我正在尝试使用 dglm 包在 R 中安装双 glm。这与 statmod 包结合使用以使用 tweedie 模型。问题的再现是：
library(dglm)
library(statmod)
p <- 1.5
y <- runif(10)
x <- runif(10)
dglm(y~x,~x,family=tweedie(link.power=0, var.power=p))
#doesnt work
dglm(y~x,~x,family=tweedie(link.power=0, var.power=1.5))
#works
var.power 需要在变量中定义，因为我想使用一个循环，其中 dglm 在它的每个条目上运行
因此，您可以通过强制dglm评估您输入的调用来解决问题p。在dglm函数中，大约在第 73 行：
if (family$family == "Tweedie") {
tweedie.p <- call$family$var.power
}
应该：
if (family$family == "Tweedie") {
tweedie.p <- eval(call$family$var.power)
}
您可以使用这样的补丁创建自己的功能：
dglm.nograpes <- function (formula = formula(data), dformula = ~1, family = gaussian,
dlink = "log", data = sys.parent(), subset = NULL, weights = NULL,
contrasts = NULL, method = "ml", mustart = NULL, betastart = NULL,
etastart = NULL, phistart = NULL, control = dglm.control(...),
ykeep = TRUE, xkeep = FALSE, zkeep = FALSE, ...)
{
call <- match.call()
if (is.character(family))
family <- get(family, mode = "function", envir = parent.frame())
if (is.function(family))
family <- family()
if (is.null(family$family)) {
print(family)
stop("'family' not recognized")
}
mnames <- c("", "formula", "data", "weights", "subset")
cnames <- names(call)
cnames <- cnames[match(mnames, cnames, 0)]
mcall <- call[cnames]
mcall[[1]] <- as.name("model.frame")
mframe <<- eval(mcall, sys.parent())
mf <- match.call(expand.dots = FALSE)
y <- model.response(mframe, "numeric")
if (is.null(dim(y))) {
N <- length(y)
}
else {
N <- dim(y)[1]
}
nobs <- N
mterms <- attr(mframe, "terms")
X <- model.matrix(mterms, mframe, contrasts)
weights <- model.weights(mframe)
if (is.null(weights))
weights <- rep(1, N)
if (is.null(weights))
weights <- rep(1, N)
if (!is.null(weights) && any(weights < 0)) {
stop("negative weights not allowed")
}
offset <- model.offset(mframe)
if (is.null(offset))
offset <- rep(0, N)
if (!is.null(offset) && length(offset) != NROW(y)) {
stop(gettextf("number of offsets is %d should equal %d (number of observations)",
length(offset), NROW(y)), domain = NA)
}
mcall$formula <- formula
mcall$formula[3] <- switch(match(length(dformula), c(0, 2,
3)), 1, dformula[2], dformula[3])
mframe <- eval(mcall, sys.parent())
dterms <- attr(mframe, "terms")
Z <- model.matrix(dterms, mframe, contrasts)
doffset <- model.extract(mframe, offset)
if (is.null(doffset))
doffset <- rep(0, N)
name.dlink <- substitute(dlink)
if (is.name(name.dlink)) {
if (is.character(dlink)) {
name.dlink <- dlink
}
else {
dlink <- name.dlink <- as.character(name.dlink)
}
}
else {
if (is.call(name.dlink))
name.dlink <- deparse(name.dlink)
}
if (!is.null(name.dlink))
name.dlink <- name.dlink
if (family$family == "Tweedie") {
tweedie.p <- eval(call$family$var.power)
}
Digamma <- family$family == "Gamma" || (family$family ==
"Tweedie" && tweedie.p == 2)
if (Digamma) {
linkinv <- make.link(name.dlink)$linkinv
linkfun <- make.link(name.dlink)$linkfun
mu.eta <- make.link(name.dlink)$mu.eta
valid.eta <- make.link(name.dlink)$valid.eta
init <- expression({
if (any(y <= 0)) {
print(y)
print(any(y <= 0))
stop("non-positive values not allowed for the DM gamma family")
}
n <- rep.int(1, nobs)
mustart <- y
})
dfamily <- structure(list(family = "Digamma", variance = varfun.digamma,
dev.resids = function(y, mu, wt) {
wt * unitdeviance.digamma(y, mu)
}, aic = function(y, n, mu, wt, dev) NA, link = name.dlink,
linkfun = linkfun, linkinv = linkinv, mu.eta = mu.eta,
initialize = init, validmu = function(mu) {
all(mu > 0)
}, valideta = valid.eta))
}
else {
eval(substitute(dfamily <- Gamma(link = lk), list(lk = name.dlink)))
}
dlink <- as.character(dfamily$link)
logdlink <- dlink == "log"
if (!is.null(call$method)) {
name.method <- substitute(method)
if (!is.character(name.method))
name.method <- deparse(name.method)
list.methods <- c("ml", "reml", "ML", "REML", "Ml", "Reml")
i.method <- pmatch(method, list.methods, nomatch = 0)
if (!i.method)
stop("Method must be ml or reml")
method <- switch(i.method, "ml", "reml", "ml", "reml",
"ml", "reml")
}
reml <- method == "reml"
if (is.null(mustart)) {
etastart <- NULL
eval(family$initialize)
mu <- mustart
mustart <- NULL
}
if (!is.null(betastart)) {
eta <- X %*% betastart
mu <- family$linkinv(eta + offset)
}
else {
if (!is.null(mustart)) {
mu <- mustart
eta <- family$linkfun(mu) - offset
}
else {
eta <- lm.fit(X, family$linkfun(mu) - offset, singular.ok = TRUE)$fitted.values
mu <- family$linkinv(eta + offset)
}
}
d <- family$dev.resids(y, mu, weights)
if (!is.null(phistart)) {
phi <- phistart
deta <- dfamily$linkfun(phi) - doffset
}
else {
deta <- lm.fit(Z, dfamily$linkfun(d + (d == 0)/6) - doffset,
singular.ok = TRUE)$fitted.values
if (logdlink)
deta <- deta + 1.27036
phi <- dfamily$linkinv(deta + offset)
}
if (any(phi <= 0)) {
cat("Some values for phi are non-positive, suggesting an inappropriate model",
"Try a different link function.\n")
}
zm <- as.vector(eta + (y - mu)/family$mu.eta(eta))
wm <- as.vector(eval(family$variance(mu)) * weights/phi)
mfit <- lm.wfit(X, zm, wm, method = "qr", singular.ok = TRUE)
eta <- mfit$fitted.values
mu <- family$linkinv(eta + offset)
cat("family:", family$family, "\n")
if (family$family == "Tweedie") {
cat("p:", tweedie.p, "\n")
if ((tweedie.p > 0) & (any(mu < 0))) {
cat("Some values for mu are negative, suggesting an inappropriate model.",
"Try a different link function.\n")
}
}
d <- family$dev.resids(y, mu, weights)
const <- dglm.constant(y, family, weights)
if (Digamma) {
h <- 2 * (lgamma(weights/phi) + (1 + log(phi/weights)) *
weights/phi)
}
else {
h <- log(phi/weights)
}
m2loglik <- const + sum(h + d/phi)
if (reml)
m2loglik <- m2loglik + 2 * log(abs(prod(diag(mfit$R))))
m2loglikold <- m2loglik + 1
epsilon <- control$epsilon
maxit <- control$maxit
trace <- control$trace
iter <- 0
while (abs(m2loglikold - m2loglik)/(abs(m2loglikold) + 1) >
epsilon && iter < maxit) {
hdot <- 1/dfamily$mu.eta(deta)
if (Digamma) {
delta <- 2 * weights * (log(weights/phi) - digamma(weights/phi))
u <- 2 * weights^2 * (trigamma(weights/phi) - phi/weights)
fdot <- phi^2/u * hdot
}
else {
delta <- phi
u <- phi^2
fdot <- hdot
}
wd <- 1/(fdot^2 * u)
if (reml) {
h <- hat(mfit$qr)
delta <- delta - phi * h
wd <- wd - 2 * (h/hdot^2/phi^2) + h^2
}
if (any(wd < 0)) {
cat(" Some weights are negative; temporarily fixing. This may be a sign of an inappropriate model.\n")
wd[wd < 0] <- 0
}
if (any(is.infinite(wd))) {
cat(" Some weights are negative; temporarily fixing. This may be a sign of an inappropriate model.\n")
wd[is.infinite(wd)] <- 100
}
zd <- deta + (d - delta) * fdot
dfit <- lm.wfit(Z, zd, wd, method = "qr", singular.ok = TRUE)
deta <- dfit$fitted.values
phi <- dfamily$linkinv(deta + doffset)
if (any(is.infinite(phi))) {
cat("*** Some values for phi are infinite, suggesting an inappropriate model",
"Try a different link function. Making an attempt to continue...\n")
phi[is.infinite(phi)] <- 10
}
zm <- eta + (y - mu)/family$mu.eta(eta)
fam.wt <- expression(weights * family$variance(mu))
wm <- eval(fam.wt)/phi
mfit <- lm.wfit(X, zm, wm, method = "qr", singular.ok = TRUE)
eta <- mfit$fitted.values
mu <- family$linkinv(eta + offset)
if (family$family == "Tweedie") {
if ((tweedie.p > 0) & (any(mu < 0))) {
cat("*** Some values for mu are negative, suggesting an inappropriate model.",
"Try a different link function. Making an attempt to continue...\n")
mu[mu <= 0] <- 1
}
}
d <- family$dev.resids(y, mu, weights)
m2loglikold <- m2loglik
if (Digamma) {
h <- 2 * (lgamma(weights/phi) + (1 + log(phi/weights)) *
weights/phi)
}
else {
h <- log(phi/weights)
}
m2loglik <- const + sum(h + d/phi)
if (reml) {
m2loglik <- m2loglik + 2 * log(abs(prod(diag(mfit$R))))
}
iter <- iter + 1
if (trace)
cat("DGLM iteration ", iter, ": -2*log-likelihood = ",
format(round(m2loglik, 4)), " \n", sep = "")
}
mfit$formula <- call$formula
mfit$call <- call
mfit$family <- family
mfit$linear.predictors <- mfit$fitted.values + offset
mfit$fitted.values <- mu
mfit$prior.weights <- weights
mfit$terms <- mterms
mfit$contrasts <- attr(X, "contrasts")
intercept <- attr(mterms, "intercept")
mfit$df.null <- N - sum(weights == 0) - as.integer(intercept)
mfit$call <- call
mfit$deviance <- sum(d/phi)
mfit$aic <- NA
mfit$null.deviance <- glm.fit(x = X, y = y, weights = weights/phi,
offset = offset, family = family)
if (length(mfit$null.deviance) > 1)
mfit$null.deviance <- mfit$null.deviance$null.deviance
if (ykeep)
mfit$y <- y
if (xkeep)
mfit$x <- X
class(mfit) <- c("glm", "lm")
dfit$family <- dfamily
dfit$prior.weights <- rep(1, N)
dfit$linear.predictors <- dfit$fitted.values + doffset
dfit$fitted.values <- phi
dfit$terms <- dterms
dfit$aic <- NA
call$formula <- call$dformula
call$dformula <- NULL
call$family <- call(dfamily$family, link = name.dlink)
dfit$call <- call
dfit$residuals <- dfamily$dev.resid(d, phi, wt = rep(1/2,
N))
dfit$deviance <- sum(dfit$residuals)
dfit$null.deviance <- glm.fit(x = Z, y = d, weights = rep(1/2,
N), offset = doffset, family = dfamily)
if (length(dfit$null.deviance) > 1)
dfit$null.deviance <- dfit$null.deviance$null.deviance
if (ykeep)
dfit$y <- d
if (zkeep)
dfit$z <- Z
dfit$formula <- as.vector(attr(dterms, "formula"))
dfit$iter <- iter
class(dfit) <- c("glm", "lm")
out <- c(mfit, list(dispersion.fit = dfit, iter = iter, method = method,
m2loglik = m2loglik))
class(out) <- c("dglm", "glm", "lm")
out
}
然后像这样运行它：
dglm.nograpes(y~x,~x,family=tweedie(link.power=0, var.power=p))