Nothing
#### copied from help page for function library
pkg <- "aster"
library(pkg, character.only = TRUE)
set.seed(42)
#### copied from help page for function aster in package aster
data(echinacea)
vars <- c("ld02", "ld03", "ld04", "fl02", "fl03", "fl04",
"hdct02", "hdct03", "hdct04")
redata <- reshape(echinacea, varying = list(vars), direction = "long",
timevar = "varb", times = as.factor(vars), v.names = "resp")
redata <- data.frame(redata, root = 1)
pred <- c(0, 1, 2, 1, 2, 3, 4, 5, 6)
fam <- c(1, 1, 1, 1, 1, 1, 3, 3, 3)
hdct <- grep("hdct", as.character(redata$varb))
hdct <- is.element(seq(along = redata$varb), hdct)
redata <- data.frame(redata, hdct = as.integer(hdct))
aout.foo <- aster(resp ~ varb + nsloc + ewloc + pop : hdct,
pred, fam, varb, id, root, data = redata, type = "conditional")
# summary(aout.foo)
beta <- aout.foo$coefficients
dbeta <- rnorm(length(beta))
pout <- predict(aout.foo, parm.type = "canonical",
model.type = "conditional", se.fit = TRUE)
theta <- pout$fit
dtheta <- as.vector(pout$gradient %*% dbeta)
pout <- predict(aout.foo, parm.type = "canonical",
model.type = "unconditional", se.fit = TRUE)
phi <- pout$fit
dphi <- as.vector(pout$gradient %*% dbeta)
pout <- predict(aout.foo, parm.type = "mean.value",
model.type = "unconditional", se.fit = TRUE)
mu <- pout$fit
dmu <- as.vector(pout$gradient %*% dbeta)
phony <- matrix(1, nrow = nrow(aout.foo$x), ncol = ncol(aout.foo$x))
pout <- predict.aster(aout.foo, x = phony, root = aout.foo$root,
modmat = aout.foo$modmat, parm.type = "mean.value",
model.type = "conditional", se.fit = TRUE)
xi <- pout$fit
dxi <- as.vector(pout$gradient %*% dbeta)
offset <- as.vector(aout.foo$origin)
modmat <- matrix(aout.foo$modmat, ncol = length(beta))
#### copied from help page for function library
detach(pos = match(paste("package", pkg, sep=":"), search()))
#### end of stuff from old aster package
rm(list = setdiff(ls(), c("beta", "theta", "phi", "xi", "mu", "tau",
"offset", "modmat", "dbeta", "dtheta", "dphi", "dxi", "dmu", "dtau")))
library(aster2)
data(echinacea)
#### saturated
myphi <- transformSaturated(theta, echinacea, from = "theta", to = "phi")
all.equal(phi, myphi)
mytheta <- transformSaturated(phi, echinacea, from = "phi", to = "theta")
all.equal(theta, mytheta)
myxi <- transformSaturated(theta, echinacea, from = "theta", to = "xi")
all.equal(xi, myxi)
mymu <- transformSaturated(xi, echinacea, from = "xi", to = "mu")
all.equal(mu, mymu)
#### unconditional from == "beta"
phi.foo <- transformConditional(beta, modmat, echinacea,
from = "beta", to = "phi", offset = offset)
all.equal(phi, phi.foo)
theta.foo <- transformConditional(beta, modmat, echinacea,
from = "beta", to = "theta", offset = offset)
all.equal(theta, theta.foo)
xi.foo <- transformConditional(beta, modmat, echinacea,
from = "beta", to = "xi", offset = offset)
all.equal(xi, xi.foo)
mu.foo <- transformConditional(beta, modmat, echinacea,
from = "beta", to = "mu", offset = offset)
all.equal(mu, mu.foo)
#### unconditional from == "beta" (differential)
dphi.foo <- transformConditional(beta, modmat, echinacea,
from = "beta", to = "phi", offset = offset, differential = dbeta)
all.equal(dphi, dphi.foo)
dtheta.foo <- transformConditional(beta, modmat, echinacea,
from = "beta", to = "theta", offset = offset, differential = dbeta)
all.equal(dtheta, dtheta.foo)
dxi.foo <- transformConditional(beta, modmat, echinacea,
from = "beta", to = "xi", offset = offset, differential = dbeta)
all.equal(dxi, dxi.foo)
dmu.foo <- transformConditional(beta, modmat, echinacea,
from = "beta", to = "mu", offset = offset, differential = dbeta)
all.equal(dmu, dmu.foo)
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