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(9.103,0,9.155,0,9.308,0,9.394,0,9.948,0,10.177,0,10.002,0,9.728,0,10.002,0,10.063,0,10.018,0,9.96,0,10.236,0,10.893,0,10.756,0,10.94,0,10.997,0,10.827,0,10.166,0,10.186,0,10.457,0,10.368,0,10.244,0,10.511,0,10.812,0,10.738,0,10.171,0,9.721,0,9.897,0,9.828,0,9.924,0,10.371,0,10.846,0,10.413,0,10.709,0,10.662,0,10.57,0,10.297,0,10.635,0,10.872,0,10.296,0,10.383,0,10.431,0,10.574,0,10.653,0,10.805,0,10.872,0,10.625,0,10.407,0,10.463,0,10.556,0,10.646,0,10.702,0,11.353,0,11.346,1,11.451,1,11.964,1,12.574,1,13.031,1,13.812,1,14.544,1,14.931,1,14.886,1,16.005,1,17.064,1,15.168,1,16.05,1,15.839,1,15.137,1,14.954,1,15.648,1,15.305,1,15.579,1,16.348,1,15.928,1,16.171,1,15.937,1,15.713,1,15.594,1,15.683,1,16.438,1,17.032,1,17.696,1,17.745,1,19.394,1,20.148,1,20.108,1,18.584,1,18.441,1,18.391,1,19.178,1,18.079,1,18.483,1,19.644,1),dim=c(2,94),dimnames=list(c('goudprijs','dummy'),1:94))
> y <- array(NA,dim=c(2,94),dimnames=list(c('goudprijs','dummy'),1:94))
> 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 = '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
goudprijs dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 9.103 0 1 0 0 0 0 0 0 0 0 0 0
2 9.155 0 0 1 0 0 0 0 0 0 0 0 0
3 9.308 0 0 0 1 0 0 0 0 0 0 0 0
4 9.394 0 0 0 0 1 0 0 0 0 0 0 0
5 9.948 0 0 0 0 0 1 0 0 0 0 0 0
6 10.177 0 0 0 0 0 0 1 0 0 0 0 0
7 10.002 0 0 0 0 0 0 0 1 0 0 0 0
8 9.728 0 0 0 0 0 0 0 0 1 0 0 0
9 10.002 0 0 0 0 0 0 0 0 0 1 0 0
10 10.063 0 0 0 0 0 0 0 0 0 0 1 0
11 10.018 0 0 0 0 0 0 0 0 0 0 0 1
12 9.960 0 0 0 0 0 0 0 0 0 0 0 0
13 10.236 0 1 0 0 0 0 0 0 0 0 0 0
14 10.893 0 0 1 0 0 0 0 0 0 0 0 0
15 10.756 0 0 0 1 0 0 0 0 0 0 0 0
16 10.940 0 0 0 0 1 0 0 0 0 0 0 0
17 10.997 0 0 0 0 0 1 0 0 0 0 0 0
18 10.827 0 0 0 0 0 0 1 0 0 0 0 0
19 10.166 0 0 0 0 0 0 0 1 0 0 0 0
20 10.186 0 0 0 0 0 0 0 0 1 0 0 0
21 10.457 0 0 0 0 0 0 0 0 0 1 0 0
22 10.368 0 0 0 0 0 0 0 0 0 0 1 0
23 10.244 0 0 0 0 0 0 0 0 0 0 0 1
24 10.511 0 0 0 0 0 0 0 0 0 0 0 0
25 10.812 0 1 0 0 0 0 0 0 0 0 0 0
26 10.738 0 0 1 0 0 0 0 0 0 0 0 0
27 10.171 0 0 0 1 0 0 0 0 0 0 0 0
28 9.721 0 0 0 0 1 0 0 0 0 0 0 0
29 9.897 0 0 0 0 0 1 0 0 0 0 0 0
30 9.828 0 0 0 0 0 0 1 0 0 0 0 0
31 9.924 0 0 0 0 0 0 0 1 0 0 0 0
32 10.371 0 0 0 0 0 0 0 0 1 0 0 0
33 10.846 0 0 0 0 0 0 0 0 0 1 0 0
34 10.413 0 0 0 0 0 0 0 0 0 0 1 0
35 10.709 0 0 0 0 0 0 0 0 0 0 0 1
36 10.662 0 0 0 0 0 0 0 0 0 0 0 0
37 10.570 0 1 0 0 0 0 0 0 0 0 0 0
38 10.297 0 0 1 0 0 0 0 0 0 0 0 0
39 10.635 0 0 0 1 0 0 0 0 0 0 0 0
40 10.872 0 0 0 0 1 0 0 0 0 0 0 0
41 10.296 0 0 0 0 0 1 0 0 0 0 0 0
42 10.383 0 0 0 0 0 0 1 0 0 0 0 0
43 10.431 0 0 0 0 0 0 0 1 0 0 0 0
44 10.574 0 0 0 0 0 0 0 0 1 0 0 0
45 10.653 0 0 0 0 0 0 0 0 0 1 0 0
46 10.805 0 0 0 0 0 0 0 0 0 0 1 0
47 10.872 0 0 0 0 0 0 0 0 0 0 0 1
48 10.625 0 0 0 0 0 0 0 0 0 0 0 0
49 10.407 0 1 0 0 0 0 0 0 0 0 0 0
50 10.463 0 0 1 0 0 0 0 0 0 0 0 0
51 10.556 0 0 0 1 0 0 0 0 0 0 0 0
52 10.646 0 0 0 0 1 0 0 0 0 0 0 0
53 10.702 0 0 0 0 0 1 0 0 0 0 0 0
54 11.353 0 0 0 0 0 0 1 0 0 0 0 0
55 11.346 1 0 0 0 0 0 0 1 0 0 0 0
56 11.451 1 0 0 0 0 0 0 0 1 0 0 0
57 11.964 1 0 0 0 0 0 0 0 0 1 0 0
58 12.574 1 0 0 0 0 0 0 0 0 0 1 0
59 13.031 1 0 0 0 0 0 0 0 0 0 0 1
60 13.812 1 0 0 0 0 0 0 0 0 0 0 0
61 14.544 1 1 0 0 0 0 0 0 0 0 0 0
62 14.931 1 0 1 0 0 0 0 0 0 0 0 0
63 14.886 1 0 0 1 0 0 0 0 0 0 0 0
64 16.005 1 0 0 0 1 0 0 0 0 0 0 0
65 17.064 1 0 0 0 0 1 0 0 0 0 0 0
66 15.168 1 0 0 0 0 0 1 0 0 0 0 0
67 16.050 1 0 0 0 0 0 0 1 0 0 0 0
68 15.839 1 0 0 0 0 0 0 0 1 0 0 0
69 15.137 1 0 0 0 0 0 0 0 0 1 0 0
70 14.954 1 0 0 0 0 0 0 0 0 0 1 0
71 15.648 1 0 0 0 0 0 0 0 0 0 0 1
72 15.305 1 0 0 0 0 0 0 0 0 0 0 0
73 15.579 1 1 0 0 0 0 0 0 0 0 0 0
74 16.348 1 0 1 0 0 0 0 0 0 0 0 0
75 15.928 1 0 0 1 0 0 0 0 0 0 0 0
76 16.171 1 0 0 0 1 0 0 0 0 0 0 0
77 15.937 1 0 0 0 0 1 0 0 0 0 0 0
78 15.713 1 0 0 0 0 0 1 0 0 0 0 0
79 15.594 1 0 0 0 0 0 0 1 0 0 0 0
80 15.683 1 0 0 0 0 0 0 0 1 0 0 0
81 16.438 1 0 0 0 0 0 0 0 0 1 0 0
82 17.032 1 0 0 0 0 0 0 0 0 0 1 0
83 17.696 1 0 0 0 0 0 0 0 0 0 0 1
84 17.745 1 0 0 0 0 0 0 0 0 0 0 0
85 19.394 1 1 0 0 0 0 0 0 0 0 0 0
86 20.148 1 0 1 0 0 0 0 0 0 0 0 0
87 20.108 1 0 0 1 0 0 0 0 0 0 0 0
88 18.584 1 0 0 0 1 0 0 0 0 0 0 0
89 18.441 1 0 0 0 0 1 0 0 0 0 0 0
90 18.391 1 0 0 0 0 0 1 0 0 0 0 0
91 19.178 1 0 0 0 0 0 0 1 0 0 0 0
92 18.079 1 0 0 0 0 0 0 0 1 0 0 0
93 18.483 1 0 0 0 0 0 0 0 0 1 0 0
94 19.644 1 0 0 0 0 0 0 0 0 0 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dummy M1 M2 M3 M4
10.14690 5.86390 0.23476 0.52576 0.44764 0.44576
M5 M6 M7 M8 M9 M10
0.56439 0.38414 -0.24248 -0.33998 -0.08135 0.15277
M11
-0.05743
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.4223 -0.6113 0.0329 0.5012 3.6496
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.14690 0.61247 16.567 <2e-16 ***
dummy 5.86390 0.33102 17.715 <2e-16 ***
M1 0.23476 0.81605 0.288 0.774
M2 0.52576 0.81605 0.644 0.521
M3 0.44764 0.81605 0.549 0.585
M4 0.44576 0.81605 0.546 0.586
M5 0.56439 0.81605 0.692 0.491
M6 0.38414 0.81605 0.471 0.639
M7 -0.24248 0.81620 -0.297 0.767
M8 -0.33998 0.81620 -0.417 0.678
M9 -0.08135 0.81620 -0.100 0.921
M10 0.15277 0.81620 0.187 0.852
M11 -0.05743 0.84261 -0.068 0.946
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.576 on 81 degrees of freedom
Multiple R-squared: 0.7954, Adjusted R-squared: 0.7651
F-statistic: 26.24 on 12 and 81 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,] 3.143277e-01 6.286553e-01 0.6856723
[2,] 2.013194e-01 4.026388e-01 0.7986806
[3,] 1.127092e-01 2.254183e-01 0.8872908
[4,] 5.518612e-02 1.103722e-01 0.9448139
[5,] 2.651887e-02 5.303775e-02 0.9734811
[6,] 1.214998e-02 2.429996e-02 0.9878500
[7,] 5.120664e-03 1.024133e-02 0.9948793
[8,] 2.025353e-03 4.050706e-03 0.9979746
[9,] 8.523488e-04 1.704698e-03 0.9991477
[10,] 6.367814e-04 1.273563e-03 0.9993632
[11,] 3.090160e-04 6.180320e-04 0.9996910
[12,] 1.140119e-04 2.280239e-04 0.9998860
[13,] 4.675610e-05 9.351219e-05 0.9999532
[14,] 2.035902e-05 4.071804e-05 0.9999796
[15,] 9.389312e-06 1.877862e-05 0.9999906
[16,] 3.103075e-06 6.206150e-06 0.9999969
[17,] 1.114935e-06 2.229869e-06 0.9999989
[18,] 4.660084e-07 9.320168e-07 0.9999995
[19,] 1.443080e-07 2.886161e-07 0.9999999
[20,] 5.605350e-08 1.121070e-07 0.9999999
[21,] 1.891290e-08 3.782580e-08 1.0000000
[22,] 6.846003e-09 1.369201e-08 1.0000000
[23,] 1.952660e-09 3.905320e-09 1.0000000
[24,] 7.209198e-10 1.441840e-09 1.0000000
[25,] 3.953093e-10 7.906185e-10 1.0000000
[26,] 1.098112e-10 2.196224e-10 1.0000000
[27,] 2.891639e-11 5.783279e-11 1.0000000
[28,] 8.620363e-12 1.724073e-11 1.0000000
[29,] 2.834629e-12 5.669258e-12 1.0000000
[30,] 7.445096e-13 1.489019e-12 1.0000000
[31,] 2.413874e-13 4.827747e-13 1.0000000
[32,] 8.709280e-14 1.741856e-13 1.0000000
[33,] 2.268360e-14 4.536720e-14 1.0000000
[34,] 5.307887e-15 1.061577e-14 1.0000000
[35,] 1.213521e-15 2.427042e-15 1.0000000
[36,] 2.975828e-16 5.951656e-16 1.0000000
[37,] 7.722737e-17 1.544547e-16 1.0000000
[38,] 2.091412e-17 4.182824e-17 1.0000000
[39,] 1.818844e-17 3.637689e-17 1.0000000
[40,] 5.105089e-17 1.021018e-16 1.0000000
[41,] 1.330233e-16 2.660466e-16 1.0000000
[42,] 3.745204e-16 7.490409e-16 1.0000000
[43,] 2.843615e-15 5.687229e-15 1.0000000
[44,] 1.147257e-14 2.294515e-14 1.0000000
[45,] 1.030242e-13 2.060485e-13 1.0000000
[46,] 7.806935e-12 1.561387e-11 1.0000000
[47,] 3.421280e-10 6.842559e-10 1.0000000
[48,] 4.015801e-09 8.031601e-09 1.0000000
[49,] 6.006724e-08 1.201345e-07 0.9999999
[50,] 1.208605e-06 2.417211e-06 0.9999988
[51,] 1.130616e-06 2.261231e-06 0.9999989
[52,] 3.964498e-06 7.928996e-06 0.9999960
[53,] 6.032365e-06 1.206473e-05 0.9999940
[54,] 6.893199e-06 1.378640e-05 0.9999931
[55,] 1.749949e-05 3.499897e-05 0.9999825
[56,] 1.661472e-05 3.322945e-05 0.9999834
[57,] 1.541487e-05 3.082975e-05 0.9999846
[58,] 4.894136e-05 9.788272e-05 0.9999511
[59,] 2.212257e-04 4.424514e-04 0.9997788
[60,] 1.492579e-03 2.985157e-03 0.9985074
[61,] 1.852827e-03 3.705655e-03 0.9981472
[62,] 2.256222e-03 4.512443e-03 0.9977438
[63,] 3.264543e-03 6.529085e-03 0.9967355
> postscript(file="/var/www/html/rcomp/tmp/1mlkz1227791816.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2t7481227791816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/352pz1227791816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4v1mz1227791816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5dcao1227791816.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 = 94
Frequency = 1
1 2 3 4 5 6
-1.27866191 -1.51766191 -1.28653691 -1.19866191 -0.76328691 -0.35403691
7 8 9 10 11 12
0.09757579 -0.07892421 -0.06354921 -0.23667421 -0.07147075 -0.18689933
13 14 15 16 17 18
-0.14566191 0.22033809 0.16146309 0.34733809 0.28571309 0.29596309
19 20 21 22 23 24
0.26157579 0.37907579 0.39145079 0.06832579 0.15452925 0.36410067
25 26 27 28 29 30
0.43033809 0.06533809 -0.42353691 -0.87166191 -0.81428691 -0.70303691
31 32 33 34 35 36
0.01957579 0.56407579 0.78045079 0.11332579 0.61952925 0.51510067
37 38 39 40 41 42
0.18833809 -0.37566191 0.04046309 0.27933809 -0.41528691 -0.14803691
43 44 45 46 47 48
0.52657579 0.76707579 0.58745079 0.50532579 0.78252925 0.47810067
49 50 51 52 53 54
0.02533809 -0.20966191 -0.03853691 0.05333809 -0.00928691 0.82196309
55 56 57 58 59 60
-4.42232579 -4.21982579 -3.96545079 -3.58957579 -2.92237233 -2.19880090
61 62 63 64 65 66
-1.70156348 -1.60556348 -1.57243848 -0.45156348 0.48881152 -1.22693848
67 68 69 70 71 72
0.28167421 0.16817421 -0.79245079 -1.20957579 -0.30537233 -0.70580090
73 74 75 76 77 78
-0.66656348 -0.18856348 -0.53043848 -0.28556348 -0.63818848 -0.68193848
79 80 81 82 83 84
-0.17432579 0.01217421 0.50854921 0.86842421 1.74262767 1.73419910
85 86 87 88 89 90
3.14843652 3.61143652 3.64956152 2.12743652 1.86581152 1.99606152
91 92 93 94
3.40967421 2.40817421 2.55354921 3.48042421
> postscript(file="/var/www/html/rcomp/tmp/6soty1227791816.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 = 94
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.27866191 NA
1 -1.51766191 -1.27866191
2 -1.28653691 -1.51766191
3 -1.19866191 -1.28653691
4 -0.76328691 -1.19866191
5 -0.35403691 -0.76328691
6 0.09757579 -0.35403691
7 -0.07892421 0.09757579
8 -0.06354921 -0.07892421
9 -0.23667421 -0.06354921
10 -0.07147075 -0.23667421
11 -0.18689933 -0.07147075
12 -0.14566191 -0.18689933
13 0.22033809 -0.14566191
14 0.16146309 0.22033809
15 0.34733809 0.16146309
16 0.28571309 0.34733809
17 0.29596309 0.28571309
18 0.26157579 0.29596309
19 0.37907579 0.26157579
20 0.39145079 0.37907579
21 0.06832579 0.39145079
22 0.15452925 0.06832579
23 0.36410067 0.15452925
24 0.43033809 0.36410067
25 0.06533809 0.43033809
26 -0.42353691 0.06533809
27 -0.87166191 -0.42353691
28 -0.81428691 -0.87166191
29 -0.70303691 -0.81428691
30 0.01957579 -0.70303691
31 0.56407579 0.01957579
32 0.78045079 0.56407579
33 0.11332579 0.78045079
34 0.61952925 0.11332579
35 0.51510067 0.61952925
36 0.18833809 0.51510067
37 -0.37566191 0.18833809
38 0.04046309 -0.37566191
39 0.27933809 0.04046309
40 -0.41528691 0.27933809
41 -0.14803691 -0.41528691
42 0.52657579 -0.14803691
43 0.76707579 0.52657579
44 0.58745079 0.76707579
45 0.50532579 0.58745079
46 0.78252925 0.50532579
47 0.47810067 0.78252925
48 0.02533809 0.47810067
49 -0.20966191 0.02533809
50 -0.03853691 -0.20966191
51 0.05333809 -0.03853691
52 -0.00928691 0.05333809
53 0.82196309 -0.00928691
54 -4.42232579 0.82196309
55 -4.21982579 -4.42232579
56 -3.96545079 -4.21982579
57 -3.58957579 -3.96545079
58 -2.92237233 -3.58957579
59 -2.19880090 -2.92237233
60 -1.70156348 -2.19880090
61 -1.60556348 -1.70156348
62 -1.57243848 -1.60556348
63 -0.45156348 -1.57243848
64 0.48881152 -0.45156348
65 -1.22693848 0.48881152
66 0.28167421 -1.22693848
67 0.16817421 0.28167421
68 -0.79245079 0.16817421
69 -1.20957579 -0.79245079
70 -0.30537233 -1.20957579
71 -0.70580090 -0.30537233
72 -0.66656348 -0.70580090
73 -0.18856348 -0.66656348
74 -0.53043848 -0.18856348
75 -0.28556348 -0.53043848
76 -0.63818848 -0.28556348
77 -0.68193848 -0.63818848
78 -0.17432579 -0.68193848
79 0.01217421 -0.17432579
80 0.50854921 0.01217421
81 0.86842421 0.50854921
82 1.74262767 0.86842421
83 1.73419910 1.74262767
84 3.14843652 1.73419910
85 3.61143652 3.14843652
86 3.64956152 3.61143652
87 2.12743652 3.64956152
88 1.86581152 2.12743652
89 1.99606152 1.86581152
90 3.40967421 1.99606152
91 2.40817421 3.40967421
92 2.55354921 2.40817421
93 3.48042421 2.55354921
94 NA 3.48042421
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.51766191 -1.27866191
[2,] -1.28653691 -1.51766191
[3,] -1.19866191 -1.28653691
[4,] -0.76328691 -1.19866191
[5,] -0.35403691 -0.76328691
[6,] 0.09757579 -0.35403691
[7,] -0.07892421 0.09757579
[8,] -0.06354921 -0.07892421
[9,] -0.23667421 -0.06354921
[10,] -0.07147075 -0.23667421
[11,] -0.18689933 -0.07147075
[12,] -0.14566191 -0.18689933
[13,] 0.22033809 -0.14566191
[14,] 0.16146309 0.22033809
[15,] 0.34733809 0.16146309
[16,] 0.28571309 0.34733809
[17,] 0.29596309 0.28571309
[18,] 0.26157579 0.29596309
[19,] 0.37907579 0.26157579
[20,] 0.39145079 0.37907579
[21,] 0.06832579 0.39145079
[22,] 0.15452925 0.06832579
[23,] 0.36410067 0.15452925
[24,] 0.43033809 0.36410067
[25,] 0.06533809 0.43033809
[26,] -0.42353691 0.06533809
[27,] -0.87166191 -0.42353691
[28,] -0.81428691 -0.87166191
[29,] -0.70303691 -0.81428691
[30,] 0.01957579 -0.70303691
[31,] 0.56407579 0.01957579
[32,] 0.78045079 0.56407579
[33,] 0.11332579 0.78045079
[34,] 0.61952925 0.11332579
[35,] 0.51510067 0.61952925
[36,] 0.18833809 0.51510067
[37,] -0.37566191 0.18833809
[38,] 0.04046309 -0.37566191
[39,] 0.27933809 0.04046309
[40,] -0.41528691 0.27933809
[41,] -0.14803691 -0.41528691
[42,] 0.52657579 -0.14803691
[43,] 0.76707579 0.52657579
[44,] 0.58745079 0.76707579
[45,] 0.50532579 0.58745079
[46,] 0.78252925 0.50532579
[47,] 0.47810067 0.78252925
[48,] 0.02533809 0.47810067
[49,] -0.20966191 0.02533809
[50,] -0.03853691 -0.20966191
[51,] 0.05333809 -0.03853691
[52,] -0.00928691 0.05333809
[53,] 0.82196309 -0.00928691
[54,] -4.42232579 0.82196309
[55,] -4.21982579 -4.42232579
[56,] -3.96545079 -4.21982579
[57,] -3.58957579 -3.96545079
[58,] -2.92237233 -3.58957579
[59,] -2.19880090 -2.92237233
[60,] -1.70156348 -2.19880090
[61,] -1.60556348 -1.70156348
[62,] -1.57243848 -1.60556348
[63,] -0.45156348 -1.57243848
[64,] 0.48881152 -0.45156348
[65,] -1.22693848 0.48881152
[66,] 0.28167421 -1.22693848
[67,] 0.16817421 0.28167421
[68,] -0.79245079 0.16817421
[69,] -1.20957579 -0.79245079
[70,] -0.30537233 -1.20957579
[71,] -0.70580090 -0.30537233
[72,] -0.66656348 -0.70580090
[73,] -0.18856348 -0.66656348
[74,] -0.53043848 -0.18856348
[75,] -0.28556348 -0.53043848
[76,] -0.63818848 -0.28556348
[77,] -0.68193848 -0.63818848
[78,] -0.17432579 -0.68193848
[79,] 0.01217421 -0.17432579
[80,] 0.50854921 0.01217421
[81,] 0.86842421 0.50854921
[82,] 1.74262767 0.86842421
[83,] 1.73419910 1.74262767
[84,] 3.14843652 1.73419910
[85,] 3.61143652 3.14843652
[86,] 3.64956152 3.61143652
[87,] 2.12743652 3.64956152
[88,] 1.86581152 2.12743652
[89,] 1.99606152 1.86581152
[90,] 3.40967421 1.99606152
[91,] 2.40817421 3.40967421
[92,] 2.55354921 2.40817421
[93,] 3.48042421 2.55354921
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.51766191 -1.27866191
2 -1.28653691 -1.51766191
3 -1.19866191 -1.28653691
4 -0.76328691 -1.19866191
5 -0.35403691 -0.76328691
6 0.09757579 -0.35403691
7 -0.07892421 0.09757579
8 -0.06354921 -0.07892421
9 -0.23667421 -0.06354921
10 -0.07147075 -0.23667421
11 -0.18689933 -0.07147075
12 -0.14566191 -0.18689933
13 0.22033809 -0.14566191
14 0.16146309 0.22033809
15 0.34733809 0.16146309
16 0.28571309 0.34733809
17 0.29596309 0.28571309
18 0.26157579 0.29596309
19 0.37907579 0.26157579
20 0.39145079 0.37907579
21 0.06832579 0.39145079
22 0.15452925 0.06832579
23 0.36410067 0.15452925
24 0.43033809 0.36410067
25 0.06533809 0.43033809
26 -0.42353691 0.06533809
27 -0.87166191 -0.42353691
28 -0.81428691 -0.87166191
29 -0.70303691 -0.81428691
30 0.01957579 -0.70303691
31 0.56407579 0.01957579
32 0.78045079 0.56407579
33 0.11332579 0.78045079
34 0.61952925 0.11332579
35 0.51510067 0.61952925
36 0.18833809 0.51510067
37 -0.37566191 0.18833809
38 0.04046309 -0.37566191
39 0.27933809 0.04046309
40 -0.41528691 0.27933809
41 -0.14803691 -0.41528691
42 0.52657579 -0.14803691
43 0.76707579 0.52657579
44 0.58745079 0.76707579
45 0.50532579 0.58745079
46 0.78252925 0.50532579
47 0.47810067 0.78252925
48 0.02533809 0.47810067
49 -0.20966191 0.02533809
50 -0.03853691 -0.20966191
51 0.05333809 -0.03853691
52 -0.00928691 0.05333809
53 0.82196309 -0.00928691
54 -4.42232579 0.82196309
55 -4.21982579 -4.42232579
56 -3.96545079 -4.21982579
57 -3.58957579 -3.96545079
58 -2.92237233 -3.58957579
59 -2.19880090 -2.92237233
60 -1.70156348 -2.19880090
61 -1.60556348 -1.70156348
62 -1.57243848 -1.60556348
63 -0.45156348 -1.57243848
64 0.48881152 -0.45156348
65 -1.22693848 0.48881152
66 0.28167421 -1.22693848
67 0.16817421 0.28167421
68 -0.79245079 0.16817421
69 -1.20957579 -0.79245079
70 -0.30537233 -1.20957579
71 -0.70580090 -0.30537233
72 -0.66656348 -0.70580090
73 -0.18856348 -0.66656348
74 -0.53043848 -0.18856348
75 -0.28556348 -0.53043848
76 -0.63818848 -0.28556348
77 -0.68193848 -0.63818848
78 -0.17432579 -0.68193848
79 0.01217421 -0.17432579
80 0.50854921 0.01217421
81 0.86842421 0.50854921
82 1.74262767 0.86842421
83 1.73419910 1.74262767
84 3.14843652 1.73419910
85 3.61143652 3.14843652
86 3.64956152 3.61143652
87 2.12743652 3.64956152
88 1.86581152 2.12743652
89 1.99606152 1.86581152
90 3.40967421 1.99606152
91 2.40817421 3.40967421
92 2.55354921 2.40817421
93 3.48042421 2.55354921
> 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/7w5ty1227791816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8a3h41227791816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yxm81227791816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10thjl1227791816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ziry1227791816.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/12r1jf1227791816.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/131x2y1227791816.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/14qtjc1227791816.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/1566841227791816.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/16zjb71227791817.tab")
+ }
>
> system("convert tmp/1mlkz1227791816.ps tmp/1mlkz1227791816.png")
> system("convert tmp/2t7481227791816.ps tmp/2t7481227791816.png")
> system("convert tmp/352pz1227791816.ps tmp/352pz1227791816.png")
> system("convert tmp/4v1mz1227791816.ps tmp/4v1mz1227791816.png")
> system("convert tmp/5dcao1227791816.ps tmp/5dcao1227791816.png")
> system("convert tmp/6soty1227791816.ps tmp/6soty1227791816.png")
> system("convert tmp/7w5ty1227791816.ps tmp/7w5ty1227791816.png")
> system("convert tmp/8a3h41227791816.ps tmp/8a3h41227791816.png")
> system("convert tmp/9yxm81227791816.ps tmp/9yxm81227791816.png")
> system("convert tmp/10thjl1227791816.ps tmp/10thjl1227791816.png")
>
>
> proc.time()
user system elapsed
2.801 1.530 3.213