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':
##
## D
Utils <- read.csv("http://www.mosaic-web.org/go/datasets/utilities.csv")
library(mosaicCalc)
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 2
library(mosaicCalc)
carPrice1 <- fitModel(
Price ~ A + B * Age + C * Mileage, data = Hondas
)
contour_plot(
carPrice1(Age = age, Mileage = miles) ~ age + miles,
domain(age=2:8, miles=range(0, 60000)))
library(mosaicCalc)
carPrice2 <- fitModel(
Price ~ A + B * Age + C * Mileage + D * Age * Mileage,
data = Hondas)
library(mosaicCalc)
carPrice2 <- fitModel(
Price ~ A + B * Age + C * Mileage + D * Age * Mileage,
data = Hondas)
library(mosaicCalc)
f <- fitModel(ccf ~ A * temp + B, data = Utils)
Utilities$fitmodel <- f(Utilities$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 45.243143
gf_point(ccf ~ temp, data = Utils) %>%
slice_plot(f(temp)~temp)
