Description Usage Arguments Details Value See Also

View source: R/zeitzeiger_cut.R

DEPRECATED: We recommend instead using limorhyde, which has support for cosinor and periodic splines, in combination with methods such as limma.

1 | ```
zeitzeigerSig(x, time, nKnots = 3, nIter = 200, dopar = TRUE)
``` |

`x` |
Matrix of measurements, with observations in rows and features in columns. Missing values are allowed. |

`time` |
Vector of values of the periodic variable for the observations, where 0 corresponds to the lowest possible value and 1 corresponds to the highest possible value. |

`nKnots` |
Number of internal knots to use for the periodic smoothing spline. |

`nIter` |
Number of permutations. |

`dopar` |
Logical indicating whether to process features in parallel.
Use |

`zeitzeigerSig`

estimates the statistical significance of the periodic
smoothing spline fit. At each permutation, the time vector is scrambled and then
zeitzeigerFit is used to fit a periodic smoothing spline for each feature as a
function of time. The p-value for each feature is calculated based on the
of permutations that had a signal-to-noise ratio at least as large as the
observed signal-to-noise ratio, adjusted by the method of Phipson and Smyth (2010).
Make sure to first register the parallel backend using `registerDoParallel`

.
For genome-scale data, this will be slow.

Vector of p-values.

jakejh/zeitzeiger documentation built on Nov. 22, 2018, 6:53 a.m.

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