# R/my_lm.R In Cherry-ty-Pan/STAT302package: Stat 302 Project 3

#### Documented in my_lm

```#' \code{my_lm} Function
#'
#' This function fits a linear model in R.
#'
#' @param formula a \code{formula} class object.
#' @param data an input data frame.
#'
#' @keywords inference
#'
#' @return a \code{table} with rows for each coefficient and columns for the \code{Estimate},
#' \code{Std. Error}, \code{t value}, and \code{Pr(>|t|)}.
#'
#' @examples
#' my_lm(mpg ~ hp, data = mtcars)
#'
#' @export
# Create the my_lm function with formula and data inputs
my_lm <- function(formula, data) {
# Get model matrix X with formula and data inputs
X <- model.matrix(formula, data)
# Get the model frame object with formula and data inputs and save as `frame`
frame <- model.frame(formula, data)
# Get the model response Y from `frame`
Y <- model.response(frame)
# Get the linear regression coefficients and save as `beta`
beta <- solve(t(X) %*% X) %*% t(X) %*% Y
# Get the df which is equal sample size(rows of data) minus number of
# covariates (columns of X)
d_f <- nrow(data) - ncol(X)
# Get the sigma square using formula and save as `sig_sqr`
sig_sqr <- sum((Y - (X %*% beta)) ^ 2 / d_f)
# Get the standard error for the coefficients using formula and save as
# `se_beta`
se_beta <- diag(sqrt(sig_sqr * solve(t(X) %*% X)))
# Get the t value for the coefficients amd save as `t_obs`
t_obs <- beta / se_beta
# Get the p value and save as `t_obs`
p_val <- 2 * pt(abs(t_obs), d_f, lower.tail = F)
# Create a list containing `Estimate`, `Std. Error`, `t value`, and `Pr(>|t|)`
result <- list("Estimate" = beta,
"Std. Error" = se_beta,
"t value" = t_obs,
"Pr(>|t|)" = p_val)
# Return the list
return(result)
}
```
Cherry-ty-Pan/STAT302package documentation built on Dec. 17, 2021, 2 p.m.