Usage
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)
Arguments
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
time.course
the number of time points we sampled