IsoGene (version 1.0-24)

IsoGenem: The five test statistics calculated for both the increasing and decreasing trends

Description

The function calculates the values for the five test statistics (the global likelihood test, Williams, Marcus, M, and the modified M) for testing increasing and decreasing alternatives.

Usage

IsoGenem(x, y)

Arguments

x

indicates the dose levels

y

gene expression for all genes

Value

A list with components

E2.up

the test statistic of global likelihood test for testing increasing alternative.

Williams.up

the test statistic of Williams for testing increasing alternative.

Marcus.up

the test statistic of Marcus for testing increasing alternative.

M.up

the M test statistic for testing increasing alternative.

ModM.up

the test statistic of the modified M for testing increasing alternative.

E2.dn

the test statistic of Williams for testing increasing alternative.

Williams.dn

the test statistic of global likelihood test for testing increasing alternative.

Marcus.dn

the test statistic of Williams for testing increasing alternative.

M.dn

the test statistic of global likelihood test for testing increasing alternative.

ModM.dn

the test statistic of Williams for testing increasing alternative.

direction

the direction with the higher likelihood of increasing (indicated by "u") or decreasing (indicated by "d") trend.

References

Modeling Dose-response Microarray Data in Early Drug Development Experiments Using R, Lin D., Shkedy Z., Yekutieli D., Amaratunga D., and Bijnens, L. (editors), (2012), Springer.

Testing for Trend in Dose-Response Microarray Experiments: a Comparison of Testing Procedures, Multiplicity, and Resampling-Based Inference, Lin et al. 2007, Stat. App. in Gen. & Mol. Bio., 6(1), article 26.

IsoGene: An R Package for Analyzing Dose-response Studies in Microarray Experiments, Pramana S., Lin D., Haldermans P., Shkedy Z., Verbeke T., De Bondt A., Talloen W., Goehlmann H., Bijnens L. 2010, R Journal 2/1.

See Also

isoreg, IsoGene1

Examples

## Not run: 
#   set.seed(1234)
#   x <- c(rep(1,3),rep(2,3),rep(3,3))
#   y1 <- matrix(rnorm(90, 1,1),10,9)  # 10 genes with no trends
#   y2 <- matrix(c(rnorm(30, 1,1), rnorm(30,2,1),
#                  rnorm(30,3,1)), 10, 9)  # 10 genes with increasing trends
#   y <- data.frame(rbind(y1, y2))  # y needs to be a data frame 
#   stat <- IsoGenem(x,y) 
#   stat
# ## End(Not run)