R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(43880,43110,44496,44164,40399,36763,37903,35532,35533,32110,33374,35462,33508,36080,34560,38737,38144,37594,36424,36843,37246,38661,40454,44928,48441,48140,45998,47369,49554,47510,44873,45344,42413,36912,43452,42142,44382,43636,44167,44423,42868,43908,42013,38846,35087,33026,34646,37135,37985,43121,43722,43630,42234,39351,39327,35704,30466,28155,29257,29998,32529,34787,33855,34556,31348,30805,28353,24514,21106,21346,23335,24379,26290,30084,29429,30632,27349,27264,27474,24482,21453,18788,19282,19713,21917,23812,23785,24696,24562,23580,24939,23899,21454,19761,19815,20780,23462,25005,24725,26198,27543,26471,26558,25317,22896,22248,23406,25073,27691,30599,31948,32946,34012,32936,32974,30951,29812,29010,31068,32447,34844,35676,35387,36488,35652,33488,32914,29781,27951),dim=c(1,129),dimnames=list(c('OPENVAC'),1:129))
> y <- array(NA,dim=c(1,129),dimnames=list(c('OPENVAC'),1:129))
> 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
OPENVAC M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 43880 1 0 0 0 0 0 0 0 0 0 0
2 43110 0 1 0 0 0 0 0 0 0 0 0
3 44496 0 0 1 0 0 0 0 0 0 0 0
4 44164 0 0 0 1 0 0 0 0 0 0 0
5 40399 0 0 0 0 1 0 0 0 0 0 0
6 36763 0 0 0 0 0 1 0 0 0 0 0
7 37903 0 0 0 0 0 0 1 0 0 0 0
8 35532 0 0 0 0 0 0 0 1 0 0 0
9 35533 0 0 0 0 0 0 0 0 1 0 0
10 32110 0 0 0 0 0 0 0 0 0 1 0
11 33374 0 0 0 0 0 0 0 0 0 0 1
12 35462 0 0 0 0 0 0 0 0 0 0 0
13 33508 1 0 0 0 0 0 0 0 0 0 0
14 36080 0 1 0 0 0 0 0 0 0 0 0
15 34560 0 0 1 0 0 0 0 0 0 0 0
16 38737 0 0 0 1 0 0 0 0 0 0 0
17 38144 0 0 0 0 1 0 0 0 0 0 0
18 37594 0 0 0 0 0 1 0 0 0 0 0
19 36424 0 0 0 0 0 0 1 0 0 0 0
20 36843 0 0 0 0 0 0 0 1 0 0 0
21 37246 0 0 0 0 0 0 0 0 1 0 0
22 38661 0 0 0 0 0 0 0 0 0 1 0
23 40454 0 0 0 0 0 0 0 0 0 0 1
24 44928 0 0 0 0 0 0 0 0 0 0 0
25 48441 1 0 0 0 0 0 0 0 0 0 0
26 48140 0 1 0 0 0 0 0 0 0 0 0
27 45998 0 0 1 0 0 0 0 0 0 0 0
28 47369 0 0 0 1 0 0 0 0 0 0 0
29 49554 0 0 0 0 1 0 0 0 0 0 0
30 47510 0 0 0 0 0 1 0 0 0 0 0
31 44873 0 0 0 0 0 0 1 0 0 0 0
32 45344 0 0 0 0 0 0 0 1 0 0 0
33 42413 0 0 0 0 0 0 0 0 1 0 0
34 36912 0 0 0 0 0 0 0 0 0 1 0
35 43452 0 0 0 0 0 0 0 0 0 0 1
36 42142 0 0 0 0 0 0 0 0 0 0 0
37 44382 1 0 0 0 0 0 0 0 0 0 0
38 43636 0 1 0 0 0 0 0 0 0 0 0
39 44167 0 0 1 0 0 0 0 0 0 0 0
40 44423 0 0 0 1 0 0 0 0 0 0 0
41 42868 0 0 0 0 1 0 0 0 0 0 0
42 43908 0 0 0 0 0 1 0 0 0 0 0
43 42013 0 0 0 0 0 0 1 0 0 0 0
44 38846 0 0 0 0 0 0 0 1 0 0 0
45 35087 0 0 0 0 0 0 0 0 1 0 0
46 33026 0 0 0 0 0 0 0 0 0 1 0
47 34646 0 0 0 0 0 0 0 0 0 0 1
48 37135 0 0 0 0 0 0 0 0 0 0 0
49 37985 1 0 0 0 0 0 0 0 0 0 0
50 43121 0 1 0 0 0 0 0 0 0 0 0
51 43722 0 0 1 0 0 0 0 0 0 0 0
52 43630 0 0 0 1 0 0 0 0 0 0 0
53 42234 0 0 0 0 1 0 0 0 0 0 0
54 39351 0 0 0 0 0 1 0 0 0 0 0
55 39327 0 0 0 0 0 0 1 0 0 0 0
56 35704 0 0 0 0 0 0 0 1 0 0 0
57 30466 0 0 0 0 0 0 0 0 1 0 0
58 28155 0 0 0 0 0 0 0 0 0 1 0
59 29257 0 0 0 0 0 0 0 0 0 0 1
60 29998 0 0 0 0 0 0 0 0 0 0 0
61 32529 1 0 0 0 0 0 0 0 0 0 0
62 34787 0 1 0 0 0 0 0 0 0 0 0
63 33855 0 0 1 0 0 0 0 0 0 0 0
64 34556 0 0 0 1 0 0 0 0 0 0 0
65 31348 0 0 0 0 1 0 0 0 0 0 0
66 30805 0 0 0 0 0 1 0 0 0 0 0
67 28353 0 0 0 0 0 0 1 0 0 0 0
68 24514 0 0 0 0 0 0 0 1 0 0 0
69 21106 0 0 0 0 0 0 0 0 1 0 0
70 21346 0 0 0 0 0 0 0 0 0 1 0
71 23335 0 0 0 0 0 0 0 0 0 0 1
72 24379 0 0 0 0 0 0 0 0 0 0 0
73 26290 1 0 0 0 0 0 0 0 0 0 0
74 30084 0 1 0 0 0 0 0 0 0 0 0
75 29429 0 0 1 0 0 0 0 0 0 0 0
76 30632 0 0 0 1 0 0 0 0 0 0 0
77 27349 0 0 0 0 1 0 0 0 0 0 0
78 27264 0 0 0 0 0 1 0 0 0 0 0
79 27474 0 0 0 0 0 0 1 0 0 0 0
80 24482 0 0 0 0 0 0 0 1 0 0 0
81 21453 0 0 0 0 0 0 0 0 1 0 0
82 18788 0 0 0 0 0 0 0 0 0 1 0
83 19282 0 0 0 0 0 0 0 0 0 0 1
84 19713 0 0 0 0 0 0 0 0 0 0 0
85 21917 1 0 0 0 0 0 0 0 0 0 0
86 23812 0 1 0 0 0 0 0 0 0 0 0
87 23785 0 0 1 0 0 0 0 0 0 0 0
88 24696 0 0 0 1 0 0 0 0 0 0 0
89 24562 0 0 0 0 1 0 0 0 0 0 0
90 23580 0 0 0 0 0 1 0 0 0 0 0
91 24939 0 0 0 0 0 0 1 0 0 0 0
92 23899 0 0 0 0 0 0 0 1 0 0 0
93 21454 0 0 0 0 0 0 0 0 1 0 0
94 19761 0 0 0 0 0 0 0 0 0 1 0
95 19815 0 0 0 0 0 0 0 0 0 0 1
96 20780 0 0 0 0 0 0 0 0 0 0 0
97 23462 1 0 0 0 0 0 0 0 0 0 0
98 25005 0 1 0 0 0 0 0 0 0 0 0
99 24725 0 0 1 0 0 0 0 0 0 0 0
100 26198 0 0 0 1 0 0 0 0 0 0 0
101 27543 0 0 0 0 1 0 0 0 0 0 0
102 26471 0 0 0 0 0 1 0 0 0 0 0
103 26558 0 0 0 0 0 0 1 0 0 0 0
104 25317 0 0 0 0 0 0 0 1 0 0 0
105 22896 0 0 0 0 0 0 0 0 1 0 0
106 22248 0 0 0 0 0 0 0 0 0 1 0
107 23406 0 0 0 0 0 0 0 0 0 0 1
108 25073 0 0 0 0 0 0 0 0 0 0 0
109 27691 1 0 0 0 0 0 0 0 0 0 0
110 30599 0 1 0 0 0 0 0 0 0 0 0
111 31948 0 0 1 0 0 0 0 0 0 0 0
112 32946 0 0 0 1 0 0 0 0 0 0 0
113 34012 0 0 0 0 1 0 0 0 0 0 0
114 32936 0 0 0 0 0 1 0 0 0 0 0
115 32974 0 0 0 0 0 0 1 0 0 0 0
116 30951 0 0 0 0 0 0 0 1 0 0 0
117 29812 0 0 0 0 0 0 0 0 1 0 0
118 29010 0 0 0 0 0 0 0 0 0 1 0
119 31068 0 0 0 0 0 0 0 0 0 0 1
120 32447 0 0 0 0 0 0 0 0 0 0 0
121 34844 1 0 0 0 0 0 0 0 0 0 0
122 35676 0 1 0 0 0 0 0 0 0 0 0
123 35387 0 0 1 0 0 0 0 0 0 0 0
124 36488 0 0 0 1 0 0 0 0 0 0 0
125 35652 0 0 0 0 1 0 0 0 0 0 0
126 33488 0 0 0 0 0 1 0 0 0 0 0
127 32914 0 0 0 0 0 0 1 0 0 0 0
128 29781 0 0 0 0 0 0 0 1 0 0 0
129 27951 0 0 0 0 0 0 0 0 1 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
31205.7 2878.8 4617.0 4437.2 5506.9 4582.0
M6 M7 M8 M9 M10 M11
3309.8 2771.8 722.8 -1622.3 -3204.0 -1396.8
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12167.5 -6611.5 -255.9 5949.6 14356.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 31205.7 2460.8 12.681 <2e-16 ***
M1 2878.8 3400.1 0.847 0.399
M2 4617.0 3400.1 1.358 0.177
M3 4437.2 3400.1 1.305 0.194
M4 5506.9 3400.1 1.620 0.108
M5 4582.0 3400.1 1.348 0.180
M6 3309.8 3400.1 0.973 0.332
M7 2771.8 3400.1 0.815 0.417
M8 722.8 3400.1 0.213 0.832
M9 -1622.3 3400.1 -0.477 0.634
M10 -3204.0 3480.1 -0.921 0.359
M11 -1396.8 3480.1 -0.401 0.689
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7782 on 117 degrees of freedom
Multiple R-squared: 0.1206, Adjusted R-squared: 0.03791
F-statistic: 1.458 on 11 and 117 DF, p-value: 0.1566
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.41940047 8.388009e-01 5.805995e-01
[2,] 0.29660846 5.932169e-01 7.033915e-01
[3,] 0.18010708 3.602142e-01 8.198929e-01
[4,] 0.10005803 2.001161e-01 8.999420e-01
[5,] 0.05287905 1.057581e-01 9.471210e-01
[6,] 0.02666976 5.333953e-02 9.733302e-01
[7,] 0.01336798 2.673596e-02 9.866320e-01
[8,] 0.01107779 2.215559e-02 9.889222e-01
[9,] 0.01002476 2.004952e-02 9.899752e-01
[10,] 0.01419308 2.838615e-02 9.858069e-01
[11,] 0.02589279 5.178558e-02 9.741072e-01
[12,] 0.03356150 6.712300e-02 9.664385e-01
[13,] 0.03198467 6.396933e-02 9.680153e-01
[14,] 0.02957557 5.915114e-02 9.704244e-01
[15,] 0.04993977 9.987954e-02 9.500602e-01
[16,] 0.07680307 1.536061e-01 9.231969e-01
[17,] 0.08437922 1.687584e-01 9.156208e-01
[18,] 0.11321871 2.264374e-01 8.867813e-01
[19,] 0.12325552 2.465110e-01 8.767445e-01
[20,] 0.10585020 2.117004e-01 8.941498e-01
[21,] 0.13034907 2.606981e-01 8.696509e-01
[22,] 0.12745770 2.549154e-01 8.725423e-01
[23,] 0.12962365 2.592473e-01 8.703764e-01
[24,] 0.11756595 2.351319e-01 8.824341e-01
[25,] 0.11263694 2.252739e-01 8.873631e-01
[26,] 0.10418774 2.083755e-01 8.958123e-01
[27,] 0.09485105 1.897021e-01 9.051490e-01
[28,] 0.10226638 2.045328e-01 8.977336e-01
[29,] 0.10143600 2.028720e-01 8.985640e-01
[30,] 0.09688079 1.937616e-01 9.031192e-01
[31,] 0.09589809 1.917962e-01 9.041019e-01
[32,] 0.09319398 1.863880e-01 9.068060e-01
[33,] 0.10002430 2.000486e-01 8.999757e-01
[34,] 0.11298828 2.259766e-01 8.870117e-01
[35,] 0.12575795 2.515159e-01 8.742421e-01
[36,] 0.14883158 2.976632e-01 8.511684e-01
[37,] 0.19202514 3.840503e-01 8.079749e-01
[38,] 0.23357204 4.671441e-01 7.664280e-01
[39,] 0.28092114 5.618423e-01 7.190789e-01
[40,] 0.31457640 6.291528e-01 6.854236e-01
[41,] 0.35567679 7.113536e-01 6.443232e-01
[42,] 0.39774280 7.954856e-01 6.022572e-01
[43,] 0.45281521 9.056304e-01 5.471848e-01
[44,] 0.49921052 9.984210e-01 5.007895e-01
[45,] 0.57652588 8.469482e-01 4.234741e-01
[46,] 0.66609374 6.678125e-01 3.339063e-01
[47,] 0.73242612 5.351478e-01 2.675739e-01
[48,] 0.77413506 4.517299e-01 2.258649e-01
[49,] 0.81017346 3.796531e-01 1.898265e-01
[50,] 0.84106316 3.178737e-01 1.589368e-01
[51,] 0.87600164 2.479967e-01 1.239984e-01
[52,] 0.89399008 2.120198e-01 1.060099e-01
[53,] 0.91607518 1.678496e-01 8.392482e-02
[54,] 0.94535755 1.092849e-01 5.464245e-02
[55,] 0.96858284 6.283432e-02 3.141716e-02
[56,] 0.97419640 5.160720e-02 2.580360e-02
[57,] 0.97875410 4.249181e-02 2.124590e-02
[58,] 0.98280864 3.438272e-02 1.719136e-02
[59,] 0.98612920 2.774160e-02 1.387080e-02
[60,] 0.98630754 2.738492e-02 1.369246e-02
[61,] 0.98631818 2.736364e-02 1.368182e-02
[62,] 0.98589484 2.821031e-02 1.410516e-02
[63,] 0.98760200 2.479599e-02 1.239800e-02
[64,] 0.98733297 2.533406e-02 1.266703e-02
[65,] 0.98588979 2.822043e-02 1.411021e-02
[66,] 0.98536636 2.926727e-02 1.463364e-02
[67,] 0.98572860 2.854281e-02 1.427140e-02
[68,] 0.98685070 2.629860e-02 1.314930e-02
[69,] 0.98921939 2.156122e-02 1.078061e-02
[70,] 0.99210475 1.579050e-02 7.895249e-03
[71,] 0.99441999 1.116001e-02 5.580005e-03
[72,] 0.99586801 8.263977e-03 4.131988e-03
[73,] 0.99691508 6.169842e-03 3.084921e-03
[74,] 0.99775061 4.498778e-03 2.249389e-03
[75,] 0.99838289 3.234215e-03 1.617107e-03
[76,] 0.99874481 2.510372e-03 1.255186e-03
[77,] 0.99869171 2.616582e-03 1.308291e-03
[78,] 0.99839395 3.212107e-03 1.606054e-03
[79,] 0.99813867 3.722663e-03 1.861331e-03
[80,] 0.99783357 4.332857e-03 2.166428e-03
[81,] 0.99802076 3.958470e-03 1.979235e-03
[82,] 0.99836857 3.262867e-03 1.631434e-03
[83,] 0.99863803 2.723947e-03 1.361974e-03
[84,] 0.99895527 2.089466e-03 1.044733e-03
[85,] 0.99936255 1.274901e-03 6.374503e-04
[86,] 0.99960228 7.954476e-04 3.977238e-04
[87,] 0.99966434 6.713272e-04 3.356636e-04
[88,] 0.99968769 6.246275e-04 3.123138e-04
[89,] 0.99968955 6.209052e-04 3.104526e-04
[90,] 0.99957102 8.579606e-04 4.289803e-04
[91,] 0.99954246 9.150732e-04 4.575366e-04
[92,] 0.99951074 9.785295e-04 4.892648e-04
[93,] 0.99965687 6.862633e-04 3.431317e-04
[94,] 0.99980162 3.967656e-04 1.983828e-04
[95,] 0.99993555 1.288966e-04 6.444831e-05
[96,] 0.99995948 8.103227e-05 4.051614e-05
[97,] 0.99993866 1.226865e-04 6.134325e-05
[98,] 0.99996084 7.831473e-05 3.915736e-05
[99,] 0.99982997 3.400502e-04 1.700251e-04
[100,] 0.99827178 3.456433e-03 1.728217e-03
> postscript(file="/var/www/html/rcomp/tmp/17z871292158159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2h8pa1292158159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3h8pa1292158159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4h8pa1292158159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5sz7v1292158159.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 = 129
Frequency = 1
1 2 3 4 5 6
9795.5455 7287.2727 8853.0909 7451.3636 4611.2727 2247.5455
7 8 9 10 11 12
3925.5455 3603.5455 5949.6364 4108.3000 3565.1000 4256.3000
13 14 15 16 17 18
-576.4545 257.2727 -1082.9091 2024.3636 2356.2727 3078.5455
19 20 21 22 23 24
2446.5455 4914.5455 7662.6364 10659.3000 10645.1000 13722.3000
25 26 27 28 29 30
14356.5455 12317.2727 10355.0909 10656.3636 13766.2727 12994.5455
31 32 33 34 35 36
10895.5455 13415.5455 12829.6364 8910.3000 13643.1000 10936.3000
37 38 39 40 41 42
10297.5455 7813.2727 8524.0909 7710.3636 7080.2727 9392.5455
43 44 45 46 47 48
8035.5455 6917.5455 5503.6364 5024.3000 4837.1000 5929.3000
49 50 51 52 53 54
3900.5455 7298.2727 8079.0909 6917.3636 6446.2727 4835.5455
55 56 57 58 59 60
5349.5455 3775.5455 882.6364 153.3000 -551.9000 -1207.7000
61 62 63 64 65 66
-1555.4545 -1035.7273 -1787.9091 -2156.6364 -4439.7273 -3710.4545
67 68 69 70 71 72
-5624.4545 -7414.4545 -8477.3636 -6655.7000 -6473.9000 -6826.7000
73 74 75 76 77 78
-7794.4545 -5738.7273 -6213.9091 -6080.6364 -8438.7273 -7251.4545
79 80 81 82 83 84
-6503.4545 -7446.4545 -8130.3636 -9213.7000 -10526.9000 -11492.7000
85 86 87 88 89 90
-12167.4545 -12010.7273 -11857.9091 -12016.6364 -11225.7273 -10935.4545
91 92 93 94 95 96
-9038.4545 -8029.4545 -8129.3636 -8240.7000 -9993.9000 -10425.7000
97 98 99 100 101 102
-10622.4545 -10817.7273 -10917.9091 -10514.6364 -8244.7273 -8044.4545
103 104 105 106 107 108
-7419.4545 -6611.4545 -6687.3636 -5753.7000 -6402.9000 -6132.7000
109 110 111 112 113 114
-6393.4545 -5223.7273 -3694.9091 -3766.6364 -1775.7273 -1579.4545
115 116 117 118 119 120
-1003.4545 -977.4545 228.6364 1008.3000 1259.1000 1241.3000
121 122 123 124 125 126
759.5455 -146.7273 -255.9091 -224.6364 -135.7273 -1027.4545
127 128 129
-1063.4545 -2147.4545 -1632.3636
> postscript(file="/var/www/html/rcomp/tmp/6sz7v1292158159.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 = 129
Frequency = 1
lag(myerror, k = 1) myerror
0 9795.5455 NA
1 7287.2727 9795.5455
2 8853.0909 7287.2727
3 7451.3636 8853.0909
4 4611.2727 7451.3636
5 2247.5455 4611.2727
6 3925.5455 2247.5455
7 3603.5455 3925.5455
8 5949.6364 3603.5455
9 4108.3000 5949.6364
10 3565.1000 4108.3000
11 4256.3000 3565.1000
12 -576.4545 4256.3000
13 257.2727 -576.4545
14 -1082.9091 257.2727
15 2024.3636 -1082.9091
16 2356.2727 2024.3636
17 3078.5455 2356.2727
18 2446.5455 3078.5455
19 4914.5455 2446.5455
20 7662.6364 4914.5455
21 10659.3000 7662.6364
22 10645.1000 10659.3000
23 13722.3000 10645.1000
24 14356.5455 13722.3000
25 12317.2727 14356.5455
26 10355.0909 12317.2727
27 10656.3636 10355.0909
28 13766.2727 10656.3636
29 12994.5455 13766.2727
30 10895.5455 12994.5455
31 13415.5455 10895.5455
32 12829.6364 13415.5455
33 8910.3000 12829.6364
34 13643.1000 8910.3000
35 10936.3000 13643.1000
36 10297.5455 10936.3000
37 7813.2727 10297.5455
38 8524.0909 7813.2727
39 7710.3636 8524.0909
40 7080.2727 7710.3636
41 9392.5455 7080.2727
42 8035.5455 9392.5455
43 6917.5455 8035.5455
44 5503.6364 6917.5455
45 5024.3000 5503.6364
46 4837.1000 5024.3000
47 5929.3000 4837.1000
48 3900.5455 5929.3000
49 7298.2727 3900.5455
50 8079.0909 7298.2727
51 6917.3636 8079.0909
52 6446.2727 6917.3636
53 4835.5455 6446.2727
54 5349.5455 4835.5455
55 3775.5455 5349.5455
56 882.6364 3775.5455
57 153.3000 882.6364
58 -551.9000 153.3000
59 -1207.7000 -551.9000
60 -1555.4545 -1207.7000
61 -1035.7273 -1555.4545
62 -1787.9091 -1035.7273
63 -2156.6364 -1787.9091
64 -4439.7273 -2156.6364
65 -3710.4545 -4439.7273
66 -5624.4545 -3710.4545
67 -7414.4545 -5624.4545
68 -8477.3636 -7414.4545
69 -6655.7000 -8477.3636
70 -6473.9000 -6655.7000
71 -6826.7000 -6473.9000
72 -7794.4545 -6826.7000
73 -5738.7273 -7794.4545
74 -6213.9091 -5738.7273
75 -6080.6364 -6213.9091
76 -8438.7273 -6080.6364
77 -7251.4545 -8438.7273
78 -6503.4545 -7251.4545
79 -7446.4545 -6503.4545
80 -8130.3636 -7446.4545
81 -9213.7000 -8130.3636
82 -10526.9000 -9213.7000
83 -11492.7000 -10526.9000
84 -12167.4545 -11492.7000
85 -12010.7273 -12167.4545
86 -11857.9091 -12010.7273
87 -12016.6364 -11857.9091
88 -11225.7273 -12016.6364
89 -10935.4545 -11225.7273
90 -9038.4545 -10935.4545
91 -8029.4545 -9038.4545
92 -8129.3636 -8029.4545
93 -8240.7000 -8129.3636
94 -9993.9000 -8240.7000
95 -10425.7000 -9993.9000
96 -10622.4545 -10425.7000
97 -10817.7273 -10622.4545
98 -10917.9091 -10817.7273
99 -10514.6364 -10917.9091
100 -8244.7273 -10514.6364
101 -8044.4545 -8244.7273
102 -7419.4545 -8044.4545
103 -6611.4545 -7419.4545
104 -6687.3636 -6611.4545
105 -5753.7000 -6687.3636
106 -6402.9000 -5753.7000
107 -6132.7000 -6402.9000
108 -6393.4545 -6132.7000
109 -5223.7273 -6393.4545
110 -3694.9091 -5223.7273
111 -3766.6364 -3694.9091
112 -1775.7273 -3766.6364
113 -1579.4545 -1775.7273
114 -1003.4545 -1579.4545
115 -977.4545 -1003.4545
116 228.6364 -977.4545
117 1008.3000 228.6364
118 1259.1000 1008.3000
119 1241.3000 1259.1000
120 759.5455 1241.3000
121 -146.7273 759.5455
122 -255.9091 -146.7273
123 -224.6364 -255.9091
124 -135.7273 -224.6364
125 -1027.4545 -135.7273
126 -1063.4545 -1027.4545
127 -2147.4545 -1063.4545
128 -1632.3636 -2147.4545
129 NA -1632.3636
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 7287.2727 9795.5455
[2,] 8853.0909 7287.2727
[3,] 7451.3636 8853.0909
[4,] 4611.2727 7451.3636
[5,] 2247.5455 4611.2727
[6,] 3925.5455 2247.5455
[7,] 3603.5455 3925.5455
[8,] 5949.6364 3603.5455
[9,] 4108.3000 5949.6364
[10,] 3565.1000 4108.3000
[11,] 4256.3000 3565.1000
[12,] -576.4545 4256.3000
[13,] 257.2727 -576.4545
[14,] -1082.9091 257.2727
[15,] 2024.3636 -1082.9091
[16,] 2356.2727 2024.3636
[17,] 3078.5455 2356.2727
[18,] 2446.5455 3078.5455
[19,] 4914.5455 2446.5455
[20,] 7662.6364 4914.5455
[21,] 10659.3000 7662.6364
[22,] 10645.1000 10659.3000
[23,] 13722.3000 10645.1000
[24,] 14356.5455 13722.3000
[25,] 12317.2727 14356.5455
[26,] 10355.0909 12317.2727
[27,] 10656.3636 10355.0909
[28,] 13766.2727 10656.3636
[29,] 12994.5455 13766.2727
[30,] 10895.5455 12994.5455
[31,] 13415.5455 10895.5455
[32,] 12829.6364 13415.5455
[33,] 8910.3000 12829.6364
[34,] 13643.1000 8910.3000
[35,] 10936.3000 13643.1000
[36,] 10297.5455 10936.3000
[37,] 7813.2727 10297.5455
[38,] 8524.0909 7813.2727
[39,] 7710.3636 8524.0909
[40,] 7080.2727 7710.3636
[41,] 9392.5455 7080.2727
[42,] 8035.5455 9392.5455
[43,] 6917.5455 8035.5455
[44,] 5503.6364 6917.5455
[45,] 5024.3000 5503.6364
[46,] 4837.1000 5024.3000
[47,] 5929.3000 4837.1000
[48,] 3900.5455 5929.3000
[49,] 7298.2727 3900.5455
[50,] 8079.0909 7298.2727
[51,] 6917.3636 8079.0909
[52,] 6446.2727 6917.3636
[53,] 4835.5455 6446.2727
[54,] 5349.5455 4835.5455
[55,] 3775.5455 5349.5455
[56,] 882.6364 3775.5455
[57,] 153.3000 882.6364
[58,] -551.9000 153.3000
[59,] -1207.7000 -551.9000
[60,] -1555.4545 -1207.7000
[61,] -1035.7273 -1555.4545
[62,] -1787.9091 -1035.7273
[63,] -2156.6364 -1787.9091
[64,] -4439.7273 -2156.6364
[65,] -3710.4545 -4439.7273
[66,] -5624.4545 -3710.4545
[67,] -7414.4545 -5624.4545
[68,] -8477.3636 -7414.4545
[69,] -6655.7000 -8477.3636
[70,] -6473.9000 -6655.7000
[71,] -6826.7000 -6473.9000
[72,] -7794.4545 -6826.7000
[73,] -5738.7273 -7794.4545
[74,] -6213.9091 -5738.7273
[75,] -6080.6364 -6213.9091
[76,] -8438.7273 -6080.6364
[77,] -7251.4545 -8438.7273
[78,] -6503.4545 -7251.4545
[79,] -7446.4545 -6503.4545
[80,] -8130.3636 -7446.4545
[81,] -9213.7000 -8130.3636
[82,] -10526.9000 -9213.7000
[83,] -11492.7000 -10526.9000
[84,] -12167.4545 -11492.7000
[85,] -12010.7273 -12167.4545
[86,] -11857.9091 -12010.7273
[87,] -12016.6364 -11857.9091
[88,] -11225.7273 -12016.6364
[89,] -10935.4545 -11225.7273
[90,] -9038.4545 -10935.4545
[91,] -8029.4545 -9038.4545
[92,] -8129.3636 -8029.4545
[93,] -8240.7000 -8129.3636
[94,] -9993.9000 -8240.7000
[95,] -10425.7000 -9993.9000
[96,] -10622.4545 -10425.7000
[97,] -10817.7273 -10622.4545
[98,] -10917.9091 -10817.7273
[99,] -10514.6364 -10917.9091
[100,] -8244.7273 -10514.6364
[101,] -8044.4545 -8244.7273
[102,] -7419.4545 -8044.4545
[103,] -6611.4545 -7419.4545
[104,] -6687.3636 -6611.4545
[105,] -5753.7000 -6687.3636
[106,] -6402.9000 -5753.7000
[107,] -6132.7000 -6402.9000
[108,] -6393.4545 -6132.7000
[109,] -5223.7273 -6393.4545
[110,] -3694.9091 -5223.7273
[111,] -3766.6364 -3694.9091
[112,] -1775.7273 -3766.6364
[113,] -1579.4545 -1775.7273
[114,] -1003.4545 -1579.4545
[115,] -977.4545 -1003.4545
[116,] 228.6364 -977.4545
[117,] 1008.3000 228.6364
[118,] 1259.1000 1008.3000
[119,] 1241.3000 1259.1000
[120,] 759.5455 1241.3000
[121,] -146.7273 759.5455
[122,] -255.9091 -146.7273
[123,] -224.6364 -255.9091
[124,] -135.7273 -224.6364
[125,] -1027.4545 -135.7273
[126,] -1063.4545 -1027.4545
[127,] -2147.4545 -1063.4545
[128,] -1632.3636 -2147.4545
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 7287.2727 9795.5455
2 8853.0909 7287.2727
3 7451.3636 8853.0909
4 4611.2727 7451.3636
5 2247.5455 4611.2727
6 3925.5455 2247.5455
7 3603.5455 3925.5455
8 5949.6364 3603.5455
9 4108.3000 5949.6364
10 3565.1000 4108.3000
11 4256.3000 3565.1000
12 -576.4545 4256.3000
13 257.2727 -576.4545
14 -1082.9091 257.2727
15 2024.3636 -1082.9091
16 2356.2727 2024.3636
17 3078.5455 2356.2727
18 2446.5455 3078.5455
19 4914.5455 2446.5455
20 7662.6364 4914.5455
21 10659.3000 7662.6364
22 10645.1000 10659.3000
23 13722.3000 10645.1000
24 14356.5455 13722.3000
25 12317.2727 14356.5455
26 10355.0909 12317.2727
27 10656.3636 10355.0909
28 13766.2727 10656.3636
29 12994.5455 13766.2727
30 10895.5455 12994.5455
31 13415.5455 10895.5455
32 12829.6364 13415.5455
33 8910.3000 12829.6364
34 13643.1000 8910.3000
35 10936.3000 13643.1000
36 10297.5455 10936.3000
37 7813.2727 10297.5455
38 8524.0909 7813.2727
39 7710.3636 8524.0909
40 7080.2727 7710.3636
41 9392.5455 7080.2727
42 8035.5455 9392.5455
43 6917.5455 8035.5455
44 5503.6364 6917.5455
45 5024.3000 5503.6364
46 4837.1000 5024.3000
47 5929.3000 4837.1000
48 3900.5455 5929.3000
49 7298.2727 3900.5455
50 8079.0909 7298.2727
51 6917.3636 8079.0909
52 6446.2727 6917.3636
53 4835.5455 6446.2727
54 5349.5455 4835.5455
55 3775.5455 5349.5455
56 882.6364 3775.5455
57 153.3000 882.6364
58 -551.9000 153.3000
59 -1207.7000 -551.9000
60 -1555.4545 -1207.7000
61 -1035.7273 -1555.4545
62 -1787.9091 -1035.7273
63 -2156.6364 -1787.9091
64 -4439.7273 -2156.6364
65 -3710.4545 -4439.7273
66 -5624.4545 -3710.4545
67 -7414.4545 -5624.4545
68 -8477.3636 -7414.4545
69 -6655.7000 -8477.3636
70 -6473.9000 -6655.7000
71 -6826.7000 -6473.9000
72 -7794.4545 -6826.7000
73 -5738.7273 -7794.4545
74 -6213.9091 -5738.7273
75 -6080.6364 -6213.9091
76 -8438.7273 -6080.6364
77 -7251.4545 -8438.7273
78 -6503.4545 -7251.4545
79 -7446.4545 -6503.4545
80 -8130.3636 -7446.4545
81 -9213.7000 -8130.3636
82 -10526.9000 -9213.7000
83 -11492.7000 -10526.9000
84 -12167.4545 -11492.7000
85 -12010.7273 -12167.4545
86 -11857.9091 -12010.7273
87 -12016.6364 -11857.9091
88 -11225.7273 -12016.6364
89 -10935.4545 -11225.7273
90 -9038.4545 -10935.4545
91 -8029.4545 -9038.4545
92 -8129.3636 -8029.4545
93 -8240.7000 -8129.3636
94 -9993.9000 -8240.7000
95 -10425.7000 -9993.9000
96 -10622.4545 -10425.7000
97 -10817.7273 -10622.4545
98 -10917.9091 -10817.7273
99 -10514.6364 -10917.9091
100 -8244.7273 -10514.6364
101 -8044.4545 -8244.7273
102 -7419.4545 -8044.4545
103 -6611.4545 -7419.4545
104 -6687.3636 -6611.4545
105 -5753.7000 -6687.3636
106 -6402.9000 -5753.7000
107 -6132.7000 -6402.9000
108 -6393.4545 -6132.7000
109 -5223.7273 -6393.4545
110 -3694.9091 -5223.7273
111 -3766.6364 -3694.9091
112 -1775.7273 -3766.6364
113 -1579.4545 -1775.7273
114 -1003.4545 -1579.4545
115 -977.4545 -1003.4545
116 228.6364 -977.4545
117 1008.3000 228.6364
118 1259.1000 1008.3000
119 1241.3000 1259.1000
120 759.5455 1241.3000
121 -146.7273 759.5455
122 -255.9091 -146.7273
123 -224.6364 -255.9091
124 -135.7273 -224.6364
125 -1027.4545 -135.7273
126 -1063.4545 -1027.4545
127 -2147.4545 -1063.4545
128 -1632.3636 -2147.4545
> 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/7lroy1292158159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8lroy1292158159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9din11292158159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10din11292158159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11h0m71292158159.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/1221kc1292158159.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/13rkzo1292158159.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/14ktg91292158159.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/15ncff1292158159.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/16jmyg1292158160.tab")
+ }
>
> try(system("convert tmp/17z871292158159.ps tmp/17z871292158159.png",intern=TRUE))
character(0)
> try(system("convert tmp/2h8pa1292158159.ps tmp/2h8pa1292158159.png",intern=TRUE))
character(0)
> try(system("convert tmp/3h8pa1292158159.ps tmp/3h8pa1292158159.png",intern=TRUE))
character(0)
> try(system("convert tmp/4h8pa1292158159.ps tmp/4h8pa1292158159.png",intern=TRUE))
character(0)
> try(system("convert tmp/5sz7v1292158159.ps tmp/5sz7v1292158159.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sz7v1292158159.ps tmp/6sz7v1292158159.png",intern=TRUE))
character(0)
> try(system("convert tmp/7lroy1292158159.ps tmp/7lroy1292158159.png",intern=TRUE))
character(0)
> try(system("convert tmp/8lroy1292158159.ps tmp/8lroy1292158159.png",intern=TRUE))
character(0)
> try(system("convert tmp/9din11292158159.ps tmp/9din11292158159.png",intern=TRUE))
character(0)
> try(system("convert tmp/10din11292158159.ps tmp/10din11292158159.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.488 1.701 7.685