利用splinefun的R-群_

我试图将我的数据按年份和CountyID分组，然后对子集数据使用splinefun（三次样条插值）。我对想法开放，但是夹板是必须的，不能改变

以下是我尝试使用的代码：

age <- seq(from = 0, by = 5, length.out = 18)

TOT_POP <- df %.%
group_by(unique(df$Year), unique(df$CountyID) %.%
splinefun(age, c(0, cumsum(df$TOT_POP)), method = "hyman")

我现在做的是将年龄分组为1:17，并将分组分为0-84岁。目前，每个小组都有5年的代表性。splinefun允许我这样做，同时为过程提供了一定程度的数学严谨性，即splinefun允许我提供美国每个县每一岁的人口总数

最后，splinefun代码本身可以工作，但在group_by函数中它不能工作，它产生：

Error: wrong result size(4), expected 68 or 1. 

splinefun代码的使用方式是这样的

TOT_POP <- splinefun(age, c(0, cumsum(df$TOT_POP)), 
           method  = "hyman") 

TOT_POP = pmax(0, diff(TOT_POP(c(0:85))))

---

TOT_POP首先，我认为我在试图实现我的道歉时使用了错误的措辞；实际上，小组并不打算解决这个问题。但是，我使用两个函数和ddply解决了这个问题。以下是解决此问题的代码：

interpolate <- function(x, ageVector){
result <- splinefun(ageVector, 
          c(0, cumsum(x)), method = "hyman")
diff(result(c(0:85)))
}

mainFunc <- function(df){

age <- seq(from = 0, by = 5, length.out = 18)
colNames <- setdiff(colnames(df)
            c("Year","CountyID","AgeGrp"))
colWiseSpline <- colwise(interpolate, .cols = true,
                 age)(df[ , colNames])

cbind(data.frame(
Year = df$Year[1],
County = df$CountyID[1],
Agegrp = 0:84
),
colWiseSpline
)
}

CompleteMainRaw <- ddply(.data = df, 
                    .variables = .(CountyID, Year), 
                    .fun = mainFunc)

interpolate让我直说吧。您希望根据两个变量分割数据帧。然后，对于每个较小的数据帧，您希望使用
splinefun
获得样条函数映射
age
到
TOT_POP
？然后您想使用该函数插值0到85岁之间所有年龄段的总人口，因为您的原始数据仅包含5、10、15、20岁的人口。。。？也许你可以通过
split
和
lappy
或
plyr
来实现这一点，然后有人会更好地准备帮助你使用
dplyr
。splinefun将用于一个县一年的年龄为1:17的数据子集。最后的年龄组是个人年龄0:84。我仍然很困惑。也许您可以更改
df
数据帧？将
Agegrp
替换为关联的
age
。例如，
df$Agegrp=df$Agegrp*5
<代码>colnames（df）[3]=“年龄”
。这可能会简化你的问题。
# Reproducible data set
set.seed(22)
df = data.frame( CountyID = rep(1001:1005,each = 100), 
                 Year = rep(2001:2010, each = 10),
                 Agegrp = sample(1:17, 500, replace=TRUE),
                 TOT_POP = rnorm(500, 10000, 2000))

# Convert Agegrp to age
df$Agegrp = df$Agegrp*5
colnames(df)[3] = "age"

# Make a spline function for every CountyID-Year combination
split.dfs = split(df, interaction(df$CountyID, df$Year))
spline.funs = lapply(split.dfs, function(x) splinefun(x[,"age"], x[,"TOT_POP"]))

# Use the spline functions to interpolate populations for all years between 0 and 85
new.split.dfs = list()
for( i in 1:length(split.dfs)) {
  new.split.dfs[[i]] = data.frame( CountyID=split.dfs[[i]]$CountyID[1],
                                   Year=split.dfs[[i]]$Year[1],
                                   age=0:85,
                                   TOT_POP=spline.funs[[i]](0:85))
}


# Does this do what you want? If so, then it will be 
# easier for others to work from here
# > head(new.split.dfs[[1]])
# CountyID Year age  TOT_POP
# 1     1001 2001   0 909033.4
# 2     1001 2001   1 833999.8
# 3     1001 2001   2 763181.8
# 4     1001 2001   3 696460.2
# 5     1001 2001   4 633716.0
# 6     1001 2001   5 574829.9
# > tail(new.split.dfs[[2]])
# CountyID Year age   TOT_POP
# 81     1002 2001  80 10201.693
# 82     1002 2001  81  9529.030
# 83     1002 2001  82  8768.306
# 84     1002 2001  83  7916.070
# 85     1002 2001  84  6968.874
# 86     1002 2001  85  5923.268

interpolate <- function(x, ageVector){
result <- splinefun(ageVector, 
          c(0, cumsum(x)), method = "hyman")
diff(result(c(0:85)))
}

mainFunc <- function(df){

age <- seq(from = 0, by = 5, length.out = 18)
colNames <- setdiff(colnames(df)
            c("Year","CountyID","AgeGrp"))
colWiseSpline <- colwise(interpolate, .cols = true,
                 age)(df[ , colNames])

cbind(data.frame(
Year = df$Year[1],
County = df$CountyID[1],
Agegrp = 0:84
),
colWiseSpline
)
}

CompleteMainRaw <- ddply(.data = df, 
                    .variables = .(CountyID, Year), 
                    .fun = mainFunc)