genSample.JointScalar: Generating sample from cross-correlated variables described...

In spup: Spatial Uncertainty Propagation Analysis

Generating sample from cross-correlated variables described by a scalar.

Description

Generating sample from cross-correlated variables described by a scalar.

Usage

## S3 method for class 'JointScalar'
genSample(UMobject, n, samplemethod, p = 0, asList = TRUE, ...)

Arguments

UMobject	object of a class JointScalar created using defineMUM.R
n	integer; number of Monte Carlo runs
samplemethod	"randomSampling" or "lhs".
p	a vector of quantiles. Optional. Only required if sample method is "lhs".
asList	logical. If asList = TRUE returns list of all samples as a list. If asList = FALSE returns samples in a format of distribution parameters in UMobject.
...	Additional parameters.

Value

Monte Carlo sample of cross-correlated scalar variables.

Author(s)

Kasia Sawicka, Gerard Heuvelink

Examples

set.seed(12345)
scalarUM <- defineUM(uncertain = TRUE, distribution = "norm",
                     distr_param = c(1, 2), id="Var1")                
scalarUM2 <- defineUM(uncertain = TRUE, distribution = "norm",
                      distr_param = c(3, 2), id="Var2")
scalarUM3 <- defineUM(uncertain = TRUE, distribution = "norm",
                      distr_param = c(10, 2.5), id="Var3")                
myMUM <- defineMUM(UMlist = list(scalarUM, scalarUM2, scalarUM3), 
               matrix(c(1,0.7,0.2,0.7,1,0.5,0.2,0.5,1), nrow = 3, ncol = 3))
my_sample <- genSample(myMUM, n = 10, samplemethod = "randomSampling", asList = FALSE)
my_sample  

spup documentation built on May 29, 2024, 5:10 a.m.