rms 包中包含大量有用的统计功能。但是，我找不到从拟合对象中提取某些拟合统计数据的正确方法。考虑一个例子：
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函数会打印很多有用的东西，包括标准错误和调整后的R2。不幸的是，如果我们检查模型拟合对象，则值似乎不会出现在任何地方。
> 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"
有a 7 year old question on R help个包创建者解释了获取这些内容的解决方案：
2010年8月11日星期三，david dav写道：
您好， 我想提取逻辑回归的系数 （在估计和标准误差中）也在lrm中，如在glm中
摘要（fit.glm）$ COEF
由于 大卫
coef（fit）sqrt（diag（vcov（fit）））
但是除了琐碎的情况之外，这些都不会很有帮助 一切都是线性的，没有任何相互作用，因素有两个层次。
谢
根据作者的解决方案不是最优的。这让人想知道如何计算显示的值。跟踪代码会导致通过未记录的包代码（包代码is on Github）进行搜索。即我们从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
...
进一步阅读我们确实发现，例如R2 adj。在打印功能中计算：
rsqa <- 1 - (1 - r2) * (n - 1) / rdf
我们也发现了一些标准误差计算，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look it up找到p值计算等。不幸的是，print命令返回NULL所以这些值在程序重用时无法在任何地方使用：
> 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？
答案 0 :(得分：2)
这是一个黑客解决方案，我们捕获print命令的输出：
#parser
get_model_stats = function(x) {
cap = capture.output(print(x))
#model stats
stats = c()
stats$R2.adj = str_match(cap, "R2 adj\\s+ (\\d\\.\\d+)") %>% na.omit() %>% .[, 2] %>% as.numeric()
#coef stats lines
coef_lines = cap[which(str_detect(cap, "Coef\\s+S\\.E\\.")):(length(cap) - 1)]
#parse
coef_lines_table = suppressWarnings(readr::read_table(coef_lines %>% stringr::str_c(collapse = "\n")))
colnames(coef_lines_table)[1] = "Predictor"
list(
stats = stats,
coefs = coef_lines_table
)
}
示例：
> get_model_stats(fit)
$stats
$stats$R2.adj
[1] 0.86
$coefs
# A tibble: 6 x 5
Predictor Coef S.E. t `Pr(>|t|)`
<chr> <dbl> <dbl> <dbl> <chr>
1 Intercept 2.17 0.280 7.8 <0.0001
2 Sepal.Width 0.50 0.086 5.8 <0.0001
3 Petal.Length 0.83 0.069 12.1 <0.0001
4 Petal.Width -0.32 0.151 -2.1 0.0389
5 Species=versicolor -0.72 0.240 -3.0 0.0031
6 Species=virginica -1.02 0.334 -3.1 0.0026
这仍有问题，例如p值不作为数值返回，只有4位数，这可能会在某些情况下引起问题。
答案 1 :(得分：1)
包broom是提取模型信息的好方法。
library(pacman)
library(rms)
library(broom)
fit = ols(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species,
data = iris)
tidy(summary.lm(fit))
# term estimate std.error statistic p.value
# 1 Intercept 2.1712663 0.27979415 7.760227 1.429502e-12
# 2 Sepal.Width 0.4958889 0.08606992 5.761466 4.867516e-08
# 3 Petal.Length 0.8292439 0.06852765 12.100867 1.073592e-23
# 4 Petal.Width -0.3151552 0.15119575 -2.084418 3.888826e-02
# 5 Species=versicolor -0.7235620 0.24016894 -3.012721 3.059634e-03
# 6 Species=virginica -1.0234978 0.33372630 -3.066878 2.584344e-03
glance(fit)
# r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC df.residual
# 1 0.8673123 0.862705 0.3068261 188.251 2.666942e-61 6 -32.55801 79.11602 100.1905 144
对象fit还包含一些易于访问的信息，您可以将这些信息存储在数据框中：
fit$coefficients
# Intercept Sepal.Width Petal.Length Petal.Width Species=versicolor Species=virginica
# 2.1712663 0.4958889 0.8292439 -0.3151552 -0.7235620 -1.0234978
fit$stats
# n Model L.R. d.f. R2 g Sigma
# 150.0000000 302.9635115 5.0000000 0.8673123 0.8820479 0.3068261