R项目和MQL4将R中的forecast对象转换为Vector

我正在R中使用forecast包，这将创建一个forecast对象

我希望将预测转换为向量，以便在MQL4代码中使用7bit包装器和R

预测代码示例：

> forecast(fit, h=5)
     Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
1057       1.605098 1.602110 1.608087 1.600528 1.609668
1058       1.605109 1.600891 1.609327 1.598658 1.611561
1059       1.604868 1.599723 1.610012 1.597000 1.612735
1060       1.604978 1.599037 1.610919 1.595892 1.614065
1061       1.605162 1.598511 1.611813 1.594990 1.615335

我希望能够以某种方式将这些预测、lo 80、hi 80等存储在一个向量中，这样我就可以将它们从R中拉出来并放入MQL4中，以便在指示器中使用

我试过：

> test1 <- forecast(fit, h=5)
> test1
     Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
1057       1.605098 1.602110 1.608087 1.600528 1.609668
1058       1.605109 1.600891 1.609327 1.598658 1.611561
1059       1.604868 1.599723 1.610012 1.597000 1.612735
1060       1.604978 1.599037 1.610919 1.595892 1.614065
1061       1.605162 1.598511 1.611813 1.594990 1.615335

如果我运行head，结构将显示为：

> head(test1)
$method
[1] "ARIMA(2,1,2)                   "

$model
Series: mt4test$close 
ARIMA(2,1,2)                    

Coefficients:
          ar1      ar2     ma1     ma2
      -0.5030  -0.9910  0.4993  0.9783
s.e.   0.0123   0.0089  0.0202  0.0140

sigma^2 estimated as 5.437e-06:  log likelihood=4897.31
AIC=-9784.61   AICc=-9784.55   BIC=-9759.81

$level
[1] 80 95

$mean
Time Series:
Start = 1057 
End = 1061 
Frequency = 1 
[1] 1.605098 1.605109 1.604868 1.604978 1.605162

$lower
          80%      95%
[1,] 1.602110 1.600528
[2,] 1.600891 1.598658
[3,] 1.599723 1.597000
[4,] 1.599037 1.595892
[5,] 1.598511 1.594990

$upper
          80%      95%
[1,] 1.608087 1.609668
[2,] 1.609327 1.611561
[3,] 1.610012 1.612735
[4,] 1.610919 1.614065
[5,] 1.611813 1.615335

任何帮助都将不胜感激。它阻止我继续我的修补工作，哈哈

提前感谢。

函数

forecast（）

生成列表。使用函数

str（）

可以检查此对象的结构，使用函数

names（）

可以查看此列表中每个元素的名称

library(forecast)
fit <- Arima(WWWusage,c(3,1,0))
test1<-forecast(fit)

names(test1)
[1] "method"    "model"     "level"     "mean"      "lower"     "upper"     "x"        
[8] "xname"     "fitted"    "residuals"

 #to extract forecast
test1$mean

Time Series:
Start = 101 
End = 110 
Frequency = 1 
 [1] 219.6608 219.2299 218.2766 217.3484 216.7633 216.3785 216.0062 215.6326 215.3175 215.0749

 #or as vector
as.vector(test1$mean)
 [1] 219.6608 219.2299 218.2766 217.3484 216.7633 216.3785 216.0062 215.6326 215.3175 215.0749

 #to extract upper interval
test1$upper

           80%      95%
 [1,] 223.5823 225.6582
 [2,] 228.5332 233.4581
 [3,] 232.7151 240.3585
 .... .... ....
[10,] 260.7719 284.9625

 #to extract lower interval
test1$lower

 #to extract only 95% upper interval
test1$upper[,2]

库（预测）
完全符合我想要的，谢谢你快速准确的回答！
library(forecast)
fit <- Arima(WWWusage,c(3,1,0))
test1<-forecast(fit)

names(test1)
[1] "method"    "model"     "level"     "mean"      "lower"     "upper"     "x"        
[8] "xname"     "fitted"    "residuals"

 #to extract forecast
test1$mean

Time Series:
Start = 101 
End = 110 
Frequency = 1 
 [1] 219.6608 219.2299 218.2766 217.3484 216.7633 216.3785 216.0062 215.6326 215.3175 215.0749

 #or as vector
as.vector(test1$mean)
 [1] 219.6608 219.2299 218.2766 217.3484 216.7633 216.3785 216.0062 215.6326 215.3175 215.0749

 #to extract upper interval
test1$upper

           80%      95%
 [1,] 223.5823 225.6582
 [2,] 228.5332 233.4581
 [3,] 232.7151 240.3585
 .... .... ....
[10,] 260.7719 284.9625

 #to extract lower interval
test1$lower

 #to extract only 95% upper interval
test1$upper[,2]