Description Usage Arguments Value Examples
A modification of the glmBayes
function to model
heteroskedasticity as a function of all or some variables.
Model Specification:
1 2 3 4 |
formula |
the model formula |
sigma.formula |
the formula for the scale parameter. Defaults to ~ 1 (intercept only) |
data |
a data frame |
s |
The desired prior scale. Defaults to 1. Is automatically squared within the model so select a number here on the standard deviation scale. |
df |
degrees of freedom for prior. |
log_lik |
Should the log likelihood be monitored? The default is FALSE. |
iter |
How many post-warmup samples? Defaults to 10000. |
warmup |
How many warmup samples? Defaults to 1000. |
adapt |
How many adaptation steps? Defaults to 1000. |
chains |
How many chains? Defaults to 4. |
thin |
Thinning interval. Defaults to 1. |
method |
Defaults to "parallel". For an alternative parallel option, choose "rjparallel" or. Otherwise, "rjags" (single core run). |
cl |
Use parallel::makeCluster(# clusters) to specify clusters for the parallel methods. Defaults to two cores. |
... |
Other arguments to run.jags. |
A run.jags object
1 | hslm()
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.