scglrTheme function

Function that fits the theme model

Calculates the components to predict all the dependent variables.

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

Arguments

• 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)

Returns

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

Details

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.

Examples

## 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)