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

## Usage

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)