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(377,370,358,357,349,348,369,381,368,361,351,351,358,354,347,345,343,340,362,370,373,371,354,357,363,364,363,358,357,357,380,378,376,380,379,384,392,394,392,396,392,396,419,421,420,418,410,418,426,428,430,424,423,427,441,449,452,462,455,461,461,463,462,456,455,456,472,472,471,465,459,465,468,467,463,460,462,461,476,476,471,453,443,442,444,438,427,424,416,406,431,434,418,412,404,409,412,406,398,397,385,390,413,413,401,397,397,409,419,424,428,430,424,433,456,459,446,441,439,454,460,457,451,444,437,443,471,469,454,444,436),dim=c(1,131),dimnames=list(c('Werkloosheid'),1:131))
> y <- array(NA,dim=c(1,131),dimnames=list(c('Werkloosheid'),1:131))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Werkloosheid M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 377 1 0 0 0 0 0 0 0 0 0 0 1
2 370 0 1 0 0 0 0 0 0 0 0 0 2
3 358 0 0 1 0 0 0 0 0 0 0 0 3
4 357 0 0 0 1 0 0 0 0 0 0 0 4
5 349 0 0 0 0 1 0 0 0 0 0 0 5
6 348 0 0 0 0 0 1 0 0 0 0 0 6
7 369 0 0 0 0 0 0 1 0 0 0 0 7
8 381 0 0 0 0 0 0 0 1 0 0 0 8
9 368 0 0 0 0 0 0 0 0 1 0 0 9
10 361 0 0 0 0 0 0 0 0 0 1 0 10
11 351 0 0 0 0 0 0 0 0 0 0 1 11
12 351 0 0 0 0 0 0 0 0 0 0 0 12
13 358 1 0 0 0 0 0 0 0 0 0 0 13
14 354 0 1 0 0 0 0 0 0 0 0 0 14
15 347 0 0 1 0 0 0 0 0 0 0 0 15
16 345 0 0 0 1 0 0 0 0 0 0 0 16
17 343 0 0 0 0 1 0 0 0 0 0 0 17
18 340 0 0 0 0 0 1 0 0 0 0 0 18
19 362 0 0 0 0 0 0 1 0 0 0 0 19
20 370 0 0 0 0 0 0 0 1 0 0 0 20
21 373 0 0 0 0 0 0 0 0 1 0 0 21
22 371 0 0 0 0 0 0 0 0 0 1 0 22
23 354 0 0 0 0 0 0 0 0 0 0 1 23
24 357 0 0 0 0 0 0 0 0 0 0 0 24
25 363 1 0 0 0 0 0 0 0 0 0 0 25
26 364 0 1 0 0 0 0 0 0 0 0 0 26
27 363 0 0 1 0 0 0 0 0 0 0 0 27
28 358 0 0 0 1 0 0 0 0 0 0 0 28
29 357 0 0 0 0 1 0 0 0 0 0 0 29
30 357 0 0 0 0 0 1 0 0 0 0 0 30
31 380 0 0 0 0 0 0 1 0 0 0 0 31
32 378 0 0 0 0 0 0 0 1 0 0 0 32
33 376 0 0 0 0 0 0 0 0 1 0 0 33
34 380 0 0 0 0 0 0 0 0 0 1 0 34
35 379 0 0 0 0 0 0 0 0 0 0 1 35
36 384 0 0 0 0 0 0 0 0 0 0 0 36
37 392 1 0 0 0 0 0 0 0 0 0 0 37
38 394 0 1 0 0 0 0 0 0 0 0 0 38
39 392 0 0 1 0 0 0 0 0 0 0 0 39
40 396 0 0 0 1 0 0 0 0 0 0 0 40
41 392 0 0 0 0 1 0 0 0 0 0 0 41
42 396 0 0 0 0 0 1 0 0 0 0 0 42
43 419 0 0 0 0 0 0 1 0 0 0 0 43
44 421 0 0 0 0 0 0 0 1 0 0 0 44
45 420 0 0 0 0 0 0 0 0 1 0 0 45
46 418 0 0 0 0 0 0 0 0 0 1 0 46
47 410 0 0 0 0 0 0 0 0 0 0 1 47
48 418 0 0 0 0 0 0 0 0 0 0 0 48
49 426 1 0 0 0 0 0 0 0 0 0 0 49
50 428 0 1 0 0 0 0 0 0 0 0 0 50
51 430 0 0 1 0 0 0 0 0 0 0 0 51
52 424 0 0 0 1 0 0 0 0 0 0 0 52
53 423 0 0 0 0 1 0 0 0 0 0 0 53
54 427 0 0 0 0 0 1 0 0 0 0 0 54
55 441 0 0 0 0 0 0 1 0 0 0 0 55
56 449 0 0 0 0 0 0 0 1 0 0 0 56
57 452 0 0 0 0 0 0 0 0 1 0 0 57
58 462 0 0 0 0 0 0 0 0 0 1 0 58
59 455 0 0 0 0 0 0 0 0 0 0 1 59
60 461 0 0 0 0 0 0 0 0 0 0 0 60
61 461 1 0 0 0 0 0 0 0 0 0 0 61
62 463 0 1 0 0 0 0 0 0 0 0 0 62
63 462 0 0 1 0 0 0 0 0 0 0 0 63
64 456 0 0 0 1 0 0 0 0 0 0 0 64
65 455 0 0 0 0 1 0 0 0 0 0 0 65
66 456 0 0 0 0 0 1 0 0 0 0 0 66
67 472 0 0 0 0 0 0 1 0 0 0 0 67
68 472 0 0 0 0 0 0 0 1 0 0 0 68
69 471 0 0 0 0 0 0 0 0 1 0 0 69
70 465 0 0 0 0 0 0 0 0 0 1 0 70
71 459 0 0 0 0 0 0 0 0 0 0 1 71
72 465 0 0 0 0 0 0 0 0 0 0 0 72
73 468 1 0 0 0 0 0 0 0 0 0 0 73
74 467 0 1 0 0 0 0 0 0 0 0 0 74
75 463 0 0 1 0 0 0 0 0 0 0 0 75
76 460 0 0 0 1 0 0 0 0 0 0 0 76
77 462 0 0 0 0 1 0 0 0 0 0 0 77
78 461 0 0 0 0 0 1 0 0 0 0 0 78
79 476 0 0 0 0 0 0 1 0 0 0 0 79
80 476 0 0 0 0 0 0 0 1 0 0 0 80
81 471 0 0 0 0 0 0 0 0 1 0 0 81
82 453 0 0 0 0 0 0 0 0 0 1 0 82
83 443 0 0 0 0 0 0 0 0 0 0 1 83
84 442 0 0 0 0 0 0 0 0 0 0 0 84
85 444 1 0 0 0 0 0 0 0 0 0 0 85
86 438 0 1 0 0 0 0 0 0 0 0 0 86
87 427 0 0 1 0 0 0 0 0 0 0 0 87
88 424 0 0 0 1 0 0 0 0 0 0 0 88
89 416 0 0 0 0 1 0 0 0 0 0 0 89
90 406 0 0 0 0 0 1 0 0 0 0 0 90
91 431 0 0 0 0 0 0 1 0 0 0 0 91
92 434 0 0 0 0 0 0 0 1 0 0 0 92
93 418 0 0 0 0 0 0 0 0 1 0 0 93
94 412 0 0 0 0 0 0 0 0 0 1 0 94
95 404 0 0 0 0 0 0 0 0 0 0 1 95
96 409 0 0 0 0 0 0 0 0 0 0 0 96
97 412 1 0 0 0 0 0 0 0 0 0 0 97
98 406 0 1 0 0 0 0 0 0 0 0 0 98
99 398 0 0 1 0 0 0 0 0 0 0 0 99
100 397 0 0 0 1 0 0 0 0 0 0 0 100
101 385 0 0 0 0 1 0 0 0 0 0 0 101
102 390 0 0 0 0 0 1 0 0 0 0 0 102
103 413 0 0 0 0 0 0 1 0 0 0 0 103
104 413 0 0 0 0 0 0 0 1 0 0 0 104
105 401 0 0 0 0 0 0 0 0 1 0 0 105
106 397 0 0 0 0 0 0 0 0 0 1 0 106
107 397 0 0 0 0 0 0 0 0 0 0 1 107
108 409 0 0 0 0 0 0 0 0 0 0 0 108
109 419 1 0 0 0 0 0 0 0 0 0 0 109
110 424 0 1 0 0 0 0 0 0 0 0 0 110
111 428 0 0 1 0 0 0 0 0 0 0 0 111
112 430 0 0 0 1 0 0 0 0 0 0 0 112
113 424 0 0 0 0 1 0 0 0 0 0 0 113
114 433 0 0 0 0 0 1 0 0 0 0 0 114
115 456 0 0 0 0 0 0 1 0 0 0 0 115
116 459 0 0 0 0 0 0 0 1 0 0 0 116
117 446 0 0 0 0 0 0 0 0 1 0 0 117
118 441 0 0 0 0 0 0 0 0 0 1 0 118
119 439 0 0 0 0 0 0 0 0 0 0 1 119
120 454 0 0 0 0 0 0 0 0 0 0 0 120
121 460 1 0 0 0 0 0 0 0 0 0 0 121
122 457 0 1 0 0 0 0 0 0 0 0 0 122
123 451 0 0 1 0 0 0 0 0 0 0 0 123
124 444 0 0 0 1 0 0 0 0 0 0 0 124
125 437 0 0 0 0 1 0 0 0 0 0 0 125
126 443 0 0 0 0 0 1 0 0 0 0 0 126
127 471 0 0 0 0 0 0 1 0 0 0 0 127
128 469 0 0 0 0 0 0 0 1 0 0 0 128
129 454 0 0 0 0 0 0 0 0 1 0 0 129
130 444 0 0 0 0 0 0 0 0 0 1 0 130
131 436 0 0 0 0 0 0 0 0 0 0 1 131
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
368.5660 4.8814 2.8142 -2.0712 -5.3202 -10.3874
M6 M7 M8 M9 M10 M11
-9.8182 10.6601 12.8656 5.6166 0.7313 -6.9723
t
0.7035
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-47.055 -22.672 -6.316 22.227 51.897
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 368.56596 10.36499 35.559 <2e-16 ***
M1 4.88137 12.89644 0.379 0.706
M2 2.81418 12.89480 0.218 0.828
M3 -2.07118 12.89353 -0.161 0.873
M4 -5.32018 12.89263 -0.413 0.681
M5 -10.38736 12.89208 -0.806 0.422
M6 -9.81818 12.89190 -0.762 0.448
M7 10.66009 12.89208 0.827 0.410
M8 12.86564 12.89263 0.998 0.320
M9 5.61663 12.89353 0.436 0.664
M10 0.73127 12.89480 0.057 0.955
M11 -6.97228 12.89644 -0.541 0.590
t 0.70355 0.06839 10.287 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 29.51 on 118 degrees of freedom
Multiple R-squared: 0.4928, Adjusted R-squared: 0.4413
F-statistic: 9.556 on 12 and 118 DF, p-value: 9.713e-13
> 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,] 8.741514e-04 1.748303e-03 9.991258e-01
[2,] 3.585845e-04 7.171691e-04 9.996414e-01
[3,] 5.716350e-05 1.143270e-04 9.999428e-01
[4,] 9.864857e-06 1.972971e-05 9.999901e-01
[5,] 1.052225e-06 2.104451e-06 9.999989e-01
[6,] 5.084617e-06 1.016923e-05 9.999949e-01
[7,] 1.313622e-05 2.627245e-05 9.999869e-01
[8,] 5.220771e-06 1.044154e-05 9.999948e-01
[9,] 2.792278e-06 5.584556e-06 9.999972e-01
[10,] 7.387533e-07 1.477507e-06 9.999993e-01
[11,] 3.264073e-07 6.528147e-07 9.999997e-01
[12,] 4.320553e-07 8.641107e-07 9.999996e-01
[13,] 2.357272e-07 4.714544e-07 9.999998e-01
[14,] 1.834942e-07 3.669885e-07 9.999998e-01
[15,] 1.615351e-07 3.230701e-07 9.999998e-01
[16,] 1.462077e-07 2.924155e-07 9.999999e-01
[17,] 6.013603e-08 1.202721e-07 9.999999e-01
[18,] 2.589588e-08 5.179176e-08 1.000000e+00
[19,] 1.993360e-08 3.986720e-08 1.000000e+00
[20,] 9.134888e-08 1.826978e-07 9.999999e-01
[21,] 4.267992e-07 8.535985e-07 9.999996e-01
[22,] 5.998612e-07 1.199722e-06 9.999994e-01
[23,] 1.200269e-06 2.400537e-06 9.999988e-01
[24,] 3.037678e-06 6.075356e-06 9.999970e-01
[25,] 1.051277e-05 2.102554e-05 9.999895e-01
[26,] 2.305636e-05 4.611272e-05 9.999769e-01
[27,] 6.232470e-05 1.246494e-04 9.999377e-01
[28,] 1.334797e-04 2.669595e-04 9.998665e-01
[29,] 1.830900e-04 3.661800e-04 9.998169e-01
[30,] 2.508891e-04 5.017782e-04 9.997491e-01
[31,] 2.980787e-04 5.961575e-04 9.997019e-01
[32,] 3.718873e-04 7.437745e-04 9.996281e-01
[33,] 5.603544e-04 1.120709e-03 9.994396e-01
[34,] 5.325611e-04 1.065122e-03 9.994674e-01
[35,] 5.480280e-04 1.096056e-03 9.994520e-01
[36,] 7.035337e-04 1.407067e-03 9.992965e-01
[37,] 6.874369e-04 1.374874e-03 9.993126e-01
[38,] 6.858545e-04 1.371709e-03 9.993141e-01
[39,] 7.524504e-04 1.504901e-03 9.992475e-01
[40,] 6.579847e-04 1.315969e-03 9.993420e-01
[41,] 5.661169e-04 1.132234e-03 9.994339e-01
[42,] 5.278939e-04 1.055788e-03 9.994721e-01
[43,] 8.024730e-04 1.604946e-03 9.991975e-01
[44,] 1.172733e-03 2.345465e-03 9.988273e-01
[45,] 1.685577e-03 3.371154e-03 9.983144e-01
[46,] 1.280191e-03 2.560381e-03 9.987198e-01
[47,] 1.043676e-03 2.087353e-03 9.989563e-01
[48,] 9.249743e-04 1.849949e-03 9.990750e-01
[49,] 7.012710e-04 1.402542e-03 9.992987e-01
[50,] 5.768651e-04 1.153730e-03 9.994231e-01
[51,] 4.669582e-04 9.339164e-04 9.995330e-01
[52,] 3.198080e-04 6.396161e-04 9.996802e-01
[53,] 2.016910e-04 4.033820e-04 9.997983e-01
[54,] 1.470119e-04 2.940238e-04 9.998530e-01
[55,] 1.128332e-04 2.256663e-04 9.998872e-01
[56,] 8.937843e-05 1.787569e-04 9.999106e-01
[57,] 7.695866e-05 1.539173e-04 9.999230e-01
[58,] 6.854701e-05 1.370940e-04 9.999315e-01
[59,] 6.720150e-05 1.344030e-04 9.999328e-01
[60,] 7.398428e-05 1.479686e-04 9.999260e-01
[61,] 8.742079e-05 1.748416e-04 9.999126e-01
[62,] 1.746642e-04 3.493284e-04 9.998253e-01
[63,] 3.734238e-04 7.468476e-04 9.996266e-01
[64,] 6.524586e-04 1.304917e-03 9.993475e-01
[65,] 1.411657e-03 2.823313e-03 9.985883e-01
[66,] 6.811956e-03 1.362391e-02 9.931880e-01
[67,] 3.186876e-02 6.373751e-02 9.681312e-01
[68,] 1.150135e-01 2.300270e-01 8.849865e-01
[69,] 2.533385e-01 5.066771e-01 7.466615e-01
[70,] 4.782028e-01 9.564056e-01 5.217972e-01
[71,] 7.058455e-01 5.883090e-01 2.941545e-01
[72,] 8.568525e-01 2.862950e-01 1.431475e-01
[73,] 9.412218e-01 1.175564e-01 5.877818e-02
[74,] 9.846168e-01 3.076632e-02 1.538316e-02
[75,] 9.936232e-01 1.275350e-02 6.376751e-03
[76,] 9.969882e-01 6.023585e-03 3.011793e-03
[77,] 9.989671e-01 2.065779e-03 1.032890e-03
[78,] 9.997506e-01 4.988230e-04 2.494115e-04
[79,] 9.999652e-01 6.952426e-05 3.476213e-05
[80,] 9.999956e-01 8.762608e-06 4.381304e-06
[81,] 9.999969e-01 6.144579e-06 3.072290e-06
[82,] 9.999970e-01 6.071836e-06 3.035918e-06
[83,] 9.999957e-01 8.552970e-06 4.276485e-06
[84,] 9.999932e-01 1.357126e-05 6.785631e-06
[85,] 9.999878e-01 2.434236e-05 1.217118e-05
[86,] 9.999809e-01 3.815943e-05 1.907972e-05
[87,] 9.999693e-01 6.131197e-05 3.065598e-05
[88,] 9.999560e-01 8.800921e-05 4.400461e-05
[89,] 9.999451e-01 1.097468e-04 5.487340e-05
[90,] 9.999338e-01 1.323991e-04 6.619957e-05
[91,] 9.999095e-01 1.809273e-04 9.046364e-05
[92,] 9.998389e-01 3.221402e-04 1.610701e-04
[93,] 9.999274e-01 1.452445e-04 7.262226e-05
[94,] 9.999799e-01 4.010247e-05 2.005124e-05
[95,] 9.999944e-01 1.126120e-05 5.630599e-06
[96,] 9.999943e-01 1.146641e-05 5.733204e-06
[97,] 9.999700e-01 5.993871e-05 2.996935e-05
[98,] 9.998377e-01 3.246978e-04 1.623489e-04
[99,] 9.988946e-01 2.210708e-03 1.105354e-03
[100,] 9.969621e-01 6.075710e-03 3.037855e-03
> postscript(file="/var/www/html/freestat/rcomp/tmp/1lu421293012842.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2el3n1293012842.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3el3n1293012842.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4el3n1293012842.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5el3n1293012842.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 = 131
Frequency = 1
1 2 3 4 5 6
2.8491296 -2.7872340 -10.6054159 -9.0599613 -12.6963250 -14.9690522
7 8 9 10 11 12
-15.1508704 -6.0599613 -12.5145068 -15.3326886 -18.3326886 -26.0085106
13 14 15 16 17 18
-24.5934236 -27.2297872 -30.0479691 -29.5025145 -27.1388781 -31.4116054
19 20 21 22 23 24
-30.5934236 -25.5025145 -15.9570600 -13.7752418 -23.7752418 -28.4510638
25 26 27 28 29 30
-28.0359768 -25.6723404 -22.4905222 -24.9450677 -21.5814313 -22.8541586
31 32 33 34 35 36
-21.0359768 -25.9450677 -21.3996132 -13.2177950 -7.2177950 -9.8936170
37 38 39 40 41 42
-7.4785300 -4.1148936 -1.9330754 4.6123791 4.9760155 7.7032882
43 44 45 46 47 48
9.5214700 8.6123791 14.1578337 16.3396518 15.3396518 15.6638298
49 50 51 52 53 54
18.0789168 21.4425532 27.6243714 24.1698259 27.5334623 30.2607350
55 56 57 58 59 60
23.0789168 28.1698259 37.7152805 51.8970986 51.8970986 50.2212766
61 62 63 64 65 66
44.6363636 48.0000000 51.1818182 47.7272727 51.0909091 50.8181818
67 68 69 70 71 72
45.6363636 42.7272727 48.2727273 46.4545455 47.4545455 45.7787234
73 74 75 76 77 78
43.1938104 43.5574468 43.7392650 43.2847195 49.6483559 47.3756286
79 80 81 82 83 84
41.1938104 38.2847195 39.8301741 26.0119923 23.0119923 14.3361702
85 86 87 88 89 90
10.7512573 6.1148936 -0.7032882 -1.1578337 -4.7941973 -16.0669246
91 92 93 94 95 96
-12.2487427 -12.1578337 -21.6123791 -23.4305609 -24.4305609 -27.1063830
97 98 99 100 101 102
-29.6912959 -34.3276596 -38.1458414 -36.6003868 -44.2367505 -40.5094778
103 104 105 106 107 108
-38.6912959 -41.6003868 -47.0549323 -46.8731141 -39.8731141 -35.5489362
109 110 111 112 113 114
-31.1338491 -24.7702128 -16.5883946 -12.0429400 -13.6793037 -5.9520309
115 116 117 118 119 120
-4.1338491 -4.0429400 -10.4974855 -11.3156673 -6.3156673 1.0085106
121 122 123 124 125 126
1.4235977 -0.2127660 -2.0309478 -6.4854932 -9.1218569 -4.3945841
127 128 129 130 131
2.4235977 -2.4854932 -10.9400387 -16.7582205 -17.7582205
> postscript(file="/var/www/html/freestat/rcomp/tmp/6pd381293012842.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 = 131
Frequency = 1
lag(myerror, k = 1) myerror
0 2.8491296 NA
1 -2.7872340 2.8491296
2 -10.6054159 -2.7872340
3 -9.0599613 -10.6054159
4 -12.6963250 -9.0599613
5 -14.9690522 -12.6963250
6 -15.1508704 -14.9690522
7 -6.0599613 -15.1508704
8 -12.5145068 -6.0599613
9 -15.3326886 -12.5145068
10 -18.3326886 -15.3326886
11 -26.0085106 -18.3326886
12 -24.5934236 -26.0085106
13 -27.2297872 -24.5934236
14 -30.0479691 -27.2297872
15 -29.5025145 -30.0479691
16 -27.1388781 -29.5025145
17 -31.4116054 -27.1388781
18 -30.5934236 -31.4116054
19 -25.5025145 -30.5934236
20 -15.9570600 -25.5025145
21 -13.7752418 -15.9570600
22 -23.7752418 -13.7752418
23 -28.4510638 -23.7752418
24 -28.0359768 -28.4510638
25 -25.6723404 -28.0359768
26 -22.4905222 -25.6723404
27 -24.9450677 -22.4905222
28 -21.5814313 -24.9450677
29 -22.8541586 -21.5814313
30 -21.0359768 -22.8541586
31 -25.9450677 -21.0359768
32 -21.3996132 -25.9450677
33 -13.2177950 -21.3996132
34 -7.2177950 -13.2177950
35 -9.8936170 -7.2177950
36 -7.4785300 -9.8936170
37 -4.1148936 -7.4785300
38 -1.9330754 -4.1148936
39 4.6123791 -1.9330754
40 4.9760155 4.6123791
41 7.7032882 4.9760155
42 9.5214700 7.7032882
43 8.6123791 9.5214700
44 14.1578337 8.6123791
45 16.3396518 14.1578337
46 15.3396518 16.3396518
47 15.6638298 15.3396518
48 18.0789168 15.6638298
49 21.4425532 18.0789168
50 27.6243714 21.4425532
51 24.1698259 27.6243714
52 27.5334623 24.1698259
53 30.2607350 27.5334623
54 23.0789168 30.2607350
55 28.1698259 23.0789168
56 37.7152805 28.1698259
57 51.8970986 37.7152805
58 51.8970986 51.8970986
59 50.2212766 51.8970986
60 44.6363636 50.2212766
61 48.0000000 44.6363636
62 51.1818182 48.0000000
63 47.7272727 51.1818182
64 51.0909091 47.7272727
65 50.8181818 51.0909091
66 45.6363636 50.8181818
67 42.7272727 45.6363636
68 48.2727273 42.7272727
69 46.4545455 48.2727273
70 47.4545455 46.4545455
71 45.7787234 47.4545455
72 43.1938104 45.7787234
73 43.5574468 43.1938104
74 43.7392650 43.5574468
75 43.2847195 43.7392650
76 49.6483559 43.2847195
77 47.3756286 49.6483559
78 41.1938104 47.3756286
79 38.2847195 41.1938104
80 39.8301741 38.2847195
81 26.0119923 39.8301741
82 23.0119923 26.0119923
83 14.3361702 23.0119923
84 10.7512573 14.3361702
85 6.1148936 10.7512573
86 -0.7032882 6.1148936
87 -1.1578337 -0.7032882
88 -4.7941973 -1.1578337
89 -16.0669246 -4.7941973
90 -12.2487427 -16.0669246
91 -12.1578337 -12.2487427
92 -21.6123791 -12.1578337
93 -23.4305609 -21.6123791
94 -24.4305609 -23.4305609
95 -27.1063830 -24.4305609
96 -29.6912959 -27.1063830
97 -34.3276596 -29.6912959
98 -38.1458414 -34.3276596
99 -36.6003868 -38.1458414
100 -44.2367505 -36.6003868
101 -40.5094778 -44.2367505
102 -38.6912959 -40.5094778
103 -41.6003868 -38.6912959
104 -47.0549323 -41.6003868
105 -46.8731141 -47.0549323
106 -39.8731141 -46.8731141
107 -35.5489362 -39.8731141
108 -31.1338491 -35.5489362
109 -24.7702128 -31.1338491
110 -16.5883946 -24.7702128
111 -12.0429400 -16.5883946
112 -13.6793037 -12.0429400
113 -5.9520309 -13.6793037
114 -4.1338491 -5.9520309
115 -4.0429400 -4.1338491
116 -10.4974855 -4.0429400
117 -11.3156673 -10.4974855
118 -6.3156673 -11.3156673
119 1.0085106 -6.3156673
120 1.4235977 1.0085106
121 -0.2127660 1.4235977
122 -2.0309478 -0.2127660
123 -6.4854932 -2.0309478
124 -9.1218569 -6.4854932
125 -4.3945841 -9.1218569
126 2.4235977 -4.3945841
127 -2.4854932 2.4235977
128 -10.9400387 -2.4854932
129 -16.7582205 -10.9400387
130 -17.7582205 -16.7582205
131 NA -17.7582205
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.7872340 2.8491296
[2,] -10.6054159 -2.7872340
[3,] -9.0599613 -10.6054159
[4,] -12.6963250 -9.0599613
[5,] -14.9690522 -12.6963250
[6,] -15.1508704 -14.9690522
[7,] -6.0599613 -15.1508704
[8,] -12.5145068 -6.0599613
[9,] -15.3326886 -12.5145068
[10,] -18.3326886 -15.3326886
[11,] -26.0085106 -18.3326886
[12,] -24.5934236 -26.0085106
[13,] -27.2297872 -24.5934236
[14,] -30.0479691 -27.2297872
[15,] -29.5025145 -30.0479691
[16,] -27.1388781 -29.5025145
[17,] -31.4116054 -27.1388781
[18,] -30.5934236 -31.4116054
[19,] -25.5025145 -30.5934236
[20,] -15.9570600 -25.5025145
[21,] -13.7752418 -15.9570600
[22,] -23.7752418 -13.7752418
[23,] -28.4510638 -23.7752418
[24,] -28.0359768 -28.4510638
[25,] -25.6723404 -28.0359768
[26,] -22.4905222 -25.6723404
[27,] -24.9450677 -22.4905222
[28,] -21.5814313 -24.9450677
[29,] -22.8541586 -21.5814313
[30,] -21.0359768 -22.8541586
[31,] -25.9450677 -21.0359768
[32,] -21.3996132 -25.9450677
[33,] -13.2177950 -21.3996132
[34,] -7.2177950 -13.2177950
[35,] -9.8936170 -7.2177950
[36,] -7.4785300 -9.8936170
[37,] -4.1148936 -7.4785300
[38,] -1.9330754 -4.1148936
[39,] 4.6123791 -1.9330754
[40,] 4.9760155 4.6123791
[41,] 7.7032882 4.9760155
[42,] 9.5214700 7.7032882
[43,] 8.6123791 9.5214700
[44,] 14.1578337 8.6123791
[45,] 16.3396518 14.1578337
[46,] 15.3396518 16.3396518
[47,] 15.6638298 15.3396518
[48,] 18.0789168 15.6638298
[49,] 21.4425532 18.0789168
[50,] 27.6243714 21.4425532
[51,] 24.1698259 27.6243714
[52,] 27.5334623 24.1698259
[53,] 30.2607350 27.5334623
[54,] 23.0789168 30.2607350
[55,] 28.1698259 23.0789168
[56,] 37.7152805 28.1698259
[57,] 51.8970986 37.7152805
[58,] 51.8970986 51.8970986
[59,] 50.2212766 51.8970986
[60,] 44.6363636 50.2212766
[61,] 48.0000000 44.6363636
[62,] 51.1818182 48.0000000
[63,] 47.7272727 51.1818182
[64,] 51.0909091 47.7272727
[65,] 50.8181818 51.0909091
[66,] 45.6363636 50.8181818
[67,] 42.7272727 45.6363636
[68,] 48.2727273 42.7272727
[69,] 46.4545455 48.2727273
[70,] 47.4545455 46.4545455
[71,] 45.7787234 47.4545455
[72,] 43.1938104 45.7787234
[73,] 43.5574468 43.1938104
[74,] 43.7392650 43.5574468
[75,] 43.2847195 43.7392650
[76,] 49.6483559 43.2847195
[77,] 47.3756286 49.6483559
[78,] 41.1938104 47.3756286
[79,] 38.2847195 41.1938104
[80,] 39.8301741 38.2847195
[81,] 26.0119923 39.8301741
[82,] 23.0119923 26.0119923
[83,] 14.3361702 23.0119923
[84,] 10.7512573 14.3361702
[85,] 6.1148936 10.7512573
[86,] -0.7032882 6.1148936
[87,] -1.1578337 -0.7032882
[88,] -4.7941973 -1.1578337
[89,] -16.0669246 -4.7941973
[90,] -12.2487427 -16.0669246
[91,] -12.1578337 -12.2487427
[92,] -21.6123791 -12.1578337
[93,] -23.4305609 -21.6123791
[94,] -24.4305609 -23.4305609
[95,] -27.1063830 -24.4305609
[96,] -29.6912959 -27.1063830
[97,] -34.3276596 -29.6912959
[98,] -38.1458414 -34.3276596
[99,] -36.6003868 -38.1458414
[100,] -44.2367505 -36.6003868
[101,] -40.5094778 -44.2367505
[102,] -38.6912959 -40.5094778
[103,] -41.6003868 -38.6912959
[104,] -47.0549323 -41.6003868
[105,] -46.8731141 -47.0549323
[106,] -39.8731141 -46.8731141
[107,] -35.5489362 -39.8731141
[108,] -31.1338491 -35.5489362
[109,] -24.7702128 -31.1338491
[110,] -16.5883946 -24.7702128
[111,] -12.0429400 -16.5883946
[112,] -13.6793037 -12.0429400
[113,] -5.9520309 -13.6793037
[114,] -4.1338491 -5.9520309
[115,] -4.0429400 -4.1338491
[116,] -10.4974855 -4.0429400
[117,] -11.3156673 -10.4974855
[118,] -6.3156673 -11.3156673
[119,] 1.0085106 -6.3156673
[120,] 1.4235977 1.0085106
[121,] -0.2127660 1.4235977
[122,] -2.0309478 -0.2127660
[123,] -6.4854932 -2.0309478
[124,] -9.1218569 -6.4854932
[125,] -4.3945841 -9.1218569
[126,] 2.4235977 -4.3945841
[127,] -2.4854932 2.4235977
[128,] -10.9400387 -2.4854932
[129,] -16.7582205 -10.9400387
[130,] -17.7582205 -16.7582205
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.7872340 2.8491296
2 -10.6054159 -2.7872340
3 -9.0599613 -10.6054159
4 -12.6963250 -9.0599613
5 -14.9690522 -12.6963250
6 -15.1508704 -14.9690522
7 -6.0599613 -15.1508704
8 -12.5145068 -6.0599613
9 -15.3326886 -12.5145068
10 -18.3326886 -15.3326886
11 -26.0085106 -18.3326886
12 -24.5934236 -26.0085106
13 -27.2297872 -24.5934236
14 -30.0479691 -27.2297872
15 -29.5025145 -30.0479691
16 -27.1388781 -29.5025145
17 -31.4116054 -27.1388781
18 -30.5934236 -31.4116054
19 -25.5025145 -30.5934236
20 -15.9570600 -25.5025145
21 -13.7752418 -15.9570600
22 -23.7752418 -13.7752418
23 -28.4510638 -23.7752418
24 -28.0359768 -28.4510638
25 -25.6723404 -28.0359768
26 -22.4905222 -25.6723404
27 -24.9450677 -22.4905222
28 -21.5814313 -24.9450677
29 -22.8541586 -21.5814313
30 -21.0359768 -22.8541586
31 -25.9450677 -21.0359768
32 -21.3996132 -25.9450677
33 -13.2177950 -21.3996132
34 -7.2177950 -13.2177950
35 -9.8936170 -7.2177950
36 -7.4785300 -9.8936170
37 -4.1148936 -7.4785300
38 -1.9330754 -4.1148936
39 4.6123791 -1.9330754
40 4.9760155 4.6123791
41 7.7032882 4.9760155
42 9.5214700 7.7032882
43 8.6123791 9.5214700
44 14.1578337 8.6123791
45 16.3396518 14.1578337
46 15.3396518 16.3396518
47 15.6638298 15.3396518
48 18.0789168 15.6638298
49 21.4425532 18.0789168
50 27.6243714 21.4425532
51 24.1698259 27.6243714
52 27.5334623 24.1698259
53 30.2607350 27.5334623
54 23.0789168 30.2607350
55 28.1698259 23.0789168
56 37.7152805 28.1698259
57 51.8970986 37.7152805
58 51.8970986 51.8970986
59 50.2212766 51.8970986
60 44.6363636 50.2212766
61 48.0000000 44.6363636
62 51.1818182 48.0000000
63 47.7272727 51.1818182
64 51.0909091 47.7272727
65 50.8181818 51.0909091
66 45.6363636 50.8181818
67 42.7272727 45.6363636
68 48.2727273 42.7272727
69 46.4545455 48.2727273
70 47.4545455 46.4545455
71 45.7787234 47.4545455
72 43.1938104 45.7787234
73 43.5574468 43.1938104
74 43.7392650 43.5574468
75 43.2847195 43.7392650
76 49.6483559 43.2847195
77 47.3756286 49.6483559
78 41.1938104 47.3756286
79 38.2847195 41.1938104
80 39.8301741 38.2847195
81 26.0119923 39.8301741
82 23.0119923 26.0119923
83 14.3361702 23.0119923
84 10.7512573 14.3361702
85 6.1148936 10.7512573
86 -0.7032882 6.1148936
87 -1.1578337 -0.7032882
88 -4.7941973 -1.1578337
89 -16.0669246 -4.7941973
90 -12.2487427 -16.0669246
91 -12.1578337 -12.2487427
92 -21.6123791 -12.1578337
93 -23.4305609 -21.6123791
94 -24.4305609 -23.4305609
95 -27.1063830 -24.4305609
96 -29.6912959 -27.1063830
97 -34.3276596 -29.6912959
98 -38.1458414 -34.3276596
99 -36.6003868 -38.1458414
100 -44.2367505 -36.6003868
101 -40.5094778 -44.2367505
102 -38.6912959 -40.5094778
103 -41.6003868 -38.6912959
104 -47.0549323 -41.6003868
105 -46.8731141 -47.0549323
106 -39.8731141 -46.8731141
107 -35.5489362 -39.8731141
108 -31.1338491 -35.5489362
109 -24.7702128 -31.1338491
110 -16.5883946 -24.7702128
111 -12.0429400 -16.5883946
112 -13.6793037 -12.0429400
113 -5.9520309 -13.6793037
114 -4.1338491 -5.9520309
115 -4.0429400 -4.1338491
116 -10.4974855 -4.0429400
117 -11.3156673 -10.4974855
118 -6.3156673 -11.3156673
119 1.0085106 -6.3156673
120 1.4235977 1.0085106
121 -0.2127660 1.4235977
122 -2.0309478 -0.2127660
123 -6.4854932 -2.0309478
124 -9.1218569 -6.4854932
125 -4.3945841 -9.1218569
126 2.4235977 -4.3945841
127 -2.4854932 2.4235977
128 -10.9400387 -2.4854932
129 -16.7582205 -10.9400387
130 -17.7582205 -16.7582205
> 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/freestat/rcomp/tmp/7hmkb1293012842.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8hmkb1293012842.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9hmkb1293012842.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10sd1e1293012842.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11vwik1293012842.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/freestat/rcomp/tmp/12zwyq1293012842.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/freestat/rcomp/tmp/13dowz1293012842.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/freestat/rcomp/tmp/14g6vm1293012842.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/freestat/rcomp/tmp/152pba1293012842.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/freestat/rcomp/tmp/16yz911293012842.tab")
+ }
>
> try(system("convert tmp/1lu421293012842.ps tmp/1lu421293012842.png",intern=TRUE))
character(0)
> try(system("convert tmp/2el3n1293012842.ps tmp/2el3n1293012842.png",intern=TRUE))
character(0)
> try(system("convert tmp/3el3n1293012842.ps tmp/3el3n1293012842.png",intern=TRUE))
character(0)
> try(system("convert tmp/4el3n1293012842.ps tmp/4el3n1293012842.png",intern=TRUE))
character(0)
> try(system("convert tmp/5el3n1293012842.ps tmp/5el3n1293012842.png",intern=TRUE))
character(0)
> try(system("convert tmp/6pd381293012842.ps tmp/6pd381293012842.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hmkb1293012842.ps tmp/7hmkb1293012842.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hmkb1293012842.ps tmp/8hmkb1293012842.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hmkb1293012842.ps tmp/9hmkb1293012842.png",intern=TRUE))
character(0)
> try(system("convert tmp/10sd1e1293012842.ps tmp/10sd1e1293012842.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.971 2.558 5.323