scglrTheme: Function that fits the theme model
In SCGLR: Supervised Component Generalized Linear Regression

Description Usage Arguments Details Value Examples

Calculates the components to predict all the dependent variables.

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scglrTheme(formula, data, H, family, size = NULL, weights = NULL,
  offset = NULL, subset = NULL, na.action = na.omit, crit = list(),
  method = methodSR(), st = FALSE)

`formula`	an object of class "`MultivariateFormula`" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under Details.
`data`	data frame.
`H`	vector of R integer. Number of components to keep for each theme
`family`	a vector of character of the same length as the number of dependent variables: "bernoulli", "binomial", "poisson" or "gaussian" is allowed.
`size`	describes the number of trials for the binomial dependent variables. A (number of statistical units * number of binomial dependent variables) matrix is expected.
`weights`	weights on individuals (not available for now)
`offset`	used for the poisson dependent variables. A vector or a matrix of size: number of observations * number of Poisson dependent variables is expected.
`subset`	an optional vector specifying a subset of observations to be used in the fitting process.
`na.action`	a function which indicates what should happen when the data contain NAs. The default is set to `na.omit`.
`crit`	a list of two elements : maxit and tol, describing respectively the maximum number of iterations and the tolerance convergence criterion for the Fisher scoring algorithm. Default is set to 50 and 10e-6 respectively.
`method`	structural relevance criterion. Object of class "method.SCGLR" built by `methodSR` for Structural Relevance.
`st`	logical (FALSE) theme build and fit order. TRUE means random, FALSE means sequential (T1, ..., Tr)

Models for theme are specified symbolically. A model as the form response ~ terms where response is the numeric response vector and terms is a series of R themes composed of predictors. Themes are separated by "|" (pipe) and are composed. ... Y1+Y2+... ~ X11+X12+...+X1_ | X21+X22+... | ...+X1_+... | A1+A2+... See multivariateFormula.

a list of SCGLRTHM class. Each element is a SCGLR object

## Not run: 
library(SCGLR)

# load sample data
data(genus)

# get variable names from dataset
n <- names(genus)
n <-n[!n%in%c("geology","surface","lon","lat","forest","altitude")]
ny <- n[grep("^gen",n)]    # Y <- names that begins with "gen"
nx1 <- n[grep("^evi",n)]   # X <- remaining names
nx2 <- n[-c(grep("^evi",n),grep("^gen",n))]


form <- multivariateFormula(ny,nx1,nx2,A=c("geology"))
fam <- rep("poisson",length(ny))
testthm <-scglrTheme(form,data=genus,H=c(2,2),family=fam,offset = genus$surface)
plot(testthm)

## End(Not run)