r-如何省略do.call产生的巨大代码

Question

我想构建一个函数additive_glm，如果需要，它将允许用户为glm函数指定附加参数。

让我们考虑一下数据：

set.seed(42)
bin_var <- sample(0:1, 125, T)
indep_1 <- rnorm(125)
indep_2 <- rexp(125)
df <- data.frame("Norm" = indep_1, "Exp" = indep_2)

我的函数additive_glm:

additive_glm <- function(y, x, glm_args = NULL){
do.call("glm", c(list(
formula = y ~ ., data = base::quote(as.data.frame(x)),
family = binomial(link = 'logit')
), glm_args))
}

但现在如果我想运行我的功能：

additive(bin_var, df)

我得到：

Call:  glm(formula = y ~ ., family = structure(list(family = "binomial", 
link = "logit", linkfun = function (mu) 
.Call(C_logit_link, mu), linkinv = function (eta) 
.Call(C_logit_linkinv, eta), variance = function (mu) 
mu * (1 - mu), dev.resids = function (y, mu, wt) 
.Call(C_binomial_dev_resids, y, mu, wt), aic = function (y, 
n, mu, wt, dev) 
{
m <- if (any(n > 1)) 
n
else wt
-2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m * 
y), round(m), mu, log = TRUE))
}, mu.eta = function (eta) 
.Call(C_logit_mu_eta, eta), initialize = expression({
if (NCOL(y) == 1) {
if (is.factor(y)) 
y <- y != levels(y)[1L]
n <- rep.int(1, nobs)
y[weights == 0] <- 0
if (any(y < 0 | y > 1)) 
stop("y values must be 0 <= y <= 1")
mustart <- (weights * y + 0.5)/(weights + 1)
m <- weights * y
if (any(abs(m - round(m)) > 0.001)) 
warning("non-integer #successes in a binomial glm!")
}
else if (NCOL(y) == 2) {
if (any(abs(y - round(y)) > 0.001)) 
warning("non-integer counts in a binomial glm!")
n <- y[, 1] + y[, 2]
y <- ifelse(n == 0, 0, y[, 1]/n)
weights <- weights * n
mustart <- (n * y + 0.5)/(n + 1)
}
else stop("for the 'binomial' family, y must be a vector of 0 and 1'snor a 2 column matrix where col 1 is no. successes and col 2 is no. failures")
}), validmu = function (mu) 
all(is.finite(mu)) && all(mu > 0 & mu < 1), valideta = function (eta) 
TRUE, simulate = function (object, nsim) 
{
ftd <- fitted(object)
n <- length(ftd)
ntot <- n * nsim
wts <- object$prior.weights
if (any(wts%%1 != 0)) 
stop("cannot simulate from non-integer prior.weights")
if (!is.null(m <- object$model)) {
y <- model.response(m)
if (is.factor(y)) {
yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd), 
labels = levels(y))
split(yy, rep(seq_len(nsim), each = n))
}
else if (is.matrix(y) && ncol(y) == 2) {
yy <- vector("list", nsim)
for (i in seq_len(nsim)) {
Y <- rbinom(n, size = wts, prob = ftd)
YY <- cbind(Y, wts - Y)
colnames(YY) <- colnames(y)
yy[[i]] <- YY
}
yy
}
else rbinom(ntot, size = wts, prob = ftd)/wts
}
else rbinom(ntot, size = wts, prob = ftd)/wts
}), class = "family"), data = as.data.frame(x))
Coefficients:
(Intercept)         Norm          Exp  
0.2235      -0.2501      -0.2612  
Degrees of Freedom: 124 Total (i.e. Null);  122 Residual
Null Deviance:      173.2 
Residual Deviance: 169.7    AIC: 175.7

所以我真的得到了我想要的东西——它前面有巨大的Call代码。我一直在寻找一些技巧来摆脱它，但我并没有那么成功。你知道如何省略这大量不必要的代码吗？

Answer 1

1(将族参数放入quote(...)中。只有标有##的行被更改。

additive_glm <- function(y, x, glm_args = NULL){
do.call("glm", c(list(
formula = y ~ ., data = base::quote(as.data.frame(x)),
family = quote(binomial(link = 'logit')) ##
), glm_args))
}
additive_glm(bin_var, df)

给予：

Call:  glm(formula = y ~ ., family = binomial(link = "logit"), data = as.data.frame(x))
Coefficients:
(Intercept)         Norm          Exp  
0.32821     -0.06504     -0.05252  
Degrees of Freedom: 124 Total (i.e. Null);  122 Residual
Null Deviance:      171 
Residual Deviance: 170.7        AIC: 176.7

2(另一种可能性是：

additive_glm2 <- function(y, x, ...){
glm(y ~ ., data = as.data.frame(x), family = binomial(link = "logit"), ...)
}
additive_glm2(bin_var, df)

给予：

Call:  glm(formula = y ~ ., family = binomial(link = "logit"), data = as.data.frame(x))
Coefficients:
(Intercept)         Norm          Exp  
0.32821     -0.06504     -0.05252  
Degrees of Freedom: 124 Total (i.e. Null);  122 Residual
Null Deviance:      171 
Residual Deviance: 170.7        AIC: 176.7

Answer 2

我不明白您为什么要使用do.call。我会这样做：

additive_glm <- function(y, x, family = binomial(link = 'logit'), ...){
mc <- match.call()
yname <- mc[["y"]] 
xname <- mc[["x"]]

x[[as.character(yname)]] <- y
assign(as.character(xname), x)

eval(substitute(glm(yname ~ ., data = xname, family = family, ...), env = environment()))
}
additive_glm(bin_var, df)
#Call:  glm(formula = bin_var ~ ., family = binomial(link = "logit"), 
#    data = df)
#
#Coefficients:
#(Intercept)         Norm          Exp  
#    0.32821     -0.06504     -0.05252  
#
#Degrees of Freedom: 124 Total (i.e. Null);  122 Residual
#Null Deviance:     171 
#Residual Deviance: 170.7   AIC: 176.7

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