profLlgm | R Documentation |
Calculates profile likelihoods and approximate joint confidence regions for covariance parameters in linear geostatistical models.
profLlgm(fit, mc.cores = 1, ...)
informationLgm(fit, ...)
fit |
Output from the |
mc.cores |
Passed to |
... |
For |
one or more vectors |
of parameter values |
logL |
A vector, matrix, or multi-dimensional array of profile likelihood values for every combination of parameter values supplied. |
full |
Data frame with profile likelihood values and estimates of model parameters |
prob , breaks |
vector of probabilities and chi-squared derived likelihood values associated with those probabilities |
MLE , maxLogL |
Maximum Likelihood Estimates of parameters and log likelihood evaluated at these values |
basepars |
combination of starting values for parameters re-estimated for each profile likelihood and values of parameters which are fixed. |
col |
vector of colours with one element fewer than the number of probabilities |
ci , ciLong |
when only one parameter is varying, a matrix of confidence intervals (in both wide and long format) is returned. |
Patrick Brown
lgm
, mcmapply
, hessian
# this example is time consuming
# the following 'if' statement ensures the CRAN
# computer doesn't run it
if(interactive() | Sys.info()['user'] =='patrick') {
library('geostatsp')
data('swissRain')
swissRain = unwrap(swissRain)
swissAltitude = unwrap(swissAltitude)
swissFit = lgm(data=swissRain, formula=rain~ CHE_alt,
grid=10, covariates=swissAltitude,
shape=1, fixShape=TRUE,
boxcox=0.5, fixBoxcox=TRUE,
aniso=TRUE,reml=TRUE,
param=c(anisoAngleDegrees=37,anisoRatio=7.5,
range=50000))
x=profLlgm(swissFit,
anisoAngleDegrees=seq(30, 43 , len=4)
)
plot(x[[1]],x[[2]], xlab=names(x)[1],
ylab='log L',
ylim=c(min(x[[2]]),x$maxLogL),
type='n')
abline(h=x$breaks[-1],
col=x$col,
lwd=1.5)
axis(2,at=x$breaks,labels=x$prob,line=-1.2,
tick=FALSE,
las=1,padj=1.2,hadj=0)
abline(v=x$ciLong$par,
lty=2,
col=x$col[as.character(x$ciLong$prob)])
lines(x[[1]],x[[2]], col='black')
}
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