get_lin_least_sq_model: get_lin_least_sq_model

In rguin26/HW4:

Description

Performs linear regression using ordinary least squares (OLS) or weighted least squares (WLS), with either an intercept included or not. This works with both simple and multiple lienar regression. It calculates and returns the beta coefficients of the least squares linear regression equation, residuals, as well as model evaluation metrics including sum of squared errors (SSE), mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), r-squared, and adjusted r-squared

Usage

get_lin_least_sq_model(x, y, intercept = TRUE, weighted = FALSE)

Arguments

x	matrix, dataframe, or vector of all predictor/predictors and its/their respective values
y	vector of target values from the matrix of known predictor values
intercept	TRUE by default, it computes the beta coefficients with an intercept included; if it is set to FALSE, then no intercept is used
weighted	FALSE by default, it computes the beta coefficients using ordinary least squares (OLS); if it is set to TRUE, then the beta coefficients are calculated using weighted least squares (WLS)

Details

works only with numeric data

Value

A list of objects, including calculations of the estimates for the beta coefficients for each predictor, along with residuals and model evaluation metrics

• beta - vector of beta coefficients, with values corresponding to their respective column in x, and starting with (intercept) if intercept = TRUE

• fitted_values - vector consisting of the fitted values of the data, x, used to build the model, calculated simply as the model's prediction value for each instance in x

• residuals - vector consisting of the residuals of each instance in x, calculated as the fitted value minus the actual value for each instance in x

• model_eval_metrics - vector consisting of evaluation metrics of the model, including sum of squared errors (SSE), mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), r-squared, and adjusted r-squared

Examples

# Example 1
x <- sample(100, 30, replace=FALSE)
y <- sample(100, 30, replace=TRUE)
ols_result <- get_lin_least_sq_model(x, y)
wls_result <- get_lin_least_sq_model(x, y, weighted = TRUE)
ols_result_no_intercept <- get_lin_least_sq_model(x, y, intercept = FALSE)
wls_result_no_intercept <- get_lin_least_sq_model(x, y, intercept = FALSE, weighted = TRUE)

# Example 2
x <- matrix(sample(1000, 30*5, replace=FALSE), ncol = 5)
y <- sample(100, 30, replace=TRUE)
ols_result <- get_lin_least_sq_model(x, y)
wls_result <- get_lin_least_sq_model(x, y, weighted = TRUE)
ols_result_no_intercept <- get_lin_least_sq_model(x, y, intercept = FALSE)
wls_result_no_intercept <- get_lin_least_sq_model(x, y, intercept = FALSE, weighted = TRUE)

# Example 3
temp_data <- data.frame(
  hours=c(1, 1, 2, 2, 2, 3, 4, 4, 4, 5, 5, 5, 6, 6, 7, 8),
  age=c(3, 6, 7, 2, 4, 5, 6, 3, 4, 5, 2, 2, 4, 5, 9, 8),
  weight=c(23, 12, 31, 24, 56, 23, 12, 23, 34, 35, 36, 14, 23, 11, 13, 56),
  score=c(48, 78, 72, 70, 66, 92, 93, 75, 75, 80, 95, 97, 90, 96, 99, 99)
)
x <- temp_data
x$score <- NULL
y <- temp_data$score
ols_result <- get_lin_least_sq_model(x, y)
wls_result <- get_lin_least_sq_model(x, y, weighted = TRUE)
ols_result_no_intercept <- get_lin_least_sq_model(x, y, intercept = FALSE)
wls_result_no_intercept <- get_lin_least_sq_model(x, y, intercept = FALSE, weighted = TRUE)

rguin26/HW4 documentation built on Dec. 22, 2021, 3:09 p.m.