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,
...
)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
A data frame, dimensioned as number of observations, and covariates in columns
A matrix, with observations on rows and number of groups across columns
Number of mcmc chains, defaults to 3
Number of mcmc iterations, defaults to 2000
Number iterations for mcmc warmup, defaults to 1/2 of the iterations
Whether or not to include overdispersion parameter, defaults to FALSE
Prior standard deviation on 1/overdispersion parameter, Defaults to inv-Cauchy(0,5)
Whether or not to return draws from posterior predictive distribution (requires more memory)
Whether to do moment matching via loo::loo_moment_match(). This increases memory by adding all temporary
parmaeters to be saved and returned
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().
# \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 4.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#>
#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 2.1e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
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#>
#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 2e-05 seconds
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# fit a model with 1 factor
design <- data.frame("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 2.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
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#>
#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 2.2e-05 seconds
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#>
#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 4.8e-05 seconds
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# }
# try a model with random effects
set.seed(123)
y <- matrix(runif(99,1,4), ncol=3)
design <- data.frame("fac" = sample(letters[1:5], size=nrow(y), replace=TRUE))
design$fac <- as.factor(design$fac)
fit <- fit_zoid(formula = ~(1|fac), design_matrix = design, data_matrix = y)
#> Warning: ‘|’ not meaningful for factors
#>
#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 1).
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#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 2).
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#> SAMPLING FOR MODEL 'dirichregmod' NOW (CHAIN 3).
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#> Warning: There were 3 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess