Summary for a Non-stationary Cosinor

The default summary method for a nsCosinor object produced by nscosinor().

Usage

# S3 method for class 'nsCosinor'
summary(object, ...)

Arguments

object: a nsCosinor object produced by nscosinor().
...: further arguments passed to or from other methods.

Value

a list with the following elements:

cycles: vector of cycles in units of time, e.g., for a six and twelve month pattern cycles=c(6,12).
niters: total number of MCMC samples.
burnin: number of MCMC samples discarded as a burn-in.
tau: vector of smoothing parameters, tau[1] for trend, tau[2] for 1st seasonal parameter, tau[3] for 2nd seasonal parameter, etc.
stats: summary statistics (mean and confidence interval) for the residual standard deviation, the standard deviation for each seasonal cycle, and the amplitude and phase for each cycle.

Details

The amplitude describes the average height of each seasonal cycle, and the phase describes the location of the peak. The results for the phase are given in radians (0 to 2\(\pi\)), they can be transformed to the time scale using the invyrfraction() making sure to first divide by 2\(\pi\).

The larger the standard deviation for the seasonal cycles, the greater the non-stationarity. This is because a larger standard deviation means more change over time.

Author

Adrian Barnett a.barnett@qut.edu.au

Examples

# \donttest{
# model to fit an annual pattern to the monthly cardiovascular disease data
f <- c(12)
tau <- c(10,50)
if (FALSE) { # \dontrun{
  res12 <- nscosinor(
    data = CVD,
    response = 'adj',
    cycles = f,
    niters = 5000,
    burnin = 1000,
    tau = tau
    )
summary(res12)
plot(res12)
} # }
# }