BAB 3.PARAMETER DAN FUNGSI
Muhammad Yasril Adim Al Amin
06/10/2022
NIM : 220605110070
KELAS : C
MATKUL : KALKULUS
DOSEN PENGAMPU : Prof.Dr.Suhartono,M.Kom
JURUSAN : TEKNIK INFORMATIKA
LEMBAGA : UNIVERSITAS ISLAM NEGERI MAULANA MALIK IBRAHIM MALANG
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':
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## D#pertimbangkan beberapa data sederhana. Kumpulan data Loblollyberisi 84 pengukuran usia dan tinggi pinus loblolly.
gf_point(height ~ age, data=datasets::Loblolly)
dataup1 = datasets::Loblolly
dataup1## height age Seed
## 1 4.51 3 301
## 15 10.89 5 301
## 29 28.72 10 301
## 43 41.74 15 301
## 57 52.70 20 301
## 71 60.92 25 301
## 2 4.55 3 303
## 16 10.92 5 303
## 30 29.07 10 303
## 44 42.83 15 303
## 58 53.88 20 303
## 72 63.39 25 303
## 3 4.79 3 305
## 17 11.37 5 305
## 31 30.21 10 305
## 45 44.40 15 305
## 59 55.82 20 305
## 73 64.10 25 305
## 4 3.91 3 307
## 18 9.48 5 307
## 32 25.66 10 307
## 46 39.07 15 307
## 60 50.78 20 307
## 74 59.07 25 307
## 5 4.81 3 309
## 19 11.20 5 309
## 33 28.66 10 309
## 47 41.66 15 309
## 61 53.31 20 309
## 75 63.05 25 309
## 6 3.88 3 311
## 20 9.40 5 311
## 34 25.99 10 311
## 48 39.55 15 311
## 62 51.46 20 311
## 76 59.64 25 311
## 7 4.32 3 315
## 21 10.43 5 315
## 35 27.16 10 315
## 49 40.85 15 315
## 63 51.33 20 315
## 77 60.07 25 315
## 8 4.57 3 319
## 22 10.57 5 319
## 36 27.90 10 319
## 50 41.13 15 319
## 64 52.43 20 319
## 78 60.69 25 319
## 9 3.77 3 321
## 23 9.03 5 321
## 37 25.45 10 321
## 51 38.98 15 321
## 65 49.76 20 321
## 79 60.28 25 321
## 10 4.33 3 323
## 24 10.79 5 323
## 38 28.97 10 323
## 52 42.44 15 323
## 66 53.17 20 323
## 80 61.62 25 323
## 11 4.38 3 325
## 25 10.48 5 325
## 39 27.93 10 325
## 53 40.20 15 325
## 67 50.06 20 325
## 81 58.49 25 325
## 12 4.12 3 327
## 26 9.92 5 327
## 40 26.54 10 327
## 54 37.82 15 327
## 68 48.43 20 327
## 82 56.81 25 327
## 13 3.93 3 329
## 27 9.34 5 329
## 41 26.08 10 329
## 55 37.79 15 329
## 69 48.31 20 329
## 83 56.43 25 329
## 14 3.46 3 331
## 28 9.05 5 331
## 42 25.85 10 331
## 56 39.15 15 331
## 70 49.12 20 331
## 84 59.49 25 331library("xlsx")
write.xlsx(dataup1,"... lobolly.xls")library("readxl")
baca_xls = read_excel("... lobolly.xls")## New names:
## • `` -> `...1`baca_xls## # A tibble: 84 × 4
## ...1 height age Seed
## <chr> <dbl> <dbl> <chr>
## 1 1 4.51 3 301
## 2 15 10.9 5 301
## 3 29 28.7 10 301
## 4 43 41.7 15 301
## 5 57 52.7 20 301
## 6 71 60.9 25 301
## 7 2 4.55 3 303
## 8 16 10.9 5 303
## 9 30 29.1 10 303
## 10 44 42.8 15 303
## # … with 74 more rows#1. Sebuah “spline kubik”, yang mengikuti kelompok titik data dan kurva dengan mulus dan anggun.
f1 <- spliner(height ~ age, data = datasets::Loblolly)## Warning in regularize.values(x, y, ties, missing(ties)): collapsing to unique
## 'x' valuesf1 ## function (age, deriv = 0)
## {
## x <- get(fnames[2])
## if (connect)
## SF(x)
## else SF(x, deriv = deriv)
## }
## <environment: 0x00000177d40aad88>#2. Sebuah “interpolant linier”, yang menghubungkan kelompok titik data dengan garis lurus.
f2 <- connector(height ~ age, data = datasets::Loblolly)## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' valuesf2## function (age)
## {
## x <- get(fnames[2])
## if (connect)
## SF(x)
## else SF(x, deriv = deriv)
## }
## <environment: 0x00000177d44c5500>#Anda dapat membuat grafiknya:
library(dplyr)
library(mosaicCalc)
gf_point(height ~ age, data = datasets::Loblolly) %>%
slice_plot(f1(age) ~ age, color = "blue") %>%
slice_plot(f2(age) ~ age, color = "red")
library(mosaicCalc)
gf_point(Seed ~ age, data=datasets::Loblolly)
#Anda bahkan dapat “menyelesaikan” mereka, misalnya menemukan usia di mana ketinggian akan menjadi 35 kaki:
findZeros(f1(age) - 35 ~ age, xlim=range(0,30))## age
## 1 12.6905findZeros(f2(age) - 35 ~ age, xlim=range(0,30))## age
## 1 12.9#bila data tidak dibagi menjadi kelompok-kelompok terpisah, seperti data pinus loblolly. Misalnya, trees.csvkumpulan data adalah pengukuran volume, ketebalan, dan tinggi pohon ceri hitam.
Cherry <- datasets::trees
gf_point(Volume ~ Girth, data = Cherry)
#Cukup mudah untuk membuat spline atau konektor linier:
g1 = spliner(Volume ~ Girth, data = Cherry)## Warning in regularize.values(x, y, ties, missing(ties)): collapsing to unique
## 'x' valuesg2 = connector(Volume ~ Girth, data = Cherry)## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' valuesslice_plot(g1(x) ~ x, domain(x = 8:18)) %>%
slice_plot(g2(x) ~ x, color ="pink") %>%
gf_point(Volume ~ Girth, data = Cherry) %>%
gf_labs(x = "Girth (inches)")
#fungsi mulus lebih tepat daripada fungsi dengan banyak pasang surut, jenis fungsi yang berbeda sesuai: lebih halus.
g3 <- smoother(Volume ~ Girth, data = Cherry, span=1.5)
gf_point(Volume~Girth, data=Cherry) %>%
slice_plot(g3(Girth) ~ Girth) %>%
gf_labs(x = "Girth (inches)")
#membangun fungsi smooth yang mendekati data. Anda memiliki kendali atas seberapa mulus fungsi tersebut. Parameter hiper span mengatur ini:
g4 <- smoother(Volume ~ Girth, data=Cherry, span=1.0)
gf_point(Volume~Girth, data = Cherry) %>%
slice_plot(g4(Girth) ~ Girth) %>%
gf_labs(x = "Girth (inches)", y = "Wood volume")
#Smoother melakukan ini dengan sangat baik; cukup tentukan variabel mana yang akan menjadi input.
g5 <- smoother(Volume ~ Girth+Height,
data = Cherry, span = 1.0)
gf_point(Height ~ Girth, data = Cherry) %>%
contour_plot(g5(Girth, Height) ~ Girth + Height) %>%
gf_labs(x = "Girth (inches)",
y = "Height (ft)",
title = "Volume (ft^3)")
#menentukan rentang yang sesuaa dengan data yang menjadi dasar fungsi,Anda dapat menemukan ini dengan range() perintah, misalnya
range(Cherry$Height)## [1] 63 87Sumber Referensi : https://dtkaplan.github.io/RforCalculus/graphing-functions.html