"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
我有一个像上面这样的数据框，其列名为：“f”，“index”，“values”，“lo.80”，“lo.95”，“hi.80”，“hi.95”
我想要做的是计算特定时间戳的不同模型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，ets和bats中都有一个相应的行具有相同的时间戳值，因此加权平均值应该计算如下：
value_arima * 1/3 + values_ets * 1/3 + values_bats * 1/3;应计算lo.80和其他列的相似值。
此结果应存储在具有所有加权平均值的新数据框中。
新数据框可能如下所示：
index(timesamp from above dataframe),avg,avg_lo_80,avg_lo_95,avg_hi_80,avg_hi_95
我认为我需要使用spread（）和mutate（）函数来实现这一点。作为R的新手，我在形成这个数据帧之后无法继续。
请帮忙。
答案 0 :(得分：1)
您提供的示例不是加权平均值，而是简单平均值。
你想要的是一个简单的聚合。
第一部分是dput提供的数据集（此处更适合分享）
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对象中，并根据需要更改列名称。
如果你真的想要加权平均值，这是获得它的一种方法（这里蝙蝠的重量为0.8，另外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