title: “Mencocokkan Fungsi ke Data” author: “Fairuz Ardhan Haunan” date: “2022-11-07” output: html_document —
BAB 6
Menyesuaikan Fungsi ke Data
Fairuz Ardhan Haunan 220605110038 UIN Maulana Malik Ibrahim Malang
Seringkali kita memiliki gagasan mengenai bentuk fungsi dari sebuah model. Kita harus memilih parameter yang akan membuat fungsi model cocok dengan hasil observasi kita. Proses pemilihan parameter yang cocok disebut “fitting model”.
Kita gunakan ilustrasi dengan mengambil data berupa utilities.csv. Data ini mencatata suhu rata-rata setiap bulan dalam Fahrenheit (F) dan juga mencatat penggunaan gas alam bulanan dalam kaki kubik (ccf). Kita deklarasikan variabel Utils berasal dari proses membaca :
http://www.mosaic-web.org/go/datasets/utilities.csvlibrary(mosaicCalc)## Loading required package: mosaicCore## Loading required package: Deriv## Loading required package: Ryacas##
## Attaching package: 'Ryacas'## The following object is masked from 'package:stats':
##
## integrate## The following objects are masked from 'package:base':
##
## %*%, diag, diag<-, lower.tri, upper.tri## Registered S3 method overwritten by 'mosaic':
## method from
## fortify.SpatialPolygonsDataFrame ggplot2##
## Attaching package: 'mosaicCalc'## The following object is masked from 'package:stats':
##
## DUtils <- read.csv("http://www.mosaic-web.org/go/datasets/utilities.csv")
Utils## month day year temp kwh ccf thermsPerDay dur totalbill gasbill elecbill
## 1 2 24 2005 29 557 166 6.0 28 213.71 166.63 47.08
## 2 3 29 2005 31 772 179 5.5 33 239.85 117.05 62.80
## 3 1 27 2005 15 891 224 7.5 30 294.96 223.92 71.04
## 4 11 23 2004 43 860 82 2.8 29 160.26 88.51 71.75
## 5 12 28 2004 23 1160 208 6.0 35 317.47 224.18 93.29
## 6 9 26 2004 71 922 15 0.5 32 117.46 21.25 96.21
## 7 8 25 2004 67 841 15 0.5 29 111.08 21.72 89.36
## 8 7 27 2004 72 860 8 0.3 30 106.65 15.59 91.06
## 9 1 28 2004 15 594 242 8.1 30 262.81 216.89 47.37
## 10 6 27 2004 64 911 18 0.6 32 119.65 25.14 94.51
## 11 5 26 2004 58 742 35 1.2 29 109.38 39.40 69.98
## 12 4 27 2004 48 709 78 2.6 30 120.08 65.67 54.41
## 13 3 28 2004 35 510 144 4.7 31 166.51 124.18 42.33
## 14 2 26 2004 16 563 216 7.6 29 239.60 193.45 46.15
## 15 12 29 2003 25 725 204 5.9 35 225.73 168.93 56.80
## 16 11 24 2003 35 570 130 4.6 29 151.62 106.61 45.01
## 17 10 26 2003 53 927 48 1.5 31 127.37 45.28 82.09
## 18 9 25 2003 69 888 16 0.5 30 108.54 21.08 87.46
## 19 8 26 2003 75 869 14 0.5 29 108.04 19.56 89.12
## 20 7 28 2003 72 934 15 0.5 32 116.29 21.28 95.01
## 21 6 26 2003 67 722 18 0.6 29 99.52 24.46 75.06
## 22 4 28 2003 46 503 100 3.2 32 127.07 86.83 40.24
## 23 3 27 2003 29 648 153 5.3 29 226.92 176.02 50.90
## 24 12 29 2002 25 1032 190 5.5 35 217.42 140.49 76.93
## 25 11 24 2002 34 865 126 4.1 31 154.93 94.67 65.02
## 26 10 24 2002 47 790 69 2.4 29 122.51 55.74 66.77
## 27 9 25 2002 69 838 16 0.5 30 99.46 18.16 82.20
## 28 8 26 2002 72 812 15 0.5 29 101.39 17.56 83.83
## 29 7 28 2002 76 925 16 0.5 32 111.65 18.61 93.04
## 30 6 26 2002 69 496 23 0.8 29 76.43 23.42 53.01
## 31 5 28 2002 51 394 60 2.0 30 87.47 48.92 38.55
## 32 4 28 2002 45 449 106 3.3 32 106.04 70.34 35.70
## 33 3 27 2002 21 471 190 6.6 29 152.32 113.63 38.69
## 34 11 26 2001 48 1046 79 2.4 33 134.50 53.60 80.90
## 35 1 28 2002 23 581 210 6.6 32 174.45 127.86 46.59
## 36 2 26 2002 28 551 178 6.2 29 147.06 102.85 44.21
## 37 6 26 2001 70 160 1 0.1 10 31.55 3.42 17.43
## 38 10 24 2001 51 828 44 1.6 29 107.58 32.38 75.20
## 39 9 25 2001 64 865 20 0.7 30 105.91 20.17 85.74
## 40 7 26 2001 76 736 7 0.2 30 92.36 12.79 79.57
## 41 10 24 2000 54 778 37 1.3 29 107.50 41.19 66.31
## 42 11 26 2000 37 617 123 3.8 33 150.13 102.52 47.61
## 43 12 27 2000 11 586 235 7.7 31 254.23 210.87 46.59
## 44 8 26 2001 75 923 15 0.5 31 114.95 18.10 96.85
## 45 2 26 2000 24 521 228 8.0 29 177.48 134.65 42.83
## 46 9 25 2000 64 864 17 0.5 32 104.86 21.39 83.47
## 47 12 29 1999 26 892 194 5.5 36 173.65 112.72 68.25
## 48 1 28 2000 18 533 164 5.6 30 139.18 95.88 43.30
## 49 8 24 2000 72 789 13 0.4 29 96.47 17.66 78.81
## 50 7 26 2000 72 935 0 0.0 32 102.44 8.08 94.36
## 51 4 28 2000 45 638 74 2.2 34 100.33 47.33 53.00
## 52 6 24 2000 66 583 23 0.9 25 85.30 25.55 59.75
## 53 5 30 2000 60 700 129 4.1 32 153.32 89.87 63.45
## 54 3 25 2000 41 554 16 0.6 28 61.27 15.32 45.95
## 55 2 26 2003 17 580 224 7.8 29 232.41 187.05 45.36
## 56 5 28 2003 56 496 43 1.4 30 92.86 43.77 49.09
## 57 4 28 2005 54 444 61 2.0 30 103.34 64.99 38.35
## 58 5 26 2005 56 645 51 1.8 28 127.22 61.81 65.41
## 59 8 25 2005 74 845 9 0.3 29 120.53 18.16 102.37
## 60 9 26 2005 69 995 11 0.3 32 135.07 22.33 112.74
## 61 7 27 2005 78 862 11 0.4 30 116.72 19.96 96.76
## 62 6 27 2005 72 939 19 0.6 32 131.02 27.30 103.72
## 63 10 25 2005 56 965 32 1.1 29 150.62 55.74 94.88
## 64 12 28 2005 21 931 176 5.8 31 324.52 240.90 83.62
## 65 11 27 2005 41 926 99 3.1 33 212.49 153.24 84.75
## 66 1 29 2006 30 927 144 4.5 32 282.25 193.84 90.28
## 67 2 27 2006 22 876 161 5.6 29 289.91 198.11 91.80
## 68 3 28 2006 34 749 116 4.0 29 210.85 138.65 72.20
## 69 4 26 2006 53 428 52 1.8 29 96.87 55.00 41.87
## 70 5 25 2006 59 450 38 1.3 29 95.04 47.39 47.65
## 71 6 26 2006 74 694 10 0.3 32 98.48 19.19 79.32
## 72 7 26 2006 78 954 7 0.2 30 131.27 16.37 114.90
## 73 8 24 2006 77 957 6 0.2 29 134.96 15.88 119.30
## 74 9 25 2006 64 1027 15 0.5 32 156.51 25.74 130.77
## 75 11 26 2006 41 663 101 3.1 33 168.24 106.54 62.72
## 76 12 27 2006 30 720 140 4.5 31 229.40 159.08 70.32
## 77 10 24 2006 50 893 47 1.6 29 144.16 46.12 98.04
## 78 1 28 2007 24 897 168 5.3 32 267.72 178.16 89.97
## 79 2 26 2007 13 808 191 6.7 29 298.50 207.53 90.97
## 80 3 26 2007 38 724 101 3.6 29 192.67 118.78 73.89
## 81 4 26 2007 46 707 77 2.6 30 159.01 82.76 76.25
## 82 5 28 2007 65 442 18 0.6 32 86.54 32.98 53.56
## 83 6 26 2007 74 305 7 0.2 29 67.19 21.41 45.78
## 84 7 27 2007 76 839 9 0.3 30 135.73 22.87 112.99
## 85 8 26 2007 75 809 6 0.2 31 123.07 19.17 103.90
## 86 9 25 2007 68 812 13 0.4 30 117.82 24.54 98.90
## 87 10 24 2007 58 761 28 1.0 29 123.40 38.59 85.81
## 88 11 26 2007 41 767 98 3.0 33 181.53 104.52 77.01
## 89 12 27 2007 18 980 182 6.0 31 296.10 194.91 101.19
## 90 3 27 2008 28 752 139 4.7 30 245.27 167.30 77.97
## 91 2 26 2008 15 804 191 6.7 29 292.12 207.32 84.80
## 92 4 27 2008 45 623 79 2.6 31 160.69 97.11 63.58
## 93 8 25 2008 75 544 12 0.4 29 103.28 26.83 76.45
## 94 5 27 2008 55 410 29 1.0 30 105.50 52.15 53.35
## 95 6 25 2008 68 196 6 0.2 29 53.92 20.97 32.95
## 96 9 25 2008 67 746 16 0.5 31 124.82 29.77 95.05
## 97 7 27 2008 76 477 11 0.3 32 99.14 69.82 29.32
## 98 10 26 2008 55 801 32 1.1 31 134.30 41.74 92.56
## 99 11 24 2008 39 868 91 3.0 29 186.18 93.60 92.58
## notes
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## 37 transfer back from England
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## 54 bad meter reading
## 55
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## 61 high efficiency gas furnace and gas water heater installed
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## 71 away for 10 days on vacation
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## 85
## 86 5.46 credit for "cost of gas"
## 87
## 88
## 89
## 90 housesitters
## 91 housesitters
## 92 housesitters
## 93
## 94 housesitters
## 95 empty house
## 96
## 97 empty house
## 98
## 99gf_point(ccf ~ temp, data = Utils) %>%
gf_labs(y = "Penggunaan Gas Alam (ccf/month)",
x = "Rata-Rata Suhu Outdor (F)")
Untuk mewakili sebuah data dapat digunakan yang namanya fungsi. Fungsi paling umum yang digunakan adalah fungsi garis luru f(x) = ax + b. Dalam hal ini, x merupakan input, sedangkan A dan B adalah parameternya. Dari data utilitas, salah satu masukkannya berupa suhu dan akan dikeluarkan dalam bentuk ccf. Tuliskan rumus dengan nama input, parameter, dan output untuk menyesuaikan fungsi model dengan data.
library(mosaic)##
## The 'mosaic' package masks several functions from core packages in order to add
## additional features. The original behavior of these functions should not be affected by this.##
## Attaching package: 'mosaic'## The following objects are masked from 'package:dplyr':
##
## count, do, tally## The following object is masked from 'package:Matrix':
##
## mean## The following object is masked from 'package:ggplot2':
##
## stat## The following objects are masked from 'package:stats':
##
## binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
## quantile, sd, t.test, var## The following objects are masked from 'package:base':
##
## max, mean, min, prod, range, sample, sumf <- fitModel(ccf ~ A * temp + B, data = Utils)Keluaran dari fitmodel dapat berupa fungsi dengan bentuk matematika yang sama dengan yang ditentukan di argumen pertama dengan nilai numerik spesifik yang diberikan parameter untuk membuat fungsi yang cocok dengan data. Segala sesuatu yang terkandung dalam data yang digunakan sebagai pemasangan adalah variabel. Dan A serta B merupakan parameter.
Kita juga akan menampilkan eror yang ada dalam data dengan Utils$eror <- (Utils$ccf-Utils$fitmodel). Perlu diingat, nilai dari Utils diambil dari http://www.mosaic-web.org/go/datasets/utilities.csv. Untuk menampilkannya kita dapat memanggilnya dengan menuliskan nama variabelnya. Dan kita akan menampilkan bentuk grafiknya.
Utils$fitmodel <- f(Utils$temp)
Utils$eror <- (Utils$ccf-Utils$fitmodel)
Utils## month day year temp kwh ccf thermsPerDay dur totalbill gasbill elecbill
## 1 2 24 2005 29 557 166 6.0 28 213.71 166.63 47.08
## 2 3 29 2005 31 772 179 5.5 33 239.85 117.05 62.80
## 3 1 27 2005 15 891 224 7.5 30 294.96 223.92 71.04
## 4 11 23 2004 43 860 82 2.8 29 160.26 88.51 71.75
## 5 12 28 2004 23 1160 208 6.0 35 317.47 224.18 93.29
## 6 9 26 2004 71 922 15 0.5 32 117.46 21.25 96.21
## 7 8 25 2004 67 841 15 0.5 29 111.08 21.72 89.36
## 8 7 27 2004 72 860 8 0.3 30 106.65 15.59 91.06
## 9 1 28 2004 15 594 242 8.1 30 262.81 216.89 47.37
## 10 6 27 2004 64 911 18 0.6 32 119.65 25.14 94.51
## 11 5 26 2004 58 742 35 1.2 29 109.38 39.40 69.98
## 12 4 27 2004 48 709 78 2.6 30 120.08 65.67 54.41
## 13 3 28 2004 35 510 144 4.7 31 166.51 124.18 42.33
## 14 2 26 2004 16 563 216 7.6 29 239.60 193.45 46.15
## 15 12 29 2003 25 725 204 5.9 35 225.73 168.93 56.80
## 16 11 24 2003 35 570 130 4.6 29 151.62 106.61 45.01
## 17 10 26 2003 53 927 48 1.5 31 127.37 45.28 82.09
## 18 9 25 2003 69 888 16 0.5 30 108.54 21.08 87.46
## 19 8 26 2003 75 869 14 0.5 29 108.04 19.56 89.12
## 20 7 28 2003 72 934 15 0.5 32 116.29 21.28 95.01
## 21 6 26 2003 67 722 18 0.6 29 99.52 24.46 75.06
## 22 4 28 2003 46 503 100 3.2 32 127.07 86.83 40.24
## 23 3 27 2003 29 648 153 5.3 29 226.92 176.02 50.90
## 24 12 29 2002 25 1032 190 5.5 35 217.42 140.49 76.93
## 25 11 24 2002 34 865 126 4.1 31 154.93 94.67 65.02
## 26 10 24 2002 47 790 69 2.4 29 122.51 55.74 66.77
## 27 9 25 2002 69 838 16 0.5 30 99.46 18.16 82.20
## 28 8 26 2002 72 812 15 0.5 29 101.39 17.56 83.83
## 29 7 28 2002 76 925 16 0.5 32 111.65 18.61 93.04
## 30 6 26 2002 69 496 23 0.8 29 76.43 23.42 53.01
## 31 5 28 2002 51 394 60 2.0 30 87.47 48.92 38.55
## 32 4 28 2002 45 449 106 3.3 32 106.04 70.34 35.70
## 33 3 27 2002 21 471 190 6.6 29 152.32 113.63 38.69
## 34 11 26 2001 48 1046 79 2.4 33 134.50 53.60 80.90
## 35 1 28 2002 23 581 210 6.6 32 174.45 127.86 46.59
## 36 2 26 2002 28 551 178 6.2 29 147.06 102.85 44.21
## 37 6 26 2001 70 160 1 0.1 10 31.55 3.42 17.43
## 38 10 24 2001 51 828 44 1.6 29 107.58 32.38 75.20
## 39 9 25 2001 64 865 20 0.7 30 105.91 20.17 85.74
## 40 7 26 2001 76 736 7 0.2 30 92.36 12.79 79.57
## 41 10 24 2000 54 778 37 1.3 29 107.50 41.19 66.31
## 42 11 26 2000 37 617 123 3.8 33 150.13 102.52 47.61
## 43 12 27 2000 11 586 235 7.7 31 254.23 210.87 46.59
## 44 8 26 2001 75 923 15 0.5 31 114.95 18.10 96.85
## 45 2 26 2000 24 521 228 8.0 29 177.48 134.65 42.83
## 46 9 25 2000 64 864 17 0.5 32 104.86 21.39 83.47
## 47 12 29 1999 26 892 194 5.5 36 173.65 112.72 68.25
## 48 1 28 2000 18 533 164 5.6 30 139.18 95.88 43.30
## 49 8 24 2000 72 789 13 0.4 29 96.47 17.66 78.81
## 50 7 26 2000 72 935 0 0.0 32 102.44 8.08 94.36
## 51 4 28 2000 45 638 74 2.2 34 100.33 47.33 53.00
## 52 6 24 2000 66 583 23 0.9 25 85.30 25.55 59.75
## 53 5 30 2000 60 700 129 4.1 32 153.32 89.87 63.45
## 54 3 25 2000 41 554 16 0.6 28 61.27 15.32 45.95
## 55 2 26 2003 17 580 224 7.8 29 232.41 187.05 45.36
## 56 5 28 2003 56 496 43 1.4 30 92.86 43.77 49.09
## 57 4 28 2005 54 444 61 2.0 30 103.34 64.99 38.35
## 58 5 26 2005 56 645 51 1.8 28 127.22 61.81 65.41
## 59 8 25 2005 74 845 9 0.3 29 120.53 18.16 102.37
## 60 9 26 2005 69 995 11 0.3 32 135.07 22.33 112.74
## 61 7 27 2005 78 862 11 0.4 30 116.72 19.96 96.76
## 62 6 27 2005 72 939 19 0.6 32 131.02 27.30 103.72
## 63 10 25 2005 56 965 32 1.1 29 150.62 55.74 94.88
## 64 12 28 2005 21 931 176 5.8 31 324.52 240.90 83.62
## 65 11 27 2005 41 926 99 3.1 33 212.49 153.24 84.75
## 66 1 29 2006 30 927 144 4.5 32 282.25 193.84 90.28
## 67 2 27 2006 22 876 161 5.6 29 289.91 198.11 91.80
## 68 3 28 2006 34 749 116 4.0 29 210.85 138.65 72.20
## 69 4 26 2006 53 428 52 1.8 29 96.87 55.00 41.87
## 70 5 25 2006 59 450 38 1.3 29 95.04 47.39 47.65
## 71 6 26 2006 74 694 10 0.3 32 98.48 19.19 79.32
## 72 7 26 2006 78 954 7 0.2 30 131.27 16.37 114.90
## 73 8 24 2006 77 957 6 0.2 29 134.96 15.88 119.30
## 74 9 25 2006 64 1027 15 0.5 32 156.51 25.74 130.77
## 75 11 26 2006 41 663 101 3.1 33 168.24 106.54 62.72
## 76 12 27 2006 30 720 140 4.5 31 229.40 159.08 70.32
## 77 10 24 2006 50 893 47 1.6 29 144.16 46.12 98.04
## 78 1 28 2007 24 897 168 5.3 32 267.72 178.16 89.97
## 79 2 26 2007 13 808 191 6.7 29 298.50 207.53 90.97
## 80 3 26 2007 38 724 101 3.6 29 192.67 118.78 73.89
## 81 4 26 2007 46 707 77 2.6 30 159.01 82.76 76.25
## 82 5 28 2007 65 442 18 0.6 32 86.54 32.98 53.56
## 83 6 26 2007 74 305 7 0.2 29 67.19 21.41 45.78
## 84 7 27 2007 76 839 9 0.3 30 135.73 22.87 112.99
## 85 8 26 2007 75 809 6 0.2 31 123.07 19.17 103.90
## 86 9 25 2007 68 812 13 0.4 30 117.82 24.54 98.90
## 87 10 24 2007 58 761 28 1.0 29 123.40 38.59 85.81
## 88 11 26 2007 41 767 98 3.0 33 181.53 104.52 77.01
## 89 12 27 2007 18 980 182 6.0 31 296.10 194.91 101.19
## 90 3 27 2008 28 752 139 4.7 30 245.27 167.30 77.97
## 91 2 26 2008 15 804 191 6.7 29 292.12 207.32 84.80
## 92 4 27 2008 45 623 79 2.6 31 160.69 97.11 63.58
## 93 8 25 2008 75 544 12 0.4 29 103.28 26.83 76.45
## 94 5 27 2008 55 410 29 1.0 30 105.50 52.15 53.35
## 95 6 25 2008 68 196 6 0.2 29 53.92 20.97 32.95
## 96 9 25 2008 67 746 16 0.5 31 124.82 29.77 95.05
## 97 7 27 2008 76 477 11 0.3 32 99.14 69.82 29.32
## 98 10 26 2008 55 801 32 1.1 31 134.30 41.74 92.56
## 99 11 24 2008 39 868 91 3.0 29 186.18 93.60 92.58
## notes fitmodel
## 1 152.634927
## 2 145.706424
## 3 201.134442
## 4 104.135411
## 5 173.420433
## 6 7.136381
## 7 20.993385
## 8 3.672130
## 9 201.134442
## 10 31.386138
## 11 52.171645
## 12 86.814156
## 13 131.849420
## 14 197.670191
## 15 166.491931
## 16 131.849420
## 17 69.492900
## 18 14.064883
## 19 -6.720624
## 20 3.672130
## 21 20.993385
## 22 93.742658
## 23 152.634927
## 24 166.491931
## 25 135.313671
## 26 90.278407
## 27 14.064883
## 28 3.672130
## 29 -10.184875
## 30 14.064883
## 31 76.421403
## 32 97.206909
## 33 180.348935
## 34 86.814156
## 35 173.420433
## 36 156.099178
## 37 transfer back from England 10.600632
## 38 76.421403
## 39 31.386138
## 40 -10.184875
## 41 66.028649
## 42 124.920918
## 43 214.991446
## 44 -6.720624
## 45 169.956182
## 46 31.386138
## 47 163.027680
## 48 190.741689
## 49 3.672130
## 50 3.672130
## 51 97.206909
## 52 24.457636
## 53 45.243143
## 54 bad meter reading 111.063913
## 55 194.205940
## 56 59.100147
## 57 66.028649
## 58 59.100147
## 59 -3.256372
## 60 14.064883
## 61 high efficiency gas furnace and gas water heater installed -17.113377
## 62 3.672130
## 63 59.100147
## 64 180.348935
## 65 111.063913
## 66 149.170675
## 67 176.884684
## 68 135.313671
## 69 69.492900
## 70 48.707394
## 71 away for 10 days on vacation -3.256372
## 72 -17.113377
## 73 -13.649126
## 74 31.386138
## 75 111.063913
## 76 149.170675
## 77 79.885654
## 78 169.956182
## 79 208.062944
## 80 121.456667
## 81 93.742658
## 82 27.921887
## 83 -3.256372
## 84 -10.184875
## 85 -6.720624
## 86 5.46 credit for "cost of gas" 17.529134
## 87 52.171645
## 88 111.063913
## 89 190.741689
## 90 housesitters 156.099178
## 91 housesitters 201.134442
## 92 housesitters 97.206909
## 93 -6.720624
## 94 housesitters 62.564398
## 95 empty house 17.529134
## 96 20.993385
## 97 empty house -10.184875
## 98 62.564398
## 99 117.992416
## eror
## 1 13.3650735
## 2 33.2935756
## 3 22.8655582
## 4 -22.1354113
## 5 34.5795669
## 6 7.8636192
## 7 -5.9933852
## 8 4.3278703
## 9 40.8655582
## 10 -13.3861384
## 11 -17.1716450
## 12 -8.8141559
## 13 12.1505800
## 14 18.3298093
## 15 37.5080691
## 16 -1.8494200
## 17 -21.4929004
## 18 1.9351170
## 19 20.7206235
## 20 11.3278703
## 21 -2.9933852
## 22 6.2573420
## 23 0.3650735
## 24 23.5080691
## 25 -9.3136711
## 26 -21.2784069
## 27 1.9351170
## 28 11.3278703
## 29 26.1848746
## 30 8.9351170
## 31 -16.4214026
## 32 8.7930909
## 33 9.6510648
## 34 -7.8141559
## 35 36.5795669
## 36 21.9008224
## 37 -9.6006319
## 38 -32.4214026
## 39 -11.3861384
## 40 17.1848746
## 41 -29.0286493
## 42 -1.9209178
## 43 20.0085539
## 44 21.7206235
## 45 58.0438180
## 46 -14.3861384
## 47 30.9723202
## 48 -26.7416885
## 49 9.3278703
## 50 -3.6721297
## 51 -23.2069091
## 52 -1.4576363
## 53 83.7568572
## 54 -95.0639135
## 55 29.7940604
## 56 -16.1001471
## 57 -5.0286493
## 58 -8.1001471
## 59 12.2563725
## 60 -3.0648830
## 61 28.1133768
## 62 15.3278703
## 63 -27.1001471
## 64 -4.3489352
## 65 -12.0639135
## 66 -5.1706755
## 67 -15.8846842
## 68 -19.3136711
## 69 -17.4929004
## 70 -10.7073939
## 71 13.2563725
## 72 24.1133768
## 73 19.6491257
## 74 -16.3861384
## 75 -10.0639135
## 76 -9.1706755
## 77 -32.8856537
## 78 -1.9561820
## 79 -17.0629440
## 80 -20.4566667
## 81 -16.7426580
## 82 -9.9218873
## 83 10.2563725
## 84 19.1848746
## 85 12.7206235
## 86 -4.5291341
## 87 -24.1716450
## 88 -13.0639135
## 89 -8.7416885
## 90 -17.0991776
## 91 -10.1344418
## 92 -18.2069091
## 93 18.7206235
## 94 -33.5643982
## 95 -11.5291341
## 96 -4.9933852
## 97 21.1848746
## 98 -30.5643982
## 99 -26.9924157Utilities$fitmodel <- f(Utilities$temp)
Utilities## month day year temp kwh ccf thermsPerDay billingDays totalbill gasbill
## 1 12 29 1999 26 892 194 5.5 36 173.65 112.72
## 2 1 28 2000 18 533 164 5.6 30 139.18 95.88
## 3 2 26 2000 24 521 228 8.0 29 177.48 134.65
## 4 3 25 2000 41 554 16 0.6 28 61.27 15.32
## 5 4 28 2000 45 638 74 2.2 34 100.33 47.33
## 6 5 30 2000 60 700 129 4.1 32 153.32 89.87
## 7 6 24 2000 66 583 23 0.9 25 85.30 25.55
## 8 7 26 2000 72 935 0 0.0 32 102.44 8.08
## 9 8 24 2000 72 789 13 0.4 29 96.47 17.66
## 10 9 25 2000 64 864 17 0.5 32 104.86 21.39
## 11 10 24 2000 54 778 37 1.3 29 107.50 41.19
## 12 11 26 2000 37 617 123 3.8 33 150.13 102.52
## 13 12 27 2000 11 586 235 7.7 31 254.23 210.87
## 14 6 26 2001 70 160 1 0.1 10 31.55 3.42
## 15 7 26 2001 76 736 7 0.2 30 92.36 12.79
## 16 8 26 2001 75 923 15 0.5 31 114.95 18.10
## 17 9 25 2001 64 865 20 0.7 30 105.91 20.17
## 18 10 24 2001 51 828 44 1.6 29 107.58 32.38
## 19 11 26 2001 48 1046 79 2.4 33 134.50 53.60
## 20 1 28 2002 23 581 210 6.6 32 174.45 127.86
## 21 2 26 2002 28 551 178 6.2 29 147.06 102.85
## 22 3 27 2002 21 471 190 6.6 29 152.32 113.63
## 23 4 28 2002 45 449 106 3.3 32 106.04 70.34
## 24 5 28 2002 51 394 60 2.0 30 87.47 48.92
## 25 6 26 2002 69 496 23 0.8 29 76.43 23.42
## 26 7 28 2002 76 925 16 0.5 32 111.65 18.61
## 27 8 26 2002 72 812 15 0.5 29 101.39 17.56
## 28 9 25 2002 69 838 16 0.5 30 99.46 18.16
## 29 10 24 2002 47 790 69 2.4 29 122.51 55.74
## 30 11 24 2002 34 865 126 4.1 31 154.93 94.67
## 31 12 29 2002 25 1032 190 5.5 35 217.42 140.49
## 32 2 26 2003 17 580 224 7.8 29 232.41 187.05
## 33 3 27 2003 29 648 153 5.3 29 226.92 176.02
## 34 4 28 2003 46 503 100 3.2 32 127.07 86.83
## 35 5 28 2003 56 496 43 1.4 30 92.86 43.77
## 36 6 26 2003 67 722 18 0.6 29 99.52 24.46
## 37 7 28 2003 72 934 15 0.5 32 116.29 21.28
## 38 8 26 2003 75 869 14 0.5 29 108.04 19.56
## 39 9 25 2003 69 888 16 0.5 30 108.54 21.08
## 40 10 26 2003 53 927 48 1.5 31 127.37 45.28
## 41 11 24 2003 35 570 130 4.6 29 151.62 106.61
## 42 12 29 2003 25 725 204 5.9 35 225.73 168.93
## 43 1 28 2004 15 594 242 8.1 30 262.81 216.89
## 44 2 26 2004 16 563 216 7.6 29 239.60 193.45
## 45 3 28 2004 35 510 144 4.7 31 166.51 124.18
## 46 4 27 2004 48 709 78 2.6 30 120.08 65.67
## 47 5 26 2004 58 742 35 1.2 29 109.38 39.40
## 48 6 27 2004 64 911 18 0.6 32 119.65 25.14
## 49 7 27 2004 72 860 8 0.3 30 106.65 15.59
## 50 8 25 2004 67 841 15 0.5 29 111.08 21.72
## 51 9 26 2004 71 922 15 0.5 32 117.46 21.25
## 52 11 23 2004 43 860 82 2.8 29 160.26 88.51
## 53 12 28 2004 23 1160 208 6.0 35 317.47 224.18
## 54 1 27 2005 15 891 224 7.5 30 294.96 223.92
## 55 2 24 2005 29 557 166 6.0 28 213.71 166.63
## 56 3 29 2005 31 772 179 5.5 33 239.85 117.05
## 57 4 28 2005 54 444 61 2.0 30 103.34 64.99
## 58 5 26 2005 56 645 51 1.8 28 127.22 61.81
## 59 6 27 2005 72 939 19 0.6 32 131.02 27.30
## 60 7 27 2005 78 862 11 0.4 30 116.72 19.96
## 61 8 25 2005 74 845 9 0.3 29 120.53 18.16
## 62 9 26 2005 69 995 11 0.3 32 135.07 22.33
## 63 10 25 2005 56 965 32 1.1 29 150.62 55.74
## 64 11 27 2005 41 926 99 3.1 33 212.49 153.24
## 65 12 28 2005 21 931 176 5.8 31 324.52 240.90
## 66 1 29 2006 30 927 144 4.5 32 282.25 193.84
## 67 2 27 2006 22 876 161 5.6 29 289.91 198.11
## 68 3 28 2006 34 749 116 4.0 29 210.85 138.65
## 69 4 26 2006 53 428 52 1.8 29 96.87 55.00
## 70 5 25 2006 59 450 38 1.3 29 95.04 47.39
## 71 6 26 2006 74 694 10 0.3 32 98.48 19.19
## 72 7 26 2006 78 954 7 0.2 30 131.27 16.37
## 73 8 24 2006 77 957 6 0.2 29 134.96 15.88
## 74 9 25 2006 64 1027 15 0.5 32 156.51 25.74
## 75 10 24 2006 50 893 47 1.6 29 144.16 46.12
## 76 11 26 2006 41 663 101 3.1 33 168.24 106.54
## 77 12 27 2006 30 720 140 4.5 31 229.40 159.08
## 78 1 28 2007 24 897 168 5.3 32 267.72 178.16
## 79 2 26 2007 13 808 191 6.7 29 298.50 207.53
## 80 3 26 2007 38 724 101 3.6 29 192.67 118.78
## 81 4 26 2007 46 707 77 2.6 30 159.01 82.76
## 82 5 28 2007 65 442 18 0.6 32 86.54 32.98
## 83 6 26 2007 74 305 7 0.2 29 67.19 21.41
## 84 7 27 2007 76 839 9 0.3 30 135.73 22.87
## 85 8 26 2007 75 809 6 0.2 31 123.07 19.17
## 86 9 25 2007 68 812 13 0.4 30 117.82 24.54
## 87 10 24 2007 58 761 28 1.0 29 123.40 38.59
## 88 11 26 2007 41 767 98 3.0 33 181.53 104.52
## 89 12 27 2007 18 980 182 6.0 31 296.10 194.91
## 90 2 26 2008 15 804 191 6.7 29 292.12 207.32
## 91 3 27 2008 28 752 139 4.7 30 245.27 167.30
## 92 4 27 2008 45 623 79 2.6 31 160.69 97.11
## 93 5 27 2008 55 410 29 1.0 30 105.50 52.15
## 94 6 25 2008 68 196 6 0.2 29 53.92 20.97
## 95 7 27 2008 76 477 11 0.3 32 99.14 69.82
## 96 8 25 2008 75 544 12 0.4 29 103.28 26.83
## 97 9 25 2008 67 746 16 0.5 31 124.82 29.77
## 98 10 26 2008 55 801 32 1.1 31 134.30 41.74
## 99 11 24 2008 39 868 91 3.0 29 186.18 93.60
## 100 12 29 2008 18 1205 199 5.8 35 332.09 209.21
## 101 1 28 2009 9 986 211 7.2 30 330.27 225.72
## 102 2 26 2009 23 870 159 5.6 29 242.12 147.82
## 103 3 29 2009 32 830 134 4.4 31 207.96 114.66
## 104 4 28 2009 47 497 74 2.5 30 113.50 58.03
## 105 5 28 2009 61 436 34 1.2 30 93.09 37.33
## 106 6 28 2009 69 579 19 0.6 31 103.70 24.88
## 107 7 28 2009 71 734 15 0.5 30 122.70 22.86
## 108 8 28 2009 72 774 10 0.3 29 125.37 19.43
## 109 9 27 2009 69 909 18 0.6 32 142.82 23.72
## 110 10 26 2009 45 842 62 2.2 29 158.02 58.38
## 111 11 24 2009 46 826 67 2.3 29 153.68 61.46
## 112 12 30 2009 22 1213 188 5.4 36 283.69 131.49
## 113 1 28 2010 15 992 206 6.9 29 291.10 180.73
## 114 2 28 2010 20 1024 187 6.1 31 268.07 163.62
## 115 3 29 2010 41 923 95 3.3 29 181.82 79.15
## 116 4 27 2010 56 814 31 1.1 29 100.61 29.44
## 117 5 36 2010 60 941 31 1.1 29 151.57 38.29
## elecbill notes
## 1 68.25
## 2 43.30
## 3 42.83
## 4 45.95 bad meter reading
## 5 53.00
## 6 63.45
## 7 59.75
## 8 94.36
## 9 78.81
## 10 83.47
## 11 66.31
## 12 47.61
## 13 46.59
## 14 17.43 transfer back from England
## 15 79.57
## 16 96.85
## 17 85.74
## 18 75.20
## 19 80.90
## 20 46.59
## 21 44.21
## 22 38.69
## 23 35.70
## 24 38.55
## 25 53.01
## 26 93.04
## 27 83.83
## 28 82.20
## 29 66.77
## 30 65.02
## 31 76.93
## 32 45.36
## 33 50.90
## 34 40.24
## 35 49.09
## 36 75.06
## 37 95.01
## 38 89.12
## 39 87.46
## 40 82.09
## 41 45.01
## 42 56.80
## 43 47.37
## 44 46.15
## 45 42.33
## 46 54.41
## 47 69.98
## 48 94.51
## 49 91.06
## 50 89.36
## 51 96.21
## 52 71.75
## 53 93.29
## 54 71.04
## 55 47.08
## 56 62.80
## 57 38.35
## 58 65.41
## 59 103.72
## 60 96.76 high efficiency gas furnace and gas water heater installed
## 61 102.37
## 62 112.74
## 63 94.88
## 64 84.75
## 65 83.62
## 66 90.28
## 67 91.80
## 68 72.20
## 69 41.87
## 70 47.65
## 71 79.32 away for 10 days on vacation
## 72 114.90
## 73 119.30
## 74 130.77
## 75 98.04
## 76 62.72
## 77 70.32
## 78 89.97
## 79 90.97
## 80 73.89
## 81 76.25
## 82 53.56
## 83 45.78
## 84 112.99
## 85 103.90
## 86 98.90 5.46 credit for "cost of gas"
## 87 85.81
## 88 77.01
## 89 101.19
## 90 84.80 housesitters
## 91 77.97 housesitters
## 92 63.58 housesitters
## 93 53.35 housesitters
## 94 32.95 empty house
## 95 29.32 empty house
## 96 76.45
## 97 95.05
## 98 92.56
## 99 92.58
## 100 122.88
## 101 104.55
## 102 94.30
## 103 93.30
## 104 55.47
## 105 55.76
## 106 78.82
## 107 99.84
## 108 105.94 Was this August?
## 109 119.10
## 110 102.52
## 111 92.22
## 112 152.20 estimated reading
## 113 110.37
## 114 114.02 9.57 escrow refund
## 115 102.67
## 116 95.22 24.05 interim elec refund
## 117 113.18
## fitmodel
## 1 163.027680
## 2 190.741689
## 3 169.956182
## 4 111.063913
## 5 97.206909
## 6 45.243143
## 7 24.457636
## 8 3.672130
## 9 3.672130
## 10 31.386138
## 11 66.028649
## 12 124.920918
## 13 214.991446
## 14 10.600632
## 15 -10.184875
## 16 -6.720624
## 17 31.386138
## 18 76.421403
## 19 86.814156
## 20 173.420433
## 21 156.099178
## 22 180.348935
## 23 97.206909
## 24 76.421403
## 25 14.064883
## 26 -10.184875
## 27 3.672130
## 28 14.064883
## 29 90.278407
## 30 135.313671
## 31 166.491931
## 32 194.205940
## 33 152.634927
## 34 93.742658
## 35 59.100147
## 36 20.993385
## 37 3.672130
## 38 -6.720624
## 39 14.064883
## 40 69.492900
## 41 131.849420
## 42 166.491931
## 43 201.134442
## 44 197.670191
## 45 131.849420
## 46 86.814156
## 47 52.171645
## 48 31.386138
## 49 3.672130
## 50 20.993385
## 51 7.136381
## 52 104.135411
## 53 173.420433
## 54 201.134442
## 55 152.634927
## 56 145.706424
## 57 66.028649
## 58 59.100147
## 59 3.672130
## 60 -17.113377
## 61 -3.256372
## 62 14.064883
## 63 59.100147
## 64 111.063913
## 65 180.348935
## 66 149.170675
## 67 176.884684
## 68 135.313671
## 69 69.492900
## 70 48.707394
## 71 -3.256372
## 72 -17.113377
## 73 -13.649126
## 74 31.386138
## 75 79.885654
## 76 111.063913
## 77 149.170675
## 78 169.956182
## 79 208.062944
## 80 121.456667
## 81 93.742658
## 82 27.921887
## 83 -3.256372
## 84 -10.184875
## 85 -6.720624
## 86 17.529134
## 87 52.171645
## 88 111.063913
## 89 190.741689
## 90 201.134442
## 91 156.099178
## 92 97.206909
## 93 62.564398
## 94 17.529134
## 95 -10.184875
## 96 -6.720624
## 97 20.993385
## 98 62.564398
## 99 117.992416
## 100 190.741689
## 101 221.919948
## 102 173.420433
## 103 142.242173
## 104 90.278407
## 105 41.778892
## 106 14.064883
## 107 7.136381
## 108 3.672130
## 109 14.064883
## 110 97.206909
## 111 93.742658
## 112 176.884684
## 113 201.134442
## 114 183.813186
## 115 111.063913
## 116 59.100147
## 117 45.243143gf_point(ccf ~ temp, data = Utils) %>%
slice_plot(f(temp) ~ temp)
Utils$eror## [1] 13.3650735 33.2935756 22.8655582 -22.1354113 34.5795669 7.8636192
## [7] -5.9933852 4.3278703 40.8655582 -13.3861384 -17.1716450 -8.8141559
## [13] 12.1505800 18.3298093 37.5080691 -1.8494200 -21.4929004 1.9351170
## [19] 20.7206235 11.3278703 -2.9933852 6.2573420 0.3650735 23.5080691
## [25] -9.3136711 -21.2784069 1.9351170 11.3278703 26.1848746 8.9351170
## [31] -16.4214026 8.7930909 9.6510648 -7.8141559 36.5795669 21.9008224
## [37] -9.6006319 -32.4214026 -11.3861384 17.1848746 -29.0286493 -1.9209178
## [43] 20.0085539 21.7206235 58.0438180 -14.3861384 30.9723202 -26.7416885
## [49] 9.3278703 -3.6721297 -23.2069091 -1.4576363 83.7568572 -95.0639135
## [55] 29.7940604 -16.1001471 -5.0286493 -8.1001471 12.2563725 -3.0648830
## [61] 28.1133768 15.3278703 -27.1001471 -4.3489352 -12.0639135 -5.1706755
## [67] -15.8846842 -19.3136711 -17.4929004 -10.7073939 13.2563725 24.1133768
## [73] 19.6491257 -16.3861384 -10.0639135 -9.1706755 -32.8856537 -1.9561820
## [79] -17.0629440 -20.4566667 -16.7426580 -9.9218873 10.2563725 19.1848746
## [85] 12.7206235 -4.5291341 -24.1716450 -13.0639135 -8.7416885 -17.0991776
## [91] -10.1344418 -18.2069091 18.7206235 -33.5643982 -11.5291341 -4.9933852
## [97] 21.1848746 -30.5643982 -26.9924157gf_point(year ~ eror,data = Utils) #eror per tahun
Kemudian kita urutkan dari data terbesar ke data terkecil menggunakan fungsi sort dengan decreasing bernilai true.
sort(Utils$eror, decreasing = TRUE)## [1] 83.7568572 58.0438180 40.8655582 37.5080691 36.5795669 34.5795669
## [7] 33.2935756 30.9723202 29.7940604 28.1133768 26.1848746 24.1133768
## [13] 23.5080691 22.8655582 21.9008224 21.7206235 21.1848746 20.7206235
## [19] 20.0085539 19.6491257 19.1848746 18.7206235 18.3298093 17.1848746
## [25] 15.3278703 13.3650735 13.2563725 12.7206235 12.2563725 12.1505800
## [31] 11.3278703 11.3278703 10.2563725 9.6510648 9.3278703 8.9351170
## [37] 8.7930909 7.8636192 6.2573420 4.3278703 1.9351170 1.9351170
## [43] 0.3650735 -1.4576363 -1.8494200 -1.9209178 -1.9561820 -2.9933852
## [49] -3.0648830 -3.6721297 -4.3489352 -4.5291341 -4.9933852 -5.0286493
## [55] -5.1706755 -5.9933852 -7.8141559 -8.1001471 -8.7416885 -8.8141559
## [61] -9.1706755 -9.3136711 -9.6006319 -9.9218873 -10.0639135 -10.1344418
## [67] -10.7073939 -11.3861384 -11.5291341 -12.0639135 -13.0639135 -13.3861384
## [73] -14.3861384 -15.8846842 -16.1001471 -16.3861384 -16.4214026 -16.7426580
## [79] -17.0629440 -17.0991776 -17.1716450 -17.4929004 -18.2069091 -19.3136711
## [85] -20.4566667 -21.2784069 -21.4929004 -22.1354113 -23.2069091 -24.1716450
## [91] -26.7416885 -26.9924157 -27.1001471 -29.0286493 -30.5643982 -32.4214026
## [97] -32.8856537 -33.5643982 -95.0639135Kita urutkan data terkecil ke terbesar dengan decreasing bernilai false.
sort(Utils$eror, decreasing = FALSE)## [1] -95.0639135 -33.5643982 -32.8856537 -32.4214026 -30.5643982 -29.0286493
## [7] -27.1001471 -26.9924157 -26.7416885 -24.1716450 -23.2069091 -22.1354113
## [13] -21.4929004 -21.2784069 -20.4566667 -19.3136711 -18.2069091 -17.4929004
## [19] -17.1716450 -17.0991776 -17.0629440 -16.7426580 -16.4214026 -16.3861384
## [25] -16.1001471 -15.8846842 -14.3861384 -13.3861384 -13.0639135 -12.0639135
## [31] -11.5291341 -11.3861384 -10.7073939 -10.1344418 -10.0639135 -9.9218873
## [37] -9.6006319 -9.3136711 -9.1706755 -8.8141559 -8.7416885 -8.1001471
## [43] -7.8141559 -5.9933852 -5.1706755 -5.0286493 -4.9933852 -4.5291341
## [49] -4.3489352 -3.6721297 -3.0648830 -2.9933852 -1.9561820 -1.9209178
## [55] -1.8494200 -1.4576363 0.3650735 1.9351170 1.9351170 4.3278703
## [61] 6.2573420 7.8636192 8.7930909 8.9351170 9.3278703 9.6510648
## [67] 10.2563725 11.3278703 11.3278703 12.1505800 12.2563725 12.7206235
## [73] 13.2563725 13.3650735 15.3278703 17.1848746 18.3298093 18.7206235
## [79] 19.1848746 19.6491257 20.0085539 20.7206235 21.1848746 21.7206235
## [85] 21.9008224 22.8655582 23.5080691 24.1133768 26.1848746 28.1133768
## [91] 29.7940604 30.9723202 33.2935756 34.5795669 36.5795669 37.5080691
## [97] 40.8655582 58.0438180 83.7568572Lalu kita jumlahkan semua elemen eror.
sum(Utils$eror)## [1] -1.355716e-11Kita coba gabungkan fungsi dengan fungsi lainnya lalu membuat grafkinya lagi dengan gf_point:
f2 <- fitModel(
ccf ~ A * temp + B + C *sqrt(temp),
data = Utils)
gf_point(
ccf ~ temp, data = Utils) %>%
slice_plot(f2(temp) ~ temp)
Menggunakan banyak variabel dalam sebuah proyeksi menjadi teknik yang sangat penting dan banyak digunakan.
Hondas <- read.csv("http://www.mosaic-web.org/go/datasets/used-hondas.csv")
head(Hondas)## Price Year Mileage Location Color Age
## 1 20746 2006 18394 St.Paul Grey 1
## 2 19787 2007 8 St.Paul Black 0
## 3 17987 2005 39998 St.Paul Grey 2
## 4 17588 2004 35882 St.Paul Black 3
## 5 16987 2004 25306 St.Paul Grey 3
## 6 16987 2005 33399 St.Paul Black 2Dapat disimpulkan dari tabel tersebut bahwasannya harga mobil bekas akan bergantung pada jarak tempuh dan tahun dikeluarkannya mobil.
carPrice1 <- fitModel(
Price ~ A + B * Age + C * Mileage, data = Hondas
)Kita akan memplot fungsi yang sesuai harga mobil tersebut
contour_plot(
carPrice1(Age = age, Mileage = miles) ~ age + miles,
domain(age=2:8, miles=range(0, 60000)))
Cara Membaca Plot
Saat harga mobil berada di harga $17.000 titik jarak berada di 20.000 miles dan umurnya adalh 4,5 tahun. Dapat disimpulkan bahwa semakin bertambah jarak 10.000 bertambah juga usia 1,5 tahun.
Model yang lebih baik dapat mencakup interaksi antara usia dan jarak tempuh. Usia dapat berbeda-beda sesuai dengan jarak yang ditempuh.
carPrice2 <- fitModel(
Price ~ A + B * Age + C * Mileage + D * Age * Mileage,
data = Hondas)contour_plot(
carPrice2(Age=age, Mileage=miles) ~ age + miles,
domain(age = range(0, 8), miles = range(0, 60000)))
bentuk contour carPrice2() sedikit berbeda
dengan carPrice1(). Bentuk yang kedua sedikit lebih
menonjol ke atas. contour didefinisikan melalui harga yang sama
antara bentuk 1 dengan bentuk 2. Menurunkan jarak tempuh 10.000 mil
diimbangi dengan penambahan usia kurang dari satu tahun.
interaksi berupa priceFun2() yang membuat pengaruh
berbeda terhadap harga mobil tergantung dari jarak tempuh dan umur.
fitModel() membantu menemukan parameter dalam model apa
pun sehingga model mendekati data