Materi: Diagnostik sisaan

Membuat Model

#memanggil ackage
library(readxl)

#input data
antar=read_excel("E:\\Praktikum-5.xlsx",sheet="Sheet1")
antar
## # A tibble: 25 x 4
##       NO     y    x1    x2
##    <dbl> <dbl> <dbl> <dbl>
##  1     1  16.7     7   560
##  2     2  11.5     3   220
##  3     3  12.0     3   340
##  4     4  14.9     4    80
##  5     5  13.8     6   150
##  6     6  18.1     7   330
##  7     7   8       2   110
##  8     8  17.8     7   210
##  9     9  79.2    30  1460
## 10    10  21.5     5   605
## # ... with 15 more rows
plot(antar) 

#melihat plot
#x1 vs y
plot(antar$x1,antar$y,main="Jumlah Minuman (X1) dan Lama Pengantaran (Y)",
     xlab="Jumlah Minuman (X1)",ylab="Lama Pengantaran (Y)")

#x2 vs y
plot(antar$x2,antar$y,main="Jarak Tempuh (X2) dan Lama Pengantaran (Y)",
     xlab="Jarak Tempuh (X2)",ylab="Lama Pengantaran (Y)")

#x1 vs x2
plot(antar$x1,antar$x2,main="Jumlah Minuman (X1) dan Jarak Tempuh (X2)", 
     xlab="Jumlah Minuman (X1)",ylab="Jarak Tempuh (X2)")

#membuat model regresi
deliver.model=lm(y~x1+x2,data=antar)
summary(deliver.model)
## 
## Call:
## lm(formula = y ~ x1 + x2, data = antar)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7880 -0.6629  0.4364  1.1566  7.4197 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.341231   1.096730   2.135 0.044170 *  
## x1          1.615907   0.170735   9.464 3.25e-09 ***
## x2          0.014385   0.003613   3.981 0.000631 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.259 on 22 degrees of freedom
## Multiple R-squared:  0.9596, Adjusted R-squared:  0.9559 
## F-statistic: 261.2 on 2 and 22 DF,  p-value: 4.687e-16

Membuat model dengan cara Manual

#y duga
yhat <- deliver.model$fit

#manual untuk dapat parameter dugaan
a=matrix(c(1),25,1)
b=matrix(antar$x1)
c=matrix(antar$x2)

x=matrix(c(a,b,c),25,3)
y=matrix(antar$y)

k=2
p=k+1
n=nrow(x)


xx=t(x)%*%x
inv_xx=solve(xx)

beta_dugaan=inv_xx%*%t(x)%*%y
beta_dugaan
##            [,1]
## [1,] 2.34123115
## [2,] 1.61590721
## [3,] 0.01438483

Manual Anova

#derajat bebas
db_r=k
db_s=n-(k+1)

#untuk dapat keragaman
Jkr=sum((deliver.model$fitted.values-mean(antar$y))^2)
Jkr
## [1] 5550.811
Jks=sum((antar$y-deliver.model$fitted.values)^2)
Jks
## [1] 233.7317
Jkt=sum((antar$y-mean(antar$y))^2)
Jkt
## [1] 5784.543
ktr=Jkr/db_r
ktr
## [1] 2775.405
kts=Jks/db_s
kts
## [1] 10.62417

Matrix Hat dan Residual

#sisaan
res=y-yhat
ei=res;ei
##             [,1]
##  [1,] -5.0280843
##  [2,]  1.1463854
##  [3,] -0.0497937
##  [4,]  4.9243539
##  [5,] -0.4443983
##  [6,] -0.2895743
##  [7,]  0.8446235
##  [8,]  1.1566049
##  [9,]  7.4197062
## [10,]  2.3764129
## [11,]  2.2374930
## [12,] -0.5930409
## [13,]  1.0270093
## [14,]  1.0675359
## [15,]  0.6712018
## [16,] -0.6629284
## [17,]  0.4363603
## [18,]  3.4486213
## [19,]  1.7931935
## [20,] -5.7879699
## [21,] -2.6141789
## [22,] -3.6865279
## [23,] -4.6075679
## [24,] -4.5728535
## [25,] -0.2125839
#matrix Hat
hat=x%*%inv_xx%*%t(x)
hat
##               [,1]          [,2]         [,3]         [,4]         [,5]
##  [1,]  0.101801776  5.836563e-02  0.090696618  0.008604070  0.003379654
##  [2,]  0.058365632  7.070164e-02  0.075871239  0.057924633  0.047448629
##  [3,]  0.090696618  7.587124e-02  0.098734764  0.036708472  0.025068223
##  [4,]  0.008604070  5.792463e-02  0.036708472  0.085374793  0.078394645
##  [5,]  0.003379654  4.744863e-02  0.025068223  0.078394645  0.075010496
##  [6,]  0.039834054  4.845723e-02  0.046874859  0.049268377  0.046275431
##  [7,]  0.040770974  7.270864e-02  0.067401659  0.074674807  0.063128457
##  [8,]  0.007503068  4.328763e-02  0.024011333  0.070484538  0.068655837
##  [9,]  0.067316359 -5.801581e-02 -0.025065952 -0.088463754 -0.053255885
## [10,]  0.138010053  7.379584e-02  0.124247745 -0.004747936 -0.014684085
## [11,]  0.027909453  3.167652e-03  0.002686503  0.010255256  0.023027114
## [12,] -0.027276043  2.326562e-02 -0.012501978  0.077694450  0.082229950
## [13,]  0.055753424  6.546363e-02  0.070051114  0.054434559  0.045756554
## [14,]  0.087440217  6.088959e-02  0.084513391  0.023232628  0.016821442
## [15,]  0.047542032  4.004907e-02  0.044380021  0.033801766  0.033939120
## [16,]  0.123871314  4.743351e-02  0.094385006 -0.021491100 -0.022398445
## [17,]  0.016850898  4.960263e-02  0.034594692  0.069554578  0.065685327
## [18,] -0.013512072  3.992739e-02  0.009150042  0.084275042  0.083203100
## [19,]  0.008791454  6.277491e-02  0.040813832  0.090456078  0.081765250
## [20,]  0.037960214 -4.558927e-05  0.005821260 -0.001544481  0.012569380
## [21,] -0.047482909  2.003462e-02 -0.026791682  0.090954550  0.096217704
## [22,] -0.059641499 -5.903461e-02 -0.098955440  0.015665223  0.048629137
## [23,]  0.048080882  4.013523e-02  0.044761080  0.033448163  0.033566113
## [24,]  0.109966564  5.485083e-02  0.092497668 -0.001958057 -0.005772556
## [25,]  0.027463812  6.094023e-02  0.050045529  0.072998699  0.065339409
##              [,6]         [,7]          [,8]         [,9]        [,10]
##  [1,] 0.039834054  0.040770974  0.0075030684  0.067316359  0.138010053
##  [2,] 0.048457229  0.072708638  0.0432876271 -0.058015811  0.073795837
##  [3,] 0.046874859  0.067401659  0.0240113335 -0.025065952  0.124247745
##  [4,] 0.049268377  0.074674807  0.0704845379 -0.088463754 -0.004747936
##  [5,] 0.046275431  0.063128457  0.0686558365 -0.053255885 -0.014684085
##  [6,] 0.042866928  0.050942683  0.0444492978  0.004162463  0.041310563
##  [7,] 0.050942683  0.081798674  0.0562496613 -0.096213407  0.047231469
##  [8,] 0.044449298  0.056249661  0.0637255914 -0.028787396 -0.009141345
##  [9,] 0.004162463 -0.096213407 -0.0287873962  0.498292159  0.063685438
## [10,] 0.041310563  0.047231469 -0.0091413449  0.063685438  0.196295947
## [11,] 0.028831654 -0.002917559  0.0293128032  0.174404907  0.014676494
## [12,] 0.041278519  0.043352619  0.0770461173 -0.003433808 -0.066088825
## [13,] 0.046960756  0.066935463  0.0423732764 -0.040411876  0.068827763
## [14,] 0.042161271  0.049330312  0.0185374732  0.032413748  0.116490876
## [15,] 0.039241034  0.037273541  0.0349100790  0.052550275  0.051555178
## [16,] 0.033880943  0.018542494 -0.0130705552  0.150606782  0.169773845
## [17,] 0.045616111  0.060917216  0.0606240475 -0.039526777  0.006337544
## [18,] 0.045477838  0.059699197  0.0762551822 -0.050204804 -0.041935085
## [19,] 0.050883528  0.080846005  0.0728446106 -0.108538928 -0.003563755
## [20,] 0.026715420 -0.010769300  0.0208485708  0.204914203  0.029468751
## [21,] 0.042267500  0.046669480  0.0890938008 -0.024027470 -0.097621267
## [22,] 0.016873425 -0.050566049  0.0567942542  0.287839521 -0.130862874
## [23,] 0.039214662  0.037185092  0.0345888074  0.053099440  0.052396043
## [24,] 0.037810124  0.033228806  0.0001632865  0.095903580  0.149859282
## [25,] 0.048345329  0.071579069  0.0592400333 -0.069243003  0.024682344
##              [,11]        [,12]        [,13]        [,14]      [,15]
##  [1,]  0.027909453 -0.027276043  0.055753424  0.087440217 0.04754203
##  [2,]  0.003167652  0.023265620  0.065463635  0.060889593 0.04004907
##  [3,]  0.002686503 -0.012501978  0.070051114  0.084513391 0.04438002
##  [4,]  0.010255256  0.077694450  0.054434559  0.023232628 0.03380177
##  [5,]  0.023027114  0.082229950  0.045756554  0.016821442 0.03393912
##  [6,]  0.028831654  0.041278519  0.046960756  0.042161271 0.03924103
##  [7,] -0.002917559  0.043352619  0.066935463  0.049330312 0.03727354
##  [8,]  0.029312803  0.077046117  0.042373276  0.018537473 0.03491008
##  [9,]  0.174404907 -0.003433808 -0.040411876  0.032413748 0.05255028
## [10,]  0.014676494 -0.066088825  0.068827763  0.116490876 0.05155518
## [11,]  0.086132602  0.048871547  0.009553581  0.021776127 0.04141105
## [12,]  0.048871547  0.113655699  0.025533370 -0.010765804 0.03150698
## [13,]  0.009553581  0.025533370  0.061124633  0.057684000 0.04011774
## [14,]  0.021776127 -0.010765804  0.057684000  0.078243316 0.04519960
## [15,]  0.041411053  0.031506980  0.040117743  0.045199604 0.04111077
## [16,]  0.046622177 -0.053557821  0.046979835  0.099675450 0.05175420
## [17,]  0.022826635  0.067326784  0.047668004  0.026664691 0.03574368
## [18,]  0.029625550  0.100295056  0.039391415  0.003182005 0.03209496
## [19,]  0.003905413  0.078109270  0.058429500  0.024666436 0.03340827
## [20,]  0.092330081  0.037130321  0.007011341  0.027823188 0.04317602
## [21,]  0.049172265  0.136010448  0.022666195 -0.025530678 0.02880013
## [22,]  0.150906073  0.139507483 -0.042552653 -0.055165020 0.03386901
## [23,]  0.041403034  0.030910854  0.040194200  0.045593334 0.04118295
## [24,]  0.034134999 -0.036930826  0.052943581  0.092109223 0.04905436
## [25,]  0.009974586  0.056830018  0.057110588  0.037013177 0.03632816
##              [,16]        [,17]        [,18]        [,19]         [,20]
##  [1,]  0.123871314  0.016850898 -0.013512072  0.008791454  3.796021e-02
##  [2,]  0.047433508  0.049602629  0.039927386  0.062774914 -4.558927e-05
##  [3,]  0.094385006  0.034594692  0.009150042  0.040813832  5.821260e-03
##  [4,] -0.021491100  0.069554578  0.084275042  0.090456078 -1.544481e-03
##  [5,] -0.022398445  0.065685327  0.083203100  0.081765250  1.256938e-02
##  [6,]  0.033880943  0.045616111  0.045477838  0.050883528  2.671542e-02
##  [7,]  0.018542494  0.060917216  0.059699197  0.080846005 -1.076930e-02
##  [8,] -0.013070555  0.060624048  0.076255182  0.072844611  2.084857e-02
##  [9,]  0.150606782 -0.039526777 -0.050204804 -0.108538928  2.049142e-01
## [10,]  0.169773845  0.006337544 -0.041935085 -0.003563755  2.946875e-02
## [11,]  0.046622177  0.022826635  0.029625550  0.003905413  9.233008e-02
## [12,] -0.053557821  0.067326784  0.100295056  0.078109270  3.713032e-02
## [13,]  0.046979835  0.047668004  0.039391415  0.058429500  7.011341e-03
## [14,]  0.099675450  0.026664691  0.003182005  0.024666436  2.782319e-02
## [15,]  0.051754197  0.035743684  0.032094958  0.033408267  4.317602e-02
## [16,]  0.165940433 -0.002835321 -0.043589029 -0.024558789  6.455784e-02
## [17,] -0.002835321  0.059432020  0.070379207  0.072614799  1.501390e-02
## [18,] -0.043589029  0.070379207  0.096260456  0.087119314  1.703512e-02
## [19,] -0.024558789  0.072614799  0.087119314  0.096448574 -9.041425e-03
## [20,]  0.064557841  0.015013901  0.017035119 -0.009041425  1.016849e-01
## [21,] -0.082902508  0.076706745  0.119530896  0.091834946  3.346354e-02
## [22,] -0.047122395  0.031995458  0.082742793  0.002177328  1.517524e-01
## [23,]  0.052536722  0.035493552  0.031582002  0.033042249  4.327380e-02
## [24,]  0.139068141  0.009913627 -0.024307158 -0.002874321  4.697276e-02
## [25,]  0.005897274  0.060799948  0.066321591  0.077645447  1.877848e-03
##             [,21]        [,22]      [,23]         [,24]        [,25]
##  [1,] -0.04748291 -0.059641499 0.04808088  0.1099665637  0.027463812
##  [2,]  0.02003462 -0.059034610 0.04013523  0.0548508305  0.060940233
##  [3,] -0.02679168 -0.098955440 0.04476108  0.0924976684  0.050045529
##  [4,]  0.09095455  0.015665223 0.03344816 -0.0019580572  0.072998699
##  [5,]  0.09621770  0.048629137 0.03356611 -0.0057725555  0.065339409
##  [6,]  0.04226750  0.016873425 0.03921466  0.0378101244  0.048345329
##  [7,]  0.04666948 -0.050566049 0.03718509  0.0332288060  0.071579069
##  [8,]  0.08909380  0.056794254 0.03458881  0.0001632865  0.059240033
##  [9,] -0.02402747  0.287839521 0.05309944  0.0959035796 -0.069243003
## [10,] -0.09762127 -0.130862874 0.05239604  0.1498592817  0.024682344
## [11,]  0.04917226  0.150906073 0.04140303  0.0341349988  0.009974586
## [12,]  0.13601045  0.139507483 0.03091085 -0.0369308259  0.056830018
## [13,]  0.02266620 -0.042552653 0.04019420  0.0529435814  0.057110588
## [14,] -0.02553068 -0.055165020 0.04559333  0.0921092230  0.037013177
## [15,]  0.02880013  0.033869007 0.04118295  0.0490543612  0.036328156
## [16,] -0.08290251 -0.047122395 0.05253672  0.1390681412  0.005897274
## [17,]  0.07670674  0.031995458 0.03549355  0.0099136269  0.060799948
## [18,]  0.11953090  0.082742793 0.03158200 -0.0243071581  0.066321591
## [19,]  0.09183495  0.002177328 0.03304225 -0.0028743209  0.077645447
## [20,]  0.03346354  0.151752372 0.04327380  0.0469727612  0.001877848
## [21,]  0.16527689  0.164458001 0.02801969 -0.0604600996  0.063639208
## [22,]  0.16445800  0.391575218 0.03320366 -0.0564664848 -0.007621928
## [23,]  0.02801969  0.033203660 0.04126005  0.0496818085  0.036146578
## [24,] -0.06046010 -0.056466485 0.04968181  0.1206082605  0.020002598
## [25,]  0.06363921 -0.007621928 0.03614658  0.0200025982  0.066643455
hii=diag(hat);hii
##  [1] 0.10180178 0.07070164 0.09873476 0.08537479 0.07501050 0.04286693
##  [7] 0.08179867 0.06372559 0.49829216 0.19629595 0.08613260 0.11365570
## [13] 0.06112463 0.07824332 0.04111077 0.16594043 0.05943202 0.09626046
## [19] 0.09644857 0.10168486 0.16527689 0.39157522 0.04126005 0.12060826
## [25] 0.06664345
#sisaan terbakukan
di=ei/sqrt(kts);di
##              [,1]
##  [1,] -1.54260631
##  [2,]  0.35170879
##  [3,] -0.01527661
##  [4,]  1.51078203
##  [5,] -0.13634053
##  [6,] -0.08884082
##  [7,]  0.25912883
##  [8,]  0.35484408
##  [9,]  2.27635117
## [10,]  0.72907878
## [11,]  0.68645843
## [12,] -0.18194377
## [13,]  0.31508443
## [14,]  0.32751789
## [15,]  0.20592338
## [16,] -0.20338513
## [17,]  0.13387449
## [18,]  1.05803019
## [19,]  0.55014821
## [20,] -1.77573772
## [21,] -0.80202492
## [22,] -1.13101946
## [23,] -1.41359270
## [24,] -1.40294240
## [25,] -0.06522033
#student residual
stud_res=ei/sqrt(kts*(1-hii));stud_res
##              [,1]
##  [1,] -1.62767993
##  [2,]  0.36484267
##  [3,] -0.01609165
##  [4,]  1.57972040
##  [5,] -0.14176094
##  [6,] -0.09080847
##  [7,]  0.27042496
##  [8,]  0.36672118
##  [9,]  3.21376278
## [10,]  0.81325432
## [11,]  0.71807970
## [12,] -0.19325733
## [13,]  0.32517935
## [14,]  0.34113547
## [15,]  0.21029137
## [16,] -0.22270023
## [17,]  0.13803929
## [18,]  1.11295196
## [19,]  0.57876634
## [20,] -1.87354643
## [21,] -0.87784258
## [22,] -1.44999541
## [23,] -1.44368977
## [24,] -1.49605875
## [25,] -0.06750861
#varians
S2=((n-p)*kts-(ei^2/(1-hii)))/(n-p-1);S2
##            [,1]
##  [1,]  9.789744
##  [2,] 11.062738
##  [3,] 11.129949
##  [4,]  9.867566
##  [5,] 11.119913
##  [6,] 11.125908
##  [7,] 11.093083
##  [8,] 11.062042
##  [9,]  5.904876
## [10,] 10.795478
## [11,] 10.869212
## [12,] 11.111185
## [13,] 11.076584
## [14,] 11.071205
## [15,] 11.107707
## [16,] 11.104989
## [17,] 11.120440
## [18,] 10.503425
## [19,] 10.960614
## [20,]  9.354237
## [21,] 10.740220
## [22,] 10.066405
## [23,] 10.075636
## [24,]  9.997750
## [25,] 11.127774
#R-Student
ti=ei/sqrt(S2*(1-hii));ti
##              [,1]
##  [1,] -1.69562881
##  [2,]  0.35753764
##  [3,] -0.01572177
##  [4,]  1.63916491
##  [5,] -0.13856493
##  [6,] -0.08873728
##  [7,]  0.26464769
##  [8,]  0.35938983
##  [9,]  4.31078012
## [10,]  0.80677584
## [11,]  0.70993906
## [12,] -0.18897451
## [13,]  0.31846924
## [14,]  0.33417725
## [15,]  0.20566324
## [16,] -0.21782566
## [17,]  0.13492400
## [18,]  1.11933065
## [19,]  0.56981420
## [20,] -1.99667657
## [21,] -0.87308697
## [22,] -1.48962473
## [23,] -1.48246718
## [24,] -1.54221512
## [25,] -0.06596332
#press
press=(ei/(1-hii))^2;press
##               [,1]
##  [1,] 3.133724e+01
##  [2,] 1.521777e+00
##  [3,] 3.052415e-03
##  [4,] 2.898759e+01
##  [5,] 2.308188e-01
##  [6,] 9.153250e-02
##  [7,] 8.461562e-01
##  [8,] 1.526032e+00
##  [9,] 2.187115e+02
## [10,] 8.742819e+00
## [11,] 5.994556e+00
## [12,] 4.476766e-01
## [13,] 1.196556e+00
## [14,] 1.341320e+00
## [15,] 4.899698e-01
## [16,] 6.317411e-01
## [17,] 2.152336e-01
## [18,] 1.456144e+01
## [19,] 3.938661e+00
## [20,] 4.151405e+01
## [21,] 9.808119e+00
## [22,] 3.671312e+01
## [23,] 2.309627e+01
## [24,] 2.704019e+01
## [25,] 5.187591e-02
sum(press)
## [1] 459.0393
#R^2 Prediction
R2_Pred=1-(sum(press)/Jkt);R2_Pred
## [1] 0.9206438
#syntax ketika menggunakan fungsi
hatvalues(deliver.model)
##          1          2          3          4          5          6          7 
## 0.10180178 0.07070164 0.09873476 0.08537479 0.07501050 0.04286693 0.08179867 
##          8          9         10         11         12         13         14 
## 0.06372559 0.49829216 0.19629595 0.08613260 0.11365570 0.06112463 0.07824332 
##         15         16         17         18         19         20         21 
## 0.04111077 0.16594043 0.05943202 0.09626046 0.09644857 0.10168486 0.16527689 
##         22         23         24         25 
## 0.39157522 0.04126005 0.12060826 0.06664345
t <- rstudent(deliver.model);t
##           1           2           3           4           5           6 
## -1.69562881  0.35753764 -0.01572177  1.63916491 -0.13856493 -0.08873728 
##           7           8           9          10          11          12 
##  0.26464769  0.35938983  4.31078012  0.80677584  0.70993906 -0.18897451 
##          13          14          15          16          17          18 
##  0.31846924  0.33417725  0.20566324 -0.21782566  0.13492400  1.11933065 
##          19          20          21          22          23          24 
##  0.56981420 -1.99667657 -0.87308697 -1.48962473 -1.48246718 -1.54221512 
##          25 
## -0.06596332