regional_mix.simulate | R Documentation |
Simulates a data set from a mixture-of-experts model for RCP (for region of common profile) types.
regional_mix.simulate(
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,
family = "bernoulli"
)
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. |
family |
Text string. Specifies the family of the species data. Current options are "bernoulli" (default), "poisson", "negative.binomial", "tweedie" and "gaussian. |
## Not run:
#generates synthetic data
set.seed( 151)
n <- 100
S <- 10
nRCP <- 3
my.dist <- "negative.binomial"
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$x1, degree=3)
pols[[2]] <- poly( X$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)
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 <- regional_mix.simulate( 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,
family=my.dist, logDisp=logDisp, offset=Offy)
## End(Not run)
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