R 从预测对象中提取价值

我正在使用fpp2包中的数据集和forecast包中的forecast函数的组合进行造林。此预测的输出是带有SNAVE_模型的对象列表。此对象包含来自消费、收入、生产和储蓄的预测

library(dplyr)
library(forecast)
library(fpp2)

MY_DATA<-uschange[,1:4]

# 1.FORECAST FUNCTION          
FORECASTING_FUNCTION_SNAIVE <- function(Z, hrz = 5) {
  timeseries <- msts(Z, start = 1970, seasonal.periods = 4)
  forecast <- snaive(timeseries, biasadj = TRUE, h =  hrz)
}     
FORECASTING_LIST_SNAIVE <- lapply(X = MY_DATA, FORECASTING_FUNCTION_SNAIVE)

# 2.FORECASTING
 SNAIVE_MODELS_ALL<-lapply(FORECASTING_LIST_SNAIVE,  forecast)  

我尝试使用这段代码，但我不能只提取平均值

test<-lapply(SNAIVE_MODELS_ALL,ts.union)

---

test您就快到了。如果您对Snave_MODELS_ALL进行lappy，它将迭代列表中的每个顶级元素，例如Snave_MODELS_ALL[[“消费”]]。所以剩下的就是调用每个元素的平均值

lapply(SNAIVE_MODELS_ALL,function(i)i$mean)
# or lapply(SNAIVE_MODELS_ALL,"[[","mean")

$Consumption
          Qtr1      Qtr2      Qtr3      Qtr4
2016                               0.5616798
2017 0.4046822 1.0477074 0.7295978 0.5616798

$Income
          Qtr1      Qtr2      Qtr3      Qtr4
2016                               0.7400626
2017 0.5190254 0.7237208 0.6447008 0.7400626

$Production
           Qtr1       Qtr2       Qtr3       Qtr4
2016                                  -0.8455464
2017 -0.4179305 -0.2033188  0.4749184 -0.8455464

$Savings
           Qtr1       Qtr2       Qtr3       Qtr4
2016                                   3.4827860
2017  2.2365341 -2.7215011 -0.5728579  3.4827860

lapply(SNAIVE_MODELS_ALL,function(i)i$mean)
# or lapply(SNAIVE_MODELS_ALL,"[[","mean")

$Consumption
          Qtr1      Qtr2      Qtr3      Qtr4
2016                               0.5616798
2017 0.4046822 1.0477074 0.7295978 0.5616798

$Income
          Qtr1      Qtr2      Qtr3      Qtr4
2016                               0.7400626
2017 0.5190254 0.7237208 0.6447008 0.7400626

$Production
           Qtr1       Qtr2       Qtr3       Qtr4
2016                                  -0.8455464
2017 -0.4179305 -0.2033188  0.4749184 -0.8455464

$Savings
           Qtr1       Qtr2       Qtr3       Qtr4
2016                                   3.4827860
2017  2.2365341 -2.7215011 -0.5728579  3.4827860