lccPlot {lcc} | R Documentation |
Plot Fitted Curves from 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, type, control, ...)
Arguments
obj |
an object inheriting from class "lcc", representing a fitted lcc model. |
type |
character string. If |
control |
a list of control values or character strings
returned by the function
|
... |
arguments to be passed to
|
Value
No return value, called for side effects
Author(s)
Thiago de Paula Oliveira, thiago.paula.oliveira@alumni.usp.br
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, 233–254, 2018.
See Also
lcc
.
Examples
data(hue)
## 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,
components=TRUE)
lccPlot(fm1, type="lcc")
lccPlot(fm1, type="lpc")
lccPlot(fm1, type="la")
## Using themes of ggplot2 package
lccPlot(fm1, type = "lpc")+
ylim(0,1) +
geom_hline(yintercept = 1, linetype = "dashed") +
scale_x_continuous(breaks = seq(1,max(hue$Time),2))+
theme_bw() +
theme(legend.position = "none", aspect.ratio = 1,
axis.line.x = element_line(color="black", size = 0.5),
axis.line.y = element_line(color="black", size = 0.5),
axis.title.x = element_text(size=14),
axis.title.y = element_text(size=14),
axis.text.x = element_text(size = 14, face = "plain"),
axis.text.y = element_text(size = 14, face = "plain"))
## Using the key (+) to constructing sophisticated graphics
lccPlot(fm1, type="lcc") +
scale_y_continuous(limits=c(-1, 1)) +
labs(title="My title",
y ="Longitudinal Concordance Correlation",
x = "Time (Days)")
## Runing all.plots = FALSE and saving plots as pdf
## Not run:
data(simulated_hue_block)
attach(simulated_hue_block)
fm2<-lcc(data = simulated_hue_block, subject = "Fruit",
resp = "Hue", method = "Method",time = "Time",
qf = 2, qr = 1, components = TRUE, covar = c("Block"),
time_lcc = list(n=50, from=min(Time), to=max(Time)))
ggsave("myplots.pdf",
lccPlot(fm2, type="lcc", scales = "free"))
## End(Not run)