R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(108.00,0,99.00,0,108.00,0,104.00,0,111.00,0,110.00,0,106.00,0,101.00,0,102.00,0,99.00,0,100.00,0,98.00,0,92.00,1,87.00,1,79.00,1,87.00,1,87.00,1,88.00,1,83.00,1,85.00,1,92.00,1,84.00,1,92.00,1,98.00,1,103.00,0,104.00,0,109.00,0,107.00,0,106.00,0,113.00,0,107.00,0,114.00,0,108.00,0,104.00,0,105.00,0,109.00,0,109.00,0,112.00,0,118.00,0,111.00,0,99.00,1,92.00,1,92.00,1,98.00,1,87.00,1,97.00,1,102.00,0,105.00,0,111.00,0,110.00,0,109.00,0,111.00,0,113.00,0,114.00,0,120.00,0,114.00,0,120.00,0,122.00,0,123.00,0,115.00,0,123.00,0,124.00,0,124.00,0,132.00,0,126.00,0,126.00,0,122.00,0,120.00,0,114.00,0,116.00,0,100.00,0,97.00,0),dim=c(2,72),dimnames=list(c('Consumentenvertrouwen','Dummy'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('Consumentenvertrouwen','Dummy'),1:72))
> 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
Consumentenvertrouwen Dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 108 0 1 0 0 0 0 0 0 0 0 0 0 1
2 99 0 0 1 0 0 0 0 0 0 0 0 0 2
3 108 0 0 0 1 0 0 0 0 0 0 0 0 3
4 104 0 0 0 0 1 0 0 0 0 0 0 0 4
5 111 0 0 0 0 0 1 0 0 0 0 0 0 5
6 110 0 0 0 0 0 0 1 0 0 0 0 0 6
7 106 0 0 0 0 0 0 0 1 0 0 0 0 7
8 101 0 0 0 0 0 0 0 0 1 0 0 0 8
9 102 0 0 0 0 0 0 0 0 0 1 0 0 9
10 99 0 0 0 0 0 0 0 0 0 0 1 0 10
11 100 0 0 0 0 0 0 0 0 0 0 0 1 11
12 98 0 0 0 0 0 0 0 0 0 0 0 0 12
13 92 1 1 0 0 0 0 0 0 0 0 0 0 13
14 87 1 0 1 0 0 0 0 0 0 0 0 0 14
15 79 1 0 0 1 0 0 0 0 0 0 0 0 15
16 87 1 0 0 0 1 0 0 0 0 0 0 0 16
17 87 1 0 0 0 0 1 0 0 0 0 0 0 17
18 88 1 0 0 0 0 0 1 0 0 0 0 0 18
19 83 1 0 0 0 0 0 0 1 0 0 0 0 19
20 85 1 0 0 0 0 0 0 0 1 0 0 0 20
21 92 1 0 0 0 0 0 0 0 0 1 0 0 21
22 84 1 0 0 0 0 0 0 0 0 0 1 0 22
23 92 1 0 0 0 0 0 0 0 0 0 0 1 23
24 98 1 0 0 0 0 0 0 0 0 0 0 0 24
25 103 0 1 0 0 0 0 0 0 0 0 0 0 25
26 104 0 0 1 0 0 0 0 0 0 0 0 0 26
27 109 0 0 0 1 0 0 0 0 0 0 0 0 27
28 107 0 0 0 0 1 0 0 0 0 0 0 0 28
29 106 0 0 0 0 0 1 0 0 0 0 0 0 29
30 113 0 0 0 0 0 0 1 0 0 0 0 0 30
31 107 0 0 0 0 0 0 0 1 0 0 0 0 31
32 114 0 0 0 0 0 0 0 0 1 0 0 0 32
33 108 0 0 0 0 0 0 0 0 0 1 0 0 33
34 104 0 0 0 0 0 0 0 0 0 0 1 0 34
35 105 0 0 0 0 0 0 0 0 0 0 0 1 35
36 109 0 0 0 0 0 0 0 0 0 0 0 0 36
37 109 0 1 0 0 0 0 0 0 0 0 0 0 37
38 112 0 0 1 0 0 0 0 0 0 0 0 0 38
39 118 0 0 0 1 0 0 0 0 0 0 0 0 39
40 111 0 0 0 0 1 0 0 0 0 0 0 0 40
41 99 1 0 0 0 0 1 0 0 0 0 0 0 41
42 92 1 0 0 0 0 0 1 0 0 0 0 0 42
43 92 1 0 0 0 0 0 0 1 0 0 0 0 43
44 98 1 0 0 0 0 0 0 0 1 0 0 0 44
45 87 1 0 0 0 0 0 0 0 0 1 0 0 45
46 97 1 0 0 0 0 0 0 0 0 0 1 0 46
47 102 0 0 0 0 0 0 0 0 0 0 0 1 47
48 105 0 0 0 0 0 0 0 0 0 0 0 0 48
49 111 0 1 0 0 0 0 0 0 0 0 0 0 49
50 110 0 0 1 0 0 0 0 0 0 0 0 0 50
51 109 0 0 0 1 0 0 0 0 0 0 0 0 51
52 111 0 0 0 0 1 0 0 0 0 0 0 0 52
53 113 0 0 0 0 0 1 0 0 0 0 0 0 53
54 114 0 0 0 0 0 0 1 0 0 0 0 0 54
55 120 0 0 0 0 0 0 0 1 0 0 0 0 55
56 114 0 0 0 0 0 0 0 0 1 0 0 0 56
57 120 0 0 0 0 0 0 0 0 0 1 0 0 57
58 122 0 0 0 0 0 0 0 0 0 0 1 0 58
59 123 0 0 0 0 0 0 0 0 0 0 0 1 59
60 115 0 0 0 0 0 0 0 0 0 0 0 0 60
61 123 0 1 0 0 0 0 0 0 0 0 0 0 61
62 124 0 0 1 0 0 0 0 0 0 0 0 0 62
63 124 0 0 0 1 0 0 0 0 0 0 0 0 63
64 132 0 0 0 0 1 0 0 0 0 0 0 0 64
65 126 0 0 0 0 0 1 0 0 0 0 0 0 65
66 126 0 0 0 0 0 0 1 0 0 0 0 0 66
67 122 0 0 0 0 0 0 0 1 0 0 0 0 67
68 120 0 0 0 0 0 0 0 0 1 0 0 0 68
69 114 0 0 0 0 0 0 0 0 0 1 0 0 69
70 116 0 0 0 0 0 0 0 0 0 0 1 0 70
71 100 0 0 0 0 0 0 0 0 0 0 0 1 71
72 97 0 0 0 0 0 0 0 0 0 0 0 0 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy M1 M2 M3 M4
96.2219 -18.2725 6.7474 4.8310 6.4146 6.9981
M5 M6 M7 M8 M9 M10
8.1271 8.0440 5.6276 5.7111 3.9614 3.5449
M11 t
0.2498 0.2498
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17.2051 -3.4482 -0.4283 4.1147 14.0562
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 96.22186 3.00379 32.033 < 2e-16 ***
Dummy -18.27246 1.77694 -10.283 1.09e-14 ***
M1 6.74744 3.56639 1.892 0.0635 .
M2 4.83100 3.56240 1.356 0.1803
M3 6.41457 3.55877 1.802 0.0767 .
M4 6.99814 3.55553 1.968 0.0538 .
M5 8.12711 3.55883 2.284 0.0261 *
M6 8.04401 3.55723 2.261 0.0275 *
M7 5.62758 3.55601 1.583 0.1190
M8 5.71114 3.55518 1.606 0.1136
M9 3.96138 3.55472 1.114 0.2697
M10 3.54494 3.55465 0.997 0.3228
M11 0.24977 3.54348 0.070 0.9440
t 0.24977 0.03684 6.779 6.88e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.137 on 58 degrees of freedom
Multiple R-squared: 0.7875, Adjusted R-squared: 0.7399
F-statistic: 16.53 on 13 and 58 DF, p-value: 6.025e-15
> 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,] 0.4267589440 0.8535178879 0.5732411
[2,] 0.2690991029 0.5381982057 0.7309009
[3,] 0.1678674580 0.3357349161 0.8321325
[4,] 0.1112903783 0.2225807567 0.8887096
[5,] 0.1337865939 0.2675731878 0.8662134
[6,] 0.0884195869 0.1768391738 0.9115804
[7,] 0.1165939213 0.2331878426 0.8834061
[8,] 0.3825096462 0.7650192923 0.6174904
[9,] 0.2919686110 0.5839372220 0.7080314
[10,] 0.2506417800 0.5012835600 0.7493582
[11,] 0.2246241797 0.4492483594 0.7753758
[12,] 0.1644488215 0.3288976429 0.8355512
[13,] 0.1269568852 0.2539137705 0.8730431
[14,] 0.0937444903 0.1874889806 0.9062555
[15,] 0.0651141101 0.1302282203 0.9348859
[16,] 0.0750700569 0.1501401139 0.9249299
[17,] 0.0482315850 0.0964631700 0.9517684
[18,] 0.0362697661 0.0725395322 0.9637302
[19,] 0.0232274809 0.0464549618 0.9767725
[20,] 0.0182163840 0.0364327679 0.9817836
[21,] 0.0108275912 0.0216551824 0.9891724
[22,] 0.0079900773 0.0159801547 0.9920099
[23,] 0.0101298247 0.0202596495 0.9898702
[24,] 0.0060630421 0.0121260842 0.9939370
[25,] 0.0041560290 0.0083120579 0.9958440
[26,] 0.0026855586 0.0053711172 0.9973144
[27,] 0.0014608262 0.0029216523 0.9985392
[28,] 0.0010067475 0.0020134951 0.9989933
[29,] 0.0008248527 0.0016497054 0.9991751
[30,] 0.0006772225 0.0013544450 0.9993228
[31,] 0.0005656540 0.0011313080 0.9994343
[32,] 0.0003929432 0.0007858863 0.9996071
[33,] 0.0002294545 0.0004589091 0.9997705
[34,] 0.0001656558 0.0003313116 0.9998343
[35,] 0.0001850155 0.0003700309 0.9998150
[36,] 0.0009906481 0.0019812962 0.9990094
[37,] 0.0022164694 0.0044329387 0.9977835
[38,] 0.0095654273 0.0191308547 0.9904346
[39,] 0.0132802711 0.0265605422 0.9867197
> postscript(file="/var/www/html/rcomp/tmp/105d51228942176.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qflc1228942176.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33zn91228942176.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4c42k1228942176.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5q9nh1228942176.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 = 72
Frequency = 1
1 2 3 4 5 6
4.7809381 -2.5523952 4.6142715 -0.2190619 5.4021956 4.2355289
7 8 9 10 11 12
2.4021956 -2.9311377 -0.4311377 -3.2644711 0.7809381 -1.2190619
13 14 15 16 17 18
4.0561876 0.7228543 -9.1104790 -1.9438124 -3.3225549 -2.4892216
19 20 21 22 23 24
-5.3225549 -3.6558882 4.8441118 -2.9892216 8.0561876 14.0561876
25 26 27 28 29 30
-6.2134731 -3.5468064 -0.3801397 -3.2134731 -5.5922156 1.2411178
31 32 33 34 35 36
-2.5922156 4.0744511 -0.4255489 -4.2588822 -0.2134731 3.7865269
37 38 39 40 41 42
-3.2106786 1.4559880 5.6226547 -2.2106786 2.6830339 -4.4836327
43 44 45 46 47 48
-2.3169661 3.3497006 -6.1502994 4.0163673 -6.2106786 -3.2106786
49 50 51 52 53 54
-4.2078842 -3.5412176 -6.3745509 -5.2078842 -4.5866267 -3.7532934
55 56 57 58 59 60
4.4133733 -1.9199601 5.5800399 7.7467066 11.7921158 3.7921158
61 62 63 64 65 66
4.7949102 7.4615768 5.6282435 12.7949102 5.4161677 5.2495010
67 68 69 70 71 72
3.4161677 1.0828343 -3.4171657 -1.2504990 -14.2050898 -17.2050898
> postscript(file="/var/www/html/rcomp/tmp/6qdjx1228942176.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 = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 4.7809381 NA
1 -2.5523952 4.7809381
2 4.6142715 -2.5523952
3 -0.2190619 4.6142715
4 5.4021956 -0.2190619
5 4.2355289 5.4021956
6 2.4021956 4.2355289
7 -2.9311377 2.4021956
8 -0.4311377 -2.9311377
9 -3.2644711 -0.4311377
10 0.7809381 -3.2644711
11 -1.2190619 0.7809381
12 4.0561876 -1.2190619
13 0.7228543 4.0561876
14 -9.1104790 0.7228543
15 -1.9438124 -9.1104790
16 -3.3225549 -1.9438124
17 -2.4892216 -3.3225549
18 -5.3225549 -2.4892216
19 -3.6558882 -5.3225549
20 4.8441118 -3.6558882
21 -2.9892216 4.8441118
22 8.0561876 -2.9892216
23 14.0561876 8.0561876
24 -6.2134731 14.0561876
25 -3.5468064 -6.2134731
26 -0.3801397 -3.5468064
27 -3.2134731 -0.3801397
28 -5.5922156 -3.2134731
29 1.2411178 -5.5922156
30 -2.5922156 1.2411178
31 4.0744511 -2.5922156
32 -0.4255489 4.0744511
33 -4.2588822 -0.4255489
34 -0.2134731 -4.2588822
35 3.7865269 -0.2134731
36 -3.2106786 3.7865269
37 1.4559880 -3.2106786
38 5.6226547 1.4559880
39 -2.2106786 5.6226547
40 2.6830339 -2.2106786
41 -4.4836327 2.6830339
42 -2.3169661 -4.4836327
43 3.3497006 -2.3169661
44 -6.1502994 3.3497006
45 4.0163673 -6.1502994
46 -6.2106786 4.0163673
47 -3.2106786 -6.2106786
48 -4.2078842 -3.2106786
49 -3.5412176 -4.2078842
50 -6.3745509 -3.5412176
51 -5.2078842 -6.3745509
52 -4.5866267 -5.2078842
53 -3.7532934 -4.5866267
54 4.4133733 -3.7532934
55 -1.9199601 4.4133733
56 5.5800399 -1.9199601
57 7.7467066 5.5800399
58 11.7921158 7.7467066
59 3.7921158 11.7921158
60 4.7949102 3.7921158
61 7.4615768 4.7949102
62 5.6282435 7.4615768
63 12.7949102 5.6282435
64 5.4161677 12.7949102
65 5.2495010 5.4161677
66 3.4161677 5.2495010
67 1.0828343 3.4161677
68 -3.4171657 1.0828343
69 -1.2504990 -3.4171657
70 -14.2050898 -1.2504990
71 -17.2050898 -14.2050898
72 NA -17.2050898
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.5523952 4.7809381
[2,] 4.6142715 -2.5523952
[3,] -0.2190619 4.6142715
[4,] 5.4021956 -0.2190619
[5,] 4.2355289 5.4021956
[6,] 2.4021956 4.2355289
[7,] -2.9311377 2.4021956
[8,] -0.4311377 -2.9311377
[9,] -3.2644711 -0.4311377
[10,] 0.7809381 -3.2644711
[11,] -1.2190619 0.7809381
[12,] 4.0561876 -1.2190619
[13,] 0.7228543 4.0561876
[14,] -9.1104790 0.7228543
[15,] -1.9438124 -9.1104790
[16,] -3.3225549 -1.9438124
[17,] -2.4892216 -3.3225549
[18,] -5.3225549 -2.4892216
[19,] -3.6558882 -5.3225549
[20,] 4.8441118 -3.6558882
[21,] -2.9892216 4.8441118
[22,] 8.0561876 -2.9892216
[23,] 14.0561876 8.0561876
[24,] -6.2134731 14.0561876
[25,] -3.5468064 -6.2134731
[26,] -0.3801397 -3.5468064
[27,] -3.2134731 -0.3801397
[28,] -5.5922156 -3.2134731
[29,] 1.2411178 -5.5922156
[30,] -2.5922156 1.2411178
[31,] 4.0744511 -2.5922156
[32,] -0.4255489 4.0744511
[33,] -4.2588822 -0.4255489
[34,] -0.2134731 -4.2588822
[35,] 3.7865269 -0.2134731
[36,] -3.2106786 3.7865269
[37,] 1.4559880 -3.2106786
[38,] 5.6226547 1.4559880
[39,] -2.2106786 5.6226547
[40,] 2.6830339 -2.2106786
[41,] -4.4836327 2.6830339
[42,] -2.3169661 -4.4836327
[43,] 3.3497006 -2.3169661
[44,] -6.1502994 3.3497006
[45,] 4.0163673 -6.1502994
[46,] -6.2106786 4.0163673
[47,] -3.2106786 -6.2106786
[48,] -4.2078842 -3.2106786
[49,] -3.5412176 -4.2078842
[50,] -6.3745509 -3.5412176
[51,] -5.2078842 -6.3745509
[52,] -4.5866267 -5.2078842
[53,] -3.7532934 -4.5866267
[54,] 4.4133733 -3.7532934
[55,] -1.9199601 4.4133733
[56,] 5.5800399 -1.9199601
[57,] 7.7467066 5.5800399
[58,] 11.7921158 7.7467066
[59,] 3.7921158 11.7921158
[60,] 4.7949102 3.7921158
[61,] 7.4615768 4.7949102
[62,] 5.6282435 7.4615768
[63,] 12.7949102 5.6282435
[64,] 5.4161677 12.7949102
[65,] 5.2495010 5.4161677
[66,] 3.4161677 5.2495010
[67,] 1.0828343 3.4161677
[68,] -3.4171657 1.0828343
[69,] -1.2504990 -3.4171657
[70,] -14.2050898 -1.2504990
[71,] -17.2050898 -14.2050898
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.5523952 4.7809381
2 4.6142715 -2.5523952
3 -0.2190619 4.6142715
4 5.4021956 -0.2190619
5 4.2355289 5.4021956
6 2.4021956 4.2355289
7 -2.9311377 2.4021956
8 -0.4311377 -2.9311377
9 -3.2644711 -0.4311377
10 0.7809381 -3.2644711
11 -1.2190619 0.7809381
12 4.0561876 -1.2190619
13 0.7228543 4.0561876
14 -9.1104790 0.7228543
15 -1.9438124 -9.1104790
16 -3.3225549 -1.9438124
17 -2.4892216 -3.3225549
18 -5.3225549 -2.4892216
19 -3.6558882 -5.3225549
20 4.8441118 -3.6558882
21 -2.9892216 4.8441118
22 8.0561876 -2.9892216
23 14.0561876 8.0561876
24 -6.2134731 14.0561876
25 -3.5468064 -6.2134731
26 -0.3801397 -3.5468064
27 -3.2134731 -0.3801397
28 -5.5922156 -3.2134731
29 1.2411178 -5.5922156
30 -2.5922156 1.2411178
31 4.0744511 -2.5922156
32 -0.4255489 4.0744511
33 -4.2588822 -0.4255489
34 -0.2134731 -4.2588822
35 3.7865269 -0.2134731
36 -3.2106786 3.7865269
37 1.4559880 -3.2106786
38 5.6226547 1.4559880
39 -2.2106786 5.6226547
40 2.6830339 -2.2106786
41 -4.4836327 2.6830339
42 -2.3169661 -4.4836327
43 3.3497006 -2.3169661
44 -6.1502994 3.3497006
45 4.0163673 -6.1502994
46 -6.2106786 4.0163673
47 -3.2106786 -6.2106786
48 -4.2078842 -3.2106786
49 -3.5412176 -4.2078842
50 -6.3745509 -3.5412176
51 -5.2078842 -6.3745509
52 -4.5866267 -5.2078842
53 -3.7532934 -4.5866267
54 4.4133733 -3.7532934
55 -1.9199601 4.4133733
56 5.5800399 -1.9199601
57 7.7467066 5.5800399
58 11.7921158 7.7467066
59 3.7921158 11.7921158
60 4.7949102 3.7921158
61 7.4615768 4.7949102
62 5.6282435 7.4615768
63 12.7949102 5.6282435
64 5.4161677 12.7949102
65 5.2495010 5.4161677
66 3.4161677 5.2495010
67 1.0828343 3.4161677
68 -3.4171657 1.0828343
69 -1.2504990 -3.4171657
70 -14.2050898 -1.2504990
71 -17.2050898 -14.2050898
> 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/7wjhr1228942176.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8dqv91228942176.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9zmvn1228942176.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xbas1228942176.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11t85p1228942176.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/12tl8n1228942176.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/13056o1228942176.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/14ducd1228942176.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/15ge3a1228942176.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/167zzp1228942176.tab")
+ }
>
> system("convert tmp/105d51228942176.ps tmp/105d51228942176.png")
> system("convert tmp/2qflc1228942176.ps tmp/2qflc1228942176.png")
> system("convert tmp/33zn91228942176.ps tmp/33zn91228942176.png")
> system("convert tmp/4c42k1228942176.ps tmp/4c42k1228942176.png")
> system("convert tmp/5q9nh1228942176.ps tmp/5q9nh1228942176.png")
> system("convert tmp/6qdjx1228942176.ps tmp/6qdjx1228942176.png")
> system("convert tmp/7wjhr1228942176.ps tmp/7wjhr1228942176.png")
> system("convert tmp/8dqv91228942176.ps tmp/8dqv91228942176.png")
> system("convert tmp/9zmvn1228942176.ps tmp/9zmvn1228942176.png")
> system("convert tmp/10xbas1228942176.ps tmp/10xbas1228942176.png")
>
>
> proc.time()
user system elapsed
2.639 1.665 4.047