predict() does not work with intercept-only models

[David Kane]

predict() does not work with intercept-only models.

library(bayesian)
#> Loading required package: brms
#> Loading required package: Rcpp
#> Loading 'brms' package (version 2.20.4). Useful instructions
#> can be found by typing help('brms'). A more detailed introduction
#> to the package is available through vignette('brms_overview').
#> 
#> Attaching package: 'brms'
#> The following object is masked from 'package:stats':
#> 
#>     ar
#> Loading required package: parsnip
library(tibble)

# Create simple data

shovel_data <- tibble(
  bead = factor(c(rep("red", 17), 
                  rep("white", 33)),
                levels = c("white", "red")))

# Fitting the model works fine

fit_obj <- bayesian(mode = "classification", 
         family = bernoulli(link = "logit")) |> 
  fit(bead ~ 1, data = shovel_data)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.008 seconds (Warm-up)
#> Chain 1:                0.007 seconds (Sampling)
#> Chain 1:                0.015 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.008 seconds (Warm-up)
#> Chain 2:                0.008 seconds (Sampling)
#> Chain 2:                0.016 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.008 seconds (Warm-up)
#> Chain 3:                0.007 seconds (Sampling)
#> Chain 3:                0.015 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 2e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
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#> Chain 4:  Elapsed Time: 0.008 seconds (Warm-up)
#> Chain 4:                0.009 seconds (Sampling)
#> Chain 4:                0.017 seconds (Total)
#> Chain 4:

# predict() does not work. I suspect that this has something to do with the fact
# that this is a model which only uses the intercept.

fit_obj |> 
  predict(new_data = shovel_data[1:5,])
#> Error in results[, 1]: incorrect number of dimensions

^{Created on 2024-03-13 with reprex v2.1.0}

[Hamada S. Badr]

Thanks for reporting! I'll look into it.

[David Kane]

Thanks for fixing!