Create a glm stat object. — stat

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()
#> [1] "ConvertedToAlzheimers ~ PLASMA_PTAU181_bl"
#> [1] "ConvertedToAlzheimers ~ PLASMA_NFL_bl"
#> [1] "ConvertedToAlzheimers ~ PLASMA_PTAU181_bl + PLASMA_NFL_bl"
#> [1] "CSF_ABETA_STATUS_bl ~ PLASMA_PTAU181_bl"
#> [1] "CSF_ABETA_STATUS_bl ~ PLASMA_NFL_bl"
#> [1] "CSF_ABETA_STATUS_bl ~ PLASMA_PTAU181_bl + PLASMA_NFL_bl"

# 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()