R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(593530.00,0,610943.00,0,612613.00,0,611324.00,0,594167.00,0,595454.00,0,590865.00,0,589379.00,0,584428.00,0,573100.00,0,567456.00,0,569028.00,0,620735.00,0,628884.00,0,628232.00,0,612117.00,0,595404.00,0,597141.00,0,593408.00,0,590072.00,0,579799.00,0,574205.00,0,572775.00,0,572942.00,0,619567.00,0,625809.00,0,619916.00,0,587625.00,0,565742.00,0,557274.00,0,560576.00,0,548854.00,0,531673.00,0,525919.00,0,511038.00,0,498662.00,0,555362.00,0,564591.00,0,541657.00,0,527070.00,0,509846.00,0,514258.00,0,516922.00,0,507561.00,0,492622.00,0,490243.00,0,469357.00,0,477580.00,0,528379.00,1,533590.00,1,517945.00,1,506174.00,1,501866.00,1,516141.00,1,528222.00,1,532638.00,1,536322.00,1,536535.00,1,523597.00,1,536214.00,1,586570.00,1,596594.00,1,580523.00,1),dim=c(2,63),dimnames=list(c('werklozen','crisis
'),1:63))
> y <- array(NA,dim=c(2,63),dimnames=list(c('werklozen','crisis
'),1:63))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
werklozen crisis\r M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 593530 0 1 0 0 0 0 0 0 0 0 0 0 1
2 610943 0 0 1 0 0 0 0 0 0 0 0 0 2
3 612613 0 0 0 1 0 0 0 0 0 0 0 0 3
4 611324 0 0 0 0 1 0 0 0 0 0 0 0 4
5 594167 0 0 0 0 0 1 0 0 0 0 0 0 5
6 595454 0 0 0 0 0 0 1 0 0 0 0 0 6
7 590865 0 0 0 0 0 0 0 1 0 0 0 0 7
8 589379 0 0 0 0 0 0 0 0 1 0 0 0 8
9 584428 0 0 0 0 0 0 0 0 0 1 0 0 9
10 573100 0 0 0 0 0 0 0 0 0 0 1 0 10
11 567456 0 0 0 0 0 0 0 0 0 0 0 1 11
12 569028 0 0 0 0 0 0 0 0 0 0 0 0 12
13 620735 0 1 0 0 0 0 0 0 0 0 0 0 13
14 628884 0 0 1 0 0 0 0 0 0 0 0 0 14
15 628232 0 0 0 1 0 0 0 0 0 0 0 0 15
16 612117 0 0 0 0 1 0 0 0 0 0 0 0 16
17 595404 0 0 0 0 0 1 0 0 0 0 0 0 17
18 597141 0 0 0 0 0 0 1 0 0 0 0 0 18
19 593408 0 0 0 0 0 0 0 1 0 0 0 0 19
20 590072 0 0 0 0 0 0 0 0 1 0 0 0 20
21 579799 0 0 0 0 0 0 0 0 0 1 0 0 21
22 574205 0 0 0 0 0 0 0 0 0 0 1 0 22
23 572775 0 0 0 0 0 0 0 0 0 0 0 1 23
24 572942 0 0 0 0 0 0 0 0 0 0 0 0 24
25 619567 0 1 0 0 0 0 0 0 0 0 0 0 25
26 625809 0 0 1 0 0 0 0 0 0 0 0 0 26
27 619916 0 0 0 1 0 0 0 0 0 0 0 0 27
28 587625 0 0 0 0 1 0 0 0 0 0 0 0 28
29 565742 0 0 0 0 0 1 0 0 0 0 0 0 29
30 557274 0 0 0 0 0 0 1 0 0 0 0 0 30
31 560576 0 0 0 0 0 0 0 1 0 0 0 0 31
32 548854 0 0 0 0 0 0 0 0 1 0 0 0 32
33 531673 0 0 0 0 0 0 0 0 0 1 0 0 33
34 525919 0 0 0 0 0 0 0 0 0 0 1 0 34
35 511038 0 0 0 0 0 0 0 0 0 0 0 1 35
36 498662 0 0 0 0 0 0 0 0 0 0 0 0 36
37 555362 0 1 0 0 0 0 0 0 0 0 0 0 37
38 564591 0 0 1 0 0 0 0 0 0 0 0 0 38
39 541657 0 0 0 1 0 0 0 0 0 0 0 0 39
40 527070 0 0 0 0 1 0 0 0 0 0 0 0 40
41 509846 0 0 0 0 0 1 0 0 0 0 0 0 41
42 514258 0 0 0 0 0 0 1 0 0 0 0 0 42
43 516922 0 0 0 0 0 0 0 1 0 0 0 0 43
44 507561 0 0 0 0 0 0 0 0 1 0 0 0 44
45 492622 0 0 0 0 0 0 0 0 0 1 0 0 45
46 490243 0 0 0 0 0 0 0 0 0 0 1 0 46
47 469357 0 0 0 0 0 0 0 0 0 0 0 1 47
48 477580 0 0 0 0 0 0 0 0 0 0 0 0 48
49 528379 1 1 0 0 0 0 0 0 0 0 0 0 49
50 533590 1 0 1 0 0 0 0 0 0 0 0 0 50
51 517945 1 0 0 1 0 0 0 0 0 0 0 0 51
52 506174 1 0 0 0 1 0 0 0 0 0 0 0 52
53 501866 1 0 0 0 0 1 0 0 0 0 0 0 53
54 516141 1 0 0 0 0 0 1 0 0 0 0 0 54
55 528222 1 0 0 0 0 0 0 1 0 0 0 0 55
56 532638 1 0 0 0 0 0 0 0 1 0 0 0 56
57 536322 1 0 0 0 0 0 0 0 0 1 0 0 57
58 536535 1 0 0 0 0 0 0 0 0 0 1 0 58
59 523597 1 0 0 0 0 0 0 0 0 0 0 1 59
60 536214 1 0 0 0 0 0 0 0 0 0 0 0 60
61 586570 1 1 0 0 0 0 0 0 0 0 0 0 61
62 596594 1 0 1 0 0 0 0 0 0 0 0 0 62
63 580523 1 0 0 1 0 0 0 0 0 0 0 0 63
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `crisis\r` M1 M2 M3 M4
598258 30273 38904 50322 42440 21660
M5 M6 M7 M8 M9 M10
8242 12930 16915 14657 7965 5036
M11 t
-4080 -2040
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-49004 -14104 -3497 18260 44200
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 598258.3 14112.3 42.393 < 2e-16 ***
`crisis\r` 30272.7 11633.4 2.602 0.01222 *
M1 38904.0 15804.7 2.462 0.01740 *
M2 50321.7 15763.6 3.192 0.00247 **
M3 42440.5 15727.0 2.699 0.00953 **
M4 21659.6 16405.2 1.320 0.19287
M5 8242.2 16371.4 0.503 0.61690
M6 12930.5 16342.0 0.791 0.43262
M7 16915.1 16317.2 1.037 0.30499
M8 14657.0 16296.8 0.899 0.37285
M9 7964.6 16280.9 0.489 0.62688
M10 5035.9 16269.6 0.310 0.75823
M11 -4080.3 16262.7 -0.251 0.80294
t -2039.7 271.8 -7.505 1.1e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 25710 on 49 degrees of freedom
Multiple R-squared: 0.7059, Adjusted R-squared: 0.6279
F-statistic: 9.047 on 13 and 49 DF, p-value: 4.584e-09
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 5.079638e-02 1.015928e-01 0.9492036163
[2,] 1.905479e-02 3.810957e-02 0.9809452150
[3,] 6.160481e-03 1.232096e-02 0.9938395189
[4,] 2.020040e-03 4.040080e-03 0.9979799599
[5,] 8.919403e-04 1.783881e-03 0.9991080597
[6,] 2.463340e-04 4.926680e-04 0.9997536660
[7,] 6.014135e-05 1.202827e-04 0.9999398586
[8,] 1.431005e-05 2.862010e-05 0.9999856899
[9,] 3.757603e-06 7.515205e-06 0.9999962424
[10,] 1.046913e-06 2.093826e-06 0.9999989531
[11,] 7.487340e-07 1.497468e-06 0.9999992513
[12,] 4.343825e-05 8.687649e-05 0.9999565618
[13,] 4.947963e-04 9.895926e-04 0.9995052037
[14,] 3.741144e-03 7.482287e-03 0.9962588563
[15,] 7.554133e-03 1.510827e-02 0.9924458666
[16,] 1.876253e-02 3.752505e-02 0.9812374737
[17,] 4.592697e-02 9.185395e-02 0.9540730258
[18,] 7.579423e-02 1.515885e-01 0.9242057660
[19,] 1.850012e-01 3.700023e-01 0.8149988454
[20,] 3.360415e-01 6.720830e-01 0.6639584902
[21,] 3.708987e-01 7.417975e-01 0.6291012684
[22,] 4.683191e-01 9.366381e-01 0.5316809350
[23,] 6.689695e-01 6.620611e-01 0.3310305269
[24,] 8.370165e-01 3.259669e-01 0.1629834708
[25,] 9.226925e-01 1.546151e-01 0.0773075472
[26,] 9.683354e-01 6.332925e-02 0.0316646230
[27,] 9.929195e-01 1.416093e-02 0.0070804649
[28,] 9.993344e-01 1.331142e-03 0.0006655709
[29,] 9.987256e-01 2.548833e-03 0.0012744164
[30,] 9.988595e-01 2.280922e-03 0.0011404612
> postscript(file="/var/www/html/rcomp/tmp/1eoa21258646990.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2yamw1258646990.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3ukdt1258646990.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/414jw1258646990.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ay9h1258646990.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 63
Frequency = 1
1 2 3 4 5 6
-41592.6291 -33557.6291 -21966.7957 -435.2145 -2135.2145 -3496.8145
7 8 9 10 11 12
-10030.8145 -7219.0145 -3438.0145 -9797.6145 -4285.8145 -4754.4145
13 14 15 16 17 18
10088.2451 8859.2451 18128.0784 24833.6596 23577.6596 22666.0596
19 20 21 22 23 24
16988.0596 17949.8596 16408.8596 15783.2596 25509.0596 23635.4596
25 26 27 28 29 30
33396.1192 30260.1192 34287.9525 24817.5338 18391.5338 7274.9338
31 32 33 34 35 36
8631.9338 1207.7338 -7241.2662 -8026.8662 -11752.0662 -26168.6662
37 38 39 40 41 42
-6333.0067 -6482.0067 -19495.1733 -11261.5921 -13028.5921 -11265.1921
43 44 45 46 47 48
-10546.1921 -15609.3921 -21816.3921 -19226.9921 -28957.1921 -22774.7921
49 50 51 52 53 54
-39112.8013 -43279.8013 -49003.9680 -37954.3868 -26805.3868 -15178.9868
55 56 57 58 59 60
-5042.9868 3670.8132 16086.8132 21268.2132 19486.0132 30062.4132
61 62 63
43554.0728 44200.0728 38049.9061
> postscript(file="/var/www/html/rcomp/tmp/6qtdb1258646990.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 63
Frequency = 1
lag(myerror, k = 1) myerror
0 -41592.6291 NA
1 -33557.6291 -41592.6291
2 -21966.7957 -33557.6291
3 -435.2145 -21966.7957
4 -2135.2145 -435.2145
5 -3496.8145 -2135.2145
6 -10030.8145 -3496.8145
7 -7219.0145 -10030.8145
8 -3438.0145 -7219.0145
9 -9797.6145 -3438.0145
10 -4285.8145 -9797.6145
11 -4754.4145 -4285.8145
12 10088.2451 -4754.4145
13 8859.2451 10088.2451
14 18128.0784 8859.2451
15 24833.6596 18128.0784
16 23577.6596 24833.6596
17 22666.0596 23577.6596
18 16988.0596 22666.0596
19 17949.8596 16988.0596
20 16408.8596 17949.8596
21 15783.2596 16408.8596
22 25509.0596 15783.2596
23 23635.4596 25509.0596
24 33396.1192 23635.4596
25 30260.1192 33396.1192
26 34287.9525 30260.1192
27 24817.5338 34287.9525
28 18391.5338 24817.5338
29 7274.9338 18391.5338
30 8631.9338 7274.9338
31 1207.7338 8631.9338
32 -7241.2662 1207.7338
33 -8026.8662 -7241.2662
34 -11752.0662 -8026.8662
35 -26168.6662 -11752.0662
36 -6333.0067 -26168.6662
37 -6482.0067 -6333.0067
38 -19495.1733 -6482.0067
39 -11261.5921 -19495.1733
40 -13028.5921 -11261.5921
41 -11265.1921 -13028.5921
42 -10546.1921 -11265.1921
43 -15609.3921 -10546.1921
44 -21816.3921 -15609.3921
45 -19226.9921 -21816.3921
46 -28957.1921 -19226.9921
47 -22774.7921 -28957.1921
48 -39112.8013 -22774.7921
49 -43279.8013 -39112.8013
50 -49003.9680 -43279.8013
51 -37954.3868 -49003.9680
52 -26805.3868 -37954.3868
53 -15178.9868 -26805.3868
54 -5042.9868 -15178.9868
55 3670.8132 -5042.9868
56 16086.8132 3670.8132
57 21268.2132 16086.8132
58 19486.0132 21268.2132
59 30062.4132 19486.0132
60 43554.0728 30062.4132
61 44200.0728 43554.0728
62 38049.9061 44200.0728
63 NA 38049.9061
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -33557.6291 -41592.6291
[2,] -21966.7957 -33557.6291
[3,] -435.2145 -21966.7957
[4,] -2135.2145 -435.2145
[5,] -3496.8145 -2135.2145
[6,] -10030.8145 -3496.8145
[7,] -7219.0145 -10030.8145
[8,] -3438.0145 -7219.0145
[9,] -9797.6145 -3438.0145
[10,] -4285.8145 -9797.6145
[11,] -4754.4145 -4285.8145
[12,] 10088.2451 -4754.4145
[13,] 8859.2451 10088.2451
[14,] 18128.0784 8859.2451
[15,] 24833.6596 18128.0784
[16,] 23577.6596 24833.6596
[17,] 22666.0596 23577.6596
[18,] 16988.0596 22666.0596
[19,] 17949.8596 16988.0596
[20,] 16408.8596 17949.8596
[21,] 15783.2596 16408.8596
[22,] 25509.0596 15783.2596
[23,] 23635.4596 25509.0596
[24,] 33396.1192 23635.4596
[25,] 30260.1192 33396.1192
[26,] 34287.9525 30260.1192
[27,] 24817.5338 34287.9525
[28,] 18391.5338 24817.5338
[29,] 7274.9338 18391.5338
[30,] 8631.9338 7274.9338
[31,] 1207.7338 8631.9338
[32,] -7241.2662 1207.7338
[33,] -8026.8662 -7241.2662
[34,] -11752.0662 -8026.8662
[35,] -26168.6662 -11752.0662
[36,] -6333.0067 -26168.6662
[37,] -6482.0067 -6333.0067
[38,] -19495.1733 -6482.0067
[39,] -11261.5921 -19495.1733
[40,] -13028.5921 -11261.5921
[41,] -11265.1921 -13028.5921
[42,] -10546.1921 -11265.1921
[43,] -15609.3921 -10546.1921
[44,] -21816.3921 -15609.3921
[45,] -19226.9921 -21816.3921
[46,] -28957.1921 -19226.9921
[47,] -22774.7921 -28957.1921
[48,] -39112.8013 -22774.7921
[49,] -43279.8013 -39112.8013
[50,] -49003.9680 -43279.8013
[51,] -37954.3868 -49003.9680
[52,] -26805.3868 -37954.3868
[53,] -15178.9868 -26805.3868
[54,] -5042.9868 -15178.9868
[55,] 3670.8132 -5042.9868
[56,] 16086.8132 3670.8132
[57,] 21268.2132 16086.8132
[58,] 19486.0132 21268.2132
[59,] 30062.4132 19486.0132
[60,] 43554.0728 30062.4132
[61,] 44200.0728 43554.0728
[62,] 38049.9061 44200.0728
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -33557.6291 -41592.6291
2 -21966.7957 -33557.6291
3 -435.2145 -21966.7957
4 -2135.2145 -435.2145
5 -3496.8145 -2135.2145
6 -10030.8145 -3496.8145
7 -7219.0145 -10030.8145
8 -3438.0145 -7219.0145
9 -9797.6145 -3438.0145
10 -4285.8145 -9797.6145
11 -4754.4145 -4285.8145
12 10088.2451 -4754.4145
13 8859.2451 10088.2451
14 18128.0784 8859.2451
15 24833.6596 18128.0784
16 23577.6596 24833.6596
17 22666.0596 23577.6596
18 16988.0596 22666.0596
19 17949.8596 16988.0596
20 16408.8596 17949.8596
21 15783.2596 16408.8596
22 25509.0596 15783.2596
23 23635.4596 25509.0596
24 33396.1192 23635.4596
25 30260.1192 33396.1192
26 34287.9525 30260.1192
27 24817.5338 34287.9525
28 18391.5338 24817.5338
29 7274.9338 18391.5338
30 8631.9338 7274.9338
31 1207.7338 8631.9338
32 -7241.2662 1207.7338
33 -8026.8662 -7241.2662
34 -11752.0662 -8026.8662
35 -26168.6662 -11752.0662
36 -6333.0067 -26168.6662
37 -6482.0067 -6333.0067
38 -19495.1733 -6482.0067
39 -11261.5921 -19495.1733
40 -13028.5921 -11261.5921
41 -11265.1921 -13028.5921
42 -10546.1921 -11265.1921
43 -15609.3921 -10546.1921
44 -21816.3921 -15609.3921
45 -19226.9921 -21816.3921
46 -28957.1921 -19226.9921
47 -22774.7921 -28957.1921
48 -39112.8013 -22774.7921
49 -43279.8013 -39112.8013
50 -49003.9680 -43279.8013
51 -37954.3868 -49003.9680
52 -26805.3868 -37954.3868
53 -15178.9868 -26805.3868
54 -5042.9868 -15178.9868
55 3670.8132 -5042.9868
56 16086.8132 3670.8132
57 21268.2132 16086.8132
58 19486.0132 21268.2132
59 30062.4132 19486.0132
60 43554.0728 30062.4132
61 44200.0728 43554.0728
62 38049.9061 44200.0728
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/77qdn1258646990.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8nczj1258646990.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90n701258646990.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10gh801258646990.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11p6e81258646990.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12ik3b1258646990.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13n21z1258646990.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14mgcy1258646990.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/153xpi1258646990.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16adm71258646990.tab")
+ }
>
> system("convert tmp/1eoa21258646990.ps tmp/1eoa21258646990.png")
> system("convert tmp/2yamw1258646990.ps tmp/2yamw1258646990.png")
> system("convert tmp/3ukdt1258646990.ps tmp/3ukdt1258646990.png")
> system("convert tmp/414jw1258646990.ps tmp/414jw1258646990.png")
> system("convert tmp/5ay9h1258646990.ps tmp/5ay9h1258646990.png")
> system("convert tmp/6qtdb1258646990.ps tmp/6qtdb1258646990.png")
> system("convert tmp/77qdn1258646990.ps tmp/77qdn1258646990.png")
> system("convert tmp/8nczj1258646990.ps tmp/8nczj1258646990.png")
> system("convert tmp/90n701258646990.ps tmp/90n701258646990.png")
> system("convert tmp/10gh801258646990.ps tmp/10gh801258646990.png")
>
>
> proc.time()
user system elapsed
2.468 1.608 5.897