R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(9
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+ ,20)
+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('Month'
+ ,'Concern'
+ ,'Doubts'
+ ,'Expectations'
+ ,'Criticisim'
+ ,'Standards'
+ ,'Organization')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('Month','Concern','Doubts','Expectations','Criticisim','Standards','Organization'),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 = '4'
> #'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
> 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
Expectations Month Concern Doubts Criticisim Standards Organization t
1 11 9 24 14 12 24 26 1
2 7 9 25 11 8 25 23 2
3 17 9 17 6 8 30 25 3
4 10 9 18 12 8 19 23 4
5 12 9 18 8 9 22 19 5
6 12 9 16 10 7 22 29 6
7 11 10 20 10 4 25 25 7
8 11 10 16 11 11 23 21 8
9 12 10 18 16 7 17 22 9
10 13 10 17 11 7 21 25 10
11 14 10 23 13 12 19 24 11
12 16 10 30 12 10 19 18 12
13 11 10 23 8 10 15 22 13
14 10 10 18 12 8 16 15 14
15 11 10 15 11 8 23 22 15
16 15 10 12 4 4 27 28 16
17 9 10 21 9 9 22 20 17
18 11 10 15 8 8 14 12 18
19 17 10 20 8 7 22 24 19
20 17 10 31 14 11 23 20 20
21 11 10 27 15 9 23 21 21
22 18 10 34 16 11 21 20 22
23 14 10 21 9 13 19 21 23
24 10 10 31 14 8 18 23 24
25 11 10 19 11 8 20 28 25
26 15 10 16 8 9 23 24 26
27 15 10 20 9 6 25 24 27
28 13 10 21 9 9 19 24 28
29 16 10 22 9 9 24 23 29
30 13 10 17 9 6 22 23 30
31 9 10 24 10 6 25 29 31
32 18 10 25 16 16 26 24 32
33 18 10 26 11 5 29 18 33
34 12 10 25 8 7 32 25 34
35 17 10 17 9 9 25 21 35
36 9 10 32 16 6 29 26 36
37 9 10 33 11 6 28 22 37
38 12 10 13 16 5 17 22 38
39 18 10 32 12 12 28 22 39
40 12 10 25 12 7 29 23 40
41 18 10 29 14 10 26 30 41
42 14 10 22 9 9 25 23 42
43 15 10 18 10 8 14 17 43
44 16 10 17 9 5 25 23 44
45 10 10 20 10 8 26 23 45
46 11 10 15 12 8 20 25 46
47 14 10 20 14 10 18 24 47
48 9 10 33 14 6 32 24 48
49 12 10 29 10 8 25 23 49
50 17 10 23 14 7 25 21 50
51 5 10 26 16 4 23 24 51
52 12 10 18 9 8 21 24 52
53 12 10 20 10 8 20 28 53
54 6 10 11 6 4 15 16 54
55 24 10 28 8 20 30 20 55
56 12 10 26 13 8 24 29 56
57 12 10 22 10 8 26 27 57
58 14 10 17 8 6 24 22 58
59 7 10 12 7 4 22 28 59
60 13 10 14 15 8 14 16 60
61 12 10 17 9 9 24 25 61
62 13 10 21 10 6 24 24 62
63 14 10 19 12 7 24 28 63
64 8 10 18 13 9 24 24 64
65 11 10 10 10 5 19 23 65
66 9 10 29 11 5 31 30 66
67 11 10 31 8 8 22 24 67
68 13 10 19 9 8 27 21 68
69 10 10 9 13 6 19 25 69
70 11 10 20 11 8 25 25 70
71 12 10 28 8 7 20 22 71
72 9 10 19 9 7 21 23 72
73 15 10 30 9 9 27 26 73
74 18 10 29 15 11 23 23 74
75 15 10 26 9 6 25 25 75
76 12 10 23 10 8 20 21 76
77 13 10 13 14 6 21 25 77
78 14 10 21 12 9 22 24 78
79 10 10 19 12 8 23 29 79
80 13 10 28 11 6 25 22 80
81 13 10 23 14 10 25 27 81
82 11 10 18 6 8 17 26 82
83 13 10 21 12 8 19 22 83
84 16 10 20 8 10 25 24 84
85 8 10 23 14 5 19 27 85
86 16 10 21 11 7 20 24 86
87 11 10 21 10 5 26 24 87
88 9 10 15 14 8 23 29 88
89 16 10 28 12 14 27 22 89
90 12 10 19 10 7 17 21 90
91 14 10 26 14 8 17 24 91
92 8 10 10 5 6 19 24 92
93 9 10 16 11 5 17 23 93
94 15 10 22 10 6 22 20 94
95 11 10 19 9 10 21 27 95
96 21 10 31 10 12 32 26 96
97 14 10 31 16 9 21 25 97
98 18 10 29 13 12 21 21 98
99 12 10 19 9 7 18 21 99
100 13 10 22 10 8 18 19 100
101 15 10 23 10 10 23 21 101
102 12 10 15 7 6 19 21 102
103 19 10 20 9 10 20 16 103
104 15 10 18 8 10 21 22 104
105 11 10 23 14 10 20 29 105
106 11 10 25 14 5 17 15 106
107 10 10 21 8 7 18 17 107
108 13 10 24 9 10 19 15 108
109 15 10 25 14 11 22 21 109
110 12 10 17 14 6 15 21 110
111 12 10 13 8 7 14 19 111
112 16 10 28 8 12 18 24 112
113 9 10 21 8 11 24 20 113
114 18 10 25 7 11 35 17 114
115 8 10 9 6 11 29 23 115
116 13 10 16 8 5 21 24 116
117 17 10 19 6 8 25 14 117
118 9 10 17 11 6 20 19 118
119 15 10 25 14 9 22 24 119
120 8 10 20 11 4 13 13 120
121 7 10 29 11 4 26 22 121
122 12 10 14 11 7 17 16 122
123 14 10 22 14 11 25 19 123
124 6 10 15 8 6 20 25 124
125 8 10 19 20 7 19 25 125
126 17 10 20 11 8 21 23 126
127 10 10 15 8 4 22 24 127
128 11 10 20 11 8 24 26 128
129 14 10 18 10 9 21 26 129
130 11 10 33 14 8 26 25 130
131 13 10 22 11 11 24 18 131
132 12 10 16 9 8 16 21 132
133 11 10 17 9 5 23 26 133
134 9 10 16 8 4 18 23 134
135 12 10 21 10 8 16 23 135
136 20 10 26 13 10 26 22 136
137 12 10 18 13 6 19 20 137
138 13 10 18 12 9 21 13 138
139 12 10 17 8 9 21 24 139
140 12 10 22 13 13 22 15 140
141 9 10 30 14 9 23 14 141
142 15 10 30 12 10 29 22 142
143 24 10 24 14 20 21 10 143
144 7 10 21 15 5 21 24 144
145 17 10 21 13 11 23 22 145
146 11 10 29 16 6 27 24 146
147 17 10 31 9 9 25 19 147
148 11 10 20 9 7 21 20 148
149 12 10 16 9 9 10 13 149
150 14 10 22 8 10 20 20 150
151 11 10 20 7 9 26 22 151
152 16 10 28 16 8 24 24 152
153 21 10 38 11 7 29 29 153
154 14 10 22 9 6 19 12 154
155 20 10 20 11 13 24 20 155
156 13 10 17 9 6 19 21 156
157 11 10 28 14 8 24 24 157
158 15 10 22 13 10 22 22 158
159 19 10 31 16 16 17 20 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month Concern Doubts Criticisim
-1.072e+01 1.672e+00 8.400e-02 -1.272e-01 6.750e-01
Standards Organization t
1.229e-01 -8.061e-02 1.835e-04
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.15120 -1.90721 -0.02066 1.81103 7.22547
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.072e+01 1.199e+01 -0.894 0.3729
Month 1.672e+00 1.196e+00 1.398 0.1640
Concern 8.400e-02 4.833e-02 1.738 0.0843 .
Doubts -1.272e-01 8.719e-02 -1.458 0.1468
Criticisim 6.750e-01 8.651e-02 7.803 9.3e-13 ***
Standards 1.229e-01 6.334e-02 1.941 0.0541 .
Organization -8.061e-02 6.312e-02 -1.277 0.2035
t 1.835e-04 5.064e-03 0.036 0.9711
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.694 on 151 degrees of freedom
Multiple R-squared: 0.4159, Adjusted R-squared: 0.3888
F-statistic: 15.36 on 7 and 151 DF, p-value: 4.314e-15
> 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,] 0.65865876 0.68268249 0.34134124
[2,] 0.54907762 0.90184477 0.45092238
[3,] 0.42649657 0.85299315 0.57350343
[4,] 0.60063394 0.79873212 0.39936606
[5,] 0.71970849 0.56058303 0.28029151
[6,] 0.64382871 0.71234257 0.35617129
[7,] 0.74055658 0.51888684 0.25944342
[8,] 0.66513259 0.66973483 0.33486741
[9,] 0.72018193 0.55963614 0.27981807
[10,] 0.67851880 0.64296241 0.32148120
[11,] 0.70178317 0.59643365 0.29821683
[12,] 0.71194412 0.57611176 0.28805588
[13,] 0.66282992 0.67434016 0.33717008
[14,] 0.74181581 0.51636839 0.25818419
[15,] 0.71190352 0.57619295 0.28809648
[16,] 0.65672774 0.68654453 0.34327226
[17,] 0.60818245 0.78363509 0.39181755
[18,] 0.54403278 0.91193443 0.45596722
[19,] 0.48577646 0.97155291 0.51422354
[20,] 0.42502731 0.85005461 0.57497269
[21,] 0.57591462 0.84817076 0.42408538
[22,] 0.52282641 0.95434718 0.47717359
[23,] 0.55700473 0.88599054 0.44299527
[24,] 0.66611028 0.66777945 0.33388972
[25,] 0.65093329 0.69813343 0.34906671
[26,] 0.72314901 0.55370197 0.27685099
[27,] 0.79040131 0.41919739 0.20959869
[28,] 0.77342449 0.45315102 0.22657551
[29,] 0.74518830 0.50962339 0.25481170
[30,] 0.72188933 0.55622133 0.27811067
[31,] 0.75924286 0.48151428 0.24075714
[32,] 0.72068664 0.55862673 0.27931336
[33,] 0.70484403 0.59031193 0.29515597
[34,] 0.74531339 0.50937322 0.25468661
[35,] 0.81513765 0.36972469 0.18486235
[36,] 0.79990731 0.40018537 0.20009269
[37,] 0.76294396 0.47411208 0.23705604
[38,] 0.80765508 0.38468984 0.19234492
[39,] 0.78623324 0.42753352 0.21376676
[40,] 0.83814328 0.32371343 0.16185672
[41,] 0.90102914 0.19794172 0.09897086
[42,] 0.88276743 0.23446513 0.11723257
[43,] 0.85678705 0.28642591 0.14321295
[44,] 0.88605877 0.22788245 0.11394123
[45,] 0.86381809 0.27236382 0.13618191
[46,] 0.83573247 0.32853506 0.16426753
[47,] 0.81003961 0.37992078 0.18996039
[48,] 0.79881423 0.40237155 0.20118577
[49,] 0.80076967 0.39846065 0.19923033
[50,] 0.78148253 0.43703494 0.21851747
[51,] 0.75729504 0.48540992 0.24270496
[52,] 0.73202507 0.53594985 0.26797493
[53,] 0.72641626 0.54716748 0.27358374
[54,] 0.81729555 0.36540890 0.18270445
[55,] 0.79870175 0.40259651 0.20129825
[56,] 0.79154720 0.41690561 0.20845280
[57,] 0.78734079 0.42531843 0.21265921
[58,] 0.75304661 0.49390677 0.24695339
[59,] 0.72292299 0.55415402 0.27707701
[60,] 0.69362427 0.61275146 0.30637573
[61,] 0.66586243 0.66827514 0.33413757
[62,] 0.66238506 0.67522988 0.33761494
[63,] 0.63165829 0.73668341 0.36834171
[64,] 0.66409749 0.67180502 0.33590251
[65,] 0.67654903 0.64690195 0.32345097
[66,] 0.63743562 0.72512875 0.36256438
[67,] 0.67026502 0.65946997 0.32973498
[68,] 0.63392727 0.73214546 0.36607273
[69,] 0.60749252 0.78501497 0.39250748
[70,] 0.56755006 0.86489987 0.43244994
[71,] 0.52263147 0.95473706 0.47736853
[72,] 0.48510360 0.97020720 0.51489640
[73,] 0.44427087 0.88854173 0.55572913
[74,] 0.41275131 0.82550262 0.58724869
[75,] 0.38231126 0.76462252 0.61768874
[76,] 0.46671967 0.93343935 0.53328033
[77,] 0.42180848 0.84361697 0.57819152
[78,] 0.40288484 0.80576969 0.59711516
[79,] 0.37862719 0.75725438 0.62137281
[80,] 0.33651291 0.67302582 0.66348709
[81,] 0.32117368 0.64234736 0.67882632
[82,] 0.31473458 0.62946916 0.68526542
[83,] 0.27408100 0.54816199 0.72591900
[84,] 0.29782339 0.59564678 0.70217661
[85,] 0.29183166 0.58366333 0.70816834
[86,] 0.32658340 0.65316680 0.67341660
[87,] 0.28922773 0.57845546 0.71077227
[88,] 0.27128659 0.54257317 0.72871341
[89,] 0.23332865 0.46665731 0.76667135
[90,] 0.19807390 0.39614780 0.80192610
[91,] 0.16781037 0.33562074 0.83218963
[92,] 0.14619788 0.29239576 0.85380212
[93,] 0.21646407 0.43292815 0.78353593
[94,] 0.19492742 0.38985483 0.80507258
[95,] 0.17376698 0.34753397 0.82623302
[96,] 0.14845276 0.29690552 0.85154724
[97,] 0.13685550 0.27371101 0.86314450
[98,] 0.11979363 0.23958726 0.88020637
[99,] 0.09772587 0.19545174 0.90227413
[100,] 0.09959941 0.19919881 0.90040059
[101,] 0.08860833 0.17721665 0.91139167
[102,] 0.07048583 0.14097165 0.92951417
[103,] 0.17466392 0.34932784 0.82533608
[104,] 0.15266083 0.30532166 0.84733917
[105,] 0.33918722 0.67837443 0.66081278
[106,] 0.34778014 0.69556028 0.65221986
[107,] 0.40222680 0.80445361 0.59777320
[108,] 0.35988556 0.71977112 0.64011444
[109,] 0.34497281 0.68994563 0.65502719
[110,] 0.31096069 0.62192138 0.68903931
[111,] 0.31924811 0.63849622 0.68075189
[112,] 0.31565914 0.63131827 0.68434086
[113,] 0.26786295 0.53572589 0.73213705
[114,] 0.34594374 0.69188749 0.65405626
[115,] 0.29887564 0.59775128 0.70112436
[116,] 0.43473945 0.86947890 0.56526055
[117,] 0.38835995 0.77671990 0.61164005
[118,] 0.34056815 0.68113630 0.65943185
[119,] 0.29276180 0.58552359 0.70723820
[120,] 0.28791464 0.57582928 0.71208536
[121,] 0.25767439 0.51534878 0.74232561
[122,] 0.20690429 0.41380858 0.79309571
[123,] 0.16407175 0.32814350 0.83592825
[124,] 0.12491039 0.24982077 0.87508961
[125,] 0.09248219 0.18496438 0.90751781
[126,] 0.19760478 0.39520956 0.80239522
[127,] 0.24371781 0.48743563 0.75628219
[128,] 0.24363190 0.48726381 0.75636810
[129,] 0.18516225 0.37032450 0.81483775
[130,] 0.18336026 0.36672052 0.81663974
[131,] 0.32297433 0.64594866 0.67702567
[132,] 0.27802788 0.55605576 0.72197212
[133,] 0.21381819 0.42763638 0.78618181
[134,] 0.17029544 0.34059087 0.82970456
[135,] 0.18706818 0.37413636 0.81293182
[136,] 0.12952868 0.25905736 0.87047132
[137,] 0.07545850 0.15091700 0.92454150
[138,] 0.04135692 0.08271384 0.95864308
> postscript(file="/var/www/rcomp/tmp/1tkmx1290604831.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2tkmx1290604831.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3lc3i1290604831.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4lc3i1290604831.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5lc3i1290604831.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
-2.519273192 -4.649680524 4.932848980 -0.197331286 -0.072408219 2.505844241
7 8 9 10 11 12
0.831313063 -3.507348749 1.478511925 1.676618797 -0.782986491 1.368047948
13 14 15 16 17 18
-2.738650008 -2.147406139 -1.319010299 4.734617553 -4.790968269 -1.400774552
19 20 21 22 23 24
4.837952683 1.531399997 -2.575018785 2.779235571 -2.042686592 -2.587826712
25 26 27 28 29 30
-0.804365080 1.699705372 3.269858221 -0.101748488 2.118804652 1.809476153
31 32 33 34 35 36
-2.536638563 -0.133886113 5.718725625 -1.733478712 3.253525216 -3.180100987
37 38 39 40 41 42
-4.099603735 3.243176584 1.061152601 -1.018357953 3.807857521 -0.006512427
43 44 45 46 47 48
3.000032346 5.113118295 -3.159836381 -0.586909332 1.062497397 -4.050639184
49 50 51 52 53 54
-1.793594065 4.732625576 -4.752505364 -0.425024921 -0.020660675 -3.426190326
55 56 57 58 59 60
1.078416871 -0.554809128 -1.007584543 2.350701641 -2.277128924 1.888093495
61 62 63 64 65 66
-1.305870568 1.429534229 2.499103737 -4.962377714 1.561980873 -2.817846234
67 68 69 70 71 72
-2.769806536 -0.491377859 0.512945206 -1.753108293 -0.758908905 -2.918295426
73 74 75 76 77 78
0.311808742 3.058475890 2.837690729 -0.841143348 3.056796493 0.901768721
79 80 81 82 83 84
-1.975295605 0.681274878 -0.814414240 -1.159069351 0.783429272 1.432213224
85 86 87 88 89 90
-1.702531118 4.369012582 -0.145910752 -2.386642435 -1.839122633 0.536067663
91 92 93 94 95 96
2.023395752 -2.673180260 -0.574102157 3.263085920 -2.625089157 3.711081031
97 98 99 100 101 102
0.770523773 2.209382284 0.284322272 0.323088419 0.435463198 0.917501307
103 104 105 106 107 108
4.525658326 0.927036977 -2.042939113 0.404156220 -2.334742133 -1.768918791
109 110 111 112 113 114
0.222580289 2.129905512 0.989431800 0.265623832 -6.531621697 0.410973259
115 116 117 118 119 120
-7.151197618 2.629075225 2.799729190 -2.028928922 1.812589515 -1.554415307
121 122 123 124 125 126
-4.183164114 0.674277850 -1.058020179 -4.759861036 -2.122150644 4.567119131
127 128 129 130 131 132
0.263125845 -1.560209320 1.174223592 -2.597498402 -2.398654806 0.101108805
133 134 135 136 137 138
0.584481959 -0.411038842 -0.031032458 5.270358985 1.341456557 -0.621046179
139 140 141 142 143 144
-1.159163153 -4.491963061 -5.540464120 -0.562678873 2.461495570 -2.905901655
145 146 147 148 149 150
2.382471216 -0.863660916 1.895820105 -1.258070053 -0.484315621 -0.455674976
151 152 153 154 155 156
-3.316391969 3.238016119 7.225474026 1.848819546 3.576141665 1.993926518
157 158 159
-2.017226115 1.094027731 1.122777497
> postscript(file="/var/www/rcomp/tmp/6wlll1290604831.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 -2.519273192 NA
1 -4.649680524 -2.519273192
2 4.932848980 -4.649680524
3 -0.197331286 4.932848980
4 -0.072408219 -0.197331286
5 2.505844241 -0.072408219
6 0.831313063 2.505844241
7 -3.507348749 0.831313063
8 1.478511925 -3.507348749
9 1.676618797 1.478511925
10 -0.782986491 1.676618797
11 1.368047948 -0.782986491
12 -2.738650008 1.368047948
13 -2.147406139 -2.738650008
14 -1.319010299 -2.147406139
15 4.734617553 -1.319010299
16 -4.790968269 4.734617553
17 -1.400774552 -4.790968269
18 4.837952683 -1.400774552
19 1.531399997 4.837952683
20 -2.575018785 1.531399997
21 2.779235571 -2.575018785
22 -2.042686592 2.779235571
23 -2.587826712 -2.042686592
24 -0.804365080 -2.587826712
25 1.699705372 -0.804365080
26 3.269858221 1.699705372
27 -0.101748488 3.269858221
28 2.118804652 -0.101748488
29 1.809476153 2.118804652
30 -2.536638563 1.809476153
31 -0.133886113 -2.536638563
32 5.718725625 -0.133886113
33 -1.733478712 5.718725625
34 3.253525216 -1.733478712
35 -3.180100987 3.253525216
36 -4.099603735 -3.180100987
37 3.243176584 -4.099603735
38 1.061152601 3.243176584
39 -1.018357953 1.061152601
40 3.807857521 -1.018357953
41 -0.006512427 3.807857521
42 3.000032346 -0.006512427
43 5.113118295 3.000032346
44 -3.159836381 5.113118295
45 -0.586909332 -3.159836381
46 1.062497397 -0.586909332
47 -4.050639184 1.062497397
48 -1.793594065 -4.050639184
49 4.732625576 -1.793594065
50 -4.752505364 4.732625576
51 -0.425024921 -4.752505364
52 -0.020660675 -0.425024921
53 -3.426190326 -0.020660675
54 1.078416871 -3.426190326
55 -0.554809128 1.078416871
56 -1.007584543 -0.554809128
57 2.350701641 -1.007584543
58 -2.277128924 2.350701641
59 1.888093495 -2.277128924
60 -1.305870568 1.888093495
61 1.429534229 -1.305870568
62 2.499103737 1.429534229
63 -4.962377714 2.499103737
64 1.561980873 -4.962377714
65 -2.817846234 1.561980873
66 -2.769806536 -2.817846234
67 -0.491377859 -2.769806536
68 0.512945206 -0.491377859
69 -1.753108293 0.512945206
70 -0.758908905 -1.753108293
71 -2.918295426 -0.758908905
72 0.311808742 -2.918295426
73 3.058475890 0.311808742
74 2.837690729 3.058475890
75 -0.841143348 2.837690729
76 3.056796493 -0.841143348
77 0.901768721 3.056796493
78 -1.975295605 0.901768721
79 0.681274878 -1.975295605
80 -0.814414240 0.681274878
81 -1.159069351 -0.814414240
82 0.783429272 -1.159069351
83 1.432213224 0.783429272
84 -1.702531118 1.432213224
85 4.369012582 -1.702531118
86 -0.145910752 4.369012582
87 -2.386642435 -0.145910752
88 -1.839122633 -2.386642435
89 0.536067663 -1.839122633
90 2.023395752 0.536067663
91 -2.673180260 2.023395752
92 -0.574102157 -2.673180260
93 3.263085920 -0.574102157
94 -2.625089157 3.263085920
95 3.711081031 -2.625089157
96 0.770523773 3.711081031
97 2.209382284 0.770523773
98 0.284322272 2.209382284
99 0.323088419 0.284322272
100 0.435463198 0.323088419
101 0.917501307 0.435463198
102 4.525658326 0.917501307
103 0.927036977 4.525658326
104 -2.042939113 0.927036977
105 0.404156220 -2.042939113
106 -2.334742133 0.404156220
107 -1.768918791 -2.334742133
108 0.222580289 -1.768918791
109 2.129905512 0.222580289
110 0.989431800 2.129905512
111 0.265623832 0.989431800
112 -6.531621697 0.265623832
113 0.410973259 -6.531621697
114 -7.151197618 0.410973259
115 2.629075225 -7.151197618
116 2.799729190 2.629075225
117 -2.028928922 2.799729190
118 1.812589515 -2.028928922
119 -1.554415307 1.812589515
120 -4.183164114 -1.554415307
121 0.674277850 -4.183164114
122 -1.058020179 0.674277850
123 -4.759861036 -1.058020179
124 -2.122150644 -4.759861036
125 4.567119131 -2.122150644
126 0.263125845 4.567119131
127 -1.560209320 0.263125845
128 1.174223592 -1.560209320
129 -2.597498402 1.174223592
130 -2.398654806 -2.597498402
131 0.101108805 -2.398654806
132 0.584481959 0.101108805
133 -0.411038842 0.584481959
134 -0.031032458 -0.411038842
135 5.270358985 -0.031032458
136 1.341456557 5.270358985
137 -0.621046179 1.341456557
138 -1.159163153 -0.621046179
139 -4.491963061 -1.159163153
140 -5.540464120 -4.491963061
141 -0.562678873 -5.540464120
142 2.461495570 -0.562678873
143 -2.905901655 2.461495570
144 2.382471216 -2.905901655
145 -0.863660916 2.382471216
146 1.895820105 -0.863660916
147 -1.258070053 1.895820105
148 -0.484315621 -1.258070053
149 -0.455674976 -0.484315621
150 -3.316391969 -0.455674976
151 3.238016119 -3.316391969
152 7.225474026 3.238016119
153 1.848819546 7.225474026
154 3.576141665 1.848819546
155 1.993926518 3.576141665
156 -2.017226115 1.993926518
157 1.094027731 -2.017226115
158 1.122777497 1.094027731
159 NA 1.122777497
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.649680524 -2.519273192
[2,] 4.932848980 -4.649680524
[3,] -0.197331286 4.932848980
[4,] -0.072408219 -0.197331286
[5,] 2.505844241 -0.072408219
[6,] 0.831313063 2.505844241
[7,] -3.507348749 0.831313063
[8,] 1.478511925 -3.507348749
[9,] 1.676618797 1.478511925
[10,] -0.782986491 1.676618797
[11,] 1.368047948 -0.782986491
[12,] -2.738650008 1.368047948
[13,] -2.147406139 -2.738650008
[14,] -1.319010299 -2.147406139
[15,] 4.734617553 -1.319010299
[16,] -4.790968269 4.734617553
[17,] -1.400774552 -4.790968269
[18,] 4.837952683 -1.400774552
[19,] 1.531399997 4.837952683
[20,] -2.575018785 1.531399997
[21,] 2.779235571 -2.575018785
[22,] -2.042686592 2.779235571
[23,] -2.587826712 -2.042686592
[24,] -0.804365080 -2.587826712
[25,] 1.699705372 -0.804365080
[26,] 3.269858221 1.699705372
[27,] -0.101748488 3.269858221
[28,] 2.118804652 -0.101748488
[29,] 1.809476153 2.118804652
[30,] -2.536638563 1.809476153
[31,] -0.133886113 -2.536638563
[32,] 5.718725625 -0.133886113
[33,] -1.733478712 5.718725625
[34,] 3.253525216 -1.733478712
[35,] -3.180100987 3.253525216
[36,] -4.099603735 -3.180100987
[37,] 3.243176584 -4.099603735
[38,] 1.061152601 3.243176584
[39,] -1.018357953 1.061152601
[40,] 3.807857521 -1.018357953
[41,] -0.006512427 3.807857521
[42,] 3.000032346 -0.006512427
[43,] 5.113118295 3.000032346
[44,] -3.159836381 5.113118295
[45,] -0.586909332 -3.159836381
[46,] 1.062497397 -0.586909332
[47,] -4.050639184 1.062497397
[48,] -1.793594065 -4.050639184
[49,] 4.732625576 -1.793594065
[50,] -4.752505364 4.732625576
[51,] -0.425024921 -4.752505364
[52,] -0.020660675 -0.425024921
[53,] -3.426190326 -0.020660675
[54,] 1.078416871 -3.426190326
[55,] -0.554809128 1.078416871
[56,] -1.007584543 -0.554809128
[57,] 2.350701641 -1.007584543
[58,] -2.277128924 2.350701641
[59,] 1.888093495 -2.277128924
[60,] -1.305870568 1.888093495
[61,] 1.429534229 -1.305870568
[62,] 2.499103737 1.429534229
[63,] -4.962377714 2.499103737
[64,] 1.561980873 -4.962377714
[65,] -2.817846234 1.561980873
[66,] -2.769806536 -2.817846234
[67,] -0.491377859 -2.769806536
[68,] 0.512945206 -0.491377859
[69,] -1.753108293 0.512945206
[70,] -0.758908905 -1.753108293
[71,] -2.918295426 -0.758908905
[72,] 0.311808742 -2.918295426
[73,] 3.058475890 0.311808742
[74,] 2.837690729 3.058475890
[75,] -0.841143348 2.837690729
[76,] 3.056796493 -0.841143348
[77,] 0.901768721 3.056796493
[78,] -1.975295605 0.901768721
[79,] 0.681274878 -1.975295605
[80,] -0.814414240 0.681274878
[81,] -1.159069351 -0.814414240
[82,] 0.783429272 -1.159069351
[83,] 1.432213224 0.783429272
[84,] -1.702531118 1.432213224
[85,] 4.369012582 -1.702531118
[86,] -0.145910752 4.369012582
[87,] -2.386642435 -0.145910752
[88,] -1.839122633 -2.386642435
[89,] 0.536067663 -1.839122633
[90,] 2.023395752 0.536067663
[91,] -2.673180260 2.023395752
[92,] -0.574102157 -2.673180260
[93,] 3.263085920 -0.574102157
[94,] -2.625089157 3.263085920
[95,] 3.711081031 -2.625089157
[96,] 0.770523773 3.711081031
[97,] 2.209382284 0.770523773
[98,] 0.284322272 2.209382284
[99,] 0.323088419 0.284322272
[100,] 0.435463198 0.323088419
[101,] 0.917501307 0.435463198
[102,] 4.525658326 0.917501307
[103,] 0.927036977 4.525658326
[104,] -2.042939113 0.927036977
[105,] 0.404156220 -2.042939113
[106,] -2.334742133 0.404156220
[107,] -1.768918791 -2.334742133
[108,] 0.222580289 -1.768918791
[109,] 2.129905512 0.222580289
[110,] 0.989431800 2.129905512
[111,] 0.265623832 0.989431800
[112,] -6.531621697 0.265623832
[113,] 0.410973259 -6.531621697
[114,] -7.151197618 0.410973259
[115,] 2.629075225 -7.151197618
[116,] 2.799729190 2.629075225
[117,] -2.028928922 2.799729190
[118,] 1.812589515 -2.028928922
[119,] -1.554415307 1.812589515
[120,] -4.183164114 -1.554415307
[121,] 0.674277850 -4.183164114
[122,] -1.058020179 0.674277850
[123,] -4.759861036 -1.058020179
[124,] -2.122150644 -4.759861036
[125,] 4.567119131 -2.122150644
[126,] 0.263125845 4.567119131
[127,] -1.560209320 0.263125845
[128,] 1.174223592 -1.560209320
[129,] -2.597498402 1.174223592
[130,] -2.398654806 -2.597498402
[131,] 0.101108805 -2.398654806
[132,] 0.584481959 0.101108805
[133,] -0.411038842 0.584481959
[134,] -0.031032458 -0.411038842
[135,] 5.270358985 -0.031032458
[136,] 1.341456557 5.270358985
[137,] -0.621046179 1.341456557
[138,] -1.159163153 -0.621046179
[139,] -4.491963061 -1.159163153
[140,] -5.540464120 -4.491963061
[141,] -0.562678873 -5.540464120
[142,] 2.461495570 -0.562678873
[143,] -2.905901655 2.461495570
[144,] 2.382471216 -2.905901655
[145,] -0.863660916 2.382471216
[146,] 1.895820105 -0.863660916
[147,] -1.258070053 1.895820105
[148,] -0.484315621 -1.258070053
[149,] -0.455674976 -0.484315621
[150,] -3.316391969 -0.455674976
[151,] 3.238016119 -3.316391969
[152,] 7.225474026 3.238016119
[153,] 1.848819546 7.225474026
[154,] 3.576141665 1.848819546
[155,] 1.993926518 3.576141665
[156,] -2.017226115 1.993926518
[157,] 1.094027731 -2.017226115
[158,] 1.122777497 1.094027731
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.649680524 -2.519273192
2 4.932848980 -4.649680524
3 -0.197331286 4.932848980
4 -0.072408219 -0.197331286
5 2.505844241 -0.072408219
6 0.831313063 2.505844241
7 -3.507348749 0.831313063
8 1.478511925 -3.507348749
9 1.676618797 1.478511925
10 -0.782986491 1.676618797
11 1.368047948 -0.782986491
12 -2.738650008 1.368047948
13 -2.147406139 -2.738650008
14 -1.319010299 -2.147406139
15 4.734617553 -1.319010299
16 -4.790968269 4.734617553
17 -1.400774552 -4.790968269
18 4.837952683 -1.400774552
19 1.531399997 4.837952683
20 -2.575018785 1.531399997
21 2.779235571 -2.575018785
22 -2.042686592 2.779235571
23 -2.587826712 -2.042686592
24 -0.804365080 -2.587826712
25 1.699705372 -0.804365080
26 3.269858221 1.699705372
27 -0.101748488 3.269858221
28 2.118804652 -0.101748488
29 1.809476153 2.118804652
30 -2.536638563 1.809476153
31 -0.133886113 -2.536638563
32 5.718725625 -0.133886113
33 -1.733478712 5.718725625
34 3.253525216 -1.733478712
35 -3.180100987 3.253525216
36 -4.099603735 -3.180100987
37 3.243176584 -4.099603735
38 1.061152601 3.243176584
39 -1.018357953 1.061152601
40 3.807857521 -1.018357953
41 -0.006512427 3.807857521
42 3.000032346 -0.006512427
43 5.113118295 3.000032346
44 -3.159836381 5.113118295
45 -0.586909332 -3.159836381
46 1.062497397 -0.586909332
47 -4.050639184 1.062497397
48 -1.793594065 -4.050639184
49 4.732625576 -1.793594065
50 -4.752505364 4.732625576
51 -0.425024921 -4.752505364
52 -0.020660675 -0.425024921
53 -3.426190326 -0.020660675
54 1.078416871 -3.426190326
55 -0.554809128 1.078416871
56 -1.007584543 -0.554809128
57 2.350701641 -1.007584543
58 -2.277128924 2.350701641
59 1.888093495 -2.277128924
60 -1.305870568 1.888093495
61 1.429534229 -1.305870568
62 2.499103737 1.429534229
63 -4.962377714 2.499103737
64 1.561980873 -4.962377714
65 -2.817846234 1.561980873
66 -2.769806536 -2.817846234
67 -0.491377859 -2.769806536
68 0.512945206 -0.491377859
69 -1.753108293 0.512945206
70 -0.758908905 -1.753108293
71 -2.918295426 -0.758908905
72 0.311808742 -2.918295426
73 3.058475890 0.311808742
74 2.837690729 3.058475890
75 -0.841143348 2.837690729
76 3.056796493 -0.841143348
77 0.901768721 3.056796493
78 -1.975295605 0.901768721
79 0.681274878 -1.975295605
80 -0.814414240 0.681274878
81 -1.159069351 -0.814414240
82 0.783429272 -1.159069351
83 1.432213224 0.783429272
84 -1.702531118 1.432213224
85 4.369012582 -1.702531118
86 -0.145910752 4.369012582
87 -2.386642435 -0.145910752
88 -1.839122633 -2.386642435
89 0.536067663 -1.839122633
90 2.023395752 0.536067663
91 -2.673180260 2.023395752
92 -0.574102157 -2.673180260
93 3.263085920 -0.574102157
94 -2.625089157 3.263085920
95 3.711081031 -2.625089157
96 0.770523773 3.711081031
97 2.209382284 0.770523773
98 0.284322272 2.209382284
99 0.323088419 0.284322272
100 0.435463198 0.323088419
101 0.917501307 0.435463198
102 4.525658326 0.917501307
103 0.927036977 4.525658326
104 -2.042939113 0.927036977
105 0.404156220 -2.042939113
106 -2.334742133 0.404156220
107 -1.768918791 -2.334742133
108 0.222580289 -1.768918791
109 2.129905512 0.222580289
110 0.989431800 2.129905512
111 0.265623832 0.989431800
112 -6.531621697 0.265623832
113 0.410973259 -6.531621697
114 -7.151197618 0.410973259
115 2.629075225 -7.151197618
116 2.799729190 2.629075225
117 -2.028928922 2.799729190
118 1.812589515 -2.028928922
119 -1.554415307 1.812589515
120 -4.183164114 -1.554415307
121 0.674277850 -4.183164114
122 -1.058020179 0.674277850
123 -4.759861036 -1.058020179
124 -2.122150644 -4.759861036
125 4.567119131 -2.122150644
126 0.263125845 4.567119131
127 -1.560209320 0.263125845
128 1.174223592 -1.560209320
129 -2.597498402 1.174223592
130 -2.398654806 -2.597498402
131 0.101108805 -2.398654806
132 0.584481959 0.101108805
133 -0.411038842 0.584481959
134 -0.031032458 -0.411038842
135 5.270358985 -0.031032458
136 1.341456557 5.270358985
137 -0.621046179 1.341456557
138 -1.159163153 -0.621046179
139 -4.491963061 -1.159163153
140 -5.540464120 -4.491963061
141 -0.562678873 -5.540464120
142 2.461495570 -0.562678873
143 -2.905901655 2.461495570
144 2.382471216 -2.905901655
145 -0.863660916 2.382471216
146 1.895820105 -0.863660916
147 -1.258070053 1.895820105
148 -0.484315621 -1.258070053
149 -0.455674976 -0.484315621
150 -3.316391969 -0.455674976
151 3.238016119 -3.316391969
152 7.225474026 3.238016119
153 1.848819546 7.225474026
154 3.576141665 1.848819546
155 1.993926518 3.576141665
156 -2.017226115 1.993926518
157 1.094027731 -2.017226115
158 1.122777497 1.094027731
> 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/rcomp/tmp/76uko1290604831.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/86uko1290604831.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/96uko1290604831.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10zl1r1290604831.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11340e1290604831.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/rcomp/tmp/12o4gk1290604831.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/rcomp/tmp/13kwet1290604831.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/rcomp/tmp/145fuz1290604831.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/rcomp/tmp/159xt51290604831.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/rcomp/tmp/16cgrb1290604831.tab")
+ }
>
> try(system("convert tmp/1tkmx1290604831.ps tmp/1tkmx1290604831.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tkmx1290604831.ps tmp/2tkmx1290604831.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lc3i1290604831.ps tmp/3lc3i1290604831.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lc3i1290604831.ps tmp/4lc3i1290604831.png",intern=TRUE))
character(0)
> try(system("convert tmp/5lc3i1290604831.ps tmp/5lc3i1290604831.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wlll1290604831.ps tmp/6wlll1290604831.png",intern=TRUE))
character(0)
> try(system("convert tmp/76uko1290604831.ps tmp/76uko1290604831.png",intern=TRUE))
character(0)
> try(system("convert tmp/86uko1290604831.ps tmp/86uko1290604831.png",intern=TRUE))
character(0)
> try(system("convert tmp/96uko1290604831.ps tmp/96uko1290604831.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zl1r1290604831.ps tmp/10zl1r1290604831.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.720 1.090 6.763