cosinor function

Cosinor Regression Model for Detecting Seasonality in Yearly Data or Circadian Patterns in Hourly Data

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

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 53 if type=weekly , or an integer between 1 and 12 if type=monthly , or a POSIXct date if type=hourly .
• 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 if type=daily , weekly or monthly ; number of cycles per 24 hours if type=hourly
• rescheck: plot the residual checks (TRUE/FALSE), see seasrescheck.
• type: daily for daily data (default), or weekly for weekly data, or monthly for monthly data, or hourly for hourly data.
• offsetmonth: include an offset to account for the uneven number of days in the month (TRUE/FALSE). Should be used for monthly counts (type=monthly ) (with family=poisson()).
• offsetpop: include an offset for the population (optional), this should be a variable in the data frame. Do not log-transform this offset, as the transform is applied by the code.
• text: add explanatory text to the returned phase value (TRUE) or return a number (FALSE). Passed to the invyrfraction function.

Returns

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: name of the 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 higher 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.

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)
seasrescheck(res$residuals) # check the residuals
## stillbirth data
data(stillbirth)
res = cosinor(stillborn~1, date='dob', data=stillbirth,
              family=binomial(link='cloglog'))
summary(res)
plot(res)
## hourly indoor temperature data
res = cosinor(bedroom~1, date='datetime', type='hourly', data=indoor)
summary(res)
# to get the p-values for the sine and cosine estimates
summary(res$glm)

References

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

See Also

summary.Cosinor, plot.Cosinor

Author(s)

Adrian Barnett a.barnett@qut.edu.au