计算 R 数据框中的加权平均值

Question

"f","index","values","lo.80","lo.95","hi.80","hi.95"

"auto.arima",2017-07-31 16:40:00,2.81613884762163,NA,NA,NA,NA

"auto.arima",2017-07-31 16:40:10,2.83441637197378,NA,NA,NA,NA

"auto.arima",2017-07-31 20:39:10,3.18497899649267,2.73259824384436,2.49312233904087,3.63735974914098,3.87683565394447

"auto.arima",2017-07-31 20:39:20,3.16981166809297,2.69309866988864,2.44074205235297,3.64652466629731,3.89888128383297

"ets",2017-07-31 16:40:00,2.93983529828936,NA,NA,NA,NA

"ets",2017-07-31 16:40:10,3.09739640066054,NA,NA,NA,NA

"ets",2017-07-31 20:39:10,3.1951571771414,2.80966705285567,2.60560090776504,3.58064730142714,3.78471344651776

"ets",2017-07-31 20:39:20,3.33876776870274,2.93593322313957,2.72268549604222,3.7416023142659,3.95485004136325

"bats",2017-07-31 16:40:00,2.82795253090081,NA,NA,NA,NA

"bats",2017-07-31 16:40:10,2.96389759682623,NA,NA,NA,NA

"bats",2017-07-31 20:39:10,3.1383560278272,2.76890864400062,2.573335012715,3.50780341165378,3.7033770429394

"bats",2017-07-31 20:39:20,3.3561357998535,2.98646195085452,2.79076843614824,3.72580964885248,3.92150316355876

我有一个类似上面的数据框，其列名称为：“f”、“index”、“values”、“lo.80”、“lo.95”、“hi.80”、“hi.95” .

我想要做的是计算来自不同模型的特定时间戳的预测结果的加权平均值。我的意思是

对于 auto.arima 中的每一行，在 ets 和 bats 中都有对应的行具有相同的时间戳值，因此加权平均值应按如下方式计算：

value_arima*1/3 + values_ets*1/3 + values_bats*1/3 ;应该计算 lo.80 和其他列的相似值。

这个结果应该与所有加权平均值一起存储在一个新的数据框中。

新的数据框可能类似于：

index(timesamp from above dataframe),avg,avg_lo_80,avg_lo_95,avg_hi_80,avg_hi_95

我想我需要使用 spread() 和 mutate() 函数来实现这一点。作为 R 的新手，我在形成此数据框后无法继续。

请帮忙。

Answer 1

您提供的示例不是加权平均，而是简单平均。 你想要的是一个简单的聚合。 第一部分是dput 提供的数据集（最好在这里分享）

d <- structure(list(f = structure(c(1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 
2L, 2L, 2L, 2L), .Label = c("auto.arima", "bats", "ets"), class = "factor"), 
index = structure(c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 
3L, 4L), .Label = c("2017-07-31 16:40:00", "2017-07-31 16:40:10", 
"2017-07-31 20:39:10", "2017-07-31 20:39:20"), class = "factor"), 
values = c(2.81613884762163, 2.83441637197378, 3.18497899649267, 
3.16981166809297, 2.93983529828936, 3.09739640066054, 3.1951571771414, 
3.33876776870274, 2.82795253090081, 2.96389759682623, 3.1383560278272, 
3.3561357998535), lo.80 = c(NA, NA, 2.73259824384436, 2.69309866988864, 
NA, NA, 2.80966705285567, 2.93593322313957, NA, NA, 2.76890864400062, 
2.98646195085452), lo.95 = c(NA, NA, 2.49312233904087, 2.44074205235297, 
NA, NA, 2.60560090776504, 2.72268549604222, NA, NA, 2.573335012715, 
2.79076843614824), hi.80 = c(NA, NA, 3.63735974914098, 3.64652466629731, 
NA, NA, 3.58064730142714, 3.7416023142659, NA, NA, 3.50780341165378, 
3.72580964885248), hi.95 = c(NA, NA, 3.87683565394447, 3.89888128383297, 
NA, NA, 3.78471344651776, 3.95485004136325, NA, NA, 3.7033770429394, 
3.92150316355876)), .Names = c("f", "index", "values", "lo.80", 
"lo.95", "hi.80", "hi.95"), class = "data.frame", row.names = c(NA, 
-12L))

> aggregate(d[,3:7], by = d["index"], FUN = mean)
                index   values    lo.80    lo.95    hi.80    hi.95
1 2017-07-31 16:40:00 2.861309       NA       NA       NA       NA
2 2017-07-31 16:40:10 2.965237       NA       NA       NA       NA
3 2017-07-31 20:39:10 3.172831 2.770391 2.557353 3.575270 3.788309
4 2017-07-31 20:39:20 3.288238 2.871831 2.651399 3.704646 3.925078

您可以将此输出保存在一个对象中并根据需要更改列名。

如果你真的想要一个加权平均值，这是一种获得它的方法（这里 bat 的权重为 0.8，其他 2 个为 0.1）：

> d$weight <- (d$f)
> levels(d$weight) # check the levels
[1] "auto.arima" "bats"       "ets"       
> levels(d$weight) <- c(0.1, 0.8, 0.1)
> # transform the factor into numbers
> # warning as.numeric(d$weight) is not correct !!
> d$weight <- as.numeric(as.character((d$weight))) 
> 
> # Here the result is saved in a data.frame called "result
> result <- aggregate(d[,3:7] * d$weight, by = d["index"], FUN = sum)
> result
                index   values    lo.80    lo.95    hi.80    hi.95
1 2017-07-31 16:40:00 2.837959       NA       NA       NA       NA
2 2017-07-31 16:40:10 2.964299       NA       NA       NA       NA
3 2017-07-31 20:39:10 3.148698 2.769353 2.568540 3.528043 3.728857
4 2017-07-31 20:39:20 3.335767 2.952073 2.748958 3.719460 3.922576