corRatio  R Documentation 
This function is a constructor for the corRatio
class,
representing a rational quadratic spatial correlation structure. Letting
d denote the range and n denote the nugget
effect, the correlation between two observations a distance
r apart is 1/(1+(r/d)^2) when no nugget effect
is present and (1n)/(1+(r/d)^2) when a
nugget effect is assumed. Objects created using this constructor need
to be later initialized using the appropriate Initialize
method.
corRatio(value, form, nugget, metric, fixed)
value 
an optional vector with the parameter values in
constrained form. If 
form 
a one sided formula of the form 
nugget 
an optional logical value indicating whether a nugget
effect is present. Defaults to 
metric 
an optional character string specifying the distance
metric to be used. The currently available options are

fixed 
an optional logical value indicating whether the
coefficients should be allowed to vary in the optimization, or kept
fixed at their initial value. Defaults to 
an object of class corRatio
, also inheriting from class
corSpatial
, representing a rational quadratic spatial correlation
structure.
JosÃ© Pinheiro and Douglas Bates bates@stat.wisc.edu
Cressie, N.A.C. (1993), "Statistics for Spatial Data", J. Wiley & Sons.
Venables, W.N. and Ripley, B.D. (2002) "Modern Applied Statistics with S", 4th Edition, SpringerVerlag.
Littel, Milliken, Stroup, and Wolfinger (1996) "SAS Systems for Mixed Models", SAS Institute.
Pinheiro, J.C., and Bates, D.M. (2000) "MixedEffects Models in S and SPLUS", Springer.
Initialize.corStruct
,
summary.corStruct
,
dist
sp1 < corRatio(form = ~ x + y + z) # example lme(..., corRatio ...) # Pinheiro and Bates, pp. 222249 fm1BW.lme < lme(weight ~ Time * Diet, BodyWeight, random = ~ Time) # p. 223 fm2BW.lme < update(fm1BW.lme, weights = varPower()) # p 246 fm3BW.lme < update(fm2BW.lme, correlation = corExp(form = ~ Time)) # p. 249 fm5BW.lme < update(fm3BW.lme, correlation = corRatio(form = ~ Time)) # example gls(..., corRatio ...) # Pinheiro and Bates, pp. 261, 263 fm1Wheat2 < gls(yield ~ variety  1, Wheat2) # p. 263 fm3Wheat2 < update(fm1Wheat2, corr = corRatio(c(12.5, 0.2), form = ~ latitude + longitude, nugget = TRUE))
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