为函数调用提供几个名称

时间:2019-01-27 16:16:17

标签: r lm

这是我先前提出的问题中的一个功能:How to let R predict user input 我想使向xname参数提供几个名称变得更容易,但是我仍然无法为该做些什么。

lmfun<-function(df,yname,xname){
  y<-deparse(substitute(yname))
  x<-deparse(substitute(xname))
  f<-as.formula(paste0(y,"~",x))
  lm.fit<-do.call("lm",list(data=quote(df),f))
  coef(lm.fit)
}

这就是我尝试过的

vals<-names(mtcars)[-1]
lmfun(mtcars,mpg,disp)#This works

我怎样才能最好地完成这项工作?我尝试了其他几种方法,但只显示了这一点:

for(name in 1:seq_along(vals)){
  name<-eval(substitute(name))
  lmfun(mtcars,mpg,name)
}

此操作失败:

  

deparse(substitute(xname))中的错误:     “ arg”应为“ mpg”,“ cyl”,“ disp”,“ hp”,“ drat”,“ wt”,“ qsec”,“ vs”,“ am”,“ gear”,“ carb”之一

也尝试过:

for(name in 1:length(vals)){
  vals<-noquote(vals)
 lmfun(mtcars,mpg,vals[name])
}

如果我能指出整合多线性回归的方法,我也将不胜感激。那就是xname+xname1+xname2 谢谢!

2 个答案:

答案 0 :(得分:1)

像这样使用lmfun调用do.call

lapply(vals, function(val) do.call("lmfun", list(mtcars, quote(mpg), as.name(val))))

这也是可行的,尽管在可能的情况下通常首选不使用eval的代码:

lapply(vals, 
  function(val) eval(substitute(lmfun(mtcars, mpg, val), list(val = as.name(val)))))

答案 1 :(得分:1)

可以在lm()中轻松地完成多个单变量finalfit。它喜欢正确指定的因素:

library(finalfit)
dependent = "mpg"
explanatory = names(mtcars)[-1]
mtcars %>% 
  dplyr::mutate(
    cyl = factor(cyl),
    vs = factor(vs),
    am = factor(am),
    gear = factor(gear)
    ) %>% 
  finalfit(dependent, explanatory)

 Dependent: mpg              Mean (sd)         Coefficient (univariable)    Coefficient (multivariable)
            cyl           4 26.7 (4.5)                                 -                              -
                          6 19.7 (1.5)  -6.92 (-10.11 to -3.73, p<0.001) -1.20 (-6.20 to 3.80, p=0.621)
                          8 15.1 (2.6) -11.56 (-14.22 to -8.91, p<0.001) 3.05 (-7.05 to 13.16, p=0.535)
           disp  [71.1,472] 20.1 (6.0)   -0.04 (-0.05 to -0.03, p<0.001)  0.01 (-0.02 to 0.05, p=0.487)
             hp    [52,335] 20.1 (6.0)   -0.07 (-0.09 to -0.05, p<0.001) -0.06 (-0.12 to 0.01, p=0.088)
           drat [2.76,4.93] 20.1 (6.0)     7.68 (4.60 to 10.76, p<0.001)  0.74 (-3.42 to 4.89, p=0.715)
             wt [1.51,5.42] 20.1 (6.0)   -5.34 (-6.49 to -4.20, p<0.001) -3.55 (-7.54 to 0.45, p=0.079)
           qsec [14.5,22.9] 20.1 (6.0)      1.41 (0.27 to 2.55, p=0.017)  0.77 (-0.81 to 2.34, p=0.320)
             vs           0 16.6 (3.9)                                 -                              -
                          1 24.6 (5.4)     7.94 (4.61 to 11.27, p<0.001)  2.49 (-2.83 to 7.81, p=0.340)
             am           0 17.1 (3.8)                                 -                              -
                          1 24.4 (6.2)     7.24 (3.64 to 10.85, p<0.001)  3.35 (-1.44 to 8.14, p=0.160)
           gear           3 16.1 (3.4)                                 -                              -
                          4 24.5 (5.3)     8.43 (4.70 to 12.16, p<0.001) -1.00 (-7.17 to 5.17, p=0.738)
                          5 21.4 (6.7)     5.27 (0.30 to 10.25, p=0.038)  1.06 (-5.27 to 7.40, p=0.729)
           carb       [1,8] 20.1 (6.0)   -2.06 (-3.22 to -0.89, p=0.001)  0.79 (-1.38 to 2.96, p=0.457)

finalfit.org此处的文档中有很多选项。