NANOVA.test: Non-parametric analysis of variance (NANOVA)
In TANOVA: Time Course Analysis of Variance for Microarray

Description Usage Arguments Value Author(s) See Also

This are an internal functions to provide non-parametric ANOVA function.

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NANOVA.test(data,f1,f2,type=2,B=100, robustify=FALSE,equal.size=FALSE,eb=FALSE)
NANOVA.test2(data,f1,f2,type,time.course,equal.size=FALSE,B=100,robustify=FALSE,eb=FALSE,df=0)
NANOVA.test3(data,f1,f2,tp,type=2,B=100,robustify=FALSE,eb=FALSE)

`data`	data matrix (gene * array). Each row is a gene. Each column is an array. If data are longitudinal (for example, time course measurements from patients), arrays from same experimental units (e.g. patient) should be adjacent to each other.
`f1`	a vector with length equal to the number of arrays. Each entry indicates the level of the first factor for corresponding array. The values of f1 should be 1,2,3,...
`f2`	a vector with length equal to the number of arrays. Each entry indicates the level of the second factor for the corresponding array. The values of f2 should be 1,2,3,... If the experimental has only one factor, let f2=0.
`tp`	a vector with length equal to the number of arrays. Each entry indicates the time point for the corresponding array. tp takes values 1,2,3 .... For non-time course data, let tp=0.
`B`	the number of bootstrap resampling. Default is 100. Large B lead to more accurate inference, but need more running time.

`robustify`	a logical indicator of whether a robust test statistic should be used. Default is FALSE.
`equal.size`	a logical indicator of whether the number of replicates under each biological condition is equal. Default is FALSE.

`type`	an indicator of TANOVA test type. 0: classifies genes into gene sets C1,C2, C3,C4 and C5 (constant genes). 1: test for interaction effect. 2: one-way NANOVA test. 3: test main effect f1. 4: test main effect f2.
`eb`	a logical indicator of whether Empirical Bayesian method should be used in the estimation of significance
`df`	degree of freedom
`time.course`	the number of time points we sampled

Return list contains

`gene.order`	A numeric vector indicating the positions in which the genes are called significant for the test
`F`	observed F-statistics
`F.null`	bootstrap generated null F-statistics
`pvalue`	a numeric vector of the corresponding p-value of NANOVA.
`delta`	a numeric vector of summary statistic for NANOVA