R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(41
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+ ,12)
+ ,dim=c(4
+ ,264)
+ ,dimnames=list(c('connected'
+ ,'separated'
+ ,'happiness'
+ ,'depression')
+ ,1:264))
> y <- array(NA,dim=c(4,264),dimnames=list(c('connected','separated','happiness','depression'),1:264))
> 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 = '4'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
depression connected separated happiness
1 12.0 41 38 14
2 11.0 39 32 18
3 14.0 30 35 11
4 12.0 31 33 12
5 21.0 34 37 16
6 12.0 35 29 18
7 22.0 39 31 14
8 11.0 34 36 14
9 10.0 36 35 15
10 13.0 37 38 15
11 10.0 38 31 17
12 8.0 36 34 19
13 15.0 38 35 10
14 14.0 39 38 16
15 10.0 33 37 18
16 14.0 32 33 14
17 14.0 36 32 14
18 11.0 38 38 17
19 10.0 39 38 14
20 13.0 32 32 16
21 9.5 32 33 18
22 14.0 31 31 11
23 12.0 39 38 14
24 14.0 37 39 12
25 11.0 39 32 17
26 9.0 41 32 9
27 11.0 36 35 16
28 15.0 33 37 14
29 14.0 33 33 15
30 13.0 34 33 11
31 9.0 31 31 16
32 15.0 27 32 13
33 10.0 37 31 17
34 11.0 34 37 15
35 13.0 34 30 14
36 8.0 32 33 16
37 20.0 29 31 9
38 12.0 36 33 15
39 10.0 29 31 17
40 10.0 35 33 13
41 9.0 37 32 15
42 14.0 34 33 16
43 8.0 38 32 16
44 14.0 35 33 12
45 11.0 38 28 15
46 13.0 37 35 11
47 9.0 38 39 15
48 11.0 33 34 15
49 15.0 36 38 17
50 11.0 38 32 13
51 10.0 32 38 16
52 14.0 32 30 14
53 18.0 32 33 11
54 14.0 34 38 12
55 11.0 32 32 12
56 14.5 37 35 15
57 13.0 39 34 16
58 9.0 29 34 15
59 10.0 37 36 12
60 15.0 35 34 12
61 20.0 30 28 8
62 12.0 38 34 13
63 12.0 34 35 11
64 14.0 31 35 14
65 13.0 34 31 15
66 11.0 35 37 10
67 17.0 36 35 11
68 12.0 30 27 12
69 13.0 39 40 15
70 14.0 35 37 15
71 13.0 38 36 14
72 15.0 31 38 16
73 13.0 34 39 15
74 10.0 38 41 15
75 11.0 34 27 13
76 19.0 39 30 12
77 13.0 37 37 17
78 17.0 34 31 13
79 13.0 28 31 15
80 9.0 37 27 13
81 11.0 33 36 15
82 9.0 35 37 15
83 12.0 37 33 16
84 12.0 32 34 15
85 13.0 33 31 14
86 13.0 38 39 15
87 12.0 33 34 14
88 15.0 29 32 13
89 22.0 33 33 7
90 13.0 31 36 17
91 15.0 36 32 13
92 13.0 35 41 15
93 15.0 32 28 14
94 12.5 29 30 13
95 11.0 39 36 16
96 16.0 37 35 12
97 11.0 35 31 14
98 11.0 37 34 17
99 10.0 32 36 15
100 10.0 38 36 17
101 16.0 37 35 12
102 12.0 36 37 16
103 11.0 32 28 11
104 16.0 33 39 15
105 19.0 40 32 9
106 11.0 38 35 16
107 16.0 41 39 15
108 15.0 36 35 10
109 24.0 43 42 10
110 14.0 30 34 15
111 15.0 31 33 11
112 11.0 32 41 13
113 15.0 32 33 14
114 12.0 37 34 18
115 10.0 37 32 16
116 14.0 33 40 14
117 13.0 34 40 14
118 9.0 33 35 14
119 15.0 38 36 14
120 15.0 33 37 12
121 14.0 31 27 14
122 11.0 38 39 15
123 8.0 37 38 15
124 11.0 36 31 15
125 11.0 31 33 13
126 8.0 39 32 17
127 10.0 44 39 17
128 11.0 33 36 19
129 13.0 35 33 15
130 11.0 32 33 13
131 20.0 28 32 9
132 10.0 40 37 15
133 15.0 27 30 15
134 12.0 37 38 15
135 14.0 32 29 16
136 23.0 28 22 11
137 14.0 34 35 14
138 16.0 30 35 11
139 11.0 35 34 15
140 12.0 31 35 13
141 10.0 32 34 15
142 14.0 30 37 16
143 12.0 30 35 14
144 12.0 31 23 15
145 11.0 40 31 16
146 12.0 32 27 16
147 13.0 36 36 11
148 11.0 32 31 12
149 19.0 35 32 9
150 12.0 38 39 16
151 17.0 42 37 13
152 9.0 34 38 16
153 12.0 35 39 12
154 19.0 38 34 9
155 18.0 33 31 13
156 15.0 36 32 13
157 14.0 32 37 14
158 11.0 33 36 19
159 9.0 34 32 13
160 18.0 32 38 12
161 16.0 34 36 13
162 24.0 27 26 10
163 14.0 31 26 14
164 20.0 38 33 16
165 18.0 34 39 10
166 23.0 24 30 11
167 12.0 30 33 14
168 14.0 26 25 12
169 16.0 34 38 9
170 18.0 27 37 9
171 20.0 37 31 11
172 12.0 36 37 16
173 12.0 41 35 9
174 17.0 29 25 13
175 13.0 36 28 16
176 9.0 32 35 13
177 16.0 37 33 9
178 18.0 30 30 12
179 10.0 31 31 16
180 14.0 38 37 11
181 11.0 36 36 14
182 9.0 35 30 13
183 11.0 31 36 15
184 10.0 38 32 14
185 11.0 22 28 16
186 19.0 32 36 13
187 14.0 36 34 14
188 12.0 39 31 15
189 14.0 28 28 13
190 21.0 32 36 11
191 13.0 32 36 11
192 10.0 38 40 14
193 15.0 32 33 15
194 16.0 35 37 11
195 14.0 32 32 15
196 12.0 37 38 12
197 19.0 34 31 14
198 15.0 33 37 14
199 19.0 33 33 8
200 13.0 26 32 13
201 17.0 30 30 9
202 12.0 24 30 15
203 11.0 34 31 17
204 14.0 34 32 13
205 11.0 33 34 15
206 13.0 34 36 15
207 12.0 35 37 14
208 15.0 35 36 16
209 14.0 36 33 13
210 12.0 34 33 16
211 17.0 34 33 9
212 11.0 41 44 16
213 18.0 32 39 11
214 13.0 30 32 10
215 17.0 35 35 11
216 13.0 28 25 15
217 11.0 33 35 17
218 12.0 39 34 14
219 22.0 36 35 8
220 14.0 36 39 15
221 12.0 35 33 11
222 12.0 38 36 16
223 17.0 33 32 10
224 9.0 31 32 15
225 21.0 34 36 9
226 10.0 32 36 16
227 11.0 31 32 19
228 12.0 33 34 12
229 23.0 34 33 8
230 13.0 34 35 11
231 12.0 34 30 14
232 16.0 33 38 9
233 9.0 32 34 15
234 17.0 41 33 13
235 9.0 34 32 16
236 14.0 36 31 11
237 17.0 37 30 12
238 13.0 36 27 13
239 11.0 29 31 10
240 12.0 37 30 11
241 10.0 27 32 12
242 19.0 35 35 8
243 16.0 28 28 12
244 16.0 35 33 12
245 14.0 37 31 15
246 20.0 29 35 11
247 15.0 32 35 13
248 23.0 36 32 14
249 20.0 19 21 10
250 16.0 21 20 12
251 14.0 31 34 15
252 17.0 33 32 13
253 11.0 36 34 13
254 13.0 33 32 13
255 17.0 37 33 12
256 15.0 34 33 12
257 21.0 35 37 9
258 18.0 31 32 9
259 15.0 37 34 15
260 8.0 35 30 10
261 12.0 27 30 14
262 12.0 34 38 15
263 22.0 40 36 7
264 12.0 29 32 14
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) connected separated happiness
26.45211 -0.05026 -0.01049 -0.79759
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.402 -1.727 0.037 1.646 9.859
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 26.45211 1.97710 13.379 <2e-16 ***
connected -0.05026 0.05180 -0.970 0.333
separated -0.01049 0.05294 -0.198 0.843
happiness -0.79759 0.07051 -11.311 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.827 on 260 degrees of freedom
Multiple R-squared: 0.3434, Adjusted R-squared: 0.3359
F-statistic: 45.34 on 3 and 260 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,] 0.999071399 0.001857202 0.0009286012
[2,] 0.998784182 0.002431636 0.0012158178
[3,] 0.998782886 0.002434229 0.0012171144
[4,] 0.997287022 0.005425957 0.0027129785
[5,] 0.996944657 0.006110687 0.0030553433
[6,] 0.996092298 0.007815404 0.0039077019
[7,] 0.993528239 0.012943522 0.0064717611
[8,] 0.989687817 0.020624366 0.0103121828
[9,] 0.983025337 0.033949327 0.0169746634
[10,] 0.973841444 0.052317113 0.0261585565
[11,] 0.960137533 0.079724935 0.0398624673
[12,] 0.943046200 0.113907600 0.0569537999
[13,] 0.945750140 0.108499721 0.0542498603
[14,] 0.924742503 0.150514995 0.0752574974
[15,] 0.903707832 0.192584335 0.0962921677
[16,] 0.876930478 0.246139043 0.1230695216
[17,] 0.845227628 0.309544744 0.1547723721
[18,] 0.803894182 0.392211635 0.1961058175
[19,] 0.767073628 0.465852743 0.2329263715
[20,] 0.901994050 0.196011901 0.0980059503
[21,] 0.877316965 0.245366071 0.1226830355
[22,] 0.858564725 0.282870550 0.1414352751
[23,] 0.829741514 0.340516972 0.1702584859
[24,] 0.798529586 0.402940828 0.2014704140
[25,] 0.809740456 0.380519089 0.1902595443
[26,] 0.777502601 0.444994799 0.2224973993
[27,] 0.743356536 0.513286929 0.2566434643
[28,] 0.712876559 0.574246882 0.2871234412
[29,] 0.665716317 0.668567367 0.3342836835
[30,] 0.712654758 0.574690484 0.2873452420
[31,] 0.767193386 0.465613228 0.2328066139
[32,] 0.726050380 0.547899240 0.2739496201
[33,] 0.698135227 0.603729546 0.3018647729
[34,] 0.713076654 0.573846691 0.2869233457
[35,] 0.714749779 0.570500443 0.2852502215
[36,] 0.698123498 0.603753005 0.3018765023
[37,] 0.708144070 0.583711860 0.2918559299
[38,] 0.666807337 0.666385325 0.3331926625
[39,] 0.623916090 0.752167821 0.3760839104
[40,] 0.588329043 0.823341914 0.4116709568
[41,] 0.594809398 0.810381204 0.4051906022
[42,] 0.559394099 0.881211801 0.4406059005
[43,] 0.597598087 0.804803826 0.4024019131
[44,] 0.570657178 0.858685643 0.4293428216
[45,] 0.554772837 0.890454325 0.4452271627
[46,] 0.517533662 0.964932676 0.4824663380
[47,] 0.535592378 0.928815243 0.4644076216
[48,] 0.491549759 0.983099519 0.5084502406
[49,] 0.503698311 0.992603379 0.4963016894
[50,] 0.495112063 0.990224126 0.5048879369
[51,] 0.469242528 0.938485057 0.5307574717
[52,] 0.502812247 0.994375505 0.4971877527
[53,] 0.536165951 0.927668098 0.4638340492
[54,] 0.503542011 0.992915978 0.4964579889
[55,] 0.529655935 0.940688130 0.4703440650
[56,] 0.494985248 0.989970496 0.5050147519
[57,] 0.491955366 0.983910732 0.5080446338
[58,] 0.454225016 0.908450032 0.5457749839
[59,] 0.415677965 0.831355929 0.5843220354
[60,] 0.463199754 0.926399509 0.5368002456
[61,] 0.463219970 0.926439940 0.5367800302
[62,] 0.455240580 0.910481160 0.5447594200
[63,] 0.422220911 0.844441822 0.5777790890
[64,] 0.398842308 0.797684616 0.6011576922
[65,] 0.361810312 0.723620623 0.6381896883
[66,] 0.365668155 0.731336309 0.6343318454
[67,] 0.329689302 0.659378603 0.6703106984
[68,] 0.314423851 0.628847703 0.6855761487
[69,] 0.305615381 0.611230761 0.6943846193
[70,] 0.412952783 0.825905565 0.5870472173
[71,] 0.395938951 0.791877903 0.6040610487
[72,] 0.416589917 0.833179834 0.5834100829
[73,] 0.379002024 0.758004047 0.6209979763
[74,] 0.433508642 0.867017284 0.5664913580
[75,] 0.405926882 0.811853764 0.5940731178
[76,] 0.420524127 0.841048253 0.5794758734
[77,] 0.384433491 0.768866983 0.6155665087
[78,] 0.349217598 0.698435195 0.6507824024
[79,] 0.314810417 0.629620833 0.6851895834
[80,] 0.285310418 0.570620836 0.7146895820
[81,] 0.258033084 0.516066169 0.7419669156
[82,] 0.233180504 0.466361008 0.7668194961
[83,] 0.289742558 0.579485116 0.7102574420
[84,] 0.270749650 0.541499301 0.7292503496
[85,] 0.249922471 0.499844942 0.7500775288
[86,] 0.222571366 0.445142731 0.7774286343
[87,] 0.207409034 0.414818068 0.7925909659
[88,] 0.189910347 0.379820695 0.8100896526
[89,] 0.165906721 0.331813441 0.8340932794
[90,] 0.154161134 0.308322267 0.8458388664
[91,] 0.143590066 0.287180131 0.8564099344
[92,] 0.123668017 0.247336034 0.8763319832
[93,] 0.120337361 0.240674722 0.8796626390
[94,] 0.103941142 0.207882284 0.8960588580
[95,] 0.095304146 0.190608292 0.9046958540
[96,] 0.080951895 0.161903790 0.9190481051
[97,] 0.101158176 0.202316352 0.8988418240
[98,] 0.111570335 0.223140671 0.8884296646
[99,] 0.116236943 0.232473886 0.8837630570
[100,] 0.099745077 0.199490153 0.9002549233
[101,] 0.116024881 0.232049762 0.8839751188
[102,] 0.101662454 0.203324907 0.8983375464
[103,] 0.269937267 0.539874535 0.7300627326
[104,] 0.250092177 0.500184355 0.7499078226
[105,] 0.223751730 0.447503461 0.7762482695
[106,] 0.230724419 0.461448839 0.7692755805
[107,] 0.216661085 0.433322169 0.7833389154
[108,] 0.204074762 0.408149524 0.7959252380
[109,] 0.186070712 0.372141424 0.8139292878
[110,] 0.164867174 0.329734347 0.8351328263
[111,] 0.144095247 0.288190494 0.8559047532
[112,] 0.169177023 0.338354045 0.8308229773
[113,] 0.157895110 0.315790220 0.8421048898
[114,] 0.137622209 0.275244418 0.8623777911
[115,] 0.122778526 0.245557052 0.8772214738
[116,] 0.109738611 0.219477221 0.8902613895
[117,] 0.134904632 0.269809263 0.8650953683
[118,] 0.120169261 0.240338521 0.8798307393
[119,] 0.121658981 0.243317962 0.8783410191
[120,] 0.120329053 0.240658107 0.8796709467
[121,] 0.104787353 0.209574707 0.8952126466
[122,] 0.094870950 0.189741899 0.9051290503
[123,] 0.081664289 0.163328577 0.9183357114
[124,] 0.082746503 0.165493006 0.9172534971
[125,] 0.085655869 0.171311737 0.9143441314
[126,] 0.080373480 0.160746959 0.9196265203
[127,] 0.077415222 0.154830444 0.9225847780
[128,] 0.065471150 0.130942301 0.9345288496
[129,] 0.062309067 0.124618134 0.9376909332
[130,] 0.150181066 0.300362133 0.8498189337
[131,] 0.132072410 0.264144821 0.8679275896
[132,] 0.114400890 0.228801779 0.8855991103
[133,] 0.101814910 0.203629820 0.8981850902
[134,] 0.094824796 0.189649591 0.9051752043
[135,] 0.091667328 0.183334656 0.9083326720
[136,] 0.085943531 0.171887062 0.9140564690
[137,] 0.075721499 0.151442998 0.9242785010
[138,] 0.064661554 0.129323108 0.9353384460
[139,] 0.054521296 0.109042593 0.9454787036
[140,] 0.045459782 0.090919564 0.9545402181
[141,] 0.043235145 0.086470289 0.9567648554
[142,] 0.050907750 0.101815499 0.9490922503
[143,] 0.046743950 0.093487900 0.9532560498
[144,] 0.038959447 0.077918894 0.9610405531
[145,] 0.042154289 0.084308579 0.9578457105
[146,] 0.040848928 0.081697856 0.9591510720
[147,] 0.040009112 0.080018223 0.9599908883
[148,] 0.036659552 0.073319105 0.9633404477
[149,] 0.043507944 0.087015888 0.9564920560
[150,] 0.036865952 0.073731905 0.9631340476
[151,] 0.030612470 0.061224941 0.9693875296
[152,] 0.026753866 0.053507731 0.9732461343
[153,] 0.040820324 0.081640648 0.9591796762
[154,] 0.042696397 0.085392794 0.9573036031
[155,] 0.038721824 0.077443647 0.9612781764
[156,] 0.098429965 0.196859929 0.9015700355
[157,] 0.084058415 0.168116831 0.9159415845
[158,] 0.251067439 0.502134877 0.7489325614
[159,] 0.231703230 0.463406459 0.7682967704
[160,] 0.382188852 0.764377704 0.6178111482
[161,] 0.355773940 0.711547881 0.6442260595
[162,] 0.328735324 0.657470648 0.6712646760
[163,] 0.301976737 0.603953474 0.6980232632
[164,] 0.271517756 0.543035513 0.7284822437
[165,] 0.315928396 0.631856792 0.6840716039
[166,] 0.284651485 0.569302969 0.7153485153
[167,] 0.354126891 0.708253783 0.6458731086
[168,] 0.351127775 0.702255551 0.6488722245
[169,] 0.326437358 0.652874716 0.6735626419
[170,] 0.405169630 0.810339260 0.5948303700
[171,] 0.376905586 0.753811172 0.6230944140
[172,] 0.380557881 0.761115762 0.6194421192
[173,] 0.357030765 0.714061529 0.6429692353
[174,] 0.334688935 0.669377869 0.6653110654
[175,] 0.321616020 0.643232041 0.6783839796
[176,] 0.397839674 0.795679347 0.6021603263
[177,] 0.372268859 0.744537718 0.6277311408
[178,] 0.384684227 0.769368454 0.6153157728
[179,] 0.353282435 0.706564870 0.6467175651
[180,] 0.422746492 0.845492985 0.5772535077
[181,] 0.387112607 0.774225214 0.6128873928
[182,] 0.352358737 0.704717475 0.6476412627
[183,] 0.316951116 0.633902233 0.6830488836
[184,] 0.406786653 0.813573307 0.5932133465
[185,] 0.401861774 0.803723549 0.5981382256
[186,] 0.416240025 0.832480050 0.5837599751
[187,] 0.406489109 0.812978218 0.5935108910
[188,] 0.368555601 0.737111203 0.6314443985
[189,] 0.341188151 0.682376303 0.6588118486
[190,] 0.347736963 0.695473926 0.6522630372
[191,] 0.464558862 0.929117725 0.5354411377
[192,] 0.437844191 0.875688382 0.5621558088
[193,] 0.399882874 0.799765749 0.6001171257
[194,] 0.365845244 0.731690488 0.6341547559
[195,] 0.328201668 0.656403335 0.6717983325
[196,] 0.293569448 0.587138895 0.7064305523
[197,] 0.258888354 0.517776708 0.7411116458
[198,] 0.225941418 0.451882836 0.7740585821
[199,] 0.202549445 0.405098890 0.7974505548
[200,] 0.174659867 0.349319734 0.8253401328
[201,] 0.154198574 0.308397149 0.8458014256
[202,] 0.162948149 0.325896299 0.8370518506
[203,] 0.137816771 0.275633542 0.8621832292
[204,] 0.115867933 0.231735866 0.8841320669
[205,] 0.096967460 0.193934919 0.9030325405
[206,] 0.081070278 0.162140555 0.9189297225
[207,] 0.073380882 0.146761764 0.9266191178
[208,] 0.081658952 0.163317905 0.9183410476
[209,] 0.067981382 0.135962764 0.9320186182
[210,] 0.055713339 0.111426677 0.9442866613
[211,] 0.044364271 0.088728542 0.9556357288
[212,] 0.036640543 0.073281087 0.9633594566
[213,] 0.039647501 0.079295001 0.9603524993
[214,] 0.033032058 0.066064116 0.9669679418
[215,] 0.039284443 0.078568886 0.9607155571
[216,] 0.030364753 0.060729506 0.9696352470
[217,] 0.023087268 0.046174537 0.9769127317
[218,] 0.024268422 0.048536844 0.9757315781
[219,] 0.026458683 0.052917367 0.9735413165
[220,] 0.021279197 0.042558395 0.9787208026
[221,] 0.017857302 0.035714605 0.9821426977
[222,] 0.017368174 0.034736347 0.9826318265
[223,] 0.025643077 0.051286153 0.9743569234
[224,] 0.024126993 0.048253987 0.9758730066
[225,] 0.018919955 0.037839911 0.9810800446
[226,] 0.014652381 0.029304761 0.9853476194
[227,] 0.017210485 0.034420969 0.9827895155
[228,] 0.015524044 0.031048088 0.9844759559
[229,] 0.015848080 0.031696159 0.9841519205
[230,] 0.012547085 0.025094171 0.9874529147
[231,] 0.009884221 0.019768441 0.9901157793
[232,] 0.007077742 0.014155483 0.9929222583
[233,] 0.016989842 0.033979684 0.9830101579
[234,] 0.023074343 0.046148686 0.9769256569
[235,] 0.050662743 0.101325485 0.9493372573
[236,] 0.036351747 0.072703494 0.9636482532
[237,] 0.025206799 0.050413598 0.9747932011
[238,] 0.017018924 0.034037848 0.9829810762
[239,] 0.011568217 0.023136435 0.9884317826
[240,] 0.011294067 0.022588134 0.9887059328
[241,] 0.006998655 0.013997310 0.9930013451
[242,] 0.184093449 0.368186897 0.8159065513
[243,] 0.173666509 0.347333018 0.8263334908
[244,] 0.281037513 0.562075026 0.7189624870
[245,] 0.216681702 0.433363404 0.7833182979
[246,] 0.282828727 0.565657454 0.7171712730
[247,] 0.288626697 0.577253394 0.7113733032
[248,] 0.201506132 0.403012264 0.7984938681
[249,] 0.190491021 0.380982042 0.8095089789
[250,] 0.119754351 0.239508702 0.8802456490
[251,] 0.064958415 0.129916830 0.9350415850
> postscript(file="/var/www/rcomp/tmp/18ofu1321634964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/27n6t1321634964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3b29y1321634964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/42l901321634964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5wuia1321634964.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
-0.826471265 1.200460168 -1.803621008 -2.976734734 9.406380790 1.967944055
7 8 9 10 11 12
8.999596403 -2.199294209 -2.311657349 0.770065701 -0.657884801 -1.131766048
13 14 15 16 17 18
-1.199100557 2.668188793 -0.048694798 0.668718387 0.859289572 0.415518822
19 20 21 22 23 24
-2.926999974 1.253420922 -0.640904080 -1.795301581 -0.926999974 -0.612231217
25 26 27 28 29 30
0.402865785 -7.877360573 -0.514062966 1.760927669 1.516577124 -2.623536054
31 32 33 34 35 36
-2.807329665 0.609316000 -0.708149156 -1.391213594 -0.262211600 -3.736092847
37 38 39 40 41 42
2.508980944 -0.332629813 -1.110263990 -3.978082934 -3.292851690 2.364435862
43 44 45 46 47 48
-3.444992953 -0.775677317 -1.284532264 -2.451770528 -3.169183713 -1.472936644
49 50 51 52 53 54
4.314990113 -2.837776103 -1.683661687 0.637259691 2.275935237 -0.773510512
55 56 57 58 59 60
-3.936956612 2.238607005 1.626243865 -3.673994061 -4.643689912 0.234808915
61 62 63 64 65 66
1.730192219 -1.816803639 -3.602563591 0.639426496 0.545869015 -5.328921156
67 68 69 70 71 72
1.497965118 -3.089916480 0.891566874 1.659050761 0.001763208 3.266073959
73 74 75 76 77 78
0.629758870 -2.148211249 -3.091264679 4.393921405 2.354768236 2.950680248
79 80 81 82 83 84
0.244282889 -4.940471616 -1.451964180 -3.340949239 0.515228925 -0.523200998
85 86 87 88 89 90
-0.301989723 0.830816287 -1.270531027 0.709844709 3.135822058 2.042695878
91 92 93 94 95 96
1.061695189 0.700995688 1.616287227 -1.811127755 -0.352783671 1.345823856
97 98 99 100 101 102
-2.201461014 0.323309540 -2.502228534 -0.605453642 1.345823856 0.506909498
103 104 105 106 107 108
-4.776495922 3.579494516 2.072375073 -0.413534257 3.981609350 -1.299629265
109 110 111 112 113 114
8.125624838 1.376270294 -0.774329117 -3.044986141 1.668718387 2.120903923
115 116 117 118 119 120
-1.495257307 0.792386365 -0.157349281 -4.260044795 2.001763208 0.165738902
121 122 123 124 125 126
0.555536641 -1.169183713 -4.229934299 -1.353602276 -3.179140351 -2.597134215
127 128 129 130 131 132
-0.272408820 1.738413353 0.617105833 -3.128875996 2.469202821 -2.089627468
133 134 135 136 137 138
2.183532303 -0.229934299 2.221962226 6.959529269 0.790219559 0.196378992
139 140 141 142 143 144
-1.372407935 -2.158167887 -2.523200998 2.205323373 -1.410837858 -0.688813903
145 146 147 148 149 150
-0.354950476 0.200989762 -2.491548650 -3.947442843 1.821053301 0.628410671
151 152 153 154 155 156
3.415712474 -2.583132978 -2.712759925 1.992818828 3.900415894 1.061695189
157 158 159 160 161 162
0.710663315 1.738413353 -5.038833520 3.125960780 2.003111408 7.153615459
163 164 165 166 167 168
0.545050409 8.565493279 1.641786954 6.842361707 -1.431810322 -1.311946361
169 170 171 172 173 174
-1.166293661 0.471369627 4.506284545 0.506909498 -4.845901877 2.636441085
175 176 177 178 179 180
1.412533411 -5.107903533 -1.067931758 2.941542216 -1.807329665 -1.380533710
181 182 183 184 185 186
-2.098765500 -5.009541629 -1.552492888 -3.040181719 -1.291167549 4.902582699
187 188 189 190 191 192
0.880262036 -0.202809214 -0.382364573 5.307393933 -2.692606067 -2.956291864
193 194 195 196 197 198
2.466312770 0.468673228 1.455826538 -2.622717449 5.748274632 1.760927669
199 200 201 202 203 204
0.933416442 -1.440948354 -0.451240934 -0.967260760 0.141057781 -0.038833520
205 206 207 208 209 210
-1.472936644 0.598300174 -1.138543623 3.446158912 0.072181421 0.364435862
211 212 213 214 215 216
-0.218724821 -0.168365107 2.338852629 -3.632674087 1.447700764 0.181365498
217 218 219 220 221 222
0.132738355 -0.968944901 4.105181968 1.730287579 -3.573271700 0.596951975
223 224 225 226 227 228
0.518118976 -3.594437816 3.812733875 -1.704634151 1.595939717 -2.865719793
229 230 231 232 233 234
4.983680796 -2.602563591 -1.262211600 -1.216558016 -3.523200998 3.323503192
235 236 237 238 239 240
-2.646050370 -1.543979810 2.293392696 -0.990735971 -5.693424673 -3.504201687
241 242 243 244 245 246
-5.188278383 1.054917614 0.820041044 1.224322683 1.696662078 4.146114638
247 248 249 250 251 252
0.892096467 9.859289572 2.699069465 0.384300708 1.426534648 2.910902126
253 254 255 256 257 258
-2.917332347 -1.089097874 2.324851392 0.174058329 3.873484461 0.619995884
259 260 261 262 263 264
2.728120774 -8.402324779 -1.614062080 -0.380727362 3.519131234 -1.492560908
> postscript(file="/var/www/rcomp/tmp/6l6xq1321634964.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 -0.826471265 NA
1 1.200460168 -0.826471265
2 -1.803621008 1.200460168
3 -2.976734734 -1.803621008
4 9.406380790 -2.976734734
5 1.967944055 9.406380790
6 8.999596403 1.967944055
7 -2.199294209 8.999596403
8 -2.311657349 -2.199294209
9 0.770065701 -2.311657349
10 -0.657884801 0.770065701
11 -1.131766048 -0.657884801
12 -1.199100557 -1.131766048
13 2.668188793 -1.199100557
14 -0.048694798 2.668188793
15 0.668718387 -0.048694798
16 0.859289572 0.668718387
17 0.415518822 0.859289572
18 -2.926999974 0.415518822
19 1.253420922 -2.926999974
20 -0.640904080 1.253420922
21 -1.795301581 -0.640904080
22 -0.926999974 -1.795301581
23 -0.612231217 -0.926999974
24 0.402865785 -0.612231217
25 -7.877360573 0.402865785
26 -0.514062966 -7.877360573
27 1.760927669 -0.514062966
28 1.516577124 1.760927669
29 -2.623536054 1.516577124
30 -2.807329665 -2.623536054
31 0.609316000 -2.807329665
32 -0.708149156 0.609316000
33 -1.391213594 -0.708149156
34 -0.262211600 -1.391213594
35 -3.736092847 -0.262211600
36 2.508980944 -3.736092847
37 -0.332629813 2.508980944
38 -1.110263990 -0.332629813
39 -3.978082934 -1.110263990
40 -3.292851690 -3.978082934
41 2.364435862 -3.292851690
42 -3.444992953 2.364435862
43 -0.775677317 -3.444992953
44 -1.284532264 -0.775677317
45 -2.451770528 -1.284532264
46 -3.169183713 -2.451770528
47 -1.472936644 -3.169183713
48 4.314990113 -1.472936644
49 -2.837776103 4.314990113
50 -1.683661687 -2.837776103
51 0.637259691 -1.683661687
52 2.275935237 0.637259691
53 -0.773510512 2.275935237
54 -3.936956612 -0.773510512
55 2.238607005 -3.936956612
56 1.626243865 2.238607005
57 -3.673994061 1.626243865
58 -4.643689912 -3.673994061
59 0.234808915 -4.643689912
60 1.730192219 0.234808915
61 -1.816803639 1.730192219
62 -3.602563591 -1.816803639
63 0.639426496 -3.602563591
64 0.545869015 0.639426496
65 -5.328921156 0.545869015
66 1.497965118 -5.328921156
67 -3.089916480 1.497965118
68 0.891566874 -3.089916480
69 1.659050761 0.891566874
70 0.001763208 1.659050761
71 3.266073959 0.001763208
72 0.629758870 3.266073959
73 -2.148211249 0.629758870
74 -3.091264679 -2.148211249
75 4.393921405 -3.091264679
76 2.354768236 4.393921405
77 2.950680248 2.354768236
78 0.244282889 2.950680248
79 -4.940471616 0.244282889
80 -1.451964180 -4.940471616
81 -3.340949239 -1.451964180
82 0.515228925 -3.340949239
83 -0.523200998 0.515228925
84 -0.301989723 -0.523200998
85 0.830816287 -0.301989723
86 -1.270531027 0.830816287
87 0.709844709 -1.270531027
88 3.135822058 0.709844709
89 2.042695878 3.135822058
90 1.061695189 2.042695878
91 0.700995688 1.061695189
92 1.616287227 0.700995688
93 -1.811127755 1.616287227
94 -0.352783671 -1.811127755
95 1.345823856 -0.352783671
96 -2.201461014 1.345823856
97 0.323309540 -2.201461014
98 -2.502228534 0.323309540
99 -0.605453642 -2.502228534
100 1.345823856 -0.605453642
101 0.506909498 1.345823856
102 -4.776495922 0.506909498
103 3.579494516 -4.776495922
104 2.072375073 3.579494516
105 -0.413534257 2.072375073
106 3.981609350 -0.413534257
107 -1.299629265 3.981609350
108 8.125624838 -1.299629265
109 1.376270294 8.125624838
110 -0.774329117 1.376270294
111 -3.044986141 -0.774329117
112 1.668718387 -3.044986141
113 2.120903923 1.668718387
114 -1.495257307 2.120903923
115 0.792386365 -1.495257307
116 -0.157349281 0.792386365
117 -4.260044795 -0.157349281
118 2.001763208 -4.260044795
119 0.165738902 2.001763208
120 0.555536641 0.165738902
121 -1.169183713 0.555536641
122 -4.229934299 -1.169183713
123 -1.353602276 -4.229934299
124 -3.179140351 -1.353602276
125 -2.597134215 -3.179140351
126 -0.272408820 -2.597134215
127 1.738413353 -0.272408820
128 0.617105833 1.738413353
129 -3.128875996 0.617105833
130 2.469202821 -3.128875996
131 -2.089627468 2.469202821
132 2.183532303 -2.089627468
133 -0.229934299 2.183532303
134 2.221962226 -0.229934299
135 6.959529269 2.221962226
136 0.790219559 6.959529269
137 0.196378992 0.790219559
138 -1.372407935 0.196378992
139 -2.158167887 -1.372407935
140 -2.523200998 -2.158167887
141 2.205323373 -2.523200998
142 -1.410837858 2.205323373
143 -0.688813903 -1.410837858
144 -0.354950476 -0.688813903
145 0.200989762 -0.354950476
146 -2.491548650 0.200989762
147 -3.947442843 -2.491548650
148 1.821053301 -3.947442843
149 0.628410671 1.821053301
150 3.415712474 0.628410671
151 -2.583132978 3.415712474
152 -2.712759925 -2.583132978
153 1.992818828 -2.712759925
154 3.900415894 1.992818828
155 1.061695189 3.900415894
156 0.710663315 1.061695189
157 1.738413353 0.710663315
158 -5.038833520 1.738413353
159 3.125960780 -5.038833520
160 2.003111408 3.125960780
161 7.153615459 2.003111408
162 0.545050409 7.153615459
163 8.565493279 0.545050409
164 1.641786954 8.565493279
165 6.842361707 1.641786954
166 -1.431810322 6.842361707
167 -1.311946361 -1.431810322
168 -1.166293661 -1.311946361
169 0.471369627 -1.166293661
170 4.506284545 0.471369627
171 0.506909498 4.506284545
172 -4.845901877 0.506909498
173 2.636441085 -4.845901877
174 1.412533411 2.636441085
175 -5.107903533 1.412533411
176 -1.067931758 -5.107903533
177 2.941542216 -1.067931758
178 -1.807329665 2.941542216
179 -1.380533710 -1.807329665
180 -2.098765500 -1.380533710
181 -5.009541629 -2.098765500
182 -1.552492888 -5.009541629
183 -3.040181719 -1.552492888
184 -1.291167549 -3.040181719
185 4.902582699 -1.291167549
186 0.880262036 4.902582699
187 -0.202809214 0.880262036
188 -0.382364573 -0.202809214
189 5.307393933 -0.382364573
190 -2.692606067 5.307393933
191 -2.956291864 -2.692606067
192 2.466312770 -2.956291864
193 0.468673228 2.466312770
194 1.455826538 0.468673228
195 -2.622717449 1.455826538
196 5.748274632 -2.622717449
197 1.760927669 5.748274632
198 0.933416442 1.760927669
199 -1.440948354 0.933416442
200 -0.451240934 -1.440948354
201 -0.967260760 -0.451240934
202 0.141057781 -0.967260760
203 -0.038833520 0.141057781
204 -1.472936644 -0.038833520
205 0.598300174 -1.472936644
206 -1.138543623 0.598300174
207 3.446158912 -1.138543623
208 0.072181421 3.446158912
209 0.364435862 0.072181421
210 -0.218724821 0.364435862
211 -0.168365107 -0.218724821
212 2.338852629 -0.168365107
213 -3.632674087 2.338852629
214 1.447700764 -3.632674087
215 0.181365498 1.447700764
216 0.132738355 0.181365498
217 -0.968944901 0.132738355
218 4.105181968 -0.968944901
219 1.730287579 4.105181968
220 -3.573271700 1.730287579
221 0.596951975 -3.573271700
222 0.518118976 0.596951975
223 -3.594437816 0.518118976
224 3.812733875 -3.594437816
225 -1.704634151 3.812733875
226 1.595939717 -1.704634151
227 -2.865719793 1.595939717
228 4.983680796 -2.865719793
229 -2.602563591 4.983680796
230 -1.262211600 -2.602563591
231 -1.216558016 -1.262211600
232 -3.523200998 -1.216558016
233 3.323503192 -3.523200998
234 -2.646050370 3.323503192
235 -1.543979810 -2.646050370
236 2.293392696 -1.543979810
237 -0.990735971 2.293392696
238 -5.693424673 -0.990735971
239 -3.504201687 -5.693424673
240 -5.188278383 -3.504201687
241 1.054917614 -5.188278383
242 0.820041044 1.054917614
243 1.224322683 0.820041044
244 1.696662078 1.224322683
245 4.146114638 1.696662078
246 0.892096467 4.146114638
247 9.859289572 0.892096467
248 2.699069465 9.859289572
249 0.384300708 2.699069465
250 1.426534648 0.384300708
251 2.910902126 1.426534648
252 -2.917332347 2.910902126
253 -1.089097874 -2.917332347
254 2.324851392 -1.089097874
255 0.174058329 2.324851392
256 3.873484461 0.174058329
257 0.619995884 3.873484461
258 2.728120774 0.619995884
259 -8.402324779 2.728120774
260 -1.614062080 -8.402324779
261 -0.380727362 -1.614062080
262 3.519131234 -0.380727362
263 -1.492560908 3.519131234
264 NA -1.492560908
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.200460168 -0.826471265
[2,] -1.803621008 1.200460168
[3,] -2.976734734 -1.803621008
[4,] 9.406380790 -2.976734734
[5,] 1.967944055 9.406380790
[6,] 8.999596403 1.967944055
[7,] -2.199294209 8.999596403
[8,] -2.311657349 -2.199294209
[9,] 0.770065701 -2.311657349
[10,] -0.657884801 0.770065701
[11,] -1.131766048 -0.657884801
[12,] -1.199100557 -1.131766048
[13,] 2.668188793 -1.199100557
[14,] -0.048694798 2.668188793
[15,] 0.668718387 -0.048694798
[16,] 0.859289572 0.668718387
[17,] 0.415518822 0.859289572
[18,] -2.926999974 0.415518822
[19,] 1.253420922 -2.926999974
[20,] -0.640904080 1.253420922
[21,] -1.795301581 -0.640904080
[22,] -0.926999974 -1.795301581
[23,] -0.612231217 -0.926999974
[24,] 0.402865785 -0.612231217
[25,] -7.877360573 0.402865785
[26,] -0.514062966 -7.877360573
[27,] 1.760927669 -0.514062966
[28,] 1.516577124 1.760927669
[29,] -2.623536054 1.516577124
[30,] -2.807329665 -2.623536054
[31,] 0.609316000 -2.807329665
[32,] -0.708149156 0.609316000
[33,] -1.391213594 -0.708149156
[34,] -0.262211600 -1.391213594
[35,] -3.736092847 -0.262211600
[36,] 2.508980944 -3.736092847
[37,] -0.332629813 2.508980944
[38,] -1.110263990 -0.332629813
[39,] -3.978082934 -1.110263990
[40,] -3.292851690 -3.978082934
[41,] 2.364435862 -3.292851690
[42,] -3.444992953 2.364435862
[43,] -0.775677317 -3.444992953
[44,] -1.284532264 -0.775677317
[45,] -2.451770528 -1.284532264
[46,] -3.169183713 -2.451770528
[47,] -1.472936644 -3.169183713
[48,] 4.314990113 -1.472936644
[49,] -2.837776103 4.314990113
[50,] -1.683661687 -2.837776103
[51,] 0.637259691 -1.683661687
[52,] 2.275935237 0.637259691
[53,] -0.773510512 2.275935237
[54,] -3.936956612 -0.773510512
[55,] 2.238607005 -3.936956612
[56,] 1.626243865 2.238607005
[57,] -3.673994061 1.626243865
[58,] -4.643689912 -3.673994061
[59,] 0.234808915 -4.643689912
[60,] 1.730192219 0.234808915
[61,] -1.816803639 1.730192219
[62,] -3.602563591 -1.816803639
[63,] 0.639426496 -3.602563591
[64,] 0.545869015 0.639426496
[65,] -5.328921156 0.545869015
[66,] 1.497965118 -5.328921156
[67,] -3.089916480 1.497965118
[68,] 0.891566874 -3.089916480
[69,] 1.659050761 0.891566874
[70,] 0.001763208 1.659050761
[71,] 3.266073959 0.001763208
[72,] 0.629758870 3.266073959
[73,] -2.148211249 0.629758870
[74,] -3.091264679 -2.148211249
[75,] 4.393921405 -3.091264679
[76,] 2.354768236 4.393921405
[77,] 2.950680248 2.354768236
[78,] 0.244282889 2.950680248
[79,] -4.940471616 0.244282889
[80,] -1.451964180 -4.940471616
[81,] -3.340949239 -1.451964180
[82,] 0.515228925 -3.340949239
[83,] -0.523200998 0.515228925
[84,] -0.301989723 -0.523200998
[85,] 0.830816287 -0.301989723
[86,] -1.270531027 0.830816287
[87,] 0.709844709 -1.270531027
[88,] 3.135822058 0.709844709
[89,] 2.042695878 3.135822058
[90,] 1.061695189 2.042695878
[91,] 0.700995688 1.061695189
[92,] 1.616287227 0.700995688
[93,] -1.811127755 1.616287227
[94,] -0.352783671 -1.811127755
[95,] 1.345823856 -0.352783671
[96,] -2.201461014 1.345823856
[97,] 0.323309540 -2.201461014
[98,] -2.502228534 0.323309540
[99,] -0.605453642 -2.502228534
[100,] 1.345823856 -0.605453642
[101,] 0.506909498 1.345823856
[102,] -4.776495922 0.506909498
[103,] 3.579494516 -4.776495922
[104,] 2.072375073 3.579494516
[105,] -0.413534257 2.072375073
[106,] 3.981609350 -0.413534257
[107,] -1.299629265 3.981609350
[108,] 8.125624838 -1.299629265
[109,] 1.376270294 8.125624838
[110,] -0.774329117 1.376270294
[111,] -3.044986141 -0.774329117
[112,] 1.668718387 -3.044986141
[113,] 2.120903923 1.668718387
[114,] -1.495257307 2.120903923
[115,] 0.792386365 -1.495257307
[116,] -0.157349281 0.792386365
[117,] -4.260044795 -0.157349281
[118,] 2.001763208 -4.260044795
[119,] 0.165738902 2.001763208
[120,] 0.555536641 0.165738902
[121,] -1.169183713 0.555536641
[122,] -4.229934299 -1.169183713
[123,] -1.353602276 -4.229934299
[124,] -3.179140351 -1.353602276
[125,] -2.597134215 -3.179140351
[126,] -0.272408820 -2.597134215
[127,] 1.738413353 -0.272408820
[128,] 0.617105833 1.738413353
[129,] -3.128875996 0.617105833
[130,] 2.469202821 -3.128875996
[131,] -2.089627468 2.469202821
[132,] 2.183532303 -2.089627468
[133,] -0.229934299 2.183532303
[134,] 2.221962226 -0.229934299
[135,] 6.959529269 2.221962226
[136,] 0.790219559 6.959529269
[137,] 0.196378992 0.790219559
[138,] -1.372407935 0.196378992
[139,] -2.158167887 -1.372407935
[140,] -2.523200998 -2.158167887
[141,] 2.205323373 -2.523200998
[142,] -1.410837858 2.205323373
[143,] -0.688813903 -1.410837858
[144,] -0.354950476 -0.688813903
[145,] 0.200989762 -0.354950476
[146,] -2.491548650 0.200989762
[147,] -3.947442843 -2.491548650
[148,] 1.821053301 -3.947442843
[149,] 0.628410671 1.821053301
[150,] 3.415712474 0.628410671
[151,] -2.583132978 3.415712474
[152,] -2.712759925 -2.583132978
[153,] 1.992818828 -2.712759925
[154,] 3.900415894 1.992818828
[155,] 1.061695189 3.900415894
[156,] 0.710663315 1.061695189
[157,] 1.738413353 0.710663315
[158,] -5.038833520 1.738413353
[159,] 3.125960780 -5.038833520
[160,] 2.003111408 3.125960780
[161,] 7.153615459 2.003111408
[162,] 0.545050409 7.153615459
[163,] 8.565493279 0.545050409
[164,] 1.641786954 8.565493279
[165,] 6.842361707 1.641786954
[166,] -1.431810322 6.842361707
[167,] -1.311946361 -1.431810322
[168,] -1.166293661 -1.311946361
[169,] 0.471369627 -1.166293661
[170,] 4.506284545 0.471369627
[171,] 0.506909498 4.506284545
[172,] -4.845901877 0.506909498
[173,] 2.636441085 -4.845901877
[174,] 1.412533411 2.636441085
[175,] -5.107903533 1.412533411
[176,] -1.067931758 -5.107903533
[177,] 2.941542216 -1.067931758
[178,] -1.807329665 2.941542216
[179,] -1.380533710 -1.807329665
[180,] -2.098765500 -1.380533710
[181,] -5.009541629 -2.098765500
[182,] -1.552492888 -5.009541629
[183,] -3.040181719 -1.552492888
[184,] -1.291167549 -3.040181719
[185,] 4.902582699 -1.291167549
[186,] 0.880262036 4.902582699
[187,] -0.202809214 0.880262036
[188,] -0.382364573 -0.202809214
[189,] 5.307393933 -0.382364573
[190,] -2.692606067 5.307393933
[191,] -2.956291864 -2.692606067
[192,] 2.466312770 -2.956291864
[193,] 0.468673228 2.466312770
[194,] 1.455826538 0.468673228
[195,] -2.622717449 1.455826538
[196,] 5.748274632 -2.622717449
[197,] 1.760927669 5.748274632
[198,] 0.933416442 1.760927669
[199,] -1.440948354 0.933416442
[200,] -0.451240934 -1.440948354
[201,] -0.967260760 -0.451240934
[202,] 0.141057781 -0.967260760
[203,] -0.038833520 0.141057781
[204,] -1.472936644 -0.038833520
[205,] 0.598300174 -1.472936644
[206,] -1.138543623 0.598300174
[207,] 3.446158912 -1.138543623
[208,] 0.072181421 3.446158912
[209,] 0.364435862 0.072181421
[210,] -0.218724821 0.364435862
[211,] -0.168365107 -0.218724821
[212,] 2.338852629 -0.168365107
[213,] -3.632674087 2.338852629
[214,] 1.447700764 -3.632674087
[215,] 0.181365498 1.447700764
[216,] 0.132738355 0.181365498
[217,] -0.968944901 0.132738355
[218,] 4.105181968 -0.968944901
[219,] 1.730287579 4.105181968
[220,] -3.573271700 1.730287579
[221,] 0.596951975 -3.573271700
[222,] 0.518118976 0.596951975
[223,] -3.594437816 0.518118976
[224,] 3.812733875 -3.594437816
[225,] -1.704634151 3.812733875
[226,] 1.595939717 -1.704634151
[227,] -2.865719793 1.595939717
[228,] 4.983680796 -2.865719793
[229,] -2.602563591 4.983680796
[230,] -1.262211600 -2.602563591
[231,] -1.216558016 -1.262211600
[232,] -3.523200998 -1.216558016
[233,] 3.323503192 -3.523200998
[234,] -2.646050370 3.323503192
[235,] -1.543979810 -2.646050370
[236,] 2.293392696 -1.543979810
[237,] -0.990735971 2.293392696
[238,] -5.693424673 -0.990735971
[239,] -3.504201687 -5.693424673
[240,] -5.188278383 -3.504201687
[241,] 1.054917614 -5.188278383
[242,] 0.820041044 1.054917614
[243,] 1.224322683 0.820041044
[244,] 1.696662078 1.224322683
[245,] 4.146114638 1.696662078
[246,] 0.892096467 4.146114638
[247,] 9.859289572 0.892096467
[248,] 2.699069465 9.859289572
[249,] 0.384300708 2.699069465
[250,] 1.426534648 0.384300708
[251,] 2.910902126 1.426534648
[252,] -2.917332347 2.910902126
[253,] -1.089097874 -2.917332347
[254,] 2.324851392 -1.089097874
[255,] 0.174058329 2.324851392
[256,] 3.873484461 0.174058329
[257,] 0.619995884 3.873484461
[258,] 2.728120774 0.619995884
[259,] -8.402324779 2.728120774
[260,] -1.614062080 -8.402324779
[261,] -0.380727362 -1.614062080
[262,] 3.519131234 -0.380727362
[263,] -1.492560908 3.519131234
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.200460168 -0.826471265
2 -1.803621008 1.200460168
3 -2.976734734 -1.803621008
4 9.406380790 -2.976734734
5 1.967944055 9.406380790
6 8.999596403 1.967944055
7 -2.199294209 8.999596403
8 -2.311657349 -2.199294209
9 0.770065701 -2.311657349
10 -0.657884801 0.770065701
11 -1.131766048 -0.657884801
12 -1.199100557 -1.131766048
13 2.668188793 -1.199100557
14 -0.048694798 2.668188793
15 0.668718387 -0.048694798
16 0.859289572 0.668718387
17 0.415518822 0.859289572
18 -2.926999974 0.415518822
19 1.253420922 -2.926999974
20 -0.640904080 1.253420922
21 -1.795301581 -0.640904080
22 -0.926999974 -1.795301581
23 -0.612231217 -0.926999974
24 0.402865785 -0.612231217
25 -7.877360573 0.402865785
26 -0.514062966 -7.877360573
27 1.760927669 -0.514062966
28 1.516577124 1.760927669
29 -2.623536054 1.516577124
30 -2.807329665 -2.623536054
31 0.609316000 -2.807329665
32 -0.708149156 0.609316000
33 -1.391213594 -0.708149156
34 -0.262211600 -1.391213594
35 -3.736092847 -0.262211600
36 2.508980944 -3.736092847
37 -0.332629813 2.508980944
38 -1.110263990 -0.332629813
39 -3.978082934 -1.110263990
40 -3.292851690 -3.978082934
41 2.364435862 -3.292851690
42 -3.444992953 2.364435862
43 -0.775677317 -3.444992953
44 -1.284532264 -0.775677317
45 -2.451770528 -1.284532264
46 -3.169183713 -2.451770528
47 -1.472936644 -3.169183713
48 4.314990113 -1.472936644
49 -2.837776103 4.314990113
50 -1.683661687 -2.837776103
51 0.637259691 -1.683661687
52 2.275935237 0.637259691
53 -0.773510512 2.275935237
54 -3.936956612 -0.773510512
55 2.238607005 -3.936956612
56 1.626243865 2.238607005
57 -3.673994061 1.626243865
58 -4.643689912 -3.673994061
59 0.234808915 -4.643689912
60 1.730192219 0.234808915
61 -1.816803639 1.730192219
62 -3.602563591 -1.816803639
63 0.639426496 -3.602563591
64 0.545869015 0.639426496
65 -5.328921156 0.545869015
66 1.497965118 -5.328921156
67 -3.089916480 1.497965118
68 0.891566874 -3.089916480
69 1.659050761 0.891566874
70 0.001763208 1.659050761
71 3.266073959 0.001763208
72 0.629758870 3.266073959
73 -2.148211249 0.629758870
74 -3.091264679 -2.148211249
75 4.393921405 -3.091264679
76 2.354768236 4.393921405
77 2.950680248 2.354768236
78 0.244282889 2.950680248
79 -4.940471616 0.244282889
80 -1.451964180 -4.940471616
81 -3.340949239 -1.451964180
82 0.515228925 -3.340949239
83 -0.523200998 0.515228925
84 -0.301989723 -0.523200998
85 0.830816287 -0.301989723
86 -1.270531027 0.830816287
87 0.709844709 -1.270531027
88 3.135822058 0.709844709
89 2.042695878 3.135822058
90 1.061695189 2.042695878
91 0.700995688 1.061695189
92 1.616287227 0.700995688
93 -1.811127755 1.616287227
94 -0.352783671 -1.811127755
95 1.345823856 -0.352783671
96 -2.201461014 1.345823856
97 0.323309540 -2.201461014
98 -2.502228534 0.323309540
99 -0.605453642 -2.502228534
100 1.345823856 -0.605453642
101 0.506909498 1.345823856
102 -4.776495922 0.506909498
103 3.579494516 -4.776495922
104 2.072375073 3.579494516
105 -0.413534257 2.072375073
106 3.981609350 -0.413534257
107 -1.299629265 3.981609350
108 8.125624838 -1.299629265
109 1.376270294 8.125624838
110 -0.774329117 1.376270294
111 -3.044986141 -0.774329117
112 1.668718387 -3.044986141
113 2.120903923 1.668718387
114 -1.495257307 2.120903923
115 0.792386365 -1.495257307
116 -0.157349281 0.792386365
117 -4.260044795 -0.157349281
118 2.001763208 -4.260044795
119 0.165738902 2.001763208
120 0.555536641 0.165738902
121 -1.169183713 0.555536641
122 -4.229934299 -1.169183713
123 -1.353602276 -4.229934299
124 -3.179140351 -1.353602276
125 -2.597134215 -3.179140351
126 -0.272408820 -2.597134215
127 1.738413353 -0.272408820
128 0.617105833 1.738413353
129 -3.128875996 0.617105833
130 2.469202821 -3.128875996
131 -2.089627468 2.469202821
132 2.183532303 -2.089627468
133 -0.229934299 2.183532303
134 2.221962226 -0.229934299
135 6.959529269 2.221962226
136 0.790219559 6.959529269
137 0.196378992 0.790219559
138 -1.372407935 0.196378992
139 -2.158167887 -1.372407935
140 -2.523200998 -2.158167887
141 2.205323373 -2.523200998
142 -1.410837858 2.205323373
143 -0.688813903 -1.410837858
144 -0.354950476 -0.688813903
145 0.200989762 -0.354950476
146 -2.491548650 0.200989762
147 -3.947442843 -2.491548650
148 1.821053301 -3.947442843
149 0.628410671 1.821053301
150 3.415712474 0.628410671
151 -2.583132978 3.415712474
152 -2.712759925 -2.583132978
153 1.992818828 -2.712759925
154 3.900415894 1.992818828
155 1.061695189 3.900415894
156 0.710663315 1.061695189
157 1.738413353 0.710663315
158 -5.038833520 1.738413353
159 3.125960780 -5.038833520
160 2.003111408 3.125960780
161 7.153615459 2.003111408
162 0.545050409 7.153615459
163 8.565493279 0.545050409
164 1.641786954 8.565493279
165 6.842361707 1.641786954
166 -1.431810322 6.842361707
167 -1.311946361 -1.431810322
168 -1.166293661 -1.311946361
169 0.471369627 -1.166293661
170 4.506284545 0.471369627
171 0.506909498 4.506284545
172 -4.845901877 0.506909498
173 2.636441085 -4.845901877
174 1.412533411 2.636441085
175 -5.107903533 1.412533411
176 -1.067931758 -5.107903533
177 2.941542216 -1.067931758
178 -1.807329665 2.941542216
179 -1.380533710 -1.807329665
180 -2.098765500 -1.380533710
181 -5.009541629 -2.098765500
182 -1.552492888 -5.009541629
183 -3.040181719 -1.552492888
184 -1.291167549 -3.040181719
185 4.902582699 -1.291167549
186 0.880262036 4.902582699
187 -0.202809214 0.880262036
188 -0.382364573 -0.202809214
189 5.307393933 -0.382364573
190 -2.692606067 5.307393933
191 -2.956291864 -2.692606067
192 2.466312770 -2.956291864
193 0.468673228 2.466312770
194 1.455826538 0.468673228
195 -2.622717449 1.455826538
196 5.748274632 -2.622717449
197 1.760927669 5.748274632
198 0.933416442 1.760927669
199 -1.440948354 0.933416442
200 -0.451240934 -1.440948354
201 -0.967260760 -0.451240934
202 0.141057781 -0.967260760
203 -0.038833520 0.141057781
204 -1.472936644 -0.038833520
205 0.598300174 -1.472936644
206 -1.138543623 0.598300174
207 3.446158912 -1.138543623
208 0.072181421 3.446158912
209 0.364435862 0.072181421
210 -0.218724821 0.364435862
211 -0.168365107 -0.218724821
212 2.338852629 -0.168365107
213 -3.632674087 2.338852629
214 1.447700764 -3.632674087
215 0.181365498 1.447700764
216 0.132738355 0.181365498
217 -0.968944901 0.132738355
218 4.105181968 -0.968944901
219 1.730287579 4.105181968
220 -3.573271700 1.730287579
221 0.596951975 -3.573271700
222 0.518118976 0.596951975
223 -3.594437816 0.518118976
224 3.812733875 -3.594437816
225 -1.704634151 3.812733875
226 1.595939717 -1.704634151
227 -2.865719793 1.595939717
228 4.983680796 -2.865719793
229 -2.602563591 4.983680796
230 -1.262211600 -2.602563591
231 -1.216558016 -1.262211600
232 -3.523200998 -1.216558016
233 3.323503192 -3.523200998
234 -2.646050370 3.323503192
235 -1.543979810 -2.646050370
236 2.293392696 -1.543979810
237 -0.990735971 2.293392696
238 -5.693424673 -0.990735971
239 -3.504201687 -5.693424673
240 -5.188278383 -3.504201687
241 1.054917614 -5.188278383
242 0.820041044 1.054917614
243 1.224322683 0.820041044
244 1.696662078 1.224322683
245 4.146114638 1.696662078
246 0.892096467 4.146114638
247 9.859289572 0.892096467
248 2.699069465 9.859289572
249 0.384300708 2.699069465
250 1.426534648 0.384300708
251 2.910902126 1.426534648
252 -2.917332347 2.910902126
253 -1.089097874 -2.917332347
254 2.324851392 -1.089097874
255 0.174058329 2.324851392
256 3.873484461 0.174058329
257 0.619995884 3.873484461
258 2.728120774 0.619995884
259 -8.402324779 2.728120774
260 -1.614062080 -8.402324779
261 -0.380727362 -1.614062080
262 3.519131234 -0.380727362
263 -1.492560908 3.519131234
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/7h2wo1321634964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8loft1321634964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9dvok1321634964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10ivet1321634964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11xnsz1321634964.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/1281051321634964.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13vifh1321634964.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14oc761321634964.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/15f4u01321634964.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/16tau61321634964.tab")
+ }
>
> try(system("convert tmp/18ofu1321634964.ps tmp/18ofu1321634964.png",intern=TRUE))
character(0)
> try(system("convert tmp/27n6t1321634964.ps tmp/27n6t1321634964.png",intern=TRUE))
character(0)
> try(system("convert tmp/3b29y1321634964.ps tmp/3b29y1321634964.png",intern=TRUE))
character(0)
> try(system("convert tmp/42l901321634964.ps tmp/42l901321634964.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wuia1321634964.ps tmp/5wuia1321634964.png",intern=TRUE))
character(0)
> try(system("convert tmp/6l6xq1321634964.ps tmp/6l6xq1321634964.png",intern=TRUE))
character(0)
> try(system("convert tmp/7h2wo1321634964.ps tmp/7h2wo1321634964.png",intern=TRUE))
character(0)
> try(system("convert tmp/8loft1321634964.ps tmp/8loft1321634964.png",intern=TRUE))
character(0)
> try(system("convert tmp/9dvok1321634964.ps tmp/9dvok1321634964.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ivet1321634964.ps tmp/10ivet1321634964.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.420 0.330 7.738