plot.summary.clme {CLME}

S3 method to plot objects of class clme

Description

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

Usage

## S3 method for class 'summary.clme'
plot(
  x,
  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,
  ...
)

Arguments

x

object of class 'clme' to be plotted.

alpha

significance level of the test.

legendx

character indicating placement of legend. See Details.

inset

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

ci

plot individual confidence intervals.

ylim

limits of the y axis.

cex

size of plotting symbols.

pch

plotting symbols.

bg

background (fill) color of the plotting symbols.

xlab

label of the x axis.

ylab

label of the y axis.

tree

logical to produce alternate graph for tree ordering.

...

additional plotting arguments.

Details

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

Examples

## 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)