R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(9
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+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('T1'
+ ,'YT'
+ ,'X1'
+ ,'X2'
+ ,'X3'
+ ,'X4'
+ ,'X5
')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('T1','YT','X1','X2','X3','X4','X5
'),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 = '2'
> 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
YT T1 X1 X2 X3 X4 X5\r t
1 24 9 14 11 12 24 26 1
2 25 9 11 7 8 25 23 2
3 17 9 6 17 8 30 25 3
4 18 9 12 10 8 19 23 4
5 18 9 8 12 9 22 19 5
6 16 9 10 12 7 22 29 6
7 20 10 10 11 4 25 25 7
8 16 10 11 11 11 23 21 8
9 18 10 16 12 7 17 22 9
10 17 10 11 13 7 21 25 10
11 23 10 13 14 12 19 24 11
12 30 10 12 16 10 19 18 12
13 23 10 8 11 10 15 22 13
14 18 10 12 10 8 16 15 14
15 15 10 11 11 8 23 22 15
16 12 10 4 15 4 27 28 16
17 21 10 9 9 9 22 20 17
18 15 10 8 11 8 14 12 18
19 20 10 8 17 7 22 24 19
20 31 10 14 17 11 23 20 20
21 27 10 15 11 9 23 21 21
22 34 10 16 18 11 21 20 22
23 21 10 9 14 13 19 21 23
24 31 10 14 10 8 18 23 24
25 19 10 11 11 8 20 28 25
26 16 10 8 15 9 23 24 26
27 20 10 9 15 6 25 24 27
28 21 10 9 13 9 19 24 28
29 22 10 9 16 9 24 23 29
30 17 10 9 13 6 22 23 30
31 24 10 10 9 6 25 29 31
32 25 10 16 18 16 26 24 32
33 26 10 11 18 5 29 18 33
34 25 10 8 12 7 32 25 34
35 17 10 9 17 9 25 21 35
36 32 10 16 9 6 29 26 36
37 33 10 11 9 6 28 22 37
38 13 10 16 12 5 17 22 38
39 32 10 12 18 12 28 22 39
40 25 10 12 12 7 29 23 40
41 29 10 14 18 10 26 30 41
42 22 10 9 14 9 25 23 42
43 18 10 10 15 8 14 17 43
44 17 10 9 16 5 25 23 44
45 20 10 10 10 8 26 23 45
46 15 10 12 11 8 20 25 46
47 20 10 14 14 10 18 24 47
48 33 10 14 9 6 32 24 48
49 29 10 10 12 8 25 23 49
50 23 10 14 17 7 25 21 50
51 26 10 16 5 4 23 24 51
52 18 10 9 12 8 21 24 52
53 20 10 10 12 8 20 28 53
54 11 10 6 6 4 15 16 54
55 28 10 8 24 20 30 20 55
56 26 10 13 12 8 24 29 56
57 22 10 10 12 8 26 27 57
58 17 10 8 14 6 24 22 58
59 12 10 7 7 4 22 28 59
60 14 10 15 13 8 14 16 60
61 17 10 9 12 9 24 25 61
62 21 10 10 13 6 24 24 62
63 19 10 12 14 7 24 28 63
64 18 10 13 8 9 24 24 64
65 10 10 10 11 5 19 23 65
66 29 10 11 9 5 31 30 66
67 31 10 8 11 8 22 24 67
68 19 10 9 13 8 27 21 68
69 9 10 13 10 6 19 25 69
70 20 10 11 11 8 25 25 70
71 28 10 8 12 7 20 22 71
72 19 10 9 9 7 21 23 72
73 30 10 9 15 9 27 26 73
74 29 10 15 18 11 23 23 74
75 26 10 9 15 6 25 25 75
76 23 10 10 12 8 20 21 76
77 13 10 14 13 6 21 25 77
78 21 10 12 14 9 22 24 78
79 19 10 12 10 8 23 29 79
80 28 10 11 13 6 25 22 80
81 23 10 14 13 10 25 27 81
82 18 10 6 11 8 17 26 82
83 21 10 12 13 8 19 22 83
84 20 10 8 16 10 25 24 84
85 23 10 14 8 5 19 27 85
86 21 10 11 16 7 20 24 86
87 21 10 10 11 5 26 24 87
88 15 10 14 9 8 23 29 88
89 28 10 12 16 14 27 22 89
90 19 10 10 12 7 17 21 90
91 26 10 14 14 8 17 24 91
92 10 10 5 8 6 19 24 92
93 16 10 11 9 5 17 23 93
94 22 10 10 15 6 22 20 94
95 19 10 9 11 10 21 27 95
96 31 10 10 21 12 32 26 96
97 31 10 16 14 9 21 25 97
98 29 10 13 18 12 21 21 98
99 19 10 9 12 7 18 21 99
100 22 10 10 13 8 18 19 100
101 23 10 10 15 10 23 21 101
102 15 10 7 12 6 19 21 102
103 20 10 9 19 10 20 16 103
104 18 10 8 15 10 21 22 104
105 23 10 14 11 10 20 29 105
106 25 10 14 11 5 17 15 106
107 21 10 8 10 7 18 17 107
108 24 10 9 13 10 19 15 108
109 25 10 14 15 11 22 21 109
110 17 10 14 12 6 15 21 110
111 13 10 8 12 7 14 19 111
112 28 10 8 16 12 18 24 112
113 21 10 8 9 11 24 20 113
114 25 10 7 18 11 35 17 114
115 9 10 6 8 11 29 23 115
116 16 10 8 13 5 21 24 116
117 19 10 6 17 8 25 14 117
118 17 10 11 9 6 20 19 118
119 25 10 14 15 9 22 24 119
120 20 10 11 8 4 13 13 120
121 29 10 11 7 4 26 22 121
122 14 10 11 12 7 17 16 122
123 22 10 14 14 11 25 19 123
124 15 10 8 6 6 20 25 124
125 19 10 20 8 7 19 25 125
126 20 10 11 17 8 21 23 126
127 15 10 8 10 4 22 24 127
128 20 10 11 11 8 24 26 128
129 18 10 10 14 9 21 26 129
130 33 10 14 11 8 26 25 130
131 22 10 11 13 11 24 18 131
132 16 10 9 12 8 16 21 132
133 17 10 9 11 5 23 26 133
134 16 10 8 9 4 18 23 134
135 21 10 10 12 8 16 23 135
136 26 10 13 20 10 26 22 136
137 18 10 13 12 6 19 20 137
138 18 10 12 13 9 21 13 138
139 17 10 8 12 9 21 24 139
140 22 10 13 12 13 22 15 140
141 30 10 14 9 9 23 14 141
142 30 10 12 15 10 29 22 142
143 24 10 14 24 20 21 10 143
144 21 10 15 7 5 21 24 144
145 21 10 13 17 11 23 22 145
146 29 10 16 11 6 27 24 146
147 31 10 9 17 9 25 19 147
148 20 10 9 11 7 21 20 148
149 16 10 9 12 9 10 13 149
150 22 10 8 14 10 20 20 150
151 20 10 7 11 9 26 22 151
152 28 10 16 16 8 24 24 152
153 38 10 11 21 7 29 29 153
154 22 10 9 14 6 19 12 154
155 20 10 11 20 13 24 20 155
156 17 10 9 13 6 19 21 156
157 28 10 14 11 8 24 24 157
158 22 10 13 15 10 22 22 158
159 31 10 16 19 16 17 20 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) T1 X1 X2 X3 X4
-18.359171 1.588385 0.798757 0.233450 0.207083 0.571863
`X5\r` t
-0.099982 0.003548
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.1552 -2.6153 -0.3347 2.7709 12.4415
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -18.359171 19.993412 -0.918 0.3599
T1 1.588385 2.002175 0.793 0.4288
X1 0.798757 0.131148 6.090 8.93e-09 ***
X2 0.233450 0.134333 1.738 0.0843 .
X3 0.207083 0.170009 1.218 0.2251
X4 0.571863 0.096247 5.942 1.87e-08 ***
`X5\r` -0.099982 0.105485 -0.948 0.3447
t 0.003548 0.008438 0.420 0.6747
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.491 on 151 degrees of freedom
Multiple R-squared: 0.4116, Adjusted R-squared: 0.3843
F-statistic: 15.09 on 7 and 151 DF, p-value: 7.363e-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,] 0.48291930 0.96583860 0.51708070
[2,] 0.55407324 0.89185352 0.44592676
[3,] 0.77857121 0.44285759 0.22142879
[4,] 0.80440138 0.39119724 0.19559862
[5,] 0.74477356 0.51045287 0.25522644
[6,] 0.66423254 0.67153491 0.33576746
[7,] 0.62352777 0.75294446 0.37647223
[8,] 0.58141591 0.83716818 0.41858409
[9,] 0.54156382 0.91687237 0.45843618
[10,] 0.56215048 0.87569904 0.43784952
[11,] 0.48534876 0.97069752 0.51465124
[12,] 0.45592491 0.91184981 0.54407509
[13,] 0.41802931 0.83605863 0.58197069
[14,] 0.52513187 0.94973626 0.47486813
[15,] 0.49659457 0.99318914 0.50340543
[16,] 0.52843031 0.94313938 0.47156969
[17,] 0.46245647 0.92491294 0.53754353
[18,] 0.39943287 0.79886574 0.60056713
[19,] 0.33749912 0.67499824 0.66250088
[20,] 0.30344943 0.60689885 0.69655057
[21,] 0.29206881 0.58413762 0.70793119
[22,] 0.42789051 0.85578103 0.57210949
[23,] 0.36777097 0.73554195 0.63222903
[24,] 0.33892162 0.67784325 0.66107838
[25,] 0.38991198 0.77982395 0.61008802
[26,] 0.35418446 0.70836892 0.64581554
[27,] 0.46542689 0.93085378 0.53457311
[28,] 0.76151440 0.47697120 0.23848560
[29,] 0.74804292 0.50391416 0.25195708
[30,] 0.71141077 0.57717846 0.28858923
[31,] 0.67644720 0.64710560 0.32355280
[32,] 0.62816508 0.74366985 0.37183492
[33,] 0.57642272 0.84715455 0.42357728
[34,] 0.56404384 0.87191233 0.43595616
[35,] 0.54714583 0.90570834 0.45285417
[36,] 0.57919269 0.84161463 0.42080731
[37,] 0.53547654 0.92904693 0.46452346
[38,] 0.52598491 0.94803018 0.47401509
[39,] 0.58621035 0.82757929 0.41378965
[40,] 0.55732133 0.88535734 0.44267867
[41,] 0.52641974 0.94716053 0.47358026
[42,] 0.47667903 0.95335805 0.52332097
[43,] 0.43300872 0.86601744 0.56699128
[44,] 0.38821469 0.77642938 0.61178531
[45,] 0.34733624 0.69467249 0.65266376
[46,] 0.31872280 0.63744560 0.68127720
[47,] 0.27585571 0.55171142 0.72414429
[48,] 0.24782998 0.49565995 0.75217002
[49,] 0.22954422 0.45908845 0.77045578
[50,] 0.26460700 0.52921401 0.73539300
[51,] 0.24812764 0.49625528 0.75187236
[52,] 0.21413018 0.42826036 0.78586982
[53,] 0.19690753 0.39381507 0.80309247
[54,] 0.21794668 0.43589336 0.78205332
[55,] 0.26697941 0.53395882 0.73302059
[56,] 0.27592913 0.55185825 0.72407087
[57,] 0.63776942 0.72446116 0.36223058
[58,] 0.62191817 0.75616365 0.37808183
[59,] 0.79519529 0.40960942 0.20480471
[60,] 0.76781475 0.46437050 0.23218525
[61,] 0.91275316 0.17449369 0.08724684
[62,] 0.89372712 0.21254576 0.10627288
[63,] 0.92429261 0.15141478 0.07570739
[64,] 0.91249702 0.17500596 0.08750298
[65,] 0.91633097 0.16733806 0.08366903
[66,] 0.91066247 0.17867506 0.08933753
[67,] 0.96209409 0.07581183 0.03790591
[68,] 0.95268524 0.09462951 0.04731476
[69,] 0.94347393 0.11305214 0.05652607
[70,] 0.94794285 0.10411429 0.05205715
[71,] 0.93826524 0.12346952 0.06173476
[72,] 0.93758272 0.12483455 0.06241728
[73,] 0.92205334 0.15589333 0.07794666
[74,] 0.90657425 0.18685150 0.09342575
[75,] 0.89676328 0.20647344 0.10323672
[76,] 0.87808005 0.24383990 0.12191995
[77,] 0.85337558 0.29324885 0.14662442
[78,] 0.90533019 0.18933962 0.09466981
[79,] 0.88615559 0.22768882 0.11384441
[80,] 0.86364771 0.27270458 0.13635229
[81,] 0.86582748 0.26834504 0.13417252
[82,] 0.85438478 0.29123044 0.14561522
[83,] 0.82983170 0.34033660 0.17016830
[84,] 0.79955375 0.40089251 0.20044625
[85,] 0.76368845 0.47262311 0.23631155
[86,] 0.73105557 0.53788885 0.26894443
[87,] 0.75174193 0.49651615 0.24825807
[88,] 0.74934651 0.50130699 0.25065349
[89,] 0.71223470 0.57553061 0.28776530
[90,] 0.68974486 0.62051027 0.31025514
[91,] 0.64943492 0.70113016 0.35056508
[92,] 0.60663485 0.78673031 0.39336515
[93,] 0.56324422 0.87351157 0.43675578
[94,] 0.51865416 0.96269169 0.48134584
[95,] 0.47588065 0.95176130 0.52411935
[96,] 0.46361214 0.92722428 0.53638786
[97,] 0.47077905 0.94155810 0.52922095
[98,] 0.50953281 0.98093439 0.49046719
[99,] 0.47097176 0.94194351 0.52902824
[100,] 0.42861169 0.85722338 0.57138831
[101,] 0.38145151 0.76290301 0.61854849
[102,] 0.71128864 0.57742272 0.28871136
[103,] 0.74739754 0.50520491 0.25260246
[104,] 0.72003227 0.55993547 0.27996773
[105,] 0.85465217 0.29069567 0.14534783
[106,] 0.82271620 0.35456760 0.17728380
[107,] 0.78959837 0.42080325 0.21040163
[108,] 0.75240502 0.49518997 0.24759498
[109,] 0.72177684 0.55644631 0.27822316
[110,] 0.77389257 0.45221485 0.22610743
[111,] 0.86094268 0.27811464 0.13905732
[112,] 0.83849371 0.32301257 0.16150629
[113,] 0.81442815 0.37114370 0.18557185
[114,] 0.77154930 0.45690141 0.22845070
[115,] 0.77469828 0.45060344 0.22530172
[116,] 0.72591221 0.54817558 0.27408779
[117,] 0.69067689 0.61864623 0.30932311
[118,] 0.63977800 0.72044399 0.36022200
[119,] 0.59073689 0.81852623 0.40926311
[120,] 0.71850322 0.56299357 0.28149678
[121,] 0.66032378 0.67935244 0.33967622
[122,] 0.59512693 0.80974614 0.40487307
[123,] 0.55371876 0.89256247 0.44628124
[124,] 0.48144577 0.96289155 0.51855423
[125,] 0.50489984 0.99020032 0.49510016
[126,] 0.43240056 0.86480113 0.56759944
[127,] 0.38385109 0.76770218 0.61614891
[128,] 0.39624875 0.79249751 0.60375125
[129,] 0.32376316 0.64752632 0.67623684
[130,] 0.25440300 0.50880600 0.74559700
[131,] 0.34404718 0.68809436 0.65595282
[132,] 0.29457591 0.58915181 0.70542409
[133,] 0.22644559 0.45289117 0.77355441
[134,] 0.15969886 0.31939772 0.84030114
[135,] 0.23993008 0.47986016 0.76006992
[136,] 0.16934831 0.33869661 0.83065169
[137,] 0.16052128 0.32104257 0.83947872
[138,] 0.08730809 0.17461618 0.91269191
> postscript(file="/var/wessaorg/rcomp/tmp/1317e1321974848.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/2sndn1321974848.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3bgdo1321974848.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4zy4b1321974848.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5t81o1321974848.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 159
Frequency = 1
1 2 3 4 5
0.699428707 4.982477533 -4.021139979 -0.092550079 0.309432197
6 7 8 9 10
-1.877648378 -0.730396894 -6.238481966 -4.109776690 -3.340495644
11 12 13 14 15
0.833321996 7.975906252 8.022016670 -0.800677630 -6.542086945
16 17 18 19 20
-5.747367265 1.680049372 -0.009510227 0.418198993 4.821988291
21 22 23 24 25
1.934533236 7.127653446 1.478748932 10.222457081 -0.262087388
26 27 28 29 30
-4.125763163 -1.450545389 2.822736033 0.159541394 -2.378681268
31 32 33 34 35
3.637116958 -5.402626037 -0.449955914 0.913587819 -5.867022028
36 37 38 39 40
4.239439239 9.401613363 -8.798498333 4.252209886 -0.787102564
41 42 43 44 45
2.005343802 0.008457742 0.870385856 -4.637207857 -2.231921021
46 47 48 49 50
-5.435293788 -2.107128831 4.878828002 6.858849478 -3.499859609
51 52 53 54 55
2.765402487 -0.965604254 1.203879954 -0.716070994 -1.010580477
56 57 58 59 60
2.609495654 -0.341469545 -3.156421048 -3.569277879 -6.816793556
61 62 63 64 65
-3.820224182 -0.334712950 -3.976381837 -5.192075888 -7.912040557
66 67 68 69 70
4.590073481 12.441523081 -3.986941409 -11.096172326 -2.580998457
71 72 73 74 75
10.344726359 0.770891644 6.821243744 1.898142550 4.479140251
76 77 78 79 80
3.422408610 -9.767389027 -1.699965699 -2.634583681 5.030842986
81 82 83 84 85
-2.697399203 4.045096838 0.238453441 -1.915796187 2.922252116
86 87 88 89 90
0.161398850 -0.893150604 -8.030577944 0.699416456 1.295409990
91 92 93 94 95
4.722794800 -3.420796193 -1.199510529 0.828654603 0.001068714
96 97 98 99 100
2.059621623 5.709441371 4.147188382 1.490373855 3.047572489
101 102 103 104 105
0.503607615 -1.287535418 -1.422852509 -1.665814523 0.743630757
106 107 108 109 110
4.091342564 4.327722848 4.431992288 -0.355023507 -2.619766861
111 112 113 114 115
-1.665955169 9.573738336 0.580323549 -3.315956539 -12.155176423
116 117 118 119 120
-2.006110633 -2.254461384 -2.610803774 0.323608466 4.432866261
121 122 123 124 125
7.128387389 -5.116785766 -5.086794180 -0.935578418 -6.626332593
126 127 128 129 130
-2.092892027 -2.709565404 -2.114928381 -2.511564958 7.237997476
131 132 133 134 135
-2.013573856 -0.690061844 -2.342042296 0.686519682 3.700500739
136 137 138 139 140
-1.799696895 -3.304233866 -5.207320015 -1.682591312 -2.979952893
141 142 143 144 145
5.074580192 2.429437275 -5.968383587 -1.296046975 -4.622770453
146 147 148 149 150
1.326039057 7.535657534 0.734410644 1.673866028 2.776335212
151 152 153 154 155
-0.752234725 0.438922517 11.109584884 2.563727727 -5.947077399
156 157 158 159
-1.310083076 3.185949922 -2.423045831 4.660187228
> postscript(file="/var/wessaorg/rcomp/tmp/6roja1321974848.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 0.699428707 NA
1 4.982477533 0.699428707
2 -4.021139979 4.982477533
3 -0.092550079 -4.021139979
4 0.309432197 -0.092550079
5 -1.877648378 0.309432197
6 -0.730396894 -1.877648378
7 -6.238481966 -0.730396894
8 -4.109776690 -6.238481966
9 -3.340495644 -4.109776690
10 0.833321996 -3.340495644
11 7.975906252 0.833321996
12 8.022016670 7.975906252
13 -0.800677630 8.022016670
14 -6.542086945 -0.800677630
15 -5.747367265 -6.542086945
16 1.680049372 -5.747367265
17 -0.009510227 1.680049372
18 0.418198993 -0.009510227
19 4.821988291 0.418198993
20 1.934533236 4.821988291
21 7.127653446 1.934533236
22 1.478748932 7.127653446
23 10.222457081 1.478748932
24 -0.262087388 10.222457081
25 -4.125763163 -0.262087388
26 -1.450545389 -4.125763163
27 2.822736033 -1.450545389
28 0.159541394 2.822736033
29 -2.378681268 0.159541394
30 3.637116958 -2.378681268
31 -5.402626037 3.637116958
32 -0.449955914 -5.402626037
33 0.913587819 -0.449955914
34 -5.867022028 0.913587819
35 4.239439239 -5.867022028
36 9.401613363 4.239439239
37 -8.798498333 9.401613363
38 4.252209886 -8.798498333
39 -0.787102564 4.252209886
40 2.005343802 -0.787102564
41 0.008457742 2.005343802
42 0.870385856 0.008457742
43 -4.637207857 0.870385856
44 -2.231921021 -4.637207857
45 -5.435293788 -2.231921021
46 -2.107128831 -5.435293788
47 4.878828002 -2.107128831
48 6.858849478 4.878828002
49 -3.499859609 6.858849478
50 2.765402487 -3.499859609
51 -0.965604254 2.765402487
52 1.203879954 -0.965604254
53 -0.716070994 1.203879954
54 -1.010580477 -0.716070994
55 2.609495654 -1.010580477
56 -0.341469545 2.609495654
57 -3.156421048 -0.341469545
58 -3.569277879 -3.156421048
59 -6.816793556 -3.569277879
60 -3.820224182 -6.816793556
61 -0.334712950 -3.820224182
62 -3.976381837 -0.334712950
63 -5.192075888 -3.976381837
64 -7.912040557 -5.192075888
65 4.590073481 -7.912040557
66 12.441523081 4.590073481
67 -3.986941409 12.441523081
68 -11.096172326 -3.986941409
69 -2.580998457 -11.096172326
70 10.344726359 -2.580998457
71 0.770891644 10.344726359
72 6.821243744 0.770891644
73 1.898142550 6.821243744
74 4.479140251 1.898142550
75 3.422408610 4.479140251
76 -9.767389027 3.422408610
77 -1.699965699 -9.767389027
78 -2.634583681 -1.699965699
79 5.030842986 -2.634583681
80 -2.697399203 5.030842986
81 4.045096838 -2.697399203
82 0.238453441 4.045096838
83 -1.915796187 0.238453441
84 2.922252116 -1.915796187
85 0.161398850 2.922252116
86 -0.893150604 0.161398850
87 -8.030577944 -0.893150604
88 0.699416456 -8.030577944
89 1.295409990 0.699416456
90 4.722794800 1.295409990
91 -3.420796193 4.722794800
92 -1.199510529 -3.420796193
93 0.828654603 -1.199510529
94 0.001068714 0.828654603
95 2.059621623 0.001068714
96 5.709441371 2.059621623
97 4.147188382 5.709441371
98 1.490373855 4.147188382
99 3.047572489 1.490373855
100 0.503607615 3.047572489
101 -1.287535418 0.503607615
102 -1.422852509 -1.287535418
103 -1.665814523 -1.422852509
104 0.743630757 -1.665814523
105 4.091342564 0.743630757
106 4.327722848 4.091342564
107 4.431992288 4.327722848
108 -0.355023507 4.431992288
109 -2.619766861 -0.355023507
110 -1.665955169 -2.619766861
111 9.573738336 -1.665955169
112 0.580323549 9.573738336
113 -3.315956539 0.580323549
114 -12.155176423 -3.315956539
115 -2.006110633 -12.155176423
116 -2.254461384 -2.006110633
117 -2.610803774 -2.254461384
118 0.323608466 -2.610803774
119 4.432866261 0.323608466
120 7.128387389 4.432866261
121 -5.116785766 7.128387389
122 -5.086794180 -5.116785766
123 -0.935578418 -5.086794180
124 -6.626332593 -0.935578418
125 -2.092892027 -6.626332593
126 -2.709565404 -2.092892027
127 -2.114928381 -2.709565404
128 -2.511564958 -2.114928381
129 7.237997476 -2.511564958
130 -2.013573856 7.237997476
131 -0.690061844 -2.013573856
132 -2.342042296 -0.690061844
133 0.686519682 -2.342042296
134 3.700500739 0.686519682
135 -1.799696895 3.700500739
136 -3.304233866 -1.799696895
137 -5.207320015 -3.304233866
138 -1.682591312 -5.207320015
139 -2.979952893 -1.682591312
140 5.074580192 -2.979952893
141 2.429437275 5.074580192
142 -5.968383587 2.429437275
143 -1.296046975 -5.968383587
144 -4.622770453 -1.296046975
145 1.326039057 -4.622770453
146 7.535657534 1.326039057
147 0.734410644 7.535657534
148 1.673866028 0.734410644
149 2.776335212 1.673866028
150 -0.752234725 2.776335212
151 0.438922517 -0.752234725
152 11.109584884 0.438922517
153 2.563727727 11.109584884
154 -5.947077399 2.563727727
155 -1.310083076 -5.947077399
156 3.185949922 -1.310083076
157 -2.423045831 3.185949922
158 4.660187228 -2.423045831
159 NA 4.660187228
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.982477533 0.699428707
[2,] -4.021139979 4.982477533
[3,] -0.092550079 -4.021139979
[4,] 0.309432197 -0.092550079
[5,] -1.877648378 0.309432197
[6,] -0.730396894 -1.877648378
[7,] -6.238481966 -0.730396894
[8,] -4.109776690 -6.238481966
[9,] -3.340495644 -4.109776690
[10,] 0.833321996 -3.340495644
[11,] 7.975906252 0.833321996
[12,] 8.022016670 7.975906252
[13,] -0.800677630 8.022016670
[14,] -6.542086945 -0.800677630
[15,] -5.747367265 -6.542086945
[16,] 1.680049372 -5.747367265
[17,] -0.009510227 1.680049372
[18,] 0.418198993 -0.009510227
[19,] 4.821988291 0.418198993
[20,] 1.934533236 4.821988291
[21,] 7.127653446 1.934533236
[22,] 1.478748932 7.127653446
[23,] 10.222457081 1.478748932
[24,] -0.262087388 10.222457081
[25,] -4.125763163 -0.262087388
[26,] -1.450545389 -4.125763163
[27,] 2.822736033 -1.450545389
[28,] 0.159541394 2.822736033
[29,] -2.378681268 0.159541394
[30,] 3.637116958 -2.378681268
[31,] -5.402626037 3.637116958
[32,] -0.449955914 -5.402626037
[33,] 0.913587819 -0.449955914
[34,] -5.867022028 0.913587819
[35,] 4.239439239 -5.867022028
[36,] 9.401613363 4.239439239
[37,] -8.798498333 9.401613363
[38,] 4.252209886 -8.798498333
[39,] -0.787102564 4.252209886
[40,] 2.005343802 -0.787102564
[41,] 0.008457742 2.005343802
[42,] 0.870385856 0.008457742
[43,] -4.637207857 0.870385856
[44,] -2.231921021 -4.637207857
[45,] -5.435293788 -2.231921021
[46,] -2.107128831 -5.435293788
[47,] 4.878828002 -2.107128831
[48,] 6.858849478 4.878828002
[49,] -3.499859609 6.858849478
[50,] 2.765402487 -3.499859609
[51,] -0.965604254 2.765402487
[52,] 1.203879954 -0.965604254
[53,] -0.716070994 1.203879954
[54,] -1.010580477 -0.716070994
[55,] 2.609495654 -1.010580477
[56,] -0.341469545 2.609495654
[57,] -3.156421048 -0.341469545
[58,] -3.569277879 -3.156421048
[59,] -6.816793556 -3.569277879
[60,] -3.820224182 -6.816793556
[61,] -0.334712950 -3.820224182
[62,] -3.976381837 -0.334712950
[63,] -5.192075888 -3.976381837
[64,] -7.912040557 -5.192075888
[65,] 4.590073481 -7.912040557
[66,] 12.441523081 4.590073481
[67,] -3.986941409 12.441523081
[68,] -11.096172326 -3.986941409
[69,] -2.580998457 -11.096172326
[70,] 10.344726359 -2.580998457
[71,] 0.770891644 10.344726359
[72,] 6.821243744 0.770891644
[73,] 1.898142550 6.821243744
[74,] 4.479140251 1.898142550
[75,] 3.422408610 4.479140251
[76,] -9.767389027 3.422408610
[77,] -1.699965699 -9.767389027
[78,] -2.634583681 -1.699965699
[79,] 5.030842986 -2.634583681
[80,] -2.697399203 5.030842986
[81,] 4.045096838 -2.697399203
[82,] 0.238453441 4.045096838
[83,] -1.915796187 0.238453441
[84,] 2.922252116 -1.915796187
[85,] 0.161398850 2.922252116
[86,] -0.893150604 0.161398850
[87,] -8.030577944 -0.893150604
[88,] 0.699416456 -8.030577944
[89,] 1.295409990 0.699416456
[90,] 4.722794800 1.295409990
[91,] -3.420796193 4.722794800
[92,] -1.199510529 -3.420796193
[93,] 0.828654603 -1.199510529
[94,] 0.001068714 0.828654603
[95,] 2.059621623 0.001068714
[96,] 5.709441371 2.059621623
[97,] 4.147188382 5.709441371
[98,] 1.490373855 4.147188382
[99,] 3.047572489 1.490373855
[100,] 0.503607615 3.047572489
[101,] -1.287535418 0.503607615
[102,] -1.422852509 -1.287535418
[103,] -1.665814523 -1.422852509
[104,] 0.743630757 -1.665814523
[105,] 4.091342564 0.743630757
[106,] 4.327722848 4.091342564
[107,] 4.431992288 4.327722848
[108,] -0.355023507 4.431992288
[109,] -2.619766861 -0.355023507
[110,] -1.665955169 -2.619766861
[111,] 9.573738336 -1.665955169
[112,] 0.580323549 9.573738336
[113,] -3.315956539 0.580323549
[114,] -12.155176423 -3.315956539
[115,] -2.006110633 -12.155176423
[116,] -2.254461384 -2.006110633
[117,] -2.610803774 -2.254461384
[118,] 0.323608466 -2.610803774
[119,] 4.432866261 0.323608466
[120,] 7.128387389 4.432866261
[121,] -5.116785766 7.128387389
[122,] -5.086794180 -5.116785766
[123,] -0.935578418 -5.086794180
[124,] -6.626332593 -0.935578418
[125,] -2.092892027 -6.626332593
[126,] -2.709565404 -2.092892027
[127,] -2.114928381 -2.709565404
[128,] -2.511564958 -2.114928381
[129,] 7.237997476 -2.511564958
[130,] -2.013573856 7.237997476
[131,] -0.690061844 -2.013573856
[132,] -2.342042296 -0.690061844
[133,] 0.686519682 -2.342042296
[134,] 3.700500739 0.686519682
[135,] -1.799696895 3.700500739
[136,] -3.304233866 -1.799696895
[137,] -5.207320015 -3.304233866
[138,] -1.682591312 -5.207320015
[139,] -2.979952893 -1.682591312
[140,] 5.074580192 -2.979952893
[141,] 2.429437275 5.074580192
[142,] -5.968383587 2.429437275
[143,] -1.296046975 -5.968383587
[144,] -4.622770453 -1.296046975
[145,] 1.326039057 -4.622770453
[146,] 7.535657534 1.326039057
[147,] 0.734410644 7.535657534
[148,] 1.673866028 0.734410644
[149,] 2.776335212 1.673866028
[150,] -0.752234725 2.776335212
[151,] 0.438922517 -0.752234725
[152,] 11.109584884 0.438922517
[153,] 2.563727727 11.109584884
[154,] -5.947077399 2.563727727
[155,] -1.310083076 -5.947077399
[156,] 3.185949922 -1.310083076
[157,] -2.423045831 3.185949922
[158,] 4.660187228 -2.423045831
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.982477533 0.699428707
2 -4.021139979 4.982477533
3 -0.092550079 -4.021139979
4 0.309432197 -0.092550079
5 -1.877648378 0.309432197
6 -0.730396894 -1.877648378
7 -6.238481966 -0.730396894
8 -4.109776690 -6.238481966
9 -3.340495644 -4.109776690
10 0.833321996 -3.340495644
11 7.975906252 0.833321996
12 8.022016670 7.975906252
13 -0.800677630 8.022016670
14 -6.542086945 -0.800677630
15 -5.747367265 -6.542086945
16 1.680049372 -5.747367265
17 -0.009510227 1.680049372
18 0.418198993 -0.009510227
19 4.821988291 0.418198993
20 1.934533236 4.821988291
21 7.127653446 1.934533236
22 1.478748932 7.127653446
23 10.222457081 1.478748932
24 -0.262087388 10.222457081
25 -4.125763163 -0.262087388
26 -1.450545389 -4.125763163
27 2.822736033 -1.450545389
28 0.159541394 2.822736033
29 -2.378681268 0.159541394
30 3.637116958 -2.378681268
31 -5.402626037 3.637116958
32 -0.449955914 -5.402626037
33 0.913587819 -0.449955914
34 -5.867022028 0.913587819
35 4.239439239 -5.867022028
36 9.401613363 4.239439239
37 -8.798498333 9.401613363
38 4.252209886 -8.798498333
39 -0.787102564 4.252209886
40 2.005343802 -0.787102564
41 0.008457742 2.005343802
42 0.870385856 0.008457742
43 -4.637207857 0.870385856
44 -2.231921021 -4.637207857
45 -5.435293788 -2.231921021
46 -2.107128831 -5.435293788
47 4.878828002 -2.107128831
48 6.858849478 4.878828002
49 -3.499859609 6.858849478
50 2.765402487 -3.499859609
51 -0.965604254 2.765402487
52 1.203879954 -0.965604254
53 -0.716070994 1.203879954
54 -1.010580477 -0.716070994
55 2.609495654 -1.010580477
56 -0.341469545 2.609495654
57 -3.156421048 -0.341469545
58 -3.569277879 -3.156421048
59 -6.816793556 -3.569277879
60 -3.820224182 -6.816793556
61 -0.334712950 -3.820224182
62 -3.976381837 -0.334712950
63 -5.192075888 -3.976381837
64 -7.912040557 -5.192075888
65 4.590073481 -7.912040557
66 12.441523081 4.590073481
67 -3.986941409 12.441523081
68 -11.096172326 -3.986941409
69 -2.580998457 -11.096172326
70 10.344726359 -2.580998457
71 0.770891644 10.344726359
72 6.821243744 0.770891644
73 1.898142550 6.821243744
74 4.479140251 1.898142550
75 3.422408610 4.479140251
76 -9.767389027 3.422408610
77 -1.699965699 -9.767389027
78 -2.634583681 -1.699965699
79 5.030842986 -2.634583681
80 -2.697399203 5.030842986
81 4.045096838 -2.697399203
82 0.238453441 4.045096838
83 -1.915796187 0.238453441
84 2.922252116 -1.915796187
85 0.161398850 2.922252116
86 -0.893150604 0.161398850
87 -8.030577944 -0.893150604
88 0.699416456 -8.030577944
89 1.295409990 0.699416456
90 4.722794800 1.295409990
91 -3.420796193 4.722794800
92 -1.199510529 -3.420796193
93 0.828654603 -1.199510529
94 0.001068714 0.828654603
95 2.059621623 0.001068714
96 5.709441371 2.059621623
97 4.147188382 5.709441371
98 1.490373855 4.147188382
99 3.047572489 1.490373855
100 0.503607615 3.047572489
101 -1.287535418 0.503607615
102 -1.422852509 -1.287535418
103 -1.665814523 -1.422852509
104 0.743630757 -1.665814523
105 4.091342564 0.743630757
106 4.327722848 4.091342564
107 4.431992288 4.327722848
108 -0.355023507 4.431992288
109 -2.619766861 -0.355023507
110 -1.665955169 -2.619766861
111 9.573738336 -1.665955169
112 0.580323549 9.573738336
113 -3.315956539 0.580323549
114 -12.155176423 -3.315956539
115 -2.006110633 -12.155176423
116 -2.254461384 -2.006110633
117 -2.610803774 -2.254461384
118 0.323608466 -2.610803774
119 4.432866261 0.323608466
120 7.128387389 4.432866261
121 -5.116785766 7.128387389
122 -5.086794180 -5.116785766
123 -0.935578418 -5.086794180
124 -6.626332593 -0.935578418
125 -2.092892027 -6.626332593
126 -2.709565404 -2.092892027
127 -2.114928381 -2.709565404
128 -2.511564958 -2.114928381
129 7.237997476 -2.511564958
130 -2.013573856 7.237997476
131 -0.690061844 -2.013573856
132 -2.342042296 -0.690061844
133 0.686519682 -2.342042296
134 3.700500739 0.686519682
135 -1.799696895 3.700500739
136 -3.304233866 -1.799696895
137 -5.207320015 -3.304233866
138 -1.682591312 -5.207320015
139 -2.979952893 -1.682591312
140 5.074580192 -2.979952893
141 2.429437275 5.074580192
142 -5.968383587 2.429437275
143 -1.296046975 -5.968383587
144 -4.622770453 -1.296046975
145 1.326039057 -4.622770453
146 7.535657534 1.326039057
147 0.734410644 7.535657534
148 1.673866028 0.734410644
149 2.776335212 1.673866028
150 -0.752234725 2.776335212
151 0.438922517 -0.752234725
152 11.109584884 0.438922517
153 2.563727727 11.109584884
154 -5.947077399 2.563727727
155 -1.310083076 -5.947077399
156 3.185949922 -1.310083076
157 -2.423045831 3.185949922
158 4.660187228 -2.423045831
> 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/7yjnm1321974848.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/8c6zf1321974848.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/986u01321974848.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/10u8o61321974848.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11o1uo1321974848.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12zzzt1321974848.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1371hy1321974848.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14p19o1321974848.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/15kxbx1321974848.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/163bdc1321974848.tab")
+ }
>
> try(system("convert tmp/1317e1321974848.ps tmp/1317e1321974848.png",intern=TRUE))
character(0)
> try(system("convert tmp/2sndn1321974848.ps tmp/2sndn1321974848.png",intern=TRUE))
character(0)
> try(system("convert tmp/3bgdo1321974848.ps tmp/3bgdo1321974848.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zy4b1321974848.ps tmp/4zy4b1321974848.png",intern=TRUE))
character(0)
> try(system("convert tmp/5t81o1321974848.ps tmp/5t81o1321974848.png",intern=TRUE))
character(0)
> try(system("convert tmp/6roja1321974848.ps tmp/6roja1321974848.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yjnm1321974848.ps tmp/7yjnm1321974848.png",intern=TRUE))
character(0)
> try(system("convert tmp/8c6zf1321974848.ps tmp/8c6zf1321974848.png",intern=TRUE))
character(0)
> try(system("convert tmp/986u01321974848.ps tmp/986u01321974848.png",intern=TRUE))
character(0)
> try(system("convert tmp/10u8o61321974848.ps tmp/10u8o61321974848.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.889 0.528 5.502