将“mFilter”对象（时间序列列表）转换为使用 ggplot2 绘图

Question

我正在使用来自mFilter 包的hpfilter，我似乎找不到将hpfilter 的时间序列 对象列表转换为的简单方法我可以与 ggplot2 一起使用的格式。我意识到我可以将它们全部拆开并重新组合在一起，但我想我忽略了一些简单的方法？我尝试了 SO 讨论中建议的代码 R list to data frame。但是，我找不到以任何简单方式将 Time-Series 对象列表转换为 data.frame 的方法。最终目标是重现mFilter 对象生成的默认图（见下文）

这是一些示例代码

# install.packages(c("mFilter"), dependencies = TRUE)
library(mFilter)
data(unemp)
unemp.hp <- hpfilter(unemp, type=c("lambda"), freq = 1606)
# str(unemp.hp)
class(unemp.hp)
# [1] "mFilter"
plot(unemp.hp)
Hit <Return> to see next plot: 

另外，为什么要我“点击<Return>”才能看到剧情？

Answer 1

plot 函数调用 plot.mFilter 参数 ask=interactive() 并设置为 TRUE 用于交互式会话， 您可以通过ask=FALSE 调用plot 来禁用此功能

plot(unemp.hp,ask=FALSE)

数据：

library(mFilter)
library(ggplot2)
library(gridExtra)
# library(zoo)


data(unemp)
unemp.hp <- hpfilter(unemp, type=c("lambda"), freq = 1606)
# str(unemp.hp)
class(unemp.hp)
# [1] "mFilter"
plot(unemp.hp,ask=FALSE)

检查对象unemp.hp的槽

names(unemp.hp)
# [1] "cycle"   "trend"   "fmatrix" "title"   "xname"   "call"    "type"    "lambda"  "method" 
#[10] "x"

相关对象为x（主要的unemp系列）、trend和cycle。所有三个对象都属于ts类，我们首先将它们转换为 data.frame 使用自定义函数并使用 ggplot 和 gridExtra 绘图（对于 grid.arrange）

objectList = list(unemp.hp$x,unemp.hp$trend,unemp.hp$cycle)
names(objectList) = c("unemp","trend","cycle")


sapply(objectList,class)
#unemp trend cycle 
# "ts"  "ts"  "ts"

ts到data.frame的转换：

fn_ts_to_DF = function(x)  {

DF = data.frame(date=zoo::as.Date(time(objectList[[x]])),tseries=as.matrix(objectList[[x]])) 
colnames(DF)[2]=names(objectList)[x]
return(DF)
}


DFList=lapply(seq_along(objectList),fn_ts_to_DF)
names(DFList) = c("unemp","trend","cycle")

seriesTrend = merge(DFList$unemp,DFList$trend,by="date")
cycleSeries = DFList$cycle

情节：

gSeries = ggplot(melt(seriesTrend,"date"),aes(x=date,y=value,color=variable)) + geom_line() +
          ggtitle('Hodrick-Prescot Filter for unemp') + 
          theme(legend.title = element_blank(),legend.justification = c(0.1, 0.8), legend.position = c(0, 1),
          legend.direction = "horizontal",legend.background = element_rect(fill="transparent",size=.5, linetype="dotted"))
gCycle = ggplot(cycleSeries,aes(x=date,y=cycle)) + geom_line(color="#619CFF") + ggtitle("Cyclical component (deviations from trend)")

gComb = grid.arrange(gSeries,gCycle,nrow=2)

Answer 2

我尝试使用先前的答案，但对我没有用。

我从 GDP 季度系列中了解趋势和周期。

这个数据是一个时间序列，所以我做了这个，并且为我工作：

list <- list(gdp_ln$x, gdp_ln$trend, gdp_ln$cycle)

names(list)=c("gdp","trend","cycle")

gdp<- data.frame((sapply(list,c)))

数据：

> dput(gdp_ln)
structure(c(16.0275785360442, 16.0477176062761, 16.0718936895007, 
16.0899963371452, 16.0875707712141, 16.0981391378223, 16.0988601288276, 
16.1110815092797, 16.1244321329861, 16.1384685077996, 16.1451472350838, 
16.148178781735, 16.161163569502, 16.1418894206861, 16.1634877625667, 
16.1965372621761, 16.2216815829736, 16.2387677536829, 16.249412380526, 
16.2690521777631, 16.2812185880068, 16.2951024427095, 16.2964024092233, 
16.3127733881018, 16.3233290487177, 16.3369922768377, 16.3486515031696, 
16.3489275708763, 16.3451264371757, 16.3524856433069, 16.3666338513045, 
16.3801691039135, 16.3959993202765, 16.4135937981601, 16.4321203154987, 
16.4488104165345, 16.4344524213544, 16.4302554348621, 16.4240722287677, 
16.425087582257, 16.4350803035092, 16.4507216431126, 16.4670532627455, 
16.4985227751756, 16.5094864456079, 16.5352746165004, 16.5504689966469, 
16.5594976247513, 16.5754312535087, 16.592641573353, 16.6003340665324, 
16.6063100774853, 16.6163655606058, 16.6370227688187, 16.6564363783854, 
16.6577160570216, 16.6543595214556, 16.6773721241902, 16.6911082706925, 
16.6935398489076, 16.6956102943815, 16.6798673418354, 16.6772670544553, 
16.6678707780266, 16.6606889172344, 16.6678398460835, 16.6668473810049, 
16.676020524389, 16.6775934319312, 16.6882821147755, 16.6957985899994, 
16.7032334217472, 16.6926036544774, 16.7027214366522, 16.7103625977254, 
16.7105344224572, 16.7042504851486, 16.7063913529457, 16.7100598555556, 
16.6960591147037, 16.686477079594, 16.5740423808036, 16.6181175035946
), .Tsp = c(2000, 2020.5, 4), class = "ts")