计算R数据帧中的加权平均值

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

I have a dataframe like above which has column names as:"f","index","values","lo.80","lo.95","hi.80","hi.95".

What I want to do is calculate the weighted average on forecast results from different models for a particular timestamp. By this what i mean is

For every row in auto.arima there is a corresponding row in ets and bats with the same timestamp value, so weighted average should be calculated something like this:

value_arima*1/3 + values_ets*1/3 + values_bats*1/3 ; similary values for lo.80 and other columns should be calculated.

This result should be stored in a new dataframe with all the weighted average values.

New dataframe can look something like:

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

I think I need to use spread() and mutate () function to achieve this. Being new to R I'm unable to proceed after forming this dataframe.

Please help.

Answer 1

The example you provide is not a weighted average but a simple average. What you want is a simple aggregate. The first part is your dataset as provided by dput (better for sharing here)

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

You can save this output in an object and change the column names as you want.

If you really want a weighted average this is a way to obtain it (here bat has a weight of 0.8 and the 2 others 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