Description Usage Arguments Details Value Author(s) References Examples

The test is testing for a monotone trend in terms of the class parameter. The number of times that an individual of a higher class has a higher gene expression forms a basis for the inference.

1 |

`data` |
A matrix with genes in rows and subjects in columns |

`class` |
the column labels, if not an ordered fctor it will be redefined to be one. |

`labs` |
the labels of the categories coded by class |

`alternative` |
two-sided, decreasing or increasing |

`ties` |
Adjustment for ties |

Assumes that groups are given in increasing order, if the class variable is not an ordered factor, it will be redefined to be one. The p-value
is calculated through a normal approximation.

The implementation owes to suggestions posted to R list.

The definition of predictive strength appears in Flandre and O'Quigley.

an object of class JT-test, which extends the class htest, and includes the following slots

`statistic` |
the observed JT statistic |

`parameter` |
the null hypothesis parameter, if other value than 0. |

`p.value` |
the p-value for the two-sided test of no trend. |

`method` |
Jonckheere-Terpstra |

`alternative` |
The relations between the levels: decreasing, increasing or two-sided |

`data.name` |
the name of the input data |

`median1 ... mediann` |
the medians for the n groups |

`trend` |
the rank correlation with category |

`S1` |
Predictive strength |

Per Broberg, acknowledging input from Christopher Andrews at SUNY Buffalo

Lehmann, EH (1975) *Nonparametrics: Statistical Methods Based on Ranks* p. 233. Holden Day

Flandre, Philippe and O'Quigley, John, *Predictive strength of Jonckheere's test for trend: an application to genotypic scores in HIV infection*,
Statistics in Medicine, 2007, 26, 24, 4441-4454

1 2 3 4 5 6 7 8 | ```
# Enter the data as a vector
A <- as.matrix(c(99,114,116,127,146,111, 125,143,148,157,133,139, 149, 160, 184))
# create the class labels
g <- c(rep(1,5),rep(2,5),rep(3,5))
# The groups have the medians
tapply(A, g, median)
# JT.test indicates that this trend is significant at the 5% level
JT.test(data = A, class = g, labs = c("GRP 1", "GRP 2", "GRP 3"), alternative = "two-sided")
``` |

```
Loading required package: multtest
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colMeans, colSums, colnames, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.min
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
1 2 3
116 143 149
class was not an ordered factor. Redefined to be one.
Jonckheere-Terpstra
data: A by g
J = 16, p-value = 0.02311
alternative hypothesis: two-sided: GRP 1two-sided: GRP 2two-sided: GRP 3
```

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