如何省略do.call产生的大量代码？

如何解决如何省略do.call产生的大量代码？

我想构建一个函数 additive_glm，它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)

还有我的函数 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))
}

但是现在如果我想运行我的函数：

additive(bin_var,df)

我明白了：

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,wt),aic = function (y,n,wt,dev) 
    {
        m <- if (any(n > 1)) 
            n
        else wt
        -2 * sum(ifelse(m > 0,(wt/m),0) * dbinom(round(m * 
            y),round(m),log = TRUE))
    },mu.eta = function (eta) 
    .Call(C_logit_mu_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,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,prob = ftd)/wts
        }
        else rbinom(ntot,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

所以我确实得到了我想要的东西 - 它前面是巨大的 Call 代码。我正在寻找一些技术来摆脱它，但是我并没有那么成功。你知道如何省略这大部分不必要的代码吗？

解决方法

1) 将家庭参数放在 quote(...) 内。仅更改了标记为 ## 的行。

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) 另一种可能是：

additive_glm2 <- function(y,...){
  glm(y ~ .,data = as.data.frame(x),...)
}
additive_glm2(bin_var,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

,

我不明白您为什么使用 do.call。我会这样做：

additive_glm <- function(y,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 ~ .,#    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

注意打印精美的电话。