R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(13
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+ ,5)
+ ,dim=c(3
+ ,156)
+ ,dimnames=list(c('IEP'
+ ,'HS'
+ ,'WP')
+ ,1:156))
> y <- array(NA,dim=c(3,156),dimnames=list(c('IEP','HS','WP'),1:156))
> 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'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
HS IEP WP t
1 14 13 5 1
2 18 12 3 2
3 11 15 0 3
4 12 12 7 4
5 16 10 4 5
6 18 12 1 6
7 14 15 6 7
8 14 9 3 8
9 15 12 12 9
10 15 11 0 10
11 17 11 5 11
12 19 11 6 12
13 10 15 6 13
14 16 7 6 14
15 18 11 2 15
16 14 11 1 16
17 14 10 5 17
18 17 14 7 18
19 14 10 3 19
20 16 6 3 20
21 18 11 3 21
22 11 15 7 22
23 14 11 8 23
24 12 12 6 24
25 17 14 3 25
26 9 15 5 26
27 16 9 5 27
28 14 13 10 28
29 15 13 2 29
30 11 16 6 30
31 16 13 4 31
32 13 12 6 32
33 17 14 8 33
34 15 11 4 34
35 14 9 5 35
36 16 16 10 36
37 9 12 6 37
38 15 10 7 38
39 17 13 4 39
40 13 16 10 40
41 15 14 4 41
42 16 15 3 42
43 16 5 3 43
44 12 8 3 44
45 12 11 3 45
46 11 16 7 46
47 15 17 15 47
48 15 9 0 48
49 17 9 0 49
50 13 13 4 50
51 16 10 5 51
52 14 6 5 52
53 11 12 2 53
54 12 8 3 54
55 12 14 0 55
56 15 12 9 56
57 16 11 2 57
58 15 16 7 58
59 12 8 7 59
60 12 15 0 60
61 8 7 0 61
62 13 16 10 62
63 11 14 2 63
64 14 16 1 64
65 15 9 8 65
66 10 14 6 66
67 11 11 11 67
68 12 13 3 68
69 15 15 8 69
70 15 5 6 70
71 14 15 9 71
72 16 13 9 72
73 15 11 8 73
74 15 11 8 74
75 13 12 7 75
76 12 12 6 76
77 17 12 5 77
78 13 12 4 78
79 15 14 6 79
80 13 6 3 80
81 15 7 2 81
82 16 14 12 82
83 15 14 8 83
84 16 10 5 84
85 15 13 9 85
86 14 12 6 86
87 15 9 5 87
88 14 12 2 88
89 13 16 4 89
90 7 10 7 90
91 17 14 5 91
92 13 10 6 92
93 15 16 7 93
94 14 15 8 94
95 13 12 6 95
96 16 10 0 96
97 12 8 1 97
98 14 8 5 98
99 17 11 5 99
100 15 13 5 100
101 17 16 7 101
102 12 16 7 102
103 16 14 1 103
104 11 11 3 104
105 15 4 4 105
106 9 14 8 106
107 16 9 6 107
108 15 14 6 108
109 10 8 2 109
110 10 8 2 110
111 15 11 3 111
112 11 12 3 112
113 13 11 0 113
114 14 14 2 114
115 18 15 8 115
116 16 16 8 116
117 14 16 0 117
118 14 11 5 118
119 14 14 9 119
120 14 14 6 120
121 12 12 6 121
122 14 14 3 122
123 15 8 9 123
124 15 13 7 124
125 15 16 8 125
126 13 12 0 126
127 17 16 7 127
128 17 12 0 128
129 19 11 5 129
130 15 4 0 130
131 13 16 14 131
132 9 15 5 132
133 15 10 2 133
134 15 13 8 134
135 15 15 4 135
136 16 12 2 136
137 11 14 6 137
138 14 7 3 138
139 11 19 5 139
140 15 12 9 140
141 13 12 3 141
142 15 13 3 142
143 16 15 0 143
144 14 8 10 144
145 15 12 4 145
146 16 10 2 146
147 16 8 3 147
148 11 10 10 148
149 12 15 7 149
150 9 16 0 150
151 16 13 6 151
152 13 16 8 152
153 16 9 0 153
154 12 14 4 154
155 9 14 10 155
156 13 12 5 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) IEP WP t
15.352157 -0.079376 0.009400 -0.005156
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.1602 -1.4654 0.3546 1.4626 5.1391
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.352157 0.841111 18.252 <2e-16 ***
IEP -0.079376 0.067010 -1.185 0.238
WP 0.009400 0.063034 0.149 0.882
t -0.005156 0.004168 -1.237 0.218
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.337 on 152 degrees of freedom
Multiple R-squared: 0.02078, Adjusted R-squared: 0.001454
F-statistic: 1.075 on 3 and 152 DF, p-value: 0.3615
> 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,] 0.8098703 0.3802595 0.1901297
[2,] 0.8489848 0.3020304 0.1510152
[3,] 0.7802105 0.4395789 0.2197895
[4,] 0.6806848 0.6386305 0.3193152
[5,] 0.6240859 0.7518283 0.3759141
[6,] 0.6675690 0.6648620 0.3324310
[7,] 0.7605468 0.4789065 0.2394532
[8,] 0.7412305 0.5175391 0.2587695
[9,] 0.7439406 0.5121187 0.2560594
[10,] 0.7081684 0.5836632 0.2918316
[11,] 0.6669604 0.6660791 0.3330396
[12,] 0.7258970 0.5482060 0.2741030
[13,] 0.6972803 0.6054394 0.3027197
[14,] 0.6476550 0.7046900 0.3523450
[15,] 0.6740828 0.6518345 0.3259172
[16,] 0.6911699 0.6176602 0.3088301
[17,] 0.6335665 0.7328670 0.3664335
[18,] 0.6200770 0.7598459 0.3799230
[19,] 0.6805113 0.6389774 0.3194887
[20,] 0.7820436 0.4359127 0.2179564
[21,] 0.7407837 0.5184327 0.2592163
[22,] 0.6940473 0.6119055 0.3059527
[23,] 0.6507067 0.6985866 0.3492933
[24,] 0.6172996 0.7654008 0.3827004
[25,] 0.6119145 0.7761709 0.3880855
[26,] 0.5655261 0.8689477 0.4344739
[27,] 0.6547518 0.6904964 0.3452482
[28,] 0.6027440 0.7945119 0.3972560
[29,] 0.5711567 0.8576866 0.4288433
[30,] 0.6173769 0.7652462 0.3826231
[31,] 0.7939239 0.4121521 0.2060761
[32,] 0.7551348 0.4897304 0.2448652
[33,] 0.7864953 0.4270095 0.2135047
[34,] 0.7467420 0.5065161 0.2532580
[35,] 0.7145496 0.5709009 0.2854504
[36,] 0.7116713 0.5766575 0.2883287
[37,] 0.6812944 0.6374112 0.3187056
[38,] 0.7170584 0.5658832 0.2829416
[39,] 0.7120264 0.5759472 0.2879736
[40,] 0.7024353 0.5951293 0.2975647
[41,] 0.6969072 0.6061856 0.3030928
[42,] 0.6565917 0.6868167 0.3434083
[43,] 0.6699606 0.6600788 0.3300394
[44,] 0.6278842 0.7442317 0.3721158
[45,] 0.6082699 0.7834601 0.3917301
[46,] 0.5740626 0.8518749 0.4259374
[47,] 0.5921265 0.8157470 0.4078735
[48,] 0.5899905 0.8200190 0.4100095
[49,] 0.5535511 0.8928978 0.4464489
[50,] 0.5213867 0.9572266 0.4786133
[51,] 0.5210440 0.9579120 0.4789560
[52,] 0.5066269 0.9867463 0.4933731
[53,] 0.5030041 0.9939917 0.4969959
[54,] 0.4656620 0.9313241 0.5343380
[55,] 0.7171987 0.5656025 0.2828013
[56,] 0.6777171 0.6445658 0.3222829
[57,] 0.6748458 0.6503083 0.3251542
[58,] 0.6469584 0.7060832 0.3530416
[59,] 0.6136356 0.7727287 0.3863644
[60,] 0.6620697 0.6758606 0.3379303
[61,] 0.6767844 0.6464313 0.3232156
[62,] 0.6566535 0.6866929 0.3433465
[63,] 0.6460012 0.7079976 0.3539988
[64,] 0.6082633 0.7834734 0.3917367
[65,] 0.5733569 0.8532861 0.4266431
[66,] 0.5810303 0.8379394 0.4189697
[67,] 0.5496510 0.9006980 0.4503490
[68,] 0.5170699 0.9658602 0.4829301
[69,] 0.4768533 0.9537066 0.5231467
[70,] 0.4579159 0.9158319 0.5420841
[71,] 0.5096201 0.9807598 0.4903799
[72,] 0.4707834 0.9415667 0.5292166
[73,] 0.4456977 0.8913954 0.5543023
[74,] 0.4128272 0.8256544 0.5871728
[75,] 0.3759180 0.7518360 0.6240820
[76,] 0.3748889 0.7497778 0.6251111
[77,] 0.3473220 0.6946440 0.6526780
[78,] 0.3387199 0.6774397 0.6612801
[79,] 0.3094710 0.6189420 0.6905290
[80,] 0.2701698 0.5403397 0.7298302
[81,] 0.2398058 0.4796116 0.7601942
[82,] 0.2066424 0.4132848 0.7933576
[83,] 0.1774382 0.3548764 0.8225618
[84,] 0.4956138 0.9912276 0.5043862
[85,] 0.5422186 0.9155629 0.4577814
[86,] 0.5053376 0.9893248 0.4946624
[87,] 0.4762470 0.9524941 0.5237530
[88,] 0.4306843 0.8613687 0.5693157
[89,] 0.3921571 0.7843142 0.6078429
[90,] 0.3803924 0.7607849 0.6196076
[91,] 0.3769223 0.7538445 0.6230777
[92,] 0.3342962 0.6685923 0.6657038
[93,] 0.3598085 0.7196170 0.6401915
[94,] 0.3265232 0.6530463 0.6734768
[95,] 0.3790103 0.7580206 0.6209897
[96,] 0.3498138 0.6996276 0.6501862
[97,] 0.3486902 0.6973804 0.6513098
[98,] 0.3718694 0.7437388 0.6281306
[99,] 0.3276790 0.6553580 0.6723210
[100,] 0.4763649 0.9527299 0.5236351
[101,] 0.4520038 0.9040076 0.5479962
[102,] 0.4143202 0.8286404 0.5856798
[103,] 0.5481996 0.9036008 0.4518004
[104,] 0.7277553 0.5444895 0.2722447
[105,] 0.6892821 0.6214358 0.3107179
[106,] 0.7716954 0.4566092 0.2283046
[107,] 0.7852699 0.4294601 0.2147301
[108,] 0.7590614 0.4818773 0.2409386
[109,] 0.8275588 0.3448823 0.1724412
[110,] 0.8247510 0.3504980 0.1752490
[111,] 0.7904301 0.4191399 0.2095699
[112,] 0.7607819 0.4784362 0.2392181
[113,] 0.7149275 0.5701449 0.2850725
[114,] 0.6671772 0.6656455 0.3328228
[115,] 0.7023967 0.5952067 0.2976033
[116,] 0.6628654 0.6742692 0.3371346
[117,] 0.6195890 0.7608221 0.3804110
[118,] 0.5651381 0.8697238 0.4348619
[119,] 0.5173035 0.9653930 0.4826965
[120,] 0.5408061 0.9183878 0.4591939
[121,] 0.5958345 0.8083310 0.4041655
[122,] 0.5775233 0.8449535 0.4224767
[123,] 0.7453736 0.5092528 0.2546264
[124,] 0.7524787 0.4950426 0.2475213
[125,] 0.7341234 0.5317531 0.2658766
[126,] 0.8789655 0.2420690 0.1210345
[127,] 0.8499187 0.3001627 0.1500813
[128,] 0.8212471 0.3575057 0.1787529
[129,] 0.7928429 0.4143141 0.2071571
[130,] 0.7570639 0.4858721 0.2429361
[131,] 0.7580925 0.4838151 0.2419075
[132,] 0.7989163 0.4021674 0.2010837
[133,] 0.7519092 0.4961817 0.2480908
[134,] 0.7029485 0.5941029 0.2970515
[135,] 0.6969790 0.6060421 0.3030210
[136,] 0.6102649 0.7794702 0.3897351
[137,] 0.5706458 0.8587084 0.4293542
[138,] 0.4803217 0.9606435 0.5196783
[139,] 0.3919387 0.7838774 0.6080613
[140,] 0.3080751 0.6161503 0.6919249
[141,] 0.2179239 0.4358478 0.7820761
[142,] 0.4410936 0.8821871 0.5589064
[143,] 0.3748516 0.7497032 0.6251484
> postscript(file="/var/www/html/freestat/rcomp/tmp/1ptt41292935039.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2ikap1292935039.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3ikap1292935039.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4ikap1292935039.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5bcrs1292935039.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 = 156
Frequency = 1
1 2 3 4 5 6
-0.36211669 3.58246398 -3.14605192 -2.44482497 1.42978014 3.62188844
7 8 9 10 11 12
-0.18182935 -0.62472743 0.53395384 0.57253687 2.53069168 4.52644744
13 14 15 16 17 18
-4.15089337 1.21925625 3.57951638 -0.40592739 -0.51774813 2.78611058
19 20 21 22 23 24
-0.48863567 1.19901714 3.60105212 -3.11388964 -0.43563707 -2.33230480
25 26 27 28 29 30
2.85980350 -5.07446518 1.45443603 -0.26990596 0.81045192 -2.98386564
31 32 33 34 35 36
1.80196344 -1.29105683 2.85405029 0.65867984 -0.50431599 2.00946940
37 38 39 40 41 42
-5.26527685 0.57172732 2.84321141 -0.96990662 0.93289920 2.02683123
43 44 45 46 47 48
1.23822926 -2.51848735 -2.27520396 -2.91076993 1.09855998 0.60971314
49 50 51 52 53 54
2.61486914 -1.10007262 1.65755575 -0.65479144 -3.14517996 -2.46692739
55 56 57 58 59 60
-1.95731590 0.80448638 1.79606823 1.15110203 -2.47874835 -1.85216012
61 62 63 64 65 66
-6.48201050 -0.85647469 -2.93486840 0.23843942 0.62216319 -3.95700136
67 68 69 70 71 72
-3.23697393 -1.99786445 1.11904196 0.34924046 0.11995372 1.96635812
73 74 75 76 77 78
0.82216276 0.82731875 -1.07874922 -2.06419298 2.95036325 -1.03508052
79 80 81 82 83 84
1.11002660 -1.49162307 0.60230896 2.06909317 1.11185011 1.82770363
85 86 87 88 89 90
1.03338607 -0.01263302 0.76379582 0.03527992 -0.66086137 -7.16016086
91 92 93 94 95 96
3.18129879 -1.14044863 1.33156191 0.24794187 -0.96622905 1.93657677
97 98 99 100 101 102
-2.22641906 -0.25886401 2.98441937 1.14832696 3.37280988 -1.62203413
103 104 105 106 107 108
2.28077169 -2.97100017 0.46912501 -4.76956197 1.85751552 1.25955050
109 110 111 112 113 114
-4.17394734 -4.16879135 1.06509180 -2.85037640 -0.89639550 0.32808742
115 116 117 118 119 120
4.35621780 2.44074959 0.52110747 0.08238331 0.28806575 0.32142245
121 122 123 124 125 126
-1.83217314 0.35993515 0.83243496 1.25327041 1.48715356 -0.74999174
127 128 129 130 131 132
3.50686579 3.26032025 5.13909927 0.63562587 -0.53831188 -4.52792956
133 134 135 136 137 138
1.10854817 1.29543014 1.49693867 2.28276775 -2.59092561 -0.11319948
139 140 141 142 143 144
-2.17433439 1.23759008 -0.70085250 1.38367929 2.57578759 -0.06868935
145 146 147 148 149 150
1.31037125 2.17557612 2.01258029 -2.88931377 -1.45907809 -4.30874464
151 152 153 154 155 156
2.40188255 -0.37363454 2.15109277 -1.48447319 -4.53571861 -0.64231303
> postscript(file="/var/www/html/freestat/rcomp/tmp/6bcrs1292935039.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.36211669 NA
1 3.58246398 -0.36211669
2 -3.14605192 3.58246398
3 -2.44482497 -3.14605192
4 1.42978014 -2.44482497
5 3.62188844 1.42978014
6 -0.18182935 3.62188844
7 -0.62472743 -0.18182935
8 0.53395384 -0.62472743
9 0.57253687 0.53395384
10 2.53069168 0.57253687
11 4.52644744 2.53069168
12 -4.15089337 4.52644744
13 1.21925625 -4.15089337
14 3.57951638 1.21925625
15 -0.40592739 3.57951638
16 -0.51774813 -0.40592739
17 2.78611058 -0.51774813
18 -0.48863567 2.78611058
19 1.19901714 -0.48863567
20 3.60105212 1.19901714
21 -3.11388964 3.60105212
22 -0.43563707 -3.11388964
23 -2.33230480 -0.43563707
24 2.85980350 -2.33230480
25 -5.07446518 2.85980350
26 1.45443603 -5.07446518
27 -0.26990596 1.45443603
28 0.81045192 -0.26990596
29 -2.98386564 0.81045192
30 1.80196344 -2.98386564
31 -1.29105683 1.80196344
32 2.85405029 -1.29105683
33 0.65867984 2.85405029
34 -0.50431599 0.65867984
35 2.00946940 -0.50431599
36 -5.26527685 2.00946940
37 0.57172732 -5.26527685
38 2.84321141 0.57172732
39 -0.96990662 2.84321141
40 0.93289920 -0.96990662
41 2.02683123 0.93289920
42 1.23822926 2.02683123
43 -2.51848735 1.23822926
44 -2.27520396 -2.51848735
45 -2.91076993 -2.27520396
46 1.09855998 -2.91076993
47 0.60971314 1.09855998
48 2.61486914 0.60971314
49 -1.10007262 2.61486914
50 1.65755575 -1.10007262
51 -0.65479144 1.65755575
52 -3.14517996 -0.65479144
53 -2.46692739 -3.14517996
54 -1.95731590 -2.46692739
55 0.80448638 -1.95731590
56 1.79606823 0.80448638
57 1.15110203 1.79606823
58 -2.47874835 1.15110203
59 -1.85216012 -2.47874835
60 -6.48201050 -1.85216012
61 -0.85647469 -6.48201050
62 -2.93486840 -0.85647469
63 0.23843942 -2.93486840
64 0.62216319 0.23843942
65 -3.95700136 0.62216319
66 -3.23697393 -3.95700136
67 -1.99786445 -3.23697393
68 1.11904196 -1.99786445
69 0.34924046 1.11904196
70 0.11995372 0.34924046
71 1.96635812 0.11995372
72 0.82216276 1.96635812
73 0.82731875 0.82216276
74 -1.07874922 0.82731875
75 -2.06419298 -1.07874922
76 2.95036325 -2.06419298
77 -1.03508052 2.95036325
78 1.11002660 -1.03508052
79 -1.49162307 1.11002660
80 0.60230896 -1.49162307
81 2.06909317 0.60230896
82 1.11185011 2.06909317
83 1.82770363 1.11185011
84 1.03338607 1.82770363
85 -0.01263302 1.03338607
86 0.76379582 -0.01263302
87 0.03527992 0.76379582
88 -0.66086137 0.03527992
89 -7.16016086 -0.66086137
90 3.18129879 -7.16016086
91 -1.14044863 3.18129879
92 1.33156191 -1.14044863
93 0.24794187 1.33156191
94 -0.96622905 0.24794187
95 1.93657677 -0.96622905
96 -2.22641906 1.93657677
97 -0.25886401 -2.22641906
98 2.98441937 -0.25886401
99 1.14832696 2.98441937
100 3.37280988 1.14832696
101 -1.62203413 3.37280988
102 2.28077169 -1.62203413
103 -2.97100017 2.28077169
104 0.46912501 -2.97100017
105 -4.76956197 0.46912501
106 1.85751552 -4.76956197
107 1.25955050 1.85751552
108 -4.17394734 1.25955050
109 -4.16879135 -4.17394734
110 1.06509180 -4.16879135
111 -2.85037640 1.06509180
112 -0.89639550 -2.85037640
113 0.32808742 -0.89639550
114 4.35621780 0.32808742
115 2.44074959 4.35621780
116 0.52110747 2.44074959
117 0.08238331 0.52110747
118 0.28806575 0.08238331
119 0.32142245 0.28806575
120 -1.83217314 0.32142245
121 0.35993515 -1.83217314
122 0.83243496 0.35993515
123 1.25327041 0.83243496
124 1.48715356 1.25327041
125 -0.74999174 1.48715356
126 3.50686579 -0.74999174
127 3.26032025 3.50686579
128 5.13909927 3.26032025
129 0.63562587 5.13909927
130 -0.53831188 0.63562587
131 -4.52792956 -0.53831188
132 1.10854817 -4.52792956
133 1.29543014 1.10854817
134 1.49693867 1.29543014
135 2.28276775 1.49693867
136 -2.59092561 2.28276775
137 -0.11319948 -2.59092561
138 -2.17433439 -0.11319948
139 1.23759008 -2.17433439
140 -0.70085250 1.23759008
141 1.38367929 -0.70085250
142 2.57578759 1.38367929
143 -0.06868935 2.57578759
144 1.31037125 -0.06868935
145 2.17557612 1.31037125
146 2.01258029 2.17557612
147 -2.88931377 2.01258029
148 -1.45907809 -2.88931377
149 -4.30874464 -1.45907809
150 2.40188255 -4.30874464
151 -0.37363454 2.40188255
152 2.15109277 -0.37363454
153 -1.48447319 2.15109277
154 -4.53571861 -1.48447319
155 -0.64231303 -4.53571861
156 NA -0.64231303
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.58246398 -0.36211669
[2,] -3.14605192 3.58246398
[3,] -2.44482497 -3.14605192
[4,] 1.42978014 -2.44482497
[5,] 3.62188844 1.42978014
[6,] -0.18182935 3.62188844
[7,] -0.62472743 -0.18182935
[8,] 0.53395384 -0.62472743
[9,] 0.57253687 0.53395384
[10,] 2.53069168 0.57253687
[11,] 4.52644744 2.53069168
[12,] -4.15089337 4.52644744
[13,] 1.21925625 -4.15089337
[14,] 3.57951638 1.21925625
[15,] -0.40592739 3.57951638
[16,] -0.51774813 -0.40592739
[17,] 2.78611058 -0.51774813
[18,] -0.48863567 2.78611058
[19,] 1.19901714 -0.48863567
[20,] 3.60105212 1.19901714
[21,] -3.11388964 3.60105212
[22,] -0.43563707 -3.11388964
[23,] -2.33230480 -0.43563707
[24,] 2.85980350 -2.33230480
[25,] -5.07446518 2.85980350
[26,] 1.45443603 -5.07446518
[27,] -0.26990596 1.45443603
[28,] 0.81045192 -0.26990596
[29,] -2.98386564 0.81045192
[30,] 1.80196344 -2.98386564
[31,] -1.29105683 1.80196344
[32,] 2.85405029 -1.29105683
[33,] 0.65867984 2.85405029
[34,] -0.50431599 0.65867984
[35,] 2.00946940 -0.50431599
[36,] -5.26527685 2.00946940
[37,] 0.57172732 -5.26527685
[38,] 2.84321141 0.57172732
[39,] -0.96990662 2.84321141
[40,] 0.93289920 -0.96990662
[41,] 2.02683123 0.93289920
[42,] 1.23822926 2.02683123
[43,] -2.51848735 1.23822926
[44,] -2.27520396 -2.51848735
[45,] -2.91076993 -2.27520396
[46,] 1.09855998 -2.91076993
[47,] 0.60971314 1.09855998
[48,] 2.61486914 0.60971314
[49,] -1.10007262 2.61486914
[50,] 1.65755575 -1.10007262
[51,] -0.65479144 1.65755575
[52,] -3.14517996 -0.65479144
[53,] -2.46692739 -3.14517996
[54,] -1.95731590 -2.46692739
[55,] 0.80448638 -1.95731590
[56,] 1.79606823 0.80448638
[57,] 1.15110203 1.79606823
[58,] -2.47874835 1.15110203
[59,] -1.85216012 -2.47874835
[60,] -6.48201050 -1.85216012
[61,] -0.85647469 -6.48201050
[62,] -2.93486840 -0.85647469
[63,] 0.23843942 -2.93486840
[64,] 0.62216319 0.23843942
[65,] -3.95700136 0.62216319
[66,] -3.23697393 -3.95700136
[67,] -1.99786445 -3.23697393
[68,] 1.11904196 -1.99786445
[69,] 0.34924046 1.11904196
[70,] 0.11995372 0.34924046
[71,] 1.96635812 0.11995372
[72,] 0.82216276 1.96635812
[73,] 0.82731875 0.82216276
[74,] -1.07874922 0.82731875
[75,] -2.06419298 -1.07874922
[76,] 2.95036325 -2.06419298
[77,] -1.03508052 2.95036325
[78,] 1.11002660 -1.03508052
[79,] -1.49162307 1.11002660
[80,] 0.60230896 -1.49162307
[81,] 2.06909317 0.60230896
[82,] 1.11185011 2.06909317
[83,] 1.82770363 1.11185011
[84,] 1.03338607 1.82770363
[85,] -0.01263302 1.03338607
[86,] 0.76379582 -0.01263302
[87,] 0.03527992 0.76379582
[88,] -0.66086137 0.03527992
[89,] -7.16016086 -0.66086137
[90,] 3.18129879 -7.16016086
[91,] -1.14044863 3.18129879
[92,] 1.33156191 -1.14044863
[93,] 0.24794187 1.33156191
[94,] -0.96622905 0.24794187
[95,] 1.93657677 -0.96622905
[96,] -2.22641906 1.93657677
[97,] -0.25886401 -2.22641906
[98,] 2.98441937 -0.25886401
[99,] 1.14832696 2.98441937
[100,] 3.37280988 1.14832696
[101,] -1.62203413 3.37280988
[102,] 2.28077169 -1.62203413
[103,] -2.97100017 2.28077169
[104,] 0.46912501 -2.97100017
[105,] -4.76956197 0.46912501
[106,] 1.85751552 -4.76956197
[107,] 1.25955050 1.85751552
[108,] -4.17394734 1.25955050
[109,] -4.16879135 -4.17394734
[110,] 1.06509180 -4.16879135
[111,] -2.85037640 1.06509180
[112,] -0.89639550 -2.85037640
[113,] 0.32808742 -0.89639550
[114,] 4.35621780 0.32808742
[115,] 2.44074959 4.35621780
[116,] 0.52110747 2.44074959
[117,] 0.08238331 0.52110747
[118,] 0.28806575 0.08238331
[119,] 0.32142245 0.28806575
[120,] -1.83217314 0.32142245
[121,] 0.35993515 -1.83217314
[122,] 0.83243496 0.35993515
[123,] 1.25327041 0.83243496
[124,] 1.48715356 1.25327041
[125,] -0.74999174 1.48715356
[126,] 3.50686579 -0.74999174
[127,] 3.26032025 3.50686579
[128,] 5.13909927 3.26032025
[129,] 0.63562587 5.13909927
[130,] -0.53831188 0.63562587
[131,] -4.52792956 -0.53831188
[132,] 1.10854817 -4.52792956
[133,] 1.29543014 1.10854817
[134,] 1.49693867 1.29543014
[135,] 2.28276775 1.49693867
[136,] -2.59092561 2.28276775
[137,] -0.11319948 -2.59092561
[138,] -2.17433439 -0.11319948
[139,] 1.23759008 -2.17433439
[140,] -0.70085250 1.23759008
[141,] 1.38367929 -0.70085250
[142,] 2.57578759 1.38367929
[143,] -0.06868935 2.57578759
[144,] 1.31037125 -0.06868935
[145,] 2.17557612 1.31037125
[146,] 2.01258029 2.17557612
[147,] -2.88931377 2.01258029
[148,] -1.45907809 -2.88931377
[149,] -4.30874464 -1.45907809
[150,] 2.40188255 -4.30874464
[151,] -0.37363454 2.40188255
[152,] 2.15109277 -0.37363454
[153,] -1.48447319 2.15109277
[154,] -4.53571861 -1.48447319
[155,] -0.64231303 -4.53571861
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.58246398 -0.36211669
2 -3.14605192 3.58246398
3 -2.44482497 -3.14605192
4 1.42978014 -2.44482497
5 3.62188844 1.42978014
6 -0.18182935 3.62188844
7 -0.62472743 -0.18182935
8 0.53395384 -0.62472743
9 0.57253687 0.53395384
10 2.53069168 0.57253687
11 4.52644744 2.53069168
12 -4.15089337 4.52644744
13 1.21925625 -4.15089337
14 3.57951638 1.21925625
15 -0.40592739 3.57951638
16 -0.51774813 -0.40592739
17 2.78611058 -0.51774813
18 -0.48863567 2.78611058
19 1.19901714 -0.48863567
20 3.60105212 1.19901714
21 -3.11388964 3.60105212
22 -0.43563707 -3.11388964
23 -2.33230480 -0.43563707
24 2.85980350 -2.33230480
25 -5.07446518 2.85980350
26 1.45443603 -5.07446518
27 -0.26990596 1.45443603
28 0.81045192 -0.26990596
29 -2.98386564 0.81045192
30 1.80196344 -2.98386564
31 -1.29105683 1.80196344
32 2.85405029 -1.29105683
33 0.65867984 2.85405029
34 -0.50431599 0.65867984
35 2.00946940 -0.50431599
36 -5.26527685 2.00946940
37 0.57172732 -5.26527685
38 2.84321141 0.57172732
39 -0.96990662 2.84321141
40 0.93289920 -0.96990662
41 2.02683123 0.93289920
42 1.23822926 2.02683123
43 -2.51848735 1.23822926
44 -2.27520396 -2.51848735
45 -2.91076993 -2.27520396
46 1.09855998 -2.91076993
47 0.60971314 1.09855998
48 2.61486914 0.60971314
49 -1.10007262 2.61486914
50 1.65755575 -1.10007262
51 -0.65479144 1.65755575
52 -3.14517996 -0.65479144
53 -2.46692739 -3.14517996
54 -1.95731590 -2.46692739
55 0.80448638 -1.95731590
56 1.79606823 0.80448638
57 1.15110203 1.79606823
58 -2.47874835 1.15110203
59 -1.85216012 -2.47874835
60 -6.48201050 -1.85216012
61 -0.85647469 -6.48201050
62 -2.93486840 -0.85647469
63 0.23843942 -2.93486840
64 0.62216319 0.23843942
65 -3.95700136 0.62216319
66 -3.23697393 -3.95700136
67 -1.99786445 -3.23697393
68 1.11904196 -1.99786445
69 0.34924046 1.11904196
70 0.11995372 0.34924046
71 1.96635812 0.11995372
72 0.82216276 1.96635812
73 0.82731875 0.82216276
74 -1.07874922 0.82731875
75 -2.06419298 -1.07874922
76 2.95036325 -2.06419298
77 -1.03508052 2.95036325
78 1.11002660 -1.03508052
79 -1.49162307 1.11002660
80 0.60230896 -1.49162307
81 2.06909317 0.60230896
82 1.11185011 2.06909317
83 1.82770363 1.11185011
84 1.03338607 1.82770363
85 -0.01263302 1.03338607
86 0.76379582 -0.01263302
87 0.03527992 0.76379582
88 -0.66086137 0.03527992
89 -7.16016086 -0.66086137
90 3.18129879 -7.16016086
91 -1.14044863 3.18129879
92 1.33156191 -1.14044863
93 0.24794187 1.33156191
94 -0.96622905 0.24794187
95 1.93657677 -0.96622905
96 -2.22641906 1.93657677
97 -0.25886401 -2.22641906
98 2.98441937 -0.25886401
99 1.14832696 2.98441937
100 3.37280988 1.14832696
101 -1.62203413 3.37280988
102 2.28077169 -1.62203413
103 -2.97100017 2.28077169
104 0.46912501 -2.97100017
105 -4.76956197 0.46912501
106 1.85751552 -4.76956197
107 1.25955050 1.85751552
108 -4.17394734 1.25955050
109 -4.16879135 -4.17394734
110 1.06509180 -4.16879135
111 -2.85037640 1.06509180
112 -0.89639550 -2.85037640
113 0.32808742 -0.89639550
114 4.35621780 0.32808742
115 2.44074959 4.35621780
116 0.52110747 2.44074959
117 0.08238331 0.52110747
118 0.28806575 0.08238331
119 0.32142245 0.28806575
120 -1.83217314 0.32142245
121 0.35993515 -1.83217314
122 0.83243496 0.35993515
123 1.25327041 0.83243496
124 1.48715356 1.25327041
125 -0.74999174 1.48715356
126 3.50686579 -0.74999174
127 3.26032025 3.50686579
128 5.13909927 3.26032025
129 0.63562587 5.13909927
130 -0.53831188 0.63562587
131 -4.52792956 -0.53831188
132 1.10854817 -4.52792956
133 1.29543014 1.10854817
134 1.49693867 1.29543014
135 2.28276775 1.49693867
136 -2.59092561 2.28276775
137 -0.11319948 -2.59092561
138 -2.17433439 -0.11319948
139 1.23759008 -2.17433439
140 -0.70085250 1.23759008
141 1.38367929 -0.70085250
142 2.57578759 1.38367929
143 -0.06868935 2.57578759
144 1.31037125 -0.06868935
145 2.17557612 1.31037125
146 2.01258029 2.17557612
147 -2.88931377 2.01258029
148 -1.45907809 -2.88931377
149 -4.30874464 -1.45907809
150 2.40188255 -4.30874464
151 -0.37363454 2.40188255
152 2.15109277 -0.37363454
153 -1.48447319 2.15109277
154 -4.53571861 -1.48447319
155 -0.64231303 -4.53571861
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/7l39v1292935039.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8l39v1292935039.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9wc8g1292935039.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10wc8g1292935039.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/11hd6m1292935039.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/12lv5a1292935039.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/13se231292935039.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/14k5j61292935039.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/freestat/rcomp/tmp/1566hu1292935039.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/freestat/rcomp/tmp/167j4r1292935039.tab")
+ }
>
> try(system("convert tmp/1ptt41292935039.ps tmp/1ptt41292935039.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ikap1292935039.ps tmp/2ikap1292935039.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ikap1292935039.ps tmp/3ikap1292935039.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ikap1292935039.ps tmp/4ikap1292935039.png",intern=TRUE))
character(0)
> try(system("convert tmp/5bcrs1292935039.ps tmp/5bcrs1292935039.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bcrs1292935039.ps tmp/6bcrs1292935039.png",intern=TRUE))
character(0)
> try(system("convert tmp/7l39v1292935039.ps tmp/7l39v1292935039.png",intern=TRUE))
character(0)
> try(system("convert tmp/8l39v1292935039.ps tmp/8l39v1292935039.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wc8g1292935039.ps tmp/9wc8g1292935039.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wc8g1292935039.ps tmp/10wc8g1292935039.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.958 2.854 32.140