Function to visualize hypotheses tested on brms models similar to those made using growthSS outputs.

Usage

brmViolin(fit, ss, hypothesis)

Arguments

fit: A brmsfit object or a dataframe of draws. If you need to combine multiple models then use combineDraws to merge their draws into a single dataframe for testing.
ss: A pcvrss object. The only component that is currently used is the pcvrForm.
hypothesis: A hypothesis expressed as a character string in the style of that used by brms::hypothesis and testGrowth. Note that currently this only supports hypotheses using two parameters from the model at a time (ie, "groupA / groupB > 1.05" works but "(groupA / groupB) - (groupC / groupD) > 1" does not). In the hypothesis "..." can be used to mean "all groups for this parameter" so that the hypothesis "... / A_group1 > 1.05" would include all the "A" coefficients for groups 1:N along the x axis, see examples.

Value

Returns a ggplot showing a brms model's posterior distributions as violins and filled by posterior probability of some hypothesis.

Examples

# \donttest{
set.seed(123)
simdf <- growthSim(
  "logistic",
  n = 20, t = 25,
  params = list("A" = c(200, 180, 190, 160), "B" = c(13, 11, 10, 10), "C" = c(3, 3, 3.25, 3.5))
)
ss <- growthSS(
  model = "logistic", form = y ~ time | id / group, sigma = "int",
  list("A" = 130, "B" = 10, "C" = 3),
  df = simdf, type = "brms"
)

fit <- fitGrowth(ss, backend = "cmdstanr", iter = 500, chains = 1, cores = 1)
#> Start sampling
#> Init values were only set for a subset of parameters. 
#> Missing init values for the following parameters:
#> b_sigma, Intercept_nu
#> 
#> To disable this message use options(cmdstanr_warn_inits = FALSE).
#> Running MCMC with 1 chain...
#> 
#> Chain 1 Iteration:   1 / 500 [  0%]  (Warmup) 
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Location parameter[1] is -nan, but must be finite! (in '/tmp/RtmpUvwz4B/model-1a7d2fac925c.stan', line 97, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1 
#> Chain 1 Iteration: 100 / 500 [ 20%]  (Warmup) 
#> Chain 1 Iteration: 200 / 500 [ 40%]  (Warmup) 
#> Chain 1 Iteration: 251 / 500 [ 50%]  (Sampling) 
#> Chain 1 Iteration: 350 / 500 [ 70%]  (Sampling) 
#> Chain 1 Iteration: 450 / 500 [ 90%]  (Sampling) 
#> Chain 1 Iteration: 500 / 500 [100%]  (Sampling) 
#> Chain 1 finished in 11.4 seconds.
brmViolin(fit, ss, ".../A_groupd > 1.05") # all groups used

brmViolin(fit, ss, "A_groupa/A_groupd > 1.05") # only these two groups

# }