R语言object探索

面对一个陌生的R语言object，如何知道这个object有哪些属性、操作函数呢？

以R自带的mtcars数据集为例，看看lm函数返回的对象是什么东东：

plot(mpg~wt, data = mtcars)

> model <- lm(mpg~wt, data = mtcars)
> model

Call:
lm(formula = mpg ~ wt, data = mtcars)

Coefficients:
(Intercept)           wt  
     37.285       -5.344  

lm返回的拟合线性模型model到底是个什么东东涅？

是什么class

> class(model)
[1] "lm"

是什么mode和type

> mode(model)
[1] "list"
> typeof(model)
[1] "list"

有哪些attributes？

> names(model)
 [1] "coefficients"  "residuals"     "effects"       "rank"         
 [5] "fitted.values" "assign"        "qr"            "df.residual"  
 [9] "xlevels"       "call"          "terms"         "model"        
> attributes(model)
$names
 [1] "coefficients"  "residuals"     "effects"       "rank"         
 [5] "fitted.values" "assign"        "qr"            "df.residual"  
 [9] "xlevels"       "call"          "terms"         "model"        

$class
[1] "lm"


> str(model)
List of 12
 $ coefficients : Named num [1:2] 37.29 -5.34
  ..- attr(*, "names")= chr [1:2] "(Intercept)" "wt"
 $ residuals    : Named num [1:32] -2.28 -0.92 -2.09 1.3 -0.2 ...
  ..- attr(*, "names")= chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
 $ effects      : Named num [1:32] -113.65 -29.116 -1.661 1.631 0.111 ...
  ..- attr(*, "names")= chr [1:32] "(Intercept)" "wt" "" "" ...
 $ rank         : int 2
 $ fitted.values: Named num [1:32] 23.3 21.9 24.9 20.1 18.9 ...
  ..- attr(*, "names")= chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
 $ assign       : int [1:2] 0 1
 $ qr           :List of 5
  ..$ qr   : num [1:32, 1:2] -5.657 0.177 0.177 0.177 0.177 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
  .. .. ..$ : chr [1:2] "(Intercept)" "wt"
  .. ..- attr(*, "assign")= int [1:2] 0 1
  ..$ qraux: num [1:2] 1.18 1.05
  ..$ pivot: int [1:2] 1 2
  ..$ tol  : num 1e-07
  ..$ rank : int 2
  ..- attr(*, "class")= chr "qr"
 $ df.residual  : int 30
 $ xlevels      : Named list()
 $ call         : language lm(formula = mpg ~ wt, data = mtcars)
 $ terms        :Classes 'terms', 'formula'  language mpg ~ wt
  .. ..- attr(*, "variables")= language list(mpg, wt)
  .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:2] "mpg" "wt"
  .. .. .. ..$ : chr "wt"
  .. ..- attr(*, "term.labels")= chr "wt"
  .. ..- attr(*, "order")= int 1
  .. ..- attr(*, "intercept")= int 1
  .. ..- attr(*, "response")= int 1
  .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
  .. ..- attr(*, "predvars")= language list(mpg, wt)
  .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. ..- attr(*, "names")= chr [1:2] "mpg" "wt"
 $ model        :'data.frame':	32 obs. of  2 variables:
  ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
  ..$ wt : num [1:32] 2.62 2.88 2.32 3.21 3.44 ...
  ..- attr(*, "terms")=Classes 'terms', 'formula'  language mpg ~ wt
  .. .. ..- attr(*, "variables")= language list(mpg, wt)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "mpg" "wt"
  .. .. .. .. ..$ : chr "wt"
  .. .. ..- attr(*, "term.labels")= chr "wt"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
  .. .. ..- attr(*, "predvars")= language list(mpg, wt)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "mpg" "wt"
 - attr(*, "class")= chr "lm"

有哪些methods？

知道了model属于哪个class，可以调用methods查看该class定义了哪些generic functions：

> methods(class = "lm")
 [1] add1           alias          anova          case.names    
 [5] coerce         confint        cooks.distance deviance      
 [9] dfbeta         dfbetas        drop1          dummy.coef    
[13] effects        extractAIC     family         formula       
[17] hatvalues      influence      initialize     kappa         
[21] labels         logLik         model.frame    model.matrix  
[25] nobs           plot           predict        print         
[29] proj           qr             residuals      rstandard     
[33] rstudent       show           simulate       slotsFromS3   
[37] summary        variable.names vcov