Creating Function to Make Groups in Data.table Based on Predicted Outcome and Compute Mean Difference Confidence Intervals

Introduction

In this blog post, we will explore how to create a function that groups data based on predicted outcomes and computes the mean difference confidence intervals for observed outcomes. We will use R and the data.table package for this task.

The problem is as follows:

We have a sample of 100,000 observations with dummy (binary), observed values, and predicted values.
We need to create groups from 1 to 20 based on predicted outcomes.
For each group, we need to compute the difference in average observed outcomes for observations with dummy == 0 and dummy == 1.
Finally, we need to create two columns with lower and upper confidence intervals (using a difference of means standard errors).

Data Preparation

First, let’s prepare our data. We have a data.table object DT containing the dummy, observed values, and predicted values.

# Load required libraries
library(data.table)
library(haven)

# Create sample data
set.seed(123)
n <- 100000
DT <- data.table(
    dummy = rbinom(n, 1, 0.4),
    observed = 50 + sample.int(52, size = n, replace = TRUE),
    predicted = sample.int(102, size = n, replace = TRUE)
)

# View the first few rows of the data
head(DT)

Creating Function to Make Groups

Next, let’s create a function that groups our data based on predicted outcomes.

# Define a function to make groups
make_groups <- function(DT) {
    # Create a factor for the group variable
    DT$group <- cut(DT$predicted, breaks = seq(1, 20), labels = FALSE)
    
    return(DT)
}

# Call the function and view the results
DT_grouped <- make_groups(DT)

# View the first few rows of the grouped data
head(DT_grouped)

Computing Mean Difference Confidence Intervals

Now, let’s compute the mean difference confidence intervals for observed outcomes.

# Define a function to compute mean differences
compute_mean_differences <- function(DT) {
    # Group by group and calculate mean differences
    DT$mean_difference <- with(DT, {
        dummies_0 <- median(observed[!dummy], na.rm = TRUE)
        dummies_1 <- median(observed[dummy], na.rm = TRUE)
        (dummies_1 - dummies_0) * group
    })
    
    # Compute confidence intervals for mean differences using delta method
    DT$lower_bound <- with(DT, {
        sd <- sd(mean_difference)
        mean_difference - 1.96 * sd / sqrt(n())
    })
    DT$upper_bound <- with(DT, {
        sd <- sd(mean_difference)
        mean_difference + 1.96 * sd / sqrt(n())
    })
    
    return(DT)
}

# Call the function and view the results
DT_with_confidence_intervals <- compute_mean_differences(DT_grouped)

# View the first few rows of the data with confidence intervals
head(DT_with_confidence_intervals)

Conclusion

In this blog post, we created a function that groups our data based on predicted outcomes and computes the mean difference confidence intervals for observed outcomes. We used R and the data.table package to achieve this.

Please note that the code provided is just an example and may need to be adapted to your specific use case.

Last modified on 2024-10-18