R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(1
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+ ,3)
+ ,dim=c(13
+ ,82)
+ ,dimnames=list(c('maand'
+ ,'X_1t'
+ ,'Yt'
+ ,'X_2t'
+ ,'X_3t'
+ ,'X_4t'
+ ,'X_5t'
+ ,'X_6t'
+ ,'X_7t'
+ ,'X_8t'
+ ,'X_9t'
+ ,'X_10t'
+ ,'X_11t')
+ ,1:82))
> y <- array(NA,dim=c(13,82),dimnames=list(c('maand','X_1t','Yt','X_2t','X_3t','X_4t','X_5t','X_6t','X_7t','X_8t','X_9t','X_10t','X_11t'),1:82))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'Linear Trend'
> par2 <- 'Do not include Seasonal 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, 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
maand X_1t Yt X_2t X_3t X_4t X_5t X_6t X_7t X_8t X_9t X_10t X_11t t
1 1 -19 -3 53 14 24 20 -9 -2 20 6 -29 17 1
2 2 -20 -4 50 16 24 19 -12 -4 21 6 -29 13 2
3 3 -21 -7 50 19 31 21 -10 -5 20 5 -27 12 3
4 4 -19 -7 51 18 25 17 -10 -2 21 5 -29 13 4
5 5 -17 -7 53 19 28 15 -11 -4 19 3 -24 10 5
6 6 -16 -3 49 20 24 18 -11 -4 22 5 -29 14 6
7 7 -10 0 54 20 25 19 -10 -5 20 5 -21 13 7
8 8 -16 -5 57 24 16 16 -13 -7 18 5 -20 10 8
9 9 -10 -3 58 18 17 21 -10 -5 16 3 -26 11 9
10 10 -8 3 56 15 11 26 -6 -6 17 6 -19 12 10
11 11 -7 2 60 25 12 23 -9 -4 18 6 -22 7 11
12 12 -15 -7 55 23 39 24 -8 -2 19 4 -22 11 12
13 13 -7 -1 54 20 19 23 -12 -3 18 6 -15 9 13
14 14 -6 0 52 20 14 19 -10 0 20 5 -16 13 14
15 15 -6 -3 55 22 15 25 -11 -4 21 4 -22 12 15
16 16 2 4 56 25 7 21 -13 -3 18 5 -21 5 16
17 17 -4 2 54 22 12 19 -10 -3 19 5 -11 13 17
18 18 -4 3 53 26 12 20 -10 -3 19 4 -10 11 18
19 19 -8 0 59 27 14 20 -11 -4 19 3 -6 8 19
20 20 -10 -10 62 41 9 17 -11 -5 21 2 -8 8 20
21 21 -16 -10 63 29 8 25 -11 -5 19 3 -15 8 21
22 22 -14 -9 64 33 4 19 -10 -6 19 2 -16 8 22
23 23 -30 -22 75 39 7 13 -13 -10 17 -1 -24 0 23
24 24 -33 -16 77 27 3 15 -12 -11 16 0 -27 3 24
25 25 -40 -18 79 27 5 15 -13 -13 16 -2 -33 0 25
26 26 -38 -14 77 25 0 13 -15 -12 17 1 -29 -1 26
27 27 -39 -12 82 19 -2 11 -16 -13 16 -2 -34 -1 27
28 28 -46 -17 83 15 6 9 -18 -12 15 -2 -37 -4 28
29 29 -50 -23 81 19 11 2 -17 -15 16 -2 -31 1 29
30 30 -55 -28 78 23 9 -2 -18 -14 16 -6 -33 -1 30
31 31 -66 -31 79 23 17 -4 -20 -16 16 -4 -25 0 31
32 32 -63 -21 79 7 21 -2 -22 -16 18 -2 -27 -1 32
33 33 -56 -19 73 1 21 1 -17 -12 19 0 -21 6 33
34 34 -66 -22 72 7 41 -13 -19 -16 16 -5 -32 0 34
35 35 -63 -22 67 4 57 -11 -18 -15 16 -4 -31 -3 35
36 36 -69 -25 67 -8 65 -14 -26 -17 16 -5 -32 -3 36
37 37 -69 -16 50 -14 68 -4 -19 -15 18 -1 -30 4 37
38 38 -72 -22 45 -10 73 -9 -23 -14 16 -2 -34 1 38
39 39 -69 -21 39 -11 71 -5 -21 -15 15 -4 -35 0 39
40 40 -67 -10 39 -10 71 -4 -27 -14 15 -1 -37 -4 40
41 41 -64 -7 37 -8 70 -8 -27 -16 16 1 -32 -2 41
42 42 -61 -5 30 -8 69 -1 -21 -11 18 1 -28 3 42
43 43 -58 -4 24 -7 65 -2 -22 -14 16 -2 -26 2 43
44 44 -47 7 27 -8 57 -1 -24 -12 19 1 -24 5 44
45 45 -44 6 19 -4 57 8 -21 -11 19 1 -27 6 45
46 46 -42 3 19 3 57 8 -21 -13 18 3 -26 6 46
47 47 -34 10 25 -5 55 6 -22 -12 17 3 -27 3 47
48 48 -38 0 16 -4 65 7 -25 -12 19 1 -27 4 48
49 49 -41 -2 20 5 65 2 -21 -10 22 1 -24 7 49
50 50 -38 -1 25 3 64 3 -26 -12 19 0 -28 5 50
51 51 -37 2 34 6 60 0 -27 -11 19 2 -23 6 51
52 52 -22 8 39 10 43 5 -22 -10 16 2 -23 1 52
53 53 -37 -6 40 16 47 -1 -22 -12 18 -1 -29 3 53
54 54 -36 -4 38 11 40 3 -20 -12 20 1 -25 6 54
55 55 -25 4 42 10 31 4 -21 -11 17 0 -24 0 55
56 56 -15 7 46 21 27 8 -16 -12 17 1 -20 3 56
57 57 -17 3 48 18 24 10 -17 -9 17 1 -22 4 57
58 58 -19 3 51 20 23 14 -19 -6 20 3 -24 7 58
59 59 -12 8 55 18 17 15 -20 -7 21 2 -27 6 59
60 60 -17 3 52 23 16 9 -20 -7 19 0 -25 6 60
61 61 -21 -3 55 28 15 8 -20 -10 18 0 -26 6 61
62 62 -10 4 58 31 8 10 -19 -8 20 3 -24 6 62
63 63 -19 -5 72 38 5 5 -20 -11 17 -2 -26 2 63
64 64 -14 -1 70 27 6 4 -25 -12 15 0 -22 2 64
65 65 -8 5 70 21 5 8 -25 -11 17 1 -20 2 65
66 66 -16 0 63 31 12 8 -22 -11 18 -1 -26 3 66
67 67 -14 -6 66 31 8 10 -19 -9 20 -2 -22 -1 67
68 68 -30 -13 65 29 17 8 -20 -9 19 -1 -29 -4 68
69 69 -33 -15 55 24 22 10 -18 -12 20 -1 -30 4 69
70 70 -37 -8 57 27 24 -8 -17 -10 22 1 -26 5 70
71 71 -47 -20 60 36 36 -6 -17 -10 20 -2 -30 3 71
72 72 -48 -10 63 35 31 -10 -21 -13 21 -5 -33 -1 72
73 73 -50 -22 65 44 34 -15 -17 -13 19 -5 -33 -4 73
74 74 -56 -25 61 39 47 -21 -22 -12 22 -6 -31 0 74
75 75 -47 -10 65 26 33 -24 -24 -14 19 -4 -36 -1 75
76 76 -37 -8 63 27 35 -15 -18 -9 21 -3 -43 -1 76
77 77 -35 -9 59 17 31 -12 -20 -12 19 -3 -40 3 77
78 78 -29 -5 56 20 35 -11 -21 -10 21 -1 -38 2 78
79 79 -28 -7 54 22 39 -11 -17 -13 18 -2 -41 -4 79
80 80 -29 -11 56 32 46 -13 -17 -11 18 -3 -38 -3 80
81 81 -33 -11 54 28 40 -10 -17 -11 20 -3 -40 -1 81
82 82 -41 -16 58 30 50 -9 -21 -11 19 -3 -41 3 82
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X_1t Yt X_2t X_3t X_4t
3.759e-15 -1.097e-16 -1.297e-16 5.643e-17 -9.627e-17 5.687e-18
X_5t X_6t X_7t X_8t X_9t X_10t
1.466e-16 2.685e-16 3.207e-16 -6.008e-16 5.210e-16 7.378e-17
X_11t t
1.998e-16 1.000e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.896e-15 -4.339e-16 1.780e-17 3.702e-16 6.820e-15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.759e-15 3.629e-15 1.036e+00 0.303982
X_1t -1.097e-16 2.840e-17 -3.864e+00 0.000251 ***
Yt -1.297e-16 4.431e-17 -2.926e+00 0.004662 **
X_2t 5.643e-17 2.686e-17 2.101e+00 0.039386 *
X_3t -9.627e-17 2.533e-17 -3.801e+00 0.000310 ***
X_4t 5.687e-18 2.244e-17 2.530e-01 0.800657
X_5t 1.466e-16 3.290e-17 4.458e+00 3.18e-05 ***
X_6t 2.685e-16 5.469e-17 4.909e+00 6.02e-06 ***
X_7t 3.207e-16 1.009e-16 3.178e+00 0.002232 **
X_8t -6.008e-16 1.158e-16 -5.190e+00 2.07e-06 ***
X_9t 5.210e-16 1.250e-16 4.168e+00 8.90e-05 ***
X_10t 7.378e-17 2.452e-17 3.009e+00 0.003670 **
X_11t 1.998e-16 6.024e-17 3.317e+00 0.001462 **
t 1.000e+00 1.526e-17 6.554e+16 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.098e-15 on 68 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 2.93e+33 on 13 and 68 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,] 1.582704e-03 3.165409e-03 9.984173e-01
[2,] 6.905604e-04 1.381121e-03 9.993094e-01
[3,] 1.095697e-04 2.191395e-04 9.998904e-01
[4,] 5.069618e-03 1.013924e-02 9.949304e-01
[5,] 9.955375e-07 1.991075e-06 9.999990e-01
[6,] 2.124280e-02 4.248561e-02 9.787572e-01
[7,] 1.235268e-04 2.470536e-04 9.998765e-01
[8,] 4.692699e-08 9.385399e-08 1.000000e+00
[9,] 5.528818e-04 1.105764e-03 9.994471e-01
[10,] 9.270078e-05 1.854016e-04 9.999073e-01
[11,] 6.774804e-01 6.450392e-01 3.225196e-01
[12,] 6.678283e-01 6.643435e-01 3.321717e-01
[13,] 1.752219e-01 3.504438e-01 8.247781e-01
[14,] 1.802578e-06 3.605157e-06 9.999982e-01
[15,] 6.235339e-01 7.529322e-01 3.764661e-01
[16,] 2.024962e-07 4.049923e-07 9.999998e-01
[17,] 2.727994e-02 5.455988e-02 9.727201e-01
[18,] 8.960965e-02 1.792193e-01 9.103903e-01
[19,] 9.975042e-01 4.991568e-03 2.495784e-03
[20,] 8.724266e-01 2.551467e-01 1.275734e-01
[21,] 3.781980e-01 7.563960e-01 6.218020e-01
[22,] 9.506961e-01 9.860774e-02 4.930387e-02
[23,] 2.761202e-01 5.522403e-01 7.238798e-01
[24,] 8.843475e-01 2.313051e-01 1.156525e-01
[25,] 1.000000e+00 1.484728e-09 7.423639e-10
[26,] 7.853870e-01 4.292260e-01 2.146130e-01
[27,] 1.661697e-09 3.323394e-09 1.000000e+00
[28,] 1.206843e-01 2.413687e-01 8.793157e-01
[29,] 6.365880e-03 1.273176e-02 9.936341e-01
[30,] 1.000000e+00 8.440504e-11 4.220252e-11
[31,] 9.965140e-01 6.972053e-03 3.486026e-03
[32,] 2.964174e-04 5.928348e-04 9.997036e-01
[33,] 9.999423e-01 1.153749e-04 5.768747e-05
[34,] 9.962267e-01 7.546525e-03 3.773262e-03
[35,] 9.999328e-01 1.344376e-04 6.721881e-05
[36,] 9.527500e-01 9.450006e-02 4.725003e-02
[37,] 9.933408e-01 1.331849e-02 6.659247e-03
[38,] 3.268791e-02 6.537581e-02 9.673121e-01
[39,] 9.864947e-01 2.701055e-02 1.350527e-02
[40,] 1.104078e-01 2.208155e-01 8.895922e-01
[41,] 2.316888e-01 4.633777e-01 7.683112e-01
[42,] 9.999961e-01 7.799375e-06 3.899688e-06
[43,] 1.000000e+00 2.907902e-08 1.453951e-08
[44,] 7.131542e-01 5.736915e-01 2.868458e-01
[45,] 9.999458e-01 1.083714e-04 5.418568e-05
[46,] 9.844534e-01 3.109311e-02 1.554656e-02
[47,] 9.946319e-01 1.073616e-02 5.368079e-03
[48,] 9.990786e-01 1.842815e-03 9.214076e-04
[49,] 9.998432e-01 3.135041e-04 1.567520e-04
> postscript(file="/var/fisher/rcomp/tmp/11n8a1352143171.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2vqjp1352143171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3vjkx1352143171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/42izd1352143171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5zbc11352143171.ps",horizontal=F,onefile=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 = 82
Frequency = 1
1 2 3 4 5
-9.512328e-16 -1.438742e-15 6.540448e-16 -4.565240e-16 -1.868753e-15
6 7 8 9 10
-3.668444e-16 1.153340e-15 6.224921e-16 9.447456e-16 1.093563e-15
11 12 13 14 15
-3.456344e-17 -1.895909e-15 -4.217511e-16 6.820291e-15 1.165337e-16
16 17 18 19 20
-1.535812e-15 -1.137197e-15 -1.331014e-15 -5.462034e-16 2.152565e-17
21 22 23 24 25
2.852899e-16 -4.356364e-16 6.306484e-16 -4.938202e-17 8.605047e-16
26 27 28 29 30
4.594887e-16 1.851516e-17 -1.437837e-17 1.477914e-16 -5.942330e-16
31 32 33 34 35
-1.792988e-16 3.962319e-16 6.745878e-18 -1.523311e-16 -5.801393e-16
36 37 38 39 40
7.506709e-16 -1.109600e-16 -7.646949e-16 2.373979e-16 2.182754e-16
41 42 43 44 45
1.635151e-16 1.382452e-16 -5.230896e-16 1.797159e-16 1.703054e-17
46 47 48 49 50
2.532106e-16 -5.026176e-17 5.755063e-16 -7.866193e-16 5.789621e-16
51 52 53 54 55
3.756725e-16 -6.322290e-17 3.237594e-16 9.902434e-17 -3.534623e-16
56 57 58 59 60
5.289917e-17 -7.428424e-16 6.728018e-16 8.989078e-17 -7.812632e-16
61 62 63 64 65
-3.667063e-16 4.347559e-16 8.263801e-17 -4.388862e-16 -2.290829e-16
66 67 68 69 70
3.270467e-16 3.067487e-17 -4.135793e-16 -1.296363e-15 -5.445114e-16
71 72 73 74 75
-3.970586e-16 6.752141e-16 2.430020e-16 9.245096e-16 -1.245218e-15
76 77 78 79 80
-4.286968e-16 -2.781318e-16 -5.256134e-17 6.717915e-16 3.537270e-16
81 82
4.984149e-16 6.570527e-16
> postscript(file="/var/fisher/rcomp/tmp/6ziqt1352143171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 82
Frequency = 1
lag(myerror, k = 1) myerror
0 -9.512328e-16 NA
1 -1.438742e-15 -9.512328e-16
2 6.540448e-16 -1.438742e-15
3 -4.565240e-16 6.540448e-16
4 -1.868753e-15 -4.565240e-16
5 -3.668444e-16 -1.868753e-15
6 1.153340e-15 -3.668444e-16
7 6.224921e-16 1.153340e-15
8 9.447456e-16 6.224921e-16
9 1.093563e-15 9.447456e-16
10 -3.456344e-17 1.093563e-15
11 -1.895909e-15 -3.456344e-17
12 -4.217511e-16 -1.895909e-15
13 6.820291e-15 -4.217511e-16
14 1.165337e-16 6.820291e-15
15 -1.535812e-15 1.165337e-16
16 -1.137197e-15 -1.535812e-15
17 -1.331014e-15 -1.137197e-15
18 -5.462034e-16 -1.331014e-15
19 2.152565e-17 -5.462034e-16
20 2.852899e-16 2.152565e-17
21 -4.356364e-16 2.852899e-16
22 6.306484e-16 -4.356364e-16
23 -4.938202e-17 6.306484e-16
24 8.605047e-16 -4.938202e-17
25 4.594887e-16 8.605047e-16
26 1.851516e-17 4.594887e-16
27 -1.437837e-17 1.851516e-17
28 1.477914e-16 -1.437837e-17
29 -5.942330e-16 1.477914e-16
30 -1.792988e-16 -5.942330e-16
31 3.962319e-16 -1.792988e-16
32 6.745878e-18 3.962319e-16
33 -1.523311e-16 6.745878e-18
34 -5.801393e-16 -1.523311e-16
35 7.506709e-16 -5.801393e-16
36 -1.109600e-16 7.506709e-16
37 -7.646949e-16 -1.109600e-16
38 2.373979e-16 -7.646949e-16
39 2.182754e-16 2.373979e-16
40 1.635151e-16 2.182754e-16
41 1.382452e-16 1.635151e-16
42 -5.230896e-16 1.382452e-16
43 1.797159e-16 -5.230896e-16
44 1.703054e-17 1.797159e-16
45 2.532106e-16 1.703054e-17
46 -5.026176e-17 2.532106e-16
47 5.755063e-16 -5.026176e-17
48 -7.866193e-16 5.755063e-16
49 5.789621e-16 -7.866193e-16
50 3.756725e-16 5.789621e-16
51 -6.322290e-17 3.756725e-16
52 3.237594e-16 -6.322290e-17
53 9.902434e-17 3.237594e-16
54 -3.534623e-16 9.902434e-17
55 5.289917e-17 -3.534623e-16
56 -7.428424e-16 5.289917e-17
57 6.728018e-16 -7.428424e-16
58 8.989078e-17 6.728018e-16
59 -7.812632e-16 8.989078e-17
60 -3.667063e-16 -7.812632e-16
61 4.347559e-16 -3.667063e-16
62 8.263801e-17 4.347559e-16
63 -4.388862e-16 8.263801e-17
64 -2.290829e-16 -4.388862e-16
65 3.270467e-16 -2.290829e-16
66 3.067487e-17 3.270467e-16
67 -4.135793e-16 3.067487e-17
68 -1.296363e-15 -4.135793e-16
69 -5.445114e-16 -1.296363e-15
70 -3.970586e-16 -5.445114e-16
71 6.752141e-16 -3.970586e-16
72 2.430020e-16 6.752141e-16
73 9.245096e-16 2.430020e-16
74 -1.245218e-15 9.245096e-16
75 -4.286968e-16 -1.245218e-15
76 -2.781318e-16 -4.286968e-16
77 -5.256134e-17 -2.781318e-16
78 6.717915e-16 -5.256134e-17
79 3.537270e-16 6.717915e-16
80 4.984149e-16 3.537270e-16
81 6.570527e-16 4.984149e-16
82 NA 6.570527e-16
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.438742e-15 -9.512328e-16
[2,] 6.540448e-16 -1.438742e-15
[3,] -4.565240e-16 6.540448e-16
[4,] -1.868753e-15 -4.565240e-16
[5,] -3.668444e-16 -1.868753e-15
[6,] 1.153340e-15 -3.668444e-16
[7,] 6.224921e-16 1.153340e-15
[8,] 9.447456e-16 6.224921e-16
[9,] 1.093563e-15 9.447456e-16
[10,] -3.456344e-17 1.093563e-15
[11,] -1.895909e-15 -3.456344e-17
[12,] -4.217511e-16 -1.895909e-15
[13,] 6.820291e-15 -4.217511e-16
[14,] 1.165337e-16 6.820291e-15
[15,] -1.535812e-15 1.165337e-16
[16,] -1.137197e-15 -1.535812e-15
[17,] -1.331014e-15 -1.137197e-15
[18,] -5.462034e-16 -1.331014e-15
[19,] 2.152565e-17 -5.462034e-16
[20,] 2.852899e-16 2.152565e-17
[21,] -4.356364e-16 2.852899e-16
[22,] 6.306484e-16 -4.356364e-16
[23,] -4.938202e-17 6.306484e-16
[24,] 8.605047e-16 -4.938202e-17
[25,] 4.594887e-16 8.605047e-16
[26,] 1.851516e-17 4.594887e-16
[27,] -1.437837e-17 1.851516e-17
[28,] 1.477914e-16 -1.437837e-17
[29,] -5.942330e-16 1.477914e-16
[30,] -1.792988e-16 -5.942330e-16
[31,] 3.962319e-16 -1.792988e-16
[32,] 6.745878e-18 3.962319e-16
[33,] -1.523311e-16 6.745878e-18
[34,] -5.801393e-16 -1.523311e-16
[35,] 7.506709e-16 -5.801393e-16
[36,] -1.109600e-16 7.506709e-16
[37,] -7.646949e-16 -1.109600e-16
[38,] 2.373979e-16 -7.646949e-16
[39,] 2.182754e-16 2.373979e-16
[40,] 1.635151e-16 2.182754e-16
[41,] 1.382452e-16 1.635151e-16
[42,] -5.230896e-16 1.382452e-16
[43,] 1.797159e-16 -5.230896e-16
[44,] 1.703054e-17 1.797159e-16
[45,] 2.532106e-16 1.703054e-17
[46,] -5.026176e-17 2.532106e-16
[47,] 5.755063e-16 -5.026176e-17
[48,] -7.866193e-16 5.755063e-16
[49,] 5.789621e-16 -7.866193e-16
[50,] 3.756725e-16 5.789621e-16
[51,] -6.322290e-17 3.756725e-16
[52,] 3.237594e-16 -6.322290e-17
[53,] 9.902434e-17 3.237594e-16
[54,] -3.534623e-16 9.902434e-17
[55,] 5.289917e-17 -3.534623e-16
[56,] -7.428424e-16 5.289917e-17
[57,] 6.728018e-16 -7.428424e-16
[58,] 8.989078e-17 6.728018e-16
[59,] -7.812632e-16 8.989078e-17
[60,] -3.667063e-16 -7.812632e-16
[61,] 4.347559e-16 -3.667063e-16
[62,] 8.263801e-17 4.347559e-16
[63,] -4.388862e-16 8.263801e-17
[64,] -2.290829e-16 -4.388862e-16
[65,] 3.270467e-16 -2.290829e-16
[66,] 3.067487e-17 3.270467e-16
[67,] -4.135793e-16 3.067487e-17
[68,] -1.296363e-15 -4.135793e-16
[69,] -5.445114e-16 -1.296363e-15
[70,] -3.970586e-16 -5.445114e-16
[71,] 6.752141e-16 -3.970586e-16
[72,] 2.430020e-16 6.752141e-16
[73,] 9.245096e-16 2.430020e-16
[74,] -1.245218e-15 9.245096e-16
[75,] -4.286968e-16 -1.245218e-15
[76,] -2.781318e-16 -4.286968e-16
[77,] -5.256134e-17 -2.781318e-16
[78,] 6.717915e-16 -5.256134e-17
[79,] 3.537270e-16 6.717915e-16
[80,] 4.984149e-16 3.537270e-16
[81,] 6.570527e-16 4.984149e-16
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.438742e-15 -9.512328e-16
2 6.540448e-16 -1.438742e-15
3 -4.565240e-16 6.540448e-16
4 -1.868753e-15 -4.565240e-16
5 -3.668444e-16 -1.868753e-15
6 1.153340e-15 -3.668444e-16
7 6.224921e-16 1.153340e-15
8 9.447456e-16 6.224921e-16
9 1.093563e-15 9.447456e-16
10 -3.456344e-17 1.093563e-15
11 -1.895909e-15 -3.456344e-17
12 -4.217511e-16 -1.895909e-15
13 6.820291e-15 -4.217511e-16
14 1.165337e-16 6.820291e-15
15 -1.535812e-15 1.165337e-16
16 -1.137197e-15 -1.535812e-15
17 -1.331014e-15 -1.137197e-15
18 -5.462034e-16 -1.331014e-15
19 2.152565e-17 -5.462034e-16
20 2.852899e-16 2.152565e-17
21 -4.356364e-16 2.852899e-16
22 6.306484e-16 -4.356364e-16
23 -4.938202e-17 6.306484e-16
24 8.605047e-16 -4.938202e-17
25 4.594887e-16 8.605047e-16
26 1.851516e-17 4.594887e-16
27 -1.437837e-17 1.851516e-17
28 1.477914e-16 -1.437837e-17
29 -5.942330e-16 1.477914e-16
30 -1.792988e-16 -5.942330e-16
31 3.962319e-16 -1.792988e-16
32 6.745878e-18 3.962319e-16
33 -1.523311e-16 6.745878e-18
34 -5.801393e-16 -1.523311e-16
35 7.506709e-16 -5.801393e-16
36 -1.109600e-16 7.506709e-16
37 -7.646949e-16 -1.109600e-16
38 2.373979e-16 -7.646949e-16
39 2.182754e-16 2.373979e-16
40 1.635151e-16 2.182754e-16
41 1.382452e-16 1.635151e-16
42 -5.230896e-16 1.382452e-16
43 1.797159e-16 -5.230896e-16
44 1.703054e-17 1.797159e-16
45 2.532106e-16 1.703054e-17
46 -5.026176e-17 2.532106e-16
47 5.755063e-16 -5.026176e-17
48 -7.866193e-16 5.755063e-16
49 5.789621e-16 -7.866193e-16
50 3.756725e-16 5.789621e-16
51 -6.322290e-17 3.756725e-16
52 3.237594e-16 -6.322290e-17
53 9.902434e-17 3.237594e-16
54 -3.534623e-16 9.902434e-17
55 5.289917e-17 -3.534623e-16
56 -7.428424e-16 5.289917e-17
57 6.728018e-16 -7.428424e-16
58 8.989078e-17 6.728018e-16
59 -7.812632e-16 8.989078e-17
60 -3.667063e-16 -7.812632e-16
61 4.347559e-16 -3.667063e-16
62 8.263801e-17 4.347559e-16
63 -4.388862e-16 8.263801e-17
64 -2.290829e-16 -4.388862e-16
65 3.270467e-16 -2.290829e-16
66 3.067487e-17 3.270467e-16
67 -4.135793e-16 3.067487e-17
68 -1.296363e-15 -4.135793e-16
69 -5.445114e-16 -1.296363e-15
70 -3.970586e-16 -5.445114e-16
71 6.752141e-16 -3.970586e-16
72 2.430020e-16 6.752141e-16
73 9.245096e-16 2.430020e-16
74 -1.245218e-15 9.245096e-16
75 -4.286968e-16 -1.245218e-15
76 -2.781318e-16 -4.286968e-16
77 -5.256134e-17 -2.781318e-16
78 6.717915e-16 -5.256134e-17
79 3.537270e-16 6.717915e-16
80 4.984149e-16 3.537270e-16
81 6.570527e-16 4.984149e-16
> 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/fisher/rcomp/tmp/7hyx41352143171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8tulh1352143171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9gmgs1352143171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10f9801352143171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11t4g91352143172.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/fisher/rcomp/tmp/126b8q1352143172.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/fisher/rcomp/tmp/13o6ag1352143172.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/fisher/rcomp/tmp/14x16k1352143172.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/fisher/rcomp/tmp/15lk9j1352143172.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/fisher/rcomp/tmp/16rrs91352143172.tab")
+ }
>
> try(system("convert tmp/11n8a1352143171.ps tmp/11n8a1352143171.png",intern=TRUE))
character(0)
> try(system("convert tmp/2vqjp1352143171.ps tmp/2vqjp1352143171.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vjkx1352143171.ps tmp/3vjkx1352143171.png",intern=TRUE))
character(0)
> try(system("convert tmp/42izd1352143171.ps tmp/42izd1352143171.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zbc11352143171.ps tmp/5zbc11352143171.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ziqt1352143171.ps tmp/6ziqt1352143171.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hyx41352143171.ps tmp/7hyx41352143171.png",intern=TRUE))
character(0)
> try(system("convert tmp/8tulh1352143171.ps tmp/8tulh1352143171.png",intern=TRUE))
character(0)
> try(system("convert tmp/9gmgs1352143171.ps tmp/9gmgs1352143171.png",intern=TRUE))
character(0)
> try(system("convert tmp/10f9801352143171.ps tmp/10f9801352143171.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.580 1.109 7.690