Fit a trinomial mixture model that optionally includes covariates to estimate effects of factor or continuous variables on proportions.

fit_zoid(
  formula = NULL,
  design_matrix,
  data_matrix,
  chains = 3,
  iter = 2000,
  warmup = floor(iter/2),
  overdispersion = FALSE,
  overdispersion_sd = 5,
  posterior_predict = FALSE,
  moment_match = FALSE,
  prior_sd = NA,
  ...
)

Arguments

formula

The model formula for the design matrix. Does not need to have a response specified. If =NULL, then the design matrix is ignored and all rows are treated as replicates

design_matrix

A data frame, dimensioned as number of observations, and covariates in columns

data_matrix

A matrix, with observations on rows and number of groups across columns

chains

Number of mcmc chains, defaults to 3

iter

Number of mcmc iterations, defaults to 2000

warmup

Number iterations for mcmc warmup, defaults to 1/2 of the iterations

overdispersion

Whether or not to include overdispersion parameter, defaults to FALSE

overdispersion_sd

Prior standard deviation on 1/overdispersion parameter, Defaults to inv-Cauchy(0,5)

posterior_predict

Whether or not to return draws from posterior predictive distribution (requires more memory)

moment_match

Whether to do moment matching via loo::loo_moment_match(). This increases memory by adding all temporary parmaeters to be saved and returned

prior_sd

Parameter to be passed in to use as standard deviation of the normal distribution in transformed space. If covariates are included this defaults to 1, but for models with single replicate, defaults to 1/n_bins.

...

Any other arguments to pass to rstan::sampling().

Examples

# \donttest{
y <- matrix(c(3.77, 6.63, 2.60, 0.9, 1.44, 0.66, 2.10, 3.57, 1.33),
  nrow = 3, byrow = TRUE
)
# fit a model with no covariates
fit <- fit_zoid(data_matrix = y)
#> 
#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 4.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
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#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.101642 seconds (Warm-up)
#> Chain 1:                0.112691 seconds (Sampling)
#> Chain 1:                0.214333 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.26 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: 0.105492 seconds (Warm-up)
#> Chain 2:                0.104494 seconds (Sampling)
#> Chain 2:                0.209986 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.106823 seconds (Warm-up)
#> Chain 3:                0.123682 seconds (Sampling)
#> Chain 3:                0.230505 seconds (Total)
#> Chain 3: 

# fit a model with 1 factor
design <- data.frame("y" = c(1, 1, 1), "fac" = c("spring", "spring", "fall"))
fit <- fit_zoid(formula = ~fac, design_matrix = design, data_matrix = y)
#> 
#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.29 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.211789 seconds (Warm-up)
#> Chain 1:                0.224391 seconds (Sampling)
#> Chain 1:                0.43618 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.25 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.215758 seconds (Warm-up)
#> Chain 2:                0.238375 seconds (Sampling)
#> Chain 2:                0.454133 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 4.1e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.41 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.2033 seconds (Warm-up)
#> Chain 3:                0.225407 seconds (Sampling)
#> Chain 3:                0.428707 seconds (Total)
#> Chain 3: 
# }