BAB 3 PARAMETER DAN FUNGSI
MUHAMMAD FAQIH
2022-10-04
NAMA MAHASISWA : MUHAMMAD FAQH
NIM : 220605110069
MATA KULIAH : KALKULUS
DOSEN PENGAMPU : Prof. Dr. Suhartono, M.Kom
JURUSAN : TEKNIK INFORMATIKA
UNIVERSITAS : UIN 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':
##
## Dgf_point(height ~ age, data=datasets::Loblolly)
Smoother dan splines tidak ditentukan oleh bentuk dan parameter aljabar, tetapi oleh data dan algoritma. Sebagai ilustrasi, pertimbangkan beberapa data sederhana. Kumpulan data Loblollyberisi 84 pengukuran usia dan tinggi PINES 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 rowslibrary(mosaicCalc)
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: 0x0000020faf144938>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: 0x0000020fae9a50a0>library(dplyr)
library(mosaicCalc)
gf_point(height ~ age, data = datasets::Loblolly) %>%
slice_plot(f1(age) ~ age) %>%
slice_plot(f2(age) ~ age, color="red")
fungsi konektor spline dan linier sangat mirip, kecuali untuk rentang input di luar rentang data.
Cherry <- datasets::trees
gf_point(Volume ~ Girth, data = Cherry)
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 ="red") %>%
gf_point(Volume ~ Girth, data = Cherry) %>%
gf_labs(x = "Girth (inches)")
Masing-masing fungsi pasti melewatiap titik data. (Satu-satunya pengecualian adalah dua titik dengan keliling 13 inci. Tidak ada fungsi yang dapat melewati kedua titik dengan keliling 13, jadi fungsi membagi selisih dan melewati rata-rata dari dua titik.)
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)")
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")
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)")
range(Cherry$Height)## [1] 63 87Saat Anda ingin memplot suatu fungsi, tentu saja Anda perlu memilih rentang untuk nilai input.
DAFTAR PUSTAKA