selection(parm, index, model, data = NULL, alpha = 0.05, type = c("canonical","mean-value"), method = c("eigen","1d"))alpha. alpha. There are four possible combinations
of outputs. They are:
method = "eigen" and
type = "mean-value" provides the user with the indices of
the eigenspace of estimated Fisher information used to construct an
envelope estimator for $\tau$ favored by AIC, BIC, and the LRT of
size alpha.
method = "eigen" and
type = "canonical" provides the user with the indices of
the eigenspace of estimated Fisher information used to construct an
envelope estimator for $\beta$ favored by AIC, BIC, and the LRT of
size alpha.
method = "1d" and
type = "mean-value" provides the user with the envelope model
dimension used to construct an envelope estimator for $\tau$ favored
by AIC, BIC, and the LRT of size alpha.
method = "1d" and
type = "canonical" provides the user with the envelope model
dimension used to construct an envelope estimator for $\beta$ favored
by AIC, BIC, and the LRT of size alpha.
When one is interested in envelope model dimensions or eigenspaces with
respect to $\beta$, then an asterdata object does not need to
be specified. On the other hand, an asterdata is needed in order to
map the estimated $\tau$ to its corresponding $\beta$ value. This
is necessary because of the interface (or lack thereof) between current
aster and aster2 software. The way in which aster model
log likelihoods are evaluated is incorporated in aster software
and changing parameterizations is carried out using aster2 software.
Cook, R.D. and Zhang, X. (2015). Algorithms for Envelope Estimation. Journal of Computational and Graphical Statistics, Published online. \Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("#1")}10.1080/10618600.2015.1029577http://doi.org/10.1080/10618600.2015.1029577doi:\ifelse{latex}{\out{~}}{ }latex~ 10.1080/10618600.2015.1029577 . Eck, D. J., Geyer, C. J., and Cook, R. D. (2016). Enveloping the aster model. in prep.
## Not run:
# set.seed(13)
# library(envlpaster)
# library(aster2)
# data(generateddata)
# m.null <- aster(resp ~ 0 + varb, fam = fam, pred = pred,
# varvar = varb, idvar = id, root = root, data = redata)
# m1 <- aster(resp ~ 0 + varb + mass + timing,
# fam = fam, pred = pred, varvar = varb, idvar = id,
# root = root, data = redata)
# m2 <- aster(resp ~ 0 + varb + mass + timing +
# I(mass^2) + I(timing^2) + I(mass*timing),
# fam = fam, pred = pred, varvar = varb, idvar = id,
# root = root, data = redata)
# anova.table <- anova(m.null,m1,m2); anova.table
# beta <- m1$coef
# a <- grepl( "offsp", names(beta))
# a <- a + grepl( "surviv", names(beta))
# b <- which(a == 1)
# target <- c(1:length(beta))[-b]
# nnode <- ncol(m1$x)
# data.aster <- asterdata(data, vars, pred, rep(0,nnode),
# fam, families = list("bernoulli", "poisson",
# fam.zero.truncated.poisson()))
# selection(parm = beta, index = target, model = m1,
# data = data.aster, alpha = 0.05, type = "canonical",
# method = "eigen")
# ## End(Not run)
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