自定义函数参数格式咨询及R语言stoch.int函数运行问题求助

Fixing Your stoch.int() Output Issue #

It sounds like you’re hitting a common snag when working with course-provided R functions for stochastic finance—let’s break down how to get stoch.int() working properly first.

You’ve defined bm = bmsim(20,20), but the function isn’t returning valid output. The root cause is almost certainly a mismatch between what bmsim() produces and what stoch.int() expects as input. Here’s how to diagnose and fix this:

1. Check what bmsim() returns: Run str(bm) in your console to see the structure of your Brownian motion object. Is it a matrix (where each column is a path, rows are time steps), a list with named elements (like $paths and $time), or something else? For example, if bmsim() returns a list, stoch.int() might require you to pass just the path matrix (e.g., stoch.int(bm$paths) instead of the whole list).

2. Look at the stoch.int() code: Type stoch.int (without parentheses) into the console to view the function’s definition. This will show you exactly what parameters it takes and how they’re used. For instance, if the function starts with:

   stoch.int <- function(bm_matrix, time_step = 1) {
     # code that operates on bm_matrix
   }
   

   You’ll know to pass a matrix of paths and optionally specify the time step.

3. Test with a simple input: Create a basic Brownian motion matrix manually to see if stoch.int() works with it. For example:

   test_bm <- matrix(cumsum(rnorm(20*20)), nrow=20, ncol=20) # 20 time steps, 20 paths
   stoch.int(test_bm)
   

   If this works, then bmsim() is returning a structure that stoch.int() doesn’t handle natively—you’ll just need to extract the relevant component (like the path matrix) before passing it.

How to Format Custom Function Parameters #

When working with any custom function (yours or from your course), following these best practices will help you avoid parameter-related errors:

• Match the expected data type: Functions are picky about input types. If a function expects a numeric matrix, passing a data frame or list will cause errors. Use class(bm) or str(bm) to confirm your input’s type before passing it.

• Use named arguments to avoid order mistakes: If a function has multiple parameters, naming your inputs makes your code clearer and prevents mix-ups. For example, instead of stoch.int(bm, 0.05), write stoch.int(paths = bm, dt = 0.05)—this guarantees you’re sending values to the right parameters.

• Leverage default values: Many functions have optional parameters with default values. Check the function definition (by typing its name) to see which parameters you can omit. For example, if stoch.int() has a default dt = 0.01, you don’t need to specify it unless you want to change it.

• Understand required input structures: Some functions expect structured inputs, like a list with specific named elements (e.g., a Brownian motion object that includes both paths and time points). If the function’s code references input$time, make sure your input has that element.

• Validate inputs upfront: Quick checks like is.matrix(bm) or nrow(bm) == 20 can help you confirm your input meets the function’s requirements before running it.

内容的提问来源于stack exchange，提问作者J Chapman