| dyn.scale {longitudinal} | R Documentation |
time2weights computes weights corresponding to time points
dyn.weights computes these weights for a given longitudinal matrix.
dyn.moments computes means and variances for the variables in
a longitudinal object.
dyn.scale centers and standardizes a longitudinal matrix.
time2weights(t)
dyn.weights(x)
dyn.moments(x)
dyn.scale(x, center=TRUE, scale=TRUE, scale.by = c("sd", "mean"))
t |
a vector with time points |
x |
a longitudinal object, or a matrix |
center |
logical value |
scale |
logical value |
scale.by |
determines whether the columns are standardized by the
standard errors of the respective variables ("sd", the default) or by the
column means ("mean"). |
The dynamical weights are computed assuming a linear spline - see Opgen-Rhein and Strimmer (2006a,b). The dynamical mean and variance etc. are then simply weighted versions of the usual empirical estimators.
A vector with weights (time2weights and dyn.weights),
a list containing the column means and variances (dyn.moments),
or a rescaled longitudinal matrix (dyn.scale).
Rainer Opgen-Rhein (http://opgen-rhein.de) and Korbinian Strimmer (http://strimmerlab.org).
Opgen-Rhein, R., and K. Strimmer. 2006a. Inferring gene dependency networks from genomic longitudinal data: a functional data approach. REVSTAT 4:53-65. (http://http://www.ine.pt/revstat/)
Opgen-Rhein, R., and K. Strimmer. 2006b. Using regularized dynamic correlation to infer gene dependency networks from time-series microarray data. The 4th International Workshop on Computational Systems Biology, WCSB 2006 (June 12-13, 2006, Tampere, Finland). (http://www.cs.tut.fi/wcsb06/)
# load "longitudinal" library
library("longitudinal")
# weights of for the data points in tcell data
data(tcell)
dyn.weights(tcell.34)
# dynamical moments
dyn.moments(tcell.34)