predict() does not work with intercept-only models
davidkane9 opened this issue · comments
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!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
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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!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
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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:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
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#> Chain 4:
#> 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
Thanks for reporting! I'll look into it.
Thanks for fixing!