rms包含大量有用的统计函数。然而,我无法找到从拟合对象中提取某些适配统计数据的正确方法。考虑以下示例:
library(pacman)
p_load(rms, stringr, readr)
#fit
> (fit = rms::ols(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species, data = iris))
Linear Regression Model
rms::ols(formula = Sepal.Length ~ Sepal.Width + Petal.Length +
Petal.Width + Species, data = iris)
Model Likelihood Discrimination
Ratio Test Indexes
Obs 150 LR chi2 302.96 R2 0.867
sigma0.3068 d.f. 5 R2 adj 0.863
d.f. 144 Pr(> chi2) 0.0000 g 0.882
Residuals
Min 1Q Median 3Q Max
-0.794236 -0.218743 0.008987 0.202546 0.731034
Coef S.E. t Pr(>|t|)
Intercept 2.1713 0.2798 7.76 <0.0001
Sepal.Width 0.4959 0.0861 5.76 <0.0001
Petal.Length 0.8292 0.0685 12.10 <0.0001
Petal.Width -0.3152 0.1512 -2.08 0.0389
Species=versicolor -0.7236 0.2402 -3.01 0.0031
Species=virginica -1.0235 0.3337 -3.07 0.0026
因此,适合的
print
函数打印了许多有用的内容,包括标准误差和调整后的R2。不幸的是,如果我们检查模型拟合对象,这些值似乎没有出现在任何地方。> str(fit)
List of 19
$ coefficients : Named num [1:6] 2.171 0.496 0.829 -0.315 -0.724 ...
..- attr(*, "names")= chr [1:6] "Intercept" "Sepal.Width" "Petal.Length" "Petal.Width" ...
$ residuals : Named num [1:150] 0.0952 0.1432 -0.0731 -0.2894 -0.0544 ...
..- attr(*, "names")= chr [1:150] "1" "2" "3" "4" ...
$ effects : Named num [1:150] -71.5659 -1.1884 9.1884 -1.3724 -0.0587 ...
..- attr(*, "names")= chr [1:150] "Intercept" "Sepal.Width" "Petal.Length" "Petal.Width" ...
$ rank : int 6
$ fitted.values : Named num [1:150] 5 4.76 4.77 4.89 5.05 ...
..- attr(*, "names")= chr [1:150] "1" "2" "3" "4" ...
$ assign :List of 4
..$ Sepal.Width : int 2
..$ Petal.Length: int 3
..$ Petal.Width : int 4
..$ Species : int [1:2] 5 6
$ qr :List of 5
..$ qr : num [1:150, 1:6] -12.2474 0.0816 0.0816 0.0816 0.0816 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:150] "1" "2" "3" "4" ...
.. .. ..$ : chr [1:6] "Intercept" "Sepal.Width" "Petal.Length" "Petal.Width" ...
..$ qraux: num [1:6] 1.08 1.02 1.11 1.02 1.02 ...
..$ pivot: int [1:6] 1 2 3 4 5 6
..$ tol : num 1e-07
..$ rank : int 6
..- attr(*, "class")= chr "qr"
$ df.residual : int 144
$ var : num [1:6, 1:6] 0.07828 -0.02258 -0.00198 0.01589 -0.02837 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:6] "Intercept" "Sepal.Width" "Petal.Length" "Petal.Width" ...
.. ..$ : chr [1:6] "Intercept" "Sepal.Width" "Petal.Length" "Petal.Width" ...
$ stats : Named num [1:6] 150 302.964 5 0.867 0.882 ...
..- attr(*, "names")= chr [1:6] "n" "Model L.R." "d.f." "R2" ...
$ linear.predictors: Named num [1:150] 5 4.76 4.77 4.89 5.05 ...
..- attr(*, "names")= chr [1:150] "1" "2" "3" "4" ...
$ call : language rms::ols(formula = Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species, data = iris)
$ terms :Classes 'terms', 'formula' language Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species
.. ..- attr(*, "variables")= language list(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, Species)
.. ..- attr(*, "factors")= int [1:5, 1:4] 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 [1:4] "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
.. ..- attr(*, "term.labels")= chr [1:4] "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
.. ..- attr(*, "order")= int [1:4] 1 1 1 1
.. ..- attr(*, "intercept")= num 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(*, "formula")=Class 'formula' language Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species
.. .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
$ Design :List of 12
..$ name : chr [1:4] "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
..$ label : chr [1:4] "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
..$ units : Named chr [1:4] "" "" "" ""
.. ..- attr(*, "names")= chr [1:4] "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
..$ colnames : chr [1:5] "Sepal.Width" "Petal.Length" "Petal.Width" "Species=versicolor" ...
..$ mmcolnames : chr [1:5] "Sepal.Width" "Petal.Length" "Petal.Width" "Speciesversicolor" ...
..$ assume : chr [1:4] "asis" "asis" "asis" "category"
..$ assume.code : int [1:4] 1 1 1 5
..$ parms :List of 1
.. ..$ Species: chr [1:3] "setosa" "versicolor" "virginica"
..$ limits : list()
..$ values : list()
..$ nonlinear :List of 4
.. ..$ Sepal.Width : logi FALSE
.. ..$ Petal.Length: logi FALSE
.. ..$ Petal.Width : logi FALSE
.. ..$ Species : logi [1:2] FALSE FALSE
..$ interactions: NULL
$ non.slopes : num 1
$ na.action : NULL
$ scale.pred : chr "Sepal.Length"
$ fail : logi FALSE
$ sformula :Class 'formula' language Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species
.. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
- attr(*, "class")= chr [1:3] "ols" "rms" "lm"
这里有一个关于R语言帮助的7年老问题,包创建者在其中解释了如何解决以下问题:
2010年8月11日,david dav写道:
嗨, 我想提取逻辑回归的系数(估计值和标准误差),与glm中的summary(fit.glm)$coef相同。
谢谢 David
coef(fit) sqrt(diag(vcov(fit)))
但是,除非一切都是线性的,没有任何交互作用,并且因子只有两个水平,否则这些内容并不会很有帮助。
Frank
根据作者的说法,这个解决方案并不是最优的。这让人们想知道显示的值是如何计算出来的。查找代码后发现需要在未记录的软件包代码(该软件包代码在Github上)中进行搜索。也就是说,我们从print.ols()
开始:
> rms:::print.ols
function (x, digits = 4, long = FALSE, coefs = TRUE, title = "Linear Regression Model",
...)
{
latex <- prType() == "latex"
k <- 0
z <- list()
if (length(zz <- x$na.action)) {
k <- k + 1
z[[k]] <- list(type = paste("naprint", class(zz)[1],
sep = "."), list(zz))
}
stats <- x$stats
...
阅读更多后,我们发现例如 R2 adj. 是在打印函数中计算的。
rsqa <- 1 - (1 - r2) * (n - 1) / rdf
我们还发现了一些标准误差的计算,但没有 p 值。
se <- sqrt(diag(x$var))
z[[k]] <- list(type='coefmatrix',
list(coef = x$coefficients,
se = se,
errordf = rdf))
所有结果都会进一步传递给
prModFit()
。我们可以查找并找到p值的计算等。不幸的是,print
命令返回NULL
,因此这些值无法在程序中重复使用:> x = print((fit = rms::ols(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species, data = iris)))
#printed output...
> x
NULL
如何获得所有统计数据?
x = print((fit = rms::ols(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species, data = iris)))
,> x NULL
。求解该问题(即我)的回答者提供了黑客解决方案。 - CoderGuy123