10/22/2020 Final Project file:///C:/Users/leesu/Downloads/project.html 1/2 Final Project Important: Please create your GitHub repository on the AU-R-Programming organization by Monday (Nov. 2nd 2020)...

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Answer To: 10/22/2020 Final Project file:///C:/Users/leesu/Downloads/project.html 1/2 Final Project Important:...

Naveen answered on Oct 31 2021
153 Votes
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#' Simple Linear Regression
#'
#' The function \code{my_lm()} is used to fit simple linear regression model.
#'
#' @param response A vector or matrix
#' @param covariates A vector or matrix
#' @param alpha level of significance, the default significance level is **0.05**.
#'
#' @return Returns \code{Residuals}, \code{beta hat},
#' \code{sigma hat}, \code{Variance of beta hat},
#' \code{Confidence Intervals of beta}, \code{fitted values},
#' \code{response} and \code{covariate} values
#'
#' @author Naveen Kumar M.Sc., \emph{Email}: \email{info@saraswathianalytics.com} OR \emph{WhatsApp}: \href{https://wa.me/918688896472}{Click Here}
#' @examples
#' y <- c(25,36,12,45,26,82,14,35,21,45,32)
#' x <- c(10,35,62,42,15,32,18,24,38,26,43)
#' lm_model <- my_lm(response, covariates, alpha = 0.1)
#' print(lm_model)
#'
#' @seealso \link{my_qqplot} for making \code{Normal Q-Q} plot, \link{my_resid_fit} getting missing percentage
#' \link{my_hist_resid} for missing count, \link{my_MSPE} getting missing percentage
#' \link{my_F} for missing count
#'
#' @export
my_lm = function(response, covariates, alpha = 0.05) {
# Make sure data formats are appropriate
response <- as.vector(response)
covariates <- as.matrix(covariates)
# Define parameters
n <- length(response)
p <- ncol(covariates)
df <- n - p
# Estimate beta through Eq. (6.1)
beta.hat <- solve(t(covariates)%*%covariates)%*%t(covariates)%*%response
# Estimate of the residual variance (sigma2) from Eq. (6.3)
# Compute residuals
fitted.val <- covariates%*%as.matrix(beta.hat)
resid <- response - fitted.val
sigma2.hat <- (1/df)*t(resid)%*%resid
# Estimate of the variance of the estimated beta from Eq. (6.2)
var.beta <- sigma2.hat*solve(t(covariates)%*%covariates)
# Estimate of the confidence interval based on alpha
quant <- 1 - alpha/2
ci.beta <- c(beta.hat - qnorm(p = quant)*sqrt(var.beta), beta.hat +
qnorm(p = quant)*sqrt(var.beta))
# Return all estimated values
return(list(residuals= resid, beta = beta.hat,
sigma2 = sigma2.hat, variance_beta = var.beta,
ci = ci.beta, fitted.values = fitted.val,
Response = response, Covariates = covariates))
}
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Testing1/.Rproj.user/E408E4C2/sources/s-0DD8B8D6/29C2EAED-contents
#' Simple Linear Regression
#'
#' The function \code{my_lm()} is used to fit simple linear regression model.
#'
#' @param response A vector or matrix
#' @param covariates A vector or matrix
#' @param alpha level of significance, the default significance level is **0.05**.
#'
#' @return Returns \code{Residuals}, \code{beta hat},
#' \code{sigma hat}, \code{Variance of beta hat},
#' \code{Confidence Intervals of beta}, \code{fitted values},
#' \code{response} and \code{covariate} values
#'
#' @author Naveen Kumar M.Sc., \emph{Email}: \email{info@saraswathianalytics.com} OR \emph{WhatsApp}: \href{https://wa.me/918688896472}{Click Here}
#' @seealso \link{my_qqplot} for making \code{Normal Q-Q} plot, \link{my_resid_fit} residual vs fitted plot,
#' \link{my_hist_resid} for histogram of residuals, \link{my_MSPE} Calculating Mean Square Percentage Error
#' \link{my_F} for F cal, F cri & p values.
#'
#'
#' @examples
#' y <- c(25,36,12,45,26,82,14,35,21,45,32)
#' x <- c(10,35,62,42,15,32,18,24,38,26,43)
#' lm_model <- my_lm(response, covariates, alpha =...
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