stat_glm: Create a glm stat object.

In aba: Automated Biomarker Analysis

Description

This function creates a glm stat object which can be passed as input to the set_stats() function when building an aba model. This stat performs a traditional logistic regression analysis using the glm function with a binary outcome. Coefficients will be presented as odds ratios. Default metrics include AUC.

Usage

stat_glm(std.beta = FALSE, complete.cases = TRUE)

Arguments

std.beta	logical. Whether to standardize model predictors and covariates prior to analysis.
complete.cases	logical. Whether to only include the subset of data with no missing data for any of the outcomes, predictors, or covariates. Note that complete cases are considering within each group - outcome combination but across all predictor sets.

Value

An abaStat object with glm stat type.

Examples

data <- adnimerge %>% dplyr::filter(VISCODE == 'bl')

# fit glm model with binary outcome variables
model <- data %>% aba_model() %>%
  set_groups(everyone()) %>%
  set_outcomes(ConvertedToAlzheimers, CSF_ABETA_STATUS_bl) %>%
  set_predictors(
    PLASMA_PTAU181_bl, PLASMA_NFL_bl,
    c(PLASMA_PTAU181_bl, PLASMA_NFL_bl)
  ) %>%
  set_stats(
    stat_glm(std.beta = TRUE)
  ) %>%
  fit()

# summarise glm model
model_summary <- model %>% summary()

# plot glm results
fig1 <- model_summary %>% aba_plot_coef()
fig2 <- model_summary %>% aba_plot_metric()
fig3 <- model_summary %>% aba_plot_roc()

aba documentation built on Dec. 17, 2021, 1:06 a.m.