Description Usage Arguments Value Note Author(s) References See Also Examples
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.
1 | IsoGenem(x, y)
|
x |
indicates the dose levels |
y |
gene expression for all genes |
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. |
This function calculates the five test statistics for both increasing and decreasing ordered alternatives for all the genes (rows in the data set).
Lin et al.
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.
1 2 3 4 5 6 7 8 9 10 11 | ## 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)
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