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(103.3,107.9,101,94.6,94.2,92.3,107.1,102.6,103.1,104.1,92.7,87,109.3,113.9,103.3,100.8,97.4,98.9,110.8,103.5,99.8,104.9,95.2,85.7,110,113.7,101.1,103.6,96.2,98.3,119.7,109.4,103.5,118.2,98.7,96.8,121.8,124,119.6,122.5,109.7,111.6,131.2,124.4,116.9,131.8,107.4,111,134,126.2,131.2,130.1,123.1,126.3,148.6,130.1,142.3,154.4,121.6,124.8,143.6,146.9,144.6,137.1,134.7,130.8,153.5,137.6,146.5,156.7,137.6,131.4,147.4,158.5,151.5,142.5,131.3,133.4,136.9,143.2,136.4,145.9,138.8,122.9),dim=c(1,84),dimnames=list(c('Bewerkende_industrie'),1:84))
> y <- array(NA,dim=c(1,84),dimnames=list(c('Bewerkende_industrie'),1:84))
> 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
Bewerkende_industrie M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 103.3 1 0 0 0 0 0 0 0 0 0 0 1
2 107.9 0 1 0 0 0 0 0 0 0 0 0 2
3 101.0 0 0 1 0 0 0 0 0 0 0 0 3
4 94.6 0 0 0 1 0 0 0 0 0 0 0 4
5 94.2 0 0 0 0 1 0 0 0 0 0 0 5
6 92.3 0 0 0 0 0 1 0 0 0 0 0 6
7 107.1 0 0 0 0 0 0 1 0 0 0 0 7
8 102.6 0 0 0 0 0 0 0 1 0 0 0 8
9 103.1 0 0 0 0 0 0 0 0 1 0 0 9
10 104.1 0 0 0 0 0 0 0 0 0 1 0 10
11 92.7 0 0 0 0 0 0 0 0 0 0 1 11
12 87.0 0 0 0 0 0 0 0 0 0 0 0 12
13 109.3 1 0 0 0 0 0 0 0 0 0 0 13
14 113.9 0 1 0 0 0 0 0 0 0 0 0 14
15 103.3 0 0 1 0 0 0 0 0 0 0 0 15
16 100.8 0 0 0 1 0 0 0 0 0 0 0 16
17 97.4 0 0 0 0 1 0 0 0 0 0 0 17
18 98.9 0 0 0 0 0 1 0 0 0 0 0 18
19 110.8 0 0 0 0 0 0 1 0 0 0 0 19
20 103.5 0 0 0 0 0 0 0 1 0 0 0 20
21 99.8 0 0 0 0 0 0 0 0 1 0 0 21
22 104.9 0 0 0 0 0 0 0 0 0 1 0 22
23 95.2 0 0 0 0 0 0 0 0 0 0 1 23
24 85.7 0 0 0 0 0 0 0 0 0 0 0 24
25 110.0 1 0 0 0 0 0 0 0 0 0 0 25
26 113.7 0 1 0 0 0 0 0 0 0 0 0 26
27 101.1 0 0 1 0 0 0 0 0 0 0 0 27
28 103.6 0 0 0 1 0 0 0 0 0 0 0 28
29 96.2 0 0 0 0 1 0 0 0 0 0 0 29
30 98.3 0 0 0 0 0 1 0 0 0 0 0 30
31 119.7 0 0 0 0 0 0 1 0 0 0 0 31
32 109.4 0 0 0 0 0 0 0 1 0 0 0 32
33 103.5 0 0 0 0 0 0 0 0 1 0 0 33
34 118.2 0 0 0 0 0 0 0 0 0 1 0 34
35 98.7 0 0 0 0 0 0 0 0 0 0 1 35
36 96.8 0 0 0 0 0 0 0 0 0 0 0 36
37 121.8 1 0 0 0 0 0 0 0 0 0 0 37
38 124.0 0 1 0 0 0 0 0 0 0 0 0 38
39 119.6 0 0 1 0 0 0 0 0 0 0 0 39
40 122.5 0 0 0 1 0 0 0 0 0 0 0 40
41 109.7 0 0 0 0 1 0 0 0 0 0 0 41
42 111.6 0 0 0 0 0 1 0 0 0 0 0 42
43 131.2 0 0 0 0 0 0 1 0 0 0 0 43
44 124.4 0 0 0 0 0 0 0 1 0 0 0 44
45 116.9 0 0 0 0 0 0 0 0 1 0 0 45
46 131.8 0 0 0 0 0 0 0 0 0 1 0 46
47 107.4 0 0 0 0 0 0 0 0 0 0 1 47
48 111.0 0 0 0 0 0 0 0 0 0 0 0 48
49 134.0 1 0 0 0 0 0 0 0 0 0 0 49
50 126.2 0 1 0 0 0 0 0 0 0 0 0 50
51 131.2 0 0 1 0 0 0 0 0 0 0 0 51
52 130.1 0 0 0 1 0 0 0 0 0 0 0 52
53 123.1 0 0 0 0 1 0 0 0 0 0 0 53
54 126.3 0 0 0 0 0 1 0 0 0 0 0 54
55 148.6 0 0 0 0 0 0 1 0 0 0 0 55
56 130.1 0 0 0 0 0 0 0 1 0 0 0 56
57 142.3 0 0 0 0 0 0 0 0 1 0 0 57
58 154.4 0 0 0 0 0 0 0 0 0 1 0 58
59 121.6 0 0 0 0 0 0 0 0 0 0 1 59
60 124.8 0 0 0 0 0 0 0 0 0 0 0 60
61 143.6 1 0 0 0 0 0 0 0 0 0 0 61
62 146.9 0 1 0 0 0 0 0 0 0 0 0 62
63 144.6 0 0 1 0 0 0 0 0 0 0 0 63
64 137.1 0 0 0 1 0 0 0 0 0 0 0 64
65 134.7 0 0 0 0 1 0 0 0 0 0 0 65
66 130.8 0 0 0 0 0 1 0 0 0 0 0 66
67 153.5 0 0 0 0 0 0 1 0 0 0 0 67
68 137.6 0 0 0 0 0 0 0 1 0 0 0 68
69 146.5 0 0 0 0 0 0 0 0 1 0 0 69
70 156.7 0 0 0 0 0 0 0 0 0 1 0 70
71 137.6 0 0 0 0 0 0 0 0 0 0 1 71
72 131.4 0 0 0 0 0 0 0 0 0 0 0 72
73 147.4 1 0 0 0 0 0 0 0 0 0 0 73
74 158.5 0 1 0 0 0 0 0 0 0 0 0 74
75 151.5 0 0 1 0 0 0 0 0 0 0 0 75
76 142.5 0 0 0 1 0 0 0 0 0 0 0 76
77 131.3 0 0 0 0 1 0 0 0 0 0 0 77
78 133.4 0 0 0 0 0 1 0 0 0 0 0 78
79 136.9 0 0 0 0 0 0 1 0 0 0 0 79
80 143.2 0 0 0 0 0 0 0 1 0 0 0 80
81 136.4 0 0 0 0 0 0 0 0 1 0 0 81
82 145.9 0 0 0 0 0 0 0 0 0 1 0 82
83 138.8 0 0 0 0 0 0 0 0 0 0 1 83
84 122.9 0 0 0 0 0 0 0 0 0 0 0 84
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
75.4798 23.2561 25.6679 19.4368 15.7343 8.6747
M6 M7 M8 M9 M10 M11
8.7007 24.6125 15.7814 14.7647 23.7193 5.3168
t
0.6882
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17.5616 -3.9917 0.7571 3.9045 15.2842
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 75.4798 2.8125 26.837 < 2e-16 ***
M1 23.2561 3.4597 6.722 3.78e-09 ***
M2 25.6679 3.4571 7.425 1.94e-10 ***
M3 19.4368 3.4547 5.626 3.42e-07 ***
M4 15.7343 3.4526 4.557 2.10e-05 ***
M5 8.6747 3.4508 2.514 0.0142 *
M6 8.7007 3.4491 2.523 0.0139 *
M7 24.6125 3.4478 7.139 6.53e-10 ***
M8 15.7814 3.4466 4.579 1.94e-05 ***
M9 14.7647 3.4458 4.285 5.65e-05 ***
M10 23.7193 3.4451 6.885 1.91e-09 ***
M11 5.3168 3.4448 1.543 0.1272
t 0.6882 0.0293 23.491 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.444 on 71 degrees of freedom
Multiple R-squared: 0.9014, Adjusted R-squared: 0.8847
F-statistic: 54.07 on 12 and 71 DF, p-value: < 2.2e-16
> 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,] 1.052478e-02 2.104956e-02 0.9894752
[2,] 2.760824e-03 5.521647e-03 0.9972392
[3,] 7.410449e-04 1.482090e-03 0.9992590
[4,] 1.581416e-04 3.162832e-04 0.9998419
[5,] 1.705524e-04 3.411048e-04 0.9998294
[6,] 1.269198e-03 2.538395e-03 0.9987308
[7,] 5.655277e-04 1.131055e-03 0.9994345
[8,] 1.710282e-04 3.420565e-04 0.9998290
[9,] 1.219828e-04 2.439656e-04 0.9998780
[10,] 3.674680e-05 7.349360e-05 0.9999633
[11,] 1.138232e-05 2.276465e-05 0.9999886
[12,] 1.825082e-05 3.650164e-05 0.9999817
[13,] 9.186592e-06 1.837318e-05 0.9999908
[14,] 5.197546e-06 1.039509e-05 0.9999948
[15,] 1.713516e-06 3.427032e-06 0.9999983
[16,] 8.671285e-06 1.734257e-05 0.9999913
[17,] 3.879802e-06 7.759604e-06 0.9999961
[18,] 2.329628e-06 4.659256e-06 0.9999977
[19,] 3.177095e-05 6.354190e-05 0.9999682
[20,] 1.598420e-05 3.196839e-05 0.9999840
[21,] 2.014153e-05 4.028306e-05 0.9999799
[22,] 4.332146e-05 8.664292e-05 0.9999567
[23,] 3.518210e-05 7.036420e-05 0.9999648
[24,] 1.661394e-04 3.322789e-04 0.9998339
[25,] 1.489398e-03 2.978797e-03 0.9985106
[26,] 1.087893e-03 2.175786e-03 0.9989121
[27,] 8.433896e-04 1.686779e-03 0.9991566
[28,] 9.507228e-04 1.901446e-03 0.9990493
[29,] 1.003578e-03 2.007156e-03 0.9989964
[30,] 1.153466e-03 2.306933e-03 0.9988465
[31,] 2.811444e-03 5.622889e-03 0.9971886
[32,] 4.664429e-03 9.328859e-03 0.9953356
[33,] 6.125808e-03 1.225162e-02 0.9938742
[34,] 5.799158e-03 1.159832e-02 0.9942008
[35,] 3.324899e-02 6.649798e-02 0.9667510
[36,] 7.164828e-02 1.432966e-01 0.9283517
[37,] 8.001649e-02 1.600330e-01 0.9199835
[38,] 8.540181e-02 1.708036e-01 0.9145982
[39,] 8.330293e-02 1.666059e-01 0.9166971
[40,] 1.178180e-01 2.356359e-01 0.8821820
[41,] 1.166222e-01 2.332443e-01 0.8833778
[42,] 1.798381e-01 3.596761e-01 0.8201619
[43,] 2.777186e-01 5.554373e-01 0.7222814
[44,] 4.729056e-01 9.458112e-01 0.5270944
[45,] 4.255395e-01 8.510791e-01 0.5744605
[46,] 3.585258e-01 7.170516e-01 0.6414742
[47,] 4.702746e-01 9.405492e-01 0.5297254
[48,] 5.168018e-01 9.663965e-01 0.4831982
[49,] 5.432692e-01 9.134616e-01 0.4567308
[50,] 4.301261e-01 8.602523e-01 0.5698739
[51,] 4.152809e-01 8.305619e-01 0.5847191
[52,] 5.097364e-01 9.805272e-01 0.4902636
[53,] 7.001451e-01 5.997098e-01 0.2998549
> postscript(file="/var/www/html/rcomp/tmp/11z341230126604.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2mutb1230126604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31aa41230126604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4io061230126604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57z0m1230126604.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 = 84
Frequency = 1
1 2 3 4 5 6
3.8758929 5.3758929 4.0187500 0.6330357 6.6044643 3.9901786
7 8 9 10 11 12
2.1901786 5.8330357 6.6616071 -1.9812500 4.3330357 3.2616071
13 14 15 16 17 18
1.6172619 3.1172619 -1.9398810 -1.4255952 1.5458333 2.3315476
19 20 21 22 23 24
-2.3684524 -1.5255952 -4.8970238 -9.4398810 -1.4255952 -6.2970238
25 26 27 28 29 30
-5.9413690 -5.3413690 -12.3985119 -6.8842262 -7.9127976 -6.5270833
31 32 33 34 35 36
-1.7270833 -3.8842262 -9.4556548 -4.3985119 -6.1842262 -3.4556548
37 38 39 40 41 42
-2.4000000 -3.3000000 -2.1571429 3.7571429 -2.6714286 -1.4857143
43 44 45 46 47 48
1.5142857 2.8571429 -4.3142857 0.9428571 -5.7428571 2.4857143
49 50 51 52 53 54
1.5413690 -9.3586310 1.1842262 3.0985119 2.4699405 4.9556548
55 56 57 58 59 60
10.6556548 0.2985119 12.8270833 15.2842262 0.1985119 8.0270833
61 62 63 64 65 66
2.8827381 3.0827381 6.3255952 1.8398810 5.8113095 1.1970238
67 68 69 70 71 72
7.2970238 -0.4601190 8.7684524 9.3255952 7.9398810 6.3684524
73 74 75 76 77 78
-1.5758929 6.4241071 4.9669643 -1.0187500 -5.8473214 -4.4616071
79 80 81 82 83 84
-17.5616071 -3.1187500 -9.5901786 -9.7330357 0.8812500 -10.3901786
> postscript(file="/var/www/html/rcomp/tmp/6079f1230126604.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 = 84
Frequency = 1
lag(myerror, k = 1) myerror
0 3.8758929 NA
1 5.3758929 3.8758929
2 4.0187500 5.3758929
3 0.6330357 4.0187500
4 6.6044643 0.6330357
5 3.9901786 6.6044643
6 2.1901786 3.9901786
7 5.8330357 2.1901786
8 6.6616071 5.8330357
9 -1.9812500 6.6616071
10 4.3330357 -1.9812500
11 3.2616071 4.3330357
12 1.6172619 3.2616071
13 3.1172619 1.6172619
14 -1.9398810 3.1172619
15 -1.4255952 -1.9398810
16 1.5458333 -1.4255952
17 2.3315476 1.5458333
18 -2.3684524 2.3315476
19 -1.5255952 -2.3684524
20 -4.8970238 -1.5255952
21 -9.4398810 -4.8970238
22 -1.4255952 -9.4398810
23 -6.2970238 -1.4255952
24 -5.9413690 -6.2970238
25 -5.3413690 -5.9413690
26 -12.3985119 -5.3413690
27 -6.8842262 -12.3985119
28 -7.9127976 -6.8842262
29 -6.5270833 -7.9127976
30 -1.7270833 -6.5270833
31 -3.8842262 -1.7270833
32 -9.4556548 -3.8842262
33 -4.3985119 -9.4556548
34 -6.1842262 -4.3985119
35 -3.4556548 -6.1842262
36 -2.4000000 -3.4556548
37 -3.3000000 -2.4000000
38 -2.1571429 -3.3000000
39 3.7571429 -2.1571429
40 -2.6714286 3.7571429
41 -1.4857143 -2.6714286
42 1.5142857 -1.4857143
43 2.8571429 1.5142857
44 -4.3142857 2.8571429
45 0.9428571 -4.3142857
46 -5.7428571 0.9428571
47 2.4857143 -5.7428571
48 1.5413690 2.4857143
49 -9.3586310 1.5413690
50 1.1842262 -9.3586310
51 3.0985119 1.1842262
52 2.4699405 3.0985119
53 4.9556548 2.4699405
54 10.6556548 4.9556548
55 0.2985119 10.6556548
56 12.8270833 0.2985119
57 15.2842262 12.8270833
58 0.1985119 15.2842262
59 8.0270833 0.1985119
60 2.8827381 8.0270833
61 3.0827381 2.8827381
62 6.3255952 3.0827381
63 1.8398810 6.3255952
64 5.8113095 1.8398810
65 1.1970238 5.8113095
66 7.2970238 1.1970238
67 -0.4601190 7.2970238
68 8.7684524 -0.4601190
69 9.3255952 8.7684524
70 7.9398810 9.3255952
71 6.3684524 7.9398810
72 -1.5758929 6.3684524
73 6.4241071 -1.5758929
74 4.9669643 6.4241071
75 -1.0187500 4.9669643
76 -5.8473214 -1.0187500
77 -4.4616071 -5.8473214
78 -17.5616071 -4.4616071
79 -3.1187500 -17.5616071
80 -9.5901786 -3.1187500
81 -9.7330357 -9.5901786
82 0.8812500 -9.7330357
83 -10.3901786 0.8812500
84 NA -10.3901786
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.3758929 3.8758929
[2,] 4.0187500 5.3758929
[3,] 0.6330357 4.0187500
[4,] 6.6044643 0.6330357
[5,] 3.9901786 6.6044643
[6,] 2.1901786 3.9901786
[7,] 5.8330357 2.1901786
[8,] 6.6616071 5.8330357
[9,] -1.9812500 6.6616071
[10,] 4.3330357 -1.9812500
[11,] 3.2616071 4.3330357
[12,] 1.6172619 3.2616071
[13,] 3.1172619 1.6172619
[14,] -1.9398810 3.1172619
[15,] -1.4255952 -1.9398810
[16,] 1.5458333 -1.4255952
[17,] 2.3315476 1.5458333
[18,] -2.3684524 2.3315476
[19,] -1.5255952 -2.3684524
[20,] -4.8970238 -1.5255952
[21,] -9.4398810 -4.8970238
[22,] -1.4255952 -9.4398810
[23,] -6.2970238 -1.4255952
[24,] -5.9413690 -6.2970238
[25,] -5.3413690 -5.9413690
[26,] -12.3985119 -5.3413690
[27,] -6.8842262 -12.3985119
[28,] -7.9127976 -6.8842262
[29,] -6.5270833 -7.9127976
[30,] -1.7270833 -6.5270833
[31,] -3.8842262 -1.7270833
[32,] -9.4556548 -3.8842262
[33,] -4.3985119 -9.4556548
[34,] -6.1842262 -4.3985119
[35,] -3.4556548 -6.1842262
[36,] -2.4000000 -3.4556548
[37,] -3.3000000 -2.4000000
[38,] -2.1571429 -3.3000000
[39,] 3.7571429 -2.1571429
[40,] -2.6714286 3.7571429
[41,] -1.4857143 -2.6714286
[42,] 1.5142857 -1.4857143
[43,] 2.8571429 1.5142857
[44,] -4.3142857 2.8571429
[45,] 0.9428571 -4.3142857
[46,] -5.7428571 0.9428571
[47,] 2.4857143 -5.7428571
[48,] 1.5413690 2.4857143
[49,] -9.3586310 1.5413690
[50,] 1.1842262 -9.3586310
[51,] 3.0985119 1.1842262
[52,] 2.4699405 3.0985119
[53,] 4.9556548 2.4699405
[54,] 10.6556548 4.9556548
[55,] 0.2985119 10.6556548
[56,] 12.8270833 0.2985119
[57,] 15.2842262 12.8270833
[58,] 0.1985119 15.2842262
[59,] 8.0270833 0.1985119
[60,] 2.8827381 8.0270833
[61,] 3.0827381 2.8827381
[62,] 6.3255952 3.0827381
[63,] 1.8398810 6.3255952
[64,] 5.8113095 1.8398810
[65,] 1.1970238 5.8113095
[66,] 7.2970238 1.1970238
[67,] -0.4601190 7.2970238
[68,] 8.7684524 -0.4601190
[69,] 9.3255952 8.7684524
[70,] 7.9398810 9.3255952
[71,] 6.3684524 7.9398810
[72,] -1.5758929 6.3684524
[73,] 6.4241071 -1.5758929
[74,] 4.9669643 6.4241071
[75,] -1.0187500 4.9669643
[76,] -5.8473214 -1.0187500
[77,] -4.4616071 -5.8473214
[78,] -17.5616071 -4.4616071
[79,] -3.1187500 -17.5616071
[80,] -9.5901786 -3.1187500
[81,] -9.7330357 -9.5901786
[82,] 0.8812500 -9.7330357
[83,] -10.3901786 0.8812500
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.3758929 3.8758929
2 4.0187500 5.3758929
3 0.6330357 4.0187500
4 6.6044643 0.6330357
5 3.9901786 6.6044643
6 2.1901786 3.9901786
7 5.8330357 2.1901786
8 6.6616071 5.8330357
9 -1.9812500 6.6616071
10 4.3330357 -1.9812500
11 3.2616071 4.3330357
12 1.6172619 3.2616071
13 3.1172619 1.6172619
14 -1.9398810 3.1172619
15 -1.4255952 -1.9398810
16 1.5458333 -1.4255952
17 2.3315476 1.5458333
18 -2.3684524 2.3315476
19 -1.5255952 -2.3684524
20 -4.8970238 -1.5255952
21 -9.4398810 -4.8970238
22 -1.4255952 -9.4398810
23 -6.2970238 -1.4255952
24 -5.9413690 -6.2970238
25 -5.3413690 -5.9413690
26 -12.3985119 -5.3413690
27 -6.8842262 -12.3985119
28 -7.9127976 -6.8842262
29 -6.5270833 -7.9127976
30 -1.7270833 -6.5270833
31 -3.8842262 -1.7270833
32 -9.4556548 -3.8842262
33 -4.3985119 -9.4556548
34 -6.1842262 -4.3985119
35 -3.4556548 -6.1842262
36 -2.4000000 -3.4556548
37 -3.3000000 -2.4000000
38 -2.1571429 -3.3000000
39 3.7571429 -2.1571429
40 -2.6714286 3.7571429
41 -1.4857143 -2.6714286
42 1.5142857 -1.4857143
43 2.8571429 1.5142857
44 -4.3142857 2.8571429
45 0.9428571 -4.3142857
46 -5.7428571 0.9428571
47 2.4857143 -5.7428571
48 1.5413690 2.4857143
49 -9.3586310 1.5413690
50 1.1842262 -9.3586310
51 3.0985119 1.1842262
52 2.4699405 3.0985119
53 4.9556548 2.4699405
54 10.6556548 4.9556548
55 0.2985119 10.6556548
56 12.8270833 0.2985119
57 15.2842262 12.8270833
58 0.1985119 15.2842262
59 8.0270833 0.1985119
60 2.8827381 8.0270833
61 3.0827381 2.8827381
62 6.3255952 3.0827381
63 1.8398810 6.3255952
64 5.8113095 1.8398810
65 1.1970238 5.8113095
66 7.2970238 1.1970238
67 -0.4601190 7.2970238
68 8.7684524 -0.4601190
69 9.3255952 8.7684524
70 7.9398810 9.3255952
71 6.3684524 7.9398810
72 -1.5758929 6.3684524
73 6.4241071 -1.5758929
74 4.9669643 6.4241071
75 -1.0187500 4.9669643
76 -5.8473214 -1.0187500
77 -4.4616071 -5.8473214
78 -17.5616071 -4.4616071
79 -3.1187500 -17.5616071
80 -9.5901786 -3.1187500
81 -9.7330357 -9.5901786
82 0.8812500 -9.7330357
83 -10.3901786 0.8812500
> 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/71rq51230126604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/847431230126604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9erlc1230126604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10cztm1230126604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/119bqw1230126604.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/12j8981230126605.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/13c8qk1230126605.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/147mk41230126605.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/15maps1230126605.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/16n7kj1230126605.tab")
+ }
>
> system("convert tmp/11z341230126604.ps tmp/11z341230126604.png")
> system("convert tmp/2mutb1230126604.ps tmp/2mutb1230126604.png")
> system("convert tmp/31aa41230126604.ps tmp/31aa41230126604.png")
> system("convert tmp/4io061230126604.ps tmp/4io061230126604.png")
> system("convert tmp/57z0m1230126604.ps tmp/57z0m1230126604.png")
> system("convert tmp/6079f1230126604.ps tmp/6079f1230126604.png")
> system("convert tmp/71rq51230126604.ps tmp/71rq51230126604.png")
> system("convert tmp/847431230126604.ps tmp/847431230126604.png")
> system("convert tmp/9erlc1230126604.ps tmp/9erlc1230126604.png")
> system("convert tmp/10cztm1230126604.ps tmp/10cztm1230126604.png")
>
>
> proc.time()
user system elapsed
2.731 1.568 3.729