使用带有lm()对象列表的预测 [英] using predict with a list of lm() objects

问题描述

我有定期进行回归分析的数据.每个大块"数据都适合不同的回归.例如，每个状态可能都有不同的函数来解释从属值.这似乎是一个典型的"split-apply-combine"类型的问题，因此我正在使用plyr软件包.我可以轻松创建一个效果很好的lm()对象列表.但是，我无法完全理解以后如何使用这些对象来预测单独data.frame中的值.

I have data which I regularly run regressions on. Each "chunk" of data gets fit a different regression. Each state, for example, might have a different function that explains the dependent value. This seems like a typical "split-apply-combine" type of problem so I'm using the plyr package. I can easily create a list of lm() objects which works well. However I can't quite wrap my head around how I use those objects later to predict values in a separate data.frame.

这是一个完全人为的例子，说明了我正在尝试做的事情:

Here's a totally contrived example illustrating what I'm trying to do:

# setting up some fake data
set.seed(1)
funct <- function(myState, myYear){
   rnorm(1, 100, 500) +  myState + (100 * myYear) 
}
state <- 50:60
year <- 10:40
myData <- expand.grid( year, state)
names(myData) <- c("year","state")
myData$value <- apply(myData, 1, function(x) funct(x[2], x[1]))
## ok, done with the fake data generation. 

require(plyr)

modelList <- dlply(myData, "state", function(x) lm(value ~ year, data=x))
## if you want to see the summaries of the lm() do this:  
    # lapply(modelList, summary)

state <- 50:60
year <- 50:60
newData <- expand.grid( year, state)
names(newData) <- c("year","state") 
## now how do I predict the values for newData$value 
   # using the regressions in modelList? 

那么我该如何使用modelList中包含的lm()对象来使用年份和状态独立于newData的值来预测值?

So how do I use the lm() objects contained in modelList to predict values using the year and state independent values from newData?

推荐答案

这是我的尝试:

predNaughty <- ddply(newData, "state", transform,
  value=predict(modelList[[paste(piece$state[1])]], newdata=piece))
head(predNaughty)
#   year state    value
# 1   50    50 5176.326
# 2   51    50 5274.907
# 3   52    50 5373.487
# 4   53    50 5472.068
# 5   54    50 5570.649
# 6   55    50 5669.229
predDiggsApproved <- ddply(newData, "state", function(x)
  transform(x, value=predict(modelList[[paste(x$state[1])]], newdata=x)))
head(predDiggsApproved)
#   year state    value
# 1   50    50 5176.326
# 2   51    50 5274.907
# 3   52    50 5373.487
# 4   53    50 5472.068
# 5   54    50 5570.649
# 6   55    50 5669.229

JD长时间编辑

我很受启发，制定了adply()选项:

I was inspired enough to work out an adply() option:

pred3 <- adply(newData, 1,  function(x)
    predict(modelList[[paste(x$state)]], newdata=x))
head(pred3)
#   year state        1
# 1   50    50 5176.326
# 2   51    50 5274.907
# 3   52    50 5373.487
# 4   53    50 5472.068
# 5   54    50 5570.649
# 6   55    50 5669.229

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