RCPmod (version 2.192)

simRCPdata: Simulates from a regimix model

Description

Simulates a data set from a mixture-of-experts model for RCP (for region of common profile) types.

Usage

simRCPdata(nRCP=3, S=20, n=200, p.x=3, p.w=0, alpha=NULL, tau=NULL, beta=NULL,
                        gamma=NULL, logDisps=NULL, powers=NULL, X=NULL, W=NULL,
                                                  offset=NULL, dist="Bernoulli")

Value

A data frame that contains the outcomes (species data) and the covariates (environmental data and species-level covariates). This data.frame has a number of special attirbutes, which are information about the model underlying the data. They are:

RCPs

the true, but unobserved, RCP types

pis

the true prior probabilities

alpha

the species overall prevalences, on linear predictor scale

tau

the deviation from alpha for each RCP type, on linear predictor scale

beta

the parameters controlling how the RCP types depend on the covariates

gamma

the parameters controlling how each species depends on the species-level covariates

logDisps

the logarithm of the dispersion parameter for each species

mu

the probabilities of each species occuring in each RCP type

Arguments

nRCP

Integer giving the number of RCPs

S

Integer giving the number of species

n

Integer giving the number of observations (sites)

p.x

Integer giving the number of covariates (including the intercept) for the model for the latent RCP types

p.w

Integer giving the number of covariates (excluding the intercept) for the model for the species data

alpha

Numeric vector of length S. Specifies the mean prevalence for each species, on the logit scale

tau

Numeric matrix of dimension c(nRCP-1,S). Specifies each species difference from the mean to each RCPs mean for the first nRCP-1 RCPs. The last RCP means are calculated using the sum-to-zero constraints

beta

Numeric matrix of dimension c(nRCP-1,p.x). Specifies the RCP's dependence on the covariates (in X)

gamma

Numeric matrix of dimension c(n,p.w). Specifies the species' dependence on the covariates (in W)

logDisps

Logartihm of the (over-)dispersion parameters for each species for negative binomial, Tweedie and Normal models

powers

Power parameters for each species for Tweedie model

X

Numeric matrix of dimension c(n,p.x). Specifies the covariates for the RCP model. Must include the intercept, if one is wanted. Default is random numbers in a matrix of the right size.

W

Numeric matrix of dimension c(n,p.w). Specifies the covariates for the species model. Must not include the intercept. Unless you want it included twice. Default is to give random levels of a two-level factor.

offset

Numeric vector of size n. Specifies any offset to be included into the species level model.

dist

Text string. Specifies the distribution of the species data. Current options are "Bernoulli" (default), "Poisson", "NegBin", "Tweedie" and "Normal".

Author

Scott D. Foster

References

Foster, S.D., Givens, G.H., Dornan, G.J., Dunstan, P.K. and Darnell, R. (2013) Modelling Regions of Common Profiles Using Biological and Environmental Data. Environmetrics.

Examples

#generates synthetic data
set.seed( 151)
n <- 100
S <- 10
nRCP <- 3
my.dist <- "NegBin"
X <- as.data.frame( cbind( x1=runif( n, min=-10, max=10), x2=runif( n, min=-10, max=10)))
Offy <- log( runif( n, min=30, max=60))
pols <- list()
pols[[1]] <- poly( X$x1, degree=3)
#important to scale covariates so that regimix can get half-way decent starting values
pols[[2]] <- poly( X$x2, degree=3)
X <- as.matrix( cbind( 1, X, pols[[1]], pols[[2]]))
colnames( X) <- c("const", 'x1', 'x2', paste( "x1",1:3,sep='.'), paste( "x2",1:3,sep='.'))
p.x <- ncol( X[,-(2:3)])
p.w <- 3
W <- matrix(sample( c(0,1), size=(n*p.w), replace=TRUE), nrow=n, ncol=p.w)
colnames( W) <- paste( "w",1:3,sep=".")
alpha <- rnorm( S)
tau.var <- 0.5
b <- sqrt( tau.var/2)
#a double exponential for RCP effects
tau <- matrix( rexp( n=(nRCP-1)*S, rate=1/b) - rexp( n=(nRCP-1)*S, rate=1/b), nrow=nRCP-1, ncol=S)
beta <- 0.2 * matrix( c(-1.2, -2.6, 0.2, -23.4, -16.7, -18.7, -59.2, -76.0, -14.2, -28.3,
  -36.8, -17.8, -92.9,-2.7), nrow=nRCP-1, ncol=p.x)
gamma <- matrix( rnorm( S*p.w), ncol=p.w, nrow=S)
logDisp <- log( rexp( S, 1))
set.seed(121)
simDat <- simRCPdata( nRCP=nRCP, S=S, p.x=p.x, p.w=p.w, n=n, alpha=alpha, tau=tau,
  beta=beta, gamma=gamma, X=X[,-(2:3)], W=W, dist=my.dist, logDisp=logDisp, offset=Offy)