lcc (version 1.0.1)

lccPlot: Plot an lcc object

Description

A plot of predictions versus the time covariate is generated. Predicted values are joined by lines while sampled observations are represented by circles. If the argument components=TRUE is considered in the lcc object, single plots of each statistics are returned on differents pages.

Usage

lccPlot(obj, control, ...)

Arguments

obj

an object inheriting from class "lcc", representing a fitted lcc model.

control

a list of control values or character strings returned by the function plotControl. Defaults to an empty list. The list may contain the following components:

shape:

draw points considering a shape parameter. Possible shape values are the numbers 0 to 25, and 32 to 127; see aes_linetype_size_shape. Default is 1.

colour:

specification for lines color. Default is "black".

size:

specification for lines size. Should be specified with a numerical value (in millimetres); see aes_linetype_size_shape. Default is 0.5.

xlab:

title for the x axis. Default is "Time".

LCC_ylab:

title for the y axis related with LCC. Default is "LCC".

LPC_ylab:

title for the y axis related with LPC. Default is "LPC".

LA_ylab:

title for the y axis related with LA. Default is "LA".

LCC_scale_y_continuous:

numeric vector of length two providing limits of the scale related to LCC. Default is c(0,1).

LPC_scale_y_continuous:

numeric vector of length two providing limits of the scale related to LPC. Default is c(0,1).

LA_scale_y_continuous:

numeric vector of length two providing limits of the scale related to LA. Default is c(0,1).

all.plot:

viewport functions for the lcc class. If TRUE, the default, returns an object created by the viewport function with multiple plots on a single page. If FALSE returns a single ggplot object by different pages.

...

not used.

References

Lin, L. A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics, 45, n. 1, 255-268, 1989.

Oliveira, T.P.; Hinde, J.; Zocchi S.S. Longitudinal Concordance Correlation Function Based on Variance Components: An Application in Fruit Color Analysis. Journal of Agricultural, Biological, and Environmental Statistics, v. 23, n. 2, 233<U+2013>254, 2018.

See Also

lcc.

Examples

# NOT RUN {
data(hue)
## Second degree polynomial model with random intercept, slope and
## quadratic term
fm1<-lcc(dataset = hue, subject = "Fruit", resp = "H_mean",
         method = "Method", time = "Time", qf = 2, qr = 2,
         components=TRUE)
lccPlot(fm1)
         
# }