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MARSYA ADELINE D
2022-10-08
“Smoothers” dan “splines” adalah dua jenis fungsi tujuan umum yang dapat menangkap pola dalam data. Smoother dan splines tidak ditentukan oleh bentuk dan parameter aljabar, tetapi oleh data dan algoritma. Sebagai ilustrasi,kumpulan data Loblollyberisi 84 pengukuran usia dan tinggi pinus loblolly.
library(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':
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## count, do, tally## The following object is masked from 'package:Matrix':
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## mean## The following object is masked from 'package:ggplot2':
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## stat## The following objects are masked from 'package:stats':
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## binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
## quantile, sd, t.test, var## The following objects are masked from 'package:base':
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## max, mean, min, prod, range, sample, sumgf_point(height ~ age, data=datasets::Loblolly)
library(mosaicCalc)## 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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## Ddataup1 = 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(mosaicCalc)
gf_point(Seed ~ age, data=datasets::Loblolly)
library("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 rowsfungsi konektor spline dan linier sangat mirip, kecuali untuk rentang input di luar rentang data. Namun, dalam rentang data tersebut, kedua jenis fungsi tersebut tepat berada di tengah setiap kelompok usia.
f1 <- spliner(height ~ age, data = datasets::Loblolly)## Warning in regularize.values(x, y, ties, missing(ties)): collapsing to unique
## 'x' valuesf2 <- connector(height ~ age, data = datasets::Loblolly)## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' valuesf1(age = 8)## [1] 20.68193## [1] 20.68193
f2(age = 8)## [1] 20.54729## [1] 20.54729
gf_point(height ~ age, data = datasets::Loblolly) %>%
slice_plot(f1(age) ~ age, color="red") %>%
slice_plot(f2(age) ~ age, color="blue") 
findZeros(f1(age) - 35 ~ age, xlim=range(0,30))## age
## 1 12.6905findZeros(f2(age) - 35 ~ age, xlim=range(0,30))## age
## 1 12.9Cherry <- 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 ="green") %>%
gf_point(Volume ~ Girth, data = Cherry) %>%
gf_labs(x = "Girth (inches)")
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)")
Smoother diberi nama yang baik: mereka membangun fungsi smooth yang
mendekati data. Anda memiliki kendali atas seberapa mulus fungsi
tersebut. Parameter hiper span.
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)")
Saat Anda ingin memplot suatu fungsi, maka perlu memilih rentang untuk
nilai input. Seringkali masuk akal untuk memilih rentang yang sesuai
dengan data yang menjadi dasar fungsi. Anda dapat menemukan ini dengan
range()perintah, misalnya
range(Cherry$Height)## [1] 63 87