simIReff (version 1.0)

effcop: Fit Vine copula models to matrices of effectiveness scores

Description

Fitting of and simulation from a copula model.

Usage

effcopFit(x, eff, ...)
reffcop(n, .effcop)

Arguments

x

a matrix or data frame of effectiveness scores to estimate dependence.

eff

a list of effectiveness distributions to use for the margins.

...

other parameters for vinecop, such as family_set, selcrit, trunc_lvl and cores.

n

number of observations to simulate.

.effcop

the effcop object representing the copula model (see effcopFit).

Value

effcopFit: an object of class effcop, with the following components:

data	the matrix of effectiveness scores used to fit the copula.
pobs	the matrix of pseudo-observations computed from data. This is stored because pseudo-observations are calculated breaking ties randomly (see pseudo_obs).
margins	the list of marginal effectiveness distributions.

These components may be altered to gain specific simulation capacity, such as systems with the same expected value.

reffcop: a matrix of random scores.

See Also

effCont and effDisc for available distributions for the margins. See package rvinecopulib for details on fitting the copulas.

Examples

# NOT RUN {
## Automatically build a gaussian copula to many systems
d <- web2010p20[,1:20] # sample P@20 data from 20 systems
effs <- effDiscFitAndSelect(d, support("p20")) # fit and select margins
cop <- effcopFit(d, effs, family_set = "gaussian") # fit copula
y <- reffcop(1000, cop) # simulate new 1000 topics

# compare observed vs. expected mean
E <- sapply(effs, function(e) e$mean)
E.hat <- colMeans(y)
plot(E, E.hat)
abline(0:1)

# compare observed vs. expected variance
Var <- sapply(effs, function(e) e$var)
Var.hat <- apply(y, 2, var)
plot(Var, Var.hat)
abline(0:1)
# }