Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/basicStatHMSC.R
The function quantile
calculates quantiles on the probabilities of a model constructed with hmscH
. Quantiles are calculated for each species included in the model.
The function basicStatHMSC
calculates the means and standard deviations on the probabilities of a model constructed with hmscH
.
The function summary
calculates the means, standard deviations, and three quantiles (0.025,0.5,0.975) on the probabilities of a model constructed with hmscH
.
1 2 3 4 5 |
x |
An object of class |
object |
An object of class |
probs |
A numeric vector of probabilities with values ranging from 0 to 1. See |
type |
an integer between 1 and 9 selecting one of the nine quantile algorithms detailed in |
stats |
A character vector defining the statistics to calculate. Any unambiguous variation of wording used in this argument is accepted. |
... |
Arguments passed to |
These functions can calculate summary statistics for any type of hmscH
object, whether they stem from a linear or a non-linear model.
For quantile
: A three-dimensional array where the first dimension represents sites, the second dimension represents species, and the third dimensions the quantiles.
For basicStatHMSC
: A matrix where the rows represents sites and the columns represents species if only the mean or the standard deviation is calculated. Otherwise, a three-dimensional array where the first dimension represents sites, the second dimension represents species, and the third dimensions the means and standard deviations.
F. Guillaume Blanchet
Hyndman, R. J. and Fan, Y. (1996) Sample quantiles in statistical packages, American Statistician, 50, 361-365.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | #==================
### Simulating data
#==================
nspecies<-20
desc<-cbind(1,scale(1:50),scale(1:50)^2)
dataBase<-communitySimulH(desc,nsp=nspecies)
#=================================
### Formatting data and parameters
#=================================
formdata<-as.HMSCdata(dataBase$data$Y,desc,Ypattern="sp",interceptX=FALSE,interceptTr=FALSE)
#========================
### Formatting parameters
#========================
startParamX<-matrix(rnorm(nspecies*ncol(desc)),nrow=nspecies,ncol=ncol(desc))
startMean<-rnorm(ncol(desc))
startSigma<-rWishart(1,ncol(desc)+1,diag(ncol(desc)))[,,1]
formparam<-as.HMSCparam(formdata,paramX=startParamX,means=startMean,sigma=startSigma)
formpriors<-as.HMSCprior(formparam,rep(0,length(formparam$means)),0,length(formparam$means),diag(length(formparam$means)))
#===================================================
### Building model using a linear modelling approach
#===================================================
modelLinear<-hmscH(formdata,formparam,formpriors,niter=200,nburn=100)
meansdHMSC<-basicStatHMSC(modelLinear)
quantileHMSC<-quantile(modelLinear)
summaryHMSC<-summary(modelLinear)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.