Autocorrelation Function for glme Residuals

DESCRIPTION:

This method function calculates the empirical autocorrelation function for the within-group residuals from a glme fit. The autocorrelation values are calculated using pairs of residuals within the innermost group level. The autocorrelation function is useful for investigating serial correlation models for equally spaced data.

USAGE:

ACF(object, maxLag, resType) 

REQUIRED ARGUMENTS:

object
an object inheriting from class glme, representing a fitted generalized linear mixed-effects model.
maxLag
an optional integer giving the maximum lag for which the autocorrelation should be calculated. Defaults to maximum lag in the within-group residuals.
resType
an optional character string specifying the type of residuals to be used. If "response", the "raw" residuals (observed - fitted) are used; else, if "pearson", the standardized residuals (raw residuals divided by the corresponding standard errors) are used; else, if "normalized", the normalized residuals (standardized residuals pre-multiplied by the inverse square-root factor of the estimated error correlation matrix) are used; else, if "deviance", the deviance residuals are used. Partial matching of arguments is used, so only the first character needs to be provided. Defaults to "pearson".

VALUE:

a data frame with columns lag and ACF representing, respectively, the lag between residuals within a pair and the corresponding empirical autocorrelation. The returned value inherits from class ACF.

REFERENCES:

Box, G. E. P., Jenkins, G. M., and Reinsel G. C. (1994). Time Series Analysis: Forecasting and Control, 3rd Edition. Holden-Day.

SEE ALSO:

,

EXAMPLES:

fm1 <- glme(resp ~ trt, Clinic, ~ 1 | clinic, family=binomial) 
ACF(fm1, maxLag=10)