summary.lcc {lcc}

Summarize an lcc Object

Description

Additional information about the fit of longitudinal concordance correlation, longitudinal Pearson correlation, and longitudinal accuracy represented by an object of class lcc. The returned object has a print method.

Usage

## S3 method for class 'lcc'
summary(object, type, adjustSigma, verbose, ...)

Arguments

object

an object inheriting from class lcc, representing a fitted longitudinal concordance correlation function.

type

an optional character string specifying the type of output to be returned. If type="model", prints the summary of the polynomial mixed-effects regression model. If type="lcc", prints the summary of the fitted and sampled values for LCC, LPC, and LA as well as the concordance correlation coefficient between fitted values from the model and observed values as goodness of fit (gof) measurement. Defaults to type="model".

adjustSigma

an optional logical value used when type = model. If TRUE and the estimation method used to obtain object was maximum likelihood, the residual standard error is multiplied by sqrt(nobs/(nobs - npar)). See summary.lme for more information. Default is TRUE.

verbose

an optional logical value used to control the amount of output in the print.summary.lme method when type = model is used. Defaults to FALSE.

...

not used.

Value

an object inheriting from class summary.lcc including:

fitted

the fitted values extracted from the lcc object.

gof

the goodness of fit (gof) measurement is calculated using the concordance correlation coefficient between fitted and observed values. Value of 1 denote perfect concordance.

AIC

the Akaike Information Criterion corresponding to object.

BIC

the Bayesian Information Criterion corresponding to object.

logLik

If REML=FALSE, returns the log-likelihood value of the linear mixed-effects model; otherwise, the restricted log-likelihood is returned

Author(s)

Thiago de Paula Oliveira, [e-mail]

See Also

AIC, BIC, print.summary.lcc, lcc

Examples

## Second degree polynomial model with random intercept, slope and
## quadratic term
fm1<-lcc(data = hue, subject = "Fruit", resp = "H_mean",
         method = "Method", time = "Time", qf = 2, qr = 2)
summary(fm1, type="model")
summary(fm1, type="lcc")