lccPlot function

Plot Fitted Curves from an 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, ...)

Arguments

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

• type: character string. If type = "lcc", the output is the LCC plot; if type = "lpc", the output is the LPC plot; and if type = "la" the output is the LA plot. Types "lpc" and "la" are available only if components = TRUE.

• 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".

  • ylab:: title for the y axis. Default is "LCC", "LPC", or "LA"

  • scale_y_continuous:: numeric vector of length two providing limits of the scale. 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 using the `marrangeGrob` function.
    

• ...: arguments to be passed to facet_wrap function

Returns

No return value, called for side effects

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)

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.

Author(s)

Thiago de Paula Oliveira, thiago.paula.oliveira@alumni.usp.br