qvcalc (version 1.0.4)

plot.qv: Plot method for objects of class qv

Description

Provides visualization of estimated contrasts using intervals based on quasi standard errors.

Usage

# S3 method for qv
plot(
  x,
  intervalWidth = 2,
  ylab = "estimate",
  xlab = "",
  ylim = NULL,
  main = "Intervals based on quasi standard errors",
  levelNames = NULL,
  ...
)

Value

invisible(x)

Arguments

x

an object of class "qv", typically the result of calling qvcalc

intervalWidth

the half-width, in quasi standard errors, of the plotted intervals

ylab

as for plot.default

xlab

as for plot.default

ylim

as for plot.default

main

as for plot.default

levelNames

labels to be used on the x axis for the levels of the factor whose effect is plotted

...

other arguments understood by plot

Author

David Firth,

Details

If levelNames is unspecified, the row names of x$qvframe will be used.

References

Easton, D. F, Peto, J. and Babiker, A. G. A. G. (1991) Floating absolute risk: an alternative to relative risk in survival and case-control analysis avoiding an arbitrary reference group. Statistics in Medicine 10, 1025--1035. tools:::Rd_expr_doi("10.1002/sim.4780100703")

Firth, D. (2000) Quasi-variances in Xlisp-Stat and on the web. Journal of Statistical Software 5.4, 1--13. tools:::Rd_expr_doi("10.18637/jss.v005.i04")

Firth, D. (2003) Overcoming the reference category problem in the presentation of statistical models. Sociological Methodology 33, 1--18. tools:::Rd_expr_doi("10.1111/j.0081-1750.2003.t01-1-00125.x")

Firth, D. and Mezezes, R. X. de (2004) Quasi-variances. Biometrika 91, 65--80. tools:::Rd_expr_doi("10.1093/biomet/91.1.65")

McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.

Menezes, R. X. (1999) More useful standard errors for group and factor effects in generalized linear models. D.Phil. Thesis, Department of Statistics, University of Oxford.

See Also

qvcalc

Examples

##  Overdispersed Poisson loglinear model for ship damage data
##  from McCullagh and Nelder (1989), Sec 6.3.2
library(MASS)
data(ships)
ships$year <- as.factor(ships$year)
ships$period <- as.factor(ships$period)
shipmodel <- glm(formula = incidents ~ type + year + period,
    family = quasipoisson,
    data = ships, subset = (service > 0), offset = log(service))
qvs <- qvcalc(shipmodel, "type")
summary(qvs, digits = 4)
plot(qvs, col = c(rep("red", 4), "blue"))
## if we want to plot in decreasing order (of estimates):
est <- qvs$qvframe$estimate
qvs2 <- qvs
qvs2$qvframe <- qvs$qvframe[order(est, decreasing = TRUE), , drop = FALSE]
plot(qvs2)