我想计算预测值和标准误差，但我不能简单地使用 predict((，因为我使用的是 15 个乘法插补数据集(生成的 Amelia 包(。我在每个数据集上运行回归模型。之后，使用使用鲁宾规则的 Amelia 函数 mi.meld(( 将结果合并为一组模型系数和标准误差。
示例数据和代码：
dd<-list()
for (i in 1:15){
dd[[i]] <- data.frame(
Age=runif(50,20,90),
Cat=factor(sample(0:4, 50, replace=T)),
Outcome = sample(0:1, 50, replace=T)
)}
b.out<-NULL
se.out<-NULL
for(i in 1:15) {
ols.out<-glm(Outcome~Age+factor(Cat), data=dd[[i]],family="binomial")
b.out <- rbind(b.out, ols.out$coef)
se.out <- rbind(se.out, coef(summary(ols.out))[,2])}
mod0 <- mi.meld(q = b.out, se = se.out)
> mod0
$q.mi
(Intercept) Age factor(Cat)1 factor(Cat)2 factor(Cat)3 factor(Cat)4
[1,] 0.0466825 -0.00577106 0.5291908 -0.09760264 0.4058684 0.3125109
$se.mi
(Intercept) Age factor(Cat)1 factor(Cat)2 factor(Cat)3
factor(Cat)4
[1,] 1.863276 0.02596468 1.604759 1.398322 1.414589
1.332743
现在是有问题的部分。我想计算以下一组预测变量值的预测值(在本例中为预测概率(和标准误差：
data.predict <- data.frame(Cat=as.factor(c(0:4)), Age=53.6)
print(data.predict)
Cat Age
1 0 53.6
2 1 53.6
3 2 53.6
4 3 53.6
5 4 53.6
如果我在 1 个数据集上拟合了一个模型，我会简单地这样做：
prediction<- predict(mod1, data.predict, type="response",se.fit=T)
但是，我没有模型对象，我只是存储了系数。 现在我已经研究了两种解决此问题的方法，第一种方法是以这种方式手动计算预测：在 r 中具有任意系数的 predict(( 但是我不知道如何到达标准错误。我的另一个想法是创建一个假的模型对象，就像这个函数创建的那样：https://gist.github.com/MrFlick/ae299d8f3760f02de6bf 并在 predict(( 中使用它，但由于不使用模型的标准误差，也没有办法计算预测的标准误差。
有人对如何解决这个问题有建议吗？我试图用示例代码清楚地解释我的问题，但是如果我的问题不清楚，请告诉我，以便我提供其他信息。感谢您的帮助！
我相信在将近几年后你不需要这个答案，但我刚刚在研究一个类似的问题，我想我会把答案放在这里供后代使用。
Andrew Heiss把这个解决方案放在gisthub上 - https://gist.github.com/andrewheiss/a3134085e92c6607db39c5b14e1b879e
我已经稍微修改了一下(部分原因是我认为自从他写这篇文章以来，"nest"的默认行为可能在整洁中被改变了？
代码(辛勤工作！(几乎完全来自Andrew Heiss。 这里的注释是我和他的混合体。
这是使用Amelia Africa数据集，对于我的现实生活中的问题，我有一个不同的数据集(显然(，并且前几步有点不同，这一切都很好。
library(tidyverse)
library(Amelia)
library(broom)
# Use the africa dataset from Amelia
data(africa)
set.seed(1234)
imp_amelia <- amelia(x = africa, m = 5, cs = "country", ts = "year", logs = "gdp_pc", p2s = 0) # do the imputations -- for me, it was fine to do this bit in 'mice'
# Gather all the imputed datasets into one data frame and run a model on each
models_imputed_df <- bind_rows(unclass(imp_amelia$imputations), .id = "m") %>%
group_by(m) %>%
nest() %>%
mutate(model = data %>% map(~ lm(gdp_pc ~ trade + civlib, data = .)))
# again - for my real life problem the models looked very different to this, and used rms - and this was also totally fine.
models_imputed_df
#> # A tibble: 5 x 3
#> m data model
#> <chr> <list> <list>
#> 1 imp1 <tibble [120 × 7]> <S3: lm>
#> 2 imp2 <tibble [120 × 7]> <S3: lm>
#> 3 imp3 <tibble [120 × 7]> <S3: lm>
#> 4 imp4 <tibble [120 × 7]> <S3: lm>
#> 5 imp5 <tibble [120 × 7]> <S3: lm>
# We want to see how GDP per capita varies with changes in civil liberties, so
# we create a new data frame with values for each of the covariates in the
# model. We include the full range of civil liberties (from 0 to 1) and the mean
# of trade.
# ie. this is a 'skelton' data frame of all your variables that you want to make predictions over.
new_data <- data_frame(civlib = seq(0, 1, 0.1),
trade = mean(africa$trade, na.rm = TRUE))
new_data
#> # A tibble: 11 x 2
#> civlib trade
#> <dbl> <dbl>
#> 1 0. 62.6
#> 2 0.100 62.6
#> 3 0.200 62.6
#> 4 0.300 62.6
#> 5 0.400 62.6
#> 6 0.500 62.6
#> 7 0.600 62.6
#> 8 0.700 62.6
#> 9 0.800 62.6
#> 10 0.900 62.6
#> 11 1.00 62.6
# write a function to meld predictions
meld_predictions <- function(x) {
# x is a data frame with m rows and two columns:
#
# m .fitted .se.fit
# 1 1.05 0.34
# 2 1.09 0.28
# x ... ...
# Meld the fitted values using Rubin's rules
x_melded <- mi.meld(matrix(x$.fitted), matrix(x$.se.fit))
data_frame(.fitted = as.numeric(x_melded$q.mi),
.se.fit = as.numeric(x_melded$se.mi))
}
# We augment/predict using new_data in each of the imputed models, then we group
# by each of the values of civil liberties (so each value, like 0.1 and 0.2 has
# 5 values, 1 from each of the imputed models), and then we meld those 5
# predicted values into a single value with meld_predictions()
predict_melded <- data_frame(models = models_imputed_df$model) %>%
mutate(m = 1:n(),
fitted = models %>% map(~ augment(., newdata = new_data))) %>%
unnest(fitted) %>%
dplyr::select(-models) %>% #### I needed to add this row to make the code work, once you've used the models to get the fit you don't need them in the data object anymore. I took this line out because it was slowing everything down, then realised the code only works with this line... not sure why?
group_by(civlib) %>%
nest(data=c(m, .fitted, .se.fit)) %>% # needed to change this here from gisthub to make the nested 'data' have all the imputations in it, not just estimates from one of the imputations.
mutate(fitted_melded = data %>% map(~ meld_predictions(.))) %>%
unnest(fitted_melded) %>%
mutate(ymin = .fitted + (qnorm(0.025) * .se.fit),
ymax = .fitted + (qnorm(0.975) * .se.fit))
## NB. this is still on the link scale -- you'd need to write an extra few lines to exponentiate everything and get your predictions and se's on the response scale
# Plot!
ggplot(predict_melded, aes(x = civlib, y = .fitted)) +
geom_line(color = "blue") +
geom_ribbon(aes(ymin = ymin, ymax = ymax), alpha = 0.2, fill = "blue")
希望这至少对其他偶然发现这一点的人有所帮助。