mvrtriangle: Simulation with a copula model and triangular distributions

mvrtriangleR Documentation

Simulation with a copula model and triangular distributions

Description

Simulates model parameters using elliptical copulas and triangular marginals.

Usage

mvrtriangle(n, object, ...)

## S4 method for signature 'numeric,FLModelSim'
mvrtriangle(n = 1, object, ...)

Arguments

n

the number of iterations

object

the FLModelSim object

...

arguments to be passed to the rMvdc and copula methods

Value

an FLModelSim object with n sets of parameters

Examples

# Set up the FLModelSim object
mm <- matrix(NA, ncol=3, nrow=3)
diag(mm) <- c(100, 0.001,0.001)
mm[upper.tri(mm)] <- mm[lower.tri(mm)] <- c(0.1,0.1,0.0003)
md <- ~linf*(1-exp(-k*(t-t0)))
prs <- FLPar(linf=120, k=0.3, t0=0.1, units=c("cm","yr^-1","yr"))
vb <- FLModelSim(model=md, params=prs, vcov=mm, distr="norm")
# Simulate from a multivariate normal distribution...
  set.seed(1)
  vbSim <- mvrnorm(10000, vb)
  mm <- predict(vbSim, t=0:20+0.5)
#...from a multivariate triangular distribution with default ranges (0.01 and
#   0.99 quantiles for min and max using a normal distribution with mean from
#   params and sigma from vcov, and with the apex located at params)...
  set.seed(1)
  vbSim1 <- mvrtriangle(10000, vb)
  mm1 <- predict(vbSim1, t=0:20+0.5)
#...and from a multivariate triangular distribution with specified ranges 
#   (note if "c" is missing, it will take the average of "a" and "b")
  set.seed(1)
  pars <- list(list(a=90, b=125, c=120), list(a=0.2, b=0.4), list(a=0, b=0.4, c=0.1))
  vbSim2 <- mvrtriangle(10000, vb, paramMargins=pars)
  mm2 <- predict(vbSim2, t=0:20+0.5)
# Plot the results
par(mfrow=c(3,1))
boxplot(t(mm), main="normal")
boxplot(t(mm1), main="triangular")
boxplot(t(mm2), main="triangular2")
splom(data.frame(t(params(vbSim)@.Data)), pch=".")
splom(data.frame(t(params(vbSim1)@.Data)), pch=".")
splom(data.frame(t(params(vbSim2)@.Data)), pch=".")

flr/FLa4a documentation built on Dec. 21, 2024, 9:28 p.m.