% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AllGenerics.R, R/deSet-methods.R
\title{Linear regression of the null and full models}
fit_models(object, stat.type = c("lrt", "odp"), weights = NULL)

\S4method{fit_models}{deSet}(object, stat.type = c("lrt", "odp"),
  weights = NULL)
\item{object}{\code{S4 object}: \code{\linkS4class{deSet}}.}

\item{stat.type}{\code{character}: type of statistic to be used. Either
"lrt" or "odp". Default is "lrt".}

\item{weights}{\code{matrix}: weights for each observation. Default is NULL.}
\code{\linkS4class{deFit}} object
\code{fit_models} fits a model matrix to each gene by using the least
squares method. Model fits can be either statistic type "odp" (optimal
discovery procedure) or "lrt" (likelihood ratio test).
If "odp" method is implemented then the null model is removed from the full 
model (see Storey 2007).  Otherwise, the statistic type has no affect on the
model fit.
\code{fit_models} does not have to be called by the user to use
\code{\link{odp}}, \code{\link{lrt}} or \code{\link{kl_clust}} as it is an
optional input and is implemented in the methods. The
\code{\linkS4class{deFit}} object can be created by the user if a different
statistical implementation is required.
# import data
age <- kidney$age
sex <- kidney$sex
kidexpr <- kidney$kidexpr
cov <- data.frame(sex = sex, age = age)

# create models
null_model <- ~sex
full_model <- ~sex + ns(age, df = 4)

# create deSet object from data
de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model,
full.model = full_model)

# retrieve statistics from linear regression for each gene
fit_lrt <- fit_models(de_obj, stat.type = "lrt") # lrt method
fit_odp <- fit_models(de_obj, stat.type = "odp") # odp method

# summarize object

John Storey
Storey JD. (2007) The optimal discovery procedure: A new approach to
simultaneous significance testing. Journal of the Royal Statistical
Society, Series B, 69: 347-368.

Storey JD, Dai JY, and Leek JT. (2007) The optimal discovery procedure for
large-scale significance testing, with applications to comparative
microarray experiments. Biostatistics, 8: 414-432.

Storey JD, Xiao W, Leek JT, Tompkins RG, and Davis RW. (2005) Significance
analysis of time course microarray experiments. Proceedings of the National
Academy of Sciences, 102: 12837-12842.
\code{\linkS4class{deFit}}, \code{\link{odp}} and