gfilmm | R Documentation |
Samples the fiducial distributions.
gfilmm( y, fixed, random, data, N, thresh = N/2, long = FALSE, seed = NULL, nthreads = parallel::detectCores() ) ## S3 method for class 'gfilmm' print(x, ...)
y |
a right-sided formula of the form |
fixed |
a right-sided formula for the fixed effects |
random |
a right-sided formula for the random effects, or |
data |
the data, a dataframe |
N |
desired number of simulations |
thresh |
threshold, default |
long |
logical, whether to use long doubles instead of doubles in the algorithm |
seed |
the seed for the C++ random numbers generator, a positive
integer, or |
nthreads |
number of threads to run the algorithm with parallelized blocks of code |
x |
a |
... |
ignored |
A list with two components: a dataframe VERTEX
, and a vector
WEIGHT
. It has class gfilmm
.
J. Cisewski and J.Hannig. Generalized fiducial inference for normal linear mixed models. The Annals of Statistics 2012, Vol. 40, No. 4, 2102–2127.
h <- 0.01 gfi <- gfilmm( ~ cbind(yield-h, yield+h), ~ 1, ~ block, data = npk, N = 5000, nthreads = 2 ) # fiducial cumulative distribution function of the intercept: Fintercept <- gfiCDF(~ `(Intercept)`, gfi) plot(Fintercept, xlim = c(40, 65)) # fiducial confidence interval of the intercept: gfiConfInt(~ `(Intercept)`, gfi) # fiducial density function of the intercept: library(kde1d) kfit <- kde1d(gfi$VERTEX[["(Intercept)"]], weights = gfi$WEIGHT) curve(dkde1d(x, kfit), from = 45, to = 65)
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