# Summary statistics of the model probabilities on an object of class hmsc

### Description

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`

.

### Usage

1 2 3 4 5 |

### Arguments

`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 |

### Details

These functions can calculate summary statistics for any type of `hmscH`

object, whether they stem from a linear or a non-linear model.

### Value

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.

### Author(s)

F. Guillaume Blanchet

### References

Hyndman, R. J. and Fan, Y. (1996) Sample quantiles in statistical packages, *American Statistician*, **50**, 361-365.

### See Also

`quantile`

, `hmscH`

### Examples

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)
``` |

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