在拟合glm modelel（来自统计数据包）时，R中是否有一个包绘制newton-raphson / fisher评分迭代的包？
答案 0 :(得分：0)
我Programming to an interface。然而，在你的情况下，事情有点简单。
请注意，当您调用glm时，它最终调用glm.fit（或您指定给method的任何其他glm参数），该参数计算循环中的解决方案路径使用.Call到C函数C_Cdqrls在第97行计算当前迭代的系数值。作为一个黑客，您可以通过修改fit$coefficients函数，在此循环内将系数的当前值提取到全局环境（glm.fit）：
glm.fit.new = function (x, y, weights = rep(1, nobs), start = NULL, etastart = NULL,
mustart = NULL, offset = rep(0, nobs), family = gaussian(),
control = list(), intercept = TRUE) {
control <- do.call("glm.control", control)
x <- as.matrix(x)
xnames <- dimnames(x)[[2L]]
ynames <- if (is.matrix(y))
rownames(y)
else names(y)
conv <- FALSE
nobs <- NROW(y)
nvars <- ncol(x)
EMPTY <- nvars == 0
if (is.null(weights))
weights <- rep.int(1, nobs)
if (is.null(offset))
offset <- rep.int(0, nobs)
variance <- family$variance
linkinv <- family$linkinv
if (!is.function(variance) || !is.function(linkinv))
stop("'family' argument seems not to be a valid family object",
call. = FALSE)
dev.resids <- family$dev.resids
aic <- family$aic
mu.eta <- family$mu.eta
unless.null <- function(x, if.null) if (is.null(x))
if.null
else x
valideta <- unless.null(family$valideta, function(eta) TRUE)
validmu <- unless.null(family$validmu, function(mu) TRUE)
if (is.null(mustart)) {
eval(family$initialize)
}
else {
mukeep <- mustart
eval(family$initialize)
mustart <- mukeep
}
if (EMPTY) {
eta <- rep.int(0, nobs) + offset
if (!valideta(eta))
stop("invalid linear predictor values in empty model",
call. = FALSE)
mu <- linkinv(eta)
if (!validmu(mu))
stop("invalid fitted means in empty model", call. = FALSE)
dev <- sum(dev.resids(y, mu, weights))
w <- ((weights * mu.eta(eta)^2)/variance(mu))^0.5
residuals <- (y - mu)/mu.eta(eta)
good <- rep_len(TRUE, length(residuals))
boundary <- conv <- TRUE
coef <- numeric()
iter <- 0L
}
else {
coefold <- NULL
eta <- if (!is.null(etastart))
etastart
else if (!is.null(start))
if (length(start) != nvars)
stop(gettextf("length of 'start' should equal %d and correspond to initial coefs for %s",
nvars, paste(deparse(xnames), collapse = ", ")),
domain = NA)
else {
coefold <- start
offset + as.vector(if (NCOL(x) == 1L)
x * start
else x %*% start)
}
else family$linkfun(mustart)
mu <- linkinv(eta)
if (!(validmu(mu) && valideta(eta)))
stop("cannot find valid starting values: please specify some",
call. = FALSE)
devold <- sum(dev.resids(y, mu, weights))
boundary <- conv <- FALSE
# EDIT: counter to create track of iterations
i <<- 1
for (iter in 1L:control$maxit) {
good <- weights > 0
varmu <- variance(mu)[good]
if (anyNA(varmu))
stop("NAs in V(mu)")
if (any(varmu == 0))
stop("0s in V(mu)")
mu.eta.val <- mu.eta(eta)
if (any(is.na(mu.eta.val[good])))
stop("NAs in d(mu)/d(eta)")
good <- (weights > 0) & (mu.eta.val != 0)
if (all(!good)) {
conv <- FALSE
warning(gettextf("no observations informative at iteration %d",
iter), domain = NA)
break
}
z <- (eta - offset)[good] + (y - mu)[good]/mu.eta.val[good]
w <- sqrt((weights[good] * mu.eta.val[good]^2)/variance(mu)[good])
fit <- .Call(stats:::C_Cdqrls, x[good, , drop = FALSE] *
w, z * w, min(1e-07, control$epsilon/1000), check = FALSE)
#======================================================
# EDIT: assign the coefficients to variables in the global namespace
#======================================================
assign(paste0("iteration_x_", i), fit$coefficients,
envir = .GlobalEnv)
i <<- i + 1 # increase the counter
if (any(!is.finite(fit$coefficients))) {
conv <- FALSE
warning(gettextf("non-finite coefficients at iteration %d",
iter), domain = NA)
break
}
if (nobs < fit$rank)
stop(sprintf(ngettext(nobs, "X matrix has rank %d, but only %d observation",
"X matrix has rank %d, but only %d observations"),
fit$rank, nobs), domain = NA)
start[fit$pivot] <- fit$coefficients
eta <- drop(x %*% start)
mu <- linkinv(eta <- eta + offset)
dev <- sum(dev.resids(y, mu, weights))
if (control$trace)
cat("Deviance = ", dev, " Iterations - ", iter,
"\n", sep = "")
boundary <- FALSE
if (!is.finite(dev)) {
if (is.null(coefold))
stop("no valid set of coefficients has been found: please supply starting values",
call. = FALSE)
warning("step size truncated due to divergence",
call. = FALSE)
ii <- 1
while (!is.finite(dev)) {
if (ii > control$maxit)
stop("inner loop 1; cannot correct step size",
call. = FALSE)
ii <- ii + 1
start <- (start + coefold)/2
eta <- drop(x %*% start)
mu <- linkinv(eta <- eta + offset)
dev <- sum(dev.resids(y, mu, weights))
}
boundary <- TRUE
if (control$trace)
cat("Step halved: new deviance = ", dev, "\n",
sep = "")
}
if (!(valideta(eta) && validmu(mu))) {
if (is.null(coefold))
stop("no valid set of coefficients has been found: please supply starting values",
call. = FALSE)
warning("step size truncated: out of bounds",
call. = FALSE)
ii <- 1
while (!(valideta(eta) && validmu(mu))) {
if (ii > control$maxit)
stop("inner loop 2; cannot correct step size",
call. = FALSE)
ii <- ii + 1
start <- (start + coefold)/2
eta <- drop(x %*% start)
mu <- linkinv(eta <- eta + offset)
}
boundary <- TRUE
dev <- sum(dev.resids(y, mu, weights))
if (control$trace)
cat("Step halved: new deviance = ", dev, "\n",
sep = "")
}
if (abs(dev - devold)/(0.1 + abs(dev)) < control$epsilon) {
conv <- TRUE
coef <- start
break
}
else {
devold <- dev
coef <- coefold <- start
}
}
if (!conv)
warning("glm.fit: algorithm did not converge", call. = FALSE)
if (boundary)
warning("glm.fit: algorithm stopped at boundary value",
call. = FALSE)
eps <- 10 * .Machine$double.eps
if (family$family == "binomial") {
if (any(mu > 1 - eps) || any(mu < eps))
warning("glm.fit: fitted probabilities numerically 0 or 1 occurred",
call. = FALSE)
}
if (family$family == "poisson") {
if (any(mu < eps))
warning("glm.fit: fitted rates numerically 0 occurred",
call. = FALSE)
}
if (fit$rank < nvars)
coef[fit$pivot][seq.int(fit$rank + 1, nvars)] <- NA
xxnames <- xnames[fit$pivot]
residuals <- (y - mu)/mu.eta(eta)
fit$qr <- as.matrix(fit$qr)
nr <- min(sum(good), nvars)
if (nr < nvars) {
Rmat <- diag(nvars)
Rmat[1L:nr, 1L:nvars] <- fit$qr[1L:nr, 1L:nvars]
}
else Rmat <- fit$qr[1L:nvars, 1L:nvars]
Rmat <- as.matrix(Rmat)
Rmat[row(Rmat) > col(Rmat)] <- 0
names(coef) <- xnames
colnames(fit$qr) <- xxnames
dimnames(Rmat) <- list(xxnames, xxnames)
}
names(residuals) <- ynames
names(mu) <- ynames
names(eta) <- ynames
wt <- rep.int(0, nobs)
wt[good] <- w^2
names(wt) <- ynames
names(weights) <- ynames
names(y) <- ynames
if (!EMPTY)
names(fit$effects) <- c(xxnames[seq_len(fit$rank)], rep.int("",
sum(good) - fit$rank))
wtdmu <- if (intercept)
sum(weights * y)/sum(weights)
else linkinv(offset)
nulldev <- sum(dev.resids(y, wtdmu, weights))
n.ok <- nobs - sum(weights == 0)
nulldf <- n.ok - as.integer(intercept)
rank <- if (EMPTY)
0
else fit$rank
resdf <- n.ok - rank
aic.model <- aic(y, n, mu, weights, dev) + 2 * rank
list(coefficients = coef, residuals = residuals, fitted.values = mu,
effects = if (!EMPTY) fit$effects, R = if (!EMPTY) Rmat,
rank = rank, qr = if (!EMPTY) structure(fit[c("qr", "rank",
"qraux", "pivot", "tol")], class = "qr"), family = family,
linear.predictors = eta, deviance = dev, aic = aic.model,
null.deviance = nulldev, iter = iter, weights = wt, prior.weights = weights,
df.residual = resdf, df.null = nulldf, y = y, converged = conv,
boundary = boundary)
}
请注意，由于以下几个原因，这是一个黑客攻击：
1.程序C_Cdrqls不会导出stats函数，因此我们必须在namespace:package:stats内查找。{1}}。
2.通过调用glm.fit.new的副作用，使用迭代值污染您的全局环境，每次迭代创建一个向量。副作用通常在像R这样的函数式语言中不受欢迎。您可以通过创建矩阵或data.frame并在其中进行分配来清理多个对象。
但是，一旦提取了迭代值，您就可以随意使用它们，包括绘制它们。
以下是使用新定义的glm方法调用glm.fit.new的方式：
counts = c(18,17,15,20,10,20,25,13,12)
outcome = gl(3,1,9)
treatment = gl(3,3)
print(d.AD = data.frame(treatment, outcome, counts))
glm.D93 = glm(counts ~ outcome + treatment, family = poisson(),
control = list(trace = TRUE, epsilon = 1e-16), method = "glm.fit.new")
您可以检查迭代参数值是否确实填充在全局环境中：
> ls(pattern = "iteration_x_")
[1] "iteration_x_1" "iteration_x_10" "iteration_x_11" "iteration_x_2"
[5] "iteration_x_3" "iteration_x_4" "iteration_x_5" "iteration_x_6"
[9] "iteration_x_7" "iteration_x_8" "iteration_x_9"