LIS 4370:Module # 5 Doing Math

 

Module # 5 Doing Math



First, two matrices, A and B, with respective dimensions of 10x10 and 10x100, are defined in the manner that is described. The determinant of matrix A is then computed by the code using the det() function, and its non-zero value indicates whether matrix A is unique or invertible. When matrix A's determinant is zero, showing that it is singular, an alert stating that "Matrix A is singular and doesn't have an inverse" is printed. 

Following this, the code tries to print matrix A's inverse, but because of its singularity, the output message is not complete and shows "Inverse of matrix A:" and a blank line. The matrix A determinant is then printed by the code. A message reading "Determinant of matrix B cannot be calculated as it is not a square matrix" is printed because matrix B is not a square matrix, making it impossible to calculate the determinant. All things considered, the process shows how to determine matrix singularity, compute the determinant, and work with non-square matrices in R. 

The following output followed, as displayed in the screenshot:

> # Define matrices A and B

> A <- matrix(1:100, nrow = 10)

> B <- matrix(1:1000, nrow = 10)

> 

> # Calculate determinant of matrix A

> det_A <- det(A)

> 

> # Check if matrix A is invertible

> if (det_A != 0) {

+   # Matrix A is invertible, so calculate its inverse

+   A_inv <- solve(A)

+ } else {

+   # Matrix A is singular and doesn't have an inverse

+   cat("Matrix A is singular and doesn't have an inverse.\n")

+   A_inv <- NULL

+ }

Matrix A is singular and doesn't have an inverse.

> 

> # Print results for matrix A

> cat("Inverse of matrix A:\n")

Inverse of matrix A:

> if (!is.null(A_inv)) {

+   print(A_inv)

+ }

> cat("\nDeterminant of matrix A:", det_A, "\n")


Determinant of matrix A: 0 

> 

> # Print message for matrix B

> cat("\nDeterminant of matrix B cannot be calculated as it is not a square matrix.\n")


Determinant of matrix B cannot be calculated as it is not a square matrix.

Comments

Post a Comment

Popular posts from this blog

LIS 4370: Module # 10 Building your own R package

Module # 10 Building your own R package  GitHub Link:  https://github.com/agremer/LIS4370/blob/main/DESCRIPTION%20File DECRIPTION File : Package : DataVisTools Title : Alec Gremer's Data Visualization Test Implementation Version : 0.1.0.9000 Authors@R : "Alec Gremer, agremer@usf.edu [aut, cre]" Description : DataVisTools is an R package designed to streamline data visualization tasks by offering a versatile set of functions. It will include interactive visualization features, customizable plot aesthetics, statistical visualization tools, geospatial mapping capabilities, and time series analysis functions. Comprehensive documentation and an MIT License will ensure ease of use and widespread adoption. Implementation will prioritize S3 classes and methods for flexibility.  Depends : R (>= 3.1.2) License : CC0 LazyData : true For the final project, I proposed an R package named DataVisTools, which will serve as a comprehensive toolkit for data visualization tasks. Thi...
Read more

LIS 4317 Final Project: Fuel Economy Data from the U.S Dept. of Energy

Image
  LIS 4317 Final Project: Fuel Economy Data from the U.S Dept. of Energy Investigating the association between vehicle features and CO2 emissions in a dataset comprising fuel efficiency data is the issue statement for my final project. The objective is to ascertain the extent to which CO2 emissions differ among various vehicle classes. In order to evaluate the effects of automobiles on the environment and pinpoint possible areas where fuel efficiency regulations could be strengthened, further investigation is essential. The idea suggests that specific vehicle classes, like larger cars or ones with bigger engines, might have more CO2 emissions than other car classes. This issue is set within the larger framework of transportation research and environmental sustainability. The relationship between vehicle characteristics and emissions, particularly CO2 emissions, has been well studied in the past. Numerous techniques, such as statistical evaluations and graphics, have been used to in...
Read more

LIS 4317: Module # 10 assignment

Image
  Module # 10 assignment Time series data is important in many fields, such as finance, economics, and weather forecasting. It is defined as observations taken at repeated time intervals. Finding patterns, drawing conclusions, and coming to wise decisions all depend on the visualization of such data. First, we take a look at the 'economics' dataset that is integrated into ggplot2, which includes economic indicators for a number of years. This dataset contains variables like population, unemployment rate, and median length of unemployed. Using a time series graphic of the unemployment rate, we first investigate patterns and variations in the dataset.  By charting the median length of unemployment across time, we can see possible long-term trends or seasonal patterns. Furthermore, we explore combination visualization methods, comparing and contrasting several time series plots for investigation of correlation or comparability. The flexible framework of ggplot2 allows analysts an...
Read more