R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(1
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+ ,4)
+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('G'
+ ,'I1'
+ ,'I2'
+ ,'I3'
+ ,'E1'
+ ,'E2'
+ ,'E3'
+ ,'A')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('G','I1','I2','I3','E1','E2','E3','A'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
G I1 I2 I3 E1 E2 E3 A
1 1 26 21 21 23 17 23 4
2 1 20 16 15 24 17 20 4
3 1 19 19 18 22 18 20 6
4 2 19 18 11 20 21 21 8
5 1 20 16 8 24 20 24 8
6 1 25 23 19 27 28 22 4
7 2 25 17 4 28 19 23 4
8 1 22 12 20 27 22 20 8
9 1 26 19 16 24 16 25 5
10 1 22 16 14 23 18 23 4
11 2 17 19 10 24 25 27 4
12 2 22 20 13 27 17 27 4
13 1 19 13 14 27 14 22 4
14 1 24 20 8 28 11 24 4
15 1 26 27 23 27 27 25 4
16 2 21 17 11 23 20 22 8
17 1 13 8 9 24 22 28 4
18 2 26 25 24 28 22 28 4
19 2 20 26 5 27 21 27 4
20 1 22 13 15 25 23 25 8
21 2 14 19 5 19 17 16 4
22 1 21 15 19 24 24 28 7
23 1 7 5 6 20 14 21 4
24 2 23 16 13 28 17 24 4
25 1 17 14 11 26 23 27 5
26 1 25 24 17 23 24 14 4
27 1 25 24 17 23 24 14 4
28 1 19 9 5 20 8 27 4
29 2 20 19 9 11 22 20 4
30 1 23 19 15 24 23 21 4
31 2 22 25 17 25 25 22 4
32 1 22 19 17 23 21 21 4
33 1 21 18 20 18 24 12 15
34 2 15 15 12 20 15 20 10
35 2 20 12 7 20 22 24 4
36 2 22 21 16 24 21 19 8
37 1 18 12 7 23 25 28 4
38 2 20 15 14 25 16 23 4
39 2 28 28 24 28 28 27 4
40 1 22 25 15 26 23 22 4
41 1 18 19 15 26 21 27 7
42 1 23 20 10 23 21 26 4
43 1 20 24 14 22 26 22 6
44 2 25 26 18 24 22 21 5
45 2 26 25 12 21 21 19 4
46 1 15 12 9 20 18 24 16
47 2 17 12 9 22 12 19 5
48 2 23 15 8 20 25 26 12
49 1 21 17 18 25 17 22 6
50 2 13 14 10 20 24 28 9
51 1 18 16 17 22 15 21 9
52 1 19 11 14 23 13 23 4
53 1 22 20 16 25 26 28 5
54 1 16 11 10 23 16 10 4
55 2 24 22 19 23 24 24 4
56 1 18 20 10 22 21 21 5
57 1 20 19 14 24 20 21 4
58 1 24 17 10 25 14 24 4
59 2 14 21 4 21 25 24 4
60 2 22 23 19 12 25 25 5
61 1 24 18 9 17 20 25 4
62 1 18 17 12 20 22 23 6
63 1 21 27 16 23 20 21 4
64 2 23 25 11 23 26 16 4
65 1 17 19 18 20 18 17 18
66 2 22 22 11 28 22 25 4
67 2 24 24 24 24 24 24 6
68 2 21 20 17 24 17 23 4
69 1 22 19 18 24 24 25 4
70 1 16 11 9 24 20 23 5
71 1 21 22 19 28 19 28 4
72 2 23 22 18 25 20 26 4
73 2 22 16 12 21 15 22 5
74 1 24 20 23 25 23 19 10
75 1 24 24 22 25 26 26 5
76 1 16 16 14 18 22 18 8
77 1 16 16 14 17 20 18 8
78 2 21 22 16 26 24 25 5
79 2 26 24 23 28 26 27 4
80 2 15 16 7 21 21 12 4
81 2 25 27 10 27 25 15 4
82 1 18 11 12 22 13 21 5
83 0 23 21 12 21 20 23 4
84 1 20 20 12 25 22 22 4
85 2 17 20 17 22 23 21 8
86 2 25 27 21 23 28 24 4
87 1 24 20 16 26 22 27 5
88 1 17 12 11 19 20 22 14
89 1 19 8 14 25 6 28 8
90 1 20 21 13 21 21 26 8
91 1 15 18 9 13 20 10 4
92 2 27 24 19 24 18 19 4
93 1 22 16 13 25 23 22 6
94 1 23 18 19 26 20 21 4
95 1 16 20 13 25 24 24 7
96 1 19 20 13 25 22 25 7
97 2 25 19 13 22 21 21 4
98 1 19 17 14 21 18 20 6
99 2 19 16 12 23 21 21 4
100 2 26 26 22 25 23 24 7
101 1 21 15 11 24 23 23 4
102 2 20 22 5 21 15 18 4
103 1 24 17 18 21 21 24 8
104 1 22 23 19 25 24 24 4
105 2 20 21 14 22 23 19 4
106 1 18 19 15 20 21 20 10
107 2 18 14 12 20 21 18 8
108 1 24 17 19 23 20 20 6
109 1 24 12 15 28 11 27 4
110 1 22 24 17 23 22 23 4
111 1 23 18 8 28 27 26 4
112 1 22 20 10 24 25 23 5
113 1 20 16 12 18 18 17 4
114 1 18 20 12 20 20 21 6
115 1 25 22 20 28 24 25 4
116 2 18 12 12 21 10 23 5
117 1 16 16 12 21 27 27 7
118 1 20 17 14 25 21 24 8
119 2 19 22 6 19 21 20 5
120 1 15 12 10 18 18 27 8
121 1 19 14 18 21 15 21 10
122 1 19 23 18 22 24 24 8
123 1 16 15 7 24 22 21 5
124 1 17 17 18 15 14 15 12
125 1 28 28 9 28 28 25 4
126 2 23 20 17 26 18 25 5
127 1 25 23 22 23 26 22 4
128 1 20 13 11 26 17 24 6
129 2 17 18 15 20 19 21 4
130 2 23 23 17 22 22 22 4
131 1 16 19 15 20 18 23 7
132 2 23 23 22 23 24 22 7
133 2 11 12 9 22 15 20 10
134 2 18 16 13 24 18 23 4
135 2 24 23 20 23 26 25 5
136 1 23 13 14 22 11 23 8
137 1 21 22 14 26 26 22 11
138 2 16 18 12 23 21 25 7
139 2 24 23 20 27 23 26 4
140 1 23 20 20 23 23 22 8
141 1 18 10 8 21 15 24 6
142 1 20 17 17 26 22 24 7
143 1 9 18 9 23 26 25 5
144 2 24 15 18 21 16 20 4
145 1 25 23 22 27 20 26 8
146 1 20 17 10 19 18 21 4
147 2 21 17 13 23 22 26 8
148 2 25 22 15 25 16 21 6
149 2 22 20 18 23 19 22 4
150 2 21 20 18 22 20 16 9
151 1 21 19 12 22 19 26 5
152 1 22 18 12 25 23 28 6
153 1 27 22 20 25 24 18 4
154 2 24 20 12 28 25 25 4
155 2 24 22 16 28 21 23 4
156 2 21 18 16 20 21 21 5
157 1 18 16 18 25 23 20 6
158 1 16 16 16 19 27 25 16
159 1 22 16 13 25 23 22 6
160 1 20 16 17 22 18 21 6
161 2 18 17 13 18 16 16 4
162 1 20 18 17 20 16 18 4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) I1 I2 I3 E1 E2
1.4860146 -0.0025839 0.0428608 -0.0150766 -0.0118362 -0.0135082
E3 A
0.0007656 -0.0167601
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.5776 -0.3719 -0.2014 0.5142 0.8068
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4860146 0.3991125 3.723 0.000275 ***
I1 -0.0025839 0.0155436 -0.166 0.868190
I2 0.0428608 0.0133994 3.199 0.001676 **
I3 -0.0150766 0.0104823 -1.438 0.152382
E1 -0.0118362 0.0150465 -0.787 0.432699
E2 -0.0135082 0.0115180 -1.173 0.242693
E3 0.0007656 0.0118528 0.065 0.948582
A -0.0167601 0.0165875 -1.010 0.313886
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4838 on 154 degrees of freedom
Multiple R-squared: 0.105, Adjusted R-squared: 0.06427
F-statistic: 2.58 on 7 and 154 DF, p-value: 0.01528
> 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,] 0.6597401 0.6805199 0.3402599
[2,] 0.6957172 0.6085656 0.3042828
[3,] 0.5817418 0.8365163 0.4182582
[4,] 0.5564906 0.8870187 0.4435094
[5,] 0.4524202 0.9048404 0.5475798
[6,] 0.4679635 0.9359269 0.5320365
[7,] 0.4734036 0.9468071 0.5265964
[8,] 0.6026481 0.7947039 0.3973519
[9,] 0.5255848 0.9488305 0.4744152
[10,] 0.4793741 0.9587482 0.5206259
[11,] 0.4389076 0.8778153 0.5610924
[12,] 0.3664528 0.7329057 0.6335472
[13,] 0.2952751 0.5905502 0.7047249
[14,] 0.4239021 0.8478042 0.5760979
[15,] 0.3938328 0.7876656 0.6061672
[16,] 0.3602301 0.7204602 0.6397699
[17,] 0.3147310 0.6294620 0.6852690
[18,] 0.2650052 0.5300104 0.7349948
[19,] 0.2707807 0.5415614 0.7292193
[20,] 0.2327069 0.4654139 0.7672931
[21,] 0.2269641 0.4539282 0.7730359
[22,] 0.1904769 0.3809538 0.8095231
[23,] 0.1547935 0.3095871 0.8452065
[24,] 0.1683363 0.3366725 0.8316637
[25,] 0.2157461 0.4314921 0.7842539
[26,] 0.2377171 0.4754343 0.7622829
[27,] 0.2311286 0.4622573 0.7688714
[28,] 0.3305864 0.6611728 0.6694136
[29,] 0.3340597 0.6681193 0.6659403
[30,] 0.3839829 0.7679658 0.6160171
[31,] 0.3976675 0.7953349 0.6023325
[32,] 0.4312428 0.8624856 0.5687572
[33,] 0.4713420 0.9426840 0.5286580
[34,] 0.4602084 0.9204167 0.5397916
[35,] 0.4262807 0.8525614 0.5737193
[36,] 0.4119434 0.8238867 0.5880566
[37,] 0.4621625 0.9243249 0.5378375
[38,] 0.5089737 0.9820526 0.4910263
[39,] 0.4692470 0.9384940 0.5307530
[40,] 0.5172641 0.9654718 0.4827359
[41,] 0.4816251 0.9632502 0.5183749
[42,] 0.4349551 0.8699103 0.5650449
[43,] 0.4154931 0.8309862 0.5845069
[44,] 0.3769948 0.7539895 0.6230052
[45,] 0.4032517 0.8065035 0.5967483
[46,] 0.4245306 0.8490611 0.5754694
[47,] 0.4117039 0.8234078 0.5882961
[48,] 0.4059578 0.8119156 0.5940422
[49,] 0.3805677 0.7611354 0.6194323
[50,] 0.3770788 0.7541575 0.6229212
[51,] 0.3993907 0.7987814 0.6006093
[52,] 0.3851129 0.7702257 0.6148871
[53,] 0.4511442 0.9022883 0.5488558
[54,] 0.4277676 0.8555352 0.5722324
[55,] 0.3979551 0.7959103 0.6020449
[56,] 0.3909283 0.7818566 0.6090717
[57,] 0.4256036 0.8512073 0.5743964
[58,] 0.4437991 0.8875981 0.5562009
[59,] 0.4159376 0.8318752 0.5840624
[60,] 0.3761173 0.7522346 0.6238827
[61,] 0.3776040 0.7552081 0.6223960
[62,] 0.3859121 0.7718241 0.6140879
[63,] 0.4175513 0.8351027 0.5824487
[64,] 0.3841970 0.7683940 0.6158030
[65,] 0.3725338 0.7450675 0.6274662
[66,] 0.3436117 0.6872235 0.6563883
[67,] 0.3178852 0.6357703 0.6821148
[68,] 0.3290844 0.6581688 0.6709156
[69,] 0.3541239 0.7082477 0.6458761
[70,] 0.3647684 0.7295368 0.6352316
[71,] 0.3299801 0.6599602 0.6700199
[72,] 0.2978975 0.5957950 0.7021025
[73,] 0.6991104 0.6017792 0.3008896
[74,] 0.6991883 0.6016234 0.3008117
[75,] 0.7258188 0.5483625 0.2741812
[76,] 0.7215804 0.5568393 0.2784196
[77,] 0.7036073 0.5927854 0.2963927
[78,] 0.6683130 0.6633741 0.3316870
[79,] 0.6345093 0.7309813 0.3654907
[80,] 0.6298089 0.7403822 0.3701911
[81,] 0.6536713 0.6926573 0.3463287
[82,] 0.6416800 0.7166400 0.3583200
[83,] 0.6064143 0.7871713 0.3935857
[84,] 0.5862201 0.8275598 0.4137799
[85,] 0.5714949 0.8570102 0.4285051
[86,] 0.5565754 0.8868491 0.4434246
[87,] 0.5769404 0.8461193 0.4230596
[88,] 0.5648339 0.8703321 0.4351661
[89,] 0.5986019 0.8027962 0.4013981
[90,] 0.6053356 0.7893288 0.3946644
[91,] 0.5706354 0.8587292 0.4293646
[92,] 0.5283318 0.9433365 0.4716682
[93,] 0.4845829 0.9691657 0.5154171
[94,] 0.4835454 0.9670907 0.5164546
[95,] 0.4801565 0.9603130 0.5198435
[96,] 0.4530220 0.9060440 0.5469780
[97,] 0.5455455 0.9089090 0.4544545
[98,] 0.5074969 0.9850062 0.4925031
[99,] 0.4942704 0.9885409 0.5057296
[100,] 0.5301143 0.9397715 0.4698857
[101,] 0.4968301 0.9936602 0.5031699
[102,] 0.4726753 0.9453506 0.5273247
[103,] 0.4595801 0.9191602 0.5404199
[104,] 0.4729084 0.9458168 0.5270916
[105,] 0.4680362 0.9360724 0.5319638
[106,] 0.4798221 0.9596442 0.5201779
[107,] 0.4318879 0.8637758 0.5681121
[108,] 0.3933710 0.7867421 0.6066290
[109,] 0.3776584 0.7553169 0.6223416
[110,] 0.3315373 0.6630746 0.6684627
[111,] 0.2981195 0.5962389 0.7018805
[112,] 0.2963614 0.5927227 0.7036386
[113,] 0.2662005 0.5324011 0.7337995
[114,] 0.2578329 0.5156658 0.7421671
[115,] 0.2927204 0.5854408 0.7072796
[116,] 0.2834359 0.5668718 0.7165641
[117,] 0.2910144 0.5820287 0.7089856
[118,] 0.2507698 0.5015395 0.7492302
[119,] 0.2369922 0.4739843 0.7630078
[120,] 0.2091843 0.4183685 0.7908157
[121,] 0.2257617 0.4515234 0.7742383
[122,] 0.2200771 0.4401542 0.7799229
[123,] 0.2849450 0.5698901 0.7150550
[124,] 0.3150265 0.6300531 0.6849735
[125,] 0.2878345 0.5756689 0.7121655
[126,] 0.2493887 0.4987773 0.7506113
[127,] 0.2382889 0.4765778 0.7617111
[128,] 0.2741620 0.5483240 0.7258380
[129,] 0.2974905 0.5949809 0.7025095
[130,] 0.2611557 0.5223113 0.7388443
[131,] 0.2152788 0.4305577 0.7847212
[132,] 0.1680089 0.3360177 0.8319911
[133,] 0.1250228 0.2500456 0.8749772
[134,] 0.2214833 0.4429665 0.7785167
[135,] 0.2028020 0.4056040 0.7971980
[136,] 0.2157752 0.4315504 0.7842248
[137,] 0.3684594 0.7369188 0.6315406
[138,] 0.2872188 0.5744376 0.7127812
[139,] 0.3412185 0.6824370 0.6587815
[140,] 0.2606348 0.5212697 0.7393652
[141,] 0.3108566 0.6217131 0.6891434
> postscript(file="/var/wessaorg/rcomp/tmp/15me11354891092.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2d11z1354891092.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3ounu1354891092.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/49arz1354891092.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5mrbz1354891092.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 = 162
Frequency = 1
1 2 3 4 5 6
-0.45099819 -0.32852410 -0.39110457 0.59582694 -0.32955748 -0.37275645
7 8 9 10 11 12
0.54775670 0.09355952 -0.42710254 -0.33905774 0.56246524 0.50519692
13 14 15 16 17 18
-0.22414935 -0.63193413 -0.49711430 0.66509034 -0.03276861 0.54568236
19 20 21 22 23 24
0.17628471 -0.03867634 0.32050596 -0.08406045 -0.11497033 0.69335686
25 26 27 28 29 30
-0.19473817 -0.54102305 -0.54102305 -0.35612590 0.36610430 -0.36907132
31 32 33 34 35 36
0.43942031 -0.38035462 -0.12225280 0.68238709 0.73943998 0.59925521
37 38 39 40 41 42
-0.19275706 0.69529133 0.50408246 -0.60591295 -0.33964790 -0.52999547
43 44 45 46 47 48
-0.55659662 0.38455279 0.27529231 -0.09623748 0.67102034 0.80676023
49 50 51 52 53 54
-0.27974601 0.78861551 -0.27119247 -0.20004644 -0.33567777 -0.21762706
55 56 57 58 59 60
0.56461171 -0.53416299 -0.43242405 -0.46818285 0.34532082 0.41588754
61 62 63 64 65 66
-0.54052646 -0.37036290 -0.75440932 0.34597414 -0.21652646 0.47023051
67 68 69 70 71 72
0.59962949 0.53047308 -0.31597974 -0.16002744 -0.45456214 0.51505981
73 74 75 76 77 78
0.58411792 -0.17480748 -0.40996243 -0.28884095 -0.32769349 0.56313350
79 80 81 82 83 84
0.62826482 0.56259276 0.28494611 -0.22632835 -1.57758688 -0.46735100
85 86 87 88 89 90
0.64608581 0.43707760 -0.37194085 -0.07772550 -0.07913641 -0.49201011
91 92 93 94 95 96
-0.59964228 0.42125707 -0.22863520 -0.28275637 -0.38684448 -0.40687475
97 98 99 100 101 102
0.55525456 -0.37752553 0.66509316 0.50401078 -0.26463353 0.20255189
103 104 105 106 107 108
-0.23331748 -0.45974440 0.50023771 -0.35502558 0.78205947 -0.23853450
109 110 111 112 113 114
-0.18580888 -0.58268145 -0.33419703 -0.44765355 -0.42896610 -0.52443032
115 116 117 118 119 120
-0.35931238 0.67691898 -0.23959515 -0.25661446 0.28765007 -0.24121114
121 122 123 124 125 126
-0.16288658 -0.45104076 -0.33307608 -0.34304821 -0.72053486 0.58805036
127 128 129 130 131 132
-0.40188792 -0.20611783 0.55690860 0.45169258 -0.45329504 0.61620835
133 134 135 136 137 138
0.77907654 0.64736635 0.57983838 -0.24724445 -0.33714575 0.61883795
139 140 141 142 143 144
0.56913299 -0.28211055 -0.21413041 -0.20280050 -0.41045838 0.72088511
145 146 147 148 149 150
-0.37161398 -0.49320619 0.71919741 0.45831347 0.56407926 0.65156156
151 152 153 154 155 156
-0.48424177 -0.33402690 -0.38429403 0.61672088 0.53880416 0.62609718
157 158 159 160 161 162
-0.16205671 -0.05058897 -0.22863520 -0.27578057 0.51183120 -0.44341430
> postscript(file="/var/wessaorg/rcomp/tmp/6szlj1354891092.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.45099819 NA
1 -0.32852410 -0.45099819
2 -0.39110457 -0.32852410
3 0.59582694 -0.39110457
4 -0.32955748 0.59582694
5 -0.37275645 -0.32955748
6 0.54775670 -0.37275645
7 0.09355952 0.54775670
8 -0.42710254 0.09355952
9 -0.33905774 -0.42710254
10 0.56246524 -0.33905774
11 0.50519692 0.56246524
12 -0.22414935 0.50519692
13 -0.63193413 -0.22414935
14 -0.49711430 -0.63193413
15 0.66509034 -0.49711430
16 -0.03276861 0.66509034
17 0.54568236 -0.03276861
18 0.17628471 0.54568236
19 -0.03867634 0.17628471
20 0.32050596 -0.03867634
21 -0.08406045 0.32050596
22 -0.11497033 -0.08406045
23 0.69335686 -0.11497033
24 -0.19473817 0.69335686
25 -0.54102305 -0.19473817
26 -0.54102305 -0.54102305
27 -0.35612590 -0.54102305
28 0.36610430 -0.35612590
29 -0.36907132 0.36610430
30 0.43942031 -0.36907132
31 -0.38035462 0.43942031
32 -0.12225280 -0.38035462
33 0.68238709 -0.12225280
34 0.73943998 0.68238709
35 0.59925521 0.73943998
36 -0.19275706 0.59925521
37 0.69529133 -0.19275706
38 0.50408246 0.69529133
39 -0.60591295 0.50408246
40 -0.33964790 -0.60591295
41 -0.52999547 -0.33964790
42 -0.55659662 -0.52999547
43 0.38455279 -0.55659662
44 0.27529231 0.38455279
45 -0.09623748 0.27529231
46 0.67102034 -0.09623748
47 0.80676023 0.67102034
48 -0.27974601 0.80676023
49 0.78861551 -0.27974601
50 -0.27119247 0.78861551
51 -0.20004644 -0.27119247
52 -0.33567777 -0.20004644
53 -0.21762706 -0.33567777
54 0.56461171 -0.21762706
55 -0.53416299 0.56461171
56 -0.43242405 -0.53416299
57 -0.46818285 -0.43242405
58 0.34532082 -0.46818285
59 0.41588754 0.34532082
60 -0.54052646 0.41588754
61 -0.37036290 -0.54052646
62 -0.75440932 -0.37036290
63 0.34597414 -0.75440932
64 -0.21652646 0.34597414
65 0.47023051 -0.21652646
66 0.59962949 0.47023051
67 0.53047308 0.59962949
68 -0.31597974 0.53047308
69 -0.16002744 -0.31597974
70 -0.45456214 -0.16002744
71 0.51505981 -0.45456214
72 0.58411792 0.51505981
73 -0.17480748 0.58411792
74 -0.40996243 -0.17480748
75 -0.28884095 -0.40996243
76 -0.32769349 -0.28884095
77 0.56313350 -0.32769349
78 0.62826482 0.56313350
79 0.56259276 0.62826482
80 0.28494611 0.56259276
81 -0.22632835 0.28494611
82 -1.57758688 -0.22632835
83 -0.46735100 -1.57758688
84 0.64608581 -0.46735100
85 0.43707760 0.64608581
86 -0.37194085 0.43707760
87 -0.07772550 -0.37194085
88 -0.07913641 -0.07772550
89 -0.49201011 -0.07913641
90 -0.59964228 -0.49201011
91 0.42125707 -0.59964228
92 -0.22863520 0.42125707
93 -0.28275637 -0.22863520
94 -0.38684448 -0.28275637
95 -0.40687475 -0.38684448
96 0.55525456 -0.40687475
97 -0.37752553 0.55525456
98 0.66509316 -0.37752553
99 0.50401078 0.66509316
100 -0.26463353 0.50401078
101 0.20255189 -0.26463353
102 -0.23331748 0.20255189
103 -0.45974440 -0.23331748
104 0.50023771 -0.45974440
105 -0.35502558 0.50023771
106 0.78205947 -0.35502558
107 -0.23853450 0.78205947
108 -0.18580888 -0.23853450
109 -0.58268145 -0.18580888
110 -0.33419703 -0.58268145
111 -0.44765355 -0.33419703
112 -0.42896610 -0.44765355
113 -0.52443032 -0.42896610
114 -0.35931238 -0.52443032
115 0.67691898 -0.35931238
116 -0.23959515 0.67691898
117 -0.25661446 -0.23959515
118 0.28765007 -0.25661446
119 -0.24121114 0.28765007
120 -0.16288658 -0.24121114
121 -0.45104076 -0.16288658
122 -0.33307608 -0.45104076
123 -0.34304821 -0.33307608
124 -0.72053486 -0.34304821
125 0.58805036 -0.72053486
126 -0.40188792 0.58805036
127 -0.20611783 -0.40188792
128 0.55690860 -0.20611783
129 0.45169258 0.55690860
130 -0.45329504 0.45169258
131 0.61620835 -0.45329504
132 0.77907654 0.61620835
133 0.64736635 0.77907654
134 0.57983838 0.64736635
135 -0.24724445 0.57983838
136 -0.33714575 -0.24724445
137 0.61883795 -0.33714575
138 0.56913299 0.61883795
139 -0.28211055 0.56913299
140 -0.21413041 -0.28211055
141 -0.20280050 -0.21413041
142 -0.41045838 -0.20280050
143 0.72088511 -0.41045838
144 -0.37161398 0.72088511
145 -0.49320619 -0.37161398
146 0.71919741 -0.49320619
147 0.45831347 0.71919741
148 0.56407926 0.45831347
149 0.65156156 0.56407926
150 -0.48424177 0.65156156
151 -0.33402690 -0.48424177
152 -0.38429403 -0.33402690
153 0.61672088 -0.38429403
154 0.53880416 0.61672088
155 0.62609718 0.53880416
156 -0.16205671 0.62609718
157 -0.05058897 -0.16205671
158 -0.22863520 -0.05058897
159 -0.27578057 -0.22863520
160 0.51183120 -0.27578057
161 -0.44341430 0.51183120
162 NA -0.44341430
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.32852410 -0.45099819
[2,] -0.39110457 -0.32852410
[3,] 0.59582694 -0.39110457
[4,] -0.32955748 0.59582694
[5,] -0.37275645 -0.32955748
[6,] 0.54775670 -0.37275645
[7,] 0.09355952 0.54775670
[8,] -0.42710254 0.09355952
[9,] -0.33905774 -0.42710254
[10,] 0.56246524 -0.33905774
[11,] 0.50519692 0.56246524
[12,] -0.22414935 0.50519692
[13,] -0.63193413 -0.22414935
[14,] -0.49711430 -0.63193413
[15,] 0.66509034 -0.49711430
[16,] -0.03276861 0.66509034
[17,] 0.54568236 -0.03276861
[18,] 0.17628471 0.54568236
[19,] -0.03867634 0.17628471
[20,] 0.32050596 -0.03867634
[21,] -0.08406045 0.32050596
[22,] -0.11497033 -0.08406045
[23,] 0.69335686 -0.11497033
[24,] -0.19473817 0.69335686
[25,] -0.54102305 -0.19473817
[26,] -0.54102305 -0.54102305
[27,] -0.35612590 -0.54102305
[28,] 0.36610430 -0.35612590
[29,] -0.36907132 0.36610430
[30,] 0.43942031 -0.36907132
[31,] -0.38035462 0.43942031
[32,] -0.12225280 -0.38035462
[33,] 0.68238709 -0.12225280
[34,] 0.73943998 0.68238709
[35,] 0.59925521 0.73943998
[36,] -0.19275706 0.59925521
[37,] 0.69529133 -0.19275706
[38,] 0.50408246 0.69529133
[39,] -0.60591295 0.50408246
[40,] -0.33964790 -0.60591295
[41,] -0.52999547 -0.33964790
[42,] -0.55659662 -0.52999547
[43,] 0.38455279 -0.55659662
[44,] 0.27529231 0.38455279
[45,] -0.09623748 0.27529231
[46,] 0.67102034 -0.09623748
[47,] 0.80676023 0.67102034
[48,] -0.27974601 0.80676023
[49,] 0.78861551 -0.27974601
[50,] -0.27119247 0.78861551
[51,] -0.20004644 -0.27119247
[52,] -0.33567777 -0.20004644
[53,] -0.21762706 -0.33567777
[54,] 0.56461171 -0.21762706
[55,] -0.53416299 0.56461171
[56,] -0.43242405 -0.53416299
[57,] -0.46818285 -0.43242405
[58,] 0.34532082 -0.46818285
[59,] 0.41588754 0.34532082
[60,] -0.54052646 0.41588754
[61,] -0.37036290 -0.54052646
[62,] -0.75440932 -0.37036290
[63,] 0.34597414 -0.75440932
[64,] -0.21652646 0.34597414
[65,] 0.47023051 -0.21652646
[66,] 0.59962949 0.47023051
[67,] 0.53047308 0.59962949
[68,] -0.31597974 0.53047308
[69,] -0.16002744 -0.31597974
[70,] -0.45456214 -0.16002744
[71,] 0.51505981 -0.45456214
[72,] 0.58411792 0.51505981
[73,] -0.17480748 0.58411792
[74,] -0.40996243 -0.17480748
[75,] -0.28884095 -0.40996243
[76,] -0.32769349 -0.28884095
[77,] 0.56313350 -0.32769349
[78,] 0.62826482 0.56313350
[79,] 0.56259276 0.62826482
[80,] 0.28494611 0.56259276
[81,] -0.22632835 0.28494611
[82,] -1.57758688 -0.22632835
[83,] -0.46735100 -1.57758688
[84,] 0.64608581 -0.46735100
[85,] 0.43707760 0.64608581
[86,] -0.37194085 0.43707760
[87,] -0.07772550 -0.37194085
[88,] -0.07913641 -0.07772550
[89,] -0.49201011 -0.07913641
[90,] -0.59964228 -0.49201011
[91,] 0.42125707 -0.59964228
[92,] -0.22863520 0.42125707
[93,] -0.28275637 -0.22863520
[94,] -0.38684448 -0.28275637
[95,] -0.40687475 -0.38684448
[96,] 0.55525456 -0.40687475
[97,] -0.37752553 0.55525456
[98,] 0.66509316 -0.37752553
[99,] 0.50401078 0.66509316
[100,] -0.26463353 0.50401078
[101,] 0.20255189 -0.26463353
[102,] -0.23331748 0.20255189
[103,] -0.45974440 -0.23331748
[104,] 0.50023771 -0.45974440
[105,] -0.35502558 0.50023771
[106,] 0.78205947 -0.35502558
[107,] -0.23853450 0.78205947
[108,] -0.18580888 -0.23853450
[109,] -0.58268145 -0.18580888
[110,] -0.33419703 -0.58268145
[111,] -0.44765355 -0.33419703
[112,] -0.42896610 -0.44765355
[113,] -0.52443032 -0.42896610
[114,] -0.35931238 -0.52443032
[115,] 0.67691898 -0.35931238
[116,] -0.23959515 0.67691898
[117,] -0.25661446 -0.23959515
[118,] 0.28765007 -0.25661446
[119,] -0.24121114 0.28765007
[120,] -0.16288658 -0.24121114
[121,] -0.45104076 -0.16288658
[122,] -0.33307608 -0.45104076
[123,] -0.34304821 -0.33307608
[124,] -0.72053486 -0.34304821
[125,] 0.58805036 -0.72053486
[126,] -0.40188792 0.58805036
[127,] -0.20611783 -0.40188792
[128,] 0.55690860 -0.20611783
[129,] 0.45169258 0.55690860
[130,] -0.45329504 0.45169258
[131,] 0.61620835 -0.45329504
[132,] 0.77907654 0.61620835
[133,] 0.64736635 0.77907654
[134,] 0.57983838 0.64736635
[135,] -0.24724445 0.57983838
[136,] -0.33714575 -0.24724445
[137,] 0.61883795 -0.33714575
[138,] 0.56913299 0.61883795
[139,] -0.28211055 0.56913299
[140,] -0.21413041 -0.28211055
[141,] -0.20280050 -0.21413041
[142,] -0.41045838 -0.20280050
[143,] 0.72088511 -0.41045838
[144,] -0.37161398 0.72088511
[145,] -0.49320619 -0.37161398
[146,] 0.71919741 -0.49320619
[147,] 0.45831347 0.71919741
[148,] 0.56407926 0.45831347
[149,] 0.65156156 0.56407926
[150,] -0.48424177 0.65156156
[151,] -0.33402690 -0.48424177
[152,] -0.38429403 -0.33402690
[153,] 0.61672088 -0.38429403
[154,] 0.53880416 0.61672088
[155,] 0.62609718 0.53880416
[156,] -0.16205671 0.62609718
[157,] -0.05058897 -0.16205671
[158,] -0.22863520 -0.05058897
[159,] -0.27578057 -0.22863520
[160,] 0.51183120 -0.27578057
[161,] -0.44341430 0.51183120
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.32852410 -0.45099819
2 -0.39110457 -0.32852410
3 0.59582694 -0.39110457
4 -0.32955748 0.59582694
5 -0.37275645 -0.32955748
6 0.54775670 -0.37275645
7 0.09355952 0.54775670
8 -0.42710254 0.09355952
9 -0.33905774 -0.42710254
10 0.56246524 -0.33905774
11 0.50519692 0.56246524
12 -0.22414935 0.50519692
13 -0.63193413 -0.22414935
14 -0.49711430 -0.63193413
15 0.66509034 -0.49711430
16 -0.03276861 0.66509034
17 0.54568236 -0.03276861
18 0.17628471 0.54568236
19 -0.03867634 0.17628471
20 0.32050596 -0.03867634
21 -0.08406045 0.32050596
22 -0.11497033 -0.08406045
23 0.69335686 -0.11497033
24 -0.19473817 0.69335686
25 -0.54102305 -0.19473817
26 -0.54102305 -0.54102305
27 -0.35612590 -0.54102305
28 0.36610430 -0.35612590
29 -0.36907132 0.36610430
30 0.43942031 -0.36907132
31 -0.38035462 0.43942031
32 -0.12225280 -0.38035462
33 0.68238709 -0.12225280
34 0.73943998 0.68238709
35 0.59925521 0.73943998
36 -0.19275706 0.59925521
37 0.69529133 -0.19275706
38 0.50408246 0.69529133
39 -0.60591295 0.50408246
40 -0.33964790 -0.60591295
41 -0.52999547 -0.33964790
42 -0.55659662 -0.52999547
43 0.38455279 -0.55659662
44 0.27529231 0.38455279
45 -0.09623748 0.27529231
46 0.67102034 -0.09623748
47 0.80676023 0.67102034
48 -0.27974601 0.80676023
49 0.78861551 -0.27974601
50 -0.27119247 0.78861551
51 -0.20004644 -0.27119247
52 -0.33567777 -0.20004644
53 -0.21762706 -0.33567777
54 0.56461171 -0.21762706
55 -0.53416299 0.56461171
56 -0.43242405 -0.53416299
57 -0.46818285 -0.43242405
58 0.34532082 -0.46818285
59 0.41588754 0.34532082
60 -0.54052646 0.41588754
61 -0.37036290 -0.54052646
62 -0.75440932 -0.37036290
63 0.34597414 -0.75440932
64 -0.21652646 0.34597414
65 0.47023051 -0.21652646
66 0.59962949 0.47023051
67 0.53047308 0.59962949
68 -0.31597974 0.53047308
69 -0.16002744 -0.31597974
70 -0.45456214 -0.16002744
71 0.51505981 -0.45456214
72 0.58411792 0.51505981
73 -0.17480748 0.58411792
74 -0.40996243 -0.17480748
75 -0.28884095 -0.40996243
76 -0.32769349 -0.28884095
77 0.56313350 -0.32769349
78 0.62826482 0.56313350
79 0.56259276 0.62826482
80 0.28494611 0.56259276
81 -0.22632835 0.28494611
82 -1.57758688 -0.22632835
83 -0.46735100 -1.57758688
84 0.64608581 -0.46735100
85 0.43707760 0.64608581
86 -0.37194085 0.43707760
87 -0.07772550 -0.37194085
88 -0.07913641 -0.07772550
89 -0.49201011 -0.07913641
90 -0.59964228 -0.49201011
91 0.42125707 -0.59964228
92 -0.22863520 0.42125707
93 -0.28275637 -0.22863520
94 -0.38684448 -0.28275637
95 -0.40687475 -0.38684448
96 0.55525456 -0.40687475
97 -0.37752553 0.55525456
98 0.66509316 -0.37752553
99 0.50401078 0.66509316
100 -0.26463353 0.50401078
101 0.20255189 -0.26463353
102 -0.23331748 0.20255189
103 -0.45974440 -0.23331748
104 0.50023771 -0.45974440
105 -0.35502558 0.50023771
106 0.78205947 -0.35502558
107 -0.23853450 0.78205947
108 -0.18580888 -0.23853450
109 -0.58268145 -0.18580888
110 -0.33419703 -0.58268145
111 -0.44765355 -0.33419703
112 -0.42896610 -0.44765355
113 -0.52443032 -0.42896610
114 -0.35931238 -0.52443032
115 0.67691898 -0.35931238
116 -0.23959515 0.67691898
117 -0.25661446 -0.23959515
118 0.28765007 -0.25661446
119 -0.24121114 0.28765007
120 -0.16288658 -0.24121114
121 -0.45104076 -0.16288658
122 -0.33307608 -0.45104076
123 -0.34304821 -0.33307608
124 -0.72053486 -0.34304821
125 0.58805036 -0.72053486
126 -0.40188792 0.58805036
127 -0.20611783 -0.40188792
128 0.55690860 -0.20611783
129 0.45169258 0.55690860
130 -0.45329504 0.45169258
131 0.61620835 -0.45329504
132 0.77907654 0.61620835
133 0.64736635 0.77907654
134 0.57983838 0.64736635
135 -0.24724445 0.57983838
136 -0.33714575 -0.24724445
137 0.61883795 -0.33714575
138 0.56913299 0.61883795
139 -0.28211055 0.56913299
140 -0.21413041 -0.28211055
141 -0.20280050 -0.21413041
142 -0.41045838 -0.20280050
143 0.72088511 -0.41045838
144 -0.37161398 0.72088511
145 -0.49320619 -0.37161398
146 0.71919741 -0.49320619
147 0.45831347 0.71919741
148 0.56407926 0.45831347
149 0.65156156 0.56407926
150 -0.48424177 0.65156156
151 -0.33402690 -0.48424177
152 -0.38429403 -0.33402690
153 0.61672088 -0.38429403
154 0.53880416 0.61672088
155 0.62609718 0.53880416
156 -0.16205671 0.62609718
157 -0.05058897 -0.16205671
158 -0.22863520 -0.05058897
159 -0.27578057 -0.22863520
160 0.51183120 -0.27578057
161 -0.44341430 0.51183120
> 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/wessaorg/rcomp/tmp/7m5mv1354891092.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/831gj1354891093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9mjx91354891093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10pyrk1354891093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11733v1354891093.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/wessaorg/rcomp/tmp/12ayv91354891093.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/wessaorg/rcomp/tmp/136hvk1354891093.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/wessaorg/rcomp/tmp/14zhj41354891093.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/wessaorg/rcomp/tmp/15r3ko1354891093.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/wessaorg/rcomp/tmp/16nstw1354891093.tab")
+ }
>
> try(system("convert tmp/15me11354891092.ps tmp/15me11354891092.png",intern=TRUE))
character(0)
> try(system("convert tmp/2d11z1354891092.ps tmp/2d11z1354891092.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ounu1354891092.ps tmp/3ounu1354891092.png",intern=TRUE))
character(0)
> try(system("convert tmp/49arz1354891092.ps tmp/49arz1354891092.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mrbz1354891092.ps tmp/5mrbz1354891092.png",intern=TRUE))
character(0)
> try(system("convert tmp/6szlj1354891092.ps tmp/6szlj1354891092.png",intern=TRUE))
character(0)
> try(system("convert tmp/7m5mv1354891092.ps tmp/7m5mv1354891092.png",intern=TRUE))
character(0)
> try(system("convert tmp/831gj1354891093.ps tmp/831gj1354891093.png",intern=TRUE))
character(0)
> try(system("convert tmp/9mjx91354891093.ps tmp/9mjx91354891093.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pyrk1354891093.ps tmp/10pyrk1354891093.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.740 1.257 10.111