title: "README" output: html_document

This package can be used to construct empirical and model-based hypervolumes to produce hypervolumes of structured or nested ecological data.

The MBH package can be installed from Github using the `install_github`

function in the **devtools** package

```
install.packages("devtools") #if not already installed
library(devtools)
install_github("susanjarvis501/MBH")
library(MBH)
```

To run the models you will need to have an installation of JAGS on your computer.

Fitting an empirical hypervolume ignores any group structure.

```
#simulate multivariate data with 4 variables, 5 groups and 20 observations per group.
dat1 <- simulate_dataMBH(nobs = 20, ngroups = 5, ndims = 4)
#fit empirical hypervolume ignoring groups
hv1 <- fitMBH(dat1$data, groups = NULL, vars = c("V1", "V2", "V3", "V4"))
#plot hypervolume
plotMBH(hv1)
```

Hypervolumes are fitted including a random effect for group.

```
#fit model-based hypervolume
hv2 <- fitMBH(dat1$data, groups = "Group", vars = c("V1", "V2", "V3", "V4"))
#plot hypervolume
plotMBH(hv2)
```

You can also use `plotMBH`

to view the individual group ellipses

```
plotMBH(hv2, groupellipses = TRUE)
```

`overlapMBH`

can be used to estimate overlap between two hypervolumes. Note this function works by simulating a large number of points in each hypervolume and testing inclusion of each point in the other hypervolume and can be computationally demanding.

```
overlapMBH(hv1, hv2, ndraws = 99, proppoints = 0.1)
```

`inclMBH`

can be used to test the probability of inclusion of new data points in a calculated hypervolume. Note the number of dimensions and variable names must be the same

```
newdat <- simulate_dataMBH(nobs = 2, ngroups = 5, ndims = 4)
inclMBH(hv2, newdat$data)
plotMBH(hv2)
points(newdat$data$V1, newdat$data$V2)
```

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