R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(6.7,0,6.7,0,6.5,0,6.3,0,6.3,0,6.3,0,6.5,0,6.6,0,6.5,0,6.3,0,6.3,0,6.5,0,7,0,7.1,0,7.3,0,7.3,0,7.4,0,7.4,0,7.3,0,7.4,0,7.5,0,7.7,0,7.7,0,7.7,0,7.7,0,7.7,0,7.8,0,8,0,8.1,0,8.1,0,8.2,0,8.2,0,8.2,0,8.1,0,8.1,0,8.2,0,8.3,0,8.3,0,8.4,0,8.5,0,8.5,0,8.4,0,8,0,7.9,0,8.1,0,8.5,0,8.8,0,8.8,0,8.6,0,8.3,0,8.3,0,8.3,0,8.4,0,8.4,0,8.5,0,8.6,0,8.6,0,8.6,0,8.6,0,8.6,0,8.5,0,8.4,0,8.4,0,8.4,0,8.5,0,8.5,0,8.6,0,8.6,0,8.4,0,8.2,0,8,0,8,0,8,0,8,0,7.9,0,7.9,0,7.8,0,7.8,0,8,0,7.8,0,7.4,0,7.2,0,7,0,7,0,7.2,0,7.2,0,7.2,0,7,0,6.9,0,6.8,0,6.8,0,6.8,0,6.9,0,7.2,0,7.2,0,7.2,0,7.1,0,7.2,1,7.3,1,7.5,1,7.6,1,7.7,1,7.7,1,7.7,1,7.8,1,8,1,8.1,1,8.1,1,8,1,8.1,1,8.2,1,8.3,1,8.4,1,8.4,1,8.4,1,8.5,1,8.5,1,8.6,1,8.6,1,8.5,1,8.5,1),dim=c(2,121),dimnames=list(c('werkloosheid','X'),1:121))
> y <- array(NA,dim=c(2,121),dimnames=list(c('werkloosheid','X'),1:121))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No 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.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
werkloosheid X
1 6.7 0
2 6.7 0
3 6.5 0
4 6.3 0
5 6.3 0
6 6.3 0
7 6.5 0
8 6.6 0
9 6.5 0
10 6.3 0
11 6.3 0
12 6.5 0
13 7.0 0
14 7.1 0
15 7.3 0
16 7.3 0
17 7.4 0
18 7.4 0
19 7.3 0
20 7.4 0
21 7.5 0
22 7.7 0
23 7.7 0
24 7.7 0
25 7.7 0
26 7.7 0
27 7.8 0
28 8.0 0
29 8.1 0
30 8.1 0
31 8.2 0
32 8.2 0
33 8.2 0
34 8.1 0
35 8.1 0
36 8.2 0
37 8.3 0
38 8.3 0
39 8.4 0
40 8.5 0
41 8.5 0
42 8.4 0
43 8.0 0
44 7.9 0
45 8.1 0
46 8.5 0
47 8.8 0
48 8.8 0
49 8.6 0
50 8.3 0
51 8.3 0
52 8.3 0
53 8.4 0
54 8.4 0
55 8.5 0
56 8.6 0
57 8.6 0
58 8.6 0
59 8.6 0
60 8.6 0
61 8.5 0
62 8.4 0
63 8.4 0
64 8.4 0
65 8.5 0
66 8.5 0
67 8.6 0
68 8.6 0
69 8.4 0
70 8.2 0
71 8.0 0
72 8.0 0
73 8.0 0
74 8.0 0
75 7.9 0
76 7.9 0
77 7.8 0
78 7.8 0
79 8.0 0
80 7.8 0
81 7.4 0
82 7.2 0
83 7.0 0
84 7.0 0
85 7.2 0
86 7.2 0
87 7.2 0
88 7.0 0
89 6.9 0
90 6.8 0
91 6.8 0
92 6.8 0
93 6.9 0
94 7.2 0
95 7.2 0
96 7.2 0
97 7.1 0
98 7.2 1
99 7.3 1
100 7.5 1
101 7.6 1
102 7.7 1
103 7.7 1
104 7.7 1
105 7.8 1
106 8.0 1
107 8.1 1
108 8.1 1
109 8.0 1
110 8.1 1
111 8.2 1
112 8.3 1
113 8.4 1
114 8.4 1
115 8.4 1
116 8.5 1
117 8.5 1
118 8.6 1
119 8.6 1
120 8.5 1
121 8.5 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
7.7196 0.3512
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.4196 -0.5196 0.1292 0.5292 1.0804
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.71959 0.06826 113.092 <2e-16 ***
X 0.35125 0.15327 2.292 0.0237 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6723 on 119 degrees of freedom
Multiple R-squared: 0.04227, Adjusted R-squared: 0.03422
F-statistic: 5.252 on 1 and 119 DF, p-value: 0.02368
> 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,] 0.0596459032 1.192918e-01 9.403541e-01
[2,] 0.0269830598 5.396612e-02 9.730169e-01
[3,] 0.0088538913 1.770778e-02 9.911461e-01
[4,] 0.0033262358 6.652472e-03 9.966738e-01
[5,] 0.0010522396 2.104479e-03 9.989478e-01
[6,] 0.0005948409 1.189682e-03 9.994052e-01
[7,] 0.0003346703 6.693406e-04 9.996653e-01
[8,] 0.0001277954 2.555908e-04 9.998722e-01
[9,] 0.0012199214 2.439843e-03 9.987801e-01
[10,] 0.0054621767 1.092435e-02 9.945378e-01
[11,] 0.0258130772 5.162615e-02 9.741869e-01
[12,] 0.0552300173 1.104600e-01 9.447700e-01
[13,] 0.1049072165 2.098144e-01 8.950928e-01
[14,] 0.1525654941 3.051310e-01 8.474345e-01
[15,] 0.1721294495 3.442589e-01 8.278706e-01
[16,] 0.2047449979 4.094900e-01 7.952550e-01
[17,] 0.2519832773 5.039666e-01 7.480167e-01
[18,] 0.3455533726 6.911067e-01 6.544466e-01
[19,] 0.4206407061 8.412814e-01 5.793593e-01
[20,] 0.4776625239 9.553250e-01 5.223375e-01
[21,] 0.5194003801 9.611992e-01 4.805996e-01
[22,] 0.5488487267 9.023025e-01 4.511513e-01
[23,] 0.5894944571 8.210111e-01 4.105055e-01
[24,] 0.6637599521 6.724801e-01 3.362400e-01
[25,] 0.7385091447 5.229817e-01 2.614909e-01
[26,] 0.7897060307 4.205879e-01 2.102940e-01
[27,] 0.8401445705 3.197109e-01 1.598554e-01
[28,] 0.8736363093 2.527274e-01 1.263637e-01
[29,] 0.8961296060 2.077408e-01 1.038704e-01
[30,] 0.9034493620 1.931013e-01 9.655064e-02
[31,] 0.9077446448 1.845107e-01 9.225536e-02
[32,] 0.9164579195 1.670842e-01 8.354208e-02
[33,] 0.9288042799 1.423914e-01 7.119572e-02
[34,] 0.9374365148 1.251270e-01 6.256349e-02
[35,] 0.9487671196 1.024658e-01 5.123288e-02
[36,] 0.9613984258 7.720315e-02 3.860157e-02
[37,] 0.9700939455 5.981211e-02 2.990605e-02
[38,] 0.9733920937 5.321581e-02 2.660791e-02
[39,] 0.9675840300 6.483194e-02 3.241597e-02
[40,] 0.9591808664 8.163827e-02 4.081913e-02
[41,] 0.9528102876 9.437942e-02 4.718971e-02
[42,] 0.9596250320 8.074994e-02 4.037497e-02
[43,] 0.9760655620 4.786888e-02 2.393444e-02
[44,] 0.9860011876 2.799762e-02 1.399881e-02
[45,] 0.9890795418 2.184092e-02 1.092046e-02
[46,] 0.9880566935 2.388661e-02 1.194331e-02
[47,] 0.9868954789 2.620904e-02 1.310452e-02
[48,] 0.9855909935 2.881801e-02 1.440901e-02
[49,] 0.9855040632 2.899187e-02 1.449594e-02
[50,] 0.9853993355 2.920133e-02 1.460066e-02
[51,] 0.9867867468 2.642651e-02 1.321325e-02
[52,] 0.9895194562 2.096109e-02 1.048054e-02
[53,] 0.9918326943 1.633461e-02 8.167306e-03
[54,] 0.9937844191 1.243116e-02 6.215581e-03
[55,] 0.9954142034 9.171593e-03 4.585797e-03
[56,] 0.9967496235 6.500753e-03 3.250377e-03
[57,] 0.9974024928 5.195014e-03 2.597507e-03
[58,] 0.9976642052 4.671590e-03 2.335795e-03
[59,] 0.9979634553 4.073089e-03 2.036545e-03
[60,] 0.9982903812 3.419238e-03 1.709619e-03
[61,] 0.9988650704 2.269859e-03 1.134930e-03
[62,] 0.9993140411 1.371918e-03 6.859589e-04
[63,] 0.9997201322 5.597356e-04 2.798678e-04
[64,] 0.9999116455 1.767089e-04 8.835445e-05
[65,] 0.9999600790 7.984208e-05 3.992104e-05
[66,] 0.9999731528 5.369437e-05 2.684719e-05
[67,] 0.9999736653 5.266942e-05 2.633471e-05
[68,] 0.9999758995 4.820107e-05 2.410054e-05
[69,] 0.9999797435 4.051298e-05 2.025649e-05
[70,] 0.9999847038 3.059246e-05 1.529623e-05
[71,] 0.9999866331 2.673389e-05 1.336695e-05
[72,] 0.9999895431 2.091373e-05 1.045687e-05
[73,] 0.9999905111 1.897788e-05 9.488939e-06
[74,] 0.9999923189 1.536224e-05 7.681121e-06
[75,] 0.9999976479 4.704185e-06 2.352092e-06
[76,] 0.9999988375 2.325017e-06 1.162508e-06
[77,] 0.9999983745 3.250943e-06 1.625471e-06
[78,] 0.9999972025 5.595057e-06 2.797529e-06
[79,] 0.9999952782 9.443551e-06 4.721775e-06
[80,] 0.9999919964 1.600728e-05 8.003641e-06
[81,] 0.9999862871 2.742573e-05 1.371286e-05
[82,] 0.9999768847 4.623058e-05 2.311529e-05
[83,] 0.9999617996 7.640084e-05 3.820042e-05
[84,] 0.9999350651 1.298698e-04 6.493491e-05
[85,] 0.9998961246 2.077509e-04 1.038754e-04
[86,] 0.9998527145 2.945710e-04 1.472855e-04
[87,] 0.9997956610 4.086780e-04 2.043390e-04
[88,] 0.9997280219 5.439563e-04 2.719781e-04
[89,] 0.9996000648 7.998704e-04 3.999352e-04
[90,] 0.9992817865 1.436427e-03 7.182135e-04
[91,] 0.9987330124 2.533975e-03 1.266988e-03
[92,] 0.9978156647 4.368671e-03 2.184335e-03
[93,] 0.9963254242 7.349152e-03 3.674576e-03
[94,] 0.9985590301 2.881940e-03 1.440970e-03
[95,] 0.9994808420 1.038316e-03 5.191580e-04
[96,] 0.9997132690 5.734620e-04 2.867310e-04
[97,] 0.9998182575 3.634850e-04 1.817425e-04
[98,] 0.9998601658 2.796685e-04 1.398342e-04
[99,] 0.9999177166 1.645668e-04 8.228340e-05
[100,] 0.9999704769 5.904620e-05 2.952310e-05
[101,] 0.9999889103 2.217932e-05 1.108966e-05
[102,] 0.9999879813 2.403742e-05 1.201871e-05
[103,] 0.9999795326 4.093484e-05 2.046742e-05
[104,] 0.9999694007 6.119853e-05 3.059926e-05
[105,] 0.9999885544 2.289127e-05 1.144563e-05
[106,] 0.9999954353 9.129463e-06 4.564732e-06
[107,] 0.9999975753 4.849313e-06 2.424657e-06
[108,] 0.9999970487 5.902667e-06 2.951334e-06
[109,] 0.9999864158 2.716839e-05 1.358419e-05
[110,] 0.9999490603 1.018794e-04 5.093971e-05
[111,] 0.9998944988 2.110024e-04 1.055012e-04
[112,] 0.9988791458 2.241708e-03 1.120854e-03
> postscript(file="/var/www/html/rcomp/tmp/16vvf1292936137.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26vvf1292936137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/36vvf1292936137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hmu01292936137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5hmu01292936137.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 = 121
Frequency = 1
1 2 3 4 5 6
-1.01958763 -1.01958763 -1.21958763 -1.41958763 -1.41958763 -1.41958763
7 8 9 10 11 12
-1.21958763 -1.11958763 -1.21958763 -1.41958763 -1.41958763 -1.21958763
13 14 15 16 17 18
-0.71958763 -0.61958763 -0.41958763 -0.41958763 -0.31958763 -0.31958763
19 20 21 22 23 24
-0.41958763 -0.31958763 -0.21958763 -0.01958763 -0.01958763 -0.01958763
25 26 27 28 29 30
-0.01958763 -0.01958763 0.08041237 0.28041237 0.38041237 0.38041237
31 32 33 34 35 36
0.48041237 0.48041237 0.48041237 0.38041237 0.38041237 0.48041237
37 38 39 40 41 42
0.58041237 0.58041237 0.68041237 0.78041237 0.78041237 0.68041237
43 44 45 46 47 48
0.28041237 0.18041237 0.38041237 0.78041237 1.08041237 1.08041237
49 50 51 52 53 54
0.88041237 0.58041237 0.58041237 0.58041237 0.68041237 0.68041237
55 56 57 58 59 60
0.78041237 0.88041237 0.88041237 0.88041237 0.88041237 0.88041237
61 62 63 64 65 66
0.78041237 0.68041237 0.68041237 0.68041237 0.78041237 0.78041237
67 68 69 70 71 72
0.88041237 0.88041237 0.68041237 0.48041237 0.28041237 0.28041237
73 74 75 76 77 78
0.28041237 0.28041237 0.18041237 0.18041237 0.08041237 0.08041237
79 80 81 82 83 84
0.28041237 0.08041237 -0.31958763 -0.51958763 -0.71958763 -0.71958763
85 86 87 88 89 90
-0.51958763 -0.51958763 -0.51958763 -0.71958763 -0.81958763 -0.91958763
91 92 93 94 95 96
-0.91958763 -0.91958763 -0.81958763 -0.51958763 -0.51958763 -0.51958763
97 98 99 100 101 102
-0.61958763 -0.87083333 -0.77083333 -0.57083333 -0.47083333 -0.37083333
103 104 105 106 107 108
-0.37083333 -0.37083333 -0.27083333 -0.07083333 0.02916667 0.02916667
109 110 111 112 113 114
-0.07083333 0.02916667 0.12916667 0.22916667 0.32916667 0.32916667
115 116 117 118 119 120
0.32916667 0.42916667 0.42916667 0.52916667 0.52916667 0.42916667
121
0.42916667
> postscript(file="/var/www/html/rcomp/tmp/6hmu01292936137.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 = 121
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.01958763 NA
1 -1.01958763 -1.01958763
2 -1.21958763 -1.01958763
3 -1.41958763 -1.21958763
4 -1.41958763 -1.41958763
5 -1.41958763 -1.41958763
6 -1.21958763 -1.41958763
7 -1.11958763 -1.21958763
8 -1.21958763 -1.11958763
9 -1.41958763 -1.21958763
10 -1.41958763 -1.41958763
11 -1.21958763 -1.41958763
12 -0.71958763 -1.21958763
13 -0.61958763 -0.71958763
14 -0.41958763 -0.61958763
15 -0.41958763 -0.41958763
16 -0.31958763 -0.41958763
17 -0.31958763 -0.31958763
18 -0.41958763 -0.31958763
19 -0.31958763 -0.41958763
20 -0.21958763 -0.31958763
21 -0.01958763 -0.21958763
22 -0.01958763 -0.01958763
23 -0.01958763 -0.01958763
24 -0.01958763 -0.01958763
25 -0.01958763 -0.01958763
26 0.08041237 -0.01958763
27 0.28041237 0.08041237
28 0.38041237 0.28041237
29 0.38041237 0.38041237
30 0.48041237 0.38041237
31 0.48041237 0.48041237
32 0.48041237 0.48041237
33 0.38041237 0.48041237
34 0.38041237 0.38041237
35 0.48041237 0.38041237
36 0.58041237 0.48041237
37 0.58041237 0.58041237
38 0.68041237 0.58041237
39 0.78041237 0.68041237
40 0.78041237 0.78041237
41 0.68041237 0.78041237
42 0.28041237 0.68041237
43 0.18041237 0.28041237
44 0.38041237 0.18041237
45 0.78041237 0.38041237
46 1.08041237 0.78041237
47 1.08041237 1.08041237
48 0.88041237 1.08041237
49 0.58041237 0.88041237
50 0.58041237 0.58041237
51 0.58041237 0.58041237
52 0.68041237 0.58041237
53 0.68041237 0.68041237
54 0.78041237 0.68041237
55 0.88041237 0.78041237
56 0.88041237 0.88041237
57 0.88041237 0.88041237
58 0.88041237 0.88041237
59 0.88041237 0.88041237
60 0.78041237 0.88041237
61 0.68041237 0.78041237
62 0.68041237 0.68041237
63 0.68041237 0.68041237
64 0.78041237 0.68041237
65 0.78041237 0.78041237
66 0.88041237 0.78041237
67 0.88041237 0.88041237
68 0.68041237 0.88041237
69 0.48041237 0.68041237
70 0.28041237 0.48041237
71 0.28041237 0.28041237
72 0.28041237 0.28041237
73 0.28041237 0.28041237
74 0.18041237 0.28041237
75 0.18041237 0.18041237
76 0.08041237 0.18041237
77 0.08041237 0.08041237
78 0.28041237 0.08041237
79 0.08041237 0.28041237
80 -0.31958763 0.08041237
81 -0.51958763 -0.31958763
82 -0.71958763 -0.51958763
83 -0.71958763 -0.71958763
84 -0.51958763 -0.71958763
85 -0.51958763 -0.51958763
86 -0.51958763 -0.51958763
87 -0.71958763 -0.51958763
88 -0.81958763 -0.71958763
89 -0.91958763 -0.81958763
90 -0.91958763 -0.91958763
91 -0.91958763 -0.91958763
92 -0.81958763 -0.91958763
93 -0.51958763 -0.81958763
94 -0.51958763 -0.51958763
95 -0.51958763 -0.51958763
96 -0.61958763 -0.51958763
97 -0.87083333 -0.61958763
98 -0.77083333 -0.87083333
99 -0.57083333 -0.77083333
100 -0.47083333 -0.57083333
101 -0.37083333 -0.47083333
102 -0.37083333 -0.37083333
103 -0.37083333 -0.37083333
104 -0.27083333 -0.37083333
105 -0.07083333 -0.27083333
106 0.02916667 -0.07083333
107 0.02916667 0.02916667
108 -0.07083333 0.02916667
109 0.02916667 -0.07083333
110 0.12916667 0.02916667
111 0.22916667 0.12916667
112 0.32916667 0.22916667
113 0.32916667 0.32916667
114 0.32916667 0.32916667
115 0.42916667 0.32916667
116 0.42916667 0.42916667
117 0.52916667 0.42916667
118 0.52916667 0.52916667
119 0.42916667 0.52916667
120 0.42916667 0.42916667
121 NA 0.42916667
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.01958763 -1.01958763
[2,] -1.21958763 -1.01958763
[3,] -1.41958763 -1.21958763
[4,] -1.41958763 -1.41958763
[5,] -1.41958763 -1.41958763
[6,] -1.21958763 -1.41958763
[7,] -1.11958763 -1.21958763
[8,] -1.21958763 -1.11958763
[9,] -1.41958763 -1.21958763
[10,] -1.41958763 -1.41958763
[11,] -1.21958763 -1.41958763
[12,] -0.71958763 -1.21958763
[13,] -0.61958763 -0.71958763
[14,] -0.41958763 -0.61958763
[15,] -0.41958763 -0.41958763
[16,] -0.31958763 -0.41958763
[17,] -0.31958763 -0.31958763
[18,] -0.41958763 -0.31958763
[19,] -0.31958763 -0.41958763
[20,] -0.21958763 -0.31958763
[21,] -0.01958763 -0.21958763
[22,] -0.01958763 -0.01958763
[23,] -0.01958763 -0.01958763
[24,] -0.01958763 -0.01958763
[25,] -0.01958763 -0.01958763
[26,] 0.08041237 -0.01958763
[27,] 0.28041237 0.08041237
[28,] 0.38041237 0.28041237
[29,] 0.38041237 0.38041237
[30,] 0.48041237 0.38041237
[31,] 0.48041237 0.48041237
[32,] 0.48041237 0.48041237
[33,] 0.38041237 0.48041237
[34,] 0.38041237 0.38041237
[35,] 0.48041237 0.38041237
[36,] 0.58041237 0.48041237
[37,] 0.58041237 0.58041237
[38,] 0.68041237 0.58041237
[39,] 0.78041237 0.68041237
[40,] 0.78041237 0.78041237
[41,] 0.68041237 0.78041237
[42,] 0.28041237 0.68041237
[43,] 0.18041237 0.28041237
[44,] 0.38041237 0.18041237
[45,] 0.78041237 0.38041237
[46,] 1.08041237 0.78041237
[47,] 1.08041237 1.08041237
[48,] 0.88041237 1.08041237
[49,] 0.58041237 0.88041237
[50,] 0.58041237 0.58041237
[51,] 0.58041237 0.58041237
[52,] 0.68041237 0.58041237
[53,] 0.68041237 0.68041237
[54,] 0.78041237 0.68041237
[55,] 0.88041237 0.78041237
[56,] 0.88041237 0.88041237
[57,] 0.88041237 0.88041237
[58,] 0.88041237 0.88041237
[59,] 0.88041237 0.88041237
[60,] 0.78041237 0.88041237
[61,] 0.68041237 0.78041237
[62,] 0.68041237 0.68041237
[63,] 0.68041237 0.68041237
[64,] 0.78041237 0.68041237
[65,] 0.78041237 0.78041237
[66,] 0.88041237 0.78041237
[67,] 0.88041237 0.88041237
[68,] 0.68041237 0.88041237
[69,] 0.48041237 0.68041237
[70,] 0.28041237 0.48041237
[71,] 0.28041237 0.28041237
[72,] 0.28041237 0.28041237
[73,] 0.28041237 0.28041237
[74,] 0.18041237 0.28041237
[75,] 0.18041237 0.18041237
[76,] 0.08041237 0.18041237
[77,] 0.08041237 0.08041237
[78,] 0.28041237 0.08041237
[79,] 0.08041237 0.28041237
[80,] -0.31958763 0.08041237
[81,] -0.51958763 -0.31958763
[82,] -0.71958763 -0.51958763
[83,] -0.71958763 -0.71958763
[84,] -0.51958763 -0.71958763
[85,] -0.51958763 -0.51958763
[86,] -0.51958763 -0.51958763
[87,] -0.71958763 -0.51958763
[88,] -0.81958763 -0.71958763
[89,] -0.91958763 -0.81958763
[90,] -0.91958763 -0.91958763
[91,] -0.91958763 -0.91958763
[92,] -0.81958763 -0.91958763
[93,] -0.51958763 -0.81958763
[94,] -0.51958763 -0.51958763
[95,] -0.51958763 -0.51958763
[96,] -0.61958763 -0.51958763
[97,] -0.87083333 -0.61958763
[98,] -0.77083333 -0.87083333
[99,] -0.57083333 -0.77083333
[100,] -0.47083333 -0.57083333
[101,] -0.37083333 -0.47083333
[102,] -0.37083333 -0.37083333
[103,] -0.37083333 -0.37083333
[104,] -0.27083333 -0.37083333
[105,] -0.07083333 -0.27083333
[106,] 0.02916667 -0.07083333
[107,] 0.02916667 0.02916667
[108,] -0.07083333 0.02916667
[109,] 0.02916667 -0.07083333
[110,] 0.12916667 0.02916667
[111,] 0.22916667 0.12916667
[112,] 0.32916667 0.22916667
[113,] 0.32916667 0.32916667
[114,] 0.32916667 0.32916667
[115,] 0.42916667 0.32916667
[116,] 0.42916667 0.42916667
[117,] 0.52916667 0.42916667
[118,] 0.52916667 0.52916667
[119,] 0.42916667 0.52916667
[120,] 0.42916667 0.42916667
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.01958763 -1.01958763
2 -1.21958763 -1.01958763
3 -1.41958763 -1.21958763
4 -1.41958763 -1.41958763
5 -1.41958763 -1.41958763
6 -1.21958763 -1.41958763
7 -1.11958763 -1.21958763
8 -1.21958763 -1.11958763
9 -1.41958763 -1.21958763
10 -1.41958763 -1.41958763
11 -1.21958763 -1.41958763
12 -0.71958763 -1.21958763
13 -0.61958763 -0.71958763
14 -0.41958763 -0.61958763
15 -0.41958763 -0.41958763
16 -0.31958763 -0.41958763
17 -0.31958763 -0.31958763
18 -0.41958763 -0.31958763
19 -0.31958763 -0.41958763
20 -0.21958763 -0.31958763
21 -0.01958763 -0.21958763
22 -0.01958763 -0.01958763
23 -0.01958763 -0.01958763
24 -0.01958763 -0.01958763
25 -0.01958763 -0.01958763
26 0.08041237 -0.01958763
27 0.28041237 0.08041237
28 0.38041237 0.28041237
29 0.38041237 0.38041237
30 0.48041237 0.38041237
31 0.48041237 0.48041237
32 0.48041237 0.48041237
33 0.38041237 0.48041237
34 0.38041237 0.38041237
35 0.48041237 0.38041237
36 0.58041237 0.48041237
37 0.58041237 0.58041237
38 0.68041237 0.58041237
39 0.78041237 0.68041237
40 0.78041237 0.78041237
41 0.68041237 0.78041237
42 0.28041237 0.68041237
43 0.18041237 0.28041237
44 0.38041237 0.18041237
45 0.78041237 0.38041237
46 1.08041237 0.78041237
47 1.08041237 1.08041237
48 0.88041237 1.08041237
49 0.58041237 0.88041237
50 0.58041237 0.58041237
51 0.58041237 0.58041237
52 0.68041237 0.58041237
53 0.68041237 0.68041237
54 0.78041237 0.68041237
55 0.88041237 0.78041237
56 0.88041237 0.88041237
57 0.88041237 0.88041237
58 0.88041237 0.88041237
59 0.88041237 0.88041237
60 0.78041237 0.88041237
61 0.68041237 0.78041237
62 0.68041237 0.68041237
63 0.68041237 0.68041237
64 0.78041237 0.68041237
65 0.78041237 0.78041237
66 0.88041237 0.78041237
67 0.88041237 0.88041237
68 0.68041237 0.88041237
69 0.48041237 0.68041237
70 0.28041237 0.48041237
71 0.28041237 0.28041237
72 0.28041237 0.28041237
73 0.28041237 0.28041237
74 0.18041237 0.28041237
75 0.18041237 0.18041237
76 0.08041237 0.18041237
77 0.08041237 0.08041237
78 0.28041237 0.08041237
79 0.08041237 0.28041237
80 -0.31958763 0.08041237
81 -0.51958763 -0.31958763
82 -0.71958763 -0.51958763
83 -0.71958763 -0.71958763
84 -0.51958763 -0.71958763
85 -0.51958763 -0.51958763
86 -0.51958763 -0.51958763
87 -0.71958763 -0.51958763
88 -0.81958763 -0.71958763
89 -0.91958763 -0.81958763
90 -0.91958763 -0.91958763
91 -0.91958763 -0.91958763
92 -0.81958763 -0.91958763
93 -0.51958763 -0.81958763
94 -0.51958763 -0.51958763
95 -0.51958763 -0.51958763
96 -0.61958763 -0.51958763
97 -0.87083333 -0.61958763
98 -0.77083333 -0.87083333
99 -0.57083333 -0.77083333
100 -0.47083333 -0.57083333
101 -0.37083333 -0.47083333
102 -0.37083333 -0.37083333
103 -0.37083333 -0.37083333
104 -0.27083333 -0.37083333
105 -0.07083333 -0.27083333
106 0.02916667 -0.07083333
107 0.02916667 0.02916667
108 -0.07083333 0.02916667
109 0.02916667 -0.07083333
110 0.12916667 0.02916667
111 0.22916667 0.12916667
112 0.32916667 0.22916667
113 0.32916667 0.32916667
114 0.32916667 0.32916667
115 0.42916667 0.32916667
116 0.42916667 0.42916667
117 0.52916667 0.42916667
118 0.52916667 0.52916667
119 0.42916667 0.52916667
120 0.42916667 0.42916667
> 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/7aet31292936137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8l5bo1292936137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9l5bo1292936137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10vwsr1292936137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zx9f1292936137.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/129oqz1292936137.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/13g75b1292936137.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/1428lh1292936137.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/15nq251292936137.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/168r0t1292936137.tab")
+ }
>
> try(system("convert tmp/16vvf1292936137.ps tmp/16vvf1292936137.png",intern=TRUE))
character(0)
> try(system("convert tmp/26vvf1292936137.ps tmp/26vvf1292936137.png",intern=TRUE))
character(0)
> try(system("convert tmp/36vvf1292936137.ps tmp/36vvf1292936137.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hmu01292936137.ps tmp/4hmu01292936137.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hmu01292936137.ps tmp/5hmu01292936137.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hmu01292936137.ps tmp/6hmu01292936137.png",intern=TRUE))
character(0)
> try(system("convert tmp/7aet31292936137.ps tmp/7aet31292936137.png",intern=TRUE))
character(0)
> try(system("convert tmp/8l5bo1292936137.ps tmp/8l5bo1292936137.png",intern=TRUE))
character(0)
> try(system("convert tmp/9l5bo1292936137.ps tmp/9l5bo1292936137.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vwsr1292936137.ps tmp/10vwsr1292936137.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.202 1.756 7.754