In hmsc-r/HMSC: Hierarchical Model of Species Communities
knitr::opts_chunk$set(echo = TRUE, warning=-1)
Introduction
The Hierarchical Modelling of Species Communities (Hmsc) framework is a statistical framework for analysis of multivariate data, typically from species communities. It uses Bayesian inference to fit latent-variable joint species distribution models. The conceptual basis of the method is outlined in @Ovaskainen2017b.
Here, we test the performance of the MCMC estimation scheme in terms of (1) computational time, (2) mixing properties, and (3) ability to recover true parameter values, by performing a set of experiments in which we use the Hmsc model to generate simulated data and then fit the model to the simulated data.
Generating simulated data
Set directories and load libraries
localDir = "."
source("load_libraries.R")
## we need to write results to subdirectories (folders)
if (!dir.exists(file.path(localDir, "data")))
dir.create(file.path(localDir, "data"))
if (!dir.exists(file.path(localDir, "models")))
dir.create(file.path(localDir, "models"))
if (!dir.exists(file.path(localDir, "performance")))
dir.create(file.path(localDir, "performance"))
The makedata function
The makedata function included in the vignette folder produces datasets based on the Hmsc model. Options include
- ny = Number of sites
- ns = Number of species
- hierarchical = T/F, where T yields data with two nested random levels, and F yields a single random level.
- spatial = T/F, where T yields spatially explicit data with sample unit locations sampled from the unit square.
makedata = function(ns, ny, hierarchical=FALSE, spatial=FALSE)
Case 1: Baseline model
As the baseline case, we consider a community of n~s~=50 species observed on n~y~=200 sampling units. We include for each sampling unit two covariates, one categorical (each unit randomized to represent one of three classes with equal probability) and one continuous (sampled from standard normal distribution). We include for each species two traits, one categorical (each species randomized to represent one of three classes with equal probability) and one continuous (sampled from standard normal distribution). We assume that the species belong to groups of 5 species, and that the phylogenetic correlation is 0.9 within a group and 0 between the groups. We sampled each element of the matrix $\Gamma$ (influence of traits on expected species parameters) from N(0, $\sigma$^2^), set the matrix V (variation among species not explained by traits) to ($\sigma$/2)^2^ I, where we set $\sigma$=0.3 (for motivation behind this value, see below). We assumed the value $\rho$=0.5 for the phylogenetic signal parameter. We generated two sampling-level latent variables, both following standard normal distributions, and sampled the latent loadings on/for these variables from N(0, $\sigma$^2^). We assumed that the data were normally distributed, obtained by adding to the linear predictor noise distributed as N(0,(2$\sigma$)^2^). We set $\sigma$=0.3, resulting in the following empirical variances for vectorized quantities: variance of data 1.16, variance of fixed effects 0.60, variance of random effects 0.21, and residual variance 0.36. Out of the fixed effects, the expectation based on traits had variance 0.30. As the data are roughly equally influenced by all model components, we expected that true values of all model parameters can be recovered if a sufficient amount of data are available.
set.seed(1)
source("makedata.R")
ns=50
ny=200
tmp=makedata(ns=ns, ny=ny)
all.data=tmp[[1]]
all.parameters=tmp[[2]]
L1 = all.parameters$L
Y1 = all.data$Y
#First five species at first five sites
all.data$Y[1:5,1:5]
#Random levels for first five sites
all.data$study.design[1:5,]
#Covariates
all.data$X.data[1:5,]
#Trait data
all.data$Tr.data[1:5,]
c(var(as.vector(all.parameters$mu)), var(as.vector(all.parameters$LF)),
var(as.vector(all.parameters$LR)), var(as.vector(all.parameters$eps)),
var(as.vector(Y1)))
save(file=file.path(localDir, "data","Case1.R"), all.data, all.parameters)
Case 2: Presence-absence model
We assumed the same linear predictor as for the baseline case but truncated the normally distributed data to 0/1 according to the probit model.
Y2 = 1*(L1 + matrix(rnorm(n = ny*ns), ncol=ns, nrow=ny)>0)
all.data$Y = Y2
#First five species at first five sites
all.data$Y[1:5,1:5]
save(file=file.path(localDir, "data","Case2.R"), all.data, all.parameters)
Case 3: Log-normal Poisson model
We assumed the same model for the baseline case but converted the normally distributed data to follow the log-normal Poisson distribution. Thus, we sampled the data for Case 3 from the Poisson distribution, the expectation of which was set to the exponential of the data from Case 1.
Y3 <- rpois(length(Y1), exp(Y1))
dim(Y3) <- dim(Y1)
all.data$Y = Y3
#First five species at first five sites
all.data$Y[1:5,1:5]
save(file=file.path(localDir, "data","Case3.R"), all.data, all.parameters)
Case 4: Large number of sites
We assumed otherwise the same model as for Case 1 but set the number of sampling units to n~y~=2000.
tmp=makedata(ns=50, ny=2000)
all.data=tmp[[1]]
all.parameters=tmp[[2]]
save(file=file.path(localDir, "data","Case4.R"), all.data, all.parameters)
Case 5: Large number of species
We assumed otherwise the same model as for Case 1 but set the number of species to n~s~=200.
tmp=makedata(ns=200, ny=200)
all.data=tmp[[1]]
all.parameters=tmp[[2]]
save(file=file.path(localDir, "data","Case5.R"), all.data, all.parameters)
Case 6: Spatially structured data
We assumed otherwise the same model as for Case 1 but included the coordinates of the sampling units, which were sampled uniformly from the unit square. The latent variables were assumed to have exponentially decaying spatial correlation structure, with the scale of decay set to 0.5 spatial units for latent variable 1 and 0.1 spatial units for latent variable 2.
tmp=makedata(ns=50, ny=200, spatial=TRUE)
all.data=tmp[[1]]
all.parameters=tmp[[2]]
#Coordinates for first five sites
all.data$xy[1:5,]
save(file=file.path(localDir, "data","Case6.R"), all.data, all.parameters)
Case 7: Hierarchically structured data
We assumed otherwise the same model as for Case 1 but assumed that the sampling units belonged to plots, each plot containing 10 sampling units. We assumed one latent variable at the plot level and another at the sampling unit level, thus keeping the variance attributed to latent variables the same as in Case 1 but changing their structure.
tmp=makedata(ns=50, ny=200, hierarchical=TRUE)
all.data=tmp[[1]]
all.parameters=tmp[[2]]
#Hierarchical random levels
head(all.data$study.design[order(all.data$study.design$plot),][c(1:3,21:23),])
save(file=file.path(localDir, "data","Case7.R"), all.data, all.parameters)
Model setup and fitting
We first set the parameters that we used in posterior samplings. There
is an alternative for test.run that runs fast, but gives no real
results, but you can use to quickly see the idea.
nChains = 4
test.run = FALSE
if (test.run){
#with this option, the vignette evaluates in ca. ? minutes in a laptop
thin = 1
samples = 10
} else {
#with this option, the vignette evaluates in ca. ? hrs in a laptop
thin = 10
samples = 1000
}
verbose = 0
We fitted Hmsc models with structure matching the data generation model, i.e. we did not examine here the influence of model misspecification or e.g. issues related to variable selection. However, we did not fix the number of latent variables to that used for generating the data, but assumed the default prior for them, as well as for all other model parameters.
We fitted each model with sampling parameters set to thin=r thin and
samples=r samples, and thus we performed r round(1.5*thin*samples)
MCMC iterations per chain. We adapted the number of latent factors
during the first r round(0.4*thin*samples) iterations, and
ignored from each chain the first r round(0.5*thin*samples)
iterations as transient. We used r nChains chains, so we ran in
total r round(nChains*1.5*thin*samples) MCMC iterations, out of
which the thinned posterior sample consisted of r nChains*samples
samples.
The code below takes a long time to run. The .Rmd file can be run much more quickly by setting test.run=TRUE, but the results will not be reliable.
For case 1, 4 and 5, we set up the model with gaussian errors, using distr = "normal"
for (case in c(1,4,5)){
set.seed(1)
load(file = file.path(localDir, "data", paste("Case",toString(case),".R", sep="")))
m = Hmsc(Y = all.data$Y,
XData = all.data$X.data, XFormula = all.data$X.formula,
TrData = all.data$Tr.data, TrFormula = all.data$Tr.formula,
C = all.data$C,
distr = "normal", studyDesign = all.data$study.design,
ranLevels = list(
"sampling.unit" = HmscRandomLevel(units = all.data$study.design[,1])))
ptm = proc.time()
m = sampleMcmc(m, samples=samples, thin=thin, transient = ceiling(0.5*samples*thin),
nChains = nChains, nParallel = nChains, verbose=verbose)
computational.time = proc.time() - ptm
save(file=file.path(localDir, "models", paste("Case",toString(case),".R", sep="")),
m, computational.time)
}
For case 2, we set up the model with probit errors, using distr = "probit"
set.seed(1)
load(file=file.path(localDir, "data","Case2.R"))
m = Hmsc(Y=all.data$Y,
XData=all.data$X.data, XFormula=all.data$X.formula,
TrData=all.data$Tr.data, TrFormula=all.data$Tr.formula,
C=all.data$C,
distr="probit", studyDesign=all.data$study.design,
ranLevels=list(
"sampling.unit"=HmscRandomLevel(units = all.data$study.design[,1])))
ptm = proc.time()
m = sampleMcmc(m, samples=samples, thin=thin, transient = ceiling(0.5*samples*thin),
nChains = nChains, nParallel = nChains, verbose=verbose)
computational.time = proc.time() - ptm
save(file=file.path(localDir, "models","Case2.R"), m, computational.time)
For case 3, we set up the model with lognormal Poisson errors, using distr = "lognormal poisson"
set.seed(1)
load(file=file.path(localDir, "data","Case3.R"))
m = Hmsc(Y=all.data$Y,
XData=all.data$X.data, XFormula=all.data$X.formula,
TrData=all.data$Tr.data, TrFormula=all.data$Tr.formula,
C=all.data$C,
distr="lognormal poisson", studyDesign=all.data$study.design,
ranLevels=list(
"sampling.unit"=HmscRandomLevel(units = all.data$study.design[,1])))
ptm = proc.time()
m = sampleMcmc(m, samples=samples, thin=thin, transient = ceiling(0.5*samples*thin),
nChains = nChains, nParallel = nChains, verbose=verbose)
computational.time = proc.time() - ptm
save(file=file.path(localDir, "models","Case3.R"), m, computational.time)
For case 6, we set up the model with a spatially structured random level, using
HmscRandomLevel(data=sRL, priors="default"). Note that sData is used instead of units.
set.seed(1)
load(file=file.path(localDir, "data","Case6.R"))
sRL = all.data$xy
colnames(sRL) = c("x","y")
rownames(sRL) = all.data$study.design[,1]
rL = HmscRandomLevel(sData=sRL)
m = Hmsc(Y=all.data$Y,
XData=all.data$X.data, XFormula=all.data$X.formula,
TrData=all.data$Tr.data, TrFormula=all.data$Tr.formula,
C=all.data$C,
distr="normal", studyDesign=all.data$study.design,
ranLevels=list("sampling.unit"=rL))
ptm = proc.time()
m = sampleMcmc(m, samples=samples, thin=thin, transient = ceiling(0.5*samples*thin),
nChains = nChains, nParallel = nChains, verbose=verbose)
computational.time = proc.time() - ptm
save(file=file.path(localDir, "models","Case6.R"), m, computational.time)
For case 7, we set up two hierarchical random levels, using HmscRandomLevel(units=...).
set.seed(1)
load(file=file.path(localDir, "data","Case7.R"))
m = Hmsc(Y=all.data$Y,
XData=all.data$X.data, XFormula=all.data$X.formula,
TrData=all.data$Tr.data, TrFormula=all.data$Tr.formula,
C=all.data$C,
distr="normal", studyDesign=all.data$study.design,
ranLevels=list(
"sampling.unit"=HmscRandomLevel(units = all.data$study.design[,1]),
"plot"=HmscRandomLevel(units = all.data$study.design[,2]))
)
ptm = proc.time()
m = sampleMcmc(m, samples=samples, thin=thin, transient = ceiling(0.5*samples*thin),
nChains = nChains, nParallel = nChains, verbose=verbose)
computational.time = proc.time() - ptm
save(file=file.path(localDir, "models","Case7.R"), m, computational.time)
Evaluating model performance
MCMC mixing
We used the coda package to compute the effective number of MCMC samples (sample size adjusted for autocorrelation) and Potential Scale Reduction Factor (Gelman and Rubin's convergence diagnostic, PSRF) for the following parameters: $\beta$, $\gamma$, $\rho$, V, $\Omega$. Note the following useful functions
computePredictedValues returns model-predicted values for the original sampling units used in model fitting. evaluateModelFit returns several measures of model fit, including RMSE (root mean square error), R^2^ (coefficient of determination), Tjur R^2^ (coefficient of discrimination), and AUC (area under the receiver operating characteristic curve).convertToCodaObject converts the posterior distributions produced by Hmsc into coda-format.
ptm = proc.time()
cases = c(1,2,3,4,5,6,7)
for (case in cases){
print(case)
set.seed(1)
load(file=file.path(localDir, "data", paste("Case",toString(case),".R", sep="")))
load(file=file.path(localDir, "models", paste("Case",toString(case),".R", sep="")))
computational.time = computational.time[3]
predY = computePredictedValues(m, expected=FALSE)
MF = evaluateModelFit(m, predY)
# ASSESS MIXING
mpost = convertToCodaObject(m)
es.beta = effectiveSize(mpost$Beta)
ge.beta = gelman.diag(mpost$Beta,multivariate=FALSE)$psrf
es.gamma = effectiveSize(mpost$Gamma)
ge.gamma = gelman.diag(mpost$Gamma,multivariate=FALSE)$psrf
es.rho = effectiveSize(mpost$Rho)
ge.rho = gelman.diag(mpost$Rho,multivariate=FALSE)$psrf
es.V = effectiveSize(mpost$V)
ge.V = gelman.diag(mpost$V,multivariate=FALSE)$psrf
mpost$temp = mpost$Omega[[1]]
for(i in 1:length(mpost$temp)){
mpost$temp[[i]] = mpost$temp[[i]][,1:50^2]
}
es.omega = effectiveSize(mpost$temp)
ge.omega = gelman.diag(mpost$temp,multivariate=FALSE)$psrf
es.alpha = NA
ge.alpha = NA
if (case==6){
es.alpha = effectiveSize(mpost$Alpha[[1]])
ge.alpha = gelman.diag(mpost$Alpha[[1]])
}
mixing = list(es.beta=es.beta, ge.beta=ge.beta,
es.gamma=es.gamma, ge.gamma=ge.gamma,
es.rho=es.rho, ge.rho=ge.rho,
es.V=es.V, ge.V=ge.V,
es.omega=es.omega, ge.omega=ge.omega)
# COMPUTE NRMSE AND COR
estimates = list()
postList=poolMcmcChains(m$postList, start = 1)
npost = length(postList)
npri = npost
for (para in 1:5){
parameter = c("beta","gamma","rho","V","omega")[para]
if (parameter=="beta"){
getpar = function(a)
return(a$Beta)
true = all.parameters$beta
prior = list()
for(i in 1:npost){
gamma = matrix(MASS::mvrnorm(n=1, mu=m$mGamma, Sigma = m$UGamma), ncol=m$nt, nrow=m$nc)
mu = tcrossprod(gamma,m$Tr)
rho = sample(size=1,x=m$rhopw[,1],prob=m$rhopw[,2])
V = solve(MCMCpack::rwish(m$f0, solve(m$V0)))
Si = kronecker(V,rho*all.data$C + (1-rho)*diag(m$ns))
prior[[i]] = matrix(MASS::mvrnorm(n=1, mu=as.vector(mu), Sigma=Si), ncol=m$ns, nrow=m$nc)
}
}
if (parameter=="rho"){
getpar = function(a)
return(a$rho)
true = all.parameters$rho
prior = list()
for(i in 1:npri){
prior[[i]] = sample(size=1,x=m$rhopw[,1],prob=m$rhopw[,2])
}
}
if (parameter=="gamma"){
getpar = function(a)
return(a$Gamma)
true = all.parameters$gamma
prior = list()
for(i in 1:npri){
prior[[i]] = matrix(MASS::mvrnorm(n=1, mu=m$mGamma, Sigma = m$UGamma), ncol=m$nt, nrow=m$nc)
}
}
if (parameter=="V"){
getpar = function(a)
return(a$V)
true = all.parameters$V
prior = list()
for(i in 1:npost){
prior[[i]] = solve(MCMCpack::rwish(m$f0, solve(m$V0)))
}
}
if (parameter=="omega"){
getpar = function(a)
return(t(a$Lambda[[1]])%*%a$Lambda[[1]])
true = t(all.parameters$lambda)%*%all.parameters$lambda
nu = m$rL[[1]]$nu
a1 = m$rL[[1]]$a1
a2 = m$rL[[1]]$a2
b1 = m$rL[[1]]$b1
b2 = m$rL[[1]]$b2
prior = list()
for(i in 1:npost){
nf = 10
delta = rep(NA,nf)
delta[1] = rgamma(1,a1,b1)
for (j in 2:nf){
delta[j] = rgamma(1,a2,b2)
}
tau = cumprod(delta)
psi = matrix(rgamma(nf*m$ns, nu/2, nu/2), nf, m$ns)
sd = sqrt(1/(psi*matrix(rep(tau,m$ns),nr=nf, nc=m$ns)))
la = rnorm(length(sd), mean=0, sd=sd)
dim(la) = dim(sd)
prior[[i]] = t(la)%*%la
}
}
post = lapply(postList, getpar)
post.mean = Reduce("+", post) / length(post)
cors = rep(0,npost)
for (i in 1:npost){
cors[i] = cor(as.vector(true),as.vector(post[[i]]))
}
se = function(a)
return((a-true)^2)
post.se = lapply(post, se)
prior.se = lapply(prior, se)
post.rmse = sqrt(Reduce("+", post.se) / length(post.se))
prior.rmse = sqrt(Reduce("+", prior.se) / length(prior.se))
nrmse = post.rmse / prior.rmse
if (parameter=="beta"){
estimates$cor.beta = cors
estimates$nrmse.beta = nrmse
}
if (parameter=="gamma"){
estimates$cor.gamma = cors
estimates$nrmse.gamma = nrmse
}
if (parameter=="rho"){
estimates$post.rho = post
estimates$nrmse.rho = nrmse
}
if (parameter=="V"){
estimates$cor.V = cors
estimates$nrmse.V = nrmse
}
if (parameter=="omega"){
estimates$cor.omega = cors
estimates$nrmse.omega = nrmse
}
}
save(file=file.path(localDir, "performance",paste("Case",toString(case),".R",sep="")),
computational.time, mixing, estimates, MF)
}
myBLAS <- extSoftVersion()["BLAS"]
if (myBLAS == "") {
myBLAS <- "unknown BLAS"
} else {
myBLAS <- unlist(strsplit(myBLAS, .Platform$file.sep))
myBLAS <- myBLAS[length(myBLAS)]
myBLAS <- unlist(strsplit(myBLAS, "\\."))[1]
}
Computational time
The timings and relative timings vary a lot. They are strongly
dependent on the hardware and its load, and our example cannot be
generalized to other environments. Parallel processing can speed up
calculations, but we can only run each chain in parallel, and the
slowest chain will determine the running time. Most of time is spent
in linear algebra in external libraries BLAS and LAPACK. BLAS provides
the basic matrix operations and LAPACK the higher level matrix
algorithms. R ships with so-called reference libraries for both, and
for BLAS it is called libRblas. Changing the reference libraries to
implementation optimized for the hardware can speed up calculations by
a factor of ten. OpenBLAS and associated LAPACK is a very popular
choice, but there are many other alternatives (see R manual on
Installation and Administration and R FAQs). Optimized BLAS and LAPACK
libraries may use parallel processing and they can use several cores
for each parallel chain. The number of available cores depends also on
the system configuration, and especially in multi-user systems, on
other CPU load and system policies. We used r myBLAS and ran
r nChains chains in parallel in a system with
r parallel::detectCores() cores.
cases = c(1,2,3,4,5,6,7)
ncases = length(cases)
mix = list()
est = list()
MFs = list()
comp = rep(0,ncases)
for (case in 1:ncases){
load(file=file.path(localDir, "performance",paste("Case",toString(cases[case]),".R",sep="")))
mix[[case]] = mixing
est[[case]] = estimates
comp[case] = computational.time
MFs[[case]] = MF
}
times=matrix(NA,ncol=7,nrow=2)
colnames(times)=1:7
times[1,]=comp/60
times[2,]=comp/comp[1]
rownames(times)=c("Time (min)", "Time (relative)")
kable(round(times,2),caption="Computational times to perform the MCMC sampling in minutes and relative to the time of the baseline case ")
```rDistributions of effective sample sizes of multivariate parameters."}
par(mfrow=c(2,2),oma=c(0,0,0,0),mar=c(4,4,3,1))
for (para in 1:4){
parameter = c("beta","gamma","V","omega")[para]
toplot = list()
for (case in 1:ncases){
if (parameter=="beta"){toplot[[case]]=as.vector(mix[[case]]$es.beta)}
if (parameter=="gamma"){toplot[[case]]=as.vector(mix[[case]]$es.gamma)}
if (parameter=="V"){toplot[[case]]=as.vector(mix[[case]]$es.V)}
if (parameter=="omega"){toplot[[case]]=as.vector(mix[[case]]$es.om)}
}
boxplot(toplot, main=parameter, names = cases, las = 1)
}
```rDistributions of upper C.I. for potential scale reduction factors for multivariate parameters."}
par(mfrow=c(2,2),oma=c(0,0,0,0),mar=c(4,4,3,1))
for (para in 1:4){
parameter = c("beta","gamma","V","omega")[para]
toplot = list()
for (case in 1:ncases){
if (parameter=="beta"){toplot[[case]]=as.vector(mix[[case]]$ge.beta[,2])}
if (parameter=="gamma"){toplot[[case]]=as.vector(mix[[case]]$ge.gamma[,2])}
if (parameter=="V"){toplot[[case]]=as.vector(mix[[case]]$ge.V[,2])}
if (parameter=="omega"){toplot[[case]]=as.vector(mix[[case]]$ge.om[,2])}
}
boxplot(toplot, main=parameter, names = cases)
}
The effective sample size (Figure 1) was close to r nChains*samples
for the $\beta$, $\gamma$, and V parameters, except for Case 3 (the
lognormal Poisson model) and to some extent Case 5 (large number of
species), for which cases a longer MCMC chain would be needed for
obtaining robust results. Mixing was more challenging to obtain for
the $\Omega$ and $\rho$ parameters, for which the effective sample
size varied much among the elements of the matrix also for the
normally distributed models.
Ability to recover true parameter values
For the same parameters that we considered for MCMC mixing, we asked how much closer the posterior distributions were to the true values than were the prior distributions. We quantified this by the normalized root mean squared errors (NRMSE), where the normalization was done with respect to the root mean squared error under the prior distribution. Further, for the matrix-valued parameters $\beta$, $\gamma$, $\rho$, V, $\Omega$ we computed the posterior distribution of the Mantel correlation between the estimated and true values. For the univariate parameter $\rho$ we compared the true value to the boxplot of the posterior distribution.
The species-specific parameters $\beta$ were better estimated for normal models than for probit and lognormal Poisson models (Figure 3). As expected, they were most accurately estimated for Case 4 with many sampling units. For the community-level parameters $\gamma$ the difference among the cases was smaller, the probit model performing slightly worse than the others. The $\Omega$ parameters were estimated best for normal models where the latent variables were independent among the sampling units. The V parameters were roughly equally well estimated in all cases. In all cases the estimation of the phylogenetic signal parameter $\rho$ was difficult, in particular for the probit model were the posterior distribution was essentially identical to the prior (Figure 5).
```rDistributions of normalized root mean squared errors (NRMSE) for multivariate parameters. Values smaller than one indicate that the posterior distribution is closer to the true value than the prior distribution."}
par(mfrow=c(2,2),oma=c(0,0,0,0),mar=c(4,4,3,1))
for (para in 1:4){
parameter = c("beta","gamma","V","omega")[para]
toplot = list()
for (case in 1:ncases){
if (parameter=="beta"){toplot[[case]]=as.vector(est[[case]]$nrmse.beta)}
if (parameter=="gamma"){toplot[[case]]=as.vector(est[[case]]$nrmse.gamma)}
if (parameter=="V"){toplot[[case]]=as.vector(est[[case]]$nrmse.V)}
if (parameter=="omega"){toplot[[case]]=as.vector(est[[case]]$nrmse.omega)}
}
boxplot(toplot, main=parameter, names = cases, las = 1)
}
```rPosterior distributions of correlation between true and estimated values for multivariate parameters."}
par(mfrow=c(2,2),oma=c(0,0,0,0),mar=c(4,4,3,1))
for (para in 1:4){
parameter = c("beta","gamma","V","omega")[para]
toplot = list()
for (case in 1:ncases){
if (parameter=="beta"){toplot[[case]]=as.vector(est[[case]]$cor.beta)}
if (parameter=="gamma"){toplot[[case]]=as.vector(est[[case]]$cor.gamma)}
if (parameter=="V"){toplot[[case]]=as.vector(est[[case]]$cor.V)}
if (parameter=="omega"){toplot[[case]]=as.vector(est[[case]]$cor.omega)}
}
boxplot(toplot, main=parameter, names = cases, las = 1)
}
```rPosterior distributions of the phylogenetic signal parameter."}
par(mfrow=c(1,1),oma=c(0,0,0,0),mar=c(4,4,3,1))
toplot = list()
for (case in 1:ncases){
toplot[[case]]=unlist(est[[case]]$post.rho)
}
boxplot(toplot, main="rho", names = cases, las = 1)
\pagebreak
```r
rhores = matrix(ncol=ncases, nrow=3)
for (case in 1:ncases){
rhores[1,case] = mix[[case]]$es.rho
rhores[2,case] = mix[[case]]$ge.rho[,2]
rhores[3,case] = est[[case]]$nrmse.rho
}
rownames(rhores)=c("Effective sample size", "Upper C.I. of PSRF", "NRMSE")
colnames(rhores)=c(1:7)
rhores[1,]=round(rhores[1,],0)
rhores[2,]=round(rhores[2,],2)
rhores[3,]=round(rhores[3,],2)
kable(rhores, caption="Performance of HMSC-R for estimating the phylogenetic signal parameter.")
References
hmsc-r/HMSC documentation built on March 5, 2025, 10:52 p.m.
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Introduction
The Hierarchical Modelling of Species Communities (Hmsc) framework is a statistical framework for analysis of multivariate data, typically from species communities. It uses Bayesian inference to fit latent-variable joint species distribution models. The conceptual basis of the method is outlined in @Ovaskainen2017b.
Here, we test the performance of the MCMC estimation scheme in terms of (1) computational time, (2) mixing properties, and (3) ability to recover true parameter values, by performing a set of experiments in which we use the Hmsc model to generate simulated data and then fit the model to the simulated data.
Generating simulated data
Set directories and load libraries
localDir = "." source("load_libraries.R") ## we need to write results to subdirectories (folders) if (!dir.exists(file.path(localDir, "data"))) dir.create(file.path(localDir, "data")) if (!dir.exists(file.path(localDir, "models"))) dir.create(file.path(localDir, "models")) if (!dir.exists(file.path(localDir, "performance"))) dir.create(file.path(localDir, "performance"))
The makedata function
The makedata function included in the vignette folder produces datasets based on the Hmsc model. Options include
- ny = Number of sites
- ns = Number of species
- hierarchical = T/F, where T yields data with two nested random levels, and F yields a single random level.
- spatial = T/F, where T yields spatially explicit data with sample unit locations sampled from the unit square.
makedata = function(ns, ny, hierarchical=FALSE, spatial=FALSE)
Case 1: Baseline model
As the baseline case, we consider a community of n~s~=50 species observed on n~y~=200 sampling units. We include for each sampling unit two covariates, one categorical (each unit randomized to represent one of three classes with equal probability) and one continuous (sampled from standard normal distribution). We include for each species two traits, one categorical (each species randomized to represent one of three classes with equal probability) and one continuous (sampled from standard normal distribution). We assume that the species belong to groups of 5 species, and that the phylogenetic correlation is 0.9 within a group and 0 between the groups. We sampled each element of the matrix $\Gamma$ (influence of traits on expected species parameters) from N(0, $\sigma$^2^), set the matrix V (variation among species not explained by traits) to ($\sigma$/2)^2^ I, where we set $\sigma$=0.3 (for motivation behind this value, see below). We assumed the value $\rho$=0.5 for the phylogenetic signal parameter. We generated two sampling-level latent variables, both following standard normal distributions, and sampled the latent loadings on/for these variables from N(0, $\sigma$^2^). We assumed that the data were normally distributed, obtained by adding to the linear predictor noise distributed as N(0,(2$\sigma$)^2^). We set $\sigma$=0.3, resulting in the following empirical variances for vectorized quantities: variance of data 1.16, variance of fixed effects 0.60, variance of random effects 0.21, and residual variance 0.36. Out of the fixed effects, the expectation based on traits had variance 0.30. As the data are roughly equally influenced by all model components, we expected that true values of all model parameters can be recovered if a sufficient amount of data are available.
set.seed(1) source("makedata.R") ns=50 ny=200 tmp=makedata(ns=ns, ny=ny) all.data=tmp[[1]] all.parameters=tmp[[2]] L1 = all.parameters$L Y1 = all.data$Y #First five species at first five sites all.data$Y[1:5,1:5] #Random levels for first five sites all.data$study.design[1:5,] #Covariates all.data$X.data[1:5,] #Trait data all.data$Tr.data[1:5,] c(var(as.vector(all.parameters$mu)), var(as.vector(all.parameters$LF)), var(as.vector(all.parameters$LR)), var(as.vector(all.parameters$eps)), var(as.vector(Y1))) save(file=file.path(localDir, "data","Case1.R"), all.data, all.parameters)
Case 2: Presence-absence model
We assumed the same linear predictor as for the baseline case but truncated the normally distributed data to 0/1 according to the probit model.
Y2 = 1*(L1 + matrix(rnorm(n = ny*ns), ncol=ns, nrow=ny)>0) all.data$Y = Y2 #First five species at first five sites all.data$Y[1:5,1:5] save(file=file.path(localDir, "data","Case2.R"), all.data, all.parameters)
Case 3: Log-normal Poisson model
We assumed the same model for the baseline case but converted the normally distributed data to follow the log-normal Poisson distribution. Thus, we sampled the data for Case 3 from the Poisson distribution, the expectation of which was set to the exponential of the data from Case 1.
Y3 <- rpois(length(Y1), exp(Y1)) dim(Y3) <- dim(Y1) all.data$Y = Y3 #First five species at first five sites all.data$Y[1:5,1:5] save(file=file.path(localDir, "data","Case3.R"), all.data, all.parameters)
Case 4: Large number of sites
We assumed otherwise the same model as for Case 1 but set the number of sampling units to n~y~=2000.
tmp=makedata(ns=50, ny=2000) all.data=tmp[[1]] all.parameters=tmp[[2]] save(file=file.path(localDir, "data","Case4.R"), all.data, all.parameters)
Case 5: Large number of species
We assumed otherwise the same model as for Case 1 but set the number of species to n~s~=200.
tmp=makedata(ns=200, ny=200) all.data=tmp[[1]] all.parameters=tmp[[2]] save(file=file.path(localDir, "data","Case5.R"), all.data, all.parameters)
Case 6: Spatially structured data
We assumed otherwise the same model as for Case 1 but included the coordinates of the sampling units, which were sampled uniformly from the unit square. The latent variables were assumed to have exponentially decaying spatial correlation structure, with the scale of decay set to 0.5 spatial units for latent variable 1 and 0.1 spatial units for latent variable 2.
tmp=makedata(ns=50, ny=200, spatial=TRUE) all.data=tmp[[1]] all.parameters=tmp[[2]] #Coordinates for first five sites all.data$xy[1:5,] save(file=file.path(localDir, "data","Case6.R"), all.data, all.parameters)
Case 7: Hierarchically structured data
We assumed otherwise the same model as for Case 1 but assumed that the sampling units belonged to plots, each plot containing 10 sampling units. We assumed one latent variable at the plot level and another at the sampling unit level, thus keeping the variance attributed to latent variables the same as in Case 1 but changing their structure.
tmp=makedata(ns=50, ny=200, hierarchical=TRUE) all.data=tmp[[1]] all.parameters=tmp[[2]] #Hierarchical random levels head(all.data$study.design[order(all.data$study.design$plot),][c(1:3,21:23),]) save(file=file.path(localDir, "data","Case7.R"), all.data, all.parameters)
Model setup and fitting
We first set the parameters that we used in posterior samplings. There
is an alternative for test.run that runs fast, but gives no real
results, but you can use to quickly see the idea.
nChains = 4 test.run = FALSE if (test.run){ #with this option, the vignette evaluates in ca. ? minutes in a laptop thin = 1 samples = 10 } else { #with this option, the vignette evaluates in ca. ? hrs in a laptop thin = 10 samples = 1000 } verbose = 0
We fitted Hmsc models with structure matching the data generation model, i.e. we did not examine here the influence of model misspecification or e.g. issues related to variable selection. However, we did not fix the number of latent variables to that used for generating the data, but assumed the default prior for them, as well as for all other model parameters.
We fitted each model with sampling parameters set to thin=r thin and
samples=r samples, and thus we performed r round(1.5*thin*samples)
MCMC iterations per chain. We adapted the number of latent factors
during the first r round(0.4*thin*samples) iterations, and
ignored from each chain the first r round(0.5*thin*samples)
iterations as transient. We used r nChains chains, so we ran in
total r round(nChains*1.5*thin*samples) MCMC iterations, out of
which the thinned posterior sample consisted of r nChains*samples
samples.
The code below takes a long time to run. The .Rmd file can be run much more quickly by setting test.run=TRUE, but the results will not be reliable.
For case 1, 4 and 5, we set up the model with gaussian errors, using distr = "normal"
for (case in c(1,4,5)){ set.seed(1) load(file = file.path(localDir, "data", paste("Case",toString(case),".R", sep=""))) m = Hmsc(Y = all.data$Y, XData = all.data$X.data, XFormula = all.data$X.formula, TrData = all.data$Tr.data, TrFormula = all.data$Tr.formula, C = all.data$C, distr = "normal", studyDesign = all.data$study.design, ranLevels = list( "sampling.unit" = HmscRandomLevel(units = all.data$study.design[,1]))) ptm = proc.time() m = sampleMcmc(m, samples=samples, thin=thin, transient = ceiling(0.5*samples*thin), nChains = nChains, nParallel = nChains, verbose=verbose) computational.time = proc.time() - ptm save(file=file.path(localDir, "models", paste("Case",toString(case),".R", sep="")), m, computational.time) }
For case 2, we set up the model with probit errors, using distr = "probit"
set.seed(1) load(file=file.path(localDir, "data","Case2.R")) m = Hmsc(Y=all.data$Y, XData=all.data$X.data, XFormula=all.data$X.formula, TrData=all.data$Tr.data, TrFormula=all.data$Tr.formula, C=all.data$C, distr="probit", studyDesign=all.data$study.design, ranLevels=list( "sampling.unit"=HmscRandomLevel(units = all.data$study.design[,1]))) ptm = proc.time() m = sampleMcmc(m, samples=samples, thin=thin, transient = ceiling(0.5*samples*thin), nChains = nChains, nParallel = nChains, verbose=verbose) computational.time = proc.time() - ptm save(file=file.path(localDir, "models","Case2.R"), m, computational.time)
For case 3, we set up the model with lognormal Poisson errors, using distr = "lognormal poisson"
set.seed(1) load(file=file.path(localDir, "data","Case3.R")) m = Hmsc(Y=all.data$Y, XData=all.data$X.data, XFormula=all.data$X.formula, TrData=all.data$Tr.data, TrFormula=all.data$Tr.formula, C=all.data$C, distr="lognormal poisson", studyDesign=all.data$study.design, ranLevels=list( "sampling.unit"=HmscRandomLevel(units = all.data$study.design[,1]))) ptm = proc.time() m = sampleMcmc(m, samples=samples, thin=thin, transient = ceiling(0.5*samples*thin), nChains = nChains, nParallel = nChains, verbose=verbose) computational.time = proc.time() - ptm save(file=file.path(localDir, "models","Case3.R"), m, computational.time)
For case 6, we set up the model with a spatially structured random level, using
HmscRandomLevel(data=sRL, priors="default"). Note that sData is used instead of units.
set.seed(1) load(file=file.path(localDir, "data","Case6.R")) sRL = all.data$xy colnames(sRL) = c("x","y") rownames(sRL) = all.data$study.design[,1] rL = HmscRandomLevel(sData=sRL) m = Hmsc(Y=all.data$Y, XData=all.data$X.data, XFormula=all.data$X.formula, TrData=all.data$Tr.data, TrFormula=all.data$Tr.formula, C=all.data$C, distr="normal", studyDesign=all.data$study.design, ranLevels=list("sampling.unit"=rL)) ptm = proc.time() m = sampleMcmc(m, samples=samples, thin=thin, transient = ceiling(0.5*samples*thin), nChains = nChains, nParallel = nChains, verbose=verbose) computational.time = proc.time() - ptm save(file=file.path(localDir, "models","Case6.R"), m, computational.time)
For case 7, we set up two hierarchical random levels, using HmscRandomLevel(units=...).
set.seed(1) load(file=file.path(localDir, "data","Case7.R")) m = Hmsc(Y=all.data$Y, XData=all.data$X.data, XFormula=all.data$X.formula, TrData=all.data$Tr.data, TrFormula=all.data$Tr.formula, C=all.data$C, distr="normal", studyDesign=all.data$study.design, ranLevels=list( "sampling.unit"=HmscRandomLevel(units = all.data$study.design[,1]), "plot"=HmscRandomLevel(units = all.data$study.design[,2])) ) ptm = proc.time() m = sampleMcmc(m, samples=samples, thin=thin, transient = ceiling(0.5*samples*thin), nChains = nChains, nParallel = nChains, verbose=verbose) computational.time = proc.time() - ptm save(file=file.path(localDir, "models","Case7.R"), m, computational.time)
Evaluating model performance
MCMC mixing
We used the coda package to compute the effective number of MCMC samples (sample size adjusted for autocorrelation) and Potential Scale Reduction Factor (Gelman and Rubin's convergence diagnostic, PSRF) for the following parameters: $\beta$, $\gamma$, $\rho$, V, $\Omega$. Note the following useful functions
computePredictedValuesreturns model-predicted values for the original sampling units used in model fitting.evaluateModelFitreturns several measures of model fit, including RMSE (root mean square error), R^2^ (coefficient of determination), Tjur R^2^ (coefficient of discrimination), and AUC (area under the receiver operating characteristic curve).convertToCodaObjectconverts the posterior distributions produced byHmscintocoda-format.
ptm = proc.time() cases = c(1,2,3,4,5,6,7) for (case in cases){ print(case) set.seed(1) load(file=file.path(localDir, "data", paste("Case",toString(case),".R", sep=""))) load(file=file.path(localDir, "models", paste("Case",toString(case),".R", sep=""))) computational.time = computational.time[3] predY = computePredictedValues(m, expected=FALSE) MF = evaluateModelFit(m, predY) # ASSESS MIXING mpost = convertToCodaObject(m) es.beta = effectiveSize(mpost$Beta) ge.beta = gelman.diag(mpost$Beta,multivariate=FALSE)$psrf es.gamma = effectiveSize(mpost$Gamma) ge.gamma = gelman.diag(mpost$Gamma,multivariate=FALSE)$psrf es.rho = effectiveSize(mpost$Rho) ge.rho = gelman.diag(mpost$Rho,multivariate=FALSE)$psrf es.V = effectiveSize(mpost$V) ge.V = gelman.diag(mpost$V,multivariate=FALSE)$psrf mpost$temp = mpost$Omega[[1]] for(i in 1:length(mpost$temp)){ mpost$temp[[i]] = mpost$temp[[i]][,1:50^2] } es.omega = effectiveSize(mpost$temp) ge.omega = gelman.diag(mpost$temp,multivariate=FALSE)$psrf es.alpha = NA ge.alpha = NA if (case==6){ es.alpha = effectiveSize(mpost$Alpha[[1]]) ge.alpha = gelman.diag(mpost$Alpha[[1]]) } mixing = list(es.beta=es.beta, ge.beta=ge.beta, es.gamma=es.gamma, ge.gamma=ge.gamma, es.rho=es.rho, ge.rho=ge.rho, es.V=es.V, ge.V=ge.V, es.omega=es.omega, ge.omega=ge.omega) # COMPUTE NRMSE AND COR estimates = list() postList=poolMcmcChains(m$postList, start = 1) npost = length(postList) npri = npost for (para in 1:5){ parameter = c("beta","gamma","rho","V","omega")[para] if (parameter=="beta"){ getpar = function(a) return(a$Beta) true = all.parameters$beta prior = list() for(i in 1:npost){ gamma = matrix(MASS::mvrnorm(n=1, mu=m$mGamma, Sigma = m$UGamma), ncol=m$nt, nrow=m$nc) mu = tcrossprod(gamma,m$Tr) rho = sample(size=1,x=m$rhopw[,1],prob=m$rhopw[,2]) V = solve(MCMCpack::rwish(m$f0, solve(m$V0))) Si = kronecker(V,rho*all.data$C + (1-rho)*diag(m$ns)) prior[[i]] = matrix(MASS::mvrnorm(n=1, mu=as.vector(mu), Sigma=Si), ncol=m$ns, nrow=m$nc) } } if (parameter=="rho"){ getpar = function(a) return(a$rho) true = all.parameters$rho prior = list() for(i in 1:npri){ prior[[i]] = sample(size=1,x=m$rhopw[,1],prob=m$rhopw[,2]) } } if (parameter=="gamma"){ getpar = function(a) return(a$Gamma) true = all.parameters$gamma prior = list() for(i in 1:npri){ prior[[i]] = matrix(MASS::mvrnorm(n=1, mu=m$mGamma, Sigma = m$UGamma), ncol=m$nt, nrow=m$nc) } } if (parameter=="V"){ getpar = function(a) return(a$V) true = all.parameters$V prior = list() for(i in 1:npost){ prior[[i]] = solve(MCMCpack::rwish(m$f0, solve(m$V0))) } } if (parameter=="omega"){ getpar = function(a) return(t(a$Lambda[[1]])%*%a$Lambda[[1]]) true = t(all.parameters$lambda)%*%all.parameters$lambda nu = m$rL[[1]]$nu a1 = m$rL[[1]]$a1 a2 = m$rL[[1]]$a2 b1 = m$rL[[1]]$b1 b2 = m$rL[[1]]$b2 prior = list() for(i in 1:npost){ nf = 10 delta = rep(NA,nf) delta[1] = rgamma(1,a1,b1) for (j in 2:nf){ delta[j] = rgamma(1,a2,b2) } tau = cumprod(delta) psi = matrix(rgamma(nf*m$ns, nu/2, nu/2), nf, m$ns) sd = sqrt(1/(psi*matrix(rep(tau,m$ns),nr=nf, nc=m$ns))) la = rnorm(length(sd), mean=0, sd=sd) dim(la) = dim(sd) prior[[i]] = t(la)%*%la } } post = lapply(postList, getpar) post.mean = Reduce("+", post) / length(post) cors = rep(0,npost) for (i in 1:npost){ cors[i] = cor(as.vector(true),as.vector(post[[i]])) } se = function(a) return((a-true)^2) post.se = lapply(post, se) prior.se = lapply(prior, se) post.rmse = sqrt(Reduce("+", post.se) / length(post.se)) prior.rmse = sqrt(Reduce("+", prior.se) / length(prior.se)) nrmse = post.rmse / prior.rmse if (parameter=="beta"){ estimates$cor.beta = cors estimates$nrmse.beta = nrmse } if (parameter=="gamma"){ estimates$cor.gamma = cors estimates$nrmse.gamma = nrmse } if (parameter=="rho"){ estimates$post.rho = post estimates$nrmse.rho = nrmse } if (parameter=="V"){ estimates$cor.V = cors estimates$nrmse.V = nrmse } if (parameter=="omega"){ estimates$cor.omega = cors estimates$nrmse.omega = nrmse } } save(file=file.path(localDir, "performance",paste("Case",toString(case),".R",sep="")), computational.time, mixing, estimates, MF) }
myBLAS <- extSoftVersion()["BLAS"] if (myBLAS == "") { myBLAS <- "unknown BLAS" } else { myBLAS <- unlist(strsplit(myBLAS, .Platform$file.sep)) myBLAS <- myBLAS[length(myBLAS)] myBLAS <- unlist(strsplit(myBLAS, "\\."))[1] }
Computational time
The timings and relative timings vary a lot. They are strongly
dependent on the hardware and its load, and our example cannot be
generalized to other environments. Parallel processing can speed up
calculations, but we can only run each chain in parallel, and the
slowest chain will determine the running time. Most of time is spent
in linear algebra in external libraries BLAS and LAPACK. BLAS provides
the basic matrix operations and LAPACK the higher level matrix
algorithms. R ships with so-called reference libraries for both, and
for BLAS it is called libRblas. Changing the reference libraries to
implementation optimized for the hardware can speed up calculations by
a factor of ten. OpenBLAS and associated LAPACK is a very popular
choice, but there are many other alternatives (see R manual on
Installation and Administration and R FAQs). Optimized BLAS and LAPACK
libraries may use parallel processing and they can use several cores
for each parallel chain. The number of available cores depends also on
the system configuration, and especially in multi-user systems, on
other CPU load and system policies. We used r myBLAS and ran
r nChains chains in parallel in a system with
r parallel::detectCores() cores.
cases = c(1,2,3,4,5,6,7) ncases = length(cases) mix = list() est = list() MFs = list() comp = rep(0,ncases) for (case in 1:ncases){ load(file=file.path(localDir, "performance",paste("Case",toString(cases[case]),".R",sep=""))) mix[[case]] = mixing est[[case]] = estimates comp[case] = computational.time MFs[[case]] = MF } times=matrix(NA,ncol=7,nrow=2) colnames(times)=1:7 times[1,]=comp/60 times[2,]=comp/comp[1] rownames(times)=c("Time (min)", "Time (relative)") kable(round(times,2),caption="Computational times to perform the MCMC sampling in minutes and relative to the time of the baseline case ")
```rDistributions of effective sample sizes of multivariate parameters."} par(mfrow=c(2,2),oma=c(0,0,0,0),mar=c(4,4,3,1)) for (para in 1:4){ parameter = c("beta","gamma","V","omega")[para] toplot = list() for (case in 1:ncases){ if (parameter=="beta"){toplot[[case]]=as.vector(mix[[case]]$es.beta)} if (parameter=="gamma"){toplot[[case]]=as.vector(mix[[case]]$es.gamma)} if (parameter=="V"){toplot[[case]]=as.vector(mix[[case]]$es.V)} if (parameter=="omega"){toplot[[case]]=as.vector(mix[[case]]$es.om)} } boxplot(toplot, main=parameter, names = cases, las = 1) }
```rDistributions of upper C.I. for potential scale reduction factors for multivariate parameters."}
par(mfrow=c(2,2),oma=c(0,0,0,0),mar=c(4,4,3,1))
for (para in 1:4){
parameter = c("beta","gamma","V","omega")[para]
toplot = list()
for (case in 1:ncases){
if (parameter=="beta"){toplot[[case]]=as.vector(mix[[case]]$ge.beta[,2])}
if (parameter=="gamma"){toplot[[case]]=as.vector(mix[[case]]$ge.gamma[,2])}
if (parameter=="V"){toplot[[case]]=as.vector(mix[[case]]$ge.V[,2])}
if (parameter=="omega"){toplot[[case]]=as.vector(mix[[case]]$ge.om[,2])}
}
boxplot(toplot, main=parameter, names = cases)
}
The effective sample size (Figure 1) was close to r nChains*samples
for the $\beta$, $\gamma$, and V parameters, except for Case 3 (the
lognormal Poisson model) and to some extent Case 5 (large number of
species), for which cases a longer MCMC chain would be needed for
obtaining robust results. Mixing was more challenging to obtain for
the $\Omega$ and $\rho$ parameters, for which the effective sample
size varied much among the elements of the matrix also for the
normally distributed models.
Ability to recover true parameter values
For the same parameters that we considered for MCMC mixing, we asked how much closer the posterior distributions were to the true values than were the prior distributions. We quantified this by the normalized root mean squared errors (NRMSE), where the normalization was done with respect to the root mean squared error under the prior distribution. Further, for the matrix-valued parameters $\beta$, $\gamma$, $\rho$, V, $\Omega$ we computed the posterior distribution of the Mantel correlation between the estimated and true values. For the univariate parameter $\rho$ we compared the true value to the boxplot of the posterior distribution.
The species-specific parameters $\beta$ were better estimated for normal models than for probit and lognormal Poisson models (Figure 3). As expected, they were most accurately estimated for Case 4 with many sampling units. For the community-level parameters $\gamma$ the difference among the cases was smaller, the probit model performing slightly worse than the others. The $\Omega$ parameters were estimated best for normal models where the latent variables were independent among the sampling units. The V parameters were roughly equally well estimated in all cases. In all cases the estimation of the phylogenetic signal parameter $\rho$ was difficult, in particular for the probit model were the posterior distribution was essentially identical to the prior (Figure 5).
```rDistributions of normalized root mean squared errors (NRMSE) for multivariate parameters. Values smaller than one indicate that the posterior distribution is closer to the true value than the prior distribution."} par(mfrow=c(2,2),oma=c(0,0,0,0),mar=c(4,4,3,1)) for (para in 1:4){ parameter = c("beta","gamma","V","omega")[para] toplot = list() for (case in 1:ncases){ if (parameter=="beta"){toplot[[case]]=as.vector(est[[case]]$nrmse.beta)} if (parameter=="gamma"){toplot[[case]]=as.vector(est[[case]]$nrmse.gamma)} if (parameter=="V"){toplot[[case]]=as.vector(est[[case]]$nrmse.V)} if (parameter=="omega"){toplot[[case]]=as.vector(est[[case]]$nrmse.omega)} } boxplot(toplot, main=parameter, names = cases, las = 1) }
```rPosterior distributions of correlation between true and estimated values for multivariate parameters."}
par(mfrow=c(2,2),oma=c(0,0,0,0),mar=c(4,4,3,1))
for (para in 1:4){
parameter = c("beta","gamma","V","omega")[para]
toplot = list()
for (case in 1:ncases){
if (parameter=="beta"){toplot[[case]]=as.vector(est[[case]]$cor.beta)}
if (parameter=="gamma"){toplot[[case]]=as.vector(est[[case]]$cor.gamma)}
if (parameter=="V"){toplot[[case]]=as.vector(est[[case]]$cor.V)}
if (parameter=="omega"){toplot[[case]]=as.vector(est[[case]]$cor.omega)}
}
boxplot(toplot, main=parameter, names = cases, las = 1)
}
```rPosterior distributions of the phylogenetic signal parameter."} par(mfrow=c(1,1),oma=c(0,0,0,0),mar=c(4,4,3,1)) toplot = list() for (case in 1:ncases){ toplot[[case]]=unlist(est[[case]]$post.rho) } boxplot(toplot, main="rho", names = cases, las = 1)
\pagebreak
```r
rhores = matrix(ncol=ncases, nrow=3)
for (case in 1:ncases){
rhores[1,case] = mix[[case]]$es.rho
rhores[2,case] = mix[[case]]$ge.rho[,2]
rhores[3,case] = est[[case]]$nrmse.rho
}
rownames(rhores)=c("Effective sample size", "Upper C.I. of PSRF", "NRMSE")
colnames(rhores)=c(1:7)
rhores[1,]=round(rhores[1,],0)
rhores[2,]=round(rhores[2,],2)
rhores[3,]=round(rhores[3,],2)
kable(rhores, caption="Performance of HMSC-R for estimating the phylogenetic signal parameter.")
References
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