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glmD <- if(.R.)
function(formula, family = gaussian, data = list(), weights = NULL,
subset = NULL, na.action = na.fail, start = NULL, offset = NULL,
control = glm.control(...), model = TRUE, method = "glm.fit",
x = FALSE, y = TRUE, contrasts = NULL, ...)
{
call <- match.call()
if (is.character(family))
family <- get(family)
if (is.function(family))
family <- family()
if (is.null(family$family)) {
print(family)
stop("`family' not recognized")
}
mt <- terms(formula, data = data)
if (missing(data))
data <- environment(formula)
mf <- match.call(expand.dots = FALSE)
mf$family <- mf$start <- mf$control <- mf$maxit <- NULL
mf$model <- mf$method <- mf$x <- mf$y <- mf$contrasts <- NULL
mf$... <- NULL
mf$drop.unused.levels <- TRUE # FEH 31jul02
mf[[1]] <- as.name("model.frame")
dul <- .Options$drop.unused.levels # FEH 31jul02
if(!length(dul) || dul) {
on.exit(options(drop.unused.levels=dul))
options(drop.unused.levels=FALSE)
}
mf <- Design(eval(mf, parent.frame())) # FEH 13Apr01
desatr <- attr(mf,'Design')
attr(mf,'Design') <- NULL
switch(method, model.frame = return(mf), glm.fit = 1, glm.fit.null = 1,
stop(paste("invalid `method':", method)))
xvars <- as.character(attr(mt, "variables"))[-1]
if ((yvar <- attr(mt, "response")) > 0)
xvars <- xvars[-yvar]
xlev <- if (length(xvars) > 0) {
xlev <- lapply(mf[xvars], levels)
xlev[!sapply(xlev, is.null)]
}
X <- if (!is.empty.model(mt))
model.matrix(mt, mf, contrasts)
Y <- model.response(mf, "numeric")
weights <- model.weights(mf)
offset <- model.offset(mf)
if (!is.null(weights) && any(weights < 0))
stop("Negative wts not allowed")
if (!is.null(offset) && length(offset) != NROW(Y))
stop(paste("Number of offsets is", length(offset), ", should equal",
NROW(Y), "(number of observations)"))
fit <- (if (is.empty.model(mt))
glm.fit.null
else glm.fit)(x = X, y = Y, weights = weights, start = start,
offset = offset, family = family, control = control,
intercept = attr(mt, "intercept") > 0)
if (any(offset) && attr(mt, "intercept") > 0) {
fit$null.deviance <- if (is.empty.model(mt))
fit$deviance
else glm.fit(x = X[, "(Intercept)", drop = FALSE], y = Y,
weights = weights, start = start, offset = offset,
family = family, control = control, intercept = TRUE)$deviance
}
if (model)
fit$model <- mf
if (x)
fit$x <- X
if (!y)
fit$y <- NULL
fit <- c(fit, list(call = call, formula = formula, terms = mt,
data = data, offset = offset, control = control, method = method,
contrasts = attr(X, "contrasts"), xlevels = xlev,
Design=desatr,
assign=DesignAssign(desatr,1,mt)))
##FEH 13Apr01 24nov02 above
class(fit) <- c("Design", 'glmD',
if (is.empty.model(mt)) "glm.null", "glm",
"lm") # FEH 13Apr01 glmD 26nov02
fit
} else function(formula = formula(data), family = gaussian,
data = sys.parent(),
weights, subset, na.action, start = eta, control = glm.control(...),
method = "glm.fit", model = FALSE, x = FALSE, y = TRUE, contrasts = NULL, ...)
{
call <- match.call()
m <- match.call(expand = FALSE)
m$family <- m$method <- m$model <- m$x <- m$y <- m$control <- m$
contrasts <- m$... <- NULL
m$drop.unused.levels <- TRUE
m[[1]] <- as.name("model.frame")
m <- Design(eval(m, sys.parent())) # FEH 13Apr01
desatr <- attr(m,'Design')
attr(m,'Design') <- NULL
Terms <- attr(m, "terms")
if(method == "model.frame")
return(m)
xvars <- as.character(attr(Terms, "variables"))
if(length(xvars) > 0) {
xlevels <- lapply(m[xvars], levels)
xlevels <- xlevels[!sapply(xlevels, is.null)]
if(length(xlevels) == 0)
xlevels <- NULL
}
else xlevels <- NULL
a <- attributes(m)
Y <- model.extract(m, response)
X <- model.matrix(Terms, m, contrasts)
w <- model.extract(m, weights)
if(!length(w))
w <- rep(1, nrow(m))
else if(any(w < 0))
stop("negative weights not allowed")
start <- model.extract(m, start)
offset <- model.extract(m, offset)
family <- as.family(family)
if(missing(method))
method <- attr(family, "method")
if(!is.null(method)) {
if(!existsFunction(method))
stop(paste("unimplemented method:", method))
}
else method <- "glm.fit"
glm.fitter <- get(method)
fit <- glm.fitter(x = X, y = Y, w = w, start = start, offset = offset,
family = family, maxit = control$maxit, epsilon = control$
epsilon, trace = control$trace, null.dev = TRUE, ...)
#
# If an offset and intercept is present, iterations are needed to
# compute the Null deviance; these are done here, unless the model
# is NULL, in which case the computations have been done already
#
if(any(offset) && attr(Terms, "intercept")) {
null.deviance <- if(length(Terms)) glm.fitter(X[, "(Intercept)", drop = FALSE], Y, w, offset = offset, family =
family, maxit = control$maxit, epsilon =
control$epsilon, null.dev = NULL)$deviance
else fit$deviance
fit$null.deviance <- null.deviance
}
oldClass(fit) <- if(.SV4.) 'Design' else
c("Design","glmD","glm","lm") # FEH 13Apr01 16aug02
## glmD 2dec02 8p
if(!is.null(xlevels))
attr(fit, "xlevels") <- xlevels
fit$terms <- Terms
fit$formula <- as.vector(attr(Terms, "formula"))
fit$call <- call
fit$Design <- desatr # FEH 13Apr01
fit$assign <- DesignAssign(desatr,1,Terms) ## 24nov02
if(model)
fit$model <- m
if(x)
fit$x <- X
if(!y)
fit$y <- NULL
fit$control <- control
if(!is.null(attr(m, "na.action")))
fit$na.action <- attr(m, "na.action")
fit$fitFunction <- c('glmD','glm','lm') ## glmD 26nov02
fit
}
## 26nov02
print.glmD <- function(x, digits=4, ...) {
cat('General Linear Model\n\n')
dput(x$call); cat('\n\n')
cof <- coef(x)
lr <- x$null.deviance - x$deviance
names(cof) <- ifelse(names(cof)=='(Intercept)','Intercept',names(cof))
dof <- x$rank - (names(cof)[1]=='Intercept')
pval <- 1 - pchisq(lr, dof)
print(c('Model L.R.'=format(lr,digits=2), 'd.f.'=format(dof),
'P'=format(pval,digits=4)), quote=FALSE)
cat('\n')
se <- sqrt(diag(Varcov(x)))
z <- cof/se
p <- 1 - pchisq(z^2, 1)
w <- cbind(format(cof, digits=digits),
format(se, digits=digits),
format(z, digits=2),
format(p, digits=4))
dimnames(w) <- list(names(cof), c('Coef','S.E.','Wald Z','P'))
print(w, quote=FALSE)
invisible()
}
## 26nov02
summary.glmD <- function(...) summary.Design(...)
## 2dec02
Varcov.glmD <- function(object, regcoef.only=FALSE, ...)
Varcov.glm(object, regcoef.only, ...)
## 6dec02
predict.glmD <-
function(object, newdata,
type=c("lp","x","data.frame","terms","adjto","adjto.data.frame",
"model.frame"),
se.fit=FALSE, conf.int=FALSE, conf.type=c('mean','individual'),
incl.non.slopes, non.slopes, kint=1,
na.action=na.keep, expand.na=TRUE, center.terms=TRUE, ...)
predictDesign(object, newdata, type, se.fit, conf.int, conf.type,
incl.non.slopes, non.slopes, kint,
na.action, expand.na, center.terms, ...)
latex.glmD <- function(...) latexDesign(...)
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