View source: R/plotmethod_lcc.R
lccPlot | R Documentation |
lcc
Object.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.
lccPlot(obj, type, control, ...)
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
|
No return value, called for side effects
Thiago de Paula Oliveira, thiago.paula.oliveira@alumni.usp.br
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.
lcc
.
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)
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