# predictTimecourses: Prediction of periodic time courses. In MoPS: MoPS - Model-based Periodicity Screening

## Description

Function that predicts periodic time courses using parameters identified by fit.periodic().

## Usage

 `1` ```predictTimecourses(res.fits) ```

## Arguments

 `res.fits` List object returned by fit.periodic().

## Details

This function takes as input the result list from MoPS function fit.periodic() and creates a list of best fitting time courses. The input list also contains information about the screening parameters, which is used in the generation of predicted time courses.

## Value

a numeric matrix containing the predicted values. The number of rows equals the number of rows of the original data matrix, the number of columns equals the number of screened phases.

## Author(s)

Philipp Eser, Achim Tresch

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22``` ```x = seq(0,40,by=1) # time points ## create 10 periodic time series with added noise mat.p = matrix(rep(x,10),nrow=10,ncol=length(x),byrow=TRUE) y = -seq(1:10) mat.p = apply(mat.p,2,function(x){ y = sin(pi*(x/41*6)+y)+rnorm(length(x),sd=1) }) ## add 10 non-periodic noisy time series mat.nonP = matrix(rep(x,10),nrow=10,ncol=length(x),byrow=TRUE) mat.nonP = apply(mat.nonP,2,function(x){ y = rnorm(length(x),sd=1) }) mat = rbind(mat.p,mat.nonP) res = fit.periodic(mat,phi=seq(0,20,1),lambda=seq(1,20,1)) time.courses = predictTimecourses(res) plot(mat[1,],type="l",main="",xlab="",ylab="") points(time.courses[1,],type="l",col="limegreen",lwd=2) ```

MoPS documentation built on Nov. 1, 2018, 4:32 a.m.