From 068a8521f5e6fc999785190f697b4ead5a4f12d0 Mon Sep 17 00:00:00 2001
From: prach896 <prach896@student.liu.se>
Date: Sun, 6 Oct 2024 17:08:57 +0200
Subject: [PATCH] Removing doc, meta from gitignore
---
.gitignore | 3 -
DESCRIPTION | 1 +
Meta/vignette.rds | Bin 0 -> 201 bytes
doc/lab4-vignette.R | 36 +++
doc/lab4-vignette.Rmd | 78 ++++++
doc/lab4-vignette.html | 532 ++++++++++++++++++++++++++++++++++++
vignettes/lab4-vignette.Rmd | 13 +-
7 files changed, 659 insertions(+), 4 deletions(-)
create mode 100644 Meta/vignette.rds
create mode 100644 doc/lab4-vignette.R
create mode 100644 doc/lab4-vignette.Rmd
create mode 100644 doc/lab4-vignette.html
diff --git a/.gitignore b/.gitignore
index c6b2f05..1f2321e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,3 @@
*.Rproj
.Rproj.user
.Rbuildignore
-inst/doc
-/doc/
-/Meta/
diff --git a/DESCRIPTION b/DESCRIPTION
index f6d9372..5b6a0b6 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -21,3 +21,4 @@ Suggests:
Config/testthat/edition: 3
Encoding: UTF-8
VignetteBuilder: knitr
+Config/build/clean-inst-doc: FALSE
diff --git a/Meta/vignette.rds b/Meta/vignette.rds
new file mode 100644
index 0000000000000000000000000000000000000000..90c8b800b9a446258d089a7f176521410d7887f6
GIT binary patch
literal 201
zcmb2|=3oE=w(bW>2?+^F35kg*2}x{5Gg}*3qy@Mc43(L=nbjwVo$>TBx~O~F=e)PB
zrsq}eQ@)PD2bh8y)YMv5X)abVXZK~bpS97UouhfF;wgt`Mv9v0XT8t(>be;>-&NhJ
zV5-lm@`rzS-Ld}}Q5JiGwNHQUPN)jFvf-f3lW8+2b8R+P+01e-k*}bz*+{ba!rg{M
zyRP4s-Jec>O{jao%394l;Rip<3pw9&o;q&dHNw4;JQu}92?p4oxXUop;lJ<>Mg|4|
D@AgfH
literal 0
HcmV?d00001
diff --git a/doc/lab4-vignette.R b/doc/lab4-vignette.R
new file mode 100644
index 0000000..9a70114
--- /dev/null
+++ b/doc/lab4-vignette.R
@@ -0,0 +1,36 @@
+## ----include = FALSE----------------------------------------------------------
+knitr::opts_chunk$set(
+ collapse = TRUE,
+ comment = "#>"
+)
+
+## ----setup--------------------------------------------------------------------
+library(lab4)
+
+## -----------------------------------------------------------------------------
+data(iris)
+
+## -----------------------------------------------------------------------------
+ linreg_mod <- linreg$new(Petal.Length~Sepal.Width+Sepal.Length, data=iris)
+
+## -----------------------------------------------------------------------------
+linreg_mod$print()
+
+## -----------------------------------------------------------------------------
+plots <- linreg_mod$plot()
+print(plots[[1]])
+print(plots[[2]])
+
+## -----------------------------------------------------------------------------
+# Get residuals
+residuals <- linreg_mod$resid()
+print(residuals)
+
+## -----------------------------------------------------------------------------
+# Get predicted values
+predictions <- linreg_mod$pred()
+print(predictions)
+
+## -----------------------------------------------------------------------------
+linreg_mod$summary()
+
diff --git a/doc/lab4-vignette.Rmd b/doc/lab4-vignette.Rmd
new file mode 100644
index 0000000..a712094
--- /dev/null
+++ b/doc/lab4-vignette.Rmd
@@ -0,0 +1,78 @@
+---
+title: "lab4"
+author: "Udaya Shanker Mohanan Nair, Pranav Pankaj Chandode"
+output: rmarkdown::html_vignette
+vignette: >
+ %\VignetteIndexEntry{lab4}
+ %\VignetteEngine{knitr::rmarkdown}
+ %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+ collapse = TRUE,
+ comment = "#>"
+)
+```
+
+```{r setup}
+library(lab4)
+```
+
+
+## Introduction
+This vignette illustrates how to use lab4 package to perform linear progression models and to perform various special functions like print(), plot(), resid(), pred(),coef() and summary().
+
+## DataSet
+For testing purpose, here we are using iris dataset, containing measurements of features of flower iris. To load data use the following:
+```{r}
+data(iris)
+```
+## Invoking linreg function
+This Function will take two parameters namely formula and data. Type of formula is an expression specifying the relationship between dependent and independent variables, whereas data is the dataset(iris in this case).
+```{r}
+ linreg_mod <- linreg$new(Petal.Length~Sepal.Width+Sepal.Length, data=iris)
+```
+## Print Function
+Printing the coefficients and coefficients name of the model generated using linreg function.This can be invoked by the following
+```{r}
+linreg_mod$print()
+```
+## Plot Function
+Plotting for residuals against Fitted Values and for Scale-Locations against sqaure root of standardized residuals.
+```{r}
+plots <- linreg_mod$plot()
+print(plots[[1]])
+print(plots[[2]])
+```
+## Resid Function
+Resid Function is to display the residuals of the model as a vector.
+```{r}
+# Get residuals
+residuals <- linreg_mod$resid()
+print(residuals)
+```
+## pred Function
+pred Function is to display the predicted values of the model.
+```{r}
+# Get predicted values
+predictions <- linreg_mod$pred()
+print(predictions)
+```
+## Summary Function
+Summary function is to display coefficients along with standard error, t-values, p-values with significance.
+```{r}
+linreg_mod$summary()
+```
+## Conclusion
+This Linreg function helps to find Linear regression of models to find out Regression coefficients, fitted values, residuals, degree of freedom, residual variance, variance of regression coefficients and different ways to print, plot the model coefficients.
+---
+
+## Instructions to Include the Vignette in Your Package
+
+1. **Save the Vignette**: Save the above content in a file named `using_linreg.Rmd` within the `vignettes/` directory of your package.
+
+2. **Build the Vignette**: Ensure that you have the necessary packages installed for building vignettes, such as `knitr` and `rmarkdown`. Then, build your package using:
+
+ ```r
+ devtools::build_vignettes()
\ No newline at end of file
diff --git a/doc/lab4-vignette.html b/doc/lab4-vignette.html
new file mode 100644
index 0000000..39d4fd8
--- /dev/null
+++ b/doc/lab4-vignette.html
@@ -0,0 +1,532 @@
+<!DOCTYPE html>
+
+<html>
+
+<head>
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+<meta name="viewport" content="width=device-width, initial-scale=1" />
+
+<meta name="author" content="Udaya Shanker Mohanan Nair, Pranav Pankaj Chandode" />
+
+
+<title>lab4</title>
+
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
+</script>
+
+<style type="text/css">
+code{white-space: pre-wrap;}
+span.smallcaps{font-variant: small-caps;}
+span.underline{text-decoration: underline;}
+div.column{display: inline-block; vertical-align: top; width: 50%;}
+div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+ul.task-list{list-style: none;}
+</style>
+
+
+
+<style type="text/css">
+code {
+white-space: pre;
+}
+.sourceCode {
+overflow: visible;
+}
+</style>
+<style type="text/css" data-origin="pandoc">
+pre > code.sourceCode { white-space: pre; position: relative; }
+pre > code.sourceCode > span { line-height: 1.25; }
+pre > code.sourceCode > span:empty { height: 1.2em; }
+.sourceCode { overflow: visible; }
+code.sourceCode > span { color: inherit; text-decoration: inherit; }
+div.sourceCode { margin: 1em 0; }
+pre.sourceCode { margin: 0; }
+@media screen {
+div.sourceCode { overflow: auto; }
+}
+@media print {
+pre > code.sourceCode { white-space: pre-wrap; }
+pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+}
+pre.numberSource code
+{ counter-reset: source-line 0; }
+pre.numberSource code > span
+{ position: relative; left: -4em; counter-increment: source-line; }
+pre.numberSource code > span > a:first-child::before
+{ content: counter(source-line);
+position: relative; left: -1em; text-align: right; vertical-align: baseline;
+border: none; display: inline-block;
+-webkit-touch-callout: none; -webkit-user-select: none;
+-khtml-user-select: none; -moz-user-select: none;
+-ms-user-select: none; user-select: none;
+padding: 0 4px; width: 4em;
+color: #aaaaaa;
+}
+pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
+div.sourceCode
+{ }
+@media screen {
+pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
+}
+code span.al { color: #ff0000; font-weight: bold; }
+code span.an { color: #60a0b0; font-weight: bold; font-style: italic; }
+code span.at { color: #7d9029; }
+code span.bn { color: #40a070; }
+code span.bu { color: #008000; }
+code span.cf { color: #007020; font-weight: bold; }
+code span.ch { color: #4070a0; }
+code span.cn { color: #880000; }
+code span.co { color: #60a0b0; font-style: italic; }
+code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; }
+code span.do { color: #ba2121; font-style: italic; }
+code span.dt { color: #902000; }
+code span.dv { color: #40a070; }
+code span.er { color: #ff0000; font-weight: bold; }
+code span.ex { }
+code span.fl { color: #40a070; }
+code span.fu { color: #06287e; }
+code span.im { color: #008000; font-weight: bold; }
+code span.in { color: #60a0b0; font-weight: bold; font-style: italic; }
+code span.kw { color: #007020; font-weight: bold; }
+code span.op { color: #666666; }
+code span.ot { color: #007020; }
+code span.pp { color: #bc7a00; }
+code span.sc { color: #4070a0; }
+code span.ss { color: #bb6688; }
+code span.st { color: #4070a0; }
+code span.va { color: #19177c; }
+code span.vs { color: #4070a0; }
+code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; }
+</style>
+<script>
+// apply pandoc div.sourceCode style to pre.sourceCode instead
+(function() {
+ var sheets = document.styleSheets;
+ for (var i = 0; i < sheets.length; i++) {
+ if (sheets[i].ownerNode.dataset["origin"] !== "pandoc") continue;
+ try { var rules = sheets[i].cssRules; } catch (e) { continue; }
+ var j = 0;
+ while (j < rules.length) {
+ var rule = rules[j];
+ // check if there is a div.sourceCode rule
+ if (rule.type !== rule.STYLE_RULE || rule.selectorText !== "div.sourceCode") {
+ j++;
+ continue;
+ }
+ var style = rule.style.cssText;
+ // check if color or background-color is set
+ if (rule.style.color === '' && rule.style.backgroundColor === '') {
+ j++;
+ continue;
+ }
+ // replace div.sourceCode by a pre.sourceCode rule
+ sheets[i].deleteRule(j);
+ sheets[i].insertRule('pre.sourceCode{' + style + '}', j);
+ }
+ }
+})();
+</script>
+
+
+
+
+<style type="text/css">body {
+background-color: #fff;
+margin: 1em auto;
+max-width: 700px;
+overflow: visible;
+padding-left: 2em;
+padding-right: 2em;
+font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
+font-size: 14px;
+line-height: 1.35;
+}
+#TOC {
+clear: both;
+margin: 0 0 10px 10px;
+padding: 4px;
+width: 400px;
+border: 1px solid #CCCCCC;
+border-radius: 5px;
+background-color: #f6f6f6;
+font-size: 13px;
+line-height: 1.3;
+}
+#TOC .toctitle {
+font-weight: bold;
+font-size: 15px;
+margin-left: 5px;
+}
+#TOC ul {
+padding-left: 40px;
+margin-left: -1.5em;
+margin-top: 5px;
+margin-bottom: 5px;
+}
+#TOC ul ul {
+margin-left: -2em;
+}
+#TOC li {
+line-height: 16px;
+}
+table {
+margin: 1em auto;
+border-width: 1px;
+border-color: #DDDDDD;
+border-style: outset;
+border-collapse: collapse;
+}
+table th {
+border-width: 2px;
+padding: 5px;
+border-style: inset;
+}
+table td {
+border-width: 1px;
+border-style: inset;
+line-height: 18px;
+padding: 5px 5px;
+}
+table, table th, table td {
+border-left-style: none;
+border-right-style: none;
+}
+table thead, table tr.even {
+background-color: #f7f7f7;
+}
+p {
+margin: 0.5em 0;
+}
+blockquote {
+background-color: #f6f6f6;
+padding: 0.25em 0.75em;
+}
+hr {
+border-style: solid;
+border: none;
+border-top: 1px solid #777;
+margin: 28px 0;
+}
+dl {
+margin-left: 0;
+}
+dl dd {
+margin-bottom: 13px;
+margin-left: 13px;
+}
+dl dt {
+font-weight: bold;
+}
+ul {
+margin-top: 0;
+}
+ul li {
+list-style: circle outside;
+}
+ul ul {
+margin-bottom: 0;
+}
+pre, code {
+background-color: #f7f7f7;
+border-radius: 3px;
+color: #333;
+white-space: pre-wrap;
+}
+pre {
+border-radius: 3px;
+margin: 5px 0px 10px 0px;
+padding: 10px;
+}
+pre:not([class]) {
+background-color: #f7f7f7;
+}
+code {
+font-family: Consolas, Monaco, 'Courier New', monospace;
+font-size: 85%;
+}
+p > code, li > code {
+padding: 2px 0px;
+}
+div.figure {
+text-align: center;
+}
+img {
+background-color: #FFFFFF;
+padding: 2px;
+border: 1px solid #DDDDDD;
+border-radius: 3px;
+border: 1px solid #CCCCCC;
+margin: 0 5px;
+}
+h1 {
+margin-top: 0;
+font-size: 35px;
+line-height: 40px;
+}
+h2 {
+border-bottom: 4px solid #f7f7f7;
+padding-top: 10px;
+padding-bottom: 2px;
+font-size: 145%;
+}
+h3 {
+border-bottom: 2px solid #f7f7f7;
+padding-top: 10px;
+font-size: 120%;
+}
+h4 {
+border-bottom: 1px solid #f7f7f7;
+margin-left: 8px;
+font-size: 105%;
+}
+h5, h6 {
+border-bottom: 1px solid #ccc;
+font-size: 105%;
+}
+a {
+color: #0033dd;
+text-decoration: none;
+}
+a:hover {
+color: #6666ff; }
+a:visited {
+color: #800080; }
+a:visited:hover {
+color: #BB00BB; }
+a[href^="http:"] {
+text-decoration: underline; }
+a[href^="https:"] {
+text-decoration: underline; }
+
+code > span.kw { color: #555; font-weight: bold; }
+code > span.dt { color: #902000; }
+code > span.dv { color: #40a070; }
+code > span.bn { color: #d14; }
+code > span.fl { color: #d14; }
+code > span.ch { color: #d14; }
+code > span.st { color: #d14; }
+code > span.co { color: #888888; font-style: italic; }
+code > span.ot { color: #007020; }
+code > span.al { color: #ff0000; font-weight: bold; }
+code > span.fu { color: #900; font-weight: bold; }
+code > span.er { color: #a61717; background-color: #e3d2d2; }
+</style>
+
+
+
+
+</head>
+
+<body>
+
+
+
+
+<h1 class="title toc-ignore">lab4</h1>
+<h4 class="author">Udaya Shanker Mohanan Nair, Pranav Pankaj
+Chandode</h4>
+
+
+
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(lab4)</span></code></pre></div>
+<div id="introduction" class="section level2">
+<h2>Introduction</h2>
+<p>This vignette illustrates how to use lab4 package to perform linear
+progression models and to perform various special functions like
+print(), plot(), resid(), pred(),coef() and summary().</p>
+</div>
+<div id="dataset" class="section level2">
+<h2>DataSet</h2>
+<p>For testing purpose, here we are using iris dataset, containing
+measurements of features of flower iris. To load data use the
+following:</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="fu">data</span>(iris)</span></code></pre></div>
+</div>
+<div id="invoking-linreg-function" class="section level2">
+<h2>Invoking linreg function</h2>
+<p>This Function will take two parameters namely formula and data. Type
+of formula is an expression specifying the relationship between
+dependent and independent variables, whereas data is the dataset(iris in
+this case).</p>
+<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a> linreg_mod <span class="ot"><-</span> linreg<span class="sc">$</span><span class="fu">new</span>(Petal.Length<span class="sc">~</span>Sepal.Width<span class="sc">+</span>Sepal.Length, <span class="at">data=</span>iris)</span>
+<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="co">#> [1] "Petal.Length ~ Sepal.Width + Sepal.Length"</span></span></code></pre></div>
+</div>
+<div id="print-function" class="section level2">
+<h2>Print Function</h2>
+<p>Printing the coefficients and coefficients name of the model
+generated using linreg function.This can be invoked by the following</p>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a>linreg_mod<span class="sc">$</span><span class="fu">print</span>()</span>
+<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="co">#> Call:</span></span>
+<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a><span class="co">#> linreg(formula = Petal.Length ~ Sepal.Width + Sepal.Length, data = iris)</span></span>
+<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a><span class="co">#> Coefficients:</span></span>
+<span id="cb4-6"><a href="#cb4-6" tabindex="-1"></a><span class="co">#> (Intercept) Sepal.Width Sepal.Length </span></span>
+<span id="cb4-7"><a href="#cb4-7" tabindex="-1"></a><span class="co">#> -2.524762 -1.338623 1.775593</span></span></code></pre></div>
+</div>
+<div id="plot-function" class="section level2">
+<h2>Plot Function</h2>
+<p>Plotting for residuals against Fitted Values and for Scale-Locations
+against sqaure root of standardized residuals.</p>
+<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a>plots <span class="ot"><-</span> linreg_mod<span class="sc">$</span><span class="fu">plot</span>()</span>
+<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="co">#> Warning in sqrt(residuals/(sqrt(abs(res_variance)))): NaNs produced</span></span>
+<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="fu">print</span>(plots[[<span class="dv">1</span>]])</span></code></pre></div>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
+<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="fu">print</span>(plots[[<span class="dv">2</span>]])</span>
+<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="co">#> Warning: Removed 79 rows containing non-finite outside the scale range</span></span>
+<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="co">#> (`stat_summary()`).</span></span>
+<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="co">#> Warning: Removed 79 rows containing missing values or values outside the scale range</span></span>
+<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a><span class="co">#> (`geom_point()`).</span></span></code></pre></div>
+<p><img src="data:image/png;base64,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" /><!-- -->
+## Resid Function Resid Function is to display the residuals of the
+model as a vector.</p>
+<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="co"># Get residuals</span></span>
+<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a>residuals <span class="ot"><-</span> linreg_mod<span class="sc">$</span><span class="fu">resid</span>()</span>
+<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a><span class="fu">print</span>(residuals)</span>
+<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a><span class="co">#> 1 2 3 4 5 6 </span></span>
+<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a><span class="co">#> -0.445578965 -0.759772100 -0.236928933 0.006767993 -0.134157381 -0.142807413 </span></span>
+<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a><span class="co">#> 7 8 9 10 11 12 </span></span>
+<span id="cb7-7"><a href="#cb7-7" tabindex="-1"></a><span class="co">#> 0.308354980 -0.301882039 -0.005838155 -0.525909771 -0.610532071 0.153236470 </span></span>
+<span id="cb7-8"><a href="#cb7-8" tabindex="-1"></a><span class="co">#> 13 14 15 16 17 18 </span></span>
+<span id="cb7-9"><a href="#cb7-9" tabindex="-1"></a><span class="co">#> -0.582212845 0.005583428 -1.219182103 -0.206173533 -0.542807413 -0.445578965 </span></span>
+<span id="cb7-10"><a href="#cb7-10" tabindex="-1"></a><span class="co">#> 19 20 21 22 23 24 </span></span>
+<span id="cb7-11"><a href="#cb7-11" tabindex="-1"></a><span class="co">#> -0.809347506 0.056008022 -0.812119057 -0.077854307 0.176079637 -0.413303622 </span></span>
+<span id="cb7-12"><a href="#cb7-12" tabindex="-1"></a><span class="co">#> 25 26 27 28 29 30 </span></span>
+<span id="cb7-13"><a href="#cb7-13" tabindex="-1"></a><span class="co">#> 0.453236470 -0.737331354 -0.201882039 -0.523138219 -0.757000548 0.063071067 </span></span>
+<span id="cb7-14"><a href="#cb7-14" tabindex="-1"></a><span class="co">#> 31 32 33 34 35 36 </span></span>
+<span id="cb7-15"><a href="#cb7-15" tabindex="-1"></a><span class="co">#> -0.248350516 -1.012119057 0.280035754 -0.218779681 -0.525909771 -0.869606697 </span></span>
+<span id="cb7-16"><a href="#cb7-16" tabindex="-1"></a><span class="co">#> 37 38 39 40 41 42 </span></span>
+<span id="cb7-17"><a href="#cb7-17" tabindex="-1"></a><span class="co">#> -1.255815983 0.043401874 0.028024174 -0.479441294 -0.368019710 -1.086571383 </span></span>
+<span id="cb7-18"><a href="#cb7-18" tabindex="-1"></a><span class="co">#> 43 44 45 46 47 48 </span></span>
+<span id="cb7-19"><a href="#cb7-19" tabindex="-1"></a><span class="co">#> 0.295748831 -0.068019710 0.456008022 -0.582212845 0.156008022 0.040630322 </span></span>
+<span id="cb7-20"><a href="#cb7-20" tabindex="-1"></a><span class="co">#> 49 50 51 52 53 54 </span></span>
+<span id="cb7-21"><a href="#cb7-21" tabindex="-1"></a><span class="co">#> -0.432972816 -0.535744368 -0.920791790 -0.055436262 -0.677094864 -0.162163930 </span></span>
+<span id="cb7-22"><a href="#cb7-22" tabindex="-1"></a><span class="co">#> 55 56 57 58 59 60 </span></span>
+<span id="cb7-23"><a href="#cb7-23" tabindex="-1"></a><span class="co">#> -0.668444832 0.652029205 0.455985322 0.337053927 -0.712141758 0.805963150 </span></span>
+<span id="cb7-24"><a href="#cb7-24" tabindex="-1"></a><span class="co">#> 61 62 63 64 65 66 </span></span>
+<span id="cb7-25"><a href="#cb7-25" tabindex="-1"></a><span class="co">#> -0.175954643 0.264635354 -1.183822532 0.275654516 0.063450789 -0.821976354 </span></span>
+<span id="cb7-26"><a href="#cb7-26" tabindex="-1"></a><span class="co">#> 67 68 69 70 71 72 </span></span>
+<span id="cb7-27"><a href="#cb7-27" tabindex="-1"></a><span class="co">#> 1.097313118 -0.059392378 -1.038941041 -0.171998527 1.132360012 -0.558207813 </span></span>
+<span id="cb7-28"><a href="#cb7-28" tabindex="-1"></a><span class="co">#> 73 74 75 76 77 78 </span></span>
+<span id="cb7-29"><a href="#cb7-29" tabindex="-1"></a><span class="co">#> -0.414913309 0.141792187 -0.657023248 -0.778279429 -1.001122596 -0.355838683 </span></span>
+<span id="cb7-30"><a href="#cb7-30" tabindex="-1"></a><span class="co">#> 79 80 81 82 83 84 </span></span>
+<span id="cb7-31"><a href="#cb7-31" tabindex="-1"></a><span class="co">#> 0.253213770 -0.615695452 -0.228301601 -0.328301601 -0.259392378 0.585489113 </span></span>
+<span id="cb7-32"><a href="#cb7-32" tabindex="-1"></a><span class="co">#> 85 86 87 88 89 90 </span></span>
+<span id="cb7-33"><a href="#cb7-33" tabindex="-1"></a><span class="co">#> 1.452431627 0.922525415 -0.521976354 -1.182637967 0.697313118 0.105560728 </span></span>
+<span id="cb7-34"><a href="#cb7-34" tabindex="-1"></a><span class="co">#> 91 92 93 94 95 96 </span></span>
+<span id="cb7-35"><a href="#cb7-35" tabindex="-1"></a><span class="co">#> 0.639423057 0.309516845 -0.293254707 0.025632344 0.395726131 0.619753863 </span></span>
+<span id="cb7-36"><a href="#cb7-36" tabindex="-1"></a><span class="co">#> 97 98 99 100 101 102 </span></span>
+<span id="cb7-37"><a href="#cb7-37" tabindex="-1"></a><span class="co">#> 0.485891534 -0.301904739 -0.184202253 0.252029205 1.755985322 0.940607622 </span></span>
+<span id="cb7-38"><a href="#cb7-38" tabindex="-1"></a><span class="co">#> 103 104 105 106 107 108 </span></span>
+<span id="cb7-39"><a href="#cb7-39" tabindex="-1"></a><span class="co">#> -0.166075702 0.820536006 0.799279826 -0.353871975 1.670916256 -0.255056540 </span></span>
+<span id="cb7-40"><a href="#cb7-40" tabindex="-1"></a><span class="co">#> 109 110 111 112 113 114 </span></span>
+<span id="cb7-41"><a href="#cb7-41" tabindex="-1"></a><span class="co">#> -0.225150328 0.659539017 0.367004484 0.075252094 -0.033397938 0.750442219 </span></span>
+<span id="cb7-42"><a href="#cb7-42" tabindex="-1"></a><span class="co">#> 115 116 117 118 119 120 </span></span>
+<span id="cb7-43"><a href="#cb7-43" tabindex="-1"></a><span class="co">#> 1.074469951 0.744563738 0.499279826 0.639467401 -0.766880545 -0.183822532 </span></span>
+<span id="cb7-44"><a href="#cb7-44" tabindex="-1"></a><span class="co">#> 121 122 123 124 125 126 </span></span>
+<span id="cb7-45"><a href="#cb7-45" tabindex="-1"></a><span class="co">#> 0.256767465 1.229588460 -0.699155888 -0.147188651 0.745748303 0.024089701 </span></span>
+<span id="cb7-46"><a href="#cb7-46" tabindex="-1"></a><span class="co">#> 127 128 129 130 131 132 </span></span>
+<span id="cb7-47"><a href="#cb7-47" tabindex="-1"></a><span class="co">#> 0.064232932 0.609516845 0.509114423 -0.443634957 -0.766478124 -0.015651108 </span></span>
+<span id="cb7-48"><a href="#cb7-48" tabindex="-1"></a><span class="co">#> 133 134 135 136 137 138 </span></span>
+<span id="cb7-49"><a href="#cb7-49" tabindex="-1"></a><span class="co">#> 0.509114423 0.186673677 0.774067529 -1.031431230 1.489847651 0.810701409 </span></span>
+<span id="cb7-50"><a href="#cb7-50" tabindex="-1"></a><span class="co">#> 139 140 141 142 143 144 </span></span>
+<span id="cb7-51"><a href="#cb7-51" tabindex="-1"></a><span class="co">#> 0.687076099 -0.177094864 0.378023646 -0.477094864 0.940607622 0.634326720 </span></span>
+<span id="cb7-52"><a href="#cb7-52" tabindex="-1"></a><span class="co">#> 145 146 147 148 149 150 </span></span>
+<span id="cb7-53"><a href="#cb7-53" tabindex="-1"></a><span class="co">#> 0.745748303 -0.155838683 -0.314913309 0.199279826 1.467406905 1.164635354</span></span></code></pre></div>
+</div>
+<div id="pred-function" class="section level2">
+<h2>pred Function</h2>
+<p>pred Function is to display the predicted values of the model.</p>
+<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="co"># Get predicted values</span></span>
+<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a>predictions <span class="ot"><-</span> linreg_mod<span class="sc">$</span><span class="fu">pred</span>()</span>
+<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a><span class="fu">print</span>(predictions)</span>
+<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a><span class="co">#> [1] 1.8455790 2.1597721 1.5369289 1.4932320 1.5341574 1.8428074 1.0916450</span></span>
+<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a><span class="co">#> [8] 1.8018820 1.4058382 2.0259098 2.1105321 1.4467635 1.9822128 1.0944166</span></span>
+<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a><span class="co">#> [15] 2.4191821 1.7061735 1.8428074 1.8455790 2.5093475 1.4439920 2.5121191</span></span>
+<span id="cb8-7"><a href="#cb8-7" tabindex="-1"></a><span class="co">#> [22] 1.5778543 0.8239204 2.1133036 1.4467635 2.3373314 1.8018820 2.0231382</span></span>
+<span id="cb8-8"><a href="#cb8-8" tabindex="-1"></a><span class="co">#> [29] 2.1570005 1.5369289 1.8483505 2.5121191 1.2199642 1.6187797 2.0259098</span></span>
+<span id="cb8-9"><a href="#cb8-9" tabindex="-1"></a><span class="co">#> [36] 2.0696067 2.5558160 1.3565981 1.2719758 1.9794413 1.6680197 2.3865714</span></span>
+<span id="cb8-10"><a href="#cb8-10" tabindex="-1"></a><span class="co">#> [43] 1.0042512 1.6680197 1.4439920 1.9822128 1.4439920 1.3593697 1.9329728</span></span>
+<span id="cb8-11"><a href="#cb8-11" tabindex="-1"></a><span class="co">#> [50] 1.9357444 5.6207918 4.5554363 5.5770949 4.1621639 5.2684448 3.8479708</span></span>
+<span id="cb8-12"><a href="#cb8-12" tabindex="-1"></a><span class="co">#> [57] 4.2440147 2.9629461 5.3121418 3.0940369 3.6759546 3.9353646 5.1838225</span></span>
+<span id="cb8-13"><a href="#cb8-13" tabindex="-1"></a><span class="co">#> [64] 4.4243455 3.5365492 5.2219764 3.4026869 4.1593924 5.5389410 4.0719985</span></span>
+<span id="cb8-14"><a href="#cb8-14" tabindex="-1"></a><span class="co">#> [71] 3.6676400 4.5582078 5.3149133 4.5582078 4.9570232 5.1782794 5.8011226</span></span>
+<span id="cb8-15"><a href="#cb8-15" tabindex="-1"></a><span class="co">#> [78] 5.3558387 4.2467862 4.1156955 4.0283016 4.0283016 4.1593924 4.5145109</span></span>
+<span id="cb8-16"><a href="#cb8-16" tabindex="-1"></a><span class="co">#> [85] 3.0475684 3.5774746 5.2219764 5.5826380 3.4026869 3.8944393 3.7605769</span></span>
+<span id="cb8-17"><a href="#cb8-17" tabindex="-1"></a><span class="co">#> [92] 4.2904832 4.2932547 3.2743677 3.8042739 3.5802461 3.7141085 4.6019047</span></span>
+<span id="cb8-18"><a href="#cb8-18" tabindex="-1"></a><span class="co">#> [99] 3.1842023 3.8479708 4.2440147 4.1593924 6.0660757 4.7794640 5.0007202</span></span>
+<span id="cb8-19"><a href="#cb8-19" tabindex="-1"></a><span class="co">#> [106] 6.9538720 2.8290837 6.5550565 6.0251503 5.4404610 4.7329955 5.2247479</span></span>
+<span id="cb8-20"><a href="#cb8-20" tabindex="-1"></a><span class="co">#> [113] 5.5333979 4.2495578 4.0255300 4.5554363 5.0007202 6.0605326 7.6668805</span></span>
+<span id="cb8-21"><a href="#cb8-21" tabindex="-1"></a><span class="co">#> [120] 5.1838225 5.4432325 3.6704115 7.3991559 5.0471887 4.9542517 5.9759103</span></span>
+<span id="cb8-22"><a href="#cb8-22" tabindex="-1"></a><span class="co">#> [127] 4.7357671 4.2904832 5.0908856 6.2436350 6.8664781 6.4156511 5.0908856</span></span>
+<span id="cb8-23"><a href="#cb8-23" tabindex="-1"></a><span class="co">#> [134] 4.9133263 4.8259325 7.1314312 4.1101523 4.6892986 4.1129239 5.5770949</span></span>
+<span id="cb8-24"><a href="#cb8-24" tabindex="-1"></a><span class="co">#> [141] 5.2219764 5.5770949 4.1593924 5.2656733 4.9542517 5.3558387 5.3149133</span></span>
+<span id="cb8-25"><a href="#cb8-25" tabindex="-1"></a><span class="co">#> [148] 5.0007202 3.9325931 3.9353646</span></span></code></pre></div>
+</div>
+<div id="summary-function" class="section level2">
+<h2>Summary Function</h2>
+<p>Summary function is to display coefficients along with standard
+error, t-values, p-values with significance.</p>
+<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>linreg_mod<span class="sc">$</span><span class="fu">summary</span>()</span>
+<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="co">#> Coefficients:</span></span>
+<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a><span class="co">#> Estimate_C Std.Error t value Pr(>|t|) </span></span>
+<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a><span class="co">#> (Intercept) -2.525 0.563 -4.481 0 ***</span></span>
+<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a><span class="co">#> Sepal.Width -1.339 0.122 -10.940 0 ***</span></span>
+<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a><span class="co">#> Sepal.Length 1.776 0.064 27.569 0 ***</span></span>
+<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a><span class="co">#> </span></span>
+<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a><span class="co">#> Residual standard error: 0.6464805 on 147 degrees of freedom</span></span></code></pre></div>
+</div>
+<div id="conclusion" class="section level2">
+<h2>Conclusion</h2>
+</div>
+<div id="this-linreg-function-helps-to-find-linear-regression-of-models-to-find-out-regression-coefficients-fitted-values-residuals-degree-of-freedom-residual-variance-variance-of-regression-coefficients-and-different-ways-to-print-plot-the-model-coefficients." class="section level2">
+<h2>This Linreg function helps to find Linear regression of models to
+find out Regression coefficients, fitted values, residuals, degree of
+freedom, residual variance, variance of regression coefficients and
+different ways to print, plot the model coefficients.</h2>
+</div>
+<div id="instructions-to-include-the-vignette-in-your-package" class="section level2">
+<h2>Instructions to Include the Vignette in Your Package</h2>
+<ol style="list-style-type: decimal">
+<li><p><strong>Save the Vignette</strong>: Save the above content in a
+file named <code>using_linreg.Rmd</code> within the
+<code>vignettes/</code> directory of your package.</p></li>
+<li><p><strong>Build the Vignette</strong>: Ensure that you have the
+necessary packages installed for building vignettes, such as
+<code>knitr</code> and <code>rmarkdown</code>. Then, build your package
+using:</p>
+<p>```r devtools::build_vignettes()</p></li>
+</ol>
+</div>
+
+
+
+<!-- code folding -->
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+ (function () {
+ var script = document.createElement("script");
+ script.type = "text/javascript";
+ script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+ document.getElementsByTagName("head")[0].appendChild(script);
+ })();
+</script>
+
+</body>
+</html>
diff --git a/vignettes/lab4-vignette.Rmd b/vignettes/lab4-vignette.Rmd
index 7549bfc..d2d819f 100644
--- a/vignettes/lab4-vignette.Rmd
+++ b/vignettes/lab4-vignette.Rmd
@@ -65,4 +65,15 @@ Summary function is to display coefficients along with standard error, t-values,
linreg_mod$summary()
```
## Conclusion
-This Linreg function helps to find Linear regression of models to find out Regression coefficients, fitted values, residuals, degree of freedom, residual variance, variance of regression coefficients and different ways to print, plot the model coefficients.
\ No newline at end of file
+This Linreg function helps to find Linear regression of models to find out Regression coefficients, fitted values, residuals, degree of freedom, residual variance, variance of regression coefficients and different ways to print, plot the model coefficients.
+
+---
+
+## Instructions to Include the Vignette in Your Package
+
+1. **Save the Vignette**: Save the above content in a file named `using_linreg.Rmd` within the `vignettes/` directory of your package.
+
+2. **Build the Vignette**: Ensure that you have the necessary packages installed for building vignettes, such as `knitr` and `rmarkdown`. Then, build your package using:
+
+ ```r
+ devtools::build_vignettes()
\ No newline at end of file
--
GitLab