Question

我有一个data.frame（x），我正在进行GAM分析。

x = data[, c("asthma3", "TargetGroup2012", "age3", "SmokingGroup_Kai", "bmi3", "PA_Score")]
x = na.omit(x)
x = data.frame(scale(x))
g = gam(formula=asthma3~s(age3)+s(bmi3)+SmokingGroup_Kai+PA_Score+TargetGroup2012, data=x)
plot(g, se=T)

生成的图具有函数s和SE。我想在data.frame中保存每个变量的s值及其SE值。

使用p = plot(g, se=T) and str(p)我可以看到值S（x）和SE，但我不知道如何访问它们。

> p = plot(g, se=T)
> p
Call:
gam(formula = asthma3 ~ s(age3) + s(bmi3) + SmokingGroup_Kai + 
    PA_Score + TargetGroup2012, data = x)

Degrees of Freedom: 3185 total; 3174 Residual
Residual Deviance: 258.4571 
> str(p)
List of 35
 $ smooth.frame       :'data.frame':    3186 obs. of  2 variables:
  ..$ s(age3):Class 'smooth'  atomic [1:3186] 55 49 58 51 52 62 62 54 49 54 ...
  .. .. ..- attr(*, "spar")= num 1
  .. .. ..- attr(*, "df")= num 4
  .. .. ..- attr(*, "call")= language gam.s(data[["s(age3)"]], z, w, spar = 1, df = 4)
  ..$ s(bmi3):Class 'smooth'  atomic [1:3186] 20.9 20.2 30.5 34.1 23.2 ...
  .. .. ..- attr(*, "spar")= num 1
  .. .. ..- attr(*, "df")= num 4
  .. .. ..- attr(*, "call")= language gam.s(data[["s(bmi3)"]], z, w, spar = 1, df = 4)
 $ coefficients       : Named num [1:6] -0.051222 -0.001759 0.005935 0.010658 0.000831 ...
  ..- attr(*, "names")= chr [1:6] "(Intercept)" "s(age3)" "s(bmi3)" "SmokingGroup_Kai" ...
 $ residuals          : Named num [1:3186] -0.0705 -0.0954 -0.1249 -0.171 -0.1079 ...
  ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
 $ fitted.values      : Named num [1:3186] 0.0705 0.0954 0.1249 0.171 0.1079 ...
  ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
 $ effects            : Named num [1:3186] -5.13778 0.00287 -1.58832 -0.97786 -0.04935 ...
  ..- attr(*, "names")= chr [1:3186] "(Intercept)" "s(age3)" "s(bmi3)" "SmokingGroup_Kai" ...
 $ weights            : Named num [1:3186] 1 1 1 1 1 1 1 1 1 1 ...
  ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
 $ rank               : int 6
 $ assign             : int [1:6] 0 1 2 3 4 5
 $ qr                 :List of 5
  ..$ qr   : num [1:3186, 1:6] -56.4447 0.0177 0.0177 0.0177 0.0177 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:3186] "1" "2" "3" "4" ...
  .. .. ..$ : chr [1:6] "(Intercept)" "s(age3)" "s(bmi3)" "SmokingGroup_Kai" ...
  .. ..- attr(*, "assign")= int [1:6] 0 1 2 3 4 5
  ..$ qraux: num [1:6] 1.02 1.02 1.02 1.02 1.02 ...
  ..$ rank : int 6
  ..$ pivot: int [1:6] 1 2 3 4 5 6
  ..$ tol  : num 1e-07
  ..- attr(*, "class")= chr "qr"
 $ smooth             : num [1:3186, 1:2] -0.00618 0.00825 -0.00368 0.0094 0.00506 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:3186] "1" "2" "3" "4" ...
  .. ..$ : chr [1:2] "s(age3)" "s(bmi3)"
 $ nl.df              : Named num [1:2] 3 3
  ..- attr(*, "names")= chr [1:2] "s(age3)" "s(bmi3)"
 $ df.residual        : num 3174
 $ var                : num [1:3186, 1:2] 0.000846 0.000722 0.000766 0.000798 0.00082 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:3186] "1" "2" "3" "4" ...
  .. ..$ : chr [1:2] "s(age3)" "s(bmi3)"
 $ additive.predictors: Named num [1:3186] 0.0705 0.0954 0.1249 0.171 0.1079 ...
  ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
 $ R                  : num [1:6, 1:6] -56.4 0 0 0 0 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:6] "(Intercept)" "s(age3)" "s(bmi3)" "SmokingGroup_Kai" ...
  .. ..$ : chr [1:6] "(Intercept)" "s(age3)" "s(bmi3)" "SmokingGroup_Kai" ...
 $ rank               : int 6
 $ family             :List of 11
  ..$ family    : chr "gaussian"
  ..$ link      : chr "identity"
  ..$ linkfun   :function (mu)  
  ..$ linkinv   :function (eta)  
  ..$ variance  :function (mu)  
  ..$ dev.resids:function (y, mu, wt)  
  ..$ aic       :function (y, n, mu, wt, dev)  
  ..$ mu.eta    :function (eta)  
  ..$ initialize:  expression({     n <- rep.int(1, nobs)     if (is.null(etastart) && is.null(start) && is.null(mustart) && ((family$link == "inverse" &&          any(y == 0)) || (family$link == "log" && any(y <= 0))))          stop("cannot find valid starting values: please specify some")     mustart <- y })
  ..$ validmu   :function (mu)  
  ..$ valideta  :function (eta)  
  ..- attr(*, "class")= chr "family"
 $ deviance           : num 258
 $ aic                : num 1065
 $ null.deviance      : num 264
 $ iter               : int 2
 $ prior.weights      : num [1:3186] 1 1 1 1 1 1 1 1 1 1 ...
 $ y                  : Named num [1:3186] 0 0 0 0 0 0 0 0 0 0 ...
  ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
 $ df.null            : int 3185
 $ nl.chisq           : Named num [1:2] 0.399 0.691
  ..- attr(*, "names")= chr [1:2] "s(age3)" "s(bmi3)"
 $ call               : language gam(formula = asthma3 ~ s(age3) + s(bmi3) + SmokingGroup_Kai + PA_Score + TargetGroup2012, data = x)
 $ formula            :Class 'formula' length 3 asthma3 ~ s(age3) + s(bmi3) + SmokingGroup_Kai + PA_Score + TargetGroup2012
  .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
 $ terms              :Classes 'terms', 'formula' length 3 asthma3 ~ s(age3) + s(bmi3) + SmokingGroup_Kai + PA_Score + TargetGroup2012
  .. ..- attr(*, "variables")= language list(asthma3, s(age3), s(bmi3), SmokingGroup_Kai, PA_Score, TargetGroup2012)
  .. ..- attr(*, "factors")= int [1:6, 1:5] 0 1 0 0 0 0 0 0 1 0 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:6] "asthma3" "s(age3)" "s(bmi3)" "SmokingGroup_Kai" ...
  .. .. .. ..$ : chr [1:5] "s(age3)" "s(bmi3)" "SmokingGroup_Kai" "PA_Score" ...
  .. ..- attr(*, "term.labels")= chr [1:5] "s(age3)" "s(bmi3)" "SmokingGroup_Kai" "PA_Score" ...
  .. ..- attr(*, "specials")=Dotted pair list of 3
  .. .. ..$ s     : int [1:2] 2 3
  .. .. ..$ lo    : NULL
  .. .. ..$ random: NULL
  .. ..- attr(*, "order")= int [1:5] 1 1 1 1 1
  .. ..- attr(*, "intercept")= int 1
  .. ..- attr(*, "response")= int 1
  .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
 $ data               :'data.frame':    3186 obs. of  6 variables:
  ..$ asthma3         : num [1:3186] 0 0 0 0 0 0 0 0 0 0 ...
  ..$ TargetGroup2012 : num [1:3186] 2 2 2 2 3 3 3 2 2 3 ...
  ..$ age3            : num [1:3186] 55 49 58 51 52 62 62 54 49 54 ...
  ..$ SmokingGroup_Kai: num [1:3186] 4 4 5 4 3 3 3 2 1 3 ...
  ..$ bmi3            : num [1:3186] 20.9 20.2 30.5 34.1 23.2 ...
  ..$ PA_Score        : num [1:3186] 2 3 3 2 3 2 2 3 3 2 ...
  ..- attr(*, "na.action")=Class 'omit'  Named int [1:77] 28 74 75 151 179 185 242 267 287 297 ...
  .. .. ..- attr(*, "names")= chr [1:77] "28" "74" "75" "151" ...
 $ offset             : NULL
 $ control            :List of 5
  ..$ epsilon   : num 1e-07
  ..$ maxit     : num 30
  ..$ bf.epsilon: num 1e-07
  ..$ bf.maxit  : num 30
  ..$ trace     : logi FALSE
 $ method             : chr "glm.fit"
 $ contrasts          : NULL
 $ xlevels            : Named list()
 $ preplot            :List of 5
  ..$ s(age3)         :List of 5
  .. ..$ x   : num [1:3186] 55 49 58 51 52 62 62 54 49 54 ...
  .. ..$ y   : Named num [1:3186] -0.00801 0.01698 -0.01078 0.01461 0.00851 ...
  .. .. ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
  .. ..$ se.y: Named num [1:3186] 0.00837 0.00929 0.00898 0.00865 0.00843 ...
  .. .. ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
  .. ..$ xlab: chr "age3"
  .. ..$ ylab: chr "s(age3)"
  .. ..- attr(*, "class")= chr "preplot.gam"
  ..$ s(bmi3)         :List of 5
  .. ..$ x   : num [1:3186] 20.9 20.2 30.5 34.1 23.2 ...
  .. ..$ y   : Named num [1:3186] -0.0213 -0.0222 0.0244 0.0566 -0.0152 ...
  .. .. ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
  .. ..$ se.y: Named num [1:3186] 0.00737 0.00917 0.00919 0.01372 0.005 ...
  .. .. ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
  .. ..$ xlab: chr "bmi3"
  .. ..$ ylab: chr "s(bmi3)"
  .. ..- attr(*, "class")= chr "preplot.gam"
  ..$ SmokingGroup_Kai:List of 5
  .. ..$ x   : num [1:3186] 4 4 5 4 3 3 3 2 1 3 ...
  .. ..$ y   : Named num [1:3186] 0.01692 0.01692 0.02758 0.01692 0.00626 ...
  .. .. ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
  .. ..$ se.y: Named num [1:3186] 0.00516 0.00516 0.00842 0.00516 0.00191 ...
  .. .. ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
  .. ..$ xlab: chr "SmokingGroup_Kai"
  .. ..$ ylab: chr "partial for SmokingGroup_Kai"
  .. ..- attr(*, "class")= chr "preplot.gam"
  ..$ PA_Score        :List of 5
  .. ..$ x   : num [1:3186] 2 3 3 2 3 2 2 3 3 2 ...
  .. ..$ y   : Named num [1:3186] -0.000292 0.000539 0.000539 -0.000292 0.000539 ...
  .. .. ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
  .. ..$ se.y: Named num [1:3186] 0.003 0.00553 0.00553 0.003 0.00553 ...
  .. .. ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
  .. ..$ xlab: chr "PA_Score"
  .. ..$ ylab: chr "partial for PA_Score"
  .. ..- attr(*, "class")= chr "preplot.gam"
  ..$ TargetGroup2012 :List of 5
  .. ..$ x   : num [1:3186] 2 2 2 2 3 3 3 2 2 3 ...
  .. ..$ y   : Named num [1:3186] -0.00786 -0.00786 -0.00786 -0.00786 0.01679 ...
  .. .. ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
  .. ..$ se.y: Named num [1:3186] 0.00289 0.00289 0.00289 0.00289 0.00617 ...
  .. .. ..- attr(*, "names")= chr [1:3186] "1" "2" "3" "4" ...
  .. ..$ xlab: chr "TargetGroup2012"
  .. ..$ ylab: chr "partial for TargetGroup2012"
  .. ..- attr(*, "class")= chr "preplot.gam"
  ..- attr(*, "class")= chr "preplot.gam"
 - attr(*, "class")= chr [1:3] "gam" "glm" "lm"
> p$preplot$s(age3)$y
Error: attempt to apply non-function
> p$preplot$s(age3)$ y
Error: attempt to apply non-function

如何在图中的每个变量中访问值SE和y？

Answer 1

问题是s(age3)种类的名称不是有效的R名称。因此，当R看到它认为您正在尝试在名为p$preplot$s的变量上评估函数age3时。您可以执行此操作来检索这些值：

p[[c("preplot", "s(age3)", "y")]]

希望这有效。

R如何从绘图中提取值

1 个答案: