Select an AFT distribution by AIC

Fits each candidate AFT distribution to the same formula/data and selects the best fit by AIC.

Usage

ertte_aft_select_distribution(
  formula,
  data,
  candidates = c("exponential", "weibull", "lognormal", "loglogistic")
)

Arguments

formula

Model formula, as for ertte_aft()

data

Data set

candidates

Character vector of candidate dist values to try. Defaults to all four tested/supported distributions.

Value

A list with two elements: comparison (a tibble with one row per candidate: dist, logLik, aic, bic, converged, sorted by AIC) and model (the best-fitting ertte_aft model, i.e. the one with lowest AIC).

Details

Ties (to floating point) are broken by the order candidates is given in, i.e. the first-listed of the tied candidates is returned as model.

candidates must be a non-empty character vector with no missing values; each element must also separately name one of the tested/supported distributions (see ertte_aft()'s dist argument). candidates = character(0) errors rather than silently fitting nothing and returning a degenerate list(comparison = <0-row tibble>, model = NULL).

Examples

# (uses ertte_aft() internally for each candidate distribution)
cmp <- ertte_aft_select_distribution(Surv(time, event) ~ aucss, ertte_data)
cmp$comparison
#> # A tibble: 4 × 5
#>   dist        logLik   aic   bic converged
#>   <chr>        <dbl> <dbl> <dbl> <lgl>    
#> 1 weibull     -1207. 2421. 2432. TRUE     
#> 2 loglogistic -1216. 2438. 2449. TRUE     
#> 3 lognormal   -1222. 2451. 2462. TRUE     
#> 4 exponential -1224. 2452. 2460. TRUE     
cmp$model
#> Call:
#> survival::survreg(formula = formula, data = data, dist = dist)
#> 
#> Coefficients:
#>   (Intercept)         aucss 
#>  4.8563375087 -0.0006407913 
#> 
#> Scale= 0.7164032 
#> 
#> Loglik(model)= -1207.3   Loglik(intercept only)= -1263.9
#> 	Chisq= 113.25 on 1 degrees of freedom, p= <2e-16 
#> n= 300