Generating sliding window to subset data for prediction task

Question

I want to write a sliding window function in order to use the model trained from t, t+1, and t+2 year to make prediction on the outcome of the t+3 year. This means that for a 10-year's data, the desired sliding window function should create 7 train-test splits and make 7 predictions (for the t+3, t+4, t+5, t+6, t+7, t+8, t+9 year).

I came up with the following code but the result doesn't ring the bell. Not only does the resulting object length differs, but even if I try to manually work through the prediction task, the predict function actually generates 3 predicted values for a single year's outcome, which doesn't make sense. It would be grateful if someone could point out the sources of the error.

# generate the data
set.seed(123)
df <- data.frame(year = 2000:2009,  # T = 10
           y = c(1, 1, 1, 1, 0, 0, 1, 0, 0, 0), 
           var1 = runif(10, min=0, max=1), 
           var2 = runif(10, min=1, max=2))

# store predicted values in a list
pred <- list()

# loop from the 1st year to the T-3 year

for(i in 2000:2007){
 df_sub1 <- subset(df, year == c(i, i+1, i+2)) 
 mod <- glm(y~var1+var2, data=df_sub1, family=binomial())
 df_sub2 <- subset(df, year == i+3)
 pred[[i]] <- predict(mod, data=df_sub2, type = "response")
}


# error message
Error in family$linkfun(mustart) : 
  Argument mu must be a nonempty numeric vector
In addition: Warning messages:
1: In year == c(i, i + 1, i + 2) :
  longer object length is not a multiple of shorter object length
2: In year == c(i, i + 1, i + 2) :
  longer object length is not a multiple of shorter object length

akrun · Accepted Answer · 2022-06-05T18:08:05.687

3

The error/warning is from using == when the rhs is of length > 1. Use %in%

pred <- vector('list', 8)
names(pred) <- 2000:2007
for(i in 2000:2007){
 df_sub1 <- subset(df, year %in% c(i, i+1, i+2)) 
 mod <- glm(y~var1+var2, data=df_sub1, family=binomial())
 df_sub2 <- subset(df, year == (i+3))
 pred[[as.character(i)]] <- tryCatch(predict(mod,
     newdata=df_sub2, type = "response"), error = function(e) NA_real_)
}

-output

> pred
$`2000`
4 
1 

$`2001`
5 
1 

$`2002`
6 
1 

$`2003`
           7 
2.220446e-16 

$`2004`
        8 
0.1467543 

$`2005`
          9 
0.001408577 

$`2006`
          10 
2.220446e-16 

$`2007`
[1] NA

edited Jun 05 '22 at 18:08

answered Jun 05 '22 at 17:53

akrun

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Thanks! But isn't the `predict()` function supposed to return a single predicted value for the `t+3` year's outcome variable? – Chris T. Jun 05 '22 at 17:58
@ChrisT. it dependds on the number of rows of `df_sub2` – akrun Jun 05 '22 at 18:02
Indeed, but `df_sub2` only has one row (just for the `t+3`-th year of data). This is where I still don't quite understand. – Chris T. Jun 05 '22 at 18:04
It is the one with the df_sub1, which returns 3 rows – akrun Jun 05 '22 at 18:05
@ChrisT. it should be `newdata=` – akrun Jun 05 '22 at 18:06
@ChrisT. I corrected in the code, now it should work. Also, added `tryCatch` when there are no rows – akrun Jun 05 '22 at 18:08
Thanks again. I think I mistook the argument `data` as `newdata` inside the `predict` function. But what does the `error = function(e) NA_real_` stands for? – Chris T. Jun 05 '22 at 18:10
@ChrisT. It is suggesting to catch the `error` and return `NA` (by default it is logical NA), so we use `N_real_` when the newdata have less than 1 row. If you don't use `N_real_` and the error i.e. `error = function(e) e)` it returns the error captured – akrun Jun 05 '22 at 18:12

score 3 · Answer 2 · answered Jun 05 '22 at 18:43

Here is another way with one of package zoo's functions to apply a function to a rolling window. The function to be applied, roll_pred is almost a copy&paste of akrun's, only the creation of the subsets is different.

# generate the data
set.seed(123)
df <- data.frame(year = 2000:2009,  # T = 10
                 y = c(1, 1, 1, 1, 0, 0, 1, 0, 0, 0), 
                 var1 = runif(10, min=0, max=1), 
                 var2 = runif(10, min=1, max=2))

library(zoo, quietly = TRUE)
#> 
#> Attaching package: 'zoo'
#> The following objects are masked from 'package:base':
#> 
#>     as.Date, as.Date.numeric

roll_pred <- function(year, X) {
  i <- match(year, X$year)
  df_sub1 <- X[i, ]
  mod <- glm(y ~ var1 + var2, data = df_sub1, family = binomial())
  df_sub2 <- X[ i[length(year)] + 1, ]
  tryCatch(predict(mod, newdata = df_sub2, type = "response"), 
           error = function(e) NA_real_)
}

rollapplyr(df$year, 3, roll_pred, X = df)
#>            4            5            6            7            8            9 
#> 1.000000e+00 1.000000e+00 1.000000e+00 2.220446e-16 1.467543e-01 1.408577e-03 
#>           10           NA 
#> 2.220446e-16           NA

^{Created on 2022-06-05 by the reprex package (v2.0.1)}

Generating sliding window to subset data for prediction task

2 Answers2