R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
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 '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(9
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+ ,7
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+ ,14)
+ ,dim=c(4
+ ,264)
+ ,dimnames=list(c('month'
+ ,'Doorzettingsvermogen'
+ ,'Zelfstandig'
+ ,'Stressbestendig')
+ ,1:264))
> y <- array(NA,dim=c(4,264),dimnames=list(c('month','Doorzettingsvermogen','Zelfstandig','Stressbestendig'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> 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
Doorzettingsvermogen month Zelfstandig Stressbestendig
1 13 9 38 14
2 16 9 32 18
3 19 9 35 11
4 15 9 33 12
5 14 9 37 16
6 13 9 29 18
7 19 9 31 14
8 15 9 36 14
9 14 9 35 15
10 15 9 38 15
11 16 9 31 17
12 16 9 34 19
13 16 9 35 10
14 16 9 38 16
15 17 9 37 18
16 15 9 33 14
17 15 9 32 14
18 20 9 38 17
19 18 9 38 14
20 16 9 32 16
21 16 9 33 18
22 16 9 31 11
23 19 9 38 14
24 16 9 39 12
25 17 9 32 17
26 17 9 32 9
27 16 9 35 16
28 15 9 37 14
29 16 9 33 15
30 14 9 33 11
31 15 9 31 16
32 12 9 32 13
33 14 9 31 17
34 16 9 37 15
35 14 9 30 14
36 10 9 33 16
37 10 9 31 9
38 14 9 33 15
39 16 9 31 17
40 16 9 33 13
41 16 9 32 15
42 14 9 33 16
43 20 9 32 16
44 14 9 33 12
45 14 9 28 15
46 11 9 35 11
47 14 9 39 15
48 15 9 34 15
49 16 9 38 17
50 14 9 32 13
51 16 9 38 16
52 14 9 30 14
53 12 9 33 11
54 16 9 38 12
55 9 9 32 12
56 14 9 35 15
57 16 9 34 16
58 16 9 34 15
59 15 9 36 12
60 16 9 34 12
61 12 9 28 8
62 16 9 34 13
63 16 9 35 11
64 14 9 35 14
65 16 9 31 15
66 17 9 34 9
67 18 10 37 10
68 18 10 35 11
69 12 10 27 12
70 16 10 40 15
71 10 10 37 15
72 14 10 36 14
73 18 10 38 16
74 18 10 39 15
75 16 10 41 15
76 17 10 27 13
77 16 10 30 12
78 16 10 37 17
79 13 10 31 13
80 16 10 31 15
81 16 10 27 13
82 16 10 36 15
83 15 10 37 15
84 15 10 33 16
85 16 10 34 15
86 14 10 31 14
87 16 10 39 15
88 16 10 34 14
89 15 10 32 13
90 12 10 33 7
91 17 10 36 17
92 16 10 32 13
93 15 10 41 15
94 13 10 28 14
95 16 10 30 13
96 16 10 36 16
97 16 10 35 12
98 16 10 31 14
99 14 10 34 17
100 16 10 36 15
101 16 10 36 17
102 20 10 35 12
103 15 10 37 16
104 16 10 28 11
105 13 10 39 15
106 17 10 32 9
107 16 10 35 16
108 16 10 39 15
109 12 10 35 10
110 16 10 42 10
111 16 10 34 15
112 17 10 33 11
113 13 10 41 13
114 12 10 33 14
115 18 10 34 18
116 14 10 32 16
117 14 10 40 14
118 13 10 40 14
119 16 10 35 14
120 13 10 36 14
121 16 10 37 12
122 13 10 27 14
123 16 10 39 15
124 15 10 38 15
125 16 10 31 15
126 15 10 33 13
127 17 10 32 17
128 15 10 39 17
129 12 10 36 19
130 16 10 33 15
131 10 10 33 13
132 16 10 32 9
133 12 10 37 15
134 14 10 30 15
135 15 10 38 15
136 13 10 29 16
137 15 10 22 11
138 11 10 35 14
139 12 10 35 11
140 11 10 34 15
141 16 10 35 13
142 15 10 34 15
143 17 10 37 16
144 16 10 35 14
145 10 10 23 15
146 18 10 31 16
147 13 10 27 16
148 16 10 36 11
149 13 10 31 12
150 10 10 32 9
151 15 10 39 16
152 16 10 37 13
153 16 10 38 16
154 14 10 39 12
155 10 10 31 13
156 17 10 32 13
157 13 10 37 14
158 15 10 36 19
159 16 10 32 13
160 12 10 38 12
161 13 10 36 13
162 13 11 26 10
163 12 11 26 14
164 17 11 33 16
165 15 11 39 10
166 10 11 30 11
167 14 11 33 14
168 11 11 25 12
169 13 11 38 9
170 16 11 37 9
171 12 11 31 11
172 16 11 37 16
173 12 11 35 9
174 9 11 25 13
175 12 11 28 16
176 15 11 35 13
177 12 11 33 9
178 12 11 30 12
179 14 11 31 16
180 12 11 37 11
181 16 11 36 14
182 11 11 30 13
183 19 11 36 15
184 15 11 32 14
185 8 11 28 16
186 16 11 36 13
187 17 11 34 14
188 12 11 31 15
189 11 11 28 13
190 11 11 36 11
191 14 11 36 11
192 16 11 40 14
193 12 11 33 15
194 16 11 37 11
195 13 11 32 15
196 15 11 38 12
197 16 11 31 14
198 16 11 37 14
199 14 11 33 8
200 16 11 32 13
201 16 11 30 9
202 14 11 30 15
203 11 11 31 17
204 12 11 32 13
205 15 11 34 15
206 15 11 36 15
207 16 11 37 14
208 16 11 36 16
209 11 11 33 13
210 15 11 33 16
211 12 11 33 9
212 12 11 44 16
213 15 11 39 11
214 15 11 32 10
215 16 11 35 11
216 14 11 25 15
217 17 11 35 17
218 14 11 34 14
219 13 11 35 8
220 15 11 39 15
221 13 11 33 11
222 14 11 36 16
223 15 11 32 10
224 12 11 32 15
225 13 11 36 9
226 8 11 36 16
227 14 11 32 19
228 14 11 34 12
229 11 11 33 8
230 12 11 35 11
231 13 11 30 14
232 10 11 38 9
233 16 11 34 15
234 18 11 33 13
235 13 11 32 16
236 11 11 31 11
237 4 11 30 12
238 13 11 27 13
239 16 11 31 10
240 10 11 30 11
241 12 11 32 12
242 12 11 35 8
243 10 11 28 12
244 13 11 33 12
245 15 11 31 15
246 12 11 35 11
247 14 11 35 13
248 10 11 32 14
249 12 11 21 10
250 12 11 20 12
251 11 11 34 15
252 10 11 32 13
253 12 11 34 13
254 16 11 32 13
255 12 11 33 12
256 14 11 33 12
257 16 11 37 9
258 14 11 32 9
259 13 11 34 15
260 4 11 30 10
261 15 11 30 14
262 11 11 38 15
263 11 11 36 7
264 14 11 32 14
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month Zelfstandig Stressbestendig
15.5210 -0.8322 0.1572 0.1405
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.7680 -1.3003 0.1866 1.4777 5.6140
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.52099 2.53917 6.113 3.57e-09 ***
month -0.83215 0.18009 -4.621 6.02e-06 ***
Zelfstandig 0.15716 0.03741 4.201 3.66e-05 ***
Stressbestendig 0.14050 0.05686 2.471 0.0141 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.225 on 260 degrees of freedom
Multiple R-squared: 0.1889, Adjusted R-squared: 0.1795
F-statistic: 20.18 on 3 and 260 DF, p-value: 8.62e-12
> 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,] 0.8120228410 0.375954318 0.1879772
[2,] 0.6899118178 0.620176364 0.3100882
[3,] 0.5739349111 0.852130178 0.4260651
[4,] 0.4786860928 0.957372186 0.5213139
[5,] 0.3809111471 0.761822294 0.6190889
[6,] 0.4057454829 0.811490966 0.5942545
[7,] 0.3254052830 0.650810566 0.6745947
[8,] 0.2915018711 0.583003742 0.7084981
[9,] 0.3194990263 0.638998053 0.6805010
[10,] 0.2562346340 0.512469268 0.7437654
[11,] 0.2019936825 0.403987365 0.7980063
[12,] 0.4566727841 0.913345568 0.5433272
[13,] 0.4334325258 0.866865052 0.5665675
[14,] 0.3622065076 0.724413015 0.6377935
[15,] 0.2962839490 0.592567898 0.7037161
[16,] 0.2381216058 0.476243212 0.7618784
[17,] 0.2672332691 0.534466538 0.7327667
[18,] 0.2195611981 0.439122396 0.7804388
[19,] 0.1911546019 0.382309204 0.8088454
[20,] 0.1594459717 0.318891943 0.8405540
[21,] 0.1224872439 0.244974488 0.8775128
[22,] 0.1040339029 0.208067806 0.8959661
[23,] 0.0780805630 0.156161126 0.9219194
[24,] 0.0778544339 0.155708868 0.9221456
[25,] 0.0590977686 0.118195537 0.9409022
[26,] 0.1069365615 0.213873123 0.8930634
[27,] 0.0914430381 0.182886076 0.9085570
[28,] 0.0696966307 0.139393261 0.9303034
[29,] 0.0565122995 0.113024599 0.9434877
[30,] 0.2196731137 0.439346227 0.7803269
[31,] 0.3846997046 0.769399409 0.6153003
[32,] 0.3494957239 0.698991448 0.6505043
[33,] 0.3129952258 0.625990452 0.6870048
[34,] 0.2761751853 0.552350371 0.7238248
[35,] 0.2422161080 0.484432216 0.7577839
[36,] 0.2183100202 0.436620040 0.7816900
[37,] 0.3843669527 0.768733905 0.6156330
[38,] 0.3473684906 0.694736981 0.6526315
[39,] 0.3038760442 0.607752088 0.6961240
[40,] 0.4184297631 0.836859526 0.5815702
[41,] 0.4235688481 0.847137696 0.5764312
[42,] 0.3792869352 0.758573870 0.6207131
[43,] 0.3364099263 0.672819853 0.6635901
[44,] 0.2999801087 0.599960217 0.7000199
[45,] 0.2610026626 0.522005325 0.7389973
[46,] 0.2275156321 0.455031264 0.7724844
[47,] 0.2414813246 0.482962649 0.7585187
[48,] 0.2087985258 0.417597052 0.7912015
[49,] 0.4125517599 0.825103520 0.5874482
[50,] 0.3921848662 0.784369732 0.6078151
[51,] 0.3521223281 0.704244656 0.6478777
[52,] 0.3155681607 0.631136321 0.6844318
[53,] 0.2790050809 0.558010162 0.7209949
[54,] 0.2549415450 0.509883090 0.7450585
[55,] 0.2307250672 0.461450134 0.7692749
[56,] 0.2061008179 0.412201636 0.7938992
[57,] 0.1857482226 0.371496445 0.8142518
[58,] 0.1716754806 0.343350961 0.8283245
[59,] 0.1529655330 0.305931066 0.8470345
[60,] 0.1653715455 0.330743091 0.8346285
[61,] 0.1520907526 0.304181505 0.8479092
[62,] 0.1416045715 0.283209143 0.8583954
[63,] 0.1699205650 0.339841130 0.8300794
[64,] 0.1611968378 0.322393676 0.8388032
[65,] 0.3695175720 0.739035144 0.6304824
[66,] 0.3400076149 0.680015230 0.6599924
[67,] 0.3417542960 0.683508592 0.6582457
[68,] 0.3344161230 0.668832246 0.6655839
[69,] 0.3017141096 0.603428219 0.6982859
[70,] 0.3450221325 0.690044265 0.6549779
[71,] 0.3287874705 0.657574941 0.6712125
[72,] 0.2953961303 0.590792261 0.7046039
[73,] 0.2813418306 0.562683661 0.7186582
[74,] 0.2580726309 0.516145262 0.7419274
[75,] 0.2518471729 0.503694346 0.7481528
[76,] 0.2232741007 0.446548201 0.7767259
[77,] 0.2007860097 0.401572019 0.7992140
[78,] 0.1762327431 0.352465486 0.8237673
[79,] 0.1549112226 0.309822445 0.8450888
[80,] 0.1364652769 0.272930554 0.8635347
[81,] 0.1171667124 0.234333425 0.8828333
[82,] 0.1021191069 0.204238214 0.8978809
[83,] 0.0867528381 0.173505676 0.9132472
[84,] 0.0857731840 0.171546368 0.9142268
[85,] 0.0756951079 0.151390216 0.9243049
[86,] 0.0676185156 0.135237031 0.9323815
[87,] 0.0607292186 0.121458437 0.9392708
[88,] 0.0538620222 0.107724044 0.9461380
[89,] 0.0497775955 0.099555191 0.9502224
[90,] 0.0412897889 0.082579578 0.9587102
[91,] 0.0354430040 0.070886008 0.9645570
[92,] 0.0313303408 0.062660682 0.9686697
[93,] 0.0286108788 0.057221758 0.9713891
[94,] 0.0234608634 0.046921727 0.9765391
[95,] 0.0189798808 0.037959762 0.9810201
[96,] 0.0481819859 0.096363972 0.9518180
[97,] 0.0413044212 0.082608842 0.9586956
[98,] 0.0418601924 0.083720385 0.9581398
[99,] 0.0498184867 0.099636973 0.9501815
[100,] 0.0577824540 0.115564908 0.9422175
[101,] 0.0490589957 0.098117991 0.9509410
[102,] 0.0408622290 0.081724458 0.9591378
[103,] 0.0464097709 0.092819542 0.9535902
[104,] 0.0388714572 0.077742914 0.9611285
[105,] 0.0333059540 0.066611908 0.9666940
[106,] 0.0366446584 0.073289317 0.9633553
[107,] 0.0419291708 0.083858342 0.9580708
[108,] 0.0488808898 0.097761780 0.9511191
[109,] 0.0527307983 0.105461597 0.9472692
[110,] 0.0461434812 0.092286962 0.9538565
[111,] 0.0429457076 0.085891415 0.9570543
[112,] 0.0470365797 0.094073159 0.9529634
[113,] 0.0408839596 0.081767919 0.9591160
[114,] 0.0407541086 0.081508217 0.9592459
[115,] 0.0353714857 0.070742971 0.9646285
[116,] 0.0311557279 0.062311456 0.9688443
[117,] 0.0256741320 0.051348264 0.9743259
[118,] 0.0210823483 0.042164697 0.9789177
[119,] 0.0190361853 0.038072371 0.9809638
[120,] 0.0157224777 0.031444955 0.9842775
[121,] 0.0157443698 0.031488740 0.9842556
[122,] 0.0130048076 0.026009615 0.9869952
[123,] 0.0196527942 0.039305588 0.9803472
[124,] 0.0172837649 0.034567530 0.9827162
[125,] 0.0342336622 0.068467324 0.9657663
[126,] 0.0352835792 0.070567158 0.9647164
[127,] 0.0440093708 0.088018742 0.9559906
[128,] 0.0371586035 0.074317207 0.9628414
[129,] 0.0306882502 0.061376500 0.9693117
[130,] 0.0272842526 0.054568505 0.9727157
[131,] 0.0309519396 0.061903879 0.9690481
[132,] 0.0451783670 0.090356734 0.9548216
[133,] 0.0468332262 0.093666452 0.9531668
[134,] 0.0663661616 0.132732323 0.9336338
[135,] 0.0597282090 0.119456418 0.9402718
[136,] 0.0501716870 0.100343374 0.9498283
[137,] 0.0465334063 0.093066813 0.9534666
[138,] 0.0413836675 0.082767335 0.9586163
[139,] 0.0507437360 0.101487472 0.9492563
[140,] 0.0702547744 0.140509549 0.9297452
[141,] 0.0612863800 0.122572760 0.9387136
[142,] 0.0578886258 0.115777252 0.9421114
[143,] 0.0503821712 0.100764342 0.9496178
[144,] 0.0646010713 0.129202143 0.9353989
[145,] 0.0545753623 0.109150725 0.9454246
[146,] 0.0487707826 0.097541565 0.9512292
[147,] 0.0416381225 0.083276245 0.9583619
[148,] 0.0353517369 0.070703474 0.9646483
[149,] 0.0502939888 0.100587978 0.9497060
[150,] 0.0614869364 0.122973873 0.9385131
[151,] 0.0570618290 0.114123658 0.9429382
[152,] 0.0477855835 0.095571167 0.9522144
[153,] 0.0537161129 0.107432226 0.9462839
[154,] 0.0530389905 0.106077981 0.9469610
[155,] 0.0467755402 0.093551080 0.9532245
[156,] 0.0414022327 0.082804465 0.9585978
[157,] 0.0355285861 0.071057172 0.9644714
[158,] 0.0406823229 0.081364646 0.9593177
[159,] 0.0341111903 0.068222381 0.9658888
[160,] 0.0390350852 0.078070170 0.9609649
[161,] 0.0322383063 0.064476613 0.9677617
[162,] 0.0281712712 0.056342542 0.9718287
[163,] 0.0235074024 0.047014805 0.9764926
[164,] 0.0238575452 0.047715090 0.9761425
[165,] 0.0200858390 0.040171678 0.9799142
[166,] 0.0173243208 0.034648642 0.9826757
[167,] 0.0149136374 0.029827275 0.9850864
[168,] 0.0185959002 0.037191800 0.9814041
[169,] 0.0156347755 0.031269551 0.9843652
[170,] 0.0132087652 0.026417530 0.9867912
[171,] 0.0107998137 0.021599627 0.9892002
[172,] 0.0087244403 0.017448881 0.9912756
[173,] 0.0068466734 0.013693347 0.9931533
[174,] 0.0062203335 0.012440667 0.9937797
[175,] 0.0057441547 0.011488309 0.9942558
[176,] 0.0053103542 0.010620708 0.9946896
[177,] 0.0126144981 0.025228996 0.9873855
[178,] 0.0111902800 0.022380560 0.9888097
[179,] 0.0268620873 0.053724175 0.9731379
[180,] 0.0261758424 0.052351685 0.9738242
[181,] 0.0332616859 0.066523372 0.9667383
[182,] 0.0289329493 0.057865899 0.9710671
[183,] 0.0259742355 0.051948471 0.9740258
[184,] 0.0272228123 0.054445625 0.9727772
[185,] 0.0219291915 0.043858383 0.9780708
[186,] 0.0192595460 0.038519092 0.9807405
[187,] 0.0170654214 0.034130843 0.9829346
[188,] 0.0173892731 0.034778546 0.9826107
[189,] 0.0137455289 0.027491058 0.9862545
[190,] 0.0114256291 0.022851258 0.9885744
[191,] 0.0127785494 0.025557099 0.9872215
[192,] 0.0121487635 0.024297527 0.9878512
[193,] 0.0103456254 0.020691251 0.9896544
[194,] 0.0118174406 0.023634881 0.9881826
[195,] 0.0178472427 0.035694485 0.9821528
[196,] 0.0143434469 0.028686894 0.9856566
[197,] 0.0154649223 0.030929845 0.9845351
[198,] 0.0126932636 0.025386527 0.9873067
[199,] 0.0105615789 0.021123158 0.9894384
[200,] 0.0085361057 0.017072211 0.9914639
[201,] 0.0082843402 0.016568680 0.9917157
[202,] 0.0078015804 0.015603161 0.9921984
[203,] 0.0075005493 0.015001099 0.9924995
[204,] 0.0062569639 0.012513928 0.9937430
[205,] 0.0047492166 0.009498433 0.9952508
[206,] 0.0056386183 0.011277237 0.9943614
[207,] 0.0046939431 0.009387886 0.9953061
[208,] 0.0049078319 0.009815664 0.9950922
[209,] 0.0061090038 0.012218008 0.9938910
[210,] 0.0050912889 0.010182578 0.9949087
[211,] 0.0065075192 0.013015038 0.9934925
[212,] 0.0049839562 0.009967912 0.9950160
[213,] 0.0037376360 0.007475272 0.9962624
[214,] 0.0029838815 0.005967763 0.9970161
[215,] 0.0021584510 0.004316902 0.9978415
[216,] 0.0015560891 0.003112178 0.9984439
[217,] 0.0017459277 0.003491855 0.9982541
[218,] 0.0012920061 0.002584012 0.9987080
[219,] 0.0009302197 0.001860439 0.9990698
[220,] 0.0051676288 0.010335258 0.9948324
[221,] 0.0036145101 0.007229020 0.9963855
[222,] 0.0027770734 0.005554147 0.9972229
[223,] 0.0020238359 0.004047672 0.9979762
[224,] 0.0014203285 0.002840657 0.9985797
[225,] 0.0009400360 0.001880072 0.9990600
[226,] 0.0010193690 0.002038738 0.9989806
[227,] 0.0010847517 0.002169503 0.9989152
[228,] 0.0047367710 0.009473542 0.9952632
[229,] 0.0032273742 0.006454748 0.9967726
[230,] 0.0023307219 0.004661444 0.9976693
[231,] 0.0820979418 0.164195884 0.9179021
[232,] 0.0640218751 0.128043750 0.9359781
[233,] 0.1034971703 0.206994341 0.8965028
[234,] 0.0957090000 0.191418000 0.9042910
[235,] 0.0722792468 0.144558494 0.9277208
[236,] 0.0529938371 0.105987674 0.9470062
[237,] 0.0483025216 0.096605043 0.9516975
[238,] 0.0342283116 0.068456623 0.9657717
[239,] 0.0322782400 0.064556480 0.9677218
[240,] 0.0219556544 0.043911309 0.9780443
[241,] 0.0161435851 0.032287170 0.9838564
[242,] 0.0157810476 0.031562095 0.9842190
[243,] 0.0099539814 0.019907963 0.9900460
[244,] 0.0066256561 0.013251312 0.9933743
[245,] 0.0053518269 0.010703654 0.9946482
[246,] 0.0048852984 0.009770597 0.9951147
[247,] 0.0027870662 0.005574132 0.9972129
[248,] 0.0036876436 0.007375287 0.9963124
[249,] 0.0016994959 0.003398992 0.9983005
[250,] 0.0008932529 0.001786506 0.9991067
[251,] 0.0016608284 0.003321657 0.9983392
> postscript(file="/var/wessaorg/rcomp/tmp/1pych1351951242.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/2islx1351951242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3gpj01351951242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4srev1351951242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5srwk1351951243.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 264
Frequency = 1
1 2 3 4 5 6
-2.970582396 0.410367813 3.922387460 0.096203846 -2.094422628 -2.118159183
7 8 9 10 11 12
4.129521282 -0.656267059 -1.639608341 -1.111081346 0.708024431 -0.044446474
13 14 15 16 17 18
1.062886410 -0.251580297 0.624579471 -0.184794054 -0.027636386 3.607920753
19 20 21 22 23 24
2.029417604 0.691365713 0.253210144 1.551018133 3.029417604 0.153257836
25 26 27 28 29 30
1.550866763 2.674858366 0.219892708 -0.813424728 0.674706995 -0.763297203
31 32 33 34 35 36
-0.151476618 -2.887137436 -1.291975569 0.046076322 -0.713321049 -5.465791955
37 38 39 40 41 42
-4.167983966 -1.325293005 0.708024431 0.955704896 0.831864664 -1.465791955
43 44 45 46 47 48
4.691365713 -0.903796154 -0.539504663 -4.077612540 -2.268239015 -0.482450673
49 50 51 52 53 54
-0.392079247 -0.887137436 -0.251580297 -0.713321049 -2.763297203 0.310415505
55 56 57 58 59 60
-5.746638485 -1.639608341 0.377050377 0.517549327 -0.375269159 0.939046178
61 62 63 64 65 66
-1.556012011 0.798547228 0.922387460 -1.499109391 0.989022332 2.360543029
67 68 69 70 71 72
3.580724473 3.754540859 -1.128696745 0.406756716 -5.121770279 -0.824113660
73 74 75 76 77 78
2.580573102 2.563914384 0.249599048 3.730804305 2.399830250 0.597231820
79 80 81 82 83 84
-0.897826369 1.821175731 2.730804305 1.035387389 -0.121770279 0.366361444
85 86 87 88 89 90
1.349702726 -0.038325319 0.563914384 1.490201676 0.945015963 -1.369148003
91 92 93 94 95 96
1.754389488 1.945015963 -0.750400952 -0.566852314 2.259331300 0.894888439
97 98 99 100 101 102
1.614041909 1.961674681 -0.931295175 1.035387389 0.754389488 5.614041909
103 104 105 106 107 108
-0.262269230 2.854644537 -2.436085616 3.507011765 1.052046107 0.563914384
109 110 111 112 113 114
-2.104960191 0.794936131 1.349702726 3.068856196 -2.469403052 -2.352640656
115 116 117 118 119 120
2.928205875 -0.476480888 -1.452744334 -2.452744334 1.333044008 -1.824113660
121 122 123 124 125 126
1.299726572 -0.409694646 0.563914384 -0.278927947 1.821175731 0.787858295
127 128 129 130 131 132
2.383020162 -0.717083517 -3.526608412 1.506860394 -4.212141705 2.507011765
133 134 135 136 137 138
-3.121770279 -0.021666601 -0.278927947 -1.005007883 2.797590547 -3.666955992
139 140 141 142 143 144
-2.245459141 -3.650297274 1.473542958 0.349702726 1.737730770 1.333044008
145 146 147 148 149 150
-2.921562923 3.680676780 -0.690692546 1.597383191 -0.757327418 -3.492988235
151 152 153 154 155 156
-0.576584566 1.159227622 0.580573102 -1.014588765 -3.897826369 2.945015963
157 158 159 160 161 162
-1.981271329 -0.526608412 1.945015963 -2.857431096 -1.683614710 1.141612223
163 164 165 166 167 168
-0.420383578 3.198514843 1.098562535 -2.627517401 0.479512743 -0.982228009
169 170 171 172 173 174
-0.603780847 2.553376822 -0.784675069 1.569884169 -1.132307842 -3.122726960
175 176 177 178 179 180
-1.015696816 1.305696357 -0.817992505 -0.768016351 0.512830179 -1.727621079
181 182 183 184 185 186
2.008039738 -1.908515301 4.867540788 1.636670412 -5.015696816 2.148538689
187 188 189 190 191 192
3.322355075 -1.346670870 -1.594199965 -2.570463411 0.429536589 1.379409065
193 194 195 196 197 198
-1.660986207 2.272378921 -0.503828539 0.974722302 2.793828080 1.850882070
199 200 201 202 203 204
1.322506445 2.777169362 3.653480500 0.810486798 -2.627668771 -1.222830638
205 206 207 208 209 210
1.181856125 0.867540788 1.850882070 1.727041838 -2.379988306 1.198514843
211 212 213 214 215 216
-0.817992505 -3.530219509 0.958063584 2.198666213 2.586694258 1.596275139
217 218 219 220 221 222
2.743700556 0.322355075 0.008191109 0.396067783 -0.098990406 -0.272958162
223 224 225 226 227 228
2.198666213 -1.503828539 -0.289465510 -6.272958162 -0.065824340 0.603352976
229 230 231 232 233 234
-1.677493555 -1.413305742 -0.049014252 -3.603780847 2.181856125 4.620011694
235 236 237 238 239 240
-0.644327489 -1.784675069 -8.768016351 0.562957704 3.355823881 -2.627517401
241 242 243 244 245 246
-1.082331688 -0.991808891 -2.453701014 -0.239489356 1.653329130 -1.413305742
247 248 249 250 251 252
0.305696357 -3.363329588 0.927400565 0.803560332 -2.818143875 -3.222830638
253 254 255 256 257 258
-1.537145975 2.777169362 -1.239489356 0.760510644 2.553376822 1.339165163
259 260 261 262 263 264
-0.818143875 -8.487018450 1.950985748 -3.446774549 -2.008467609 0.636670412
> postscript(file="/var/wessaorg/rcomp/tmp/6fm4v1351951243.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 = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.970582396 NA
1 0.410367813 -2.970582396
2 3.922387460 0.410367813
3 0.096203846 3.922387460
4 -2.094422628 0.096203846
5 -2.118159183 -2.094422628
6 4.129521282 -2.118159183
7 -0.656267059 4.129521282
8 -1.639608341 -0.656267059
9 -1.111081346 -1.639608341
10 0.708024431 -1.111081346
11 -0.044446474 0.708024431
12 1.062886410 -0.044446474
13 -0.251580297 1.062886410
14 0.624579471 -0.251580297
15 -0.184794054 0.624579471
16 -0.027636386 -0.184794054
17 3.607920753 -0.027636386
18 2.029417604 3.607920753
19 0.691365713 2.029417604
20 0.253210144 0.691365713
21 1.551018133 0.253210144
22 3.029417604 1.551018133
23 0.153257836 3.029417604
24 1.550866763 0.153257836
25 2.674858366 1.550866763
26 0.219892708 2.674858366
27 -0.813424728 0.219892708
28 0.674706995 -0.813424728
29 -0.763297203 0.674706995
30 -0.151476618 -0.763297203
31 -2.887137436 -0.151476618
32 -1.291975569 -2.887137436
33 0.046076322 -1.291975569
34 -0.713321049 0.046076322
35 -5.465791955 -0.713321049
36 -4.167983966 -5.465791955
37 -1.325293005 -4.167983966
38 0.708024431 -1.325293005
39 0.955704896 0.708024431
40 0.831864664 0.955704896
41 -1.465791955 0.831864664
42 4.691365713 -1.465791955
43 -0.903796154 4.691365713
44 -0.539504663 -0.903796154
45 -4.077612540 -0.539504663
46 -2.268239015 -4.077612540
47 -0.482450673 -2.268239015
48 -0.392079247 -0.482450673
49 -0.887137436 -0.392079247
50 -0.251580297 -0.887137436
51 -0.713321049 -0.251580297
52 -2.763297203 -0.713321049
53 0.310415505 -2.763297203
54 -5.746638485 0.310415505
55 -1.639608341 -5.746638485
56 0.377050377 -1.639608341
57 0.517549327 0.377050377
58 -0.375269159 0.517549327
59 0.939046178 -0.375269159
60 -1.556012011 0.939046178
61 0.798547228 -1.556012011
62 0.922387460 0.798547228
63 -1.499109391 0.922387460
64 0.989022332 -1.499109391
65 2.360543029 0.989022332
66 3.580724473 2.360543029
67 3.754540859 3.580724473
68 -1.128696745 3.754540859
69 0.406756716 -1.128696745
70 -5.121770279 0.406756716
71 -0.824113660 -5.121770279
72 2.580573102 -0.824113660
73 2.563914384 2.580573102
74 0.249599048 2.563914384
75 3.730804305 0.249599048
76 2.399830250 3.730804305
77 0.597231820 2.399830250
78 -0.897826369 0.597231820
79 1.821175731 -0.897826369
80 2.730804305 1.821175731
81 1.035387389 2.730804305
82 -0.121770279 1.035387389
83 0.366361444 -0.121770279
84 1.349702726 0.366361444
85 -0.038325319 1.349702726
86 0.563914384 -0.038325319
87 1.490201676 0.563914384
88 0.945015963 1.490201676
89 -1.369148003 0.945015963
90 1.754389488 -1.369148003
91 1.945015963 1.754389488
92 -0.750400952 1.945015963
93 -0.566852314 -0.750400952
94 2.259331300 -0.566852314
95 0.894888439 2.259331300
96 1.614041909 0.894888439
97 1.961674681 1.614041909
98 -0.931295175 1.961674681
99 1.035387389 -0.931295175
100 0.754389488 1.035387389
101 5.614041909 0.754389488
102 -0.262269230 5.614041909
103 2.854644537 -0.262269230
104 -2.436085616 2.854644537
105 3.507011765 -2.436085616
106 1.052046107 3.507011765
107 0.563914384 1.052046107
108 -2.104960191 0.563914384
109 0.794936131 -2.104960191
110 1.349702726 0.794936131
111 3.068856196 1.349702726
112 -2.469403052 3.068856196
113 -2.352640656 -2.469403052
114 2.928205875 -2.352640656
115 -0.476480888 2.928205875
116 -1.452744334 -0.476480888
117 -2.452744334 -1.452744334
118 1.333044008 -2.452744334
119 -1.824113660 1.333044008
120 1.299726572 -1.824113660
121 -0.409694646 1.299726572
122 0.563914384 -0.409694646
123 -0.278927947 0.563914384
124 1.821175731 -0.278927947
125 0.787858295 1.821175731
126 2.383020162 0.787858295
127 -0.717083517 2.383020162
128 -3.526608412 -0.717083517
129 1.506860394 -3.526608412
130 -4.212141705 1.506860394
131 2.507011765 -4.212141705
132 -3.121770279 2.507011765
133 -0.021666601 -3.121770279
134 -0.278927947 -0.021666601
135 -1.005007883 -0.278927947
136 2.797590547 -1.005007883
137 -3.666955992 2.797590547
138 -2.245459141 -3.666955992
139 -3.650297274 -2.245459141
140 1.473542958 -3.650297274
141 0.349702726 1.473542958
142 1.737730770 0.349702726
143 1.333044008 1.737730770
144 -2.921562923 1.333044008
145 3.680676780 -2.921562923
146 -0.690692546 3.680676780
147 1.597383191 -0.690692546
148 -0.757327418 1.597383191
149 -3.492988235 -0.757327418
150 -0.576584566 -3.492988235
151 1.159227622 -0.576584566
152 0.580573102 1.159227622
153 -1.014588765 0.580573102
154 -3.897826369 -1.014588765
155 2.945015963 -3.897826369
156 -1.981271329 2.945015963
157 -0.526608412 -1.981271329
158 1.945015963 -0.526608412
159 -2.857431096 1.945015963
160 -1.683614710 -2.857431096
161 1.141612223 -1.683614710
162 -0.420383578 1.141612223
163 3.198514843 -0.420383578
164 1.098562535 3.198514843
165 -2.627517401 1.098562535
166 0.479512743 -2.627517401
167 -0.982228009 0.479512743
168 -0.603780847 -0.982228009
169 2.553376822 -0.603780847
170 -0.784675069 2.553376822
171 1.569884169 -0.784675069
172 -1.132307842 1.569884169
173 -3.122726960 -1.132307842
174 -1.015696816 -3.122726960
175 1.305696357 -1.015696816
176 -0.817992505 1.305696357
177 -0.768016351 -0.817992505
178 0.512830179 -0.768016351
179 -1.727621079 0.512830179
180 2.008039738 -1.727621079
181 -1.908515301 2.008039738
182 4.867540788 -1.908515301
183 1.636670412 4.867540788
184 -5.015696816 1.636670412
185 2.148538689 -5.015696816
186 3.322355075 2.148538689
187 -1.346670870 3.322355075
188 -1.594199965 -1.346670870
189 -2.570463411 -1.594199965
190 0.429536589 -2.570463411
191 1.379409065 0.429536589
192 -1.660986207 1.379409065
193 2.272378921 -1.660986207
194 -0.503828539 2.272378921
195 0.974722302 -0.503828539
196 2.793828080 0.974722302
197 1.850882070 2.793828080
198 1.322506445 1.850882070
199 2.777169362 1.322506445
200 3.653480500 2.777169362
201 0.810486798 3.653480500
202 -2.627668771 0.810486798
203 -1.222830638 -2.627668771
204 1.181856125 -1.222830638
205 0.867540788 1.181856125
206 1.850882070 0.867540788
207 1.727041838 1.850882070
208 -2.379988306 1.727041838
209 1.198514843 -2.379988306
210 -0.817992505 1.198514843
211 -3.530219509 -0.817992505
212 0.958063584 -3.530219509
213 2.198666213 0.958063584
214 2.586694258 2.198666213
215 1.596275139 2.586694258
216 2.743700556 1.596275139
217 0.322355075 2.743700556
218 0.008191109 0.322355075
219 0.396067783 0.008191109
220 -0.098990406 0.396067783
221 -0.272958162 -0.098990406
222 2.198666213 -0.272958162
223 -1.503828539 2.198666213
224 -0.289465510 -1.503828539
225 -6.272958162 -0.289465510
226 -0.065824340 -6.272958162
227 0.603352976 -0.065824340
228 -1.677493555 0.603352976
229 -1.413305742 -1.677493555
230 -0.049014252 -1.413305742
231 -3.603780847 -0.049014252
232 2.181856125 -3.603780847
233 4.620011694 2.181856125
234 -0.644327489 4.620011694
235 -1.784675069 -0.644327489
236 -8.768016351 -1.784675069
237 0.562957704 -8.768016351
238 3.355823881 0.562957704
239 -2.627517401 3.355823881
240 -1.082331688 -2.627517401
241 -0.991808891 -1.082331688
242 -2.453701014 -0.991808891
243 -0.239489356 -2.453701014
244 1.653329130 -0.239489356
245 -1.413305742 1.653329130
246 0.305696357 -1.413305742
247 -3.363329588 0.305696357
248 0.927400565 -3.363329588
249 0.803560332 0.927400565
250 -2.818143875 0.803560332
251 -3.222830638 -2.818143875
252 -1.537145975 -3.222830638
253 2.777169362 -1.537145975
254 -1.239489356 2.777169362
255 0.760510644 -1.239489356
256 2.553376822 0.760510644
257 1.339165163 2.553376822
258 -0.818143875 1.339165163
259 -8.487018450 -0.818143875
260 1.950985748 -8.487018450
261 -3.446774549 1.950985748
262 -2.008467609 -3.446774549
263 0.636670412 -2.008467609
264 NA 0.636670412
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.410367813 -2.970582396
[2,] 3.922387460 0.410367813
[3,] 0.096203846 3.922387460
[4,] -2.094422628 0.096203846
[5,] -2.118159183 -2.094422628
[6,] 4.129521282 -2.118159183
[7,] -0.656267059 4.129521282
[8,] -1.639608341 -0.656267059
[9,] -1.111081346 -1.639608341
[10,] 0.708024431 -1.111081346
[11,] -0.044446474 0.708024431
[12,] 1.062886410 -0.044446474
[13,] -0.251580297 1.062886410
[14,] 0.624579471 -0.251580297
[15,] -0.184794054 0.624579471
[16,] -0.027636386 -0.184794054
[17,] 3.607920753 -0.027636386
[18,] 2.029417604 3.607920753
[19,] 0.691365713 2.029417604
[20,] 0.253210144 0.691365713
[21,] 1.551018133 0.253210144
[22,] 3.029417604 1.551018133
[23,] 0.153257836 3.029417604
[24,] 1.550866763 0.153257836
[25,] 2.674858366 1.550866763
[26,] 0.219892708 2.674858366
[27,] -0.813424728 0.219892708
[28,] 0.674706995 -0.813424728
[29,] -0.763297203 0.674706995
[30,] -0.151476618 -0.763297203
[31,] -2.887137436 -0.151476618
[32,] -1.291975569 -2.887137436
[33,] 0.046076322 -1.291975569
[34,] -0.713321049 0.046076322
[35,] -5.465791955 -0.713321049
[36,] -4.167983966 -5.465791955
[37,] -1.325293005 -4.167983966
[38,] 0.708024431 -1.325293005
[39,] 0.955704896 0.708024431
[40,] 0.831864664 0.955704896
[41,] -1.465791955 0.831864664
[42,] 4.691365713 -1.465791955
[43,] -0.903796154 4.691365713
[44,] -0.539504663 -0.903796154
[45,] -4.077612540 -0.539504663
[46,] -2.268239015 -4.077612540
[47,] -0.482450673 -2.268239015
[48,] -0.392079247 -0.482450673
[49,] -0.887137436 -0.392079247
[50,] -0.251580297 -0.887137436
[51,] -0.713321049 -0.251580297
[52,] -2.763297203 -0.713321049
[53,] 0.310415505 -2.763297203
[54,] -5.746638485 0.310415505
[55,] -1.639608341 -5.746638485
[56,] 0.377050377 -1.639608341
[57,] 0.517549327 0.377050377
[58,] -0.375269159 0.517549327
[59,] 0.939046178 -0.375269159
[60,] -1.556012011 0.939046178
[61,] 0.798547228 -1.556012011
[62,] 0.922387460 0.798547228
[63,] -1.499109391 0.922387460
[64,] 0.989022332 -1.499109391
[65,] 2.360543029 0.989022332
[66,] 3.580724473 2.360543029
[67,] 3.754540859 3.580724473
[68,] -1.128696745 3.754540859
[69,] 0.406756716 -1.128696745
[70,] -5.121770279 0.406756716
[71,] -0.824113660 -5.121770279
[72,] 2.580573102 -0.824113660
[73,] 2.563914384 2.580573102
[74,] 0.249599048 2.563914384
[75,] 3.730804305 0.249599048
[76,] 2.399830250 3.730804305
[77,] 0.597231820 2.399830250
[78,] -0.897826369 0.597231820
[79,] 1.821175731 -0.897826369
[80,] 2.730804305 1.821175731
[81,] 1.035387389 2.730804305
[82,] -0.121770279 1.035387389
[83,] 0.366361444 -0.121770279
[84,] 1.349702726 0.366361444
[85,] -0.038325319 1.349702726
[86,] 0.563914384 -0.038325319
[87,] 1.490201676 0.563914384
[88,] 0.945015963 1.490201676
[89,] -1.369148003 0.945015963
[90,] 1.754389488 -1.369148003
[91,] 1.945015963 1.754389488
[92,] -0.750400952 1.945015963
[93,] -0.566852314 -0.750400952
[94,] 2.259331300 -0.566852314
[95,] 0.894888439 2.259331300
[96,] 1.614041909 0.894888439
[97,] 1.961674681 1.614041909
[98,] -0.931295175 1.961674681
[99,] 1.035387389 -0.931295175
[100,] 0.754389488 1.035387389
[101,] 5.614041909 0.754389488
[102,] -0.262269230 5.614041909
[103,] 2.854644537 -0.262269230
[104,] -2.436085616 2.854644537
[105,] 3.507011765 -2.436085616
[106,] 1.052046107 3.507011765
[107,] 0.563914384 1.052046107
[108,] -2.104960191 0.563914384
[109,] 0.794936131 -2.104960191
[110,] 1.349702726 0.794936131
[111,] 3.068856196 1.349702726
[112,] -2.469403052 3.068856196
[113,] -2.352640656 -2.469403052
[114,] 2.928205875 -2.352640656
[115,] -0.476480888 2.928205875
[116,] -1.452744334 -0.476480888
[117,] -2.452744334 -1.452744334
[118,] 1.333044008 -2.452744334
[119,] -1.824113660 1.333044008
[120,] 1.299726572 -1.824113660
[121,] -0.409694646 1.299726572
[122,] 0.563914384 -0.409694646
[123,] -0.278927947 0.563914384
[124,] 1.821175731 -0.278927947
[125,] 0.787858295 1.821175731
[126,] 2.383020162 0.787858295
[127,] -0.717083517 2.383020162
[128,] -3.526608412 -0.717083517
[129,] 1.506860394 -3.526608412
[130,] -4.212141705 1.506860394
[131,] 2.507011765 -4.212141705
[132,] -3.121770279 2.507011765
[133,] -0.021666601 -3.121770279
[134,] -0.278927947 -0.021666601
[135,] -1.005007883 -0.278927947
[136,] 2.797590547 -1.005007883
[137,] -3.666955992 2.797590547
[138,] -2.245459141 -3.666955992
[139,] -3.650297274 -2.245459141
[140,] 1.473542958 -3.650297274
[141,] 0.349702726 1.473542958
[142,] 1.737730770 0.349702726
[143,] 1.333044008 1.737730770
[144,] -2.921562923 1.333044008
[145,] 3.680676780 -2.921562923
[146,] -0.690692546 3.680676780
[147,] 1.597383191 -0.690692546
[148,] -0.757327418 1.597383191
[149,] -3.492988235 -0.757327418
[150,] -0.576584566 -3.492988235
[151,] 1.159227622 -0.576584566
[152,] 0.580573102 1.159227622
[153,] -1.014588765 0.580573102
[154,] -3.897826369 -1.014588765
[155,] 2.945015963 -3.897826369
[156,] -1.981271329 2.945015963
[157,] -0.526608412 -1.981271329
[158,] 1.945015963 -0.526608412
[159,] -2.857431096 1.945015963
[160,] -1.683614710 -2.857431096
[161,] 1.141612223 -1.683614710
[162,] -0.420383578 1.141612223
[163,] 3.198514843 -0.420383578
[164,] 1.098562535 3.198514843
[165,] -2.627517401 1.098562535
[166,] 0.479512743 -2.627517401
[167,] -0.982228009 0.479512743
[168,] -0.603780847 -0.982228009
[169,] 2.553376822 -0.603780847
[170,] -0.784675069 2.553376822
[171,] 1.569884169 -0.784675069
[172,] -1.132307842 1.569884169
[173,] -3.122726960 -1.132307842
[174,] -1.015696816 -3.122726960
[175,] 1.305696357 -1.015696816
[176,] -0.817992505 1.305696357
[177,] -0.768016351 -0.817992505
[178,] 0.512830179 -0.768016351
[179,] -1.727621079 0.512830179
[180,] 2.008039738 -1.727621079
[181,] -1.908515301 2.008039738
[182,] 4.867540788 -1.908515301
[183,] 1.636670412 4.867540788
[184,] -5.015696816 1.636670412
[185,] 2.148538689 -5.015696816
[186,] 3.322355075 2.148538689
[187,] -1.346670870 3.322355075
[188,] -1.594199965 -1.346670870
[189,] -2.570463411 -1.594199965
[190,] 0.429536589 -2.570463411
[191,] 1.379409065 0.429536589
[192,] -1.660986207 1.379409065
[193,] 2.272378921 -1.660986207
[194,] -0.503828539 2.272378921
[195,] 0.974722302 -0.503828539
[196,] 2.793828080 0.974722302
[197,] 1.850882070 2.793828080
[198,] 1.322506445 1.850882070
[199,] 2.777169362 1.322506445
[200,] 3.653480500 2.777169362
[201,] 0.810486798 3.653480500
[202,] -2.627668771 0.810486798
[203,] -1.222830638 -2.627668771
[204,] 1.181856125 -1.222830638
[205,] 0.867540788 1.181856125
[206,] 1.850882070 0.867540788
[207,] 1.727041838 1.850882070
[208,] -2.379988306 1.727041838
[209,] 1.198514843 -2.379988306
[210,] -0.817992505 1.198514843
[211,] -3.530219509 -0.817992505
[212,] 0.958063584 -3.530219509
[213,] 2.198666213 0.958063584
[214,] 2.586694258 2.198666213
[215,] 1.596275139 2.586694258
[216,] 2.743700556 1.596275139
[217,] 0.322355075 2.743700556
[218,] 0.008191109 0.322355075
[219,] 0.396067783 0.008191109
[220,] -0.098990406 0.396067783
[221,] -0.272958162 -0.098990406
[222,] 2.198666213 -0.272958162
[223,] -1.503828539 2.198666213
[224,] -0.289465510 -1.503828539
[225,] -6.272958162 -0.289465510
[226,] -0.065824340 -6.272958162
[227,] 0.603352976 -0.065824340
[228,] -1.677493555 0.603352976
[229,] -1.413305742 -1.677493555
[230,] -0.049014252 -1.413305742
[231,] -3.603780847 -0.049014252
[232,] 2.181856125 -3.603780847
[233,] 4.620011694 2.181856125
[234,] -0.644327489 4.620011694
[235,] -1.784675069 -0.644327489
[236,] -8.768016351 -1.784675069
[237,] 0.562957704 -8.768016351
[238,] 3.355823881 0.562957704
[239,] -2.627517401 3.355823881
[240,] -1.082331688 -2.627517401
[241,] -0.991808891 -1.082331688
[242,] -2.453701014 -0.991808891
[243,] -0.239489356 -2.453701014
[244,] 1.653329130 -0.239489356
[245,] -1.413305742 1.653329130
[246,] 0.305696357 -1.413305742
[247,] -3.363329588 0.305696357
[248,] 0.927400565 -3.363329588
[249,] 0.803560332 0.927400565
[250,] -2.818143875 0.803560332
[251,] -3.222830638 -2.818143875
[252,] -1.537145975 -3.222830638
[253,] 2.777169362 -1.537145975
[254,] -1.239489356 2.777169362
[255,] 0.760510644 -1.239489356
[256,] 2.553376822 0.760510644
[257,] 1.339165163 2.553376822
[258,] -0.818143875 1.339165163
[259,] -8.487018450 -0.818143875
[260,] 1.950985748 -8.487018450
[261,] -3.446774549 1.950985748
[262,] -2.008467609 -3.446774549
[263,] 0.636670412 -2.008467609
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.410367813 -2.970582396
2 3.922387460 0.410367813
3 0.096203846 3.922387460
4 -2.094422628 0.096203846
5 -2.118159183 -2.094422628
6 4.129521282 -2.118159183
7 -0.656267059 4.129521282
8 -1.639608341 -0.656267059
9 -1.111081346 -1.639608341
10 0.708024431 -1.111081346
11 -0.044446474 0.708024431
12 1.062886410 -0.044446474
13 -0.251580297 1.062886410
14 0.624579471 -0.251580297
15 -0.184794054 0.624579471
16 -0.027636386 -0.184794054
17 3.607920753 -0.027636386
18 2.029417604 3.607920753
19 0.691365713 2.029417604
20 0.253210144 0.691365713
21 1.551018133 0.253210144
22 3.029417604 1.551018133
23 0.153257836 3.029417604
24 1.550866763 0.153257836
25 2.674858366 1.550866763
26 0.219892708 2.674858366
27 -0.813424728 0.219892708
28 0.674706995 -0.813424728
29 -0.763297203 0.674706995
30 -0.151476618 -0.763297203
31 -2.887137436 -0.151476618
32 -1.291975569 -2.887137436
33 0.046076322 -1.291975569
34 -0.713321049 0.046076322
35 -5.465791955 -0.713321049
36 -4.167983966 -5.465791955
37 -1.325293005 -4.167983966
38 0.708024431 -1.325293005
39 0.955704896 0.708024431
40 0.831864664 0.955704896
41 -1.465791955 0.831864664
42 4.691365713 -1.465791955
43 -0.903796154 4.691365713
44 -0.539504663 -0.903796154
45 -4.077612540 -0.539504663
46 -2.268239015 -4.077612540
47 -0.482450673 -2.268239015
48 -0.392079247 -0.482450673
49 -0.887137436 -0.392079247
50 -0.251580297 -0.887137436
51 -0.713321049 -0.251580297
52 -2.763297203 -0.713321049
53 0.310415505 -2.763297203
54 -5.746638485 0.310415505
55 -1.639608341 -5.746638485
56 0.377050377 -1.639608341
57 0.517549327 0.377050377
58 -0.375269159 0.517549327
59 0.939046178 -0.375269159
60 -1.556012011 0.939046178
61 0.798547228 -1.556012011
62 0.922387460 0.798547228
63 -1.499109391 0.922387460
64 0.989022332 -1.499109391
65 2.360543029 0.989022332
66 3.580724473 2.360543029
67 3.754540859 3.580724473
68 -1.128696745 3.754540859
69 0.406756716 -1.128696745
70 -5.121770279 0.406756716
71 -0.824113660 -5.121770279
72 2.580573102 -0.824113660
73 2.563914384 2.580573102
74 0.249599048 2.563914384
75 3.730804305 0.249599048
76 2.399830250 3.730804305
77 0.597231820 2.399830250
78 -0.897826369 0.597231820
79 1.821175731 -0.897826369
80 2.730804305 1.821175731
81 1.035387389 2.730804305
82 -0.121770279 1.035387389
83 0.366361444 -0.121770279
84 1.349702726 0.366361444
85 -0.038325319 1.349702726
86 0.563914384 -0.038325319
87 1.490201676 0.563914384
88 0.945015963 1.490201676
89 -1.369148003 0.945015963
90 1.754389488 -1.369148003
91 1.945015963 1.754389488
92 -0.750400952 1.945015963
93 -0.566852314 -0.750400952
94 2.259331300 -0.566852314
95 0.894888439 2.259331300
96 1.614041909 0.894888439
97 1.961674681 1.614041909
98 -0.931295175 1.961674681
99 1.035387389 -0.931295175
100 0.754389488 1.035387389
101 5.614041909 0.754389488
102 -0.262269230 5.614041909
103 2.854644537 -0.262269230
104 -2.436085616 2.854644537
105 3.507011765 -2.436085616
106 1.052046107 3.507011765
107 0.563914384 1.052046107
108 -2.104960191 0.563914384
109 0.794936131 -2.104960191
110 1.349702726 0.794936131
111 3.068856196 1.349702726
112 -2.469403052 3.068856196
113 -2.352640656 -2.469403052
114 2.928205875 -2.352640656
115 -0.476480888 2.928205875
116 -1.452744334 -0.476480888
117 -2.452744334 -1.452744334
118 1.333044008 -2.452744334
119 -1.824113660 1.333044008
120 1.299726572 -1.824113660
121 -0.409694646 1.299726572
122 0.563914384 -0.409694646
123 -0.278927947 0.563914384
124 1.821175731 -0.278927947
125 0.787858295 1.821175731
126 2.383020162 0.787858295
127 -0.717083517 2.383020162
128 -3.526608412 -0.717083517
129 1.506860394 -3.526608412
130 -4.212141705 1.506860394
131 2.507011765 -4.212141705
132 -3.121770279 2.507011765
133 -0.021666601 -3.121770279
134 -0.278927947 -0.021666601
135 -1.005007883 -0.278927947
136 2.797590547 -1.005007883
137 -3.666955992 2.797590547
138 -2.245459141 -3.666955992
139 -3.650297274 -2.245459141
140 1.473542958 -3.650297274
141 0.349702726 1.473542958
142 1.737730770 0.349702726
143 1.333044008 1.737730770
144 -2.921562923 1.333044008
145 3.680676780 -2.921562923
146 -0.690692546 3.680676780
147 1.597383191 -0.690692546
148 -0.757327418 1.597383191
149 -3.492988235 -0.757327418
150 -0.576584566 -3.492988235
151 1.159227622 -0.576584566
152 0.580573102 1.159227622
153 -1.014588765 0.580573102
154 -3.897826369 -1.014588765
155 2.945015963 -3.897826369
156 -1.981271329 2.945015963
157 -0.526608412 -1.981271329
158 1.945015963 -0.526608412
159 -2.857431096 1.945015963
160 -1.683614710 -2.857431096
161 1.141612223 -1.683614710
162 -0.420383578 1.141612223
163 3.198514843 -0.420383578
164 1.098562535 3.198514843
165 -2.627517401 1.098562535
166 0.479512743 -2.627517401
167 -0.982228009 0.479512743
168 -0.603780847 -0.982228009
169 2.553376822 -0.603780847
170 -0.784675069 2.553376822
171 1.569884169 -0.784675069
172 -1.132307842 1.569884169
173 -3.122726960 -1.132307842
174 -1.015696816 -3.122726960
175 1.305696357 -1.015696816
176 -0.817992505 1.305696357
177 -0.768016351 -0.817992505
178 0.512830179 -0.768016351
179 -1.727621079 0.512830179
180 2.008039738 -1.727621079
181 -1.908515301 2.008039738
182 4.867540788 -1.908515301
183 1.636670412 4.867540788
184 -5.015696816 1.636670412
185 2.148538689 -5.015696816
186 3.322355075 2.148538689
187 -1.346670870 3.322355075
188 -1.594199965 -1.346670870
189 -2.570463411 -1.594199965
190 0.429536589 -2.570463411
191 1.379409065 0.429536589
192 -1.660986207 1.379409065
193 2.272378921 -1.660986207
194 -0.503828539 2.272378921
195 0.974722302 -0.503828539
196 2.793828080 0.974722302
197 1.850882070 2.793828080
198 1.322506445 1.850882070
199 2.777169362 1.322506445
200 3.653480500 2.777169362
201 0.810486798 3.653480500
202 -2.627668771 0.810486798
203 -1.222830638 -2.627668771
204 1.181856125 -1.222830638
205 0.867540788 1.181856125
206 1.850882070 0.867540788
207 1.727041838 1.850882070
208 -2.379988306 1.727041838
209 1.198514843 -2.379988306
210 -0.817992505 1.198514843
211 -3.530219509 -0.817992505
212 0.958063584 -3.530219509
213 2.198666213 0.958063584
214 2.586694258 2.198666213
215 1.596275139 2.586694258
216 2.743700556 1.596275139
217 0.322355075 2.743700556
218 0.008191109 0.322355075
219 0.396067783 0.008191109
220 -0.098990406 0.396067783
221 -0.272958162 -0.098990406
222 2.198666213 -0.272958162
223 -1.503828539 2.198666213
224 -0.289465510 -1.503828539
225 -6.272958162 -0.289465510
226 -0.065824340 -6.272958162
227 0.603352976 -0.065824340
228 -1.677493555 0.603352976
229 -1.413305742 -1.677493555
230 -0.049014252 -1.413305742
231 -3.603780847 -0.049014252
232 2.181856125 -3.603780847
233 4.620011694 2.181856125
234 -0.644327489 4.620011694
235 -1.784675069 -0.644327489
236 -8.768016351 -1.784675069
237 0.562957704 -8.768016351
238 3.355823881 0.562957704
239 -2.627517401 3.355823881
240 -1.082331688 -2.627517401
241 -0.991808891 -1.082331688
242 -2.453701014 -0.991808891
243 -0.239489356 -2.453701014
244 1.653329130 -0.239489356
245 -1.413305742 1.653329130
246 0.305696357 -1.413305742
247 -3.363329588 0.305696357
248 0.927400565 -3.363329588
249 0.803560332 0.927400565
250 -2.818143875 0.803560332
251 -3.222830638 -2.818143875
252 -1.537145975 -3.222830638
253 2.777169362 -1.537145975
254 -1.239489356 2.777169362
255 0.760510644 -1.239489356
256 2.553376822 0.760510644
257 1.339165163 2.553376822
258 -0.818143875 1.339165163
259 -8.487018450 -0.818143875
260 1.950985748 -8.487018450
261 -3.446774549 1.950985748
262 -2.008467609 -3.446774549
263 0.636670412 -2.008467609
> 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/7tfyh1351951243.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/8d45f1351951243.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/96aih1351951243.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/104eoa1351951243.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/117y6t1351951243.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12a1dd1351951243.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/134wx41351951243.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/149tc31351951243.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/15b47a1351951243.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/160hfh1351951243.tab")
+ }
>
> try(system("convert tmp/1pych1351951242.ps tmp/1pych1351951242.png",intern=TRUE))
character(0)
> try(system("convert tmp/2islx1351951242.ps tmp/2islx1351951242.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gpj01351951242.ps tmp/3gpj01351951242.png",intern=TRUE))
character(0)
> try(system("convert tmp/4srev1351951242.ps tmp/4srev1351951242.png",intern=TRUE))
character(0)
> try(system("convert tmp/5srwk1351951243.ps tmp/5srwk1351951243.png",intern=TRUE))
character(0)
> try(system("convert tmp/6fm4v1351951243.ps tmp/6fm4v1351951243.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tfyh1351951243.ps tmp/7tfyh1351951243.png",intern=TRUE))
character(0)
> try(system("convert tmp/8d45f1351951243.ps tmp/8d45f1351951243.png",intern=TRUE))
character(0)
> try(system("convert tmp/96aih1351951243.ps tmp/96aih1351951243.png",intern=TRUE))
character(0)
> try(system("convert tmp/104eoa1351951243.ps tmp/104eoa1351951243.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.487 1.364 13.008