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*.Rproj
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DESCRIPTION
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Config/testthat/edition: 3
Encoding: UTF-8
VignetteBuilder: knitr
Config/build/clean-inst-doc: FALSE
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## ----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$summary()
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---
title: "lab4"
author: "Udaya Shanker Mohanan Nair, Pranav Pankaj Chandode"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{lab4}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$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()
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<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">&lt;-</span> linreg<span class="sc">$</span><span class="fu">new</span>(Petal.Length<span class="sc">~</span>Sepal.Width<span class="sc">+</span>Sepal.Length, <span class="at">data=</span>iris)</span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="co">#&gt; [1] &quot;Petal.Length ~ Sepal.Width + Sepal.Length&quot;</span></span></code></pre></div>
</div>
<div id="print-function" class="section level2">
<h2>Print Function</h2>
<p>Printing the coefficients and coefficients name of the model
generated using linreg function.This can be invoked by the following</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a>linreg_mod<span class="sc">$</span><span class="fu">print</span>()</span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="co">#&gt; Call:</span></span>
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; </span></span>
<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a><span class="co">#&gt; Coefficients:</span></span>
<span id="cb4-6"><a href="#cb4-6" tabindex="-1"></a><span class="co">#&gt; (Intercept) Sepal.Width Sepal.Length </span></span>
<span id="cb4-7"><a href="#cb4-7" tabindex="-1"></a><span class="co">#&gt; -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">&lt;-</span> linreg_mod<span class="sc">$</span><span class="fu">plot</span>()</span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="co">#&gt; Warning in sqrt(residuals/(sqrt(abs(res_variance)))): NaNs produced</span></span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="fu">print</span>(plots[[<span class="dv">1</span>]])</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="fu">print</span>(plots[[<span class="dv">2</span>]])</span>
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="co">#&gt; Warning: Removed 79 rows containing non-finite outside the scale range</span></span>
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="co">#&gt; (`stat_summary()`).</span></span>
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="co">#&gt; Warning: Removed 79 rows containing missing values or values outside the scale range</span></span>
<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a><span class="co">#&gt; (`geom_point()`).</span></span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- -->
## Resid Function Resid Function is to display the residuals of the
model as a vector.</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="co"># Get residuals</span></span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a>residuals <span class="ot">&lt;-</span> linreg_mod<span class="sc">$</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">#&gt; 1 2 3 4 5 6 </span></span>
<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a><span class="co">#&gt; -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">#&gt; 7 8 9 10 11 12 </span></span>
<span id="cb7-7"><a href="#cb7-7" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; 13 14 15 16 17 18 </span></span>
<span id="cb7-9"><a href="#cb7-9" tabindex="-1"></a><span class="co">#&gt; -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">#&gt; 19 20 21 22 23 24 </span></span>
<span id="cb7-11"><a href="#cb7-11" tabindex="-1"></a><span class="co">#&gt; -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">#&gt; 25 26 27 28 29 30 </span></span>
<span id="cb7-13"><a href="#cb7-13" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; 31 32 33 34 35 36 </span></span>
<span id="cb7-15"><a href="#cb7-15" tabindex="-1"></a><span class="co">#&gt; -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">#&gt; 37 38 39 40 41 42 </span></span>
<span id="cb7-17"><a href="#cb7-17" tabindex="-1"></a><span class="co">#&gt; -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">#&gt; 43 44 45 46 47 48 </span></span>
<span id="cb7-19"><a href="#cb7-19" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; 49 50 51 52 53 54 </span></span>
<span id="cb7-21"><a href="#cb7-21" tabindex="-1"></a><span class="co">#&gt; -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">#&gt; 55 56 57 58 59 60 </span></span>
<span id="cb7-23"><a href="#cb7-23" tabindex="-1"></a><span class="co">#&gt; -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">#&gt; 61 62 63 64 65 66 </span></span>
<span id="cb7-25"><a href="#cb7-25" tabindex="-1"></a><span class="co">#&gt; -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">#&gt; 67 68 69 70 71 72 </span></span>
<span id="cb7-27"><a href="#cb7-27" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; 73 74 75 76 77 78 </span></span>
<span id="cb7-29"><a href="#cb7-29" tabindex="-1"></a><span class="co">#&gt; -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">#&gt; 79 80 81 82 83 84 </span></span>
<span id="cb7-31"><a href="#cb7-31" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; 85 86 87 88 89 90 </span></span>
<span id="cb7-33"><a href="#cb7-33" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; 91 92 93 94 95 96 </span></span>
<span id="cb7-35"><a href="#cb7-35" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; 97 98 99 100 101 102 </span></span>
<span id="cb7-37"><a href="#cb7-37" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; 103 104 105 106 107 108 </span></span>
<span id="cb7-39"><a href="#cb7-39" tabindex="-1"></a><span class="co">#&gt; -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">#&gt; 109 110 111 112 113 114 </span></span>
<span id="cb7-41"><a href="#cb7-41" tabindex="-1"></a><span class="co">#&gt; -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">#&gt; 115 116 117 118 119 120 </span></span>
<span id="cb7-43"><a href="#cb7-43" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; 121 122 123 124 125 126 </span></span>
<span id="cb7-45"><a href="#cb7-45" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; 127 128 129 130 131 132 </span></span>
<span id="cb7-47"><a href="#cb7-47" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; 133 134 135 136 137 138 </span></span>
<span id="cb7-49"><a href="#cb7-49" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; 139 140 141 142 143 144 </span></span>
<span id="cb7-51"><a href="#cb7-51" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; 145 146 147 148 149 150 </span></span>
<span id="cb7-53"><a href="#cb7-53" tabindex="-1"></a><span class="co">#&gt; 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">&lt;-</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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; [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">#&gt; Coefficients:</span></span>
<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a><span class="co">#&gt; Estimate_C Std.Error t value Pr(&gt;|t|) </span></span>
<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a><span class="co">#&gt; (Intercept) -2.525 0.563 -4.481 0 ***</span></span>
<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a><span class="co">#&gt; 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">#&gt; 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">#&gt; </span></span>
<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a><span class="co">#&gt; 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>
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vignettes/lab4-vignette.Rmd
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......@@ -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
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