aba_summary: Summarise a fitted aba model.

In aba: Automated Biomarker Analysis

Description

This function concisely summarises coefficients and metrics for the stat fits from the different group - outcome - stat combinations. This is the primary function to use if you want to see the results of a fitted aba model. It is also the way to generate publication-ready tables of model results.

Usage

aba_summary(
  object,
  control = aba_control(),
  adjust = aba_adjust(),
  verbose = FALSE
)

Arguments

object	abaModel. The fitted aba model which you want to summarise.
control	abaControl. An aba control object which allows users to customize the summary process – e.g., whether to include covariates in the table.
adjust	abaAdjust. An aba adjust object which allows users to specify p-value adjustment using a variety of methods and across arbitrary model factors.
verbose	logical. Whether to provide a progress bar to track status.

Value

an abaSummary object which contains coefficients and metrics from the different statistical fits summarised into publication-ready tables.

Examples

# use built-in data
data <- adnimerge %>% dplyr::filter(VISCODE == 'bl')

# fit an aba model
model <- data %>% aba_model() %>%
  set_groups(everyone()) %>%
  set_outcomes(PET_ABETA_STATUS_bl) %>%
  set_predictors(
    PLASMA_PTAU181_bl,
    PLASMA_NFL_bl,
    c(PLASMA_PTAU181_bl, PLASMA_NFL_bl)
  ) %>%
  set_covariates(AGE, GENDER, EDUCATION) %>%
  set_stats('glm') %>%
  fit()

# default aba summary
model_summary <- model %>% aba_summary()

# create an aba control object to customize the summary
my_control <- aba_control(include_covariates = FALSE)

# summarise model with th custom aba control - notice covariates
# wont be included in the tables when you print the summary to console
model_summary2 <- model %>% aba_summary(control = my_control)

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