dropped
|
||
#> Warning: problem with term 1 in model.matrix: no columns are assigned
|
||
broom::tidy(m)
|
||
#> # A tibble: 3 × 5
|
||
#> term estimate std.error statistic p.value
|
||
#> <chr> <dbl> <dbl> <dbl> <dbl>
|
||
#> 1 (Intercept) 26.7 0.972 27.4 2.69e-22
|
||
#> 2 cyl6 -6.92 1.56 -4.44 1.19e- 4
|
||
#> 3 cyl8 -11.6 1.30 -8.90 8.57e-10
|
||
if (FALSE) { # \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()
|
||
} # }
|
||
if (FALSE) { # \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)
|
||
} # }
|
||
if (FALSE) { # \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()
|
||
} # }
|
||
```
|