serf.design: Helper function for Serfling models

In ensoesie/epidemics: A Method for Defining Influenza Epidemic Onset

Description

Given a time series and a specified number of harmonic and polynomial terms, this function builds the dataframe needed for the call to lm(). See serf.fit function for example on how to fit model.

Usage

serf.design(ts = g.ts, t1 = 1, t2 = length(ts), period = 52.2,
  trend.poly = 1, harmonic = c(1, 2), covar = NULL)

Arguments

ts	response vector of numeric values representing the time series to be modeled. By default this is set to 'g.ts' the global time series which MUST be specified by the user before calling this function
t1	is the integer index of the parameter 'ts' where model fitting begins; defaults to 1
t2	is the integer index of the parameter 'ts' where model fitting ends; defaults to length(ts)
period	numeric value; defaults to 52.2
trend.poly	is specified as an integer vector representing the degrees of the polynomial terms to be included; defaults to 1 i.e. single linear trend term
harmonic	is also specified as an integer vector representing fractional multiples of the period e.g. 'harmonic=c(1,2)' specifies two harmonic pairs (two sine terms and two cosine terms) of one period and one half-period respectively
covar	adds additional covariance factors but this feature not currently implemented; defaults to NULL

Value

Data frame containing predictors for model fitting and forecasting

ensoesie/epidemics documentation built on May 28, 2019, 8:37 p.m.