Memanggil Packages yang Dibutuhkan
library(readxl)
## Warning: package 'readxl' was built under R version 4.4.3
library(MVN)
## Warning: package 'MVN' was built under R version 4.4.3
library(MVTests)
## Warning: package 'MVTests' was built under R version 4.4.3
##
## Attaching package: 'MVTests'
## The following object is masked from 'package:datasets':
##
## iris
library(profileR)
## Warning: package 'profileR' was built under R version 4.4.3
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 4.4.3
## Loading required package: RColorBrewer
## Loading required package: reshape
## Warning: package 'reshape' was built under R version 4.4.3
## Loading required package: lavaan
## Warning: package 'lavaan' was built under R version 4.4.3
## This is lavaan 0.6-20
## lavaan is FREE software! Please report any bugs.
Mengubah Variabel Menjadi Bentuk Matriks
y1 <- as.matrix(data_anmul$`Produksi`, nrow=80, ncol=1)
y2 <- as.matrix(data_anmul$`Luas Panen`, nrow=80, ncol=1)
y3 <- as.matrix(data_anmul$`Curah hujan`, nrow=80, ncol=1)
perlakuan <- as.matrix(data_anmul$Provinsi, nrow=80, ncol=1)
Membuat Data Frame
datafix=data.frame(perlakuan,y1,y2,y3)
datafix
## perlakuan y1 y2 y3
## 1 Aceh 1788738.0 352281.00 1986.0
## 2 Aceh 1772962.0 380686.00 1268.0
## 3 Aceh 1582393.0 387803.00 1098.0
## 4 Aceh 2331046.0 419183.00 1623.6
## 5 Aceh 1820062.0 376137.00 2264.4
## 6 Aceh 1956940.0 461060.00 1575.0
## 7 Aceh 2180754.0 293067.00 1096.0
## 8 Aceh 2478922.0 294483.00 1905.9
## 9 Aceh 1714437.6 310012.46 1931.4
## 10 Aceh 1861567.1 317869.41 1619.2
## 11 Sumatera Utara 3582302.0 754674.00 1903.3
## 12 Sumatera Utara 3607403.0 757547.00 2042.0
## 13 Sumatera Utara 3715514.0 765099.00 3175.0
## 14 Sumatera Utara 3727249.0 742968.00 2627.0
## 15 Sumatera Utara 3631039.0 717318.00 2148.0
## 16 Sumatera Utara 4044829.0 781769.00 975.9
## 17 Sumatera Utara 4387035.9 423029.00 2384.0
## 18 Sumatera Utara 4669777.5 415675.00 3190.0
## 19 Sumatera Utara 2078901.6 413141.24 1401.6
## 20 Sumatera Utara 2076280.0 388591.22 1648.3
## 21 Sumatera Barat 2211248.0 460497.00 5228.0
## 22 Sumatera Barat 2279602.0 461709.00 4959.5
## 23 Sumatera Barat 2368390.0 476422.00 4339.0
## 24 Sumatera Barat 2430384.0 487820.00 4627.9
## 25 Sumatera Barat 2519020.0 503198.00 2838.4
## 26 Sumatera Barat 2550609.0 507545.00 3548.0
## 27 Sumatera Barat 2487929.0 222482.00 4205.2
## 28 Sumatera Barat 2810425.0 222021.00 4553.0
## 29 Sumatera Barat 1482996.0 311671.23 4757.5
## 30 Sumatera Barat 1450839.7 295664.47 4072.7
## 31 Riau 574864.0 156088.00 3390.0
## 32 Riau 535788.0 145242.00 2405.0
## 33 Riau 512152.0 144015.00 2636.0
## 34 Riau 434144.0 118518.00 2628.0
## 35 Riau 385475.0 106037.00 2343.0
## 36 Riau 393917.0 107546.00 2048.3
## 37 Riau 325826.0 72151.00 2105.6
## 38 Riau 337421.0 70016.00 2982.9
## 39 Riau 230874.0 63142.04 1608.3
## 40 Riau 269344.0 64733.13 2584.9
## 41 Jambi 628828.0 153897.00 3207.0
## 42 Jambi 646641.0 157441.00 2295.0
## 43 Jambi 625164.0 149369.00 1874.0
## 44 Jambi 664535.0 153243.00 2093.0
## 45 Jambi 664720.0 145990.00 1781.0
## 46 Jambi 541486.0 122214.00 1694.9
## 47 Jambi 642095.0 96588.00 1502.4
## 48 Jambi 678127.0 97690.00 2193.2
## 49 Jambi 309932.7 69536.06 1773.4
## 50 Jambi 374376.3 84772.93 2303.8
## 51 Sumatera Selatan 3272451.0 769478.00 3396.0
## 52 Sumatera Selatan 3384670.0 784820.00 2593.0
## 53 Sumatera Selatan 3295247.0 769725.00 3083.0
## 54 Sumatera Selatan 3676723.0 800036.00 3409.0
## 55 Sumatera Selatan 3670435.0 810900.00 1668.3
## 56 Sumatera Selatan 4247922.0 872737.00 1947.2
## 57 Sumatera Selatan 4881089.0 615184.00 3477.9
## 58 Sumatera Selatan 4807430.0 621903.00 2489.5
## 59 Sumatera Selatan 2603396.2 539316.52 1655.5
## 60 Sumatera Selatan 2696877.5 551320.76 2300.2
## 61 Bengkulu 516869.0 133629.00 3822.0
## 62 Bengkulu 502552.0 127934.00 2465.7
## 63 Bengkulu 581910.0 144448.00 2545.0
## 64 Bengkulu 622832.0 147680.00 3980.9
## 65 Bengkulu 593194.0 147572.00 3323.0
## 66 Bengkulu 578654.0 128833.00 2668.9
## 67 Bengkulu 629224.0 83449.00 3350.1
## 68 Bengkulu 714017.0 82429.00 3949.8
## 69 Bengkulu 296472.1 64406.86 1786.2
## 70 Bengkulu 296925.2 64137.28 4144.0
## 71 Lampung 2807676.0 590608.00 2710.0
## 72 Lampung 2940795.0 606973.00 1568.0
## 73 Lampung 3101455.0 641876.00 1685.0
## 74 Lampung 3207002.0 638090.00 2456.7
## 75 Lampung 3320064.0 648731.00 1682.5
## 76 Lampung 3641895.0 707266.00 1628.1
## 77 Lampung 3831923.0 390799.00 2317.6
## 78 Lampung 4090654.0 396559.00 1825.1
## 79 Lampung 2164089.3 464103.42 1706.4
## 80 Lampung 2604913.3 545149.05 2211.3
Mengecek Keberadaan Package MVN
exists("mvn", where = "package:MVN")
## [1] TRUE
Melakukan Uji Normalitas Multivariat
norm.test = mvn(data = datafix, subset = "perlakuan", mvn_test = "mardia")
norm.test$multivariate_normality
## Group Test Statistic p.value MVN
## 1 Aceh Mardia Skewness 5.189 0.878 ✓ Normal
## 2 Aceh Mardia Kurtosis -1.213 0.225 ✓ Normal
## 3 Bengkulu Mardia Skewness 16.071 0.098 ✓ Normal
## 4 Bengkulu Mardia Kurtosis -0.449 0.654 ✓ Normal
## 5 Jambi Mardia Skewness 14.461 0.153 ✓ Normal
## 6 Jambi Mardia Kurtosis -0.695 0.487 ✓ Normal
## 7 Lampung Mardia Skewness 12.953 0.226 ✓ Normal
## 8 Lampung Mardia Kurtosis -0.921 0.357 ✓ Normal
## 9 Riau Mardia Skewness 6.532 0.769 ✓ Normal
## 10 Riau Mardia Kurtosis -0.886 0.376 ✓ Normal
## 11 Sumatera Barat Mardia Skewness 15.281 0.122 ✓ Normal
## 12 Sumatera Barat Mardia Kurtosis -1.021 0.307 ✓ Normal
## 13 Sumatera Selatan Mardia Skewness 12.440 0.257 ✓ Normal
## 14 Sumatera Selatan Mardia Kurtosis -1.195 0.232 ✓ Normal
## 15 Sumatera Utara Mardia Skewness 16.601 0.084 ✓ Normal
## 16 Sumatera Utara Mardia Kurtosis -0.660 0.509 ✓ Normal
Melakukan Uji Homogenitas Ragam
ujiboxm<-BoxM(datafix[,2:4], datafix$`perlakuan`)
summary(ujiboxm)
## Box's M Test
##
## Chi-Squared Value = 149.4296 , df = 42 and p-value: 5.84e-14
Melakukan Uji MANOVA
ujimanova <- manova(cbind(y1,y2,y3)~perlakuan,data=datafix)
summary(ujimanova, test="Pillai")
## Df Pillai approx F num Df den Df Pr(>F)
## perlakuan 7 1.6135 11.969 21 216 < 2.2e-16 ***
## Residuals 72
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(ujimanova, test="Roy")
## Df Roy approx F num Df den Df Pr(>F)
## perlakuan 7 11.674 120.07 7 72 < 2.2e-16 ***
## Residuals 72
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(ujimanova, test="Wilks")
## Df Wilks approx F num Df den Df Pr(>F)
## perlakuan 7 0.02629 24.481 21 201.55 < 2.2e-16 ***
## Residuals 72
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(ujimanova, test="Hotelling-Lawley")
## Df Hotelling-Lawley approx F num Df den Df Pr(>F)
## perlakuan 7 13.598 44.464 21 206 < 2.2e-16 ***
## Residuals 72
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Melakukan Uji ANOVA Setiap Variabel
summary.aov(ujimanova)
## Response y1 :
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan 7 1.3318e+14 1.9026e+13 75.203 < 2.2e-16 ***
## Residuals 72 1.8215e+13 2.5299e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response y2 :
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan 7 4.1409e+12 5.9156e+11 60.014 < 2.2e-16 ***
## Residuals 72 7.0971e+11 9.8570e+09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response y3 :
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan 7 50926052 7275150 19.646 1.899e-14 ***
## Residuals 72 26662534 370313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot Analisis Profil
profil <- pbg(datafix[,2:4], datafix[,1], profile.plot = TRUE)
## Warning in plot.xy(xy.coords(x, y), type = type, ...): unimplemented pch value
## '26'
## Warning in plot.xy(xy.coords(x, y), type = type, ...): unimplemented pch value
## '26'
## Warning in plot.xy(xy.coords(x, y), type = type, ...): unimplemented pch value
## '26'
## Warning in plot.xy(xy.coords(x, y), type = type, ...): unimplemented pch value
## '27'
## Warning in plot.xy(xy.coords(x, y), type = type, ...): unimplemented pch value
## '27'
## Warning in plot.xy(xy.coords(x, y), type = type, ...): unimplemented pch value
## '27'
## Warning in plot.xy(xy.coords(x, y), type = type, ...): unimplemented pch value
## '28'
## Warning in plot.xy(xy.coords(x, y), type = type, ...): unimplemented pch value
## '28'
## Warning in plot.xy(xy.coords(x, y), type = type, ...): unimplemented pch value
## '28'
## Warning in plot.xy(xy.coords(x, y), type = type, ...): unimplemented pch value
## '26'
## Warning in plot.xy(xy.coords(x, y), type = type, ...): unimplemented pch value
## '27'
## Warning in plot.xy(xy.coords(x, y), type = type, ...): unimplemented pch value
## '28'
Uji Pada Analisis Profil
summary(profil)
## Call:
## pbg(data = datafix[, 2:4], group = datafix[, 1], profile.plot = TRUE)
##
## Hypothesis Tests:
## $`Ho: Profiles are parallel`
## Multivariate.Test Statistic Approx.F num.df den.df p.value
## 1 Wilks 0.07828946 26.107198 14 142 1.820973e-32
## 2 Pillai 0.95814723 9.459329 14 144 1.380184e-14
## 3 Hotelling-Lawley 11.30770241 56.538512 14 140 2.026386e-50
## 4 Roy 11.26639284 115.882898 7 72 1.370939e-36
##
## $`Ho: Profiles have equal levels`
## Df Sum Sq Mean Sq F value Pr(>F)
## group 7 2.045e+13 2.921e+12 94 <2e-16 ***
## Residuals 72 2.237e+12 3.107e+10
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Ho: Profiles are flat`
## F df1 df2 p-value
## 1 1012.456 2 71 6.470383e-53