tof_get_model_x: Get a 'tof_model”s processed predictor matrix (for glmnet)

In keyes-timothy/tidytof: Analyze High-dimensional Cytometry Data Using Tidy Data Principles

Get a ‘tof_model'’s processed predictor matrix (for glmnet)

Description

Get a ‘tof_model'’s processed predictor matrix (for glmnet)

Usage

tof_get_model_x(tof_model)

Arguments

tof_model

A tof_model

Value

An x value formatted for glmnet

Examples

feature_tibble <-
    dplyr::tibble(
        sample = as.character(1:100),
        cd45 = runif(n = 100),
        pstat5 = runif(n = 100),
        cd34 = runif(n = 100),
        outcome = (3 * cd45) + (4 * pstat5) + rnorm(100),
        class =
            as.factor(
                dplyr::if_else(outcome > median(outcome), "class1", "class2")
            ),
        multiclass =
            as.factor(
                c(rep("class1", 30), rep("class2", 30), rep("class3", 40))
            ),
        event = c(rep(0, times = 30), rep(1, times = 70)),
        time_to_event = rnorm(n = 100, mean = 10, sd = 2)
    )

split_data <- tof_split_data(feature_tibble, split_method = "simple")

# train a regression model
regression_model <-
    tof_train_model(
        split_data = split_data,
        predictor_cols = c(cd45, pstat5, cd34),
        response_col = outcome,
        model_type = "linear"
    )

tof_get_model_x(regression_model)

keyes-timothy/tidytof documentation built on Aug. 28, 2024, 8:37 a.m.