aba (version 0.0.9)

aba_selection: Run model selection on an aba model.

Description

This function allows you to run model selection on a fitted aba model. The function supports both forward and backward selection algorithms, both AIC and p-value as selection criteria, and arbitrary thresholds.

Usage

aba_selection(
  model,
  method = c("forward", "backward"),
  criteria = c("aic", "pval"),
  threshold = NULL,
  verbose = FALSE
)

Arguments

model

abaModel. The fitted aba model to run selection on.

method

string. The selection algorithm to use (forward or backward).

criteria

string. Which metric to use when selecting the next model (aic or pval).

threshold

numeric. Which threshold to use for the selected metric (defaults to -2 for aic; defaults to 0.1 for pval).

verbose

logical. Whether to print out results of each selection round.

Value

an abaSelection object which contains model summary information such as coefficients and metrics for each selection round across the different groups/outcomes/stats.

Details

Forward selection starts from covariates-only and tests the addition of all predictor sets individually, then adds the predictor set which improves the model criteria the most. Backward selection starts from the inclusion of all covariates + predictor sets and tests the removal of all predictor sets individually, then removes the predictor set which improves the model criteria the most. If there are no predictor sets whose addition/removal results in an improvement in the selected criteria by a value at least as good as the selected threshold, then the selection stops and the current model is frozen. Also, note that the model selection procedure is run separately for each group - outcome - stat combination.

Examples

# NOT RUN {
df <- aba::adnimerge %>% dplyr::filter(VISCODE == 'bl')

# standard model selection
model <- df %>% aba_model() %>%
  set_outcomes(ConvertedToAlzheimers) %>%
  set_predictors(
    CDRSB_bl, ADAS13_bl, MMSE_bl,
    CSF_ABETA_bl, CSF_PTAU_bl, CSF_TAU_bl,
    PLASMA_ABETA_bl, PLASMA_PTAU181_bl, PLASMA_NFL_bl,
    MRI_HIPP_bl,
    PET_ABETA_bl
  ) %>%
  set_covariates(AGE, GENDER, EDUCATION) %>%
  set_stats('glm') %>%
  aba_fit()

model_summary <- model %>% aba_summary()

# default selection - forward selection by AIC with threshold = -2
# }
# NOT RUN {
model_selection <- model %>% aba_selection(verbose=TRUE)
# }
# NOT RUN {
# selection with p-value and threshold = 0.1
# }
# NOT RUN {
model_selection <- model %>%
  aba_selection(criteria = 'pval', threshold=0.1, verbose=TRUE)
# }
# NOT RUN {
# selection by group
model2 <- model %>%
  set_predictors(
    c(CDRSB_bl,ADAS13_bl,MMSE_bl),
    c(CSF_ABETA_bl,CSF_PTAU_bl,CSF_TAU_bl),
    c(PLASMA_ABETA_bl, PLASMA_PTAU181_bl, PLASMA_NFL_bl),
    c(MRI_HIPP_bl),
    c(PET_ABETA_bl)
  ) %>%
  aba_fit()

model_summary2 <- model2 %>% aba_summary()

# }
# NOT RUN {
model_selection2 <- model2 %>%
  aba_selection(criteria='pval', threshold=0.1, verbose=TRUE)
# }
# NOT RUN {
# add more outcomes
model3 <- model2 %>%
  set_outcomes(ConvertedToAlzheimers, ConvertedToDementia) %>%
  aba_fit()

# }
# NOT RUN {
model_selection3 <- model3 %>%
  aba_selection(criteria='pval', threshold=0.1, verbose=TRUE)
# }
# NOT RUN {
# add more groups
model4 <- model3 %>%
  set_groups(everyone(), DX_bl %in% c('MCI','AD')) %>%
  aba_fit()

# }
# NOT RUN {
model_selection4 <- model4 %>%
  aba_selection(criteria='pval', threshold=0.1, verbose=TRUE)
# }
# NOT RUN {
# }