plot.qv: Plot method for objects of class qv

In qvcalc: Quasi Variances for Factor Effects in Statistical Models

Plot method for objects of class qv

Description

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

Usage

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

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

Details

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

Value

invisible(x)

Author(s)

David Firth, [e-mail]

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.

Firth, D. (2000) Quasi-variances in Xlisp-Stat and on the web. Journal of Statistical Software 5.4, 1–13. \Sexpr[results=rd]{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. \Sexpr[results=rd]{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. \Sexpr[results=rd]{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)

qvcalc documentation built on May 31, 2023, 9:03 p.m.