fit-methods: Fit model on time series data
In Rnits: R Normalization and Inference of Time Series data

Description Usage Arguments Details Value Examples

Fit a model comparing time series data set Rnits objects

fit(object, cluster = TRUE, B = 100, verbatim = FALSE, nclus = NULL,
  modelhistplot = FALSE, seed = 123, gene.level = TRUE,
  clusterallsamples = FALSE, model = NULL)

## S4 method for signature 'Rnits'
fit(object, cluster = TRUE, B = 100, verbatim = FALSE,
  nclus = NULL, modelhistplot = FALSE, seed = 123, gene.level = TRUE,
  clusterallsamples = FALSE, model = NULL)

`object`	`Rnits` object
`cluster`	if `TRUE`, perform clustering to identify groups of genes/probes with similar expression profiles.
`B`	Default `100`. Number of bootstrap iterations for p-value calculation
`verbatim`	If `FALSE`, print out details of fitting models.
`nclus`	Default `NULL`. Number of clusters to use for k-means clustering.
`modelhistplot`	If `TRUE`, p-value histograms of multiple models are plotted.
`seed`	Random seed for bootstrap iterations
`gene.level`	If `TRUE`, collapse probes to gene level information.
`clusterallsamples`	If `TRUE`, Use all time series for clustering. By default, only the sample labeled 'control' is used or the lexically first sample is used.
`model`	A data frame with fields 'degree' and 'df' indicating a specific B-spline model to be used. If provided, model selection is not run.

The function compares multiple time-series expression data sets by i) (optional) summarizing probes into gene-level information ii) (optional) identifying a set of co-expressed genes by clustering iii) For each cluster (or for all genes /probes), fit a series of B-splines with varying curvature and degrees of freedom. Under the null hypothesis H_0, a single model is fit for all data sets, while under H_1, each data set is fit separately. P-values from the hypothesis test are then plotted and the least complex spline parameters that result in uniformly distributed null p-values are automatically chosen.

An object of S4 class Rnits with fitted results data containing cluster information, ratio statistics and p-values.

# load pre-compiled expressionSet object for Ronen and Botstein yeast  chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')
## Not run: 
# Fit model using gene-level summarization
rnitsobj <- fit(rnitsobj, gene.level = TRUE, clusterallsamples = FALSE)

## End(Not run)