Convert a periodic time variable into components usable in linear models

Decompose a periodic time variable into multiple components based on either the first harmonic of a Fourier series or on a periodic smoothing spline.

limorhyde(
  time,
  colnamePrefix = NULL,
  period = 24,
  sinusoid = TRUE,
  nKnots = 3,
  intercept = FALSE
)

Arguments

time	Numeric vector of times, e.g., at which samples were acquired.
colnamePrefix	Character string with which to prefix the column names of the basis.
period	Number corresponding to the period to use for the decomposition (in the same units as time).
sinusoid	If TRUE, the decomposition is based on cosinor, i.e., cosine and sine. If FALSE, the decomposition is based on a periodic smoothing spline from the pbs package.
nKnots	Number of internal knots for the periodic spline. Only used if sinusoid is FALSE.
intercept	If TRUE, a column of ones will be included in the basis.

Value

A matrix with a row for each sample and a column for each component of the time decomposition.

Examples

# create an example data frame
nSamples = 12
d = data.frame(
  sample = paste0('sample_', 1:nSamples),
  genotype = factor(rep(c('WT', 'KO'), each = nSamples / 2),
                    levels = c('WT', 'KO')),
  zt = rep(seq(0, 24 - 24 / nSamples * 2, 24 / nSamples * 2), times = 2),
  stringsAsFactors = FALSE)

# call limorhyde
limo = limorhyde(d$zt, 'zt_')
d = cbind(d, limo)

# create a design matrix that could be used with methods such as limma
design = model.matrix(~ genotype * (zt_cos + zt_sin), data = d)