Family objects for models

Family objects provide a convenient way to specify the details of the models used by netReg. See also stats::family for more details.

family(object, ...)

gaussian(link = c("identity"))

binomial(link = c("logit", "probit", "log"))

poisson(link = c("log"))

gamma(link = c("inverse"))

beta(link = c("logit", "probit", "log"))

inverse.gaussian(link = c("1/mu^2"))

Arguments

object	a object for which the family shoulr be retured (e.g. edgenet)
...	further arguments passed to methods
link	name of a link function

Value

An object of class netReg.family

family

name of the family

link

name of the link function

linkinv

inverse link function

loss

loss function

Examples

gaussian()
#> family: gaussian
#> link: identity
binomial("probit")$link
#> [1] "probit"
poisson()$linkinv
#> function (x) 
#> tf$maximum(tf$exp(x), tf$float32$min)
#> <bytecode: 0x5578c5d265e0>
#> <environment: namespace:netReg>
gamma()$linkinv
#> Warning: family gamma is still experimental. enjoy with care.
#> function (x) 
#> 1/x
#> <bytecode: 0x5578c5d938d8>
#> <environment: namespace:netReg>
beta()$loss
#> Warning: family beta is still experimental. enjoy with care.
#> function (y, mu.hat, ...) 
#> {
#>     obj <- 0
#>     eps <- .Machine$double.eps * 1e+09
#>     for (j in seq(ncol(y))) {
#>         mu <- mu.hat[, j]
#>         phi <- 1
#>         p <- mu * phi
#>         q <- (1 - mu) * phi
#>         p.trans <- tf$math$maximum(p, eps)
#>         q.trans <- tf$math$maximum(q, eps)
#>         prob <- tfprobability::tfd_beta(concentration1 = p.trans, 
#>             concentration0 = q.trans)
#>         obj <- obj + tf$reduce_sum(prob$log_prob(y[, j]))
#>     }
#>     -obj
#> }
#> <bytecode: 0x5578c5dff240>
#> <environment: namespace:netReg>
inverse.gaussian()$loss
#> Warning: family inverse.gaussian is still experimental. enjoy with care.
#> function (y, mu.hat, ...) 
#> {
#>     obj <- 0
#>     for (j in seq(ncol(y))) {
#>         prob <- tfprobability::tfd_inverse_gaussian(loc = mu.hat[, 
#>             j], concentration = 1)
#>         obj <- obj + tf$reduce_sum(prob$log_prob(y[, j]))
#>     }
#>     -obj
#> }
#> <bytecode: 0x5578c5e68170>
#> <environment: namespace:netReg>