tests/testthat/test_RCP_S3_class.R
In skiptoniam/ecomix: Fitting Finite Mixture Models to Ecological Data
context('regional_mix bernoulli functions')
library(ecomix)
testthat::test_that('testing regional mix S3 functions', {
library(ecomix)
rm(list = ls())
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$x1, degree=3)
#important to scale covariates so that regimix can get half-way decent starting values
pols[[2]] <- poly( X$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 <- 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)
#fit the model
my.form.RCP <- paste( paste( paste(
'cbind(', paste( paste( 'spp', 1:S, sep=''), collapse=','), sep=''),
')',sep=''),
'~x1.1+x1.2+x1.3+x2.1+x2.2+x2.3',sep='')
my.form.spp <- ~w.1+w.2+w.3
fm2 <- regional_mix(rcp_formula = my.form.RCP, species_formula = my.form.spp,
data = simDat, family = "negative.binomial", nRCP = 3, inits = "random2")
testthat::expect_is(AIC(fm2),'numeric')
testthat::expect_is(BIC(fm2),'numeric')
cdres <- cooks.distance(fm2,times=10)
testthat::expect_s3_class(cdres,"regiCooksD")
plot(fm2)
plot(fm2,fitted.scale = "log")
plot(fm2, type = "deviance" )
plot(fm2, type = "deviance" , alpha.conf = c(0.75))
plot(fm2, type = "deviance" , species = fm2$names$spp[1])
print(fm2)
resres <- residuals(fm2)
testthat::expect_is(resres,'matrix')
})
testthat::test_that('Test the prediction functions in RCP',{
library(ecomix)
rm(list = ls())
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$x1, degree=3)
#important to scale covariates so that regimix can get half-way decent starting values
pols[[2]] <- poly( X$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 <- 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)
#fit the model
my.form.RCP <- paste( paste( paste(
'cbind(', paste( paste( 'spp', 1:S, sep=''), collapse=','), sep=''),
')',sep=''),
'~x1.1+x1.2+x1.3+x2.1+x2.2+x2.3',sep='')
my.form.spp <- ~w.1+w.2+w.3
fm2 <- regional_mix(rcp_formula = my.form.RCP, species_formula = my.form.spp,
data = simDat, family = "negative.binomial", nRCP = 3, inits = "random2")
preds <- predict(fm2)
testthat::expect_is(preds,'matrix')
bootmod <- regional_mix.bootstrap(fm2,nboot = 10)
bootmod1 <- regional_mix.bootstrap(fm2,nboot = 10,type = "SimpleBoot")
predsboot <- predict(fm2, bootmod)
predsboot1 <- predict(fm2, bootmod1)
testthat::expect_is(predsboot,'list')
testthat::expect_is(predsboot1,'list')
xwnew <- rbind(replicate(2,cbind(X,W))[,,1],replicate(2,cbind(X,W))[,,2])
preds_new <- predict(fm2,newdata = xwnew)
extractAIC(fm2)
fm2 <- regional_mix(rcp_formula = my.form.RCP, species_formula = my.form.spp,
data = simDat, family = "negative.binomial",
nRCP = 3, inits = "random2",
control = list(getScores=TRUE))
})
testthat::test_that('stability',{
library(ecomix)
rm(list = ls())
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$x1, degree=3)
#important to scale covariates so that regimix can get half-way decent starting values
pols[[2]] <- poly( X$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 <- 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)
#fit the model
my.form.RCP <- paste( paste( paste(
'cbind(', paste( paste( 'spp', 1:S, sep=''), collapse=','), sep=''),
')',sep=''),
'~x1.1+x1.2+x1.3+x2.1+x2.2+x2.3',sep='')
my.form.spp <- ~w.1+w.2+w.3
fm2 <- regional_mix(rcp_formula = my.form.RCP, species_formula = my.form.spp,
data = simDat, family = "negative.binomial",
nRCP = 3, inits = "random2")
stab <- stability.regional_mix(fm2,oosSizeRange=c(1,5,10), mc.cores=1, times = 10, doPlot=FALSE)
plot(stab)
testthat::expect_error(residuals(fm2,type='RQRS.sim'))
# test_res <- residuals(fm2,type='RQR.sim',mc.cores=1)
# testthat::expect_is(test_res,"matrix")
vcov(fm2)
tmp <- vcov(fm2,method="FiniteDifference")
vcov(fm2,method="BayesBoot",nboot=10)
vcov(fm2,method="SimpleBoot",nboot=10)
vcov(fm2,method="EmpiricalInfo")
fm2$vcov <- tmp
# regional_mix_bootParametric(fm2, nboot = 10)
summary(fm2)
})
testthat::test_that("RCP membership",{
library(ecomix)
rm(list = ls())
set.seed( 151)
n <- 100
S <- 10
nRCP <- 3
my.dist <- "poisson"
X <- as.data.frame( cbind( x1=runif( n, min=-10, max=10), x2=runif( n, min=-10, max=10)))
Offy <- log( runif( n, min=30, max=60))
pols <- list()
pols[[1]] <- poly( X$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)
simDat1 <- regional_mix.simulate( nRCP=nRCP, S=S, p.x=p.x, n=n, #p.w=p.w,
alpha=alpha, tau=tau, beta=beta, #gamma=gamma,
X=X[,-(2:3)], #W=W,
family=my.dist, offset=Offy)
simDat2 <- 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, offset=Offy)
#fit the model
my.form.RCP <- paste( paste( paste(
'cbind(', paste( paste( 'spp', 1:S, sep=''), collapse=','), sep=''),
')',sep=''),
'~x1.1+x1.2+x1.3+x2.1+x2.2+x2.3',sep='')
my.form.spp <- ~1#w.1+w.2+w.3
fm1 <- regional_mix(rcp_formula = my.form.RCP, species_formula = my.form.spp,
data = simDat1, family = "poisson",
nRCP = 3, inits = "random2")
my.form.spp <- ~w.1+w.2+w.3
my.form.RCP <- paste( paste( paste(
'cbind(', paste( paste( 'spp', c(1,1:S), sep=''), collapse=','), sep=''),
')',sep=''),
'~x1.1+x1.2+x1.3+x2.1+x2.2+x2.3',sep='')
testthat::expect_null(fm2 <- regional_mix.multifit(rcp_formula = my.form.RCP, species_formula = my.form.spp,
data = simDat2, family = "poisson",
nRCP = 3, inits = "random"))
testthat::expect_null(fm2 <- regional_mix.multifit(rcp_formula = NULL, species_formula = my.form.spp,
data = simDat2, family = "poisson",
nRCP = 3, inits = "random"))
fm2 <- regional_mix(rcp_formula = my.form.RCP, species_formula = my.form.spp,
data = simDat2, family = "poisson",
nRCP = 3, inits = "random2")
tmpboot1 <- regional_mix.bootstrap(fm1,nboot = 10)
regional_mix.species_profile(fm1)
regional_mix.species_profile(fm1,tmpboot1)
AIC(fm1,k=NULL)
extractAIC(fm1,k=NULL)
predict(fm1)
plot(fm1)
plot(fm1,species = fm1$names$spp[1])
testthat::expect_error(plot(fm1,type='blah'))
testthat::expect_error(plot(fm1,species='blah'))
})
skiptoniam/ecomix documentation built on Sept. 14, 2023, 6:04 a.m.
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context('regional_mix bernoulli functions')
library(ecomix)
testthat::test_that('testing regional mix S3 functions', {
library(ecomix)
rm(list = ls())
set.seed( 151)
n <- 100
S <- 10
nRCP <- 3
my.dist <- "negative.binomial"
X <- as.data.frame( cbind( x1=runif( n, min=-10, max=10), x2=runif( n, min=-10, max=10)))
Offy <- log( runif( n, min=30, max=60))
pols <- list()
pols[[1]] <- poly( X$x1, degree=3)
#important to scale covariates so that regimix can get half-way decent starting values
pols[[2]] <- poly( X$x2, degree=3)
X <- as.matrix( cbind( 1, X, pols[[1]], pols[[2]]))
colnames( X) <- c("const", 'x1', 'x2', paste( "x1",1:3,sep='.'), paste( "x2",1:3,sep='.'))
p.x <- ncol( X[,-(2:3)])
p.w <- 3
W <- matrix(sample( c(0,1), size=(n*p.w), replace=TRUE), nrow=n, ncol=p.w)
colnames( W) <- paste( "w",1:3,sep=".")
alpha <- rnorm( S)
tau.var <- 0.5
b <- sqrt( tau.var/2)
#a double exponential for RCP effects
tau <- matrix( rexp( n=(nRCP-1)*S, rate=1/b) - rexp( n=(nRCP-1)*S, rate=1/b), nrow=nRCP-1, ncol=S)
beta <- 0.2 * matrix( c(-1.2, -2.6, 0.2, -23.4, -16.7, -18.7, -59.2, -76.0, -14.2, -28.3,
-36.8, -17.8, -92.9,-2.7), nrow=nRCP-1, ncol=p.x)
gamma <- matrix( rnorm( S*p.w), ncol=p.w, nrow=S)
logDisp <- log( rexp( S, 1))
set.seed(121)
simDat <- regional_mix.simulate( nRCP=nRCP, S=S, p.x=p.x, p.w=p.w, n=n,
alpha=alpha, tau=tau, beta=beta, gamma=gamma,
X=X[,-(2:3)], W=W, family=my.dist,
logDisp=logDisp, offset=Offy)
#fit the model
my.form.RCP <- paste( paste( paste(
'cbind(', paste( paste( 'spp', 1:S, sep=''), collapse=','), sep=''),
')',sep=''),
'~x1.1+x1.2+x1.3+x2.1+x2.2+x2.3',sep='')
my.form.spp <- ~w.1+w.2+w.3
fm2 <- regional_mix(rcp_formula = my.form.RCP, species_formula = my.form.spp,
data = simDat, family = "negative.binomial", nRCP = 3, inits = "random2")
testthat::expect_is(AIC(fm2),'numeric')
testthat::expect_is(BIC(fm2),'numeric')
cdres <- cooks.distance(fm2,times=10)
testthat::expect_s3_class(cdres,"regiCooksD")
plot(fm2)
plot(fm2,fitted.scale = "log")
plot(fm2, type = "deviance" )
plot(fm2, type = "deviance" , alpha.conf = c(0.75))
plot(fm2, type = "deviance" , species = fm2$names$spp[1])
print(fm2)
resres <- residuals(fm2)
testthat::expect_is(resres,'matrix')
})
testthat::test_that('Test the prediction functions in RCP',{
library(ecomix)
rm(list = ls())
set.seed( 151)
n <- 100
S <- 10
nRCP <- 3
my.dist <- "negative.binomial"
X <- as.data.frame( cbind( x1=runif( n, min=-10, max=10), x2=runif( n, min=-10, max=10)))
Offy <- log( runif( n, min=30, max=60))
pols <- list()
pols[[1]] <- poly( X$x1, degree=3)
#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 <- 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)
#fit the model
my.form.RCP <- paste( paste( paste(
'cbind(', paste( paste( 'spp', 1:S, sep=''), collapse=','), sep=''),
')',sep=''),
'~x1.1+x1.2+x1.3+x2.1+x2.2+x2.3',sep='')
my.form.spp <- ~w.1+w.2+w.3
fm2 <- regional_mix(rcp_formula = my.form.RCP, species_formula = my.form.spp,
data = simDat, family = "negative.binomial", nRCP = 3, inits = "random2")
preds <- predict(fm2)
testthat::expect_is(preds,'matrix')
bootmod <- regional_mix.bootstrap(fm2,nboot = 10)
bootmod1 <- regional_mix.bootstrap(fm2,nboot = 10,type = "SimpleBoot")
predsboot <- predict(fm2, bootmod)
predsboot1 <- predict(fm2, bootmod1)
testthat::expect_is(predsboot,'list')
testthat::expect_is(predsboot1,'list')
xwnew <- rbind(replicate(2,cbind(X,W))[,,1],replicate(2,cbind(X,W))[,,2])
preds_new <- predict(fm2,newdata = xwnew)
extractAIC(fm2)
fm2 <- regional_mix(rcp_formula = my.form.RCP, species_formula = my.form.spp,
data = simDat, family = "negative.binomial",
nRCP = 3, inits = "random2",
control = list(getScores=TRUE))
})
testthat::test_that('stability',{
library(ecomix)
rm(list = ls())
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)
#important to scale covariates so that regimix can get half-way decent starting values
pols[[2]] <- poly( X$x2, degree=3)
X <- as.matrix( cbind( 1, X, pols[[1]], pols[[2]]))
colnames( X) <- c("const", 'x1', 'x2', paste( "x1",1:3,sep='.'), paste( "x2",1:3,sep='.'))
p.x <- ncol( X[,-(2:3)])
p.w <- 3
W <- matrix(sample( c(0,1), size=(n*p.w), replace=TRUE), nrow=n, ncol=p.w)
colnames( W) <- paste( "w",1:3,sep=".")
alpha <- rnorm( S)
tau.var <- 0.5
b <- sqrt( tau.var/2)
#a double exponential for RCP effects
tau <- matrix( rexp( n=(nRCP-1)*S, rate=1/b) - rexp( n=(nRCP-1)*S, rate=1/b), nrow=nRCP-1, ncol=S)
beta <- 0.2 * matrix( c(-1.2, -2.6, 0.2, -23.4, -16.7, -18.7, -59.2, -76.0, -14.2, -28.3,
-36.8, -17.8, -92.9,-2.7), nrow=nRCP-1, ncol=p.x)
gamma <- matrix( rnorm( S*p.w), ncol=p.w, nrow=S)
logDisp <- log( rexp( S, 1))
set.seed(121)
simDat <- regional_mix.simulate( nRCP=nRCP, S=S, p.x=p.x, p.w=p.w, n=n,
alpha=alpha, tau=tau, beta=beta, gamma=gamma,
X=X[,-(2:3)], W=W, family=my.dist,
logDisp=logDisp, offset=Offy)
#fit the model
my.form.RCP <- paste( paste( paste(
'cbind(', paste( paste( 'spp', 1:S, sep=''), collapse=','), sep=''),
')',sep=''),
'~x1.1+x1.2+x1.3+x2.1+x2.2+x2.3',sep='')
my.form.spp <- ~w.1+w.2+w.3
fm2 <- regional_mix(rcp_formula = my.form.RCP, species_formula = my.form.spp,
data = simDat, family = "negative.binomial",
nRCP = 3, inits = "random2")
stab <- stability.regional_mix(fm2,oosSizeRange=c(1,5,10), mc.cores=1, times = 10, doPlot=FALSE)
plot(stab)
testthat::expect_error(residuals(fm2,type='RQRS.sim'))
# test_res <- residuals(fm2,type='RQR.sim',mc.cores=1)
# testthat::expect_is(test_res,"matrix")
vcov(fm2)
tmp <- vcov(fm2,method="FiniteDifference")
vcov(fm2,method="BayesBoot",nboot=10)
vcov(fm2,method="SimpleBoot",nboot=10)
vcov(fm2,method="EmpiricalInfo")
fm2$vcov <- tmp
# regional_mix_bootParametric(fm2, nboot = 10)
summary(fm2)
})
testthat::test_that("RCP membership",{
library(ecomix)
rm(list = ls())
set.seed( 151)
n <- 100
S <- 10
nRCP <- 3
my.dist <- "poisson"
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)
simDat1 <- regional_mix.simulate( nRCP=nRCP, S=S, p.x=p.x, n=n, #p.w=p.w,
alpha=alpha, tau=tau, beta=beta, #gamma=gamma,
X=X[,-(2:3)], #W=W,
family=my.dist, offset=Offy)
simDat2 <- 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, offset=Offy)
#fit the model
my.form.RCP <- paste( paste( paste(
'cbind(', paste( paste( 'spp', 1:S, sep=''), collapse=','), sep=''),
')',sep=''),
'~x1.1+x1.2+x1.3+x2.1+x2.2+x2.3',sep='')
my.form.spp <- ~1#w.1+w.2+w.3
fm1 <- regional_mix(rcp_formula = my.form.RCP, species_formula = my.form.spp,
data = simDat1, family = "poisson",
nRCP = 3, inits = "random2")
my.form.spp <- ~w.1+w.2+w.3
my.form.RCP <- paste( paste( paste(
'cbind(', paste( paste( 'spp', c(1,1:S), sep=''), collapse=','), sep=''),
')',sep=''),
'~x1.1+x1.2+x1.3+x2.1+x2.2+x2.3',sep='')
testthat::expect_null(fm2 <- regional_mix.multifit(rcp_formula = my.form.RCP, species_formula = my.form.spp,
data = simDat2, family = "poisson",
nRCP = 3, inits = "random"))
testthat::expect_null(fm2 <- regional_mix.multifit(rcp_formula = NULL, species_formula = my.form.spp,
data = simDat2, family = "poisson",
nRCP = 3, inits = "random"))
fm2 <- regional_mix(rcp_formula = my.form.RCP, species_formula = my.form.spp,
data = simDat2, family = "poisson",
nRCP = 3, inits = "random2")
tmpboot1 <- regional_mix.bootstrap(fm1,nboot = 10)
regional_mix.species_profile(fm1)
regional_mix.species_profile(fm1,tmpboot1)
AIC(fm1,k=NULL)
extractAIC(fm1,k=NULL)
predict(fm1)
plot(fm1)
plot(fm1,species = fm1$names$spp[1])
testthat::expect_error(plot(fm1,type='blah'))
testthat::expect_error(plot(fm1,species='blah'))
})
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