问题描述
由于某种原因，当我指定glms时(结果也显示lm时)，R不能预测数据的缺失值.这是一个示例:
For some reason, when I specify glms (and lm's too, it turns out), R is not predicting missing values of the data. Here is an example:
y = round(runif(50))
y = c(y,rep(NA,50))
x = rnorm(100)
m = glm(y~x, family=binomial(link="logit"))
p = predict(m,na.action=na.pass)
length(p)
y = round(runif(50))
y = c(y,rep(NA,50))
x = rnorm(100)
m = lm(y~x)
p = predict(m)
length(p)
p的长度应为100，但应为50.奇怪的是，我在同一脚本中还有其他预测可以根据丢失的数据进行预测.
The length of p should be 100, but its 50. The weird thing is that I have other predicts in the same script that do predict from missing data.
事实证明那些其他预测是完全错误的-我在做imputed.value = rnorm(N,mean.from.predict,var.of.prediction.interval).这个 recycled 来自length(predict)<N时lm预测或glm预测函数的均值和sd向量，这与我正在寻找的完全不同.
It turns out that those other predicts were quite wrong -- I was doing imputed.value = rnorm(N,mean.from.predict,var.of.prediction.interval). This recycled the mean and sd vectors from the lm predict or glm predict functions when length(predict)<N, which was quite different from what I was seeking.
所以我的问题是我的示例代码如何阻止glm和lm预测缺失值?
谢谢！
推荐答案
当glm拟合模型时，它仅使用没有缺失值的情况.通过构造数据框并将其传递给predict.glm，您仍然可以获得有关y值缺失的情况的预测.
When glm fits the model, it uses only the cases where there are no missing values. You can still get predictions for the cases where your y values are missing, by constructing a data frame and passing that to predict.glm.
predict(m, newdata=data.frame(y, x))
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