Project 6 - Menyesuaikan Fungsi ke Data / Model Fitting Bagian 1
Muhammad Annas Bintar Putra - UIN Malang
2022-11-16
Model Fitting
Model fitting merupakan proses pemodelan matematika yang berfungsi sebagai pencocokan data atau improvisasi data untuk mencari data yang mendekati dengan target dalam bentuk grafik
Model fitting ini sangat berguna untuk programmer, data scientist, data analytics, dan sebagainya karena, model fitting dapat mencari data kualitas / kuantitas yang terbaik maupun yang mendekati. Sehingga kita dapat memilih dan menelaah apa yang kita cari.
Pada contoh kali ini, saya menggunakan data bawaan yang bernama ‘utilities.csv’ (dari mosaic-web.org) Berikut proses pengambilan datanya :
library(mosaicCalc)## Loading required package: mosaic## Registered S3 method overwritten by 'mosaic':
## method from
## fortify.SpatialPolygonsDataFrame ggplot2##
## 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, sum## Loading required package: mosaicCore##
## Attaching package: 'mosaicCore'## The following objects are masked from 'package:dplyr':
##
## count, tally##
## Attaching package: 'mosaicCalc'## The following object is masked from 'package:stats':
##
## DUtils <- read.csv("http://www.mosaic-web.org/go/datasets/utilities.csv")
gf_point(ccf ~ temp, data = Utils) %>%
gf_labs(y = "Natural gas usage (ccf/month)",
x = "Average outdoor temperature (F)")
data tersebut akan disimpan dalam Environment Variables, jika anda menggunakan RStudio anda akan melihat data tersebut dengan nama variable = ‘Utils’
Untuk melakukan model fitting, kita dapat menggunakan fungsi
fitModel()
Berikut contohnya :
f <- fitModel(ccf ~ A * temp + B, data = Utils)Pada fitModel() kali ini, kita akan membuat fungsi untuk
membandingkan data tabel ccf dan temp, dimana ccf sebagai variabel A
yang dimana akan menjadi titik riil atau titik pusat
Anda juga bisa melihat menggunakan visual grafik dari data ril jadi data model
keterangan variable : A * x + B
berikut contohnya :
gf_point(ccf ~ temp, data = Utils) %>%
slice_plot(f(temp) ~ temp)
untuk melihat datanya, dapat digunakan dengan cara berikut :
Utils$newTemp <- f(Utils$temp)
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 newTemp
## 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
## 99 117.992416Hal ini juga berfungsi untuk menambah tabel data pada file tersebut
gf_point(newTemp ~ temp, data = Utils) %>%
gf_point(ccf ~ temp, data = Utils, color = "red")
f2 <- fitModel(
ccf ~ A * temp + B + C *sqrt(temp),
data = Utils)
gf_point(
ccf ~ temp, data = Utils) %>%
slice_plot(f2(temp) ~ temp)
Utils$newTemp2 <- f2(Utils$temp)
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 newTemp
## 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
## newTemp2
## 1 146.5959484
## 2 137.8095225
## 3 222.8906446
## 4 91.7861669
## 5 175.5805494
## 6 12.2432940
## 7 21.9529277
## 8 9.8833696
## 9 222.8906446
## 10 29.5357088
## 11 45.5559294
## 12 75.2391949
## 13 121.3038562
## 14 216.2398366
## 15 165.4307935
## 16 121.3038562
## 17 59.8842038
## 18 17.0429561
## 19 2.9566952
## 20 9.8833696
## 21 21.9529277
## 22 81.7042215
## 23 146.5959484
## 24 165.4307935
## 25 125.3069559
## 26 78.4471839
## 27 17.0429561
## 28 9.8833696
## 29 0.6971296
## 30 17.0429561
## 31 65.8946514
## 32 85.0119162
## 33 186.3046994
## 34 75.2391949
## 35 175.5805494
## 36 151.1384846
## 37 14.6296334
## 38 65.8946514
## 39 29.5357088
## 40 0.6971296
## 41 56.9410118
## 42 113.5235012
## 43 252.5463173
## 44 2.9566952
## 45 170.4384355
## 46 29.5357088
## 47 160.5496309
## 48 203.6583262
## 49 9.8833696
## 50 9.8833696
## 51 85.0119162
## 52 24.4508206
## 53 40.0819840
## 54 98.7847354
## 55 209.8362372
## 56 51.1730250
## 57 56.9410118
## 58 51.1730250
## 59 5.2405910
## 60 17.0429561
## 61 -3.7509104
## 62 9.8833696
## 63 51.1730250
## 64 186.3046994
## 65 98.7847354
## 66 142.1546347
## 67 180.8660067
## 68 125.3069559
## 69 59.8842038
## 70 42.8015208
## 71 5.2405910
## 72 -3.7509104
## 73 -1.5385846
## 74 29.5357088
## 75 98.7847354
## 76 142.1546347
## 77 68.9643403
## 78 170.4384355
## 79 237.0391706
## 80 109.7401390
## 81 81.7042215
## 82 26.9781866
## 83 5.2405910
## 84 0.6971296
## 85 2.9566952
## 86 19.4838507
## 87 45.5559294
## 88 98.7847354
## 89 203.6583262
## 90 151.1384846
## 91 222.8906446
## 92 85.0119162
## 93 2.9566952
## 94 54.0376460
## 95 19.4838507
## 96 21.9529277
## 97 0.6971296
## 98 54.0376460
## 99 106.0242500gf_point(newTemp ~ temp, data = Utils, color = "red") %>%
gf_point(ccf ~ temp, data = Utils, color = "black") %>%
gf_point(newTemp2 ~ temp, data = Utils, color = "blue")
Note! Suatu data dikatakan bagus jika data model mendekati/sesuai data riil