qvcalc {qvcalc} | R Documentation |
Computes a set of `quasi-variances' (and corresponding `quasi standard errors') for estimated model coefficients relating to the levels of a categorical (i.e., factor) explanatory variable. For details of the method see Firth (2000) or Firth and Menezes (2002).
qvcalc(object, factorname=NULL, labels = NULL, dispersion = NULL, estimates=NULL, modelcall=NULL)
object |
A model (of class lm, glm, etc.), or the covariance (sub)matrix for the estimates of interest |
factorname |
If object is a model, the name of the factor of
interest |
labels |
An optional vector of row names for the qvframe
component of the result (redundant if object is a model) |
dispersion |
an optional scalar multiplier for the covariance matrix, to cope with overdispersion for example |
estimates |
an optional vector of estimated coefficients (redundant
if object is a model) |
modelcall |
optional, the call expression for the model of interest
(redundant if object is a model) |
A list of class qv
, with components
covmat |
the full variance-covariance matrix for the estimated coefficients corresponding to the factor of interest |
qvframe |
a data frame with variables
estimate , SE , quasiSE and quasiVar ,
the last two being a quasi standard error and quasi-variance
for each level of the factor of interest |
relerrs |
relative errors for approximating the standard errors of all simple contrasts |
factorname |
the factor name if given |
modelcall |
if object is a model, object$call ;
otherwise NULL |
David Firth, david.firth@nuffield.ox.ac.uk
Firth, D. (2000) Quasi-variances in Xlisp-Stat and on the web. Journal of Statistical Software 5.4, 113. At http://www.jstatsoft.org
Firth, D. and Mezezes, R. X. de (2002) Quasi-variances. Submitted for publication. At http://www.stats.ox.ac.uk/~firth/papers/.
McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.
Menezes, R. X. (1999) More useful standard errors for group and factor effects in generalized linear models. D.Phil. Thesis, Department of Statistics, University of Oxford.
## Overdispersed Poisson loglinear model for ship damage data ## from McCullagh and Nelder (1989), Sec 6.3.2 library(MASS) data(ships) ships$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)) shiptype.qvs <- qvcalc(shipmodel, "type") summary(shiptype.qvs, digits=4) plot(shiptype.qvs)