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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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(14
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+ ,76.347)
+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('Doubts'
+ ,'Concern'
+ ,'Expectations'
+ ,'Criticism'
+ ,'Standards'
+ ,'Organization'
+ ,'Time')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('Doubts','Concern','Expectations','Criticism','Standards','Organization','Time'),1:159))
> 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 = '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.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
Doubts Concern Expectations Criticism Standards Organization Time t
1 14 24 11 12 24 26 237.588 1
2 11 25 7 8 25 23 164.083 2
3 6 17 17 8 30 25 278.261 3
4 12 18 10 8 19 23 220.360 4
5 8 18 12 9 22 19 253.967 5
6 10 16 12 7 22 29 422.310 6
7 10 20 11 4 25 25 136.921 7
8 11 16 11 11 23 21 143.495 8
9 16 18 12 7 17 22 189.785 9
10 11 17 13 7 21 25 219.529 10
11 13 23 14 12 19 24 217.761 11
12 12 30 16 10 19 18 221.754 12
13 8 23 11 10 15 22 159.854 13
14 12 18 10 8 16 15 209.464 14
15 11 15 11 8 23 22 174.283 15
16 4 12 15 4 27 28 154.550 16
17 9 21 9 9 22 20 153.024 17
18 8 15 11 8 14 12 162.490 18
19 8 20 17 7 22 24 154.462 19
20 14 31 17 11 23 20 249.671 20
21 15 27 11 9 23 21 259.473 21
22 16 34 18 11 21 20 155.337 22
23 9 21 14 13 19 21 151.289 23
24 14 31 10 8 18 23 276.614 24
25 11 19 11 8 20 28 188.214 25
26 8 16 15 9 23 24 181.098 26
27 9 20 15 6 25 24 240.898 27
28 9 21 13 9 19 24 244.551 28
29 9 22 16 9 24 23 250.238 29
30 9 17 13 6 22 23 183.129 30
31 10 24 9 6 25 29 310.331 31
32 16 25 18 16 26 24 281.942 32
33 11 26 18 5 29 18 230.343 33
34 8 25 12 7 32 25 161.563 34
35 9 17 17 9 25 21 392.527 35
36 16 32 9 6 29 26 1077.414 36
37 11 33 9 6 28 22 248.275 37
38 16 13 12 5 17 22 557.386 38
39 12 32 18 12 28 22 731.874 39
40 12 25 12 7 29 23 301.429 40
41 14 29 18 10 26 30 226.360 41
42 9 22 14 9 25 23 215.018 42
43 10 18 15 8 14 17 157.672 43
44 9 17 16 5 25 23 219.118 44
45 10 20 10 8 26 23 213.019 45
46 12 15 11 8 20 25 390.642 46
47 14 20 14 10 18 24 157.124 47
48 14 33 9 6 32 24 227.652 48
49 10 29 12 8 25 23 239.266 49
50 14 23 17 7 25 21 506.343 50
51 16 26 5 4 23 24 149.219 51
52 9 18 12 8 21 24 213.351 52
53 10 20 12 8 20 28 174.517 53
54 6 11 6 4 15 16 172.531 54
55 8 28 24 20 30 20 320.656 55
56 13 26 12 8 24 29 305.011 56
57 10 22 12 8 26 27 266.495 57
58 8 17 14 6 24 22 361.511 58
59 7 12 7 4 22 28 361.019 59
60 15 14 13 8 14 16 382.187 60
61 9 17 12 9 24 25 196.763 61
62 10 21 13 6 24 24 273.212 62
63 12 19 14 7 24 28 186.397 63
64 13 18 8 9 24 24 294.205 64
65 10 10 11 5 19 23 364.685 65
66 11 29 9 5 31 30 230.501 66
67 8 31 11 8 22 24 217.510 67
68 9 19 13 8 27 21 262.297 68
69 13 9 10 6 19 25 169.246 69
70 11 20 11 8 25 25 260.428 70
71 8 28 12 7 20 22 348.187 71
72 9 19 9 7 21 23 512.937 72
73 9 30 15 9 27 26 164.496 73
74 15 29 18 11 23 23 111.187 74
75 9 26 15 6 25 25 169.999 75
76 10 23 12 8 20 21 240.187 76
77 14 13 13 6 21 25 187.158 77
78 12 21 14 9 22 24 194.096 78
79 12 19 10 8 23 29 265.846 79
80 11 28 13 6 25 22 283.319 80
81 14 23 13 10 25 27 356.938 81
82 6 18 11 8 17 26 240.802 82
83 12 21 13 8 19 22 326.662 83
84 8 20 16 10 25 24 249.266 84
85 14 23 8 5 19 27 277.368 85
86 11 21 16 7 20 24 394.618 86
87 10 21 11 5 26 24 235.686 87
88 14 15 9 8 23 29 227.641 88
89 12 28 16 14 27 22 159.593 89
90 10 19 12 7 17 21 268.866 90
91 14 26 14 8 17 24 206.466 91
92 5 10 8 6 19 24 233.064 92
93 11 16 9 5 17 23 133.824 93
94 10 22 15 6 22 20 486.783 94
95 9 19 11 10 21 27 228.859 95
96 10 31 21 12 32 26 155.238 96
97 16 31 14 9 21 25 2042.451 97
98 13 29 18 12 21 21 205.218 98
99 9 19 12 7 18 21 373.648 99
100 10 22 13 8 18 19 229.151 100
101 10 23 15 10 23 21 199.156 101
102 7 15 12 6 19 21 234.410 102
103 9 20 19 10 20 16 56.519 103
104 8 18 15 10 21 22 289.239 104
105 14 23 11 10 20 29 199.227 105
106 14 25 11 5 17 15 274.513 106
107 8 21 10 7 18 17 174.499 107
108 9 24 13 10 19 15 217.714 108
109 14 25 15 11 22 21 239.717 109
110 14 17 12 6 15 21 241.529 110
111 8 13 12 7 14 19 155.561 111
112 8 28 16 12 18 24 204.107 112
113 8 21 9 11 24 20 745.970 113
114 7 25 18 11 35 17 241.772 114
115 6 9 8 11 29 23 110.267 115
116 8 16 13 5 21 24 186.580 116
117 6 19 17 8 25 14 227.906 117
118 11 17 9 6 20 19 197.518 118
119 14 25 15 9 22 24 254.094 119
120 11 20 8 4 13 13 173.942 120
121 11 29 7 4 26 22 294.420 121
122 11 14 12 7 17 16 211.924 122
123 14 22 14 11 25 19 262.479 123
124 8 15 6 6 20 25 193.495 124
125 20 19 8 7 19 25 165.972 125
126 11 20 17 8 21 23 237.352 126
127 8 15 10 4 22 24 205.814 127
128 11 20 11 8 24 26 227.526 128
129 10 18 14 9 21 26 250.439 129
130 14 33 11 8 26 25 470.849 130
131 11 22 13 11 24 18 176.469 131
132 9 16 12 8 16 21 298.691 132
133 9 17 11 5 23 26 193.922 133
134 8 16 9 4 18 23 212.422 134
135 10 21 12 8 16 23 203.284 135
136 13 26 20 10 26 22 240.560 136
137 13 18 12 6 19 20 445.327 137
138 12 18 13 9 21 13 248.984 138
139 8 17 12 9 21 24 174.440 139
140 13 22 12 13 22 15 165.024 140
141 14 30 9 9 23 14 249.681 141
142 12 30 15 10 29 22 238.312 142
143 14 24 24 20 21 10 250.437 143
144 15 21 7 5 21 24 174.750 144
145 13 21 17 11 23 22 4941.633 145
146 16 29 11 6 27 24 138.936 146
147 9 31 17 9 25 19 203.181 147
148 9 20 11 7 21 20 187.747 148
149 9 16 12 9 10 13 270.950 149
150 8 22 14 10 20 20 307.688 150
151 7 20 11 9 26 22 184.477 151
152 16 28 16 8 24 24 230.916 152
153 11 38 21 7 29 29 187.286 153
154 9 22 14 6 19 12 169.376 154
155 11 20 20 13 24 20 182.838 155
156 9 17 13 6 19 21 176.081 156
157 14 28 11 8 24 24 248.056 157
158 13 22 15 10 22 22 235.240 158
159 16 31 19 16 17 20 76.347 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Concern Expectations Criticism Standards
7.3720025 0.2462405 -0.1122430 0.1447157 -0.1911164
Organization Time t
0.1077412 0.0007767 0.0007507
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.5957 -1.7771 -0.3116 1.5982 8.5493
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.3720025 1.6890273 4.365 2.35e-05 ***
Concern 0.2462405 0.0400728 6.145 6.80e-09 ***
Expectations -0.1122430 0.0738946 -1.519 0.13086
Criticism 0.1447157 0.0927008 1.561 0.12059
Standards -0.1911164 0.0567367 -3.368 0.00096 ***
Organization 0.1077412 0.0577125 1.867 0.06386 .
Time 0.0007767 0.0004791 1.621 0.10705
t 0.0007507 0.0044428 0.169 0.86605
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.476 on 151 degrees of freedom
Multiple R-squared: 0.2531, Adjusted R-squared: 0.2184
F-statistic: 7.308 on 7 and 151 DF, p-value: 1.519e-07
> 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,] 0.32043261 0.6408652 0.6795674
[2,] 0.19351417 0.3870283 0.8064858
[3,] 0.83794485 0.3241103 0.1620551
[4,] 0.82058785 0.3588243 0.1794121
[5,] 0.78534792 0.4293042 0.2146521
[6,] 0.78424302 0.4315140 0.2157570
[7,] 0.70811708 0.5837658 0.2918829
[8,] 0.68535112 0.6292978 0.3146489
[9,] 0.60563211 0.7887358 0.3943679
[10,] 0.68109790 0.6378042 0.3189021
[11,] 0.68706633 0.6258673 0.3129337
[12,] 0.67237645 0.6552471 0.3276235
[13,] 0.64814513 0.7037097 0.3518549
[14,] 0.59807266 0.8038547 0.4019273
[15,] 0.52817373 0.9436525 0.4718263
[16,] 0.45776886 0.9155377 0.5422311
[17,] 0.38795517 0.7759103 0.6120448
[18,] 0.36165602 0.7233120 0.6383440
[19,] 0.30312980 0.6062596 0.6968702
[20,] 0.25356410 0.5071282 0.7464359
[21,] 0.21549505 0.4309901 0.7845049
[22,] 0.32470681 0.6494136 0.6752932
[23,] 0.29506862 0.5901372 0.7049314
[24,] 0.26682037 0.5336407 0.7331796
[25,] 0.21827680 0.4365536 0.7817232
[26,] 0.18617453 0.3723491 0.8138255
[27,] 0.15739165 0.3147833 0.8426083
[28,] 0.37360117 0.7472023 0.6263988
[29,] 0.40176666 0.8035333 0.5982333
[30,] 0.37872516 0.7574503 0.6212748
[31,] 0.35903146 0.7180629 0.6409685
[32,] 0.32085344 0.6417069 0.6791466
[33,] 0.27680697 0.5536139 0.7231930
[34,] 0.23655158 0.4731032 0.7634484
[35,] 0.19672909 0.3934582 0.8032709
[36,] 0.17008758 0.3401752 0.8299124
[37,] 0.16979317 0.3395863 0.8302068
[38,] 0.16702654 0.3340531 0.8329735
[39,] 0.16634510 0.3326902 0.8336549
[40,] 0.18465772 0.3693154 0.8153423
[41,] 0.24705042 0.4941008 0.7529496
[42,] 0.23179505 0.4635901 0.7682049
[43,] 0.21020308 0.4204062 0.7897969
[44,] 0.23608343 0.4721669 0.7639166
[45,] 0.25313521 0.5062704 0.7468648
[46,] 0.21617412 0.4323482 0.7838259
[47,] 0.18317829 0.3663566 0.8168217
[48,] 0.15834834 0.3166967 0.8416517
[49,] 0.15480264 0.3096053 0.8451974
[50,] 0.24821746 0.4964349 0.7517825
[51,] 0.21149748 0.4229950 0.7885025
[52,] 0.17802737 0.3560547 0.8219726
[53,] 0.17307997 0.3461599 0.8269200
[54,] 0.18074246 0.3614849 0.8192575
[55,] 0.15837432 0.3167486 0.8416257
[56,] 0.13160888 0.2632178 0.8683911
[57,] 0.25749508 0.5149902 0.7425049
[58,] 0.22036627 0.4407325 0.7796337
[59,] 0.32400361 0.6480072 0.6759964
[60,] 0.28710210 0.5742042 0.7128979
[61,] 0.41672113 0.8334423 0.5832789
[62,] 0.41749324 0.8349865 0.5825068
[63,] 0.42258783 0.8451757 0.5774122
[64,] 0.46254602 0.9250920 0.5374540
[65,] 0.43471001 0.8694200 0.5652900
[66,] 0.40049574 0.8009915 0.5995043
[67,] 0.58974412 0.8205118 0.4102559
[68,] 0.56056331 0.8788734 0.4394367
[69,] 0.52337544 0.9532491 0.4766246
[70,] 0.47664811 0.9532962 0.5233519
[71,] 0.48220278 0.9644056 0.5177972
[72,] 0.65058813 0.6988237 0.3494119
[73,] 0.61265442 0.7746912 0.3873456
[74,] 0.58584540 0.8283092 0.4141546
[75,] 0.56203183 0.8759363 0.4379682
[76,] 0.52022795 0.9595441 0.4797721
[77,] 0.47639986 0.9527997 0.5236001
[78,] 0.57653115 0.8469377 0.4234688
[79,] 0.53246077 0.9350785 0.4675392
[80,] 0.48992732 0.9798546 0.5100727
[81,] 0.46031458 0.9206292 0.5396854
[82,] 0.50644002 0.9871200 0.4935600
[83,] 0.47214226 0.9442845 0.5278577
[84,] 0.43062469 0.8612494 0.5693753
[85,] 0.41128924 0.8225785 0.5887108
[86,] 0.36691707 0.7338341 0.6330829
[87,] 0.35302848 0.7060570 0.6469715
[88,] 0.31342512 0.6268502 0.6865749
[89,] 0.28438403 0.5687681 0.7156160
[90,] 0.24789862 0.4957972 0.7521014
[91,] 0.21111316 0.4222263 0.7888868
[92,] 0.19448077 0.3889615 0.8055192
[93,] 0.16218838 0.3243768 0.8378116
[94,] 0.14615097 0.2923019 0.8538490
[95,] 0.12880836 0.2576167 0.8711916
[96,] 0.13582556 0.2716511 0.8641744
[97,] 0.13483962 0.2696792 0.8651604
[98,] 0.12889945 0.2577989 0.8711006
[99,] 0.12869234 0.2573847 0.8713077
[100,] 0.16900799 0.3380160 0.8309920
[101,] 0.14324365 0.2864873 0.8567564
[102,] 0.29934939 0.5986988 0.7006506
[103,] 0.38274959 0.7654992 0.6172504
[104,] 0.35147166 0.7029433 0.6485283
[105,] 0.34062367 0.6812473 0.6593763
[106,] 0.29997782 0.5999556 0.7000222
[107,] 0.30077498 0.6015500 0.6992250
[108,] 0.26271099 0.5254220 0.7372890
[109,] 0.23851298 0.4770260 0.7614870
[110,] 0.19798259 0.3959652 0.8020174
[111,] 0.18894585 0.3778917 0.8110542
[112,] 0.16837114 0.3367423 0.8316289
[113,] 0.17418688 0.3483738 0.8258131
[114,] 0.18820039 0.3764008 0.8117996
[115,] 0.73434725 0.5313055 0.2656527
[116,] 0.69519351 0.6096130 0.3048065
[117,] 0.63947494 0.7210501 0.3605251
[118,] 0.57751642 0.8449672 0.4224836
[119,] 0.51234878 0.9753024 0.4876512
[120,] 0.44993100 0.8998620 0.5500690
[121,] 0.39271329 0.7854266 0.6072867
[122,] 0.33893084 0.6778617 0.6610692
[123,] 0.27856188 0.5571238 0.7214381
[124,] 0.24378508 0.4875702 0.7562149
[125,] 0.23784857 0.4756971 0.7621514
[126,] 0.20278046 0.4055609 0.7972195
[127,] 0.21894958 0.4378992 0.7810504
[128,] 0.23690329 0.4738066 0.7630967
[129,] 0.22853213 0.4570643 0.7714679
[130,] 0.17494690 0.3498938 0.8250531
[131,] 0.12680821 0.2536164 0.8731918
[132,] 0.09133363 0.1826673 0.9086664
[133,] 0.12091663 0.2418333 0.8790834
[134,] 0.13197358 0.2639472 0.8680264
[135,] 0.11015887 0.2203177 0.8898411
[136,] 0.30631503 0.6126301 0.6936850
[137,] 0.20294185 0.4058837 0.7970582
[138,] 0.14010248 0.2802050 0.8598975
> postscript(file="/var/www/html/freestat/rcomp/tmp/19peb1290526886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/22gve1290526886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/32gve1290526886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/42gve1290526886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/52gve1290526886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 159
Frequency = 1
1 2 3 4 5 6
1.81653983 -0.72912650 -1.98610925 1.13937367 -1.80339614 -0.23040445
7 8 9 10 11 12
0.33177285 1.34659976 5.25407914 1.02995090 0.66730331 -0.89986736
13 14 15 16 17 18
-4.88550060 1.42891082 1.89007747 -3.21077097 -1.91718993 -1.74565024
19 20 21 22 23 24
-1.91715694 1.34271339 2.82754319 2.40577323 -2.61908462 -0.31157671
25 26 27 28 29 30
-0.33300846 -1.28093981 -0.49672104 -2.55188152 -1.40323742 -0.40547457
31 32 33 34 35 36
-1.75078091 4.31713115 1.92188848 -1.92292676 0.23178217 2.76741155
37 38 39 40 41 42
-1.59571027 6.46741545 -0.58470629 1.60606363 1.59043276 -1.41900904
43 44 45 46 47 48
-0.58912995 0.61085642 -0.04036729 1.80218045 2.52441480 1.96103438
49 50 51 52 53 54
-2.24655163 3.94410600 3.86379874 -1.39223624 -1.47738551 -3.01771198
55 56 57 58 59 60
-3.17890009 0.59828498 -0.78987170 -0.96283140 -2.25694746 5.09193736
61 62 63 64 65 66
-0.81897486 -0.20993658 1.88578832 2.51561524 1.49779519 -0.76257658
67 68 69 70 71 72
-5.52897946 -0.10633997 4.42039603 0.60968609 -4.80455367 -1.97046086
73 74 75 76 77 78
-3.20170899 2.69124328 -2.06286722 -1.53019175 5.13447791 1.13536790
79 80 81 82 83 84
0.91952145 -0.54838342 2.50731685 -5.52826949 0.70325182 -2.01262837
85 86 87 88 89 90
1.58178382 0.10529487 0.10290719 3.81515954 0.10219899 -1.00664866
91 92 93 94 95 96
1.07393218 -4.00942414 0.57193252 -0.37286814 -2.40769894 -1.26313049
97 98 99 100 101 102
0.92416173 0.28872410 -1.90367596 -1.34790252 -0.89644114 -2.47698288
103 104 105 106 107 108
-0.63410104 -2.22743541 1.21624981 2.32314743 -3.04099747 -2.50485538
109 110 111 112 113 114
2.23773529 3.25453551 -1.81482864 -5.59574079 -3.35701363 -1.51540642
115 116 117 118 119 120
-1.38974118 -1.38061344 -2.29548192 1.11705068 2.18526935 -0.11903913
121 122 123 124 125 126
-1.02693381 1.78346753 3.62485621 -2.37502395 8.54929539 0.71004779
127 128 129 130 131 132
-1.15846684 0.29284616 -0.61455685 0.39119531 0.49003961 -1.65845328
133 134 135 136 137 138
-0.70305356 -2.18406221 -2.03328422 2.33322758 2.70193723 2.66821026
139 140 141 142 143 144
-2.32579553 2.03148967 1.53605066 0.35764199 3.15190937 3.70289624
145 146 147 148 149 150
-1.14860571 4.21024407 -2.93710467 -1.47345560 -2.07915174 -3.34913477
151 152 153 154 155 156
-3.02249935 4.07897087 -2.22748873 -0.99502943 1.24034494 -0.85245040
157 158 159
1.50068920 1.98012627 1.72720687
> postscript(file="/var/www/html/freestat/rcomp/tmp/6c7cz1290526886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 1.81653983 NA
1 -0.72912650 1.81653983
2 -1.98610925 -0.72912650
3 1.13937367 -1.98610925
4 -1.80339614 1.13937367
5 -0.23040445 -1.80339614
6 0.33177285 -0.23040445
7 1.34659976 0.33177285
8 5.25407914 1.34659976
9 1.02995090 5.25407914
10 0.66730331 1.02995090
11 -0.89986736 0.66730331
12 -4.88550060 -0.89986736
13 1.42891082 -4.88550060
14 1.89007747 1.42891082
15 -3.21077097 1.89007747
16 -1.91718993 -3.21077097
17 -1.74565024 -1.91718993
18 -1.91715694 -1.74565024
19 1.34271339 -1.91715694
20 2.82754319 1.34271339
21 2.40577323 2.82754319
22 -2.61908462 2.40577323
23 -0.31157671 -2.61908462
24 -0.33300846 -0.31157671
25 -1.28093981 -0.33300846
26 -0.49672104 -1.28093981
27 -2.55188152 -0.49672104
28 -1.40323742 -2.55188152
29 -0.40547457 -1.40323742
30 -1.75078091 -0.40547457
31 4.31713115 -1.75078091
32 1.92188848 4.31713115
33 -1.92292676 1.92188848
34 0.23178217 -1.92292676
35 2.76741155 0.23178217
36 -1.59571027 2.76741155
37 6.46741545 -1.59571027
38 -0.58470629 6.46741545
39 1.60606363 -0.58470629
40 1.59043276 1.60606363
41 -1.41900904 1.59043276
42 -0.58912995 -1.41900904
43 0.61085642 -0.58912995
44 -0.04036729 0.61085642
45 1.80218045 -0.04036729
46 2.52441480 1.80218045
47 1.96103438 2.52441480
48 -2.24655163 1.96103438
49 3.94410600 -2.24655163
50 3.86379874 3.94410600
51 -1.39223624 3.86379874
52 -1.47738551 -1.39223624
53 -3.01771198 -1.47738551
54 -3.17890009 -3.01771198
55 0.59828498 -3.17890009
56 -0.78987170 0.59828498
57 -0.96283140 -0.78987170
58 -2.25694746 -0.96283140
59 5.09193736 -2.25694746
60 -0.81897486 5.09193736
61 -0.20993658 -0.81897486
62 1.88578832 -0.20993658
63 2.51561524 1.88578832
64 1.49779519 2.51561524
65 -0.76257658 1.49779519
66 -5.52897946 -0.76257658
67 -0.10633997 -5.52897946
68 4.42039603 -0.10633997
69 0.60968609 4.42039603
70 -4.80455367 0.60968609
71 -1.97046086 -4.80455367
72 -3.20170899 -1.97046086
73 2.69124328 -3.20170899
74 -2.06286722 2.69124328
75 -1.53019175 -2.06286722
76 5.13447791 -1.53019175
77 1.13536790 5.13447791
78 0.91952145 1.13536790
79 -0.54838342 0.91952145
80 2.50731685 -0.54838342
81 -5.52826949 2.50731685
82 0.70325182 -5.52826949
83 -2.01262837 0.70325182
84 1.58178382 -2.01262837
85 0.10529487 1.58178382
86 0.10290719 0.10529487
87 3.81515954 0.10290719
88 0.10219899 3.81515954
89 -1.00664866 0.10219899
90 1.07393218 -1.00664866
91 -4.00942414 1.07393218
92 0.57193252 -4.00942414
93 -0.37286814 0.57193252
94 -2.40769894 -0.37286814
95 -1.26313049 -2.40769894
96 0.92416173 -1.26313049
97 0.28872410 0.92416173
98 -1.90367596 0.28872410
99 -1.34790252 -1.90367596
100 -0.89644114 -1.34790252
101 -2.47698288 -0.89644114
102 -0.63410104 -2.47698288
103 -2.22743541 -0.63410104
104 1.21624981 -2.22743541
105 2.32314743 1.21624981
106 -3.04099747 2.32314743
107 -2.50485538 -3.04099747
108 2.23773529 -2.50485538
109 3.25453551 2.23773529
110 -1.81482864 3.25453551
111 -5.59574079 -1.81482864
112 -3.35701363 -5.59574079
113 -1.51540642 -3.35701363
114 -1.38974118 -1.51540642
115 -1.38061344 -1.38974118
116 -2.29548192 -1.38061344
117 1.11705068 -2.29548192
118 2.18526935 1.11705068
119 -0.11903913 2.18526935
120 -1.02693381 -0.11903913
121 1.78346753 -1.02693381
122 3.62485621 1.78346753
123 -2.37502395 3.62485621
124 8.54929539 -2.37502395
125 0.71004779 8.54929539
126 -1.15846684 0.71004779
127 0.29284616 -1.15846684
128 -0.61455685 0.29284616
129 0.39119531 -0.61455685
130 0.49003961 0.39119531
131 -1.65845328 0.49003961
132 -0.70305356 -1.65845328
133 -2.18406221 -0.70305356
134 -2.03328422 -2.18406221
135 2.33322758 -2.03328422
136 2.70193723 2.33322758
137 2.66821026 2.70193723
138 -2.32579553 2.66821026
139 2.03148967 -2.32579553
140 1.53605066 2.03148967
141 0.35764199 1.53605066
142 3.15190937 0.35764199
143 3.70289624 3.15190937
144 -1.14860571 3.70289624
145 4.21024407 -1.14860571
146 -2.93710467 4.21024407
147 -1.47345560 -2.93710467
148 -2.07915174 -1.47345560
149 -3.34913477 -2.07915174
150 -3.02249935 -3.34913477
151 4.07897087 -3.02249935
152 -2.22748873 4.07897087
153 -0.99502943 -2.22748873
154 1.24034494 -0.99502943
155 -0.85245040 1.24034494
156 1.50068920 -0.85245040
157 1.98012627 1.50068920
158 1.72720687 1.98012627
159 NA 1.72720687
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.72912650 1.81653983
[2,] -1.98610925 -0.72912650
[3,] 1.13937367 -1.98610925
[4,] -1.80339614 1.13937367
[5,] -0.23040445 -1.80339614
[6,] 0.33177285 -0.23040445
[7,] 1.34659976 0.33177285
[8,] 5.25407914 1.34659976
[9,] 1.02995090 5.25407914
[10,] 0.66730331 1.02995090
[11,] -0.89986736 0.66730331
[12,] -4.88550060 -0.89986736
[13,] 1.42891082 -4.88550060
[14,] 1.89007747 1.42891082
[15,] -3.21077097 1.89007747
[16,] -1.91718993 -3.21077097
[17,] -1.74565024 -1.91718993
[18,] -1.91715694 -1.74565024
[19,] 1.34271339 -1.91715694
[20,] 2.82754319 1.34271339
[21,] 2.40577323 2.82754319
[22,] -2.61908462 2.40577323
[23,] -0.31157671 -2.61908462
[24,] -0.33300846 -0.31157671
[25,] -1.28093981 -0.33300846
[26,] -0.49672104 -1.28093981
[27,] -2.55188152 -0.49672104
[28,] -1.40323742 -2.55188152
[29,] -0.40547457 -1.40323742
[30,] -1.75078091 -0.40547457
[31,] 4.31713115 -1.75078091
[32,] 1.92188848 4.31713115
[33,] -1.92292676 1.92188848
[34,] 0.23178217 -1.92292676
[35,] 2.76741155 0.23178217
[36,] -1.59571027 2.76741155
[37,] 6.46741545 -1.59571027
[38,] -0.58470629 6.46741545
[39,] 1.60606363 -0.58470629
[40,] 1.59043276 1.60606363
[41,] -1.41900904 1.59043276
[42,] -0.58912995 -1.41900904
[43,] 0.61085642 -0.58912995
[44,] -0.04036729 0.61085642
[45,] 1.80218045 -0.04036729
[46,] 2.52441480 1.80218045
[47,] 1.96103438 2.52441480
[48,] -2.24655163 1.96103438
[49,] 3.94410600 -2.24655163
[50,] 3.86379874 3.94410600
[51,] -1.39223624 3.86379874
[52,] -1.47738551 -1.39223624
[53,] -3.01771198 -1.47738551
[54,] -3.17890009 -3.01771198
[55,] 0.59828498 -3.17890009
[56,] -0.78987170 0.59828498
[57,] -0.96283140 -0.78987170
[58,] -2.25694746 -0.96283140
[59,] 5.09193736 -2.25694746
[60,] -0.81897486 5.09193736
[61,] -0.20993658 -0.81897486
[62,] 1.88578832 -0.20993658
[63,] 2.51561524 1.88578832
[64,] 1.49779519 2.51561524
[65,] -0.76257658 1.49779519
[66,] -5.52897946 -0.76257658
[67,] -0.10633997 -5.52897946
[68,] 4.42039603 -0.10633997
[69,] 0.60968609 4.42039603
[70,] -4.80455367 0.60968609
[71,] -1.97046086 -4.80455367
[72,] -3.20170899 -1.97046086
[73,] 2.69124328 -3.20170899
[74,] -2.06286722 2.69124328
[75,] -1.53019175 -2.06286722
[76,] 5.13447791 -1.53019175
[77,] 1.13536790 5.13447791
[78,] 0.91952145 1.13536790
[79,] -0.54838342 0.91952145
[80,] 2.50731685 -0.54838342
[81,] -5.52826949 2.50731685
[82,] 0.70325182 -5.52826949
[83,] -2.01262837 0.70325182
[84,] 1.58178382 -2.01262837
[85,] 0.10529487 1.58178382
[86,] 0.10290719 0.10529487
[87,] 3.81515954 0.10290719
[88,] 0.10219899 3.81515954
[89,] -1.00664866 0.10219899
[90,] 1.07393218 -1.00664866
[91,] -4.00942414 1.07393218
[92,] 0.57193252 -4.00942414
[93,] -0.37286814 0.57193252
[94,] -2.40769894 -0.37286814
[95,] -1.26313049 -2.40769894
[96,] 0.92416173 -1.26313049
[97,] 0.28872410 0.92416173
[98,] -1.90367596 0.28872410
[99,] -1.34790252 -1.90367596
[100,] -0.89644114 -1.34790252
[101,] -2.47698288 -0.89644114
[102,] -0.63410104 -2.47698288
[103,] -2.22743541 -0.63410104
[104,] 1.21624981 -2.22743541
[105,] 2.32314743 1.21624981
[106,] -3.04099747 2.32314743
[107,] -2.50485538 -3.04099747
[108,] 2.23773529 -2.50485538
[109,] 3.25453551 2.23773529
[110,] -1.81482864 3.25453551
[111,] -5.59574079 -1.81482864
[112,] -3.35701363 -5.59574079
[113,] -1.51540642 -3.35701363
[114,] -1.38974118 -1.51540642
[115,] -1.38061344 -1.38974118
[116,] -2.29548192 -1.38061344
[117,] 1.11705068 -2.29548192
[118,] 2.18526935 1.11705068
[119,] -0.11903913 2.18526935
[120,] -1.02693381 -0.11903913
[121,] 1.78346753 -1.02693381
[122,] 3.62485621 1.78346753
[123,] -2.37502395 3.62485621
[124,] 8.54929539 -2.37502395
[125,] 0.71004779 8.54929539
[126,] -1.15846684 0.71004779
[127,] 0.29284616 -1.15846684
[128,] -0.61455685 0.29284616
[129,] 0.39119531 -0.61455685
[130,] 0.49003961 0.39119531
[131,] -1.65845328 0.49003961
[132,] -0.70305356 -1.65845328
[133,] -2.18406221 -0.70305356
[134,] -2.03328422 -2.18406221
[135,] 2.33322758 -2.03328422
[136,] 2.70193723 2.33322758
[137,] 2.66821026 2.70193723
[138,] -2.32579553 2.66821026
[139,] 2.03148967 -2.32579553
[140,] 1.53605066 2.03148967
[141,] 0.35764199 1.53605066
[142,] 3.15190937 0.35764199
[143,] 3.70289624 3.15190937
[144,] -1.14860571 3.70289624
[145,] 4.21024407 -1.14860571
[146,] -2.93710467 4.21024407
[147,] -1.47345560 -2.93710467
[148,] -2.07915174 -1.47345560
[149,] -3.34913477 -2.07915174
[150,] -3.02249935 -3.34913477
[151,] 4.07897087 -3.02249935
[152,] -2.22748873 4.07897087
[153,] -0.99502943 -2.22748873
[154,] 1.24034494 -0.99502943
[155,] -0.85245040 1.24034494
[156,] 1.50068920 -0.85245040
[157,] 1.98012627 1.50068920
[158,] 1.72720687 1.98012627
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.72912650 1.81653983
2 -1.98610925 -0.72912650
3 1.13937367 -1.98610925
4 -1.80339614 1.13937367
5 -0.23040445 -1.80339614
6 0.33177285 -0.23040445
7 1.34659976 0.33177285
8 5.25407914 1.34659976
9 1.02995090 5.25407914
10 0.66730331 1.02995090
11 -0.89986736 0.66730331
12 -4.88550060 -0.89986736
13 1.42891082 -4.88550060
14 1.89007747 1.42891082
15 -3.21077097 1.89007747
16 -1.91718993 -3.21077097
17 -1.74565024 -1.91718993
18 -1.91715694 -1.74565024
19 1.34271339 -1.91715694
20 2.82754319 1.34271339
21 2.40577323 2.82754319
22 -2.61908462 2.40577323
23 -0.31157671 -2.61908462
24 -0.33300846 -0.31157671
25 -1.28093981 -0.33300846
26 -0.49672104 -1.28093981
27 -2.55188152 -0.49672104
28 -1.40323742 -2.55188152
29 -0.40547457 -1.40323742
30 -1.75078091 -0.40547457
31 4.31713115 -1.75078091
32 1.92188848 4.31713115
33 -1.92292676 1.92188848
34 0.23178217 -1.92292676
35 2.76741155 0.23178217
36 -1.59571027 2.76741155
37 6.46741545 -1.59571027
38 -0.58470629 6.46741545
39 1.60606363 -0.58470629
40 1.59043276 1.60606363
41 -1.41900904 1.59043276
42 -0.58912995 -1.41900904
43 0.61085642 -0.58912995
44 -0.04036729 0.61085642
45 1.80218045 -0.04036729
46 2.52441480 1.80218045
47 1.96103438 2.52441480
48 -2.24655163 1.96103438
49 3.94410600 -2.24655163
50 3.86379874 3.94410600
51 -1.39223624 3.86379874
52 -1.47738551 -1.39223624
53 -3.01771198 -1.47738551
54 -3.17890009 -3.01771198
55 0.59828498 -3.17890009
56 -0.78987170 0.59828498
57 -0.96283140 -0.78987170
58 -2.25694746 -0.96283140
59 5.09193736 -2.25694746
60 -0.81897486 5.09193736
61 -0.20993658 -0.81897486
62 1.88578832 -0.20993658
63 2.51561524 1.88578832
64 1.49779519 2.51561524
65 -0.76257658 1.49779519
66 -5.52897946 -0.76257658
67 -0.10633997 -5.52897946
68 4.42039603 -0.10633997
69 0.60968609 4.42039603
70 -4.80455367 0.60968609
71 -1.97046086 -4.80455367
72 -3.20170899 -1.97046086
73 2.69124328 -3.20170899
74 -2.06286722 2.69124328
75 -1.53019175 -2.06286722
76 5.13447791 -1.53019175
77 1.13536790 5.13447791
78 0.91952145 1.13536790
79 -0.54838342 0.91952145
80 2.50731685 -0.54838342
81 -5.52826949 2.50731685
82 0.70325182 -5.52826949
83 -2.01262837 0.70325182
84 1.58178382 -2.01262837
85 0.10529487 1.58178382
86 0.10290719 0.10529487
87 3.81515954 0.10290719
88 0.10219899 3.81515954
89 -1.00664866 0.10219899
90 1.07393218 -1.00664866
91 -4.00942414 1.07393218
92 0.57193252 -4.00942414
93 -0.37286814 0.57193252
94 -2.40769894 -0.37286814
95 -1.26313049 -2.40769894
96 0.92416173 -1.26313049
97 0.28872410 0.92416173
98 -1.90367596 0.28872410
99 -1.34790252 -1.90367596
100 -0.89644114 -1.34790252
101 -2.47698288 -0.89644114
102 -0.63410104 -2.47698288
103 -2.22743541 -0.63410104
104 1.21624981 -2.22743541
105 2.32314743 1.21624981
106 -3.04099747 2.32314743
107 -2.50485538 -3.04099747
108 2.23773529 -2.50485538
109 3.25453551 2.23773529
110 -1.81482864 3.25453551
111 -5.59574079 -1.81482864
112 -3.35701363 -5.59574079
113 -1.51540642 -3.35701363
114 -1.38974118 -1.51540642
115 -1.38061344 -1.38974118
116 -2.29548192 -1.38061344
117 1.11705068 -2.29548192
118 2.18526935 1.11705068
119 -0.11903913 2.18526935
120 -1.02693381 -0.11903913
121 1.78346753 -1.02693381
122 3.62485621 1.78346753
123 -2.37502395 3.62485621
124 8.54929539 -2.37502395
125 0.71004779 8.54929539
126 -1.15846684 0.71004779
127 0.29284616 -1.15846684
128 -0.61455685 0.29284616
129 0.39119531 -0.61455685
130 0.49003961 0.39119531
131 -1.65845328 0.49003961
132 -0.70305356 -1.65845328
133 -2.18406221 -0.70305356
134 -2.03328422 -2.18406221
135 2.33322758 -2.03328422
136 2.70193723 2.33322758
137 2.66821026 2.70193723
138 -2.32579553 2.66821026
139 2.03148967 -2.32579553
140 1.53605066 2.03148967
141 0.35764199 1.53605066
142 3.15190937 0.35764199
143 3.70289624 3.15190937
144 -1.14860571 3.70289624
145 4.21024407 -1.14860571
146 -2.93710467 4.21024407
147 -1.47345560 -2.93710467
148 -2.07915174 -1.47345560
149 -3.34913477 -2.07915174
150 -3.02249935 -3.34913477
151 4.07897087 -3.02249935
152 -2.22748873 4.07897087
153 -0.99502943 -2.22748873
154 1.24034494 -0.99502943
155 -0.85245040 1.24034494
156 1.50068920 -0.85245040
157 1.98012627 1.50068920
158 1.72720687 1.98012627
> 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/75zt21290526886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/85zt21290526886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9qiv01290526887.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10qiv01290526887.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11u0c51290526887.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/12x1st1290526887.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/13mkpn1290526887.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/14pk6t1290526887.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/150cnw1290526887.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/16w43n1290526887.tab")
+ }
>
> try(system("convert tmp/19peb1290526886.ps tmp/19peb1290526886.png",intern=TRUE))
character(0)
> try(system("convert tmp/22gve1290526886.ps tmp/22gve1290526886.png",intern=TRUE))
character(0)
> try(system("convert tmp/32gve1290526886.ps tmp/32gve1290526886.png",intern=TRUE))
character(0)
> try(system("convert tmp/42gve1290526886.ps tmp/42gve1290526886.png",intern=TRUE))
character(0)
> try(system("convert tmp/52gve1290526886.ps tmp/52gve1290526886.png",intern=TRUE))
character(0)
> try(system("convert tmp/6c7cz1290526886.ps tmp/6c7cz1290526886.png",intern=TRUE))
character(0)
> try(system("convert tmp/75zt21290526886.ps tmp/75zt21290526886.png",intern=TRUE))
character(0)
> try(system("convert tmp/85zt21290526886.ps tmp/85zt21290526886.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qiv01290526887.ps tmp/9qiv01290526887.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qiv01290526887.ps tmp/10qiv01290526887.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.247 2.902 20.460