Building a nxn Matrix of Functions and Parameters

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In this article, we will explore how to build a square matrix (nxn) where each column represents a function and each row represents a parameter. We’ll start by understanding the problem statement and then dive into the code.

Problem Statement

We are given a set of functions (FUN1 to FUN10) that take in two parameters: data and a parameter value (P1 to P10). Each function returns a result. Our goal is to create an nxn matrix where each column represents one of the functions, and each row represents one of the parameter values.

Solution Overview

We can solve this problem using various methods, including:

Using expand.grid from base R
Using tidyverse packages (dplyr, tidyr, stringr)
Applying functional programming concepts

In this article, we’ll explore the first two approaches.

Approach 1: Using `expand.grid`

We can use expand.grid to create a data frame with all possible combinations of:

The sequence of n, where n is the size of our matrix
The functions (FUN) from 1 to 10
The parameter names (P) from 1 to 10

# Define n and generate the grid
n <- 10
d1 <- expand.grid(n = seq_len(n), f = paste0("FUN", 1:10),
       p = paste0("P", 1:10), stringsAsFactors = FALSE)

Next, we can loop over each row in the d1 data frame and apply the corresponding function to our data. We’ll use match.fun to convert the function name into a valid R function object.

# Apply the functions to our data
a <- do.call(rbind, lapply(seq_len(nrow(d1)), function(i) 
       match.fun(d1$f[i])(data, get(d1$p[i]), d1$n[i])))

Finally, we can convert the resulting list of vectors into a matrix.

# Convert to a matrix
MM <- matrix(a, nrow = 10, byrow = FALSE)

Approach 2: Using `tidyverse`

Alternatively, we can use the tidyverse packages (dplyr, tidyr, stringr) to achieve the same result.

# Load necessary libraries
library(dplyr)
library(tidyr)
library(stringr)

# Define n and create a crossing data frame
n <- 10
crossing(n = seq_len(n), f = paste0("FUN", 1:10),
         p = str_c("P", 1:10)) %>%

Next, we can use pmap to apply the functions to our data.

# Apply the functions to our data using pmap
result <- crossing(n = seq_len(n), f = paste0("FUN", 1:10),
       p = str_c("P", 1:10)) %>%
  pmap(~ match.fun(..2)(data, get(..3), ..1))

Finally, we can use unlist and matrix to convert the resulting list of vectors into a matrix.

# Convert to a matrix
MM <- result %>% unlist() %>% matrix(., nrow = 10, byrow = FALSE)

Conclusion

In this article, we’ve explored two approaches to building an nxn matrix where each column represents a function and each row represents a parameter. We used expand.grid from base R and the tidyverse packages (dplyr, tidyr, stringr) to achieve our goal.

We hope this article has been helpful in understanding how to build such an matrix. Let us know if you have any questions or need further clarification on any of the steps.

Last modified on 2024-11-14