引導在logistic模型

我有以下的邏輯模型引導在logistic模型

Call: 
glm(formula = Y ~ ., family = binomial, data = datasim) 

Deviance Residuals: 
Min  1Q Median  3Q  Max 
-1.79670 -1.06758 0.00754 1.08200 1.69251 

Coefficients: 
      Estimate Std. Error z value Pr(>|z|) 
(Intercept) -0.11504 1.02968 -0.112 0.91104 
X1   0.17173 0.31899 0.538 0.59034 
X2   -0.01573 0.28294 -0.056 0.95567 
X3   -0.36905 0.29577 -1.248 0.21212 
X4   -0.34710 0.29518 -1.176 0.23965 
X5   0.01733 0.36088 0.048 0.96170 
X6   -0.33492 0.38217 -0.876 0.38083 
X7   0.20179 0.31615 0.638 0.52328 
X8   -0.42734 0.26011 -1.643 0.10040 
X9   -0.02750 0.29571 -0.093 0.92590 
X10   0.76991 0.26840 2.868 0.00412 ** 
--- 
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for binomial family taken to be 1) 

Null deviance: 138.63 on 99 degrees of freedom 
Residual deviance: 125.09 on 89 degrees of freedom 
AIC: 147.09 

Number of Fisher Scoring iterations: 4

我計算「得分」，該模型例如X = $ null.deviance- $越軌的X。在這種情況下，X = 138.63-125.09。現在，我想使用bootstrap來計算得分的均值和置信區間。我如何在R中實現這個？

來源

2016-01-21 Shima

看看包裝boot。這裏有個模擬例子，適合你的情況：

library(boot) 
x <- rnorm(100) 
Y <- exp(x + rnorm(100)) > 1 
datasim <- data.frame(Y, x) 

dDeviance <- function(data, indices){ 
    fit <- glm(formula = Y ~ ., family = binomial, data = data[indices, ]) 
    with(fit, null.deviance - deviance) 
} 
boot(data = datasim, statistic = dDeviance, R = 100)

隨着輸出：

ORDINARY NONPARAMETRIC BOOTSTRAP 


Call: 
boot(data = datasim, statistic = dDeviance, R = 100) 


Bootstrap Statistics : 
    original bias std. error 
t1* 41.02692 1.445287 9.712626

來源

2016-01-21 16:39:33

引導在logistic模型

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