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()
#> [1] "PET_ABETA_STATUS_bl ~ AGE + GENDER + EDUCATION"
#> [1] "PET_ABETA_STATUS_bl ~ AGE + GENDER + EDUCATION + PLASMA_PTAU181_bl"
#> [1] "PET_ABETA_STATUS_bl ~ AGE + GENDER + EDUCATION + PLASMA_NFL_bl"
#> [1] "PET_ABETA_STATUS_bl ~ AGE + GENDER + EDUCATION + PLASMA_PTAU181_bl + PLASMA_NFL_bl"
# 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)