# Obtaining the delta table in the SAM procedure

### Description

The function obtains the delta table in the SAM procedure for the five test statistics (the global likelihood test, Williams, Marcus, M, and the modified M).

### Usage

1 | ```
Isoallfdr(qqstat, ddelta, stat)
``` |

### Arguments

`qqstat` |
output from function Isoqqstat containing the test statistics of permutations |

`ddelta` |
give a list of values as cut-off to find the number of significant genes in the SAM procedure. If unspecified, the default value is assigned using the centiles of the absolute difference between the observed and expected test statistics. |

`stat` |
choose one of the five test statistics to use |

### Value

dtable: the delta table in the SAM procedure containing six columns. The first column is the cut-off value to find the number of significant genes, the second column is the meidan number of false positives, the third column is the 90% percentile number of false positives, the fourth column is the number of significant genes, the fifth column is the median FDR, and the last column is the 90% FDR.

### Note

This function calculates the delta table in the SAM procedure for the five test statistics. To use the SAM procedure, the number of genes in the dataset is preferably larger than 500.

### Author(s)

Lin et al.

### 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.

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`

, `Isoqqstat`

,
`Isoqval`

,`IsoTestSAM`

,
`IsoSAMPlot`

### Examples

1 2 3 4 5 6 7 8 9 10 11 | ```
set.seed(1234)
x <- c(rep(1,3),rep(2,3),rep(3,3))
y1 <- matrix(rnorm(4500, 1,1),500,9) ## 500 genes with no trends
y2 <- matrix(c(rnorm(1500, 1,1),rnorm(1500,2,1),
rnorm(1500,3,1)),500,9) ## 500 genes with increasing trends
y <- data.frame(rbind(y1, y2)) ##y needs to be a data frame
qqstat <- Isoqqstat(x, y, fudge="pooled",niter=50)
allfdr <- Isoallfdr(qqstat,,stat="E2")
``` |

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