A wrapper for creating a vector of distributions.
Returns an R6 object of class VectorDistribution.
VectorDistribution$new(distlist = NULL, distribution = NULL, params = NULL, name = NULL, short_name = NULL, description = NULL, decorators = NULL)
| Argument | Type | Details |
distlist |
list | List of distributions. |
distribution |
character | Distribution to wrap. |
params |
a R object | Either list of parameters or matrix-type frame, see examples. |
shared_params |
a R object | Either list of shared parameters or matrix-type frame, see examples. |
name |
list | Optional new name for distribution. |
short_name |
list | Optional new short_name for distribution. |
description |
list | Optional new description for distribution. |
decorators |
list | Decorators to pass to wrapped distributions on construction. |
A vector distribution can either be constructed by a list of
distributions passed to distlist or by passing the name of one or more distributions
implemented in distr6 to distribution, as well as a list or table of parameters to params.
The former case provides more flexibility in the ability to use wrapped distributions
but the latter is vastly faster for distributions of class SDistribution or custom distributions.
The shared_params parameter decreases memory usage and improves speed by storing any parameters
shared between distributions only once (instead of repeated in a list).
| Variable | Return |
name |
Name of distribution. |
short_name |
Id of distribution. |
description |
Brief description of distribution. |
| Accessor Methods | Link |
wrappedModels(model = NULL) |
wrappedModels |
decorators() |
decorators |
traits() |
traits |
valueSupport() |
valueSupport |
variateForm() |
variateForm |
type() |
type |
properties() |
properties |
support() |
support |
symmetry() |
symmetry |
sup() |
sup |
inf() |
inf |
dmax() |
dmax |
dmin() |
dmin |
skewnessType() |
skewnessType |
kurtosisType() |
kurtosisType |
| d/p/q/r Methods | Link |
pdf(x1, ..., log = FALSE, simplify = TRUE) |
pdf |
cdf(x1, ..., lower.tail = TRUE, log.p = FALSE, simplify = TRUE) |
cdf |
quantile(p, ..., lower.tail = TRUE, log.p = FALSE, simplify = TRUE) |
quantile.Distribution |
rand(n, simplify = TRUE) |
rand |
| Statistical Methods | Link |
prec() |
prec |
stdev() |
stdev |
median() |
median.Distribution |
iqr() |
iqr |
cor() |
cor |
| Parameter Methods | Link |
parameters(id) |
parameters |
getParameterValue(id, error = "warn") |
getParameterValue |
setParameterValue(..., lst = NULL, error = "warn") |
setParameterValue |
| Validation Methods | Link |
liesInSupport(x, all = TRUE, bound = FALSE) |
liesInSupport |
liesInType(x, all = TRUE, bound = FALSE) |
liesInType |
| Representation Methods | Link |
strprint(n = 2) |
strprint |
print(n = 2) |
print |
summary(full = T) |
summary.Distribution |
A vector distribution is intented to vectorize distributions more efficiently than storing a list of distributions. To improve speed and reduce memory usage, distributions are only constructed when methods (e.g. d/p/q/r) are called. Whilst it is recommended to first extract distributions using `[` before querying them for results, all common methods are available in `VectorDistribution` as they are wrapped in `apply`.
# NOT RUN {
vecDist <- VectorDistribution$new(list(Binomial$new(prob = 0.5,
size = 10), Normal$new(mean = 15)))
vecDist$pdf(x1 = 2, x2 =3)
# Equivalently
vecDist[1]$pdf(2); vecDist[2]$pdf(3)
# Or to evaluate every distribution at the same point
vecDist$pdf(1)
# Same wrapping for statistical functions
vecDist$mean()
c(vecDist[1]$mean(), vecDist[2]$mean())
vecDist$entropy()
c(vecDist[1]$entropy(), vecDist[2]$entropy())
vecDist$cdf(1:5, 12:16)
vecDist$rand(10)
vecBin = VectorDistribution$new(distribution = "Binomial",
params = list(list(prob = 0.1, size = 2),
list(prob = 0.6, size = 4),
list(prob = 0.2, size = 6)))
vecBin$pdf(x1=1,x2=2,x3=3)
vecBin$cdf(x1=1,x2=2,x3=3)
vecBin$rand(10)
#Equivalently
vecBin = VectorDistribution$new(distribution = "Binomial",
params = data.table::data.table(prob = c(0.1,0.6,0.2), size = c(2,4,6)))
vecBin$pdf(x1=1,x2=2,x3=3)
vecBin$cdf(x1=1,x2=2,x3=3)
vecBin$rand(10)
# sharedparams is very useful for vectorized custom distributions
shared_params = list(name = "A Distribution", short_name = "Dist", type = Reals$new())
params = list(list(pdf = function(x) return(1)), list(pdf = function(x) return(2)))
vecdist = VectorDistribution$new(distribution = "Distribution", params = params,
shared_params = shared_params)
vecdist$pdf(1)
# }
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