时间戳

首页 > 解决方案 > 如何省略do.call产生的大量代码？

r - 如何省略do.call产生的大量代码？

问题描述

我想构建一个函数additive_glm，允许用户在需要时为 glm 函数指定附加参数。

让我们考虑数据：

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)

我的功能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))
}

但是现在如果我想运行我的函数：

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's\nor 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，然而——它前面有大量的Call代码。我一直在寻找一些技术来摆脱它，但是我没有那么成功。你知道如何省略这大部分不必要的代码吗？

标签： rlistfunctiondo.call

解决方案

1)将家庭论点放在quote(...) . 只有标记为## 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

推荐阅读