计算R dataframe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，其列名为：“f”，“index”，“values”，“lo.80”，“lo.95”，“hi 0.80" ， “hi.95”。

我想要做的是计算来自特定时间戳的不同模型的预测结果的加权平均值。通过这我的意思是

对于auto.arima每一行有在ETS和蝙蝠相同的时间戳值对应的行，所以加权平均数来计算是这样的：

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。

这个结果应该存储在一个新的数据框中，并加上所有的加权平均值。

新的数据帧可以是这个样子：

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

我想我需要使用传播（）和变异（）函数来实现这一目标。对R来说是新的，我无法在形成这个数据框后继续。

请帮忙。

Answer 1

您提供的示例不是加权平均值，而是简单的平均值。 你想要的是一个简单的聚合。 第一部分是由dput（更好地分享这里）提供的数据集

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 

，你想，你可以保存在一个对象这个输出和改变的列名。

如果你真的想要一个加权平均值，这是一种方式来获得它（这里蝙蝠具有0.8的权重和2个其他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