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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> x <- array(list(162687
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+ ,28)
+ ,dim=c(7
+ ,144)
+ ,dimnames=list(c('timeRFC'
+ ,'compshared'
+ ,'blogged'
+ ,'reviewedcomp'
+ ,'characters'
+ ,'seconds'
+ ,'inclhyperlinks')
+ ,1:144))
> y <- array(NA,dim=c(7,144),dimnames=list(c('timeRFC','compshared','blogged','reviewedcomp','characters','seconds','inclhyperlinks'),1:144))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> 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
timeRFC compshared blogged reviewedcomp characters seconds inclhyperlinks
1 162687 0 48 21 20465 23975 39
2 201906 1 58 20 33629 85634 46
3 7215 0 0 0 1423 1929 0
4 146367 0 67 27 25629 36294 54
5 257045 0 83 31 54002 72255 93
6 524450 1 136 36 151036 189748 198
7 188294 1 65 23 33287 61834 42
8 195674 0 86 30 31172 68167 59
9 177020 0 62 30 28113 38462 49
10 325899 1 71 27 57803 101219 83
11 121844 2 50 24 49830 43270 49
12 203938 0 88 30 52143 76183 83
13 113213 0 61 22 21055 31476 39
14 220751 4 79 28 47007 62157 93
15 172905 4 56 18 28735 46261 31
16 156326 3 54 22 59147 50063 29
17 145178 0 81 37 78950 64483 104
18 89171 5 13 15 13497 2341 2
19 172624 0 74 34 46154 48149 46
20 39790 0 18 18 53249 12743 27
21 87927 0 31 15 10726 18743 16
22 241285 0 99 30 83700 97057 108
23 195820 1 38 25 40400 17675 36
24 146946 1 59 34 33797 33106 33
25 159763 1 54 21 36205 53311 46
26 207078 0 63 21 30165 42754 65
27 212394 0 66 25 58534 59056 80
28 201536 0 90 31 44663 101621 81
29 394662 0 72 31 92556 118120 69
30 217892 0 61 20 40078 79572 69
31 182286 0 61 28 34711 42744 37
32 181740 2 61 22 31076 65931 45
33 137978 4 53 17 74608 38575 62
34 255929 0 118 25 58092 28795 33
35 236489 1 73 25 42009 94440 77
36 0 0 0 0 0 0 0
37 230761 0 54 31 36022 38229 34
38 132807 3 54 14 23333 31972 44
39 157118 9 46 35 53349 40071 43
40 253254 0 83 34 92596 132480 117
41 269329 2 106 22 49598 62797 125
42 161273 0 44 34 44093 40429 49
43 107181 2 27 23 84205 45545 76
44 195891 1 64 24 63369 57568 81
45 139667 2 71 26 60132 39019 111
46 171101 2 44 23 37403 53866 61
47 81407 1 23 35 24460 38345 56
48 247563 0 78 24 46456 50210 54
49 239807 1 60 31 66616 80947 47
50 172743 8 73 30 41554 43461 55
51 48188 0 12 22 22346 14812 14
52 169355 0 104 23 30874 37819 44
53 315622 0 83 27 68701 102738 115
54 241518 0 57 30 35728 54509 57
55 195583 1 67 33 29010 62956 48
56 159913 8 44 12 23110 55411 40
57 220241 0 53 26 38844 50611 51
58 101694 1 26 26 27084 26692 32
59 157258 0 67 23 35139 60056 36
60 202536 10 36 38 57476 25155 47
61 173505 6 56 32 33277 42840 51
62 150518 0 52 21 31141 39358 37
63 141491 11 54 22 61281 47241 52
64 125612 3 57 26 25820 49611 42
65 166049 0 27 28 23284 41833 11
66 124197 0 58 33 35378 48930 47
67 195043 8 76 36 74990 110600 59
68 138708 2 93 25 29653 52235 82
69 116552 0 59 25 64622 53986 49
70 31970 0 5 21 4157 4105 6
71 258158 3 57 19 29245 59331 83
72 151184 1 42 12 50008 47796 56
73 135926 2 88 30 52338 38302 114
74 119629 1 53 21 13310 14063 46
75 171518 0 81 39 92901 54414 46
76 108949 2 35 32 10956 9903 2
77 183471 1 102 28 34241 53987 51
78 159966 0 71 29 75043 88937 96
79 93786 0 28 21 21152 21928 20
80 84971 0 34 31 42249 29487 57
81 88882 0 54 26 42005 35334 49
82 304603 0 49 29 41152 57596 51
83 75101 1 30 23 14399 29750 40
84 145043 0 57 25 28263 41029 40
85 95827 0 54 22 17215 12416 36
86 173924 0 38 26 48140 51158 64
87 241957 0 63 33 62897 79935 117
88 115367 0 58 24 22883 26552 40
89 118408 7 46 24 41622 25807 46
90 164078 0 46 21 40715 50620 61
91 158931 5 51 28 65897 61467 59
92 184139 1 87 28 76542 65292 94
93 152856 0 39 25 37477 55516 36
94 144014 0 28 15 53216 42006 51
95 62535 0 26 13 40911 26273 39
96 245196 0 52 36 57021 90248 62
97 199841 0 96 27 73116 61476 79
98 19349 0 13 1 3895 9604 14
99 247280 3 43 24 46609 45108 45
100 159408 0 42 31 29351 47232 43
101 72128 0 30 4 2325 3439 8
102 104253 0 59 21 31747 30553 41
103 151090 0 73 27 32665 24751 25
104 137382 1 39 23 19249 34458 22
105 87448 1 36 12 15292 24649 18
106 27676 0 2 16 5842 2342 3
107 165507 0 102 29 33994 52739 54
108 132148 1 30 26 13018 6245 6
109 0 0 0 0 0 0 0
110 95778 0 46 25 98177 35381 50
111 109001 0 25 21 37941 19595 33
112 158833 0 59 24 31032 50848 54
113 147690 1 60 21 32683 39443 63
114 89887 0 36 21 34545 27023 56
115 3616 0 0 0 0 0 0
116 0 0 0 0 0 0 0
117 199005 0 45 23 27525 61022 49
118 160930 0 79 33 66856 63528 90
119 177948 2 30 32 28549 34835 51
120 136061 0 43 23 38610 37172 29
121 43410 0 7 1 2781 13 1
122 184277 1 80 29 41211 62548 68
123 108858 0 32 20 22698 31334 29
124 141744 8 81 33 41194 20839 27
125 60493 3 3 12 32689 5084 4
126 19764 1 10 2 5752 9927 10
127 177559 3 47 21 26757 53229 47
128 140281 0 35 28 22527 29877 44
129 164249 0 54 35 44810 37310 53
130 11796 0 1 2 0 0 0
131 10674 0 0 0 0 0 0
132 151322 0 46 18 100674 50067 40
133 6836 0 0 1 0 0 0
134 174712 6 51 21 57786 47708 57
135 5118 0 5 0 0 0 0
136 40248 1 8 4 5444 6012 6
137 0 0 0 0 0 0 0
138 127628 0 38 29 28470 27749 24
139 88837 0 21 26 61849 47555 34
140 7131 1 0 0 0 0 0
141 9056 0 0 4 2179 1336 10
142 87957 1 18 19 8019 11017 16
143 144470 0 53 22 39644 55184 93
144 111408 1 17 22 23494 43485 28
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) compshared blogged reviewedcomp characters
1.537e+04 1.234e+03 7.289e+02 1.278e+03 -9.199e-02
seconds inclhyperlinks
1.628e+00 -2.288e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-103418 -21820 -6511 16828 127631
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.537e+04 8.162e+03 1.883 0.061820 .
compshared 1.234e+03 1.475e+03 0.836 0.404359
blogged 7.289e+02 1.999e+02 3.646 0.000377 ***
reviewedcomp 1.278e+03 4.774e+02 2.678 0.008310 **
characters -9.199e-02 2.182e-01 -0.422 0.674014
seconds 1.628e+00 2.090e-01 7.789 1.5e-12 ***
inclhyperlinks -2.288e+01 2.056e+02 -0.111 0.911545
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 38220 on 137 degrees of freedom
Multiple R-squared: 0.7859, Adjusted R-squared: 0.7765
F-statistic: 83.81 on 6 and 137 DF, p-value: < 2.2e-16
> 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,] 0.3029745 6.059490e-01 6.970255e-01
[2,] 0.4199218 8.398436e-01 5.800782e-01
[3,] 0.4250234 8.500468e-01 5.749766e-01
[4,] 0.3048920 6.097839e-01 6.951080e-01
[5,] 0.2043245 4.086490e-01 7.956755e-01
[6,] 0.3557246 7.114491e-01 6.442754e-01
[7,] 0.2687489 5.374979e-01 7.312511e-01
[8,] 0.5710463 8.579075e-01 4.289537e-01
[9,] 0.5167519 9.664963e-01 4.832481e-01
[10,] 0.4550763 9.101526e-01 5.449237e-01
[11,] 0.3747470 7.494941e-01 6.252530e-01
[12,] 0.3004026 6.008053e-01 6.995974e-01
[13,] 0.2584315 5.168630e-01 7.415685e-01
[14,] 0.6868259 6.263482e-01 3.131741e-01
[15,] 0.6198839 7.602322e-01 3.801161e-01
[16,] 0.5547690 8.904620e-01 4.452310e-01
[17,] 0.6129857 7.740286e-01 3.870143e-01
[18,] 0.5672852 8.654296e-01 4.327148e-01
[19,] 0.7554412 4.891175e-01 2.445588e-01
[20,] 0.9303215 1.393571e-01 6.967854e-02
[21,] 0.9091621 1.816758e-01 9.083789e-02
[22,] 0.8944087 2.111826e-01 1.055913e-01
[23,] 0.8703364 2.593272e-01 1.296636e-01
[24,] 0.8374934 3.250132e-01 1.625066e-01
[25,] 0.9422555 1.154890e-01 5.774448e-02
[26,] 0.9246383 1.507234e-01 7.536172e-02
[27,] 0.9065355 1.869290e-01 9.346448e-02
[28,] 0.9534642 9.307159e-02 4.653580e-02
[29,] 0.9387431 1.225138e-01 6.125691e-02
[30,] 0.9228557 1.542887e-01 7.714435e-02
[31,] 0.9604762 7.904751e-02 3.952376e-02
[32,] 0.9669172 6.616565e-02 3.308283e-02
[33,] 0.9570191 8.596182e-02 4.298091e-02
[34,] 0.9506820 9.863601e-02 4.931801e-02
[35,] 0.9378759 1.242482e-01 6.212411e-02
[36,] 0.9239660 1.520680e-01 7.603400e-02
[37,] 0.9061487 1.877026e-01 9.385130e-02
[38,] 0.9153649 1.692702e-01 8.463512e-02
[39,] 0.9464734 1.070532e-01 5.352662e-02
[40,] 0.9344173 1.311653e-01 6.558265e-02
[41,] 0.9188661 1.622678e-01 8.113392e-02
[42,] 0.9071653 1.856693e-01 9.283467e-02
[43,] 0.9101898 1.796204e-01 8.981021e-02
[44,] 0.9303824 1.392353e-01 6.961764e-02
[45,] 0.9601755 7.964906e-02 3.982453e-02
[46,] 0.9482193 1.035614e-01 5.178068e-02
[47,] 0.9338013 1.323975e-01 6.619873e-02
[48,] 0.9539648 9.207042e-02 4.603521e-02
[49,] 0.9419568 1.160864e-01 5.804321e-02
[50,] 0.9384158 1.231684e-01 6.158420e-02
[51,] 0.9560950 8.780991e-02 4.390496e-02
[52,] 0.9432812 1.134375e-01 5.671876e-02
[53,] 0.9303578 1.392844e-01 6.964220e-02
[54,] 0.9258875 1.482250e-01 7.411252e-02
[55,] 0.9301806 1.396387e-01 6.981937e-02
[56,] 0.9235486 1.529029e-01 7.645143e-02
[57,] 0.9366161 1.267677e-01 6.338386e-02
[58,] 0.9918350 1.632998e-02 8.164988e-03
[59,] 0.9945736 1.085285e-02 5.426427e-03
[60,] 0.9966194 6.761197e-03 3.380598e-03
[61,] 0.9958574 8.285297e-03 4.142649e-03
[62,] 0.9991677 1.664622e-03 8.323111e-04
[63,] 0.9989835 2.032954e-03 1.016477e-03
[64,] 0.9988524 2.295220e-03 1.147610e-03
[65,] 0.9985403 2.919497e-03 1.459749e-03
[66,] 0.9983332 3.333681e-03 1.666840e-03
[67,] 0.9976596 4.680851e-03 2.340425e-03
[68,] 0.9969232 6.153695e-03 3.076848e-03
[69,] 0.9992067 1.586590e-03 7.932952e-04
[70,] 0.9987878 2.424414e-03 1.212207e-03
[71,] 0.9988544 2.291272e-03 1.145636e-03
[72,] 0.9992283 1.543354e-03 7.716770e-04
[73,] 0.9999992 1.684354e-06 8.421769e-07
[74,] 0.9999996 8.663388e-07 4.331694e-07
[75,] 0.9999992 1.694982e-06 8.474908e-07
[76,] 0.9999984 3.280979e-06 1.640490e-06
[77,] 0.9999975 5.067469e-06 2.533734e-06
[78,] 0.9999966 6.864361e-06 3.432180e-06
[79,] 0.9999938 1.234159e-05 6.170797e-06
[80,] 0.9999905 1.901503e-05 9.507513e-06
[81,] 0.9999858 2.841409e-05 1.420704e-05
[82,] 0.9999894 2.110670e-05 1.055335e-05
[83,] 0.9999829 3.412314e-05 1.706157e-05
[84,] 0.9999710 5.792617e-05 2.896309e-05
[85,] 0.9999747 5.066096e-05 2.533048e-05
[86,] 0.9999629 7.427362e-05 3.713681e-05
[87,] 0.9999332 1.336252e-04 6.681260e-05
[88,] 0.9999013 1.973552e-04 9.867759e-05
[89,] 0.9998441 3.118444e-04 1.559222e-04
[90,] 0.9999995 1.040977e-06 5.204887e-07
[91,] 0.9999989 2.220751e-06 1.110375e-06
[92,] 0.9999994 1.295960e-06 6.479801e-07
[93,] 0.9999988 2.321013e-06 1.160506e-06
[94,] 0.9999984 3.203038e-06 1.601519e-06
[95,] 0.9999966 6.853474e-06 3.426737e-06
[96,] 0.9999927 1.456264e-05 7.281321e-06
[97,] 0.9999921 1.585517e-05 7.927583e-06
[98,] 0.9999883 2.332843e-05 1.166421e-05
[99,] 0.9999939 1.229390e-05 6.146950e-06
[100,] 0.9999878 2.436696e-05 1.218348e-05
[101,] 0.9999805 3.909718e-05 1.954859e-05
[102,] 0.9999737 5.256722e-05 2.628361e-05
[103,] 0.9999435 1.130527e-04 5.652634e-05
[104,] 0.9999038 1.924030e-04 9.620149e-05
[105,] 0.9998161 3.677512e-04 1.838756e-04
[106,] 0.9996365 7.269629e-04 3.634815e-04
[107,] 0.9993326 1.334864e-03 6.674320e-04
[108,] 0.9995353 9.294859e-04 4.647430e-04
[109,] 0.9997528 4.944749e-04 2.472374e-04
[110,] 0.9998383 3.234685e-04 1.617342e-04
[111,] 0.9996729 6.541917e-04 3.270959e-04
[112,] 0.9997150 5.699797e-04 2.849899e-04
[113,] 0.9994398 1.120322e-03 5.601608e-04
[114,] 0.9987229 2.554119e-03 1.277060e-03
[115,] 0.9999895 2.090337e-05 1.045168e-05
[116,] 0.9999804 3.923237e-05 1.961618e-05
[117,] 0.9999763 4.734410e-05 2.367205e-05
[118,] 0.9999150 1.700558e-04 8.502789e-05
[119,] 0.9999294 1.411130e-04 7.055649e-05
[120,] 0.9997220 5.559427e-04 2.779714e-04
[121,] 0.9989464 2.107185e-03 1.053593e-03
[122,] 0.9965346 6.930785e-03 3.465392e-03
[123,] 0.9999549 9.015088e-05 4.507544e-05
[124,] 0.9996563 6.874137e-04 3.437069e-04
[125,] 0.9979430 4.113958e-03 2.056979e-03
> postscript(file="/var/wessaorg/rcomp/tmp/1xwky1324652057.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/28xlf1324652057.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/387mc1324652057.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4cpmx1324652057.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5g3rc1324652057.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 144
Frequency = 1
1 2 3 4 5
49226.58667 -17803.58282 -11164.05784 -7849.90089 31012.19297
6 7 8 9 10
72217.88921 -1731.89299 -27490.27358 19197.05294 65463.34078
11 12 13 14 15
-27857.34423 -31255.22296 -23158.63032 12330.35837 16812.32795
16 17 18 19 20
-5626.57548 -71868.87230 36456.84009 -13238.55862 -26941.14111
21 22 23 24 25
1624.23926 -32432.55267 95322.51207 -6161.66885 -5453.07037
26 27 28 29 30
53600.22954 28030.14593 -78538.50329 104980.31746 8217.16673
31 32 33 34 35
21110.59298 -12129.64277 2792.09299 81807.82319 -13402.09572
36 37 38 39 40
-15369.70386 78254.64190 7581.49017 -6971.60414 -70556.32092
41 42 43 44 45
51294.08909 9724.66537 -24400.16222 15919.93322 -18612.09789
46 47 48 49 50
8933.53341 -55599.40516 68425.20265 15263.88978 -9730.70600
51 52 53 54 55
-25792.02895 -8946.92818 46934.72070 62099.96140 -10768.22306
56 57 58 59 60
96.99299 55347.57443 -7330.40288 -30064.63931 65419.88524
61 62 63 64 65
3491.08394 10035.94885 -25013.45660 -45673.89165 29493.85930
66 67 68 69 70
-50964.20547 -103417.96242 -59310.89524 -54605.03953 -20055.26160
71 72 73 74 75
81251.18016 16697.86039 -39339.80915 16928.33048 -31737.86626
76 77 78 79 80
9621.01300 -26849.37231 -79915.50839 -2135.14546 -37626.57131
81 82 83 84 85
-51625.54369 127631.11174 -38965.75342 -7113.94566 -4835.88321
86 87 88 89 90
20226.52493 16810.60658 -13167.88893 -6940.74388 11065.67184
91 92 93 94 95
-28230.26609 -28775.95635 -9007.95972 26737.26324 -26521.38202
96 97 98 99 100
5644.62116 -11568.42591 -21731.08954 98067.59787 584.72886
101 102 103 104 105
24575.25575 -26850.37673 11274.45154 9125.04562 -9046.18846
106 107 108 109 110
-12813.71172 -42780.73543 51605.18409 -15369.70386 -32505.82629
111 112 113 114 115
20906.48366 -8912.83517 741.37351 -18104.04079 -11753.70386
116 117 118 119 120
-15369.70386 25743.03393 -49423.34440 44416.80417 3644.99088
121 122 123 124 125
21916.92482 -24192.08308 -3664.45056 -14243.38889 18716.50952
126 127 128 129 130
-22087.85928 14265.47799 18043.30752 9367.94530 -6859.57991
131 132 133 134 135
-4695.70386 8080.76808 -9812.18143 16873.18146 -13896.30837
136 137 138 139 140
3550.49125 -15369.70386 5478.42167 -46028.64525 -9472.25608
141 142 143 144
-13173.26939 17111.04668 -21719.47675 -13701.67877
> postscript(file="/var/wessaorg/rcomp/tmp/6q7vx1324652057.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 = 144
Frequency = 1
lag(myerror, k = 1) myerror
0 49226.58667 NA
1 -17803.58282 49226.58667
2 -11164.05784 -17803.58282
3 -7849.90089 -11164.05784
4 31012.19297 -7849.90089
5 72217.88921 31012.19297
6 -1731.89299 72217.88921
7 -27490.27358 -1731.89299
8 19197.05294 -27490.27358
9 65463.34078 19197.05294
10 -27857.34423 65463.34078
11 -31255.22296 -27857.34423
12 -23158.63032 -31255.22296
13 12330.35837 -23158.63032
14 16812.32795 12330.35837
15 -5626.57548 16812.32795
16 -71868.87230 -5626.57548
17 36456.84009 -71868.87230
18 -13238.55862 36456.84009
19 -26941.14111 -13238.55862
20 1624.23926 -26941.14111
21 -32432.55267 1624.23926
22 95322.51207 -32432.55267
23 -6161.66885 95322.51207
24 -5453.07037 -6161.66885
25 53600.22954 -5453.07037
26 28030.14593 53600.22954
27 -78538.50329 28030.14593
28 104980.31746 -78538.50329
29 8217.16673 104980.31746
30 21110.59298 8217.16673
31 -12129.64277 21110.59298
32 2792.09299 -12129.64277
33 81807.82319 2792.09299
34 -13402.09572 81807.82319
35 -15369.70386 -13402.09572
36 78254.64190 -15369.70386
37 7581.49017 78254.64190
38 -6971.60414 7581.49017
39 -70556.32092 -6971.60414
40 51294.08909 -70556.32092
41 9724.66537 51294.08909
42 -24400.16222 9724.66537
43 15919.93322 -24400.16222
44 -18612.09789 15919.93322
45 8933.53341 -18612.09789
46 -55599.40516 8933.53341
47 68425.20265 -55599.40516
48 15263.88978 68425.20265
49 -9730.70600 15263.88978
50 -25792.02895 -9730.70600
51 -8946.92818 -25792.02895
52 46934.72070 -8946.92818
53 62099.96140 46934.72070
54 -10768.22306 62099.96140
55 96.99299 -10768.22306
56 55347.57443 96.99299
57 -7330.40288 55347.57443
58 -30064.63931 -7330.40288
59 65419.88524 -30064.63931
60 3491.08394 65419.88524
61 10035.94885 3491.08394
62 -25013.45660 10035.94885
63 -45673.89165 -25013.45660
64 29493.85930 -45673.89165
65 -50964.20547 29493.85930
66 -103417.96242 -50964.20547
67 -59310.89524 -103417.96242
68 -54605.03953 -59310.89524
69 -20055.26160 -54605.03953
70 81251.18016 -20055.26160
71 16697.86039 81251.18016
72 -39339.80915 16697.86039
73 16928.33048 -39339.80915
74 -31737.86626 16928.33048
75 9621.01300 -31737.86626
76 -26849.37231 9621.01300
77 -79915.50839 -26849.37231
78 -2135.14546 -79915.50839
79 -37626.57131 -2135.14546
80 -51625.54369 -37626.57131
81 127631.11174 -51625.54369
82 -38965.75342 127631.11174
83 -7113.94566 -38965.75342
84 -4835.88321 -7113.94566
85 20226.52493 -4835.88321
86 16810.60658 20226.52493
87 -13167.88893 16810.60658
88 -6940.74388 -13167.88893
89 11065.67184 -6940.74388
90 -28230.26609 11065.67184
91 -28775.95635 -28230.26609
92 -9007.95972 -28775.95635
93 26737.26324 -9007.95972
94 -26521.38202 26737.26324
95 5644.62116 -26521.38202
96 -11568.42591 5644.62116
97 -21731.08954 -11568.42591
98 98067.59787 -21731.08954
99 584.72886 98067.59787
100 24575.25575 584.72886
101 -26850.37673 24575.25575
102 11274.45154 -26850.37673
103 9125.04562 11274.45154
104 -9046.18846 9125.04562
105 -12813.71172 -9046.18846
106 -42780.73543 -12813.71172
107 51605.18409 -42780.73543
108 -15369.70386 51605.18409
109 -32505.82629 -15369.70386
110 20906.48366 -32505.82629
111 -8912.83517 20906.48366
112 741.37351 -8912.83517
113 -18104.04079 741.37351
114 -11753.70386 -18104.04079
115 -15369.70386 -11753.70386
116 25743.03393 -15369.70386
117 -49423.34440 25743.03393
118 44416.80417 -49423.34440
119 3644.99088 44416.80417
120 21916.92482 3644.99088
121 -24192.08308 21916.92482
122 -3664.45056 -24192.08308
123 -14243.38889 -3664.45056
124 18716.50952 -14243.38889
125 -22087.85928 18716.50952
126 14265.47799 -22087.85928
127 18043.30752 14265.47799
128 9367.94530 18043.30752
129 -6859.57991 9367.94530
130 -4695.70386 -6859.57991
131 8080.76808 -4695.70386
132 -9812.18143 8080.76808
133 16873.18146 -9812.18143
134 -13896.30837 16873.18146
135 3550.49125 -13896.30837
136 -15369.70386 3550.49125
137 5478.42167 -15369.70386
138 -46028.64525 5478.42167
139 -9472.25608 -46028.64525
140 -13173.26939 -9472.25608
141 17111.04668 -13173.26939
142 -21719.47675 17111.04668
143 -13701.67877 -21719.47675
144 NA -13701.67877
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -17803.58282 49226.58667
[2,] -11164.05784 -17803.58282
[3,] -7849.90089 -11164.05784
[4,] 31012.19297 -7849.90089
[5,] 72217.88921 31012.19297
[6,] -1731.89299 72217.88921
[7,] -27490.27358 -1731.89299
[8,] 19197.05294 -27490.27358
[9,] 65463.34078 19197.05294
[10,] -27857.34423 65463.34078
[11,] -31255.22296 -27857.34423
[12,] -23158.63032 -31255.22296
[13,] 12330.35837 -23158.63032
[14,] 16812.32795 12330.35837
[15,] -5626.57548 16812.32795
[16,] -71868.87230 -5626.57548
[17,] 36456.84009 -71868.87230
[18,] -13238.55862 36456.84009
[19,] -26941.14111 -13238.55862
[20,] 1624.23926 -26941.14111
[21,] -32432.55267 1624.23926
[22,] 95322.51207 -32432.55267
[23,] -6161.66885 95322.51207
[24,] -5453.07037 -6161.66885
[25,] 53600.22954 -5453.07037
[26,] 28030.14593 53600.22954
[27,] -78538.50329 28030.14593
[28,] 104980.31746 -78538.50329
[29,] 8217.16673 104980.31746
[30,] 21110.59298 8217.16673
[31,] -12129.64277 21110.59298
[32,] 2792.09299 -12129.64277
[33,] 81807.82319 2792.09299
[34,] -13402.09572 81807.82319
[35,] -15369.70386 -13402.09572
[36,] 78254.64190 -15369.70386
[37,] 7581.49017 78254.64190
[38,] -6971.60414 7581.49017
[39,] -70556.32092 -6971.60414
[40,] 51294.08909 -70556.32092
[41,] 9724.66537 51294.08909
[42,] -24400.16222 9724.66537
[43,] 15919.93322 -24400.16222
[44,] -18612.09789 15919.93322
[45,] 8933.53341 -18612.09789
[46,] -55599.40516 8933.53341
[47,] 68425.20265 -55599.40516
[48,] 15263.88978 68425.20265
[49,] -9730.70600 15263.88978
[50,] -25792.02895 -9730.70600
[51,] -8946.92818 -25792.02895
[52,] 46934.72070 -8946.92818
[53,] 62099.96140 46934.72070
[54,] -10768.22306 62099.96140
[55,] 96.99299 -10768.22306
[56,] 55347.57443 96.99299
[57,] -7330.40288 55347.57443
[58,] -30064.63931 -7330.40288
[59,] 65419.88524 -30064.63931
[60,] 3491.08394 65419.88524
[61,] 10035.94885 3491.08394
[62,] -25013.45660 10035.94885
[63,] -45673.89165 -25013.45660
[64,] 29493.85930 -45673.89165
[65,] -50964.20547 29493.85930
[66,] -103417.96242 -50964.20547
[67,] -59310.89524 -103417.96242
[68,] -54605.03953 -59310.89524
[69,] -20055.26160 -54605.03953
[70,] 81251.18016 -20055.26160
[71,] 16697.86039 81251.18016
[72,] -39339.80915 16697.86039
[73,] 16928.33048 -39339.80915
[74,] -31737.86626 16928.33048
[75,] 9621.01300 -31737.86626
[76,] -26849.37231 9621.01300
[77,] -79915.50839 -26849.37231
[78,] -2135.14546 -79915.50839
[79,] -37626.57131 -2135.14546
[80,] -51625.54369 -37626.57131
[81,] 127631.11174 -51625.54369
[82,] -38965.75342 127631.11174
[83,] -7113.94566 -38965.75342
[84,] -4835.88321 -7113.94566
[85,] 20226.52493 -4835.88321
[86,] 16810.60658 20226.52493
[87,] -13167.88893 16810.60658
[88,] -6940.74388 -13167.88893
[89,] 11065.67184 -6940.74388
[90,] -28230.26609 11065.67184
[91,] -28775.95635 -28230.26609
[92,] -9007.95972 -28775.95635
[93,] 26737.26324 -9007.95972
[94,] -26521.38202 26737.26324
[95,] 5644.62116 -26521.38202
[96,] -11568.42591 5644.62116
[97,] -21731.08954 -11568.42591
[98,] 98067.59787 -21731.08954
[99,] 584.72886 98067.59787
[100,] 24575.25575 584.72886
[101,] -26850.37673 24575.25575
[102,] 11274.45154 -26850.37673
[103,] 9125.04562 11274.45154
[104,] -9046.18846 9125.04562
[105,] -12813.71172 -9046.18846
[106,] -42780.73543 -12813.71172
[107,] 51605.18409 -42780.73543
[108,] -15369.70386 51605.18409
[109,] -32505.82629 -15369.70386
[110,] 20906.48366 -32505.82629
[111,] -8912.83517 20906.48366
[112,] 741.37351 -8912.83517
[113,] -18104.04079 741.37351
[114,] -11753.70386 -18104.04079
[115,] -15369.70386 -11753.70386
[116,] 25743.03393 -15369.70386
[117,] -49423.34440 25743.03393
[118,] 44416.80417 -49423.34440
[119,] 3644.99088 44416.80417
[120,] 21916.92482 3644.99088
[121,] -24192.08308 21916.92482
[122,] -3664.45056 -24192.08308
[123,] -14243.38889 -3664.45056
[124,] 18716.50952 -14243.38889
[125,] -22087.85928 18716.50952
[126,] 14265.47799 -22087.85928
[127,] 18043.30752 14265.47799
[128,] 9367.94530 18043.30752
[129,] -6859.57991 9367.94530
[130,] -4695.70386 -6859.57991
[131,] 8080.76808 -4695.70386
[132,] -9812.18143 8080.76808
[133,] 16873.18146 -9812.18143
[134,] -13896.30837 16873.18146
[135,] 3550.49125 -13896.30837
[136,] -15369.70386 3550.49125
[137,] 5478.42167 -15369.70386
[138,] -46028.64525 5478.42167
[139,] -9472.25608 -46028.64525
[140,] -13173.26939 -9472.25608
[141,] 17111.04668 -13173.26939
[142,] -21719.47675 17111.04668
[143,] -13701.67877 -21719.47675
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -17803.58282 49226.58667
2 -11164.05784 -17803.58282
3 -7849.90089 -11164.05784
4 31012.19297 -7849.90089
5 72217.88921 31012.19297
6 -1731.89299 72217.88921
7 -27490.27358 -1731.89299
8 19197.05294 -27490.27358
9 65463.34078 19197.05294
10 -27857.34423 65463.34078
11 -31255.22296 -27857.34423
12 -23158.63032 -31255.22296
13 12330.35837 -23158.63032
14 16812.32795 12330.35837
15 -5626.57548 16812.32795
16 -71868.87230 -5626.57548
17 36456.84009 -71868.87230
18 -13238.55862 36456.84009
19 -26941.14111 -13238.55862
20 1624.23926 -26941.14111
21 -32432.55267 1624.23926
22 95322.51207 -32432.55267
23 -6161.66885 95322.51207
24 -5453.07037 -6161.66885
25 53600.22954 -5453.07037
26 28030.14593 53600.22954
27 -78538.50329 28030.14593
28 104980.31746 -78538.50329
29 8217.16673 104980.31746
30 21110.59298 8217.16673
31 -12129.64277 21110.59298
32 2792.09299 -12129.64277
33 81807.82319 2792.09299
34 -13402.09572 81807.82319
35 -15369.70386 -13402.09572
36 78254.64190 -15369.70386
37 7581.49017 78254.64190
38 -6971.60414 7581.49017
39 -70556.32092 -6971.60414
40 51294.08909 -70556.32092
41 9724.66537 51294.08909
42 -24400.16222 9724.66537
43 15919.93322 -24400.16222
44 -18612.09789 15919.93322
45 8933.53341 -18612.09789
46 -55599.40516 8933.53341
47 68425.20265 -55599.40516
48 15263.88978 68425.20265
49 -9730.70600 15263.88978
50 -25792.02895 -9730.70600
51 -8946.92818 -25792.02895
52 46934.72070 -8946.92818
53 62099.96140 46934.72070
54 -10768.22306 62099.96140
55 96.99299 -10768.22306
56 55347.57443 96.99299
57 -7330.40288 55347.57443
58 -30064.63931 -7330.40288
59 65419.88524 -30064.63931
60 3491.08394 65419.88524
61 10035.94885 3491.08394
62 -25013.45660 10035.94885
63 -45673.89165 -25013.45660
64 29493.85930 -45673.89165
65 -50964.20547 29493.85930
66 -103417.96242 -50964.20547
67 -59310.89524 -103417.96242
68 -54605.03953 -59310.89524
69 -20055.26160 -54605.03953
70 81251.18016 -20055.26160
71 16697.86039 81251.18016
72 -39339.80915 16697.86039
73 16928.33048 -39339.80915
74 -31737.86626 16928.33048
75 9621.01300 -31737.86626
76 -26849.37231 9621.01300
77 -79915.50839 -26849.37231
78 -2135.14546 -79915.50839
79 -37626.57131 -2135.14546
80 -51625.54369 -37626.57131
81 127631.11174 -51625.54369
82 -38965.75342 127631.11174
83 -7113.94566 -38965.75342
84 -4835.88321 -7113.94566
85 20226.52493 -4835.88321
86 16810.60658 20226.52493
87 -13167.88893 16810.60658
88 -6940.74388 -13167.88893
89 11065.67184 -6940.74388
90 -28230.26609 11065.67184
91 -28775.95635 -28230.26609
92 -9007.95972 -28775.95635
93 26737.26324 -9007.95972
94 -26521.38202 26737.26324
95 5644.62116 -26521.38202
96 -11568.42591 5644.62116
97 -21731.08954 -11568.42591
98 98067.59787 -21731.08954
99 584.72886 98067.59787
100 24575.25575 584.72886
101 -26850.37673 24575.25575
102 11274.45154 -26850.37673
103 9125.04562 11274.45154
104 -9046.18846 9125.04562
105 -12813.71172 -9046.18846
106 -42780.73543 -12813.71172
107 51605.18409 -42780.73543
108 -15369.70386 51605.18409
109 -32505.82629 -15369.70386
110 20906.48366 -32505.82629
111 -8912.83517 20906.48366
112 741.37351 -8912.83517
113 -18104.04079 741.37351
114 -11753.70386 -18104.04079
115 -15369.70386 -11753.70386
116 25743.03393 -15369.70386
117 -49423.34440 25743.03393
118 44416.80417 -49423.34440
119 3644.99088 44416.80417
120 21916.92482 3644.99088
121 -24192.08308 21916.92482
122 -3664.45056 -24192.08308
123 -14243.38889 -3664.45056
124 18716.50952 -14243.38889
125 -22087.85928 18716.50952
126 14265.47799 -22087.85928
127 18043.30752 14265.47799
128 9367.94530 18043.30752
129 -6859.57991 9367.94530
130 -4695.70386 -6859.57991
131 8080.76808 -4695.70386
132 -9812.18143 8080.76808
133 16873.18146 -9812.18143
134 -13896.30837 16873.18146
135 3550.49125 -13896.30837
136 -15369.70386 3550.49125
137 5478.42167 -15369.70386
138 -46028.64525 5478.42167
139 -9472.25608 -46028.64525
140 -13173.26939 -9472.25608
141 17111.04668 -13173.26939
142 -21719.47675 17111.04668
143 -13701.67877 -21719.47675
> 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/7fhi51324652057.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/8uwsr1324652057.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/9tcq11324652057.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/10abhi1324652057.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11i3vv1324652057.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12shs91324652057.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13ljlb1324652057.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14rg871324652057.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/15yrcs1324652057.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/16n5kg1324652057.tab")
+ }
>
> try(system("convert tmp/1xwky1324652057.ps tmp/1xwky1324652057.png",intern=TRUE))
character(0)
> try(system("convert tmp/28xlf1324652057.ps tmp/28xlf1324652057.png",intern=TRUE))
character(0)
> try(system("convert tmp/387mc1324652057.ps tmp/387mc1324652057.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cpmx1324652057.ps tmp/4cpmx1324652057.png",intern=TRUE))
character(0)
> try(system("convert tmp/5g3rc1324652057.ps tmp/5g3rc1324652057.png",intern=TRUE))
character(0)
> try(system("convert tmp/6q7vx1324652057.ps tmp/6q7vx1324652057.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fhi51324652057.ps tmp/7fhi51324652057.png",intern=TRUE))
character(0)
> try(system("convert tmp/8uwsr1324652057.ps tmp/8uwsr1324652057.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tcq11324652057.ps tmp/9tcq11324652057.png",intern=TRUE))
character(0)
> try(system("convert tmp/10abhi1324652057.ps tmp/10abhi1324652057.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.535 0.555 5.099