Analisis Multivariat
Desy Amelia Eka Putri Pratama Wati
2025-02-27
1️ Membaca Dataset dari CSV
housing_data <- read.csv("C:\\Users\\User\\Downloads\\Housing.csv", header = TRUE, sep = ",")
head(housing_data)## price area bedrooms bathrooms stories mainroad guestroom basement
## 1 13300000 7420 4 2 3 yes no no
## 2 12250000 8960 4 4 4 yes no no
## 3 12250000 9960 3 2 2 yes no yes
## 4 12215000 7500 4 2 2 yes no yes
## 5 11410000 7420 4 1 2 yes yes yes
## 6 10850000 7500 3 3 1 yes no yes
## hotwaterheating airconditioning parking prefarea furnishingstatus
## 1 no yes 2 yes furnished
## 2 no yes 3 no furnished
## 3 no no 2 yes semi-furnished
## 4 no yes 3 yes furnished
## 5 no yes 2 no furnished
## 6 no yes 2 yes semi-furnished2️# Mengecek tipe data
str(housing_data)## 'data.frame': 545 obs. of 13 variables:
## $ price : int 13300000 12250000 12250000 12215000 11410000 10850000 10150000 10150000 9870000 9800000 ...
## $ area : int 7420 8960 9960 7500 7420 7500 8580 16200 8100 5750 ...
## $ bedrooms : int 4 4 3 4 4 3 4 5 4 3 ...
## $ bathrooms : int 2 4 2 2 1 3 3 3 1 2 ...
## $ stories : int 3 4 2 2 2 1 4 2 2 4 ...
## $ mainroad : chr "yes" "yes" "yes" "yes" ...
## $ guestroom : chr "no" "no" "no" "no" ...
## $ basement : chr "no" "no" "yes" "yes" ...
## $ hotwaterheating : chr "no" "no" "no" "no" ...
## $ airconditioning : chr "yes" "yes" "no" "yes" ...
## $ parking : int 2 3 2 3 2 2 2 0 2 1 ...
## $ prefarea : chr "yes" "no" "yes" "yes" ...
## $ furnishingstatus: chr "furnished" "furnished" "semi-furnished" "furnished" ...3️ Menghapus Kolom yang memiliki tipe data teks
housing_data <- Filter(function(x) !is.character(x), housing_data)
str(housing_data)## 'data.frame': 545 obs. of 6 variables:
## $ price : int 13300000 12250000 12250000 12215000 11410000 10850000 10150000 10150000 9870000 9800000 ...
## $ area : int 7420 8960 9960 7500 7420 7500 8580 16200 8100 5750 ...
## $ bedrooms : int 4 4 3 4 4 3 4 5 4 3 ...
## $ bathrooms: int 2 4 2 2 1 3 3 3 1 2 ...
## $ stories : int 3 4 2 2 2 1 4 2 2 4 ...
## $ parking : int 2 3 2 3 2 2 2 0 2 1 ...4️ Mengonversi dataset ke dalam bentuk matriks
housing_matrix <- as.matrix(housing_data)
housing_matrix## price area bedrooms bathrooms stories parking
## [1,] 13300000 7420 4 2 3 2
## [2,] 12250000 8960 4 4 4 3
## [3,] 12250000 9960 3 2 2 2
## [4,] 12215000 7500 4 2 2 3
## [5,] 11410000 7420 4 1 2 2
## [6,] 10850000 7500 3 3 1 2
## [7,] 10150000 8580 4 3 4 2
## [8,] 10150000 16200 5 3 2 0
## [9,] 9870000 8100 4 1 2 2
## [10,] 9800000 5750 3 2 4 1
## [11,] 9800000 13200 3 1 2 2
## [12,] 9681000 6000 4 3 2 2
## [13,] 9310000 6550 4 2 2 1
## [14,] 9240000 3500 4 2 2 2
## [15,] 9240000 7800 3 2 2 0
## [16,] 9100000 6000 4 1 2 2
## [17,] 9100000 6600 4 2 2 1
## [18,] 8960000 8500 3 2 4 2
## [19,] 8890000 4600 3 2 2 2
## [20,] 8855000 6420 3 2 2 1
## [21,] 8750000 4320 3 1 2 2
## [22,] 8680000 7155 3 2 1 2
## [23,] 8645000 8050 3 1 1 1
## [24,] 8645000 4560 3 2 2 1
## [25,] 8575000 8800 3 2 2 2
## [26,] 8540000 6540 4 2 2 2
## [27,] 8463000 6000 3 2 4 0
## [28,] 8400000 8875 3 1 1 1
## [29,] 8400000 7950 5 2 2 2
## [30,] 8400000 5500 4 2 2 1
## [31,] 8400000 7475 3 2 4 2
## [32,] 8400000 7000 3 1 4 2
## [33,] 8295000 4880 4 2 2 1
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## [36,] 8080940 7000 3 2 4 2
## [37,] 8043000 7482 3 2 3 1
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## [39,] 7962500 6000 3 1 4 2
## [40,] 7910000 6000 4 2 4 1
## [41,] 7875000 6550 3 1 2 0
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## [43,] 7700000 6480 3 2 4 2
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## [476,] 2961000 3000 2 1 2 0
## [477,] 2940000 5850 3 1 2 1
## [478,] 2940000 4960 2 1 1 0
## [479,] 2940000 3600 3 1 2 1
## [480,] 2940000 3660 4 1 2 0
## [481,] 2940000 3480 3 1 2 1
## [482,] 2940000 2700 2 1 1 0
## [483,] 2940000 3150 3 1 2 0
## [484,] 2940000 6615 3 1 2 0
## [485,] 2870000 3040 2 1 1 0
## [486,] 2870000 3630 2 1 1 0
## [487,] 2870000 6000 2 1 1 0
## [488,] 2870000 5400 4 1 2 0
## [489,] 2852500 5200 4 1 3 0
## [490,] 2835000 3300 3 1 2 1
## [491,] 2835000 4350 3 1 2 1
## [492,] 2835000 2640 2 1 1 1
## [493,] 2800000 2650 3 1 2 1
## [494,] 2800000 3960 3 1 1 0
## [495,] 2730000 6800 2 1 1 0
## [496,] 2730000 4000 3 1 2 1
## [497,] 2695000 4000 2 1 1 0
## [498,] 2660000 3934 2 1 1 0
## [499,] 2660000 2000 2 1 2 0
## [500,] 2660000 3630 3 3 2 0
## [501,] 2660000 2800 3 1 1 0
## [502,] 2660000 2430 3 1 1 0
## [503,] 2660000 3480 2 1 1 1
## [504,] 2660000 4000 3 1 1 0
## [505,] 2653000 3185 2 1 1 0
## [506,] 2653000 4000 3 1 2 0
## [507,] 2604000 2910 2 1 1 0
## [508,] 2590000 3600 2 1 1 0
## [509,] 2590000 4400 2 1 1 0
## [510,] 2590000 3600 2 2 2 1
## [511,] 2520000 2880 3 1 1 0
## [512,] 2520000 3180 3 1 1 0
## [513,] 2520000 3000 2 1 2 0
## [514,] 2485000 4400 3 1 2 0
## [515,] 2485000 3000 3 1 2 0
## [516,] 2450000 3210 3 1 2 0
## [517,] 2450000 3240 2 1 1 1
## [518,] 2450000 3000 2 1 1 1
## [519,] 2450000 3500 2 1 1 0
## [520,] 2450000 4840 2 1 2 0
## [521,] 2450000 7700 2 1 1 0
## [522,] 2408000 3635 2 1 1 0
## [523,] 2380000 2475 3 1 2 0
## [524,] 2380000 2787 4 2 2 0
## [525,] 2380000 3264 2 1 1 0
## [526,] 2345000 3640 2 1 1 0
## [527,] 2310000 3180 2 1 1 0
## [528,] 2275000 1836 2 1 1 0
## [529,] 2275000 3970 1 1 1 0
## [530,] 2275000 3970 3 1 2 0
## [531,] 2240000 1950 3 1 1 0
## [532,] 2233000 5300 3 1 1 0
## [533,] 2135000 3000 2 1 1 0
## [534,] 2100000 2400 3 1 2 0
## [535,] 2100000 3000 4 1 2 0
## [536,] 2100000 3360 2 1 1 1
## [537,] 1960000 3420 5 1 2 0
## [538,] 1890000 1700 3 1 2 0
## [539,] 1890000 3649 2 1 1 0
## [540,] 1855000 2990 2 1 1 1
## [541,] 1820000 3000 2 1 1 2
## [542,] 1767150 2400 3 1 1 0
## [543,] 1750000 3620 2 1 1 0
## [544,] 1750000 2910 3 1 1 0
## [545,] 1750000 3850 3 1 2 05️ Menghitung Eigenvalue dan Eigenvector
# Menghitung kovarians untuk mencari eigenvalues dan eigenvectors
eigen_kovarian <- eigen(cov(housing_matrix))
# Menampilkan hasil Eigenvalues dan Eigenvectors
print("eigen values")## [1] "eigen values"print(eigen_kovarian$values)## [1] 3.498546e+12 3.356500e+06 7.345801e-01 5.958731e-01 3.626262e-01
## [6] 1.677009e-01print("eigen vectors")## [1] "eigen vectors"print(eigen_kovarian$vectors)## [,1] [,2] [,3] [,4] [,5]
## [1,] 9.999998e-01 6.218809e-04 2.350022e-07 2.360030e-07 -1.896406e-08
## [2,] 6.218809e-04 -9.999998e-01 -1.068146e-04 4.693651e-05 1.227917e-05
## [3,] 1.446162e-07 2.127405e-05 -4.831235e-01 -3.524835e-01 -7.742078e-01
## [4,] 1.390319e-07 2.715391e-05 -1.156247e-01 -7.418855e-02 -1.559105e-01
## [5,] 1.951224e-07 7.936672e-05 -7.760615e-01 -2.230034e-01 5.897316e-01
## [6,] 1.770643e-07 -8.185750e-05 3.885243e-01 -9.058261e-01 1.688515e-01
## [,6]
## [1,] 1.167426e-07
## [2,] -2.065080e-05
## [3,] 2.072422e-01
## [4,] -9.781712e-01
## [5,] 1.465063e-02
## [6,] -4.137110e-036️ Menghitung Variance-Covariance Matrix
cov_matrix <- cov(housing_matrix)
print(cov_matrix)## price area bedrooms bathrooms stories
## price 3.498544e+12 2.175676e+09 5.059464e+05 4.864093e+05 6.826446e+05
## area 2.175676e+09 4.709512e+06 2.432321e+02 2.113466e+02 1.581294e+02
## bedrooms 5.059464e+05 2.432321e+02 5.447383e-01 1.386738e-01 2.615893e-01
## bathrooms 4.864093e+05 2.113466e+02 1.386738e-01 2.524757e-01 1.421715e-01
## stories 6.826446e+05 1.581294e+02 2.615893e-01 1.421715e-01 7.525432e-01
## parking 6.194673e+05 6.599897e+02 8.856247e-02 7.684161e-02 3.404277e-02
## parking
## price 6.194673e+05
## area 6.599897e+02
## bedrooms 8.856247e-02
## bathrooms 7.684161e-02
## stories 3.404277e-02
## parking 7.423300e-017️ Menghitung Correlation Matrix
cor_matrix <- cor(housing_matrix)
print(cor_matrix)## price area bedrooms bathrooms stories parking
## price 1.0000000 0.53599735 0.3664940 0.5175453 0.42071237 0.38439365
## area 0.5359973 1.00000000 0.1518585 0.1938195 0.08399605 0.35298048
## bedrooms 0.3664940 0.15185849 1.0000000 0.3739302 0.40856424 0.13926990
## bathrooms 0.5175453 0.19381953 0.3739302 1.0000000 0.32616471 0.17749582
## stories 0.4207124 0.08399605 0.4085642 0.3261647 1.00000000 0.04554709
## parking 0.3843936 0.35298048 0.1392699 0.1774958 0.04554709 1.00000000R Markdown
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