R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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(13363
+ ,0
+ ,12530
+ ,0
+ ,11420
+ ,0
+ ,10948
+ ,0
+ ,10173
+ ,0
+ ,10602
+ ,0
+ ,16094
+ ,0
+ ,19631
+ ,0
+ ,17140
+ ,0
+ ,14345
+ ,0
+ ,12632
+ ,0
+ ,12894
+ ,0
+ ,11808
+ ,0
+ ,10673
+ ,0
+ ,9939
+ ,0
+ ,9890
+ ,0
+ ,9283
+ ,0
+ ,10131
+ ,0
+ ,15864
+ ,0
+ ,19283
+ ,0
+ ,16203
+ ,0
+ ,13919
+ ,0
+ ,11937
+ ,0
+ ,11795
+ ,0
+ ,11268
+ ,0
+ ,10522
+ ,0
+ ,9929
+ ,0
+ ,9725
+ ,0
+ ,9372
+ ,0
+ ,10068
+ ,0
+ ,16230
+ ,0
+ ,19115
+ ,0
+ ,18351
+ ,0
+ ,16265
+ ,0
+ ,14103
+ ,0
+ ,14115
+ ,0
+ ,13327
+ ,0
+ ,12618
+ ,0
+ ,12129
+ ,0
+ ,11775
+ ,0
+ ,11493
+ ,0
+ ,12470
+ ,0
+ ,20792
+ ,0
+ ,22337
+ ,0
+ ,21325
+ ,0
+ ,18581
+ ,0
+ ,16475
+ ,0
+ ,16581
+ ,0
+ ,15745
+ ,0
+ ,14453
+ ,0
+ ,13712
+ ,0
+ ,13766
+ ,0
+ ,13336
+ ,0
+ ,15346
+ ,0
+ ,24446
+ ,0
+ ,26178
+ ,0
+ ,24628
+ ,0
+ ,21282
+ ,0
+ ,18850
+ ,0
+ ,18822
+ ,0
+ ,18060
+ ,0
+ ,17536
+ ,0
+ ,16417
+ ,0
+ ,15842
+ ,0
+ ,15188
+ ,0
+ ,16905
+ ,0
+ ,25430
+ ,0
+ ,27962
+ ,0
+ ,26607
+ ,0
+ ,23364
+ ,0
+ ,20827
+ ,0
+ ,20506
+ ,0
+ ,19181
+ ,0
+ ,18016
+ ,0
+ ,17354
+ ,0
+ ,16256
+ ,0
+ ,15770
+ ,0
+ ,17538
+ ,0
+ ,26899
+ ,0
+ ,28915
+ ,0
+ ,25247
+ ,0
+ ,22856
+ ,0
+ ,19980
+ ,0
+ ,19856
+ ,0
+ ,16994
+ ,0
+ ,16839
+ ,0
+ ,15618
+ ,0
+ ,15883
+ ,0
+ ,15513
+ ,0
+ ,17106
+ ,0
+ ,25272
+ ,0
+ ,26731
+ ,0
+ ,22891
+ ,0
+ ,19583
+ ,0
+ ,16939
+ ,0
+ ,16757
+ ,0
+ ,15435
+ ,0
+ ,14786
+ ,0
+ ,13680
+ ,0
+ ,13208
+ ,0
+ ,12707
+ ,0
+ ,14277
+ ,0
+ ,22436
+ ,1
+ ,23229
+ ,1
+ ,18241
+ ,1
+ ,16145
+ ,1
+ ,13994
+ ,1
+ ,14780
+ ,1
+ ,13100
+ ,1
+ ,12329
+ ,1
+ ,12463
+ ,1
+ ,11532
+ ,1
+ ,10784
+ ,1
+ ,13106
+ ,1
+ ,19491
+ ,1
+ ,20418
+ ,1
+ ,16094
+ ,1
+ ,14491
+ ,1
+ ,13067
+ ,1)
+ ,dim=c(2
+ ,119)
+ ,dimnames=list(c('NWWZPB'
+ ,'Dummy')
+ ,1:119))
> y <- array(NA,dim=c(2,119),dimnames=list(c('NWWZPB','Dummy'),1:119))
> 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
NWWZPB Dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 13363 0 1 0 0 0 0 0 0 0 0 0 0
2 12530 0 0 1 0 0 0 0 0 0 0 0 0
3 11420 0 0 0 1 0 0 0 0 0 0 0 0
4 10948 0 0 0 0 1 0 0 0 0 0 0 0
5 10173 0 0 0 0 0 1 0 0 0 0 0 0
6 10602 0 0 0 0 0 0 1 0 0 0 0 0
7 16094 0 0 0 0 0 0 0 1 0 0 0 0
8 19631 0 0 0 0 0 0 0 0 1 0 0 0
9 17140 0 0 0 0 0 0 0 0 0 1 0 0
10 14345 0 0 0 0 0 0 0 0 0 0 1 0
11 12632 0 0 0 0 0 0 0 0 0 0 0 1
12 12894 0 0 0 0 0 0 0 0 0 0 0 0
13 11808 0 1 0 0 0 0 0 0 0 0 0 0
14 10673 0 0 1 0 0 0 0 0 0 0 0 0
15 9939 0 0 0 1 0 0 0 0 0 0 0 0
16 9890 0 0 0 0 1 0 0 0 0 0 0 0
17 9283 0 0 0 0 0 1 0 0 0 0 0 0
18 10131 0 0 0 0 0 0 1 0 0 0 0 0
19 15864 0 0 0 0 0 0 0 1 0 0 0 0
20 19283 0 0 0 0 0 0 0 0 1 0 0 0
21 16203 0 0 0 0 0 0 0 0 0 1 0 0
22 13919 0 0 0 0 0 0 0 0 0 0 1 0
23 11937 0 0 0 0 0 0 0 0 0 0 0 1
24 11795 0 0 0 0 0 0 0 0 0 0 0 0
25 11268 0 1 0 0 0 0 0 0 0 0 0 0
26 10522 0 0 1 0 0 0 0 0 0 0 0 0
27 9929 0 0 0 1 0 0 0 0 0 0 0 0
28 9725 0 0 0 0 1 0 0 0 0 0 0 0
29 9372 0 0 0 0 0 1 0 0 0 0 0 0
30 10068 0 0 0 0 0 0 1 0 0 0 0 0
31 16230 0 0 0 0 0 0 0 1 0 0 0 0
32 19115 0 0 0 0 0 0 0 0 1 0 0 0
33 18351 0 0 0 0 0 0 0 0 0 1 0 0
34 16265 0 0 0 0 0 0 0 0 0 0 1 0
35 14103 0 0 0 0 0 0 0 0 0 0 0 1
36 14115 0 0 0 0 0 0 0 0 0 0 0 0
37 13327 0 1 0 0 0 0 0 0 0 0 0 0
38 12618 0 0 1 0 0 0 0 0 0 0 0 0
39 12129 0 0 0 1 0 0 0 0 0 0 0 0
40 11775 0 0 0 0 1 0 0 0 0 0 0 0
41 11493 0 0 0 0 0 1 0 0 0 0 0 0
42 12470 0 0 0 0 0 0 1 0 0 0 0 0
43 20792 0 0 0 0 0 0 0 1 0 0 0 0
44 22337 0 0 0 0 0 0 0 0 1 0 0 0
45 21325 0 0 0 0 0 0 0 0 0 1 0 0
46 18581 0 0 0 0 0 0 0 0 0 0 1 0
47 16475 0 0 0 0 0 0 0 0 0 0 0 1
48 16581 0 0 0 0 0 0 0 0 0 0 0 0
49 15745 0 1 0 0 0 0 0 0 0 0 0 0
50 14453 0 0 1 0 0 0 0 0 0 0 0 0
51 13712 0 0 0 1 0 0 0 0 0 0 0 0
52 13766 0 0 0 0 1 0 0 0 0 0 0 0
53 13336 0 0 0 0 0 1 0 0 0 0 0 0
54 15346 0 0 0 0 0 0 1 0 0 0 0 0
55 24446 0 0 0 0 0 0 0 1 0 0 0 0
56 26178 0 0 0 0 0 0 0 0 1 0 0 0
57 24628 0 0 0 0 0 0 0 0 0 1 0 0
58 21282 0 0 0 0 0 0 0 0 0 0 1 0
59 18850 0 0 0 0 0 0 0 0 0 0 0 1
60 18822 0 0 0 0 0 0 0 0 0 0 0 0
61 18060 0 1 0 0 0 0 0 0 0 0 0 0
62 17536 0 0 1 0 0 0 0 0 0 0 0 0
63 16417 0 0 0 1 0 0 0 0 0 0 0 0
64 15842 0 0 0 0 1 0 0 0 0 0 0 0
65 15188 0 0 0 0 0 1 0 0 0 0 0 0
66 16905 0 0 0 0 0 0 1 0 0 0 0 0
67 25430 0 0 0 0 0 0 0 1 0 0 0 0
68 27962 0 0 0 0 0 0 0 0 1 0 0 0
69 26607 0 0 0 0 0 0 0 0 0 1 0 0
70 23364 0 0 0 0 0 0 0 0 0 0 1 0
71 20827 0 0 0 0 0 0 0 0 0 0 0 1
72 20506 0 0 0 0 0 0 0 0 0 0 0 0
73 19181 0 1 0 0 0 0 0 0 0 0 0 0
74 18016 0 0 1 0 0 0 0 0 0 0 0 0
75 17354 0 0 0 1 0 0 0 0 0 0 0 0
76 16256 0 0 0 0 1 0 0 0 0 0 0 0
77 15770 0 0 0 0 0 1 0 0 0 0 0 0
78 17538 0 0 0 0 0 0 1 0 0 0 0 0
79 26899 0 0 0 0 0 0 0 1 0 0 0 0
80 28915 0 0 0 0 0 0 0 0 1 0 0 0
81 25247 0 0 0 0 0 0 0 0 0 1 0 0
82 22856 0 0 0 0 0 0 0 0 0 0 1 0
83 19980 0 0 0 0 0 0 0 0 0 0 0 1
84 19856 0 0 0 0 0 0 0 0 0 0 0 0
85 16994 0 1 0 0 0 0 0 0 0 0 0 0
86 16839 0 0 1 0 0 0 0 0 0 0 0 0
87 15618 0 0 0 1 0 0 0 0 0 0 0 0
88 15883 0 0 0 0 1 0 0 0 0 0 0 0
89 15513 0 0 0 0 0 1 0 0 0 0 0 0
90 17106 0 0 0 0 0 0 1 0 0 0 0 0
91 25272 0 0 0 0 0 0 0 1 0 0 0 0
92 26731 0 0 0 0 0 0 0 0 1 0 0 0
93 22891 0 0 0 0 0 0 0 0 0 1 0 0
94 19583 0 0 0 0 0 0 0 0 0 0 1 0
95 16939 0 0 0 0 0 0 0 0 0 0 0 1
96 16757 0 0 0 0 0 0 0 0 0 0 0 0
97 15435 0 1 0 0 0 0 0 0 0 0 0 0
98 14786 0 0 1 0 0 0 0 0 0 0 0 0
99 13680 0 0 0 1 0 0 0 0 0 0 0 0
100 13208 0 0 0 0 1 0 0 0 0 0 0 0
101 12707 0 0 0 0 0 1 0 0 0 0 0 0
102 14277 0 0 0 0 0 0 1 0 0 0 0 0
103 22436 1 0 0 0 0 0 0 1 0 0 0 0
104 23229 1 0 0 0 0 0 0 0 1 0 0 0
105 18241 1 0 0 0 0 0 0 0 0 1 0 0
106 16145 1 0 0 0 0 0 0 0 0 0 1 0
107 13994 1 0 0 0 0 0 0 0 0 0 0 1
108 14780 1 0 0 0 0 0 0 0 0 0 0 0
109 13100 1 1 0 0 0 0 0 0 0 0 0 0
110 12329 1 0 1 0 0 0 0 0 0 0 0 0
111 12463 1 0 0 1 0 0 0 0 0 0 0 0
112 11532 1 0 0 0 1 0 0 0 0 0 0 0
113 10784 1 0 0 0 0 1 0 0 0 0 0 0
114 13106 1 0 0 0 0 0 1 0 0 0 0 0
115 19491 1 0 0 0 0 0 0 1 0 0 0 0
116 20418 1 0 0 0 0 0 0 0 1 0 0 0
117 16094 1 0 0 0 0 0 0 0 0 1 0 0
118 14491 1 0 0 0 0 0 0 0 0 0 1 0
119 13067 1 0 0 0 0 0 0 0 0 0 0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy M1 M2 M3 M4
16469.5 -2119.2 -1429.4 -2227.3 -2991.4 -3375.0
M5 M6 M7 M8 M9 M10
-3895.6 -2502.6 5249.8 7334.3 4627.1 2037.5
M11
-165.2
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5855.2 -2377.6 206.1 2761.3 5510.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16469.5 1044.8 15.763 < 2e-16 ***
Dummy -2119.2 826.0 -2.566 0.011697 *
M1 -1429.4 1434.6 -0.996 0.321329
M2 -2227.3 1434.6 -1.553 0.123508
M3 -2991.4 1434.6 -2.085 0.039455 *
M4 -3375.0 1434.6 -2.353 0.020490 *
M5 -3895.6 1434.6 -2.715 0.007729 **
M6 -2502.6 1434.6 -1.744 0.083977 .
M7 5249.8 1436.5 3.655 0.000402 ***
M8 7334.3 1436.5 5.106 1.46e-06 ***
M9 4627.1 1436.5 3.221 0.001696 **
M10 2037.5 1436.5 1.418 0.159012
M11 -165.2 1436.5 -0.115 0.908644
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3122 on 106 degrees of freedom
Multiple R-squared: 0.5985, Adjusted R-squared: 0.5531
F-statistic: 13.17 on 12 and 106 DF, p-value: 3.688e-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,] 8.303826e-02 1.660765e-01 9.169617e-01
[2,] 3.255486e-02 6.510972e-02 9.674451e-01
[3,] 1.126869e-02 2.253737e-02 9.887313e-01
[4,] 3.998508e-03 7.997016e-03 9.960015e-01
[5,] 1.334125e-03 2.668250e-03 9.986659e-01
[6,] 5.861314e-04 1.172263e-03 9.994139e-01
[7,] 2.075043e-04 4.150085e-04 9.997925e-01
[8,] 8.231651e-05 1.646330e-04 9.999177e-01
[9,] 4.650739e-05 9.301478e-05 9.999535e-01
[10,] 3.869344e-05 7.738688e-05 9.999613e-01
[11,] 2.525606e-05 5.051212e-05 9.999747e-01
[12,] 1.284198e-05 2.568397e-05 9.999872e-01
[13,] 6.366203e-06 1.273241e-05 9.999936e-01
[14,] 2.688458e-06 5.376917e-06 9.999973e-01
[15,] 1.338280e-06 2.676561e-06 9.999987e-01
[16,] 1.377907e-06 2.755813e-06 9.999986e-01
[17,] 1.296582e-06 2.593164e-06 9.999987e-01
[18,] 3.535856e-06 7.071712e-06 9.999965e-01
[19,] 1.604542e-05 3.209085e-05 9.999840e-01
[20,] 3.593480e-05 7.186960e-05 9.999641e-01
[21,] 7.617545e-05 1.523509e-04 9.999238e-01
[22,] 8.593250e-05 1.718650e-04 9.999141e-01
[23,] 1.174737e-04 2.349474e-04 9.998825e-01
[24,] 2.062627e-04 4.125253e-04 9.997937e-01
[25,] 3.144826e-04 6.289652e-04 9.996855e-01
[26,] 5.833544e-04 1.166709e-03 9.994166e-01
[27,] 1.725297e-03 3.450594e-03 9.982747e-01
[28,] 5.408366e-02 1.081673e-01 9.459163e-01
[29,] 1.542137e-01 3.084275e-01 8.457863e-01
[30,] 3.364142e-01 6.728285e-01 6.635858e-01
[31,] 5.059780e-01 9.880441e-01 4.940220e-01
[32,] 6.397668e-01 7.204664e-01 3.602332e-01
[33,] 7.562462e-01 4.875076e-01 2.437538e-01
[34,] 8.153194e-01 3.693613e-01 1.846806e-01
[35,] 8.598947e-01 2.802105e-01 1.401053e-01
[36,] 8.945775e-01 2.108450e-01 1.054225e-01
[37,] 9.191094e-01 1.617813e-01 8.089064e-02
[38,] 9.376370e-01 1.247261e-01 6.236305e-02
[39,] 9.658990e-01 6.820201e-02 3.410101e-02
[40,] 9.944460e-01 1.110794e-02 5.553971e-03
[41,] 9.981700e-01 3.660039e-03 1.830019e-03
[42,] 9.993520e-01 1.295964e-03 6.479820e-04
[43,] 9.996108e-01 7.784901e-04 3.892450e-04
[44,] 9.997061e-01 5.878601e-04 2.939300e-04
[45,] 9.997614e-01 4.772930e-04 2.386465e-04
[46,] 9.998037e-01 3.926093e-04 1.963047e-04
[47,] 9.998520e-01 2.960471e-04 1.480236e-04
[48,] 9.998630e-01 2.739518e-04 1.369759e-04
[49,] 9.998554e-01 2.891598e-04 1.445799e-04
[50,] 9.998361e-01 3.278264e-04 1.639132e-04
[51,] 9.998358e-01 3.283868e-04 1.641934e-04
[52,] 9.998861e-01 2.277649e-04 1.138824e-04
[53,] 9.999116e-01 1.768626e-04 8.843130e-05
[54,] 9.999773e-01 4.530817e-05 2.265408e-05
[55,] 9.999891e-01 2.175970e-05 1.087985e-05
[56,] 9.999934e-01 1.322921e-05 6.614607e-06
[57,] 9.999939e-01 1.225072e-05 6.125360e-06
[58,] 9.999954e-01 9.192351e-06 4.596176e-06
[59,] 9.999951e-01 9.846324e-06 4.923162e-06
[60,] 9.999949e-01 1.020833e-05 5.104165e-06
[61,] 9.999923e-01 1.535118e-05 7.675591e-06
[62,] 9.999886e-01 2.283219e-05 1.141610e-05
[63,] 9.999838e-01 3.241019e-05 1.620509e-05
[64,] 9.999840e-01 3.205775e-05 1.602887e-05
[65,] 9.999887e-01 2.250973e-05 1.125486e-05
[66,] 9.999948e-01 1.044925e-05 5.224623e-06
[67,] 9.999982e-01 3.656496e-06 1.828248e-06
[68,] 9.999989e-01 2.255413e-06 1.127706e-06
[69,] 9.999988e-01 2.367846e-06 1.183923e-06
[70,] 9.999973e-01 5.499658e-06 2.749829e-06
[71,] 9.999951e-01 9.742704e-06 4.871352e-06
[72,] 9.999886e-01 2.288493e-05 1.144247e-05
[73,] 9.999827e-01 3.468721e-05 1.734361e-05
[74,] 9.999784e-01 4.327013e-05 2.163506e-05
[75,] 9.999679e-01 6.427658e-05 3.213829e-05
[76,] 9.999372e-01 1.255693e-04 6.278465e-05
[77,] 9.999142e-01 1.716149e-04 8.580743e-05
[78,] 9.999619e-01 7.618312e-05 3.809156e-05
[79,] 9.999529e-01 9.422508e-05 4.711254e-05
[80,] 9.999066e-01 1.867662e-04 9.338310e-05
[81,] 9.996932e-01 6.136807e-04 3.068403e-04
[82,] 9.990862e-01 1.827549e-03 9.137743e-04
[83,] 9.975291e-01 4.941869e-03 2.470935e-03
[84,] 9.930052e-01 1.398960e-02 6.994799e-03
[85,] 9.811809e-01 3.763826e-02 1.881913e-02
[86,] 9.539389e-01 9.212217e-02 4.606109e-02
[87,] 8.930289e-01 2.139422e-01 1.069711e-01
[88,] 8.671512e-01 2.656975e-01 1.328488e-01
> postscript(file="/var/www/html/rcomp/tmp/1a8ul1229267023.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/202ya1229267023.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3slf11229267023.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fhgg1229267023.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50v441229267023.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 = 119
Frequency = 1
1 2 3 4 5 6
-1677.01781 -1712.11781 -2058.01781 -2146.41781 -2400.81781 -3364.81781
7 8 9 10 11 12
-5625.23561 -4172.73561 -3956.53561 -4161.93561 -3672.23561 -3575.46423
13 14 15 16 17 18
-3232.01781 -3569.11781 -3539.01781 -3204.41781 -3290.81781 -3835.81781
19 20 21 22 23 24
-5855.23561 -4520.73561 -4893.53561 -4587.93561 -4367.23561 -4674.46423
25 26 27 28 29 30
-3772.01781 -3720.11781 -3549.01781 -3369.41781 -3201.81781 -3898.81781
31 32 33 34 35 36
-5489.23561 -4688.73561 -2745.53561 -2241.93561 -2201.23561 -2354.46423
37 38 39 40 41 42
-1713.01781 -1624.11781 -1349.01781 -1319.41781 -1080.81781 -1496.81781
43 44 45 46 47 48
-927.23561 -1466.73561 228.46439 74.06439 170.76439 111.53577
49 50 51 52 53 54
704.98219 210.88219 233.98219 671.58219 762.18219 1379.18219
55 56 57 58 59 60
2726.76439 2374.26439 3531.46439 2775.06439 2545.76439 2352.53577
61 62 63 64 65 66
3019.98219 3293.88219 2938.98219 2747.58219 2614.18219 2938.18219
67 68 69 70 71 72
3710.76439 4158.26439 5510.46439 4857.06439 4522.76439 4036.53577
73 74 75 76 77 78
4140.98219 3773.88219 3875.98219 3161.58219 3196.18219 3571.18219
79 80 81 82 83 84
5179.76439 5111.26439 4150.46439 4349.06439 3675.76439 3386.53577
85 86 87 88 89 90
1953.98219 2596.88219 2139.98219 2788.58219 2939.18219 3139.18219
91 92 93 94 95 96
3552.76439 2927.26439 1794.46439 1076.06439 634.76439 287.53577
97 98 99 100 101 102
394.98219 543.88219 201.98219 113.58219 133.18219 310.18219
103 104 105 106 107 108
2835.94246 1544.44246 -736.35754 -242.75754 -191.05754 429.71384
109 110 111 112 113 114
179.16026 206.06026 1104.16026 556.76026 329.36026 1258.36026
115 116 117 118 119
-109.05754 -1266.55754 -2883.35754 -1896.75754 -1118.05754
> postscript(file="/var/www/html/rcomp/tmp/659lt1229267023.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 = 119
Frequency = 1
lag(myerror, k = 1) myerror
0 -1677.01781 NA
1 -1712.11781 -1677.01781
2 -2058.01781 -1712.11781
3 -2146.41781 -2058.01781
4 -2400.81781 -2146.41781
5 -3364.81781 -2400.81781
6 -5625.23561 -3364.81781
7 -4172.73561 -5625.23561
8 -3956.53561 -4172.73561
9 -4161.93561 -3956.53561
10 -3672.23561 -4161.93561
11 -3575.46423 -3672.23561
12 -3232.01781 -3575.46423
13 -3569.11781 -3232.01781
14 -3539.01781 -3569.11781
15 -3204.41781 -3539.01781
16 -3290.81781 -3204.41781
17 -3835.81781 -3290.81781
18 -5855.23561 -3835.81781
19 -4520.73561 -5855.23561
20 -4893.53561 -4520.73561
21 -4587.93561 -4893.53561
22 -4367.23561 -4587.93561
23 -4674.46423 -4367.23561
24 -3772.01781 -4674.46423
25 -3720.11781 -3772.01781
26 -3549.01781 -3720.11781
27 -3369.41781 -3549.01781
28 -3201.81781 -3369.41781
29 -3898.81781 -3201.81781
30 -5489.23561 -3898.81781
31 -4688.73561 -5489.23561
32 -2745.53561 -4688.73561
33 -2241.93561 -2745.53561
34 -2201.23561 -2241.93561
35 -2354.46423 -2201.23561
36 -1713.01781 -2354.46423
37 -1624.11781 -1713.01781
38 -1349.01781 -1624.11781
39 -1319.41781 -1349.01781
40 -1080.81781 -1319.41781
41 -1496.81781 -1080.81781
42 -927.23561 -1496.81781
43 -1466.73561 -927.23561
44 228.46439 -1466.73561
45 74.06439 228.46439
46 170.76439 74.06439
47 111.53577 170.76439
48 704.98219 111.53577
49 210.88219 704.98219
50 233.98219 210.88219
51 671.58219 233.98219
52 762.18219 671.58219
53 1379.18219 762.18219
54 2726.76439 1379.18219
55 2374.26439 2726.76439
56 3531.46439 2374.26439
57 2775.06439 3531.46439
58 2545.76439 2775.06439
59 2352.53577 2545.76439
60 3019.98219 2352.53577
61 3293.88219 3019.98219
62 2938.98219 3293.88219
63 2747.58219 2938.98219
64 2614.18219 2747.58219
65 2938.18219 2614.18219
66 3710.76439 2938.18219
67 4158.26439 3710.76439
68 5510.46439 4158.26439
69 4857.06439 5510.46439
70 4522.76439 4857.06439
71 4036.53577 4522.76439
72 4140.98219 4036.53577
73 3773.88219 4140.98219
74 3875.98219 3773.88219
75 3161.58219 3875.98219
76 3196.18219 3161.58219
77 3571.18219 3196.18219
78 5179.76439 3571.18219
79 5111.26439 5179.76439
80 4150.46439 5111.26439
81 4349.06439 4150.46439
82 3675.76439 4349.06439
83 3386.53577 3675.76439
84 1953.98219 3386.53577
85 2596.88219 1953.98219
86 2139.98219 2596.88219
87 2788.58219 2139.98219
88 2939.18219 2788.58219
89 3139.18219 2939.18219
90 3552.76439 3139.18219
91 2927.26439 3552.76439
92 1794.46439 2927.26439
93 1076.06439 1794.46439
94 634.76439 1076.06439
95 287.53577 634.76439
96 394.98219 287.53577
97 543.88219 394.98219
98 201.98219 543.88219
99 113.58219 201.98219
100 133.18219 113.58219
101 310.18219 133.18219
102 2835.94246 310.18219
103 1544.44246 2835.94246
104 -736.35754 1544.44246
105 -242.75754 -736.35754
106 -191.05754 -242.75754
107 429.71384 -191.05754
108 179.16026 429.71384
109 206.06026 179.16026
110 1104.16026 206.06026
111 556.76026 1104.16026
112 329.36026 556.76026
113 1258.36026 329.36026
114 -109.05754 1258.36026
115 -1266.55754 -109.05754
116 -2883.35754 -1266.55754
117 -1896.75754 -2883.35754
118 -1118.05754 -1896.75754
119 NA -1118.05754
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1712.11781 -1677.01781
[2,] -2058.01781 -1712.11781
[3,] -2146.41781 -2058.01781
[4,] -2400.81781 -2146.41781
[5,] -3364.81781 -2400.81781
[6,] -5625.23561 -3364.81781
[7,] -4172.73561 -5625.23561
[8,] -3956.53561 -4172.73561
[9,] -4161.93561 -3956.53561
[10,] -3672.23561 -4161.93561
[11,] -3575.46423 -3672.23561
[12,] -3232.01781 -3575.46423
[13,] -3569.11781 -3232.01781
[14,] -3539.01781 -3569.11781
[15,] -3204.41781 -3539.01781
[16,] -3290.81781 -3204.41781
[17,] -3835.81781 -3290.81781
[18,] -5855.23561 -3835.81781
[19,] -4520.73561 -5855.23561
[20,] -4893.53561 -4520.73561
[21,] -4587.93561 -4893.53561
[22,] -4367.23561 -4587.93561
[23,] -4674.46423 -4367.23561
[24,] -3772.01781 -4674.46423
[25,] -3720.11781 -3772.01781
[26,] -3549.01781 -3720.11781
[27,] -3369.41781 -3549.01781
[28,] -3201.81781 -3369.41781
[29,] -3898.81781 -3201.81781
[30,] -5489.23561 -3898.81781
[31,] -4688.73561 -5489.23561
[32,] -2745.53561 -4688.73561
[33,] -2241.93561 -2745.53561
[34,] -2201.23561 -2241.93561
[35,] -2354.46423 -2201.23561
[36,] -1713.01781 -2354.46423
[37,] -1624.11781 -1713.01781
[38,] -1349.01781 -1624.11781
[39,] -1319.41781 -1349.01781
[40,] -1080.81781 -1319.41781
[41,] -1496.81781 -1080.81781
[42,] -927.23561 -1496.81781
[43,] -1466.73561 -927.23561
[44,] 228.46439 -1466.73561
[45,] 74.06439 228.46439
[46,] 170.76439 74.06439
[47,] 111.53577 170.76439
[48,] 704.98219 111.53577
[49,] 210.88219 704.98219
[50,] 233.98219 210.88219
[51,] 671.58219 233.98219
[52,] 762.18219 671.58219
[53,] 1379.18219 762.18219
[54,] 2726.76439 1379.18219
[55,] 2374.26439 2726.76439
[56,] 3531.46439 2374.26439
[57,] 2775.06439 3531.46439
[58,] 2545.76439 2775.06439
[59,] 2352.53577 2545.76439
[60,] 3019.98219 2352.53577
[61,] 3293.88219 3019.98219
[62,] 2938.98219 3293.88219
[63,] 2747.58219 2938.98219
[64,] 2614.18219 2747.58219
[65,] 2938.18219 2614.18219
[66,] 3710.76439 2938.18219
[67,] 4158.26439 3710.76439
[68,] 5510.46439 4158.26439
[69,] 4857.06439 5510.46439
[70,] 4522.76439 4857.06439
[71,] 4036.53577 4522.76439
[72,] 4140.98219 4036.53577
[73,] 3773.88219 4140.98219
[74,] 3875.98219 3773.88219
[75,] 3161.58219 3875.98219
[76,] 3196.18219 3161.58219
[77,] 3571.18219 3196.18219
[78,] 5179.76439 3571.18219
[79,] 5111.26439 5179.76439
[80,] 4150.46439 5111.26439
[81,] 4349.06439 4150.46439
[82,] 3675.76439 4349.06439
[83,] 3386.53577 3675.76439
[84,] 1953.98219 3386.53577
[85,] 2596.88219 1953.98219
[86,] 2139.98219 2596.88219
[87,] 2788.58219 2139.98219
[88,] 2939.18219 2788.58219
[89,] 3139.18219 2939.18219
[90,] 3552.76439 3139.18219
[91,] 2927.26439 3552.76439
[92,] 1794.46439 2927.26439
[93,] 1076.06439 1794.46439
[94,] 634.76439 1076.06439
[95,] 287.53577 634.76439
[96,] 394.98219 287.53577
[97,] 543.88219 394.98219
[98,] 201.98219 543.88219
[99,] 113.58219 201.98219
[100,] 133.18219 113.58219
[101,] 310.18219 133.18219
[102,] 2835.94246 310.18219
[103,] 1544.44246 2835.94246
[104,] -736.35754 1544.44246
[105,] -242.75754 -736.35754
[106,] -191.05754 -242.75754
[107,] 429.71384 -191.05754
[108,] 179.16026 429.71384
[109,] 206.06026 179.16026
[110,] 1104.16026 206.06026
[111,] 556.76026 1104.16026
[112,] 329.36026 556.76026
[113,] 1258.36026 329.36026
[114,] -109.05754 1258.36026
[115,] -1266.55754 -109.05754
[116,] -2883.35754 -1266.55754
[117,] -1896.75754 -2883.35754
[118,] -1118.05754 -1896.75754
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1712.11781 -1677.01781
2 -2058.01781 -1712.11781
3 -2146.41781 -2058.01781
4 -2400.81781 -2146.41781
5 -3364.81781 -2400.81781
6 -5625.23561 -3364.81781
7 -4172.73561 -5625.23561
8 -3956.53561 -4172.73561
9 -4161.93561 -3956.53561
10 -3672.23561 -4161.93561
11 -3575.46423 -3672.23561
12 -3232.01781 -3575.46423
13 -3569.11781 -3232.01781
14 -3539.01781 -3569.11781
15 -3204.41781 -3539.01781
16 -3290.81781 -3204.41781
17 -3835.81781 -3290.81781
18 -5855.23561 -3835.81781
19 -4520.73561 -5855.23561
20 -4893.53561 -4520.73561
21 -4587.93561 -4893.53561
22 -4367.23561 -4587.93561
23 -4674.46423 -4367.23561
24 -3772.01781 -4674.46423
25 -3720.11781 -3772.01781
26 -3549.01781 -3720.11781
27 -3369.41781 -3549.01781
28 -3201.81781 -3369.41781
29 -3898.81781 -3201.81781
30 -5489.23561 -3898.81781
31 -4688.73561 -5489.23561
32 -2745.53561 -4688.73561
33 -2241.93561 -2745.53561
34 -2201.23561 -2241.93561
35 -2354.46423 -2201.23561
36 -1713.01781 -2354.46423
37 -1624.11781 -1713.01781
38 -1349.01781 -1624.11781
39 -1319.41781 -1349.01781
40 -1080.81781 -1319.41781
41 -1496.81781 -1080.81781
42 -927.23561 -1496.81781
43 -1466.73561 -927.23561
44 228.46439 -1466.73561
45 74.06439 228.46439
46 170.76439 74.06439
47 111.53577 170.76439
48 704.98219 111.53577
49 210.88219 704.98219
50 233.98219 210.88219
51 671.58219 233.98219
52 762.18219 671.58219
53 1379.18219 762.18219
54 2726.76439 1379.18219
55 2374.26439 2726.76439
56 3531.46439 2374.26439
57 2775.06439 3531.46439
58 2545.76439 2775.06439
59 2352.53577 2545.76439
60 3019.98219 2352.53577
61 3293.88219 3019.98219
62 2938.98219 3293.88219
63 2747.58219 2938.98219
64 2614.18219 2747.58219
65 2938.18219 2614.18219
66 3710.76439 2938.18219
67 4158.26439 3710.76439
68 5510.46439 4158.26439
69 4857.06439 5510.46439
70 4522.76439 4857.06439
71 4036.53577 4522.76439
72 4140.98219 4036.53577
73 3773.88219 4140.98219
74 3875.98219 3773.88219
75 3161.58219 3875.98219
76 3196.18219 3161.58219
77 3571.18219 3196.18219
78 5179.76439 3571.18219
79 5111.26439 5179.76439
80 4150.46439 5111.26439
81 4349.06439 4150.46439
82 3675.76439 4349.06439
83 3386.53577 3675.76439
84 1953.98219 3386.53577
85 2596.88219 1953.98219
86 2139.98219 2596.88219
87 2788.58219 2139.98219
88 2939.18219 2788.58219
89 3139.18219 2939.18219
90 3552.76439 3139.18219
91 2927.26439 3552.76439
92 1794.46439 2927.26439
93 1076.06439 1794.46439
94 634.76439 1076.06439
95 287.53577 634.76439
96 394.98219 287.53577
97 543.88219 394.98219
98 201.98219 543.88219
99 113.58219 201.98219
100 133.18219 113.58219
101 310.18219 133.18219
102 2835.94246 310.18219
103 1544.44246 2835.94246
104 -736.35754 1544.44246
105 -242.75754 -736.35754
106 -191.05754 -242.75754
107 429.71384 -191.05754
108 179.16026 429.71384
109 206.06026 179.16026
110 1104.16026 206.06026
111 556.76026 1104.16026
112 329.36026 556.76026
113 1258.36026 329.36026
114 -109.05754 1258.36026
115 -1266.55754 -109.05754
116 -2883.35754 -1266.55754
117 -1896.75754 -2883.35754
118 -1118.05754 -1896.75754
> 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/79p0y1229267024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8561k1229267024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/917651229267024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bcdf1229267024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11fla11229267024.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/12699u1229267024.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/13wbrl1229267024.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/14wjzw1229267024.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/15r5wr1229267024.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/168efj1229267024.tab")
+ }
>
> system("convert tmp/1a8ul1229267023.ps tmp/1a8ul1229267023.png")
> system("convert tmp/202ya1229267023.ps tmp/202ya1229267023.png")
> system("convert tmp/3slf11229267023.ps tmp/3slf11229267023.png")
> system("convert tmp/4fhgg1229267023.ps tmp/4fhgg1229267023.png")
> system("convert tmp/50v441229267023.ps tmp/50v441229267023.png")
> system("convert tmp/659lt1229267023.ps tmp/659lt1229267023.png")
> system("convert tmp/79p0y1229267024.ps tmp/79p0y1229267024.png")
> system("convert tmp/8561k1229267024.ps tmp/8561k1229267024.png")
> system("convert tmp/917651229267024.ps tmp/917651229267024.png")
> system("convert tmp/10bcdf1229267024.ps tmp/10bcdf1229267024.png")
>
>
> proc.time()
user system elapsed
3.350 1.629 3.709