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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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(4
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+ ,dim=c(6
+ ,154)
+ ,dimnames=list(c('Weeks_Treatment'
+ ,'UseLimit'
+ ,'Used'
+ ,'CorrectAnalysis'
+ ,'Useful'
+ ,'Outcome
')
+ ,1:154))
> y <- array(NA,dim=c(6,154),dimnames=list(c('Weeks_Treatment','UseLimit','Used','CorrectAnalysis','Useful','Outcome
'),1:154))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> 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
CorrectAnalysis Weeks_Treatment UseLimit Used Useful Outcome\r t
1 0 4 1 0 0 1 1
2 0 0 0 0 0 0 2
3 0 0 0 0 0 0 3
4 0 0 0 0 0 0 4
5 0 0 0 0 0 0 5
6 0 0 1 0 1 1 6
7 0 0 0 0 0 0 7
8 0 4 0 0 0 0 8
9 0 0 0 0 0 1 9
10 0 0 1 0 0 0 10
11 0 4 1 0 0 0 11
12 0 0 0 0 0 0 12
13 0 0 0 1 1 0 13
14 0 4 1 0 0 0 14
15 0 0 0 1 1 1 15
16 0 4 0 1 1 1 16
17 1 4 1 1 1 0 17
18 0 4 1 0 0 0 18
19 0 0 0 0 0 1 19
20 1 4 0 1 1 1 20
21 0 0 1 0 1 0 21
22 0 0 1 1 1 1 22
23 0 0 0 0 1 1 23
24 0 0 1 0 1 1 24
25 0 4 0 1 0 1 25
26 0 0 0 1 1 0 26
27 0 0 1 0 0 1 27
28 0 0 0 1 0 0 28
29 0 0 0 0 0 1 29
30 0 0 0 0 1 0 30
31 0 0 0 0 0 0 31
32 0 0 1 0 0 0 32
33 0 0 1 0 1 0 33
34 0 4 0 0 0 1 34
35 0 0 0 0 0 0 35
36 0 0 0 0 0 0 36
37 0 4 1 1 1 0 37
38 0 0 0 1 0 1 38
39 0 0 0 0 1 1 39
40 0 4 0 0 1 0 40
41 1 0 0 1 1 1 41
42 0 0 0 1 0 1 42
43 0 0 1 0 1 1 43
44 0 4 1 0 0 0 44
45 0 0 0 0 1 0 45
46 0 0 0 0 1 1 46
47 0 0 0 0 0 0 47
48 0 0 0 0 0 1 48
49 0 0 0 0 1 1 49
50 0 0 0 0 0 0 50
51 0 4 0 1 0 0 51
52 1 4 1 1 1 0 52
53 0 0 0 0 0 1 53
54 1 0 0 1 0 0 54
55 0 0 0 0 0 0 55
56 0 4 0 1 0 1 56
57 0 0 0 1 1 1 57
58 0 0 0 0 0 1 58
59 0 0 0 0 0 1 59
60 1 4 1 1 1 1 60
61 0 4 1 0 0 1 61
62 0 0 0 1 1 0 62
63 0 0 0 0 0 0 63
64 0 4 1 0 0 1 64
65 0 0 0 0 0 0 65
66 0 0 0 0 0 0 66
67 1 4 0 1 1 0 67
68 0 0 1 0 0 0 68
69 0 0 0 0 0 1 69
70 0 0 0 1 0 0 70
71 0 0 0 0 0 0 71
72 0 0 0 0 0 1 72
73 0 0 0 1 0 1 73
74 0 0 1 1 0 0 74
75 0 0 0 0 0 1 75
76 0 4 0 0 1 1 76
77 0 0 0 0 0 1 77
78 0 0 0 1 1 1 78
79 1 4 0 1 0 1 79
80 0 4 0 0 1 0 80
81 0 0 0 0 0 0 81
82 0 0 1 1 0 1 82
83 0 0 0 0 0 0 83
84 1 0 0 1 0 0 84
85 0 0 0 0 1 1 85
86 0 0 1 0 0 0 86
87 0 0 1 0 0 1 87
88 0 2 1 1 0 1 88
89 0 0 0 0 0 0 89
90 0 0 0 0 0 1 90
91 0 0 0 0 1 0 91
92 0 2 1 0 0 0 92
93 0 0 1 0 1 0 93
94 0 0 0 0 0 0 94
95 0 2 0 0 0 0 95
96 0 0 0 0 0 1 96
97 0 2 1 0 0 0 97
98 0 0 0 0 0 0 98
99 0 0 1 0 0 0 99
100 0 0 0 0 0 1 100
101 0 0 1 0 0 1 101
102 0 0 0 0 0 0 102
103 0 0 0 0 0 0 103
104 0 0 0 0 0 0 104
105 0 2 0 1 0 0 105
106 0 0 0 0 0 0 106
107 0 0 0 0 0 0 107
108 0 2 1 1 0 0 108
109 0 0 0 0 0 0 109
110 0 0 1 0 0 0 110
111 0 2 1 1 1 0 111
112 0 2 0 0 0 0 112
113 0 0 0 1 0 0 113
114 0 2 1 1 0 0 114
115 0 0 1 0 0 0 115
116 0 0 0 0 0 0 116
117 0 0 1 0 0 1 117
118 0 0 1 0 0 0 118
119 0 0 0 0 0 0 119
120 0 0 0 0 0 1 120
121 0 0 1 0 0 0 121
122 0 0 0 0 0 0 122
123 0 2 1 1 0 0 123
124 0 0 0 1 1 1 124
125 0 0 0 0 0 1 125
126 0 2 0 0 0 0 126
127 0 0 0 0 1 0 127
128 0 0 0 0 0 1 128
129 0 0 0 0 0 0 129
130 0 0 0 0 0 1 130
131 0 0 1 0 0 0 131
132 0 0 1 0 0 1 132
133 0 0 1 1 0 0 133
134 0 0 0 0 0 0 134
135 0 0 0 0 0 0 135
136 0 0 0 0 0 0 136
137 0 0 1 1 1 1 137
138 0 2 1 1 1 1 138
139 0 2 0 0 0 0 139
140 0 0 0 0 0 0 140
141 1 0 0 1 0 1 141
142 0 2 0 1 0 1 142
143 0 0 1 0 0 0 143
144 0 0 0 0 1 1 144
145 0 0 0 0 1 0 145
146 0 2 0 0 0 1 146
147 0 2 0 1 0 0 147
148 0 2 0 0 0 0 148
149 0 0 1 0 0 0 149
150 0 0 0 0 1 1 150
151 0 0 0 0 0 1 151
152 1 0 1 1 0 0 152
153 1 0 1 1 1 0 153
154 0 0 1 1 0 0 154
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Weeks_Treatment UseLimit Used
-0.0301764 0.0248250 -0.0109502 0.2342283
Useful `Outcome\\r` t
0.0642435 -0.0242136 0.0001985
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.36399 -0.06258 0.00625 0.02913 0.79217
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0301764 0.0496553 -0.608 0.5443
Weeks_Treatment 0.0248250 0.0141025 1.760 0.0804 .
UseLimit -0.0109502 0.0424871 -0.258 0.7970
Used 0.2342283 0.0456825 5.127 9.12e-07 ***
Useful 0.0642435 0.0467406 1.374 0.1714
`Outcome\\r` -0.0242136 0.0405244 -0.598 0.5511
t 0.0001985 0.0004555 0.436 0.6636
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2405 on 147 degrees of freedom
Multiple R-squared: 0.2319, Adjusted R-squared: 0.2005
F-statistic: 7.397 on 6 and 147 DF, p-value: 6.261e-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,] 0.000000000 0.000000000 1.000000000
[2,] 0.000000000 0.000000000 1.000000000
[3,] 0.000000000 0.000000000 1.000000000
[4,] 0.000000000 0.000000000 1.000000000
[5,] 0.000000000 0.000000000 1.000000000
[6,] 0.000000000 0.000000000 1.000000000
[7,] 0.000000000 0.000000000 1.000000000
[8,] 0.395078920 0.790157841 0.604921080
[9,] 0.321701721 0.643403441 0.678298279
[10,] 0.318057974 0.636115948 0.681942026
[11,] 0.811623107 0.376753786 0.188376893
[12,] 0.760169624 0.479660752 0.239830376
[13,] 0.750058960 0.499882080 0.249941040
[14,] 0.686738467 0.626523066 0.313261533
[15,] 0.618821709 0.762356582 0.381178291
[16,] 0.609563457 0.780873086 0.390436543
[17,] 0.597706854 0.804586291 0.402293146
[18,] 0.567011442 0.865977116 0.432988558
[19,] 0.509631543 0.980736915 0.490368457
[20,] 0.458881096 0.917762193 0.541118904
[21,] 0.399571581 0.799143163 0.600428419
[22,] 0.343347829 0.686695658 0.656652171
[23,] 0.289756812 0.579513623 0.710243188
[24,] 0.241235652 0.482471303 0.758764348
[25,] 0.199875794 0.399751588 0.800124206
[26,] 0.162170396 0.324340792 0.837829604
[27,] 0.128908369 0.257816738 0.871091631
[28,] 0.167562054 0.335124108 0.832437946
[29,] 0.138978378 0.277956756 0.861021622
[30,] 0.108873886 0.217747772 0.891126114
[31,] 0.095394318 0.190788636 0.904605682
[32,] 0.561139928 0.877720145 0.438860072
[33,] 0.527757126 0.944485748 0.472242874
[34,] 0.473747475 0.947494950 0.526252525
[35,] 0.421656082 0.843312164 0.578343918
[36,] 0.371059749 0.742119498 0.628940251
[37,] 0.322417464 0.644834929 0.677582536
[38,] 0.278858793 0.557717585 0.721141207
[39,] 0.237306424 0.474612848 0.762693576
[40,] 0.199263716 0.398527433 0.800736284
[41,] 0.165962990 0.331925980 0.834037010
[42,] 0.172127731 0.344255461 0.827872269
[43,] 0.439846390 0.879692779 0.560153610
[44,] 0.391681421 0.783362842 0.608318579
[45,] 0.795245710 0.409508579 0.204754290
[46,] 0.758319727 0.483360547 0.241680273
[47,] 0.780632142 0.438735717 0.219367858
[48,] 0.788824504 0.422350992 0.211175496
[49,] 0.751938075 0.496123850 0.248061925
[50,] 0.711775338 0.576449323 0.288224662
[51,] 0.889948797 0.220102406 0.110051203
[52,] 0.869628836 0.260742329 0.130371164
[53,] 0.884395612 0.231208777 0.115604388
[54,] 0.858886087 0.282227826 0.141113913
[55,] 0.834241845 0.331516310 0.165758155
[56,] 0.802209618 0.395580764 0.197790382
[57,] 0.766757134 0.466485732 0.233242866
[58,] 0.927874763 0.144250474 0.072125237
[59,] 0.911196867 0.177606265 0.088803133
[60,] 0.890407145 0.219185711 0.109592855
[61,] 0.890991450 0.218017099 0.109008550
[62,] 0.866853706 0.266292588 0.133146294
[63,] 0.839466618 0.321066765 0.160533382
[64,] 0.834966763 0.330066474 0.165033237
[65,] 0.831809539 0.336380922 0.168190461
[66,] 0.800273938 0.399452124 0.199726062
[67,] 0.785041891 0.429916217 0.214958109
[68,] 0.748971201 0.502057598 0.251028799
[69,] 0.761680938 0.476638124 0.238319062
[70,] 0.977747319 0.044505363 0.022252681
[71,] 0.980896519 0.038206962 0.019103481
[72,] 0.974500763 0.050998475 0.025499237
[73,] 0.974205615 0.051588770 0.025794385
[74,] 0.966058931 0.067882138 0.033941069
[75,] 0.998861922 0.002276155 0.001138078
[76,] 0.998370737 0.003258526 0.001629263
[77,] 0.997604281 0.004791438 0.002395719
[78,] 0.996558664 0.006882673 0.003441336
[79,] 0.995924293 0.008151414 0.004075707
[80,] 0.994200228 0.011599544 0.005799772
[81,] 0.991964625 0.016070751 0.008035375
[82,] 0.989222504 0.021554992 0.010777496
[83,] 0.987287126 0.025425749 0.012712874
[84,] 0.983211779 0.033576441 0.016788221
[85,] 0.977398118 0.045203764 0.022601882
[86,] 0.975062299 0.049875403 0.024937701
[87,] 0.967622728 0.064754544 0.032377272
[88,] 0.966248901 0.067502197 0.033751099
[89,] 0.956142930 0.087714141 0.043857070
[90,] 0.943991075 0.112017851 0.056008925
[91,] 0.930287631 0.139424737 0.069712369
[92,] 0.915981558 0.168036883 0.084018442
[93,] 0.895716692 0.208566616 0.104283308
[94,] 0.872123160 0.255753679 0.127876840
[95,] 0.845083544 0.309832912 0.154916456
[96,] 0.831180706 0.337638589 0.168819294
[97,] 0.798246638 0.403506724 0.201753362
[98,] 0.761824751 0.476350497 0.238175249
[99,] 0.736937516 0.526124968 0.263062484
[100,] 0.694430838 0.611138324 0.305569162
[101,] 0.649806802 0.700386396 0.350193198
[102,] 0.628275520 0.743448960 0.371724480
[103,] 0.629817204 0.740365592 0.370182796
[104,] 0.618193258 0.763613484 0.381806742
[105,] 0.575843988 0.848312023 0.424156012
[106,] 0.525182645 0.949634709 0.474817355
[107,] 0.469738692 0.939477384 0.530261308
[108,] 0.425915184 0.851830368 0.574084816
[109,] 0.377855540 0.755711080 0.622144460
[110,] 0.324828046 0.649656092 0.675171954
[111,] 0.279105858 0.558211716 0.720894142
[112,] 0.239439943 0.478879885 0.760560057
[113,] 0.196440316 0.392880633 0.803559684
[114,] 0.163593191 0.327186382 0.836406809
[115,] 0.156351222 0.312702445 0.843648778
[116,] 0.122691276 0.245382552 0.877308724
[117,] 0.123206222 0.246412444 0.876793778
[118,] 0.096551571 0.193103143 0.903448429
[119,] 0.072339640 0.144679281 0.927660360
[120,] 0.052425187 0.104850374 0.947574813
[121,] 0.037174690 0.074349381 0.962825310
[122,] 0.027606823 0.055213646 0.972393177
[123,] 0.023048828 0.046097655 0.976951172
[124,] 0.021971406 0.043942813 0.978028594
[125,] 0.013855133 0.027710266 0.986144867
[126,] 0.008402515 0.016805030 0.991597485
[127,] 0.004936148 0.009872296 0.995063852
[128,] 0.006757388 0.013514776 0.993242612
[129,] 0.007072705 0.014145410 0.992927295
[130,] 0.005035892 0.010071784 0.994964108
[131,] 0.002536126 0.005072253 0.997463874
[132,] 0.050229044 0.100458087 0.949770956
[133,] 0.030091049 0.060182099 0.969908951
[134,] 0.014727767 0.029455534 0.985272233
[135,] 0.006552333 0.013104666 0.993447667
> postscript(file="/var/wessaorg/rcomp/tmp/1q9w11356141851.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/2hvo51356141851.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/36gp51356141851.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4m51x1356141851.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5sjzv1356141851.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 154
Frequency = 1
1 2 3 4 5
-3.415824e-02 2.977930e-02 2.958077e-02 2.938223e-02 2.918370e-02
6 7 8 9 10
-9.450509e-05 2.878663e-02 -7.071185e-02 5.260321e-02 3.914127e-02
11 12 13 14 15
-6.035722e-02 2.779397e-02 -2.708764e-01 -6.095282e-02 -2.470598e-01
16 17 18 19 20
-3.465583e-01 6.399798e-01 -6.174696e-02 5.061787e-02 6.526476e-01
21 22 23 24 25
-2.728615e-02 -2.374993e-01 -1.441981e-02 -3.668111e-03 -2.841015e-01
26 27 28 29 30
-2.734573e-01 5.997984e-02 -2.096108e-01 4.863254e-02 -4.002319e-02
31 32 33 34 35
2.402183e-02 3.477352e-02 -2.966856e-02 -5.166009e-02 2.322769e-02
36 37 38 39 40
2.302916e-02 -3.639909e-01 -1.873825e-01 -1.759635e-02 -1.413085e-01
41 42 43 44 45
7.477783e-01 -1.881767e-01 -7.440250e-03 -6.690883e-02 -4.300119e-02
46 47 48 49 50
-1.898608e-02 2.084529e-02 4.486040e-02 -1.958168e-02 2.024969e-02
51 52 53 54 55
-3.134771e-01 6.330311e-01 4.386773e-02 7.852273e-01 1.925702e-02
56 57 58 59 60
-2.902561e-01 -2.553982e-01 4.287506e-02 4.267653e-02 6.556565e-01
61 62 63 64 65
-4.607026e-02 -2.806045e-01 1.766875e-02 -4.666586e-02 1.727168e-02
66 67 68 69 70
1.707315e-02 6.191029e-01 2.762631e-02 4.069119e-02 -2.179492e-01
71 72 73 74 75
1.608048e-02 4.009559e-02 -1.943312e-01 -2.077931e-01 3.949999e-02
76 77 78 79 80
-1.242420e-01 3.910292e-02 -2.595674e-01 7.051776e-01 -1.492498e-01
81 82 83 84 85
1.409514e-02 -1.851678e-01 1.369808e-02 7.792713e-01 -2.672890e-02
86 87 88 89 90
2.405271e-02 4.806782e-02 -2.360090e-01 1.250687e-02 3.652198e-02
91 92 93 94 95
-5.213374e-02 -2.678847e-02 -4.158057e-02 1.151421e-02 -3.833430e-02
96 97 98 99 100
3.533078e-02 -2.778114e-02 1.072007e-02 2.147177e-02 3.453665e-02
101 102 103 104 105
4.528834e-02 9.925936e-03 9.727403e-03 9.528869e-03 -2.745479e-01
106 107 108 109 110
9.131802e-03 8.933268e-03 -2.641933e-01 8.536201e-03 1.928790e-02
111 112 113 114 115
-3.290324e-01 -4.170938e-02 -2.264862e-01 -2.653845e-01 1.829523e-02
116 117 118 119 120
7.146465e-03 4.211181e-02 1.769963e-02 6.550864e-03 3.056597e-02
121 122 123 124 125
1.710403e-02 5.955263e-03 -2.671713e-01 -2.687000e-01 2.957330e-02
126 127 128 129 130
-4.448885e-02 -5.928095e-02 2.897770e-02 4.565528e-03 2.858064e-02
131 132 133 134 135
1.511869e-02 3.913380e-02 -2.195066e-01 3.572860e-03 3.374326e-03
136 137 138 139 140
3.175792e-03 -2.603307e-01 -3.101792e-01 -4.706979e-02 2.381658e-03
141 142 143 144 145
7.921685e-01 -2.576800e-01 1.273629e-02 -3.844238e-02 -6.285456e-02
146 147 148 149 150
-2.424588e-02 -2.828863e-01 -4.885659e-02 1.154509e-02 -3.963358e-02
151 152 153 154
2.441143e-02 7.767212e-01 7.122791e-01 -2.236758e-01
> postscript(file="/var/wessaorg/rcomp/tmp/6po6m1356141851.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 = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.415824e-02 NA
1 2.977930e-02 -3.415824e-02
2 2.958077e-02 2.977930e-02
3 2.938223e-02 2.958077e-02
4 2.918370e-02 2.938223e-02
5 -9.450509e-05 2.918370e-02
6 2.878663e-02 -9.450509e-05
7 -7.071185e-02 2.878663e-02
8 5.260321e-02 -7.071185e-02
9 3.914127e-02 5.260321e-02
10 -6.035722e-02 3.914127e-02
11 2.779397e-02 -6.035722e-02
12 -2.708764e-01 2.779397e-02
13 -6.095282e-02 -2.708764e-01
14 -2.470598e-01 -6.095282e-02
15 -3.465583e-01 -2.470598e-01
16 6.399798e-01 -3.465583e-01
17 -6.174696e-02 6.399798e-01
18 5.061787e-02 -6.174696e-02
19 6.526476e-01 5.061787e-02
20 -2.728615e-02 6.526476e-01
21 -2.374993e-01 -2.728615e-02
22 -1.441981e-02 -2.374993e-01
23 -3.668111e-03 -1.441981e-02
24 -2.841015e-01 -3.668111e-03
25 -2.734573e-01 -2.841015e-01
26 5.997984e-02 -2.734573e-01
27 -2.096108e-01 5.997984e-02
28 4.863254e-02 -2.096108e-01
29 -4.002319e-02 4.863254e-02
30 2.402183e-02 -4.002319e-02
31 3.477352e-02 2.402183e-02
32 -2.966856e-02 3.477352e-02
33 -5.166009e-02 -2.966856e-02
34 2.322769e-02 -5.166009e-02
35 2.302916e-02 2.322769e-02
36 -3.639909e-01 2.302916e-02
37 -1.873825e-01 -3.639909e-01
38 -1.759635e-02 -1.873825e-01
39 -1.413085e-01 -1.759635e-02
40 7.477783e-01 -1.413085e-01
41 -1.881767e-01 7.477783e-01
42 -7.440250e-03 -1.881767e-01
43 -6.690883e-02 -7.440250e-03
44 -4.300119e-02 -6.690883e-02
45 -1.898608e-02 -4.300119e-02
46 2.084529e-02 -1.898608e-02
47 4.486040e-02 2.084529e-02
48 -1.958168e-02 4.486040e-02
49 2.024969e-02 -1.958168e-02
50 -3.134771e-01 2.024969e-02
51 6.330311e-01 -3.134771e-01
52 4.386773e-02 6.330311e-01
53 7.852273e-01 4.386773e-02
54 1.925702e-02 7.852273e-01
55 -2.902561e-01 1.925702e-02
56 -2.553982e-01 -2.902561e-01
57 4.287506e-02 -2.553982e-01
58 4.267653e-02 4.287506e-02
59 6.556565e-01 4.267653e-02
60 -4.607026e-02 6.556565e-01
61 -2.806045e-01 -4.607026e-02
62 1.766875e-02 -2.806045e-01
63 -4.666586e-02 1.766875e-02
64 1.727168e-02 -4.666586e-02
65 1.707315e-02 1.727168e-02
66 6.191029e-01 1.707315e-02
67 2.762631e-02 6.191029e-01
68 4.069119e-02 2.762631e-02
69 -2.179492e-01 4.069119e-02
70 1.608048e-02 -2.179492e-01
71 4.009559e-02 1.608048e-02
72 -1.943312e-01 4.009559e-02
73 -2.077931e-01 -1.943312e-01
74 3.949999e-02 -2.077931e-01
75 -1.242420e-01 3.949999e-02
76 3.910292e-02 -1.242420e-01
77 -2.595674e-01 3.910292e-02
78 7.051776e-01 -2.595674e-01
79 -1.492498e-01 7.051776e-01
80 1.409514e-02 -1.492498e-01
81 -1.851678e-01 1.409514e-02
82 1.369808e-02 -1.851678e-01
83 7.792713e-01 1.369808e-02
84 -2.672890e-02 7.792713e-01
85 2.405271e-02 -2.672890e-02
86 4.806782e-02 2.405271e-02
87 -2.360090e-01 4.806782e-02
88 1.250687e-02 -2.360090e-01
89 3.652198e-02 1.250687e-02
90 -5.213374e-02 3.652198e-02
91 -2.678847e-02 -5.213374e-02
92 -4.158057e-02 -2.678847e-02
93 1.151421e-02 -4.158057e-02
94 -3.833430e-02 1.151421e-02
95 3.533078e-02 -3.833430e-02
96 -2.778114e-02 3.533078e-02
97 1.072007e-02 -2.778114e-02
98 2.147177e-02 1.072007e-02
99 3.453665e-02 2.147177e-02
100 4.528834e-02 3.453665e-02
101 9.925936e-03 4.528834e-02
102 9.727403e-03 9.925936e-03
103 9.528869e-03 9.727403e-03
104 -2.745479e-01 9.528869e-03
105 9.131802e-03 -2.745479e-01
106 8.933268e-03 9.131802e-03
107 -2.641933e-01 8.933268e-03
108 8.536201e-03 -2.641933e-01
109 1.928790e-02 8.536201e-03
110 -3.290324e-01 1.928790e-02
111 -4.170938e-02 -3.290324e-01
112 -2.264862e-01 -4.170938e-02
113 -2.653845e-01 -2.264862e-01
114 1.829523e-02 -2.653845e-01
115 7.146465e-03 1.829523e-02
116 4.211181e-02 7.146465e-03
117 1.769963e-02 4.211181e-02
118 6.550864e-03 1.769963e-02
119 3.056597e-02 6.550864e-03
120 1.710403e-02 3.056597e-02
121 5.955263e-03 1.710403e-02
122 -2.671713e-01 5.955263e-03
123 -2.687000e-01 -2.671713e-01
124 2.957330e-02 -2.687000e-01
125 -4.448885e-02 2.957330e-02
126 -5.928095e-02 -4.448885e-02
127 2.897770e-02 -5.928095e-02
128 4.565528e-03 2.897770e-02
129 2.858064e-02 4.565528e-03
130 1.511869e-02 2.858064e-02
131 3.913380e-02 1.511869e-02
132 -2.195066e-01 3.913380e-02
133 3.572860e-03 -2.195066e-01
134 3.374326e-03 3.572860e-03
135 3.175792e-03 3.374326e-03
136 -2.603307e-01 3.175792e-03
137 -3.101792e-01 -2.603307e-01
138 -4.706979e-02 -3.101792e-01
139 2.381658e-03 -4.706979e-02
140 7.921685e-01 2.381658e-03
141 -2.576800e-01 7.921685e-01
142 1.273629e-02 -2.576800e-01
143 -3.844238e-02 1.273629e-02
144 -6.285456e-02 -3.844238e-02
145 -2.424588e-02 -6.285456e-02
146 -2.828863e-01 -2.424588e-02
147 -4.885659e-02 -2.828863e-01
148 1.154509e-02 -4.885659e-02
149 -3.963358e-02 1.154509e-02
150 2.441143e-02 -3.963358e-02
151 7.767212e-01 2.441143e-02
152 7.122791e-01 7.767212e-01
153 -2.236758e-01 7.122791e-01
154 NA -2.236758e-01
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.977930e-02 -3.415824e-02
[2,] 2.958077e-02 2.977930e-02
[3,] 2.938223e-02 2.958077e-02
[4,] 2.918370e-02 2.938223e-02
[5,] -9.450509e-05 2.918370e-02
[6,] 2.878663e-02 -9.450509e-05
[7,] -7.071185e-02 2.878663e-02
[8,] 5.260321e-02 -7.071185e-02
[9,] 3.914127e-02 5.260321e-02
[10,] -6.035722e-02 3.914127e-02
[11,] 2.779397e-02 -6.035722e-02
[12,] -2.708764e-01 2.779397e-02
[13,] -6.095282e-02 -2.708764e-01
[14,] -2.470598e-01 -6.095282e-02
[15,] -3.465583e-01 -2.470598e-01
[16,] 6.399798e-01 -3.465583e-01
[17,] -6.174696e-02 6.399798e-01
[18,] 5.061787e-02 -6.174696e-02
[19,] 6.526476e-01 5.061787e-02
[20,] -2.728615e-02 6.526476e-01
[21,] -2.374993e-01 -2.728615e-02
[22,] -1.441981e-02 -2.374993e-01
[23,] -3.668111e-03 -1.441981e-02
[24,] -2.841015e-01 -3.668111e-03
[25,] -2.734573e-01 -2.841015e-01
[26,] 5.997984e-02 -2.734573e-01
[27,] -2.096108e-01 5.997984e-02
[28,] 4.863254e-02 -2.096108e-01
[29,] -4.002319e-02 4.863254e-02
[30,] 2.402183e-02 -4.002319e-02
[31,] 3.477352e-02 2.402183e-02
[32,] -2.966856e-02 3.477352e-02
[33,] -5.166009e-02 -2.966856e-02
[34,] 2.322769e-02 -5.166009e-02
[35,] 2.302916e-02 2.322769e-02
[36,] -3.639909e-01 2.302916e-02
[37,] -1.873825e-01 -3.639909e-01
[38,] -1.759635e-02 -1.873825e-01
[39,] -1.413085e-01 -1.759635e-02
[40,] 7.477783e-01 -1.413085e-01
[41,] -1.881767e-01 7.477783e-01
[42,] -7.440250e-03 -1.881767e-01
[43,] -6.690883e-02 -7.440250e-03
[44,] -4.300119e-02 -6.690883e-02
[45,] -1.898608e-02 -4.300119e-02
[46,] 2.084529e-02 -1.898608e-02
[47,] 4.486040e-02 2.084529e-02
[48,] -1.958168e-02 4.486040e-02
[49,] 2.024969e-02 -1.958168e-02
[50,] -3.134771e-01 2.024969e-02
[51,] 6.330311e-01 -3.134771e-01
[52,] 4.386773e-02 6.330311e-01
[53,] 7.852273e-01 4.386773e-02
[54,] 1.925702e-02 7.852273e-01
[55,] -2.902561e-01 1.925702e-02
[56,] -2.553982e-01 -2.902561e-01
[57,] 4.287506e-02 -2.553982e-01
[58,] 4.267653e-02 4.287506e-02
[59,] 6.556565e-01 4.267653e-02
[60,] -4.607026e-02 6.556565e-01
[61,] -2.806045e-01 -4.607026e-02
[62,] 1.766875e-02 -2.806045e-01
[63,] -4.666586e-02 1.766875e-02
[64,] 1.727168e-02 -4.666586e-02
[65,] 1.707315e-02 1.727168e-02
[66,] 6.191029e-01 1.707315e-02
[67,] 2.762631e-02 6.191029e-01
[68,] 4.069119e-02 2.762631e-02
[69,] -2.179492e-01 4.069119e-02
[70,] 1.608048e-02 -2.179492e-01
[71,] 4.009559e-02 1.608048e-02
[72,] -1.943312e-01 4.009559e-02
[73,] -2.077931e-01 -1.943312e-01
[74,] 3.949999e-02 -2.077931e-01
[75,] -1.242420e-01 3.949999e-02
[76,] 3.910292e-02 -1.242420e-01
[77,] -2.595674e-01 3.910292e-02
[78,] 7.051776e-01 -2.595674e-01
[79,] -1.492498e-01 7.051776e-01
[80,] 1.409514e-02 -1.492498e-01
[81,] -1.851678e-01 1.409514e-02
[82,] 1.369808e-02 -1.851678e-01
[83,] 7.792713e-01 1.369808e-02
[84,] -2.672890e-02 7.792713e-01
[85,] 2.405271e-02 -2.672890e-02
[86,] 4.806782e-02 2.405271e-02
[87,] -2.360090e-01 4.806782e-02
[88,] 1.250687e-02 -2.360090e-01
[89,] 3.652198e-02 1.250687e-02
[90,] -5.213374e-02 3.652198e-02
[91,] -2.678847e-02 -5.213374e-02
[92,] -4.158057e-02 -2.678847e-02
[93,] 1.151421e-02 -4.158057e-02
[94,] -3.833430e-02 1.151421e-02
[95,] 3.533078e-02 -3.833430e-02
[96,] -2.778114e-02 3.533078e-02
[97,] 1.072007e-02 -2.778114e-02
[98,] 2.147177e-02 1.072007e-02
[99,] 3.453665e-02 2.147177e-02
[100,] 4.528834e-02 3.453665e-02
[101,] 9.925936e-03 4.528834e-02
[102,] 9.727403e-03 9.925936e-03
[103,] 9.528869e-03 9.727403e-03
[104,] -2.745479e-01 9.528869e-03
[105,] 9.131802e-03 -2.745479e-01
[106,] 8.933268e-03 9.131802e-03
[107,] -2.641933e-01 8.933268e-03
[108,] 8.536201e-03 -2.641933e-01
[109,] 1.928790e-02 8.536201e-03
[110,] -3.290324e-01 1.928790e-02
[111,] -4.170938e-02 -3.290324e-01
[112,] -2.264862e-01 -4.170938e-02
[113,] -2.653845e-01 -2.264862e-01
[114,] 1.829523e-02 -2.653845e-01
[115,] 7.146465e-03 1.829523e-02
[116,] 4.211181e-02 7.146465e-03
[117,] 1.769963e-02 4.211181e-02
[118,] 6.550864e-03 1.769963e-02
[119,] 3.056597e-02 6.550864e-03
[120,] 1.710403e-02 3.056597e-02
[121,] 5.955263e-03 1.710403e-02
[122,] -2.671713e-01 5.955263e-03
[123,] -2.687000e-01 -2.671713e-01
[124,] 2.957330e-02 -2.687000e-01
[125,] -4.448885e-02 2.957330e-02
[126,] -5.928095e-02 -4.448885e-02
[127,] 2.897770e-02 -5.928095e-02
[128,] 4.565528e-03 2.897770e-02
[129,] 2.858064e-02 4.565528e-03
[130,] 1.511869e-02 2.858064e-02
[131,] 3.913380e-02 1.511869e-02
[132,] -2.195066e-01 3.913380e-02
[133,] 3.572860e-03 -2.195066e-01
[134,] 3.374326e-03 3.572860e-03
[135,] 3.175792e-03 3.374326e-03
[136,] -2.603307e-01 3.175792e-03
[137,] -3.101792e-01 -2.603307e-01
[138,] -4.706979e-02 -3.101792e-01
[139,] 2.381658e-03 -4.706979e-02
[140,] 7.921685e-01 2.381658e-03
[141,] -2.576800e-01 7.921685e-01
[142,] 1.273629e-02 -2.576800e-01
[143,] -3.844238e-02 1.273629e-02
[144,] -6.285456e-02 -3.844238e-02
[145,] -2.424588e-02 -6.285456e-02
[146,] -2.828863e-01 -2.424588e-02
[147,] -4.885659e-02 -2.828863e-01
[148,] 1.154509e-02 -4.885659e-02
[149,] -3.963358e-02 1.154509e-02
[150,] 2.441143e-02 -3.963358e-02
[151,] 7.767212e-01 2.441143e-02
[152,] 7.122791e-01 7.767212e-01
[153,] -2.236758e-01 7.122791e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.977930e-02 -3.415824e-02
2 2.958077e-02 2.977930e-02
3 2.938223e-02 2.958077e-02
4 2.918370e-02 2.938223e-02
5 -9.450509e-05 2.918370e-02
6 2.878663e-02 -9.450509e-05
7 -7.071185e-02 2.878663e-02
8 5.260321e-02 -7.071185e-02
9 3.914127e-02 5.260321e-02
10 -6.035722e-02 3.914127e-02
11 2.779397e-02 -6.035722e-02
12 -2.708764e-01 2.779397e-02
13 -6.095282e-02 -2.708764e-01
14 -2.470598e-01 -6.095282e-02
15 -3.465583e-01 -2.470598e-01
16 6.399798e-01 -3.465583e-01
17 -6.174696e-02 6.399798e-01
18 5.061787e-02 -6.174696e-02
19 6.526476e-01 5.061787e-02
20 -2.728615e-02 6.526476e-01
21 -2.374993e-01 -2.728615e-02
22 -1.441981e-02 -2.374993e-01
23 -3.668111e-03 -1.441981e-02
24 -2.841015e-01 -3.668111e-03
25 -2.734573e-01 -2.841015e-01
26 5.997984e-02 -2.734573e-01
27 -2.096108e-01 5.997984e-02
28 4.863254e-02 -2.096108e-01
29 -4.002319e-02 4.863254e-02
30 2.402183e-02 -4.002319e-02
31 3.477352e-02 2.402183e-02
32 -2.966856e-02 3.477352e-02
33 -5.166009e-02 -2.966856e-02
34 2.322769e-02 -5.166009e-02
35 2.302916e-02 2.322769e-02
36 -3.639909e-01 2.302916e-02
37 -1.873825e-01 -3.639909e-01
38 -1.759635e-02 -1.873825e-01
39 -1.413085e-01 -1.759635e-02
40 7.477783e-01 -1.413085e-01
41 -1.881767e-01 7.477783e-01
42 -7.440250e-03 -1.881767e-01
43 -6.690883e-02 -7.440250e-03
44 -4.300119e-02 -6.690883e-02
45 -1.898608e-02 -4.300119e-02
46 2.084529e-02 -1.898608e-02
47 4.486040e-02 2.084529e-02
48 -1.958168e-02 4.486040e-02
49 2.024969e-02 -1.958168e-02
50 -3.134771e-01 2.024969e-02
51 6.330311e-01 -3.134771e-01
52 4.386773e-02 6.330311e-01
53 7.852273e-01 4.386773e-02
54 1.925702e-02 7.852273e-01
55 -2.902561e-01 1.925702e-02
56 -2.553982e-01 -2.902561e-01
57 4.287506e-02 -2.553982e-01
58 4.267653e-02 4.287506e-02
59 6.556565e-01 4.267653e-02
60 -4.607026e-02 6.556565e-01
61 -2.806045e-01 -4.607026e-02
62 1.766875e-02 -2.806045e-01
63 -4.666586e-02 1.766875e-02
64 1.727168e-02 -4.666586e-02
65 1.707315e-02 1.727168e-02
66 6.191029e-01 1.707315e-02
67 2.762631e-02 6.191029e-01
68 4.069119e-02 2.762631e-02
69 -2.179492e-01 4.069119e-02
70 1.608048e-02 -2.179492e-01
71 4.009559e-02 1.608048e-02
72 -1.943312e-01 4.009559e-02
73 -2.077931e-01 -1.943312e-01
74 3.949999e-02 -2.077931e-01
75 -1.242420e-01 3.949999e-02
76 3.910292e-02 -1.242420e-01
77 -2.595674e-01 3.910292e-02
78 7.051776e-01 -2.595674e-01
79 -1.492498e-01 7.051776e-01
80 1.409514e-02 -1.492498e-01
81 -1.851678e-01 1.409514e-02
82 1.369808e-02 -1.851678e-01
83 7.792713e-01 1.369808e-02
84 -2.672890e-02 7.792713e-01
85 2.405271e-02 -2.672890e-02
86 4.806782e-02 2.405271e-02
87 -2.360090e-01 4.806782e-02
88 1.250687e-02 -2.360090e-01
89 3.652198e-02 1.250687e-02
90 -5.213374e-02 3.652198e-02
91 -2.678847e-02 -5.213374e-02
92 -4.158057e-02 -2.678847e-02
93 1.151421e-02 -4.158057e-02
94 -3.833430e-02 1.151421e-02
95 3.533078e-02 -3.833430e-02
96 -2.778114e-02 3.533078e-02
97 1.072007e-02 -2.778114e-02
98 2.147177e-02 1.072007e-02
99 3.453665e-02 2.147177e-02
100 4.528834e-02 3.453665e-02
101 9.925936e-03 4.528834e-02
102 9.727403e-03 9.925936e-03
103 9.528869e-03 9.727403e-03
104 -2.745479e-01 9.528869e-03
105 9.131802e-03 -2.745479e-01
106 8.933268e-03 9.131802e-03
107 -2.641933e-01 8.933268e-03
108 8.536201e-03 -2.641933e-01
109 1.928790e-02 8.536201e-03
110 -3.290324e-01 1.928790e-02
111 -4.170938e-02 -3.290324e-01
112 -2.264862e-01 -4.170938e-02
113 -2.653845e-01 -2.264862e-01
114 1.829523e-02 -2.653845e-01
115 7.146465e-03 1.829523e-02
116 4.211181e-02 7.146465e-03
117 1.769963e-02 4.211181e-02
118 6.550864e-03 1.769963e-02
119 3.056597e-02 6.550864e-03
120 1.710403e-02 3.056597e-02
121 5.955263e-03 1.710403e-02
122 -2.671713e-01 5.955263e-03
123 -2.687000e-01 -2.671713e-01
124 2.957330e-02 -2.687000e-01
125 -4.448885e-02 2.957330e-02
126 -5.928095e-02 -4.448885e-02
127 2.897770e-02 -5.928095e-02
128 4.565528e-03 2.897770e-02
129 2.858064e-02 4.565528e-03
130 1.511869e-02 2.858064e-02
131 3.913380e-02 1.511869e-02
132 -2.195066e-01 3.913380e-02
133 3.572860e-03 -2.195066e-01
134 3.374326e-03 3.572860e-03
135 3.175792e-03 3.374326e-03
136 -2.603307e-01 3.175792e-03
137 -3.101792e-01 -2.603307e-01
138 -4.706979e-02 -3.101792e-01
139 2.381658e-03 -4.706979e-02
140 7.921685e-01 2.381658e-03
141 -2.576800e-01 7.921685e-01
142 1.273629e-02 -2.576800e-01
143 -3.844238e-02 1.273629e-02
144 -6.285456e-02 -3.844238e-02
145 -2.424588e-02 -6.285456e-02
146 -2.828863e-01 -2.424588e-02
147 -4.885659e-02 -2.828863e-01
148 1.154509e-02 -4.885659e-02
149 -3.963358e-02 1.154509e-02
150 2.441143e-02 -3.963358e-02
151 7.767212e-01 2.441143e-02
152 7.122791e-01 7.767212e-01
153 -2.236758e-01 7.122791e-01
> 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/70f7d1356141851.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/8de621356141851.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/9cjq11356141851.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/10a3f21356141851.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1168zh1356141851.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12oy0r1356141851.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13ewgz1356141851.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14dxzz1356141851.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/15h5y31356141851.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/16jq301356141851.tab")
+ }
>
> try(system("convert tmp/1q9w11356141851.ps tmp/1q9w11356141851.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hvo51356141851.ps tmp/2hvo51356141851.png",intern=TRUE))
character(0)
> try(system("convert tmp/36gp51356141851.ps tmp/36gp51356141851.png",intern=TRUE))
character(0)
> try(system("convert tmp/4m51x1356141851.ps tmp/4m51x1356141851.png",intern=TRUE))
character(0)
> try(system("convert tmp/5sjzv1356141851.ps tmp/5sjzv1356141851.png",intern=TRUE))
character(0)
> try(system("convert tmp/6po6m1356141851.ps tmp/6po6m1356141851.png",intern=TRUE))
character(0)
> try(system("convert tmp/70f7d1356141851.ps tmp/70f7d1356141851.png",intern=TRUE))
character(0)
> try(system("convert tmp/8de621356141851.ps tmp/8de621356141851.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cjq11356141851.ps tmp/9cjq11356141851.png",intern=TRUE))
character(0)
> try(system("convert tmp/10a3f21356141851.ps tmp/10a3f21356141851.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.838 1.712 12.536