str
関数を使うとオブジェクトの中身が見えるので、そこから情報を取り出していろいろ利用することができます。
※ちなみにstr
ってStructureの略です。Stringの略ではないんですね~
sample.R
model <- lm(Sepal.Length ~ . -Species, iris)
model
str(model)
attr(model$terms, "factors")
row <- rownames(attr(model$terms, "factors"))
col <- colnames(attr(model$terms, "factors"))
coefs <- model$coefficients
coefs[names(coefs) != "(Intercept)"]
formula(paste("Sepal.Length ~ ", paste(col, collapse="+")))
as.formula(model)
実行結果は以下のとおり
> model <- lm(Sepal.Length ~ . -Species, iris)
> model
Call:
lm(formula = Sepal.Length ~ . - Species, data = iris)
Coefficients:
(Intercept) Sepal.Width Petal.Length Petal.Width
1.8560 0.6508 0.7091 -0.5565
モデルはSepal.Lengthを目的変数に、Speciesの項を除いた他すべてを説明変数にしています。
モデル式の中でピリオド(ドット)を指定すると「他すべて」を指定できるのは便利ですね~
> str(model)
List of 13
$ coefficients : Named num [1:4] 1.856 0.651 0.709 -0.556
..- attr(*, "names")= chr [1:4] "(Intercept)" "Sepal.Width" "Petal.Length" "Petal.Width"
$ residuals : Named num [1:150] 0.0846 0.21 -0.0493 -0.226 -0.0805 ...
..- attr(*, "names")= chr [1:150] "1" "2" "3" "4" ...
$ effects : Named num [1:150] -71.5659 -1.1884 9.1884 -1.3724 -0.0599 ...
..- attr(*, "names")= chr [1:150] "(Intercept)" "Sepal.Width" "Petal.Length" "Petal.Width" ...
$ rank : int 4
$ fitted.values: Named num [1:150] 5.02 4.69 4.75 4.83 5.08 ...
..- attr(*, "names")= chr [1:150] "1" "2" "3" "4" ...
$ assign : int [1:4] 0 1 2 3
$ qr :List of 5
..$ qr : num [1:150, 1:4] -12.2474 0.0816 0.0816 0.0816 0.0816 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:150] "1" "2" "3" "4" ...
.. .. ..$ : chr [1:4] "(Intercept)" "Sepal.Width" "Petal.Length" "Petal.Width"
.. ..- attr(*, "assign")= int [1:4] 0 1 2 3
.. ..- attr(*, "contrasts")=List of 1
.. .. ..$ Species: chr "contr.treatment"
..$ qraux: num [1:4] 1.08 1.02 1.11 1.02
..$ pivot: int [1:4] 1 2 3 4
..$ tol : num 1e-07
..$ rank : int 4
..- attr(*, "class")= chr "qr"
$ df.residual : int 146
$ contrasts :List of 1
..$ Species: chr "contr.treatment"
$ xlevels :List of 1
..$ Species: chr [1:3] "setosa" "versicolor" "virginica"
$ call : language lm(formula = Sepal.Length ~ . - Species, data = iris)
$ terms :Classes 'terms', 'formula' length 3 Sepal.Length ~ (Sepal.Width + Petal.Length + Petal.Width + Species) - Species
.. ..- attr(*, "variables")= language list(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, Species)
.. ..- attr(*, "factors")= int [1:5, 1:3] 0 1 0 0 0 0 0 1 0 0 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:5] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" ...
.. .. .. ..$ : chr [1:3] "Sepal.Width" "Petal.Length" "Petal.Width"
.. ..- attr(*, "term.labels")= chr [1:3] "Sepal.Width" "Petal.Length" "Petal.Width"
.. ..- attr(*, "order")= int [1:3] 1 1 1
.. ..- attr(*, "intercept")= int 1
.. ..- attr(*, "response")= int 1
.. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
.. ..- attr(*, "predvars")= language list(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, Species)
.. ..- attr(*, "dataClasses")= Named chr [1:5] "numeric" "numeric" "numeric" "numeric" ...
.. .. ..- attr(*, "names")= chr [1:5] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" ...
$ model :'data.frame': 150 obs. of 5 variables:
..$ Sepal.Length: num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
..$ Sepal.Width : num [1:150] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
..$ Petal.Length: num [1:150] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
..$ Petal.Width : num [1:150] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
..$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
..- attr(*, "terms")=Classes 'terms', 'formula' length 3 Sepal.Length ~ (Sepal.Width + Petal.Length + Petal.Width + Species) - Species
.. .. ..- attr(*, "variables")= language list(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, Species)
.. .. ..- attr(*, "factors")= int [1:5, 1:3] 0 1 0 0 0 0 0 1 0 0 ...
.. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. ..$ : chr [1:5] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" ...
.. .. .. .. ..$ : chr [1:3] "Sepal.Width" "Petal.Length" "Petal.Width"
.. .. ..- attr(*, "term.labels")= chr [1:3] "Sepal.Width" "Petal.Length" "Petal.Width"
.. .. ..- attr(*, "order")= int [1:3] 1 1 1
.. .. ..- attr(*, "intercept")= int 1
.. .. ..- attr(*, "response")= int 1
.. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
.. .. ..- attr(*, "predvars")= language list(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, Species)
.. .. ..- attr(*, "dataClasses")= Named chr [1:5] "numeric" "numeric" "numeric" "numeric" ...
.. .. .. ..- attr(*, "names")= chr [1:5] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" ...
- attr(*, "class")= chr "lm"
>
str
で見てみると、termsやcoefficientsあたりからモデル式の項の名前を取り出せそうです。
> attr(model$terms, "factors")
Sepal.Width Petal.Length Petal.Width
Sepal.Length 0 0 0
Sepal.Width 1 0 0
Petal.Length 0 1 0
Petal.Width 0 0 1
Species 0 0 0
> row <- rownames(attr(model$terms, "factors"))
> col <- colnames(attr(model$terms, "factors"))
>
> coefs <- model$coefficients
> coefs[names(coefs) != "(Intercept)"]
Sepal.Width Petal.Length Petal.Width
0.6508372 0.7091320 -0.5564827
>
こんな感じで名前をとりだして、上記のcolを使ってモデル式を作ってみると
> formula(paste("Sepal.Length ~ ", paste(col, collapse="+")))
Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width
このとおり、formula
関数を使って文字列からモデル式を構築することができました。
> as.formula(model)
Sepal.Length ~ (Sepal.Width + Petal.Length + Petal.Width + Species) -
Species
>
ちなみに単にモデル式を取り出したい場合、as.formula
関数を使うこともできます。
こっちでは削除した項の情報も入ってるんですね~