Nama: Marsya Adeline Deswintarani , NIM : 220605110065 ,Kelas : C , MataKuliah : Kalkulus , Dosen Pengampu : Prof. Dr. Suhartono, M.Kom. Jurusan : Teknik Informatika, Universitas :UIN MALIK IBRAHIM MALANG
MARSYA ADELINE D
2022-12-01
Bab 6 Menyesuaikan fungsi ke data
LATIHAN SOAL
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)")
library(mosaicCalc)
f <- fitModel(ccf ~ A * temp + B, data = Utils)gf_point(ccf ~ temp, data = Utils) %>%
slice_plot(f(temp) ~ temp)
f2 <- fitModel(
ccf ~ A * temp + B + C *sqrt(temp),
data = Utils)
gf_point(
ccf ~ temp, data = Utils) %>%
slice_plot(f2(temp) ~ temp)
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 2library(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, 70000)))
carPrice2 <- fitModel(
Price ~ A + B * Age + C * Mileage + D * Age * Mileage,
data = Hondas)Latihan 1
library(mosaicCalc)
contour_plot(
carPrice2(Age=age, Mileage=miles) ~ age + miles,
domain(age = range(0, 16), miles = range(0, 28000)))
Latihan 2
logPrice2 <- fitModel(
logPrice ~ A + B * Age + C * Mileage + D * Age * Mileage,
data = Hondas %>% mutate(logPrice = log10(Price)))contour_plot(
logPrice2(Age=age, Mileage=miles) ~ age + miles,
domain(age = range(0, 10), miles = range(0, 28000)))
contour_plot(
logPrice2(Age=age, Mileage=miles) ~ age + miles,
domain(age = range(4, 3), miles = range(0, 4500)))## Warning: Computation failed in `stat_contour_fill()`:
## factor level [2] is duplicated
carPrice3 <- fitModel(
Price ~ A + B * Age + C * Mileage + D * Age * Mileage +
E * Age^2 + F * Mileage^2 + G * Age^2 * Mileage +
H * Age * Mileage^2,
data = Hondas)
gf_point(Mileage ~ Age, data = Hondas, fill = NA) %>%
contour_plot(
carPrice3(Age=Age, Mileage=Mileage) ~ Age + Mileage)
Linier Curva
Utilities = read.csv("http://www.mosaic-web.org/go/datasets/utilities.csv")
gf_point(ccf ~ temp, data = Utilities)
project(ccf ~ temp + 1, data = Utilities)## (Intercept) temp
## 253.098208 -3.464251model_fun = makeFun( 253.098 - 3.464*temp ~ temp)
gf_point(ccf ~ temp, data=Utils) %>%
slice_plot(model_fun(temp) ~ temp)
project(ccf ~ temp + sqrt(temp) + 1, data = Utils)## (Intercept) temp sqrt(temp)
## 447.029273 1.377666 -63.208025mod2 <- makeFun(447.03 + 1.378*temp - 63.21*sqrt(temp) ~ temp)
gf_point(ccf ~ temp, data=Utils) %>% # the data
slice_plot(mod2(temp) ~ temp) %>%
gf_labs(x = "Temperature (F)",
y = "Natural gas used (ccf)")
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 2project(Price ~ Age + Mileage + 1, data = Hondas)## (Intercept) Age Mileage
## 2.133049e+04 -5.382931e+02 -7.668922e-02car_price <- makeFun(21330-5.383e2*age-7.669e-2*miles ~ age & miles)
contour_plot(car_price(age, miles) ~ age + miles,
domain(age=range(2, 8), miles=range(0, 60000))) %>%
gf_labs(title = "Miles per gallon")
project(Price ~ Age + Mileage + Age*Mileage + 1, data = Hondas)## (Intercept) Age Mileage Age:Mileage
## 2.213744e+04 -7.494928e+02 -9.413962e-02 3.450033e-03car_price2 <- makeFun(22137 - 7.495e2*age - 9.414e-2*miles +
3.450e-3*age*miles ~ age & miles)
contour_plot(
car_price2(Age, Mileage) ~ Age + Mileage,
domain(Age = range(0, 10), Mileage = range(0, 100000))) %>%
gf_labs(title = "Price of car (USD)")
Utilities = read.csv("http://www.mosaic-web.org/go/datasets/utilities.csv")
project(ccf ~ 1 + temp + I(temp^2), data = Utilities)## (Intercept) temp I(temp^2)
## 317.58743630 -6.85301947 0.03609138ccfQuad <- makeFun(317.587 - 6.853*T + 0.0361*T^2 ~ T)
gf_point(ccf ~ temp, data = Utilities) %>%
slice_plot(ccfQuad(temp) ~ temp) 
ccfQuad(T=72)## [1] 11.3134jawab
project(ccf ~ 1 + temp + I(temp^2) + I(temp^3), data = Utils)## (Intercept) temp I(temp^2) I(temp^3)
## 2.550709e+02 -1.427408e+00 -9.643482e-02 9.609511e-04ccfCubic <-
makeFun(2.551e2 - 1.427*T -
9.643e-2*T^2 + 9.6095e-4*T^3 ~ T)
gf_point(ccf ~ temp, data = Utils) %>%
slice_plot(ccfCubic(temp) ~ temp) 
ccfCubic(32)## [1] 142.18011 Pasang polinomial orde ke-4 dari ccfversus tempke data utilitas. Berapa nilai model ini untuk suhu 32 derajat? {87.103.128.140, 143 ,168,184}
project(ccf ~ 1 + temp + I(temp^2) + I(temp^3) + I(temp^4),
data = Utils)## (Intercept) temp I(temp^2) I(temp^3) I(temp^4)
## 1.757579e+02 8.225746e+00 -4.815403e-01 7.102673e-03 -3.384490e-05ccfQuad <- makeFun(1.7576e2 + 8.225*T -4.815e-1*T^2 +
7.103e-3*T^3 - 3.384e-5*T^4 ~ T)
gf_point(ccf ~ temp, data = Utils) %>%
slice_plot(ccfQuad(temp) ~ temp) %>%
gf_labs(y = "Natural gas use (ccf)", x = "Temperature (F)")
library(mosaicCalc)
ccfQuad(32)## [1] 143.1713- Buat plot perbedaan antara model orde ke-3 dan ke-4 pada rentang suhu dari 20 hingga 60 derajat. Apa perbedaan terbesar (dalam nilai absolut) antara output dari kedua model?
slice_plot(ccfQuad(temp) - ccfCubic(temp) ~ temp,
domain(temp = range(20, 60)))
Cars = read.csv("http://www.mosaic-web.org/go/datasets/cardata.csv")
head(Cars)## 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 1project(mpg ~ pounds + 1, data = Cars)## (Intercept) pounds
## 43.188646127 -0.00720077343.1886 - 0.00720*2000## [1] 28.7886Gunakan data agar sesuai dengan model ekonomi bahan bakar berikut (variabel mpg): =kamu0+kamu1+kamu2. a. Berapa nilai model untuk input 2000 pound dan 150 tenaga kuda? {14.9, 19.4 ,21.1,25.0,28.8,33.9,35.2} b. Berapa nilai model untuk input 2000 pound dan 50 tenaga kuda? {14.9,19.4,21.1,25.0,28.8, 33.9 ,35.2}
jawaban
project(mpg ~ pounds + horsepower + 1, data = Cars)## (Intercept) pounds horsepower
## 46.932738241 -0.002902265 -0.144930546mod_fun <- makeFun(46.933 - 0.00290*lbs - 0.1449*hp ~ lbs + hp)
mod_fun(lbs = 2000, hp = 50)## [1] 33.888Fungsi dengan parameter nonlinier
- Fungsi eksponensial
Families <- read.csv("http://www.mosaic-web.org/go/datasets/Income-Housing.csv")
gf_point(TwoVehicles ~ Income, data = Families)
kguess <- log(0.5) / 25000
kguess## [1] -2.772589e-05project( TwoVehicles ~ 1 + exp(Income*kguess), data = Families)## (Intercept) exp(Income * kguess)
## 110.4263 -101.5666f <- makeFun( 110.43 - 101.57*exp(Income * k) ~ Income, k = kguess)
gf_point(TwoVehicles ~ Income, data = Families) %>%
slice_plot(f(Income) ~ Income) 
f(Income = 10000)## [1] 33.45433f(Income = 50000)## [1] 85.0375sum_square_resids <- Vectorize(function(k) {
sum((Families$TwoVehicles - f(Income=Families$Income, k)) ^ 2)
})
slice_plot(
sum_square_resids(k) ~ k,
domain(k = range(log(0.8)/28000,log(0.5)/25000)))
Utilities = read.csv("http://www.mosaic-web.org/go/datasets/utilities.csv")
project(ccf ~ 1 + temp + I(temp^2), data = Utilities)## (Intercept) temp I(temp^2)
## 317.58743630 -6.85301947 0.03609138ccfQuad <- makeFun(317.587 - 6.853*T + 0.0361*T^2 ~ T)
gf_point(ccf ~ temp, data = Utilities) %>%
slice_plot(ccfQuad(temp) ~ temp)
a. Pasang polinomial orde ke-3 dari versus ke data utilitas. Berapa
nilai model ini untuk suhu 32 derajat? {87.103.128, 142
,143.168.184}
jawab
ccfCubic <-
makeFun(2.551e2 - 1.427*T -
9.643e-2*T^2 + 9.6095e-4*T^3 ~ T)
gf_point(ccf ~ temp, data = Utils) %>%
slice_plot(ccfCubic(temp) ~ temp)
1. Pasang polinomial orde ke-4 dari ccfversus tempke data utilitas.
Berapa nilai model ini untuk suhu 32 derajat? {87.103.128.140, 143
,168,184}
jawab
ccfQuad <- makeFun(1.7576e2 + 8.225*T -4.815e-1*T^2 +
7.103e-3*T^3 - 3.384e-5*T^4 ~ T)
gf_point(ccf ~ temp, data = Utils) %>%
slice_plot(ccfQuad(temp) ~ temp) %>%
gf_labs(y = "Natural gas use (ccf)", x = "Temperature (F)")
REFERENSI