Description Usage Arguments Value Note References Examples

A method to compute the Jonckheere-Terpstra test statistics for large numbers of dependent and independent variables, with optional multi-threaded execution. The calculation of the standardized test statistic employs the null variance equation as defined by Hollander and Wolfe (1999, eq. 6.19) to account for ties in the data.

1 |

`X` |
A matrix of dependent variables, e.g. marker levels. Row names are sample IDs, and column names are marker IDs. Required. |

`G` |
A matrix of independent variables, e.g. SNP counts. Row names are sample IDs, and column names are SNP IDs. Required. |

`outTopN` |
An integer to indicate the number of top hits to be reported for each marker, based on the standardized Jonckheere-Terpstra test statistics. Optional, and the default value is |

`numThreads` |
A integer to indicate the number of threads used in the computation. Optional, and the default value is |

`standardized` |
A boolean to specify whether to return standardized statistics ( |

A `list`

with two objects

`J` |
A matrix of the standardized/non-standardized Jonckheere-Terpstra test statistics,
depending on the value of the |

`gSnipID` |
If |

Rows (samples) are assumed to be in the same order in `X`

and `G`

.

Hollander, M. and Wolfe, D. A. (1999) *Nonparametric Statistical Methods*. New York: Wiley, 2nd edition.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
# Generate dummy data
num_patient <- 100
num_marker <- 10
num_SNP <- 500
set.seed(12345)
X_pat_mak <- matrix(rnorm(num_patient*num_marker), num_patient, num_marker)
G_pat_SNP <- matrix(rbinom(num_patient*num_SNP, 2, 0.5), num_patient, num_SNP)
colnames(X_pat_mak) <- colnames(X_pat_mak, do.NULL=FALSE, prefix="Mrk:")
colnames(G_pat_SNP) <- colnames(G_pat_SNP, do.NULL=FALSE, prefix="SNP:")
res <- jtGWAS(X_pat_mak, G_pat_SNP, outTopN=5)
res
res <- jtGWAS(X_pat_mak, G_pat_SNP, outTopN=NA)
head(res)
``` |

jtGWAS documentation built on Aug. 15, 2017, 1:02 a.m.

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