MDMnMod: MDMnMod
In RAD: Fit RAD models to biological data

Description Usage Arguments Details Value Authors Examples

MDMnMod

1	MDMnMod(MDMn.form, data, ID, dist="MDMn", scale.covar=FALSE, est.var=TRUE, calc.resid=TRUE, trace=TRUE)

`MDMn.form`	an object of class "formula" (or one that can be coerced to that class):a symbolic description of the model to be fitted. Must include both the response and covariates
`data`	a data frame containing the variables in the formula. Must contain a column with the ordered (most to least) abundances for each site. Covariates must be repeated for each row belonging to a site.
`ID`	Vector of site identifiers for all observations. For every abundance (row) that comes from a site, the same identifier must be used. The length of ID must equal nrows(data)
`dist`	one of either multinomial "Mn" or Dirichlet Multinomial "DMN" or Modified Dirichlet Multinomial "MDMn" (default)
`scale.covar`	Should the model matrix be scaled (TRUE/FALSE)? Useful if models do not converge. Setting this to TRUE will save the column means and sd for later prediction.
`est.var`	estimate the variance-covariance matrix using newton-raphson
`calc.resid`	should residuals be calculated?
`trace`	print model trace

Fits a selected distribution to the vector of relative abundances. See Foster and Dunstan 2009 for details.

`coef`	coefficents
`vcov`	variance-covariance matrix
`logl`	log-likelihood
`AIC`	AIC
`residuals`	model residuals, calculated using PIT
`fitted`	matrix of fitted values (nij and pij)
`mean.X`	column means for model matrix if scale.covar is TRUE
`sd.X`	column sds for model matrix if scale.covar is TRUE
`formula`	model formula

Piers Dunstan and Scott Foster

head(n.data)
## format for the data object
n.data$N.scale <- n.data$N/n.data$area
n.data$S.scale <- n.data$S/n.data$area
nij.form <- nij~1 + N.scale + S.scale  + depth + O2_AV + temp_AV
model.nij <- MDMnMod(nij.form, data=n.data, ID=n.data$i, dist="MDMn", scale.covar=TRUE, est.var=FALSE, calc.resid=TRUE, trace=TRUE)
plot(model.nij$fitted$nij,model.nij$residuals)
plot(log(model.nij$fitted$nij),model.nij$residuals)