plot.summary.clme {CLME}R Documentation

S3 method to plot objects of class clme


Generates a basic plot of estimated coefficients which are subject to constraints (theta_1 ). Lines indicate individual constraints (not global tests) and significance.


## S3 method for class 'summary.clme'
  alpha = 0.05,
  legendx = "below",
  inset = 0.01,
  ci = FALSE,
  ylim = NULL,
  cex = 1.75,
  pch = 21,
  bg = "white",
  xlab = expression(paste("Component of ", theta[1])),
  ylab = expression(paste("Estimated Value of ", theta[1])),
  tree = NULL,



object of class 'clme' to be plotted.


significance level of the test.


character indicating placement of legend. See Details.


inset distance(s) from the margins as a fraction of the plot region when legend is placed by keyword.


plot individual confidence intervals.


limits of the y axis.


size of plotting symbols.


plotting symbols.


background (fill) color of the plotting symbols.


label of the x axis.


label of the y axis.


logical to produce alternate graph for tree ordering.


additional plotting arguments.


All of the individual contrasts in the constraints\$A matrix are tested and plotted. The global test is not represented (unless it happens to coincide with an individual contrast). Only the elements of theta which appear in any constraints (e.g. the elements of theta_1) are plotted. Coefficients for the covariates are not plotted. Solid lines denote no significant difference, while dashed lines denote statistical significance. Significance is determined by the individual p-value being less than or equal to the supplied alpha threshold. By default a legend denoting the meaning of solid and dashed lines will be placed below the graph. Argument legendx may be set to a legend keyword (e.g. legend=''bottomright'') to place it inside the graph at the specified location. Setting legendx to FALSE or to a non-supported keyword suppresses the legend. Confidence intervals for the coefficients may be plotted. They are individual confidence intervals, and are computed using the covariance matrix of the unconstrained estimates of theta_1. These confidence intervals have higher coverage probability than the nominal value, and as such may appear to be in conflict with the significance tests. Alternate forms of confidence intervals may be provided in future updates.#'

See Also

CLME-package clme


## Not run: 
  set.seed( 42 )
  data( rat.blood )
  cons <- list(order = "simple", decreasing = FALSE, node = 1 )
  clme.out <- clme(mcv ~ time + temp + sex + (1|id), data = rat.blood , 
                   constraints = cons, seed = 42, nsim = 10)
  clme.out2 <- summary( clme.out )
  plot( clme.out2 )

## End(Not run)

[Package CLME version 2.0-12 Index]