Fitting functions Bab 6

Kurva dan model linier untuk cardata

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

Car <- read.csv("https://www.mosaic-web.org/go/datasets/cardata.csv")
head(Car)

##    mpg  pounds horsepower cylinders tons constant
## 1 16.9 3967.60        155         8  2.0        1
## 2 15.5 3689.14        142         8  1.8        1
## 3 19.2 3280.55        125         8  1.6        1
## 4 18.5 3585.40        150         8  1.8        1
## 5 30.0 1961.05         68         4  1.0        1
## 6 27.5 2329.60         95         4  1.2        1

gf_point(mpg ~ pounds, data = Car) %>%
  gf_labs(y = "konsumsi bahan bakar", 
          x = "pounds")

f <- fitModel(mpg ~ A * pounds + B, data = Car)

gf_point(mpg ~ pounds, data = Car) %>%
  slice_plot(f(pounds) ~ pounds)

f2 <- fitModel(
  mpg ~ A * pounds + B + C *sqrt(pounds),
  data = Car)
gf_point(
  mpg ~ pounds, data = Car) %>%
  slice_plot(f2(pounds) ~ pounds)

Carr1 <- fitModel(
  horsepower ~ A + B * mpg + C * pounds,
  data = Car)
contour_plot(
  Carr1(mpg=Mpg, pounds=Pounds) ~ Mpg + Pounds,
  domain(Mpg = 2:8, Pounds = range(0, 60000)))

logCar2 <- fitModel(
  horsepower ~ A + B * mpg + C * pounds + D * mpg * pounds,
  data = Car %>% mutate(logPrice = log10(horsepower)))

contour_plot(
  logCar2(mpg=mp, pounds=po) ~ mp + po,
  domain(mp = range(0, 8),po = range(0, 60000)))

car3 <- fitModel(
  horsepower ~ A + B * mpg + C * pounds + D * mpg * pounds +
    E * mpg^2 + F * pounds^2 + G * mpg^2 * pounds + 
    H * mpg * pounds^2,
  data = Car)
gf_point(pounds ~ mpg, data = Car, fill = NA) %>%
contour_plot(
  car3(mpg=mpg, pounds=pounds) ~ mpg + pounds)