> library(readxl)
> library(car)
> library(CCA)
> library(candisc)
Loading required package: heplots Loading required package: broom Attaching package: ‘candisc’
The following object is masked from ‘package:stats’:
cancor
> data <- read_excel("datakanonik.xlsx")
> summary(data)
Y1 Y2 Y3 Y4 Y5 X1
Min. : 65.0 Min. : 510 Min. : 0.00 Min. : 6.0 Min. : 1.00 Min. : 12.00
1st Qu.:143.5 1st Qu.: 928 1st Qu.: 38.75 1st Qu.: 28.5 1st Qu.: 3.00 1st Qu.: 29.50 Median :211.0 Median :1349 Median : 73.50 Median : 96.5 Median : 9.50 Median : 47.50
Mean :264.7 Mean :1532 Mean :114.00 Mean :113.1 Mean : 16.08 Mean : 47.83
3rd Qu.:313.5 3rd Qu.:1634 3rd Qu.:155.50 3rd Qu.:161.0 3rd Qu.: 16.75 3rd Qu.:
59.25
Max. :888.0 Max. :5203 Max. :578.00 Max. :457.0 Max. :138.00 Max. :123.00 X2 X3
Min. :10.00 Min. : 8.00 1st Qu.:16.75 1st Qu.:16.75 Median :22.50 Median :24.50 Mean :25.46 Mean :27.67 3rd Qu.:30.00 3rd Qu.:34.00 Max. :64.00 Max. :64.00
> X <- (data[,1:5])
> Y <- (data[,6:8])
> dataCCA <- cbind(Y,X)
> CCA <- as.matrix(dataCCA)
> rata2 <- colMeans(CCA)
> n <- nrow(dataCCA)
> p <- ncol(dataCCA)
> kovarian <- cov(CCA)
> d <- mahalanobis(CCA,rata2,kovarian)
> qqplot(qchisq(ppoints(n),df=p),d, xlim=c(1,20),pch=20,col="black", + ylim=c(0, 30),main="QQ-Plot Data", ylab="Jarak Mahalanobis")
> abline(a=0,b=1,col="red")
> model=lm(Y1+Y2+Y3+Y4+Y5~X1+X2+X3,data=dataCCA)
> vif(model)
X1 X2 X3
1.510482 2.950806 2.558688
> korelasi <- matcor(X, Y)
> print(korelasi)
$Xcor
Y1 Y2 Y3 Y4 Y5
Y1 1.0000000 0.5396499 0.7159781 0.2882816 0.5960664 Y2 0.5396499 1.0000000 0.2575132 0.3143730 0.8092408 Y3 0.7159781 0.2575132 1.0000000 0.4051420 0.2354192
Y4 0.2882816 0.3143730 0.4051420 1.0000000 0.3155242 Y5 0.5960664 0.8092408 0.2354192 0.3155242 1.0000000
$Ycor
X1 X2 X3
X1 1.0000000 0.5791205 0.4832472 X2 0.5791205 1.0000000 0.7795204 X3 0.4832472 0.7795204 1.0000000
$XYcor
Y1 Y2 Y3 Y4 Y5 X1 X2 X3
Y1 1.0000000 0.5396499 0.7159781 0.2882816 0.5960664 0.5856479 0.7178298 0.5375613
Y2 0.5396499 1.0000000 0.2575132 0.3143730 0.8092408 0.2985924 0.5489339 0.4345682
Y3 0.7159781 0.2575132 1.0000000 0.4051420 0.2354192 0.2315573 0.4858632 0.2708149
Y4 0.2882816 0.3143730 0.4051420 1.0000000 0.3155242 0.1423312 0.3939374 0.4092346
Y5 0.5960664 0.8092408 0.2354192 0.3155242 1.0000000 0.3689140 0.6537535 0.4997001
X1 0.5856479 0.2985924 0.2315573 0.1423312 0.3689140 1.0000000 0.5791205 0.4832472
X2 0.7178298 0.5489339 0.4858632 0.3939374 0.6537535 0.5791205 1.0000000 0.7795204
X3 0.5375613 0.4345682 0.2708149 0.4092346 0.4997001 0.4832472 0.7795204 1.0000000
> img.matcor(korelasi, type=2)
> analisis <- candisc::cancor(X,Y)
> summary(analisis)
Canonical correlation analysis of:
5 X variables: Y1, Y2, Y3, Y4, Y5 with 3 Y variables: X1, X2, X3
CanR CanRSQ Eigen percent cum scree
1 0.7954 0.63258 1.7217 80.463 80.46 ******************************
2 0.4897 0.23976 0.3154 14.739 95.20 *****
3 0.3051 0.09311 0.1027 4.798 100.00 **
Test of H0: The canonical correlations in the current row and all that follow are zero
CanR LR test stat approx F numDF denDF Pr(> F) 1 0.79535 0.25332 1.91487 15 44.57 0.04759 * 2 0.48965 0.68946 0.86842 8 34.00 0.55202 3 0.30514 0.90689 0.61600 3 18.00 0.61351 ---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Raw canonical coefficients
X variables:
Xcan1 Xcan2 Xcan3
Y1 -4.0841e-03 -8.2260e-03 -0.0016459 Y2 7.8961e-05 5.1029e-05 0.0002600 Y3 1.1874e-03 1.2070e-02 -0.0037115 Y4 -1.7914e-03 -2.6658e-03 0.0086697 Y5 -1.2633e-02 3.4341e-02 -0.0022221 Y variables:
Ycan1 Ycan2 Ycan3
X1 -0.0093331 -0.039276 -0.025936 X2 -0.0644868 0.108298 -0.048554 X3 -0.0019821 -0.055750 0.098150
> hasil <- cc(X,Y)
> plot(hasil$cor,type="b")
> hasil$cor
[1] 0.7953504 0.4896528 0.3051361
> hasil$xcoef
[,1] [,2] [,3]
Y1 -4.084071e-03 -8.225976e-03 -0.0016458793 Y2 7.896097e-05 5.102865e-05 0.0002599966 Y3 1.187422e-03 1.207023e-02 -0.0037115439 Y4 -1.791388e-03 -2.665799e-03 0.0086697020 Y5 -1.263297e-02 3.434097e-02 -0.0022221024
> hasil$ycoef
[,1] [,2] [,3]
X1 -0.009333094 -0.03927584 -0.02593574 X2 -0.064486769 0.10829757 -0.04855435 X3 -0.001982099 -0.05575015 0.09814991
> hasil$scores
$xscores
[,1] [,2] [,3]
[1,] 0.9340792 0.84959615 -0.79072712 [2,] -2.0385283 0.24169856 -2.31599465 [3,] 0.6784625 -0.62209133 0.24343649 [4,] 0.2739619 -0.60135207 0.08971677 [5,] 0.7566707 0.62066107 0.71560416 [6,] -0.7652235 1.60904901 2.20942739 [7,] 0.3043312 -0.19636217 -0.42455603 [8,] -0.4253247 0.11330384 2.03476002 [9,] -0.3271384 -1.25438233 1.10863586 [10,] 0.6006510 -0.50097226 -0.51318714 [11,] -0.4148288 -1.09622087 -0.70754686 [12,] 0.1405033 0.27953574 0.14459784 [13,] -0.7618419 -0.11414626 -0.22225701 [14,] -0.1898865 -1.67924073 0.22002598 [15,] -0.1309194 -1.88206852 -0.50169983 [16,] 0.2895659 -0.72647515 1.32533055 [17,] 0.5765901 -0.01677423 -0.50754546 [18,] 1.1189861 1.76156742 -0.65043674 [19,] 0.7264865 1.68138994 -0.96998641 [20,] 0.1234639 -0.68993446 -0.13938513 [21,] 0.6808177 -0.13784059 -0.70210689 [22,] -3.3593234 1.08455922 0.02553784 [23,] 0.5006042 0.74288830 0.84277514 [24,] 0.7078408 0.53361168 -0.51441877
$yscores
[,1] [,2] [,3]
[1,] 0.99380722 0.38485872 0.05534032 [2,] -1.42956356 0.96102095 -0.45206138 [3,] 0.35573796 0.35742492 -0.80325481 [4,] -0.02839086 -0.16666722 0.54471478 [5,] -0.46140679 2.18710232 -0.18137755 [6,] -0.40934758 1.45679674 -0.84328593 [7,] 0.53498881 0.40102019 2.20964665 [8,] -1.08682653 -1.15474258 2.51524811 [9,] -0.35168718 -0.81483589 0.97117215 [10,] 0.03188182 0.27620944 -0.66233704 [11,] -1.74803630 -2.51419581 -0.51210349 [12,] 0.57292343 -0.05686189 0.78288167 [13,] 0.47337871 -0.76062430 0.16829295 [14,] -0.11294851 0.04680342 0.02575358 [15,] 0.57949682 -1.35905723 -2.05589861 [16,] 1.22798669 0.08909477 0.62513177 [17,] -0.27132603 0.32929578 -0.56769771 [18,] 1.33575213 0.30515099 0.48297932 [19,] 0.39108823 0.45877813 -0.60136196 [20,] 0.60716133 -0.48249070 0.20209762 [21,] 0.41086679 1.06431184 0.23318641 [22,] -2.97444697 0.72539670 -0.18966774 [23,] 0.88606662 -0.68565784 -0.81632985 [24,] 0.47284374 -1.04813143 -1.13106925
$corr.X.xscores
[,1] [,2] [,3]
Y1 -0.9351888 -0.05131952 -0.2777144 Y2 -0.6711181 0.22952094 0.2196279 Y3 -0.5801530 0.45400903 -0.2531046 Y4 -0.4634341 0.15545861 0.7580257 Y5 -0.8026378 0.27480794 0.1512506
$corr.Y.xscores
[,1] [,2] [,3]
X1 -0.5786750 -0.28894644 -0.10676056 X2 -0.7798014 0.08761879 0.02497243 X3 -0.6226831 -0.09912212 0.17950768
$corr.X.yscores
[,1] [,2] [,3]
Y1 -0.7438028 -0.02512875 -0.08474070 Y2 -0.5337741 0.11238557 0.06701639 Y3 -0.4614250 0.22230680 -0.07723136 Y4 -0.3685925 0.07612074 0.23130102 Y5 -0.6383783 0.13456048 0.04615203
$corr.Y.yscores
[,1] [,2] [,3]
X1 -0.7275723 -0.5901047 -0.34987850 X2 -0.9804501 0.1789407 0.08184029 X3 -0.7829041 -0.2024335 0.58828723
