Description Usage Arguments Details Value References See Also Examples
View source: R/qvcalc.PlackettLuce.R
A method for qvcalc
to compute a set of quasi variances (and
corresponding quasi standard errors) for estimated item parameters from a
PlackettLuce model.
1 2 
object 
a 
ref 
An integer or character string specifying the reference item (for
which log worth will be set to zero). If 
... 
additional arguments, currently ignored.. 
For details of the method see Firth (2000), Firth (2003) or Firth and de Menezes (2004). Quasi variances generalize and improve the accuracy of “floating absolute risk” (Easton et al., 1991). This device for economical model summary was first suggested by Ridout (1989).
Ordinarily the quasi variances are positive and so their square roots (the quasi standard errors) exist and can be used in plots, etc.
A list of class "qv"
, with components
covmat 
The full variancecovariance matrix for the item parameters. 
qvframe 
A data frame with variables 
dispersion 

relerrs 
Relative errors for approximating the standard errors of all simple contrasts. 
factorname 

coef.indices 

modelcall 
The call to 
Easton, D. F, Peto, J. and Babiker, A. G. A. G. (1991) Floating absolute risk: an alternative to relative risk in survival and casecontrol analysis avoiding an arbitrary reference group. Statistics in Medicine 10, 1025–1035.
Firth, D. (2000) Quasivariances in XlispStat and on the web. Journal of Statistical Software 5.4, 1–13. At https://www.jstatsoft.org
Firth, D. (2003) Overcoming the reference category problem in the presentation of statistical models. Sociological Methodology 33, 1–18.
Firth, D. and de Menezes, R. X. (2004) Quasivariances. Biometrika 91, 65–80.
Menezes, R. X. de (1999) More useful standard errors for group and factor effects in generalized linear models. D.Phil. Thesis, Department of Statistics, University of Oxford.
Ridout, M.S. (1989). Summarizing the results of fitting generalized linear models to data from designed experiments. In: Statistical Modelling: Proceedings of GLIM89 and the 4th International Workshop on Statistical Modelling held in Trento, Italy, July 17–21, 1989 (A. Decarli et al., eds.), pp 262–269. New York: Springer.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  # Six partial rankings of four objects, 1 is top rank, e.g
# first ranking: item 1, item 2
# second ranking: item 2, item 3, item 4, item 1
# third ranking: items 2, 3, 4 tie for first place, item 1 second
R < matrix(c(1, 2, 0, 0,
4, 1, 2, 3,
2, 1, 1, 1,
1, 2, 3, 0,
2, 1, 1, 0,
1, 0, 3, 2), nrow = 6, byrow = TRUE)
colnames(R) < c("apple", "banana", "orange", "pear")
mod < PlackettLuce(R)
qv < qvcalc(mod)
qv
plot(qv)

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