plotcompare function

plotcompare

This function compares the predictive posterior surfaces of two fitted models.

plotcompare(
  m1,
  m2,
  level = 0.95,
  title = paste("Comparative Predictive Posterior Contours"),
  m1.name = "Model.1",
  m2.name = "Model.2",
  group = NULL,
  limits.x = c(0, 1),
  limits.y = c(0, 1),
  group.colors = c("blue", "red")
)

Arguments

• m1: A model fitted to the data. This is an object generated by the metadiag function.
• m2: A second model fitted to the data. This is an object generated by the metadiag function.
• level: Credibility level of the predictive curves.
• title: The title of the plot.
• m1.name: Label of the model 1.
• m2.name: Label of the model 2.
• group: An optional argument, which is a variable name indicating a group factor. This argument is used to compare results from two subgroups.
• limits.x: A vector with the limits of the horizontal axis.
• limits.y: A vector with the limits of the vertical axis.
• group.colors: A character vector with two color names.

Examples

## execute analysis
## Not run:

# Comparing results from two models same data

data(glas)
glas.t <- glas[glas$marker == "Telomerase", 1:4]
glas.m1 <- metadiag(glas.t)
glas.m2 <- metadiag(glas.t, re = "sm")
plotcompare(m1 = glas.m1, m2 = glas.m2)

# Comparing results from two models fitted to two subgroups of data:
# studies with retrospective design and studies with prospective design

data("ct")
ct$design = factor(ct$design, labels = c("Prospective", "Retrospective"))

m1.ct <- metadiag(ct[ct$design=="Prospective", ])
m2.ct <- metadiag(ct[ct$design=="Retrospective", ])

plotcompare(m1.ct, m2.ct,m1.name = "Retrospective design",
m2.name = "Prospective design",group = "design",
limits.x = c(0, 0.75), limits.y = c(0.65, 1))
## End(Not run)

See Also

metadiag.