R 应用线性模型并存储拟合值

我有一些时间序列数据，我想在它的基础上运行“迷你”回归模型，但在滚动的基础上

我可以运行线性模型，并使用以下方法绘制：

mod <- lm(Y1 ~ time(data), data = data)
ggplot(data, aes(x = time(data), y = Y1)) +
  geom_line() +
  geom_line(aes(x = as.Date(rownames(data.frame(mod$fitted.values))),
                y = mod$fitted.values), colour = "blue")

但是，这不会存储安装的值。我正在尝试使用

rollapply

函数来执行此操作

library(zoo)
lmFunction <- function(x){
  lm(Y1 ~ time(x), data = x)
}


data %>% 
  zoo() %>% 
  rollapply(
    width = 40,
    FUN = lmFunction
    #fill = NA,
    #by = 30,
    #by.column = FALSE,
    #align = "right"
)

图书馆（动物园）
lmFunction%
zoo（）%>%
滚涂(
宽度=40，
FUN=lm函数
#填充=不适用，
#按=30，
#by.column=FALSE，
#align=“right”
)

然而，我似乎无法让它发挥作用。一旦得到结果，我想将所有（滚动）拟合值绘制为（许多不同的）曲线

数据：

---

datarollapply中使用的函数必须生成普通向量。
lm
对象不是可接受的输出

此外，默认情况下，它只将对象的coredata传递到函数中，但如果您希望传递zoo对象以便获得其时间，则使用
coredata=FALSE

lm
传递的zoo对象可能有问题，请使用其coredata

在问题所示的lm函数中，它试图使用lm（…，x），就好像x是数据帧，但x不是数据帧一样

下面的两个示例分别返回拟合值和系数

为每个时间点绘制一条线看起来像是一团乱麻，但我们在最后展示了如何生成它

library(xts)

Y1 <- as.zoo(data)$Y1

fitfun <- function(x) fitted(lm(coredata(x) ~ time(x)))
r1 <- rollapplyr(Y1, 40, fitfun, coredata = FALSE, fill = NA)
dim(r1)
## [1] 837  40

coeffun <- function(x) coef(lm(coredata(x) ~ time(x)))
r2 <- rollapplyr(Y1, 40, coeffun, coredata = FALSE, fill = NA)
dim(r2)
## [1] 837  2

# plot
r2na <- na.omit(r2)
plot(Y1)
junk <- Map(abline, a = r2na[, 1], b = r2na[, 2])

库（xts）
Y1
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1577404800, 1577664000), tzone = "UTC", tclass = "Date"), class = c("xts", 
"zoo"), src = "yahoo", updated = structure(1585412559.29406, class = c("POSIXct", 
"POSIXt")), .Dim = c(837L, 1L), .Dimnames = list(NULL, "Y1"))

library(xts)

Y1 <- as.zoo(data)$Y1

fitfun <- function(x) fitted(lm(coredata(x) ~ time(x)))
r1 <- rollapplyr(Y1, 40, fitfun, coredata = FALSE, fill = NA)
dim(r1)
## [1] 837  40

coeffun <- function(x) coef(lm(coredata(x) ~ time(x)))
r2 <- rollapplyr(Y1, 40, coeffun, coredata = FALSE, fill = NA)
dim(r2)
## [1] 837  2

# plot
r2na <- na.omit(r2)
plot(Y1)
junk <- Map(abline, a = r2na[, 1], b = r2na[, 2])