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

CodePudding user response：

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

CodePudding user response：

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)}