What_is_Finlay_Wilkinson_Regression_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN9CRYRDF81vWQNmCPyWuRt"
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    },
    "language_info": {
      "name": "R"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "library(dplyr)\n",
        "library(tidyr)\n",
        "library(broom)\n",
        "library(ggplot2)"
      ],
      "metadata": {
        "id": "o4cokXsZa5Ix"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "IpW-d0V-7Hod",
        "outputId": "ab61a89e-feee-4f12-8fc8-5341af937f54"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 4 × 6</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>Env</th><th scope=col>CV1</th><th scope=col>CV2</th><th scope=col>CV3</th><th scope=col>CV4</th><th scope=col>CV5</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>High_inoc   </td><td> 30</td><td>74</td><td> 78</td><td>86</td><td>74</td></tr>\n",
              "\t<tr><td>High_NO_inoc</td><td>150</td><td>99</td><td>106</td><td>92</td><td>98</td></tr>\n",
              "\t<tr><td>Low_inoc    </td><td> 20</td><td>49</td><td> 56</td><td>66</td><td>57</td></tr>\n",
              "\t<tr><td>Low_NO_inoc </td><td>100</td><td>73</td><td> 69</td><td>70</td><td>79</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 4 × 6\n\n| Env &lt;chr&gt; | CV1 &lt;dbl&gt; | CV2 &lt;dbl&gt; | CV3 &lt;dbl&gt; | CV4 &lt;dbl&gt; | CV5 &lt;dbl&gt; |\n|---|---|---|---|---|---|\n| High_inoc    |  30 | 74 |  78 | 86 | 74 |\n| High_NO_inoc | 150 | 99 | 106 | 92 | 98 |\n| Low_inoc     |  20 | 49 |  56 | 66 | 57 |\n| Low_NO_inoc  | 100 | 73 |  69 | 70 | 79 |\n\n",
            "text/latex": "A data.frame: 4 × 6\n\\begin{tabular}{llllll}\n Env & CV1 & CV2 & CV3 & CV4 & CV5\\\\\n <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t High\\_inoc    &  30 & 74 &  78 & 86 & 74\\\\\n\t High\\_NO\\_inoc & 150 & 99 & 106 & 92 & 98\\\\\n\t Low\\_inoc     &  20 & 49 &  56 & 66 & 57\\\\\n\t Low\\_NO\\_inoc  & 100 & 73 &  69 & 70 & 79\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  Env          CV1 CV2 CV3 CV4 CV5\n",
              "1 High_inoc     30 74   78 86  74 \n",
              "2 High_NO_inoc 150 99  106 92  98 \n",
              "3 Low_inoc      20 49   56 66  57 \n",
              "4 Low_NO_inoc  100 73   69 70  79 "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "Env= c(\"High_inoc\",\"High_NO_inoc\",\"Low_inoc\", \"Low_NO_inoc\")\n",
        "CV1= c(30,150,20,100)\n",
        "CV2= c(74,99,49,73)\n",
        "CV3= c(78,106,56,69)\n",
        "CV4= c(86,92,66,70)\n",
        "CV5= c(74,98,57,79)\n",
        "Data= data.frame(Env,CV1,CV2,CV3,CV4,CV5)\n",
        "Data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Data$Mean= rowMeans (Data %>% select(-Env))\n",
        "Data= rbind(Data, c(\"Mean\", colMeans(Data %>% select(-Env))))\n",
        "Data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "A0H97z9E70uw",
        "outputId": "d41731d7-39b8-4ff1-9940-fbf5ccc9a0f9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 5 × 7</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>Env</th><th scope=col>CV1</th><th scope=col>CV2</th><th scope=col>CV3</th><th scope=col>CV4</th><th scope=col>CV5</th><th scope=col>Mean</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>High_inoc   </td><td>30 </td><td>74   </td><td>78   </td><td>86  </td><td>74</td><td>68.4</td></tr>\n",
              "\t<tr><td>High_NO_inoc</td><td>150</td><td>99   </td><td>106  </td><td>92  </td><td>98</td><td>109 </td></tr>\n",
              "\t<tr><td>Low_inoc    </td><td>20 </td><td>49   </td><td>56   </td><td>66  </td><td>57</td><td>49.6</td></tr>\n",
              "\t<tr><td>Low_NO_inoc </td><td>100</td><td>73   </td><td>69   </td><td>70  </td><td>79</td><td>78.2</td></tr>\n",
              "\t<tr><td>Mean        </td><td>75 </td><td>73.75</td><td>77.25</td><td>78.5</td><td>77</td><td>76.3</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 5 × 7\n\n| Env &lt;chr&gt; | CV1 &lt;chr&gt; | CV2 &lt;chr&gt; | CV3 &lt;chr&gt; | CV4 &lt;chr&gt; | CV5 &lt;chr&gt; | Mean &lt;chr&gt; |\n|---|---|---|---|---|---|---|\n| High_inoc    | 30  | 74    | 78    | 86   | 74 | 68.4 |\n| High_NO_inoc | 150 | 99    | 106   | 92   | 98 | 109  |\n| Low_inoc     | 20  | 49    | 56    | 66   | 57 | 49.6 |\n| Low_NO_inoc  | 100 | 73    | 69    | 70   | 79 | 78.2 |\n| Mean         | 75  | 73.75 | 77.25 | 78.5 | 77 | 76.3 |\n\n",
            "text/latex": "A data.frame: 5 × 7\n\\begin{tabular}{lllllll}\n Env & CV1 & CV2 & CV3 & CV4 & CV5 & Mean\\\\\n <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n\\hline\n\t High\\_inoc    & 30  & 74    & 78    & 86   & 74 & 68.4\\\\\n\t High\\_NO\\_inoc & 150 & 99    & 106   & 92   & 98 & 109 \\\\\n\t Low\\_inoc     & 20  & 49    & 56    & 66   & 57 & 49.6\\\\\n\t Low\\_NO\\_inoc  & 100 & 73    & 69    & 70   & 79 & 78.2\\\\\n\t Mean         & 75  & 73.75 & 77.25 & 78.5 & 77 & 76.3\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  Env          CV1 CV2   CV3   CV4  CV5 Mean\n",
              "1 High_inoc    30  74    78    86   74  68.4\n",
              "2 High_NO_inoc 150 99    106   92   98  109 \n",
              "3 Low_inoc     20  49    56    66   57  49.6\n",
              "4 Low_NO_inoc  100 73    69    70   79  78.2\n",
              "5 Mean         75  73.75 77.25 78.5 77  76.3"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Data$Mean= as.numeric(Data$Mean)\n",
        "Data$Env_index= Data$Mean - Data$Mean[nrow(Data)]\n",
        "Data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "E3Vmno6o8jcG",
        "outputId": "a7bcf2b9-e364-4ca6-8f56-5570784bfa52"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 5 × 8</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>Env</th><th scope=col>CV1</th><th scope=col>CV2</th><th scope=col>CV3</th><th scope=col>CV4</th><th scope=col>CV5</th><th scope=col>Mean</th><th scope=col>Env_index</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>High_inoc   </td><td>30 </td><td>74   </td><td>78   </td><td>86  </td><td>74</td><td> 68.4</td><td> -7.9</td></tr>\n",
              "\t<tr><td>High_NO_inoc</td><td>150</td><td>99   </td><td>106  </td><td>92  </td><td>98</td><td>109.0</td><td> 32.7</td></tr>\n",
              "\t<tr><td>Low_inoc    </td><td>20 </td><td>49   </td><td>56   </td><td>66  </td><td>57</td><td> 49.6</td><td>-26.7</td></tr>\n",
              "\t<tr><td>Low_NO_inoc </td><td>100</td><td>73   </td><td>69   </td><td>70  </td><td>79</td><td> 78.2</td><td>  1.9</td></tr>\n",
              "\t<tr><td>Mean        </td><td>75 </td><td>73.75</td><td>77.25</td><td>78.5</td><td>77</td><td> 76.3</td><td>  0.0</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 5 × 8\n\n| Env &lt;chr&gt; | CV1 &lt;chr&gt; | CV2 &lt;chr&gt; | CV3 &lt;chr&gt; | CV4 &lt;chr&gt; | CV5 &lt;chr&gt; | Mean &lt;dbl&gt; | Env_index &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|\n| High_inoc    | 30  | 74    | 78    | 86   | 74 |  68.4 |  -7.9 |\n| High_NO_inoc | 150 | 99    | 106   | 92   | 98 | 109.0 |  32.7 |\n| Low_inoc     | 20  | 49    | 56    | 66   | 57 |  49.6 | -26.7 |\n| Low_NO_inoc  | 100 | 73    | 69    | 70   | 79 |  78.2 |   1.9 |\n| Mean         | 75  | 73.75 | 77.25 | 78.5 | 77 |  76.3 |   0.0 |\n\n",
            "text/latex": "A data.frame: 5 × 8\n\\begin{tabular}{llllllll}\n Env & CV1 & CV2 & CV3 & CV4 & CV5 & Mean & Env\\_index\\\\\n <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl>\\\\\n\\hline\n\t High\\_inoc    & 30  & 74    & 78    & 86   & 74 &  68.4 &  -7.9\\\\\n\t High\\_NO\\_inoc & 150 & 99    & 106   & 92   & 98 & 109.0 &  32.7\\\\\n\t Low\\_inoc     & 20  & 49    & 56    & 66   & 57 &  49.6 & -26.7\\\\\n\t Low\\_NO\\_inoc  & 100 & 73    & 69    & 70   & 79 &  78.2 &   1.9\\\\\n\t Mean         & 75  & 73.75 & 77.25 & 78.5 & 77 &  76.3 &   0.0\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  Env          CV1 CV2   CV3   CV4  CV5 Mean  Env_index\n",
              "1 High_inoc    30  74    78    86   74   68.4  -7.9    \n",
              "2 High_NO_inoc 150 99    106   92   98  109.0  32.7    \n",
              "3 Low_inoc     20  49    56    66   57   49.6 -26.7    \n",
              "4 Low_NO_inoc  100 73    69    70   79   78.2   1.9    \n",
              "5 Mean         75  73.75 77.25 78.5 77   76.3   0.0    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Data= Data [-5,-7]  # delete 5th row and 7th column\n",
        "Data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "fe9X9ngT9NOM",
        "outputId": "be2e3e8a-8403-4df0-bc48-f6c4b52673be"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 4 × 7</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Env</th><th scope=col>CV1</th><th scope=col>CV2</th><th scope=col>CV3</th><th scope=col>CV4</th><th scope=col>CV5</th><th scope=col>Env_index</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>High_inoc   </td><td>30 </td><td>74</td><td>78 </td><td>86</td><td>74</td><td> -7.9</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>High_NO_inoc</td><td>150</td><td>99</td><td>106</td><td>92</td><td>98</td><td> 32.7</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>Low_inoc    </td><td>20 </td><td>49</td><td>56 </td><td>66</td><td>57</td><td>-26.7</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>Low_NO_inoc </td><td>100</td><td>73</td><td>69 </td><td>70</td><td>79</td><td>  1.9</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 4 × 7\n\n| <!--/--> | Env &lt;chr&gt; | CV1 &lt;chr&gt; | CV2 &lt;chr&gt; | CV3 &lt;chr&gt; | CV4 &lt;chr&gt; | CV5 &lt;chr&gt; | Env_index &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|\n| 1 | High_inoc    | 30  | 74 | 78  | 86 | 74 |  -7.9 |\n| 2 | High_NO_inoc | 150 | 99 | 106 | 92 | 98 |  32.7 |\n| 3 | Low_inoc     | 20  | 49 | 56  | 66 | 57 | -26.7 |\n| 4 | Low_NO_inoc  | 100 | 73 | 69  | 70 | 79 |   1.9 |\n\n",
            "text/latex": "A data.frame: 4 × 7\n\\begin{tabular}{r|lllllll}\n  & Env & CV1 & CV2 & CV3 & CV4 & CV5 & Env\\_index\\\\\n  & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl>\\\\\n\\hline\n\t1 & High\\_inoc    & 30  & 74 & 78  & 86 & 74 &  -7.9\\\\\n\t2 & High\\_NO\\_inoc & 150 & 99 & 106 & 92 & 98 &  32.7\\\\\n\t3 & Low\\_inoc     & 20  & 49 & 56  & 66 & 57 & -26.7\\\\\n\t4 & Low\\_NO\\_inoc  & 100 & 73 & 69  & 70 & 79 &   1.9\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  Env          CV1 CV2 CV3 CV4 CV5 Env_index\n",
              "1 High_inoc    30  74  78  86  74   -7.9    \n",
              "2 High_NO_inoc 150 99  106 92  98   32.7    \n",
              "3 Low_inoc     20  49  56  66  57  -26.7    \n",
              "4 Low_NO_inoc  100 73  69  70  79    1.9    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df= data.frame(\n",
        "               Data %>%\n",
        "               pivot_longer(\n",
        "               cols=c(CV1, CV2, CV3, CV4, CV5),\n",
        "               names_to=\"Genotype\", values_to=\"Yield\")\n",
        "               )\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "v3QFz3CTbCmL",
        "outputId": "5503f565-3896-4b6d-b187-3b2fb98b021e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 20 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>Env</th><th scope=col>Env_index</th><th scope=col>Genotype</th><th scope=col>Yield</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>High_inoc   </td><td> -7.9</td><td>CV1</td><td>30 </td></tr>\n",
              "\t<tr><td>High_inoc   </td><td> -7.9</td><td>CV2</td><td>74 </td></tr>\n",
              "\t<tr><td>High_inoc   </td><td> -7.9</td><td>CV3</td><td>78 </td></tr>\n",
              "\t<tr><td>High_inoc   </td><td> -7.9</td><td>CV4</td><td>86 </td></tr>\n",
              "\t<tr><td>High_inoc   </td><td> -7.9</td><td>CV5</td><td>74 </td></tr>\n",
              "\t<tr><td>High_NO_inoc</td><td> 32.7</td><td>CV1</td><td>150</td></tr>\n",
              "\t<tr><td>High_NO_inoc</td><td> 32.7</td><td>CV2</td><td>99 </td></tr>\n",
              "\t<tr><td>High_NO_inoc</td><td> 32.7</td><td>CV3</td><td>106</td></tr>\n",
              "\t<tr><td>High_NO_inoc</td><td> 32.7</td><td>CV4</td><td>92 </td></tr>\n",
              "\t<tr><td>High_NO_inoc</td><td> 32.7</td><td>CV5</td><td>98 </td></tr>\n",
              "\t<tr><td>Low_inoc    </td><td>-26.7</td><td>CV1</td><td>20 </td></tr>\n",
              "\t<tr><td>Low_inoc    </td><td>-26.7</td><td>CV2</td><td>49 </td></tr>\n",
              "\t<tr><td>Low_inoc    </td><td>-26.7</td><td>CV3</td><td>56 </td></tr>\n",
              "\t<tr><td>Low_inoc    </td><td>-26.7</td><td>CV4</td><td>66 </td></tr>\n",
              "\t<tr><td>Low_inoc    </td><td>-26.7</td><td>CV5</td><td>57 </td></tr>\n",
              "\t<tr><td>Low_NO_inoc </td><td>  1.9</td><td>CV1</td><td>100</td></tr>\n",
              "\t<tr><td>Low_NO_inoc </td><td>  1.9</td><td>CV2</td><td>73 </td></tr>\n",
              "\t<tr><td>Low_NO_inoc </td><td>  1.9</td><td>CV3</td><td>69 </td></tr>\n",
              "\t<tr><td>Low_NO_inoc </td><td>  1.9</td><td>CV4</td><td>70 </td></tr>\n",
              "\t<tr><td>Low_NO_inoc </td><td>  1.9</td><td>CV5</td><td>79 </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 20 × 4\n\n| Env &lt;chr&gt; | Env_index &lt;dbl&gt; | Genotype &lt;chr&gt; | Yield &lt;chr&gt; |\n|---|---|---|---|\n| High_inoc    |  -7.9 | CV1 | 30  |\n| High_inoc    |  -7.9 | CV2 | 74  |\n| High_inoc    |  -7.9 | CV3 | 78  |\n| High_inoc    |  -7.9 | CV4 | 86  |\n| High_inoc    |  -7.9 | CV5 | 74  |\n| High_NO_inoc |  32.7 | CV1 | 150 |\n| High_NO_inoc |  32.7 | CV2 | 99  |\n| High_NO_inoc |  32.7 | CV3 | 106 |\n| High_NO_inoc |  32.7 | CV4 | 92  |\n| High_NO_inoc |  32.7 | CV5 | 98  |\n| Low_inoc     | -26.7 | CV1 | 20  |\n| Low_inoc     | -26.7 | CV2 | 49  |\n| Low_inoc     | -26.7 | CV3 | 56  |\n| Low_inoc     | -26.7 | CV4 | 66  |\n| Low_inoc     | -26.7 | CV5 | 57  |\n| Low_NO_inoc  |   1.9 | CV1 | 100 |\n| Low_NO_inoc  |   1.9 | CV2 | 73  |\n| Low_NO_inoc  |   1.9 | CV3 | 69  |\n| Low_NO_inoc  |   1.9 | CV4 | 70  |\n| Low_NO_inoc  |   1.9 | CV5 | 79  |\n\n",
            "text/latex": "A data.frame: 20 × 4\n\\begin{tabular}{llll}\n Env & Env\\_index & Genotype & Yield\\\\\n <chr> & <dbl> & <chr> & <chr>\\\\\n\\hline\n\t High\\_inoc    &  -7.9 & CV1 & 30 \\\\\n\t High\\_inoc    &  -7.9 & CV2 & 74 \\\\\n\t High\\_inoc    &  -7.9 & CV3 & 78 \\\\\n\t High\\_inoc    &  -7.9 & CV4 & 86 \\\\\n\t High\\_inoc    &  -7.9 & CV5 & 74 \\\\\n\t High\\_NO\\_inoc &  32.7 & CV1 & 150\\\\\n\t High\\_NO\\_inoc &  32.7 & CV2 & 99 \\\\\n\t High\\_NO\\_inoc &  32.7 & CV3 & 106\\\\\n\t High\\_NO\\_inoc &  32.7 & CV4 & 92 \\\\\n\t High\\_NO\\_inoc &  32.7 & CV5 & 98 \\\\\n\t Low\\_inoc     & -26.7 & CV1 & 20 \\\\\n\t Low\\_inoc     & -26.7 & CV2 & 49 \\\\\n\t Low\\_inoc     & -26.7 & CV3 & 56 \\\\\n\t Low\\_inoc     & -26.7 & CV4 & 66 \\\\\n\t Low\\_inoc     & -26.7 & CV5 & 57 \\\\\n\t Low\\_NO\\_inoc  &   1.9 & CV1 & 100\\\\\n\t Low\\_NO\\_inoc  &   1.9 & CV2 & 73 \\\\\n\t Low\\_NO\\_inoc  &   1.9 & CV3 & 69 \\\\\n\t Low\\_NO\\_inoc  &   1.9 & CV4 & 70 \\\\\n\t Low\\_NO\\_inoc  &   1.9 & CV5 & 79 \\\\\n\\end{tabular}\n",
            "text/plain": [
              "   Env          Env_index Genotype Yield\n",
              "1  High_inoc     -7.9     CV1      30   \n",
              "2  High_inoc     -7.9     CV2      74   \n",
              "3  High_inoc     -7.9     CV3      78   \n",
              "4  High_inoc     -7.9     CV4      86   \n",
              "5  High_inoc     -7.9     CV5      74   \n",
              "6  High_NO_inoc  32.7     CV1      150  \n",
              "7  High_NO_inoc  32.7     CV2      99   \n",
              "8  High_NO_inoc  32.7     CV3      106  \n",
              "9  High_NO_inoc  32.7     CV4      92   \n",
              "10 High_NO_inoc  32.7     CV5      98   \n",
              "11 Low_inoc     -26.7     CV1      20   \n",
              "12 Low_inoc     -26.7     CV2      49   \n",
              "13 Low_inoc     -26.7     CV3      56   \n",
              "14 Low_inoc     -26.7     CV4      66   \n",
              "15 Low_inoc     -26.7     CV5      57   \n",
              "16 Low_NO_inoc    1.9     CV1      100  \n",
              "17 Low_NO_inoc    1.9     CV2      73   \n",
              "18 Low_NO_inoc    1.9     CV3      69   \n",
              "19 Low_NO_inoc    1.9     CV4      70   \n",
              "20 Low_NO_inoc    1.9     CV5      79   "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "summary(lm (Yield ~ Env_index, data=df %>% filter (Genotype==\"CV1\")))\n",
        "summary(lm (Yield ~ Env_index, data=df %>% filter (Genotype==\"CV2\")))\n",
        "summary(lm (Yield ~ Env_index, data=df %>% filter (Genotype==\"CV3\")))\n",
        "summary(lm (Yield ~ Env_index, data=df %>% filter (Genotype==\"CV4\")))\n",
        "summary(lm (Yield ~ Env_index, data=df %>% filter (Genotype==\"CV5\")))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "W_ChJQ2DdqqQ",
        "outputId": "e8ce02f9-5847-4fde-b641-6c80424a423e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = Yield ~ Env_index, data = df %>% filter(Genotype == \n",
              "    \"CV1\"))\n",
              "\n",
              "Residuals:\n",
              "      1       2       3       4 \n",
              "-26.517  -1.504   7.467  20.555 \n",
              "\n",
              "Coefficients:\n",
              "            Estimate Std. Error t value Pr(>|t|)  \n",
              "(Intercept)  75.0000    12.1639   6.166   0.0253 *\n",
              "Env_index     2.3396     0.5659   4.134   0.0538 .\n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 24.33 on 2 degrees of freedom\n",
              "Multiple R-squared:  0.8952,\tAdjusted R-squared:  0.8429 \n",
              "F-statistic: 17.09 on 1 and 2 DF,  p-value: 0.05382\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = Yield ~ Env_index, data = df %>% filter(Genotype == \n",
              "    \"CV2\"))\n",
              "\n",
              "Residuals:\n",
              "      1       2       3       4 \n",
              " 6.5894 -0.9903 -3.3244 -2.2747 \n",
              "\n",
              "Coefficients:\n",
              "            Estimate Std. Error t value Pr(>|t|)   \n",
              "(Intercept)  73.7500     2.7529  26.790  0.00139 **\n",
              "Env_index     0.8025     0.1281   6.266  0.02454 * \n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 5.506 on 2 degrees of freedom\n",
              "Multiple R-squared:  0.9515,\tAdjusted R-squared:  0.9273 \n",
              "F-statistic: 39.26 on 1 and 2 DF,  p-value: 0.02454\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = Yield ~ Env_index, data = df %>% filter(Genotype == \n",
              "    \"CV3\"))\n",
              "\n",
              "Residuals:\n",
              "     1      2      3      4 \n",
              " 7.101  2.460  0.216 -9.778 \n",
              "\n",
              "Coefficients:\n",
              "            Estimate Std. Error t value Pr(>|t|)   \n",
              "(Intercept)  77.2500     4.3607  17.715  0.00317 **\n",
              "Env_index     0.8040     0.2029   3.963  0.05817 . \n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 8.721 on 2 degrees of freedom\n",
              "Multiple R-squared:  0.887,\tAdjusted R-squared:  0.8306 \n",
              "F-statistic: 15.71 on 1 and 2 DF,  p-value: 0.05817\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = Yield ~ Env_index, data = df %>% filter(Genotype == \n",
              "    \"CV4\"))\n",
              "\n",
              "Residuals:\n",
              "     1      2      3      4 \n",
              "10.491  1.119 -2.390 -9.219 \n",
              "\n",
              "Coefficients:\n",
              "            Estimate Std. Error t value Pr(>|t|)   \n",
              "(Intercept)  78.5000     5.0253   15.62  0.00407 **\n",
              "Env_index     0.3786     0.2338    1.62  0.24675   \n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 10.05 on 2 degrees of freedom\n",
              "Multiple R-squared:  0.5674,\tAdjusted R-squared:  0.3511 \n",
              "F-statistic: 2.623 on 1 and 2 DF,  p-value: 0.2467\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = Yield ~ Env_index, data = df %>% filter(Genotype == \n",
              "    \"CV5\"))\n",
              "\n",
              "Residuals:\n",
              "      1       2       3       4 \n",
              " 2.3353 -1.0843 -1.9679  0.7168 \n",
              "\n",
              "Coefficients:\n",
              "            Estimate Std. Error t value Pr(>|t|)    \n",
              "(Intercept) 77.00000    1.17345   65.62 0.000232 ***\n",
              "Env_index    0.67536    0.05459   12.37 0.006471 ** \n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 2.347 on 2 degrees of freedom\n",
              "Multiple R-squared:  0.9871,\tAdjusted R-squared:  0.9807 \n",
              "F-statistic:   153 on 1 and 2 DF,  p-value: 0.006471\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "regression= df%>% group_by(Genotype) %>% do(model= lm(Yield~Env_index, data=.))\n",
        "regression$model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        },
        "id": "4F9ivr3Fd0Ro",
        "outputId": "8f2e52e7-1535-4f20-e591-dd1ed923e528"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[[1]]\n",
              "\n",
              "Call:\n",
              "lm(formula = Yield ~ Env_index, data = .)\n",
              "\n",
              "Coefficients:\n",
              "(Intercept)    Env_index  \n",
              "      75.00         2.34  \n",
              "\n",
              "\n",
              "[[2]]\n",
              "\n",
              "Call:\n",
              "lm(formula = Yield ~ Env_index, data = .)\n",
              "\n",
              "Coefficients:\n",
              "(Intercept)    Env_index  \n",
              "    73.7500       0.8025  \n",
              "\n",
              "\n",
              "[[3]]\n",
              "\n",
              "Call:\n",
              "lm(formula = Yield ~ Env_index, data = .)\n",
              "\n",
              "Coefficients:\n",
              "(Intercept)    Env_index  \n",
              "     77.250        0.804  \n",
              "\n",
              "\n",
              "[[4]]\n",
              "\n",
              "Call:\n",
              "lm(formula = Yield ~ Env_index, data = .)\n",
              "\n",
              "Coefficients:\n",
              "(Intercept)    Env_index  \n",
              "    78.5000       0.3786  \n",
              "\n",
              "\n",
              "[[5]]\n",
              "\n",
              "Call:\n",
              "lm(formula = Yield ~ Env_index, data = .)\n",
              "\n",
              "Coefficients:\n",
              "(Intercept)    Env_index  \n",
              "    77.0000       0.6754  \n",
              "\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "library(broom)\n",
        "regression1= df %>% group_by(Genotype) %>% do(tidy(lm(Yield ~ Env_index, data=.)))\n",
        "regression1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "0YmyDOOjeGmS",
        "outputId": "23f20fc6-a126-47c5-e58a-65f980641e70"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A grouped_df: 10 × 6</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>Genotype</th><th scope=col>term</th><th scope=col>estimate</th><th scope=col>std.error</th><th scope=col>statistic</th><th scope=col>p.value</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>CV1</td><td>(Intercept)</td><td>75.0000000</td><td>12.1638994</td><td> 6.165786</td><td>0.025309723</td></tr>\n",
              "\t<tr><td>CV1</td><td>Env_index  </td><td> 2.3395736</td><td> 0.5658852</td><td> 4.134361</td><td>0.053823885</td></tr>\n",
              "\t<tr><td>CV2</td><td>(Intercept)</td><td>73.7500000</td><td> 2.7528814</td><td>26.790112</td><td>0.001390415</td></tr>\n",
              "\t<tr><td>CV2</td><td>Env_index  </td><td> 0.8024564</td><td> 0.1280687</td><td> 6.265828</td><td>0.024537235</td></tr>\n",
              "\t<tr><td>CV3</td><td>(Intercept)</td><td>77.2500000</td><td> 4.3607461</td><td>17.714859</td><td>0.003171427</td></tr>\n",
              "\t<tr><td>CV3</td><td>Env_index  </td><td> 0.8039714</td><td> 0.2028693</td><td> 3.963002</td><td>0.058171978</td></tr>\n",
              "\t<tr><td>CV4</td><td>(Intercept)</td><td>78.5000000</td><td> 5.0252942</td><td>15.620976</td><td>0.004073090</td></tr>\n",
              "\t<tr><td>CV4</td><td>Env_index  </td><td> 0.3786387</td><td> 0.2337852</td><td> 1.619601</td><td>0.246746749</td></tr>\n",
              "\t<tr><td>CV5</td><td>(Intercept)</td><td>77.0000000</td><td> 1.1734503</td><td>65.618456</td><td>0.000232165</td></tr>\n",
              "\t<tr><td>CV5</td><td>Env_index  </td><td> 0.6753598</td><td> 0.0545909</td><td>12.371290</td><td>0.006470516</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA grouped_df: 10 × 6\n\n| Genotype &lt;chr&gt; | term &lt;chr&gt; | estimate &lt;dbl&gt; | std.error &lt;dbl&gt; | statistic &lt;dbl&gt; | p.value &lt;dbl&gt; |\n|---|---|---|---|---|---|\n| CV1 | (Intercept) | 75.0000000 | 12.1638994 |  6.165786 | 0.025309723 |\n| CV1 | Env_index   |  2.3395736 |  0.5658852 |  4.134361 | 0.053823885 |\n| CV2 | (Intercept) | 73.7500000 |  2.7528814 | 26.790112 | 0.001390415 |\n| CV2 | Env_index   |  0.8024564 |  0.1280687 |  6.265828 | 0.024537235 |\n| CV3 | (Intercept) | 77.2500000 |  4.3607461 | 17.714859 | 0.003171427 |\n| CV3 | Env_index   |  0.8039714 |  0.2028693 |  3.963002 | 0.058171978 |\n| CV4 | (Intercept) | 78.5000000 |  5.0252942 | 15.620976 | 0.004073090 |\n| CV4 | Env_index   |  0.3786387 |  0.2337852 |  1.619601 | 0.246746749 |\n| CV5 | (Intercept) | 77.0000000 |  1.1734503 | 65.618456 | 0.000232165 |\n| CV5 | Env_index   |  0.6753598 |  0.0545909 | 12.371290 | 0.006470516 |\n\n",
            "text/latex": "A grouped\\_df: 10 × 6\n\\begin{tabular}{llllll}\n Genotype & term & estimate & std.error & statistic & p.value\\\\\n <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t CV1 & (Intercept) & 75.0000000 & 12.1638994 &  6.165786 & 0.025309723\\\\\n\t CV1 & Env\\_index   &  2.3395736 &  0.5658852 &  4.134361 & 0.053823885\\\\\n\t CV2 & (Intercept) & 73.7500000 &  2.7528814 & 26.790112 & 0.001390415\\\\\n\t CV2 & Env\\_index   &  0.8024564 &  0.1280687 &  6.265828 & 0.024537235\\\\\n\t CV3 & (Intercept) & 77.2500000 &  4.3607461 & 17.714859 & 0.003171427\\\\\n\t CV3 & Env\\_index   &  0.8039714 &  0.2028693 &  3.963002 & 0.058171978\\\\\n\t CV4 & (Intercept) & 78.5000000 &  5.0252942 & 15.620976 & 0.004073090\\\\\n\t CV4 & Env\\_index   &  0.3786387 &  0.2337852 &  1.619601 & 0.246746749\\\\\n\t CV5 & (Intercept) & 77.0000000 &  1.1734503 & 65.618456 & 0.000232165\\\\\n\t CV5 & Env\\_index   &  0.6753598 &  0.0545909 & 12.371290 & 0.006470516\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   Genotype term        estimate   std.error  statistic p.value    \n",
              "1  CV1      (Intercept) 75.0000000 12.1638994  6.165786 0.025309723\n",
              "2  CV1      Env_index    2.3395736  0.5658852  4.134361 0.053823885\n",
              "3  CV2      (Intercept) 73.7500000  2.7528814 26.790112 0.001390415\n",
              "4  CV2      Env_index    0.8024564  0.1280687  6.265828 0.024537235\n",
              "5  CV3      (Intercept) 77.2500000  4.3607461 17.714859 0.003171427\n",
              "6  CV3      Env_index    0.8039714  0.2028693  3.963002 0.058171978\n",
              "7  CV4      (Intercept) 78.5000000  5.0252942 15.620976 0.004073090\n",
              "8  CV4      Env_index    0.3786387  0.2337852  1.619601 0.246746749\n",
              "9  CV5      (Intercept) 77.0000000  1.1734503 65.618456 0.000232165\n",
              "10 CV5      Env_index    0.6753598  0.0545909 12.371290 0.006470516"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ggplot(data=df, aes(x=as.numeric(Env_index), y=as.numeric(Yield), group=Genotype)) +\n",
        "  geom_smooth(method = lm, level=0.95, se=FALSE, linetype=1, color=\"Dark red\", linewidth=0.5, formula= y ~ x) +\n",
        "  geom_point (aes(shape=Genotype, fill=Genotype), col=\"Black\", size=5, stroke = 0.5) +\n",
        "  scale_shape_manual(values = c(21, 22, 23, 24, 25)) +\n",
        "  scale_fill_manual(values = c(\"Black\",\"Red\",'Blue',\"Orange\",\"Green\")) +\n",
        "  scale_x_continuous(breaks = seq(-40,40,10), limits = c(-40,40)) +\n",
        "  scale_y_continuous(breaks = seq(0,200,20), limits = c(0,200)) +\n",
        "  labs(x=\"Environmental Index\", y=\"Yield\") +\n",
        "  theme_classic(base_size=18, base_family=\"serif\")+\n",
        "  theme(legend.position=c(0.89,0.17),\n",
        "        legend.title=element_blank(),\n",
        "        legend.key=element_rect(color=\"white\", fill=\"white\"),\n",
        "        legend.text=element_text(family=\"serif\", face=\"plain\",\n",
        "                                 size=15, color= \"Black\"),\n",
        "        legend.background=element_rect(fill=\"white\"),\n",
        "        axis.line=element_line(linewidth=0.5, colour=\"black\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "onqIqnbHeGdE",
        "outputId": "19ec7f44-86c3-4f09-bc2e-46c4753a2989"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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Wqwq1evXkFBQWBgYMnKJi4uLo0aNVq0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m1l1veHY2/HAE3i+Dpf37AAzDHjHD8A8J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  ]
}