Nothing
as.lm.cusp <-
function (object, what = c("y", "alpha", "beta"))
{
what = match.arg(what)
object$coefficients = object$coefficients[grep(sub("a", switch(what,
alpha = "a", beta = "b", y = "w"), "^a\\["), names(object$coefficients))]
object$terms = object$terms[[what]]
object$call$formula = object$call[[switch(what, alpha = "alpha",
beta = "beta", y = "formula")]]
object$model = object$model[[what]]
object$rank = attr(object$rank, "ranks")[switch(what, alpha = "ranka",
beta = "rankb", y = "ranky")]
object$contrasts = object$contrasts[[what]]
object$xlevels = object$xlevels[[what]]
class(object) = "lm"
object
}
predict.cusp <-
function (object, newdata, se.fit = FALSE, interval = c("none",
"confidence", "prediction"), level = 0.95, type = c("response",
"terms"), terms = NULL, na.action = na.pass, pred.var = res.var/weights,
weights = 1, method = c("delay", "maxwell", "expected"),
keep.linear.predictors = FALSE, ...)
{
if (!all(missing(se.fit), missing(interval), missing(level),
missing(type), missing(terms), missing(pred.var), missing(weights)))
.NotYetUsed(intersect(names(match.call()), c("se.fit",
"interval", "level", "type", "terms", "pred.var",
"weights")), error = TRUE)
interval = match.arg(interval)
if (se.fit || interval != "none") {
res.var <- (1-summary(object)$r2cusp.r.squared[1]) * var(drop(object$y))
}
method = match.arg(method)
if (missing(newdata)) {
alpha = object$linear.predictors[, "alpha"]
beta = object$linear.predictors[, "beta"]
}
else {
alpha = predict(as.lm.cusp(object, "alpha"), newdata,
se.fit = se.fit, interval = interval, level = level,
type = type, terms = terms, na.action = na.action,
pred.var = pred.var, weights = weights, ...)
alpha = if (is.list(alpha))
alpha$fit
else as.matrix(alpha)[, 1]
beta = predict(as.lm.cusp(object, "beta"), newdata, se.fit = se.fit,
interval = interval, level = level, type = type,
terms = terms, na.action = na.action, pred.var = pred.var,
weights = weights, ...)
beta = if (is.list(beta))
beta$fit
else as.matrix(beta)[, 1]
}
if (method == "delay") {
if (missing(newdata)) {
y = drop(object$y)
}
else {
y = predict(as.lm.cusp(object, "y"), newdata, se.fit = se.fit,
interval = interval, level = level, type = type,
terms = terms, na.action = na.action, pred.var = pred.var,
weights = weights, ...)
y = if (is.list(y))
y$fit
else as.matrix(y)[, 1]
}
pred = cusp.expected(alpha, beta, y, method = method)
}
else {
pred = cusp.expected(alpha, beta, method = method)
}
if (keep.linear.predictors && !missing(newdata)) {
if (method == "delay")
attr(pred, "data") = cbind(newdata, alpha = alpha,
beta = beta, y = y)
else attr(pred, "data") = cbind(newdata, alpha = alpha,
beta = beta)
}
pred
}
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