make_distribution: Make an 'distribution' from a gamlss distribution

In davharris/mistnet2: Neural Networks with Latent Random Variables

Description

Make an distribution from a gamlss distribution

Usage

make_distribution(family_function, ...)

Arguments

family_function	Either a character vector containing the name of a function that produces a gamlss.family (e.g. family_function = "NO" for the normal distribution), or a user-created function that returns an object with the same structure. [[Add explanation of how to do this: see dEXAMPLE in test-distributions.R]] gamlss.family.
...	Additional arguments, possibly including bd (binomial denominator), sigma (scale), nu (shape), or tau (shape), depending on the distribution. These must be named, and partial matching is not allowed.

Value

An distribution object, consisting of the following functions:

• log_prob(x, mu): log probability of the distribution with location parameter mu evaluated at x.

• sample(n, mu): draw n random samples from the distribution with location parameter mu.

• Functions for calculating partial derivatives evaluated at x with regard to mu (dldm), sigma (dldd), nu (dldv), and tau (dldt). For a few distributions, dldx is also defined: these distributions can be used as prior distributions on the model's weights. Users can add dldx functions by creating their own distributions that include a function called dldx.

Examples

distribution = make_distribution("NO", sigma = 1/3)

# Sample 10 random values with mean=2 (and sigma=1/3 as defined above)
samples = draw_samples(distribution, n = 10, mu = 2)

# find the log_prob of those samples under a distribution with mean=1
log_prob(distribution, x = samples, mu = 1)

# The gradient of the log_prob with respect to the mean indicates that
# the log_prob would be higher if mu were larger (more positive)
grad(distribution, "mu", y = samples, mu = 1)

# The gradient with respect to x shows that the log_prob would also be
# higher if we reduced the values of x (more negative)
grad(distribution, "x", x = samples, mu = 1)

# The gradient with respect to x isn't defined for all distributions,
# however. For example, this code would return an error that
# " gradient with respect to x (dldx) is not defined for the distribution PO"
## Not run: 
another_distribution = make_distribution("PO")
another_distribution$dldx(x = 1:10, mu = 1)

## End(Not run)

davharris/mistnet2 documentation built on May 14, 2019, 9:28 p.m.