Description Usage Arguments Details Value Data Shuffling Author(s) References See Also Examples
This is the function to perform F or T test on one or multiple experimental factor(s). Permutation test will be carried upon request.
1 2 3 4 5 
data 
An object of class 
anovaobj 
An object of class 
term 
The term(s) to be tested. It can be multiple terms. Note that the tested term must be fixed. If the term to be tested is a random term, it will be converted to a fixed term than do test. 
Contrast 
The contrast matrix for the term. The number of
columns equals to the number of levels in the term. The number of
rows is the number of Ttest you want to carry. Note that it must
be a matrix. Use 
n.perm 
An integer for number of permutations. 
nnodes 
Number of nodes in the MPI cluster. If 1, the permutation test will be running on the local computer. 
critical 
percentile of Fdistribution used to get a subset to calculate pvalue. Default is 90th percentile of Fdistribution, and permutation analysis is conducted based on genes whose test statistics are smaller than 90th percentile of the Fdistribution. 
test.type 
Test type. It could be Ftest or Ttest. If the Contrast matrix is missing, this should be a "ftest" and the contrast matrix is generated automatically to cover the whole linear space except for testing covariates. If the Contrast matrix is given, this could be "ftest" or "ttest". The default is "ttest" (for backward compatability). For Ttest, the code will do a series of Ttest, where each Ttest corresponds to a row in the contrast matrix. 
shuffle.method 
Data shuffling method. "sample" for sample shuffling and "resid" for residual shuffling. Read "Data Shuffling" section for detail. 
MME.method 
The method used to solve the Mixed Model Equations. See

test.method 
An integer vector of two elements to indicate which F test to carry. Default is c(1,1), which means do F1 and Fs test. 
pval.pool 
A logical value to indicate whether to use pooled permutation F values to calculate the P values. 
verbose 
A logical value to indicate whether to display some message for calculation progress. 
If user provide a comparison matrix, this function will perform Ttest on the given comparison(s). Otherwise, this function will perform Ftest for the given term.
There are three types of tests available. All three tests are based on the genespecific ANOVA model. F1 is the usual F statistic, Fs is based on the JamesStein shrinkage estimates of the error variance.
Permutation tests can run on MPI cluster. This feature is only available for Unix/Linux system. Several other R packages (such like SNOW, Rmpi, etc.) are needed for using cluster. You may need help from your system administrator to setup LAMMPI correctly. For detailed information on LAMMPI cluster setup and the cluster usage in R, read "MPI\_README" distributed with the package.
An object of class matest
, which is a list of the following
components:
model 
Input model object. 
term 
The input term(s) to be tested. 
dfde 
Denominator's degree of freedom for the test. 
dfnu 
Numerator's degree of freedom for the test. Note that this is always 1 for Ttest. 
obsAnova 
An object of 
Contrast 
The contrast matrix used in the test. 
n.perm 
Number of permutations. 
shuffle 
Shuffle style 
pval.pool 
Use pooled P value or not. 
F1, Fs 
Objects of four different F tests results. All or any of them could be there according to the requested F test method. Each of them contains the following fields:
All the F values and P values are matrices. The number of rows in the matrices equals to the number of genes. For Ftest, the number of columns will be one. For Ttest, the number of columns equals to the number of tests carried. 
Data shuffling method is a crucial part in the permutation test. Currently there are two shuffling method available, residual shuffling and sample shuffling.
Residual shuffling is to shuffle the null model residuals within gene without replacement.
Sample shuffling is to shuffle the samples based on the nesting relationship among the experimental factors in the model. For sample shuffling, you need to make sure you have a good sample size. Otherwise the result may be biased.
Hao Wu
Cui, X. and Churchill,GA (2003), Statistical tests for differential expression in cDNA Microarray experiments, Genome Biology 4:210.
Cui, X., Hwang, J.T.G., Blades N., Qiu J. and Churchill GA (2003), Improved statistical tests for differential gene expression by shrinking variance components, to be submitted.
1 2 3 4 5 6 7 8 9 10 11  # load in abf1 data
data(abf1)
## Not run:
fit.full.mix < fitmaanova(abf1, formula = ~Strain+Sample,
random = ~Sample)
ftest.all = matest(abf1, fit.full.mix, test.method=c(1,1),
shuffle.method="sample", term="Strain", n.perm= 100)
C = matrix(c(1,1,0,1,0,1), ncol=3, byrow=T)
ftest.pair = matest(abf1, fit.full.mix, Contrast = C,
term="Strain", n.perm=100)
## End(Not run)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.