season (version 0.2-6)

cosinor: Cosinor regression model for detecting seasonality

Description

Fits a cosinor model as part of a generalized linear model.

Usage

cosinor(formula, date, data, family=gaussian() ,alpha=0.05, cycles=1,
               rescheck=FALSE, type='daily', offsetmonth=FALSE, offsetpop=NULL, text=TRUE)

Arguments

formula

regression formula.

date

a date variable if type=daily, or an integer between 1 and 12 if type=monthly.

data

data set as a data frame.

family

a description of the error distribution and link function to be used in the model. Available link functions: identity, log, logit, cloglog. Note, it must have the parentheses.

alpha

significance level, set to 0.05 (default).

cycles

number of seasonal cycles per year.

rescheck

plot the residual checks (TRUE/FALSE), see seasrescheck.

type

daily for daily data (default), or monthly for monthly data.

offsetmonth

include an offset to account for the uneven number of days in the month (TRUE/FALSE). Should be used for monthly counts (with family=poisson()).

offsetpop

include an offset for the population (optional), this should be a variable in the data frame.

text

add explanatory text to the returned phase value (TRUE) or return a number (FALSE). Passed to the invyrfraction function.

Value

• Returns an object of class Cosinor with the following parts:
• call
  the original call to the cosinor function.
• glm
  an object of class glm (see glm).
• fitted
  fitted values for intercept and cosinor only (ignoring other independent variables).
• fitted.plus
  standard fitted values, including all other independent variables.
• residuals
  residuals.
• date
  date variable (in Date format when type=daily).

Details

The cosinor model captures a seasonal pattern using a sinusoid. It is therefore suitable for relatively simple seasonal patterns that are symmetric and stationary. The default is to fit an annual seasonal pattern (cycle=1), but other frequencies are possible (e.g., twice per year: cycle=2). The model is fitted using a sine and cosine term that together describe the sinusoid. These parameters are added to a generalized linear model, so the model can be fitted to a range of dependent data (e.g., Normal, Poisson, Binomial). Unlike the nscosinor model, the cosinor model can be applied to unequally spaced data.

References

Barnett, A.G., Dobson, A.J. (2010) Analysing Seasonal Health Data. Springer.

See Also

summary.Cosinor, plot.Cosinor

Examples

## cardiovascular disease data (offset based on number of days in... 
## ...the month scaled to an average month length)
data(CVD)
res = cosinor(cvd~1, date=month, data=CVD, type='monthly',
              family=poisson(), offsetmonth=TRUE)
summary(res)
# stillbirth data
data(stillbirth)
res = cosinor(stillborn~1, date=dob, data=stillbirth,
              family=binomial(link='cloglog'))
summary(res)
plot(res)