tests/testthat/_snaps/inference.md

In greta: Simple and Scalable Statistical Modelling in R

opt converges with SciPy optimisers

Code
  o <- opt(m, optimiser = optmr(), max_iterations = 500)

Code
  o <- opt(m, optimiser = optmr(), max_iterations = 500)
Condition
  Warning:
  This optimiser is deprecated and will be removed in greta 0.4.0.
  Please use a different optimiser.

Code
  o <- opt(m, optimiser = optmr(), max_iterations = 500)
Condition
  Warning:
  This optimiser is deprecated and will be removed in greta 0.4.0.
  Please use a different optimiser.

Code
  o <- opt(m, optimiser = optmr(), max_iterations = 500)

Code
  o <- opt(m, optimiser = optmr(), max_iterations = 500)
Condition
  Warning:
  This optimiser is deprecated and will be removed in greta 0.4.0.
  Please use a different optimiser.

Code
  o <- opt(m, optimiser = optmr(), max_iterations = 500)
Condition
  Warning:
  This optimiser is deprecated and will be removed in greta 0.4.0.
  Please use a different optimiser.

Code
  o <- opt(m, optimiser = optmr(), max_iterations = 500)
Condition
  Warning:
  This optimiser is deprecated and will be removed in greta 0.4.0.
  Please use a different optimiser.

Code
  o <- opt(m, optimiser = optmr(), max_iterations = 500)
Condition
  Warning:
  This optimiser is deprecated and will be removed in greta 0.4.0.
  Please use a different optimiser.

Code
  o <- opt(m, optimiser = optmr(), max_iterations = 500)
Condition
  Warning:
  This optimiser is deprecated and will be removed in greta 0.4.0.
  Please use a different optimiser.

greta arrays passed into mcmc fail appropriately

MCMC requires input to be a <greta_model> not a <greta_array>
x `x` is a <greta_array> not a <greta_model>
i You can convert `x` into a <greta_model> by running:
`model(x)`

bad mcmc proposals are rejected

The log density could not be evaluated at these initial values
Try using these initials as the values argument in `calculate()` to see what values of subsequent <greta_array>s these initial values lead to.

Could not find reasonable starting values after 20 attempts.
Please specify initial values manually via the `initial_values` argument

mcmc handles initial values nicely

the number of provided initial values does not match chains
3 sets of initial values were provided, but there are 2 chains

the initial values provided have different dimensions than the named <greta_array>s

Code
  mcmc(m, warmup = 10, n_samples = 10, chains = 2, initial_values = inits,
    verbose = FALSE)
Message
  only one set of initial values was provided, and was used for all chains
Output
  $`11`
  Markov Chain Monte Carlo (MCMC) output:
  Start = 1 
  End = 10 
  Thinning interval = 1 
             z
  1  0.3742083
  2  0.3742083
  3  0.3742083
  4  0.3742083
  5  0.3742083
  6  0.3742083
  7  0.3742083
  8  0.3742083
  9  0.3742083
  10 0.3742083

  $`12`
  Markov Chain Monte Carlo (MCMC) output:
  Start = 1 
  End = 10 
  Thinning interval = 1 
              z
  1  0.04036782
  2  0.04036782
  3  0.04036782
  4  0.04036782
  5  0.04036782
  6  0.04036782
  7  0.04036782
  8  0.04036782
  9  0.04036782
  10 0.04036782

  attr(,"class")
  [1] "greta_mcmc_list" "mcmc.list"      
  attr(,"model_info")
  attr(,"model_info")$raw_draws
  $`11`
  Markov Chain Monte Carlo (MCMC) output:
  Start = 1 
  End = 10 
  Thinning interval = 1 
         draws
  1  0.3742083
  2  0.3742083
  3  0.3742083
  4  0.3742083
  5  0.3742083
  6  0.3742083
  7  0.3742083
  8  0.3742083
  9  0.3742083
  10 0.3742083

  $`12`
  Markov Chain Monte Carlo (MCMC) output:
  Start = 1 
  End = 10 
  Thinning interval = 1 
          draws
  1  0.04036782
  2  0.04036782
  3  0.04036782
  4  0.04036782
  5  0.04036782
  6  0.04036782
  7  0.04036782
  8  0.04036782
  9  0.04036782
  10 0.04036782

  attr(,"class")
  [1] "mcmc.list"

  attr(,"model_info")$samplers
  attr(,"model_info")$samplers$`1`
  hmc_sampler object with parameters:
    Lmin = 5, Lmax = 10, epsilon = 0.7326749, diag_sd = 1

  attr(,"model_info")$model
  greta model

progress bar gives a range of messages

Code
  draws <- mock_mcmc(1010)
Message

    sampling          1010/1010 | eta:  0s | <1% bad

Code
  draws <- mock_mcmc(500)
Message

    sampling            500/500 | eta:  0s | 2% bad

Code
  draws <- mock_mcmc(10)
Message

    sampling =========== 10/10 | eta:  0s | 100% bad

samples has object names

Code
  rownames(summary(draws)$statistics)
Output
  [1] "a"      "b[1,1]" "b[2,1]" "b[3,1]"

Code
  rownames(summary(c_draws)$statistics)
Output
  [1] "c[1,1]" "c[2,1]" "c[3,1]"

model errors nicely

`model()` arguments must be <greta_array>s
The following object passed to `model()` is not a <greta array>:
"a"

mcmc doesn't support slice sampler with double precision models

slice sampler can only currently be used for models defined with single precision
set `model(..., precision = 'single')` instead

numerical issues are handled in mcmc

TensorFlow hit a numerical problem that caused it to error
greta can handle these as bad proposals if you rerun `mcmc()` with the argument `one_by_one = TRUE`.
This will slow down the sampler slightly.
The error encountered can be recovered and viewed with:
`greta_notes_tf_num_error()`

mcmc errors for invalid parallel plans

parallel mcmc samplers cannot be run with `plan(multicore)`

parallel mcmc samplers cannot be run with a fork cluster

initials works

initial values must be numeric

all initial values must be named

Code
  initials(a = 3)
Output
  a greta initials object with values:

  $a
       [,1]
  [1,]    3

prep_initials errors informatively

`initial_values` must be an initials object created with `initials()`, or a simple list of initials objects

`initial_values` must be an initials object created with `initials()`, or a simple list of initials objects

some <greta_array>s passed to `initials()` are not associated with the model:
`g`

initial values can only be set for variable <greta_array>s

initial values can only be set for variable <greta_array>s

some provided initial values are outside the range of values their variables can take

some provided initial values are outside the range of values their variables can take

some provided initial values are outside the range of values their variables can take

samplers print informatively

Code
  hmc()
Output
  hmc sampler object with parameters:
    Lmin = 5, Lmax = 10, epsilon = 0.1, diag_sd = 1

Code
  rwmh()
Output
  rwmh sampler object with parameters:
    proposal = normal, epsilon = 0.1, diag_sd = 1

Code
  slice()
Output
  slice sampler object with parameters:
    max_doublings = 5

Code
  hmc(Lmin = 1)
Output
  hmc sampler object with parameters:
    Lmin = 1, Lmax = 10, epsilon = 0.1, diag_sd = 1

greta documentation built on May 29, 2024, 5:56 a.m.