R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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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(56
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+ ,0)
+ ,dim=c(6
+ ,149)
+ ,dimnames=list(c('Long_feedback_messages'
+ ,'Page_views'
+ ,'Blogs'
+ ,'Peer_reviews'
+ ,'Compendium_hours'
+ ,'RFC_hours')
+ ,1:149))
> y <- array(NA,dim=c(6,149),dimnames=list(c('Long_feedback_messages','Page_views','Blogs','Peer_reviews','Compendium_hours','RFC_hours'),1:149))
> 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
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Long_feedback_messages Page_views Blogs Peer_reviews Compendium_hours
1 56 1901 61 17 21
2 73 2508 74 19 15
3 62 2114 57 18 17
4 42 1331 50 15 20
5 59 1399 48 15 12
6 27 7333 2 12 4
7 78 1507 61 14 12
8 56 1107 36 15 9
9 59 2051 46 13 14
10 51 1138 29 20 11
11 47 1290 30 17 11
12 35 819 49 10 14
13 47 1178 54 16 9
14 47 1451 12 12 7
15 55 1502 14 13 4
16 54 1514 44 15 14
17 60 883 40 15 13
18 55 1405 57 15 11
19 48 927 29 12 9
20 47 1314 28 12 9
21 47 1307 40 15 11
22 52 1352 32 13 8
23 48 1097 19 9 4
24 48 1100 67 12 10
25 27 1316 25 13 10
26 12 1243 54 12 7
27 51 1232 56 12 15
28 58 903 42 16 13
29 60 929 28 15 10
30 46 1049 57 13 10
31 45 1469 35 13 8
32 42 1239 30 12 11
33 41 820 32 15 11
34 47 1462 24 12 10
35 32 1372 28 12 6
36 56 821 10 12 7
37 42 1380 23 8 10
38 41 868 49 15 5
39 47 1228 19 14 5
40 47 707 17 15 5
41 49 1091 33 12 10
42 52 1202 42 12 8
43 42 1106 3 13 2
44 55 1671 37 13 13
45 48 1429 56 12 9
46 48 1579 26 12 7
47 38 1165 30 12 9
48 48 1156 34 13 5
49 50 968 12 9 5
50 39 1374 28 13 10
51 48 934 22 13 7
52 36 774 19 12 5
53 49 1375 35 12 8
54 39 1223 38 12 5
55 41 1111 15 13 7
56 45 804 38 15 10
57 60 962 45 15 10
58 45 613 27 14 9
59 41 1153 35 14 10
60 52 729 23 12 10
61 46 813 51 12 8
62 39 912 23 9 5
63 32 813 33 12 10
64 52 1178 26 14 8
65 54 1199 32 16 6
66 51 1165 35 15 7
67 52 705 18 13 6
68 45 837 56 12 9
69 57 814 18 16 3
70 47 884 39 12 11
71 41 1082 41 12 9
72 27 913 37 10 9
73 43 586 35 12 10
74 31 627 16 10 5
75 32 758 33 12 6
76 41 778 0 12 0
77 40 501 13 13 5
78 46 1009 35 15 10
79 32 547 26 15 7
80 9 848 7 9 6
81 64 849 54 12 8
82 30 480 40 12 10
83 46 719 30 13 7
84 37 847 22 12 6
85 22 634 9 16 5
86 20 714 29 12 6
87 21 871 25 12 4
88 44 815 32 12 7
89 24 811 40 12 5
90 33 776 17 14 3
91 45 642 18 13 0
92 35 562 15 8 5
93 31 626 17 16 5
94 20 528 24 12 8
95 13 636 28 12 5
96 33 935 18 11 5
97 58 473 16 15 6
98 26 566 2 13 0
99 36 929 17 12 6
100 32 656 25 13 4
101 34 765 10 12 8
102 15 835 28 13 5
103 40 479 7 8 3
104 37 567 16 16 3
105 26 558 7 12 2
106 31 582 27 14 8
107 47 607 25 12 3
108 21 705 9 12 2
109 21 433 28 8 3
110 9 507 16 9 2
111 28 488 0 5 1
112 24 394 10 11 2
113 15 504 0 4 1
114 19 368 2 8 2
115 35 386 5 13 7
116 45 580 10 12 1
117 20 510 14 12 3
118 1 565 43 13 6
119 29 451 36 13 4
120 33 495 12 12 2
121 32 412 8 12 2
122 11 596 15 10 3
123 10 446 10 13 4
124 18 338 39 5 5
125 41 418 0 12 0
126 0 349 10 9 3
127 10 335 7 6 0
128 24 308 3 12 2
129 28 228 0 11 1
130 38 455 8 15 0
131 4 428 8 3 3
132 25 244 8 0 4
133 40 242 1 8 0
134 0 352 0 12 0
135 23 269 3 9 1
136 13 213 0 9 0
137 6 242 0 4 0
138 31 291 0 14 2
139 0 135 0 0 1
140 3 210 3 1 3
141 0 231 0 0 0
142 7 225 0 6 0
143 0 340 0 0 0
144 2 44 0 0 0
145 0 126 0 6 0
146 0 141 2 2 0
147 5 25 0 0 0
148 0 104 0 0 0
149 0 11 0 0 0
RFC_hours
1 51
2 45
3 44
4 42
5 38
6 38
7 35
8 34
9 33
10 32
11 32
12 31
13 30
14 30
15 30
16 29
17 29
18 29
19 28
20 27
21 27
22 27
23 26
24 26
25 26
26 26
27 26
28 25
29 25
30 25
31 24
32 24
33 24
34 24
35 24
36 24
37 24
38 23
39 23
40 23
41 23
42 23
43 22
44 22
45 22
46 22
47 22
48 21
49 21
50 21
51 21
52 21
53 21
54 21
55 21
56 21
57 21
58 20
59 20
60 20
61 20
62 20
63 20
64 20
65 19
66 19
67 18
68 17
69 17
70 17
71 17
72 16
73 16
74 15
75 15
76 15
77 15
78 15
79 15
80 15
81 15
82 15
83 15
84 15
85 15
86 14
87 14
88 14
89 14
90 14
91 13
92 13
93 13
94 13
95 13
96 13
97 12
98 12
99 12
100 12
101 12
102 12
103 11
104 11
105 11
106 11
107 11
108 11
109 11
110 10
111 10
112 10
113 9
114 9
115 9
116 9
117 9
118 9
119 9
120 8
121 8
122 8
123 7
124 7
125 7
126 6
127 6
128 5
129 5
130 5
131 5
132 5
133 5
134 5
135 5
136 4
137 4
138 4
139 3
140 3
141 2
142 2
143 2
144 2
145 2
146 1
147 1
148 1
149 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Page_views Blogs Peer_reviews
1.655620 -0.003048 0.061678 1.595503
Compendium_hours RFC_hours
-0.048627 0.986249
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-33.646 -6.113 0.331 6.954 28.051
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.655620 2.649415 0.625 0.533
Page_views -0.003048 0.001864 -1.635 0.104
Blogs 0.061678 0.087419 0.706 0.482
Peer_reviews 1.595503 0.284997 5.598 1.07e-07 ***
Compendium_hours -0.048627 0.448099 -0.109 0.914
RFC_hours 0.986249 0.216226 4.561 1.08e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 10.74 on 143 degrees of freedom
Multiple R-squared: 0.6445, Adjusted R-squared: 0.6321
F-statistic: 51.85 on 5 and 143 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,] 0.161132306 0.322264613 0.838867694
[2,] 0.082939319 0.165878638 0.917060681
[3,] 0.038652592 0.077305185 0.961347408
[4,] 0.325490396 0.650980791 0.674509604
[5,] 0.654723038 0.690553923 0.345276962
[6,] 0.623874202 0.752251595 0.376125798
[7,] 0.540737902 0.918524196 0.459262098
[8,] 0.515135381 0.969729238 0.484864619
[9,] 0.504769781 0.990460438 0.495230219
[10,] 0.438837238 0.877674477 0.561162762
[11,] 0.355028857 0.710057715 0.644971143
[12,] 0.279999350 0.559998701 0.720000650
[13,] 0.225867975 0.451735951 0.774132025
[14,] 0.171125896 0.342251792 0.828874104
[15,] 0.126089636 0.252179272 0.873910364
[16,] 0.138762658 0.277525316 0.861237342
[17,] 0.224812929 0.449625859 0.775187071
[18,] 0.928310997 0.143378006 0.071689003
[19,] 0.918331926 0.163336148 0.081668074
[20,] 0.906870135 0.186259730 0.093129865
[21,] 0.904311102 0.191377796 0.095688898
[22,] 0.877298139 0.245403723 0.122701861
[23,] 0.844214776 0.311570448 0.155785224
[24,] 0.808016837 0.383966326 0.191983163
[25,] 0.792779750 0.414440500 0.207220250
[26,] 0.758589348 0.482821303 0.241410652
[27,] 0.775292539 0.449414921 0.224707461
[28,] 0.781867583 0.436264835 0.218132417
[29,] 0.746042238 0.507915523 0.253957762
[30,] 0.729719499 0.540561002 0.270280501
[31,] 0.683150893 0.633698214 0.316849107
[32,] 0.635415799 0.729168402 0.364584201
[33,] 0.592133466 0.815733067 0.407866534
[34,] 0.564239504 0.871520991 0.435760496
[35,] 0.516052629 0.967894743 0.483947371
[36,] 0.513299363 0.973401274 0.486700637
[37,] 0.464666344 0.929332687 0.535333656
[38,] 0.422329739 0.844659477 0.577670261
[39,] 0.393213387 0.786426775 0.606786613
[40,] 0.347112945 0.694225890 0.652887055
[41,] 0.341106274 0.682212547 0.658893726
[42,] 0.308307590 0.616615181 0.691692410
[43,] 0.266054966 0.532109932 0.733945034
[44,] 0.255091171 0.510182342 0.744908829
[45,] 0.225909981 0.451819963 0.774090019
[46,] 0.197635405 0.395270810 0.802364595
[47,] 0.169058116 0.338116233 0.830941884
[48,] 0.143068244 0.286136487 0.856931756
[49,] 0.148712293 0.297424585 0.851287707
[50,] 0.122703146 0.245406291 0.877296854
[51,] 0.104315651 0.208631302 0.895684349
[52,] 0.093943603 0.187887205 0.906056397
[53,] 0.074730743 0.149461486 0.925269257
[54,] 0.058855428 0.117710855 0.941144572
[55,] 0.067358535 0.134717071 0.932641465
[56,] 0.058123168 0.116246335 0.941876832
[57,] 0.050578543 0.101157086 0.949421457
[58,] 0.041491641 0.082983283 0.958508359
[59,] 0.037299378 0.074598757 0.962700622
[60,] 0.029654093 0.059308186 0.970345907
[61,] 0.030126488 0.060252975 0.969873512
[62,] 0.025568545 0.051137089 0.974431455
[63,] 0.020240827 0.040481654 0.979759173
[64,] 0.021304928 0.042609856 0.978695072
[65,] 0.016644392 0.033288784 0.983355608
[66,] 0.013908636 0.027817273 0.986091364
[67,] 0.012489347 0.024978694 0.987510653
[68,] 0.009409997 0.018819993 0.990590003
[69,] 0.007012641 0.014025282 0.992987359
[70,] 0.005825321 0.011650642 0.994174679
[71,] 0.006850138 0.013700277 0.993149862
[72,] 0.026620212 0.053240424 0.973379788
[73,] 0.128987246 0.257974492 0.871012754
[74,] 0.123664790 0.247329580 0.876335210
[75,] 0.118077190 0.236154380 0.881922810
[76,] 0.098715318 0.197430636 0.901284682
[77,] 0.208702373 0.417404745 0.791297627
[78,] 0.257898273 0.515796546 0.742101727
[79,] 0.287345462 0.574690923 0.712654538
[80,] 0.300663829 0.601327658 0.699336171
[81,] 0.302269178 0.604538357 0.697730822
[82,] 0.270519285 0.541038571 0.729480715
[83,] 0.263184324 0.526368647 0.736815676
[84,] 0.232792227 0.465584454 0.767207773
[85,] 0.223095610 0.446191220 0.776904390
[86,] 0.269889415 0.539778830 0.730110585
[87,] 0.400491337 0.800982675 0.599508663
[88,] 0.362952626 0.725905253 0.637047374
[89,] 0.460206706 0.920413411 0.539793294
[90,] 0.468211715 0.936423430 0.531788285
[91,] 0.460335870 0.920671740 0.539664130
[92,] 0.414905074 0.829810147 0.585094926
[93,] 0.378866926 0.757733853 0.621133074
[94,] 0.415140422 0.830280845 0.584859578
[95,] 0.423333842 0.846667684 0.576666158
[96,] 0.372757304 0.745514608 0.627242696
[97,] 0.342129678 0.684259356 0.657870322
[98,] 0.302925678 0.605851356 0.697074322
[99,] 0.414537373 0.829074746 0.585462627
[100,] 0.393759764 0.787519528 0.606240236
[101,] 0.353995464 0.707990927 0.646004536
[102,] 0.437465206 0.874930413 0.562534794
[103,] 0.388370121 0.776740243 0.611629879
[104,] 0.372378900 0.744757800 0.627621100
[105,] 0.343865619 0.687731238 0.656134381
[106,] 0.378710554 0.757421108 0.621289446
[107,] 0.332464447 0.664928895 0.667535553
[108,] 0.383105397 0.766210795 0.616894603
[109,] 0.362264188 0.724528377 0.637735812
[110,] 0.537803814 0.924392372 0.462196186
[111,] 0.476196185 0.952392370 0.523803815
[112,] 0.432655044 0.865310088 0.567344956
[113,] 0.373687158 0.747374315 0.626312842
[114,] 0.361811478 0.723622955 0.638188522
[115,] 0.443214809 0.886429618 0.556785191
[116,] 0.383482510 0.766965020 0.616517490
[117,] 0.419609997 0.839219995 0.580390003
[118,] 0.750357147 0.499285705 0.249642853
[119,] 0.779044072 0.441911855 0.220955928
[120,] 0.717701229 0.564597541 0.282298771
[121,] 0.658198794 0.683602412 0.341801206
[122,] 0.636209269 0.727581462 0.363790731
[123,] 0.647634331 0.704731337 0.352365669
[124,] 0.577744116 0.844511767 0.422255884
[125,] 0.942985645 0.114028709 0.057014355
[126,] 0.995133220 0.009733559 0.004866780
[127,] 0.997225157 0.005549686 0.002774843
[128,] 0.992398317 0.015203367 0.007601683
[129,] 0.982052616 0.035894768 0.017947384
[130,] 0.995262711 0.009474578 0.004737289
[131,] 0.985413742 0.029172517 0.014586258
[132,] 0.947675834 0.104648331 0.052324166
> postscript(file="/var/wessaorg/rcomp/tmp/125tv1352045343.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/2x3ni1352045343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3hb071352045343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4fucs1352045343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5zk551352045343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 149
Frequency = 1
1 2 3 4 5 6
-20.02475827 0.45831882 -8.01507589 -23.06506167 -2.17845379 -8.85665104
7 8 9 10 11 12
20.90316849 -1.52922711 8.15173404 -11.91075240 -10.72261910 -13.02948689
13 14 15 16 17 18
-9.07353024 0.63383122 6.92454246 2.39228093 6.66705007 2.11234680
19 20 21 22 23 24
1.05787627 2.28539773 -3.16533296 5.51038105 8.70870072 1.26253975
25 26 27 28 29 30
-18.08409842 -33.64565210 5.58647564 6.95414863 11.34651644 -0.88538167
31 32 33 34 35 36
1.64071544 -0.01056134 -7.19755780 5.99059593 -9.72494764 13.75441395
37 38 39 40 41 42
7.18434572 -7.40529220 3.13785494 0.07767643 7.29091568 9.97689136
43 44 45 46 47 48
1.18871763 14.34869577 6.84017818 9.05047956 -2.36087148 6.56122312
49 50 51 52 53 54
15.72712331 -1.16110027 6.72194905 -4.08245397 9.90844965 -0.88576795
55 56 57 58 59 60
0.69320103 -0.70627493 14.34356863 1.92313448 -0.87571989 12.76305314
61 62 63 64 65 66
5.19484476 4.86422117 -7.59769349 10.65833236 10.05026219 8.40572329
67 68 69 70 71 72
13.18078057 6.96698093 14.56687858 10.25602260 4.63892610 -5.45222582
73 74 75 76 77 78
6.53204044 -0.23698090 -3.02859621 7.77598751 3.77749150 7.02110480
79 80 81 82 83 84
-7.97786814 -19.36412894 28.05078588 -6.11319450 9.49068850 2.92114071
85 86 87 88 89 90
-18.35691382 -13.92974799 -12.30174488 10.24169653 -10.36117487 -3.33751553
91 92 93 94 95 96
10.62824029 8.79007849 -7.90222573 -13.10479093 -20.16819471 2.95545653
97 98 99 100 101 102
21.32348010 -6.63030994 5.43821951 -1.58007921 3.46734086 -18.17088707
103 104 105 106 107 108
15.90576197 -0.14513692 -4.28408031 -3.34373837 15.80369200 -8.95937399
109 110 111 112 113 114
-4.52969149 -16.22187720 10.04044672 -4.38724260 -0.32903246 -3.20030803
115 116 117 118 119 120
4.93514084 16.52181384 -8.84100923 -30.91165908 -2.92464178 5.17424923
121 122 123 124 125 126
4.16797439 -13.46330058 -18.36374894 0.33104198 14.56868475 -21.33977545
127 128 129 130 131 132
-6.55678461 -0.88188344 4.60618385 8.37402642 -6.41635702 18.85793755
133 134 135 136 137 138
21.32506016 -24.65998782 2.73712467 -6.31090852 -5.24500100 4.04657811
139 140 141 142 143 144
-4.15425478 -2.60893571 -2.92401968 -5.51532497 -2.59178262 -1.49400436
145 146 147 148 149
-12.81708157 -5.52645679 2.43433201 -2.32487233 -1.62209140
> postscript(file="/var/wessaorg/rcomp/tmp/6ujng1352045343.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 = 149
Frequency = 1
lag(myerror, k = 1) myerror
0 -20.02475827 NA
1 0.45831882 -20.02475827
2 -8.01507589 0.45831882
3 -23.06506167 -8.01507589
4 -2.17845379 -23.06506167
5 -8.85665104 -2.17845379
6 20.90316849 -8.85665104
7 -1.52922711 20.90316849
8 8.15173404 -1.52922711
9 -11.91075240 8.15173404
10 -10.72261910 -11.91075240
11 -13.02948689 -10.72261910
12 -9.07353024 -13.02948689
13 0.63383122 -9.07353024
14 6.92454246 0.63383122
15 2.39228093 6.92454246
16 6.66705007 2.39228093
17 2.11234680 6.66705007
18 1.05787627 2.11234680
19 2.28539773 1.05787627
20 -3.16533296 2.28539773
21 5.51038105 -3.16533296
22 8.70870072 5.51038105
23 1.26253975 8.70870072
24 -18.08409842 1.26253975
25 -33.64565210 -18.08409842
26 5.58647564 -33.64565210
27 6.95414863 5.58647564
28 11.34651644 6.95414863
29 -0.88538167 11.34651644
30 1.64071544 -0.88538167
31 -0.01056134 1.64071544
32 -7.19755780 -0.01056134
33 5.99059593 -7.19755780
34 -9.72494764 5.99059593
35 13.75441395 -9.72494764
36 7.18434572 13.75441395
37 -7.40529220 7.18434572
38 3.13785494 -7.40529220
39 0.07767643 3.13785494
40 7.29091568 0.07767643
41 9.97689136 7.29091568
42 1.18871763 9.97689136
43 14.34869577 1.18871763
44 6.84017818 14.34869577
45 9.05047956 6.84017818
46 -2.36087148 9.05047956
47 6.56122312 -2.36087148
48 15.72712331 6.56122312
49 -1.16110027 15.72712331
50 6.72194905 -1.16110027
51 -4.08245397 6.72194905
52 9.90844965 -4.08245397
53 -0.88576795 9.90844965
54 0.69320103 -0.88576795
55 -0.70627493 0.69320103
56 14.34356863 -0.70627493
57 1.92313448 14.34356863
58 -0.87571989 1.92313448
59 12.76305314 -0.87571989
60 5.19484476 12.76305314
61 4.86422117 5.19484476
62 -7.59769349 4.86422117
63 10.65833236 -7.59769349
64 10.05026219 10.65833236
65 8.40572329 10.05026219
66 13.18078057 8.40572329
67 6.96698093 13.18078057
68 14.56687858 6.96698093
69 10.25602260 14.56687858
70 4.63892610 10.25602260
71 -5.45222582 4.63892610
72 6.53204044 -5.45222582
73 -0.23698090 6.53204044
74 -3.02859621 -0.23698090
75 7.77598751 -3.02859621
76 3.77749150 7.77598751
77 7.02110480 3.77749150
78 -7.97786814 7.02110480
79 -19.36412894 -7.97786814
80 28.05078588 -19.36412894
81 -6.11319450 28.05078588
82 9.49068850 -6.11319450
83 2.92114071 9.49068850
84 -18.35691382 2.92114071
85 -13.92974799 -18.35691382
86 -12.30174488 -13.92974799
87 10.24169653 -12.30174488
88 -10.36117487 10.24169653
89 -3.33751553 -10.36117487
90 10.62824029 -3.33751553
91 8.79007849 10.62824029
92 -7.90222573 8.79007849
93 -13.10479093 -7.90222573
94 -20.16819471 -13.10479093
95 2.95545653 -20.16819471
96 21.32348010 2.95545653
97 -6.63030994 21.32348010
98 5.43821951 -6.63030994
99 -1.58007921 5.43821951
100 3.46734086 -1.58007921
101 -18.17088707 3.46734086
102 15.90576197 -18.17088707
103 -0.14513692 15.90576197
104 -4.28408031 -0.14513692
105 -3.34373837 -4.28408031
106 15.80369200 -3.34373837
107 -8.95937399 15.80369200
108 -4.52969149 -8.95937399
109 -16.22187720 -4.52969149
110 10.04044672 -16.22187720
111 -4.38724260 10.04044672
112 -0.32903246 -4.38724260
113 -3.20030803 -0.32903246
114 4.93514084 -3.20030803
115 16.52181384 4.93514084
116 -8.84100923 16.52181384
117 -30.91165908 -8.84100923
118 -2.92464178 -30.91165908
119 5.17424923 -2.92464178
120 4.16797439 5.17424923
121 -13.46330058 4.16797439
122 -18.36374894 -13.46330058
123 0.33104198 -18.36374894
124 14.56868475 0.33104198
125 -21.33977545 14.56868475
126 -6.55678461 -21.33977545
127 -0.88188344 -6.55678461
128 4.60618385 -0.88188344
129 8.37402642 4.60618385
130 -6.41635702 8.37402642
131 18.85793755 -6.41635702
132 21.32506016 18.85793755
133 -24.65998782 21.32506016
134 2.73712467 -24.65998782
135 -6.31090852 2.73712467
136 -5.24500100 -6.31090852
137 4.04657811 -5.24500100
138 -4.15425478 4.04657811
139 -2.60893571 -4.15425478
140 -2.92401968 -2.60893571
141 -5.51532497 -2.92401968
142 -2.59178262 -5.51532497
143 -1.49400436 -2.59178262
144 -12.81708157 -1.49400436
145 -5.52645679 -12.81708157
146 2.43433201 -5.52645679
147 -2.32487233 2.43433201
148 -1.62209140 -2.32487233
149 NA -1.62209140
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.45831882 -20.02475827
[2,] -8.01507589 0.45831882
[3,] -23.06506167 -8.01507589
[4,] -2.17845379 -23.06506167
[5,] -8.85665104 -2.17845379
[6,] 20.90316849 -8.85665104
[7,] -1.52922711 20.90316849
[8,] 8.15173404 -1.52922711
[9,] -11.91075240 8.15173404
[10,] -10.72261910 -11.91075240
[11,] -13.02948689 -10.72261910
[12,] -9.07353024 -13.02948689
[13,] 0.63383122 -9.07353024
[14,] 6.92454246 0.63383122
[15,] 2.39228093 6.92454246
[16,] 6.66705007 2.39228093
[17,] 2.11234680 6.66705007
[18,] 1.05787627 2.11234680
[19,] 2.28539773 1.05787627
[20,] -3.16533296 2.28539773
[21,] 5.51038105 -3.16533296
[22,] 8.70870072 5.51038105
[23,] 1.26253975 8.70870072
[24,] -18.08409842 1.26253975
[25,] -33.64565210 -18.08409842
[26,] 5.58647564 -33.64565210
[27,] 6.95414863 5.58647564
[28,] 11.34651644 6.95414863
[29,] -0.88538167 11.34651644
[30,] 1.64071544 -0.88538167
[31,] -0.01056134 1.64071544
[32,] -7.19755780 -0.01056134
[33,] 5.99059593 -7.19755780
[34,] -9.72494764 5.99059593
[35,] 13.75441395 -9.72494764
[36,] 7.18434572 13.75441395
[37,] -7.40529220 7.18434572
[38,] 3.13785494 -7.40529220
[39,] 0.07767643 3.13785494
[40,] 7.29091568 0.07767643
[41,] 9.97689136 7.29091568
[42,] 1.18871763 9.97689136
[43,] 14.34869577 1.18871763
[44,] 6.84017818 14.34869577
[45,] 9.05047956 6.84017818
[46,] -2.36087148 9.05047956
[47,] 6.56122312 -2.36087148
[48,] 15.72712331 6.56122312
[49,] -1.16110027 15.72712331
[50,] 6.72194905 -1.16110027
[51,] -4.08245397 6.72194905
[52,] 9.90844965 -4.08245397
[53,] -0.88576795 9.90844965
[54,] 0.69320103 -0.88576795
[55,] -0.70627493 0.69320103
[56,] 14.34356863 -0.70627493
[57,] 1.92313448 14.34356863
[58,] -0.87571989 1.92313448
[59,] 12.76305314 -0.87571989
[60,] 5.19484476 12.76305314
[61,] 4.86422117 5.19484476
[62,] -7.59769349 4.86422117
[63,] 10.65833236 -7.59769349
[64,] 10.05026219 10.65833236
[65,] 8.40572329 10.05026219
[66,] 13.18078057 8.40572329
[67,] 6.96698093 13.18078057
[68,] 14.56687858 6.96698093
[69,] 10.25602260 14.56687858
[70,] 4.63892610 10.25602260
[71,] -5.45222582 4.63892610
[72,] 6.53204044 -5.45222582
[73,] -0.23698090 6.53204044
[74,] -3.02859621 -0.23698090
[75,] 7.77598751 -3.02859621
[76,] 3.77749150 7.77598751
[77,] 7.02110480 3.77749150
[78,] -7.97786814 7.02110480
[79,] -19.36412894 -7.97786814
[80,] 28.05078588 -19.36412894
[81,] -6.11319450 28.05078588
[82,] 9.49068850 -6.11319450
[83,] 2.92114071 9.49068850
[84,] -18.35691382 2.92114071
[85,] -13.92974799 -18.35691382
[86,] -12.30174488 -13.92974799
[87,] 10.24169653 -12.30174488
[88,] -10.36117487 10.24169653
[89,] -3.33751553 -10.36117487
[90,] 10.62824029 -3.33751553
[91,] 8.79007849 10.62824029
[92,] -7.90222573 8.79007849
[93,] -13.10479093 -7.90222573
[94,] -20.16819471 -13.10479093
[95,] 2.95545653 -20.16819471
[96,] 21.32348010 2.95545653
[97,] -6.63030994 21.32348010
[98,] 5.43821951 -6.63030994
[99,] -1.58007921 5.43821951
[100,] 3.46734086 -1.58007921
[101,] -18.17088707 3.46734086
[102,] 15.90576197 -18.17088707
[103,] -0.14513692 15.90576197
[104,] -4.28408031 -0.14513692
[105,] -3.34373837 -4.28408031
[106,] 15.80369200 -3.34373837
[107,] -8.95937399 15.80369200
[108,] -4.52969149 -8.95937399
[109,] -16.22187720 -4.52969149
[110,] 10.04044672 -16.22187720
[111,] -4.38724260 10.04044672
[112,] -0.32903246 -4.38724260
[113,] -3.20030803 -0.32903246
[114,] 4.93514084 -3.20030803
[115,] 16.52181384 4.93514084
[116,] -8.84100923 16.52181384
[117,] -30.91165908 -8.84100923
[118,] -2.92464178 -30.91165908
[119,] 5.17424923 -2.92464178
[120,] 4.16797439 5.17424923
[121,] -13.46330058 4.16797439
[122,] -18.36374894 -13.46330058
[123,] 0.33104198 -18.36374894
[124,] 14.56868475 0.33104198
[125,] -21.33977545 14.56868475
[126,] -6.55678461 -21.33977545
[127,] -0.88188344 -6.55678461
[128,] 4.60618385 -0.88188344
[129,] 8.37402642 4.60618385
[130,] -6.41635702 8.37402642
[131,] 18.85793755 -6.41635702
[132,] 21.32506016 18.85793755
[133,] -24.65998782 21.32506016
[134,] 2.73712467 -24.65998782
[135,] -6.31090852 2.73712467
[136,] -5.24500100 -6.31090852
[137,] 4.04657811 -5.24500100
[138,] -4.15425478 4.04657811
[139,] -2.60893571 -4.15425478
[140,] -2.92401968 -2.60893571
[141,] -5.51532497 -2.92401968
[142,] -2.59178262 -5.51532497
[143,] -1.49400436 -2.59178262
[144,] -12.81708157 -1.49400436
[145,] -5.52645679 -12.81708157
[146,] 2.43433201 -5.52645679
[147,] -2.32487233 2.43433201
[148,] -1.62209140 -2.32487233
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.45831882 -20.02475827
2 -8.01507589 0.45831882
3 -23.06506167 -8.01507589
4 -2.17845379 -23.06506167
5 -8.85665104 -2.17845379
6 20.90316849 -8.85665104
7 -1.52922711 20.90316849
8 8.15173404 -1.52922711
9 -11.91075240 8.15173404
10 -10.72261910 -11.91075240
11 -13.02948689 -10.72261910
12 -9.07353024 -13.02948689
13 0.63383122 -9.07353024
14 6.92454246 0.63383122
15 2.39228093 6.92454246
16 6.66705007 2.39228093
17 2.11234680 6.66705007
18 1.05787627 2.11234680
19 2.28539773 1.05787627
20 -3.16533296 2.28539773
21 5.51038105 -3.16533296
22 8.70870072 5.51038105
23 1.26253975 8.70870072
24 -18.08409842 1.26253975
25 -33.64565210 -18.08409842
26 5.58647564 -33.64565210
27 6.95414863 5.58647564
28 11.34651644 6.95414863
29 -0.88538167 11.34651644
30 1.64071544 -0.88538167
31 -0.01056134 1.64071544
32 -7.19755780 -0.01056134
33 5.99059593 -7.19755780
34 -9.72494764 5.99059593
35 13.75441395 -9.72494764
36 7.18434572 13.75441395
37 -7.40529220 7.18434572
38 3.13785494 -7.40529220
39 0.07767643 3.13785494
40 7.29091568 0.07767643
41 9.97689136 7.29091568
42 1.18871763 9.97689136
43 14.34869577 1.18871763
44 6.84017818 14.34869577
45 9.05047956 6.84017818
46 -2.36087148 9.05047956
47 6.56122312 -2.36087148
48 15.72712331 6.56122312
49 -1.16110027 15.72712331
50 6.72194905 -1.16110027
51 -4.08245397 6.72194905
52 9.90844965 -4.08245397
53 -0.88576795 9.90844965
54 0.69320103 -0.88576795
55 -0.70627493 0.69320103
56 14.34356863 -0.70627493
57 1.92313448 14.34356863
58 -0.87571989 1.92313448
59 12.76305314 -0.87571989
60 5.19484476 12.76305314
61 4.86422117 5.19484476
62 -7.59769349 4.86422117
63 10.65833236 -7.59769349
64 10.05026219 10.65833236
65 8.40572329 10.05026219
66 13.18078057 8.40572329
67 6.96698093 13.18078057
68 14.56687858 6.96698093
69 10.25602260 14.56687858
70 4.63892610 10.25602260
71 -5.45222582 4.63892610
72 6.53204044 -5.45222582
73 -0.23698090 6.53204044
74 -3.02859621 -0.23698090
75 7.77598751 -3.02859621
76 3.77749150 7.77598751
77 7.02110480 3.77749150
78 -7.97786814 7.02110480
79 -19.36412894 -7.97786814
80 28.05078588 -19.36412894
81 -6.11319450 28.05078588
82 9.49068850 -6.11319450
83 2.92114071 9.49068850
84 -18.35691382 2.92114071
85 -13.92974799 -18.35691382
86 -12.30174488 -13.92974799
87 10.24169653 -12.30174488
88 -10.36117487 10.24169653
89 -3.33751553 -10.36117487
90 10.62824029 -3.33751553
91 8.79007849 10.62824029
92 -7.90222573 8.79007849
93 -13.10479093 -7.90222573
94 -20.16819471 -13.10479093
95 2.95545653 -20.16819471
96 21.32348010 2.95545653
97 -6.63030994 21.32348010
98 5.43821951 -6.63030994
99 -1.58007921 5.43821951
100 3.46734086 -1.58007921
101 -18.17088707 3.46734086
102 15.90576197 -18.17088707
103 -0.14513692 15.90576197
104 -4.28408031 -0.14513692
105 -3.34373837 -4.28408031
106 15.80369200 -3.34373837
107 -8.95937399 15.80369200
108 -4.52969149 -8.95937399
109 -16.22187720 -4.52969149
110 10.04044672 -16.22187720
111 -4.38724260 10.04044672
112 -0.32903246 -4.38724260
113 -3.20030803 -0.32903246
114 4.93514084 -3.20030803
115 16.52181384 4.93514084
116 -8.84100923 16.52181384
117 -30.91165908 -8.84100923
118 -2.92464178 -30.91165908
119 5.17424923 -2.92464178
120 4.16797439 5.17424923
121 -13.46330058 4.16797439
122 -18.36374894 -13.46330058
123 0.33104198 -18.36374894
124 14.56868475 0.33104198
125 -21.33977545 14.56868475
126 -6.55678461 -21.33977545
127 -0.88188344 -6.55678461
128 4.60618385 -0.88188344
129 8.37402642 4.60618385
130 -6.41635702 8.37402642
131 18.85793755 -6.41635702
132 21.32506016 18.85793755
133 -24.65998782 21.32506016
134 2.73712467 -24.65998782
135 -6.31090852 2.73712467
136 -5.24500100 -6.31090852
137 4.04657811 -5.24500100
138 -4.15425478 4.04657811
139 -2.60893571 -4.15425478
140 -2.92401968 -2.60893571
141 -5.51532497 -2.92401968
142 -2.59178262 -5.51532497
143 -1.49400436 -2.59178262
144 -12.81708157 -1.49400436
145 -5.52645679 -12.81708157
146 2.43433201 -5.52645679
147 -2.32487233 2.43433201
148 -1.62209140 -2.32487233
> 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/76z231352045343.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/8ah2t1352045343.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/9ca6q1352045343.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/10afc01352045343.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11iek01352045344.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1225m11352045344.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13aemb1352045344.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14m1971352045344.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/15imi91352045344.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/16cx1d1352045344.tab")
+ }
>
> try(system("convert tmp/125tv1352045343.ps tmp/125tv1352045343.png",intern=TRUE))
character(0)
> try(system("convert tmp/2x3ni1352045343.ps tmp/2x3ni1352045343.png",intern=TRUE))
character(0)
> try(system("convert tmp/3hb071352045343.ps tmp/3hb071352045343.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fucs1352045343.ps tmp/4fucs1352045343.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zk551352045343.ps tmp/5zk551352045343.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ujng1352045343.ps tmp/6ujng1352045343.png",intern=TRUE))
character(0)
> try(system("convert tmp/76z231352045343.ps tmp/76z231352045343.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ah2t1352045343.ps tmp/8ah2t1352045343.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ca6q1352045343.ps tmp/9ca6q1352045343.png",intern=TRUE))
character(0)
> try(system("convert tmp/10afc01352045343.ps tmp/10afc01352045343.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.282 1.672 12.999