R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'q()' to quit R.
> x <- array(list(27
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+ ,18)
+ ,dim=c(6
+ ,195)
+ ,dimnames=list(c('leeftijd'
+ ,'opleiding'
+ ,'Neuroticisme'
+ ,'Extraversie'
+ ,'Openheid'
+ ,'Extrinsieke_waarden')
+ ,1:195))
> y <- array(NA,dim=c(6,195),dimnames=list(c('leeftijd','opleiding','Neuroticisme','Extraversie','Openheid','Extrinsieke_waarden'),1:195))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
leeftijd opleiding Neuroticisme Extraversie Openheid Extrinsieke_waarden
1 27 5 26 49 35 18
2 36 4 25 45 34 10
3 25 4 17 54 13 23
4 27 3 37 36 35 14
5 25 3 35 36 28 20
6 44 3 15 53 32 15
7 50 4 27 46 35 18
8 41 4 36 42 36 19
9 48 5 25 41 27 19
10 43 4 30 45 29 14
11 47 2 27 47 27 15
12 41 3 33 42 28 14
13 44 2 29 45 29 16
14 47 5 30 40 28 13
15 40 3 25 45 30 13
16 46 3 23 40 25 14
17 28 3 26 42 15 23
18 56 3 24 45 33 17
19 49 4 35 47 31 14
20 25 4 39 31 37 21
21 41 4 23 46 37 15
22 26 3 32 34 34 19
23 50 5 29 43 32 20
24 47 4 26 45 21 18
25 52 2 21 42 25 13
26 37 5 35 51 32 20
27 41 3 23 44 28 12
28 45 4 21 47 22 17
29 26 4 28 47 25 13
30 3 30 41 26 17 52
31 4 21 44 34 16 46
32 2 29 51 34 20 58
33 3 28 46 36 18 54
34 5 19 47 36 9 29
35 3 26 46 26 14 50
36 3 33 38 26 12 43
37 2 34 50 34 21 30
38 3 33 48 33 16 47
39 2 40 36 31 12 45
40 3 24 51 33 20 48
41 1 35 35 22 18 48
42 3 35 49 29 22 26
43 4 32 38 24 17 46
44 5 20 47 37 16 3
45 35 36 32 14 50 3
46 35 47 23 19 25 4
47 21 46 29 21 47 2
48 33 43 35 18 47 2
49 40 53 20 23 41 3
50 22 55 28 20 45 2
51 35 39 26 10 41 4
52 20 55 36 16 45 5
53 28 41 26 18 40 3
54 46 33 33 12 29 4
55 18 52 25 15 34 5
56 22 42 29 19 45 5
57 20 56 32 11 52 3
58 25 46 35 16 41 4
59 31 33 24 12 48 3
60 21 51 31 18 45 3
61 23 46 29 14 54 2
62 26 46 27 20 25 3
63 34 50 29 15 26 4
64 31 46 29 17 28 4
65 23 51 27 20 50 4
66 31 48 34 14 48 4
67 26 44 32 16 51 3
68 36 38 31 15 53 3
69 28 42 31 17 37 3
70 34 39 31 20 56 2
71 25 45 16 14 43 3
72 33 31 25 20 34 3
73 46 29 27 20 42 3
74 24 48 32 15 32 3
75 32 38 28 21 31 5
76 33 55 25 22 46 3
77 42 32 25 11 30 5
78 17 51 36 20 47 4
79 36 53 36 17 33 4
80 40 47 36 19 25 4
81 30 45 27 17 25 5
82 19 33 29 15 21 4
83 33 49 32 20 36 5
84 35 46 29 12 50 3
85 23 42 31 13 48 3
86 15 56 34 18 48 2
87 38 35 27 19 25 3
88 37 40 28 13 48 4
89 23 44 32 12 49 5
90 41 46 33 16 27 5
91 34 46 29 21 28 3
92 38 39 32 19 43 2
93 45 35 35 19 48 3
94 27 48 33 12 48 4
95 46 42 27 22 25 1
96 26 39 16 9 49 4
97 44 39 32 9 26 3
98 36 41 26 18 51 3
99 20 52 32 14 25 4
100 44 45 38 14 29 3
101 27 42 24 23 29 4
102 27 44 26 19 43 2
103 41 33 19 24 46 3
104 30 42 37 12 44 3
105 33 46 25 20 25 3
106 37 45 24 21 51 2
107 30 40 23 18 42 5
108 20 48 28 20 53 5
109 44 32 38 18 25 4
110 20 53 28 18 49 2
111 33 39 28 17 51 3
112 31 45 26 18 20 3
113 23 36 21 14 44 3
114 33 38 35 23 38 4
115 33 49 31 19 46 5
116 32 46 34 14 42 4
117 25 43 30 17 29 22
118 37 30 22 46 4 16
119 48 24 10 49 2 36
120 45 27 16 51 3 35
121 32 26 14 38 3 25
122 46 30 19 41 1 27
123 20 15 14 47 3 32
124 42 28 18 44 3 36
125 45 34 19 47 3 51
126 29 29 21 46 3 30
127 51 26 13 44 4 20
128 55 31 17 28 3 29
129 50 28 11 47 4 26
130 44 33 16 28 4 20
131 41 32 22 41 5 40
132 40 33 19 45 4 29
133 47 31 17 46 4 32
134 42 37 25 46 4 33
135 40 27 17 22 3 32
136 51 19 23 33 3 34
137 43 27 21 41 4 24
138 45 31 12 47 5 25
139 41 38 18 25 3 41
140 41 22 15 42 3 39
141 37 35 17 47 3 21
142 46 35 11 50 3 38
143 38 30 17 55 5 28
144 39 41 13 21 3 37
145 45 25 17 3 26 46
146 28 16 52 3 30 39
147 45 14 49 4 25 21
148 21 15 46 4 38 31
149 33 20 4 31 35 25
150 14 45 3 31 49 29
151 16 52 3 27 40 31
152 14 3 21 45 29 13
153 40 4 26 46 31 15
154 49 4 37 45 31 13
155 38 5 28 34 25 13
156 32 5 29 41 27 23
157 46 4 33 43 26 18
158 32 3 41 45 26 21
159 41 3 19 48 23 14
160 43 3 37 43 27 12
161 44 4 36 45 24 17
162 47 5 27 45 35 11
163 28 3 33 34 24 15
164 52 1 29 40 32 14
165 27 2 42 40 24 19
166 45 5 27 55 24 12
167 27 4 47 44 38 14
168 25 4 17 44 36 18
169 28 4 34 48 24 25
170 25 3 32 51 18 22
171 52 4 25 49 34 15
172 44 3 27 33 23 18
173 43 3 37 43 35 18
174 47 4 34 44 22 12
175 52 4 27 44 34 12
176 40 2 37 41 28 16
177 42 3 32 45 34 22
178 45 5 26 44 32 15
179 45 2 29 44 24 16
180 50 5 28 40 34 11
181 49 3 19 48 33 20
182 52 2 46 49 33 14
183 48 3 31 46 29 20
184 51 3 42 49 38 15
185 49 4 33 55 24 12
186 31 4 39 51 25 18
187 43 3 27 46 37 18
188 31 3 35 37 33 11
189 28 4 23 43 30 13
190 43 4 32 41 22 15
191 31 3 22 45 28 19
192 51 3 17 39 24 13
193 58 4 35 38 33 19
194 25 5 34 41 37 18
195 27 5 26 49 35 18
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) opleiding Neuroticisme
77.37544 -0.38124 -0.52405
Extraversie Openheid Extrinsieke_waarden
-0.08222 -0.29369 -0.51313
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-32.3389 -6.2392 0.8485 7.2667 23.0571
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 77.37544 6.52304 11.862 < 2e-16 ***
opleiding -0.38124 0.06365 -5.990 1.04e-08 ***
Neuroticisme -0.52405 0.08409 -6.232 2.93e-09 ***
Extraversie -0.08222 0.09282 -0.886 0.376835
Openheid -0.29369 0.07498 -3.917 0.000125 ***
Extrinsieke_waarden -0.51313 0.07394 -6.940 6.10e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 9.813 on 189 degrees of freedom
Multiple R-squared: 0.458, Adjusted R-squared: 0.4437
F-statistic: 31.94 on 5 and 189 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,] 0.75359675 0.4928065 0.2464033
[2,] 0.82927555 0.3414489 0.1707245
[3,] 0.86190093 0.2761981 0.1380991
[4,] 0.80493929 0.3901214 0.1950607
[5,] 0.73369772 0.5326046 0.2663023
[6,] 0.70425427 0.5914915 0.2957457
[7,] 0.61794837 0.7641033 0.3820516
[8,] 0.53373011 0.9325398 0.4662699
[9,] 0.46440849 0.9288170 0.5355915
[10,] 0.53376399 0.9324720 0.4662360
[11,] 0.56122276 0.8775545 0.4387772
[12,] 0.52343810 0.9531238 0.4765619
[13,] 0.45303043 0.9060609 0.5469696
[14,] 0.42352988 0.8470598 0.5764701
[15,] 0.49612561 0.9922512 0.5038744
[16,] 0.47378911 0.9475782 0.5262109
[17,] 0.45762447 0.9152489 0.5423755
[18,] 0.39094902 0.7818980 0.6090510
[19,] 0.34536793 0.6907359 0.6546321
[20,] 0.28984705 0.5796941 0.7101530
[21,] 0.42905264 0.8581053 0.5709474
[22,] 0.37175247 0.7435049 0.6282475
[23,] 0.32724672 0.6544934 0.6727533
[24,] 0.28566416 0.5713283 0.7143358
[25,] 0.23708548 0.4741710 0.7629145
[26,] 0.24904254 0.4980851 0.7509575
[27,] 0.21464392 0.4292878 0.7853561
[28,] 0.19349573 0.3869915 0.8065043
[29,] 0.17507561 0.3501512 0.8249244
[30,] 0.15219028 0.3043806 0.8478097
[31,] 0.13898041 0.2779608 0.8610196
[32,] 0.12326050 0.2465210 0.8767395
[33,] 0.12763990 0.2552798 0.8723601
[34,] 0.13526460 0.2705292 0.8647354
[35,] 0.15387092 0.3077418 0.8461291
[36,] 0.28311597 0.5662319 0.7168840
[37,] 0.26413731 0.5282746 0.7358627
[38,] 0.29377377 0.5875475 0.7062262
[39,] 0.31921427 0.6384285 0.6807857
[40,] 0.29523548 0.5904710 0.7047645
[41,] 0.29163116 0.5832623 0.7083688
[42,] 0.27386304 0.5477261 0.7261370
[43,] 0.23800473 0.4760095 0.7619953
[44,] 0.20555923 0.4111185 0.7944408
[45,] 0.17988712 0.3597742 0.8201129
[46,] 0.31919727 0.6383945 0.6808027
[47,] 0.33093874 0.6618775 0.6690613
[48,] 0.33519084 0.6703817 0.6648092
[49,] 0.30737066 0.6147413 0.6926293
[50,] 0.27206522 0.5441304 0.7279348
[51,] 0.26893323 0.5378665 0.7310668
[52,] 0.24311054 0.4862211 0.7568895
[53,] 0.23389936 0.4677987 0.7661006
[54,] 0.21282626 0.4256525 0.7871737
[55,] 0.23644690 0.4728938 0.7635531
[56,] 0.21800198 0.4360040 0.7819980
[57,] 0.19431919 0.3886384 0.8056808
[58,] 0.17955189 0.3591038 0.8204481
[59,] 0.15346806 0.3069361 0.8465319
[60,] 0.13679901 0.2735980 0.8632010
[61,] 0.11554843 0.2310969 0.8844516
[62,] 0.09893849 0.1978770 0.9010615
[63,] 0.11343143 0.2268629 0.8865686
[64,] 0.09530833 0.1906167 0.9046917
[65,] 0.10512821 0.2102564 0.8948718
[66,] 0.09288229 0.1857646 0.9071177
[67,] 0.07788597 0.1557719 0.9221140
[68,] 0.07129605 0.1425921 0.9287039
[69,] 0.06613386 0.1322677 0.9338661
[70,] 0.06199358 0.1239872 0.9380064
[71,] 0.09496514 0.1899303 0.9050349
[72,] 0.14343006 0.2868601 0.8565699
[73,] 0.12387229 0.2477446 0.8761277
[74,] 0.19158454 0.3831691 0.8084155
[75,] 0.18246475 0.3649295 0.8175353
[76,] 0.16774174 0.3354835 0.8322583
[77,] 0.15908297 0.3181659 0.8409170
[78,] 0.16703732 0.3340746 0.8329627
[79,] 0.14711170 0.2942234 0.8528883
[80,] 0.13415007 0.2683001 0.8658499
[81,] 0.12042897 0.2408579 0.8795710
[82,] 0.15284987 0.3056997 0.8471501
[83,] 0.13557441 0.2711488 0.8644256
[84,] 0.12654072 0.2530814 0.8734593
[85,] 0.16836444 0.3367289 0.8316356
[86,] 0.14326139 0.2865228 0.8567386
[87,] 0.16552343 0.3310469 0.8344766
[88,] 0.17580763 0.3516153 0.8241924
[89,] 0.19159903 0.3831981 0.8084010
[90,] 0.17708668 0.3541734 0.8229133
[91,] 0.18730004 0.3746001 0.8127000
[92,] 0.24222186 0.4844437 0.7577781
[93,] 0.22957306 0.4591461 0.7704269
[94,] 0.20666030 0.4133206 0.7933397
[95,] 0.19702442 0.3940488 0.8029756
[96,] 0.16972686 0.3394537 0.8302731
[97,] 0.14573350 0.2914670 0.8542665
[98,] 0.14243343 0.2848669 0.8575666
[99,] 0.12224685 0.2444937 0.8777532
[100,] 0.11188698 0.2237740 0.8881130
[101,] 0.11600601 0.2320120 0.8839940
[102,] 0.10596799 0.2119360 0.8940320
[103,] 0.09170494 0.1834099 0.9082951
[104,] 0.07898231 0.1579646 0.9210177
[105,] 0.09107666 0.1821533 0.9089233
[106,] 0.07585015 0.1517003 0.9241499
[107,] 0.06867840 0.1373568 0.9313216
[108,] 0.05849659 0.1169932 0.9415034
[109,] 0.05198741 0.1039748 0.9480126
[110,] 0.04735030 0.0947006 0.9526497
[111,] 0.06445884 0.1289177 0.9355412
[112,] 0.07219382 0.1443876 0.9278062
[113,] 0.07361455 0.1472291 0.9263854
[114,] 0.07420066 0.1484013 0.9257993
[115,] 0.18122795 0.3624559 0.8187721
[116,] 0.18100575 0.3620115 0.8189943
[117,] 0.26204900 0.5240980 0.7379510
[118,] 0.27975292 0.5595058 0.7202471
[119,] 0.25865602 0.5173120 0.7413440
[120,] 0.33099906 0.6619981 0.6690009
[121,] 0.31238486 0.6247697 0.6876151
[122,] 0.27655072 0.5531014 0.7234493
[123,] 0.26905896 0.5381179 0.7309410
[124,] 0.23628391 0.4725678 0.7637161
[125,] 0.22635277 0.4527055 0.7736472
[126,] 0.21613345 0.4322669 0.7838666
[127,] 0.18785971 0.3757194 0.8121403
[128,] 0.20355891 0.4071178 0.7964411
[129,] 0.17401963 0.3480393 0.8259804
[130,] 0.14974147 0.2994829 0.8502585
[131,] 0.13986485 0.2797297 0.8601351
[132,] 0.11646345 0.2329269 0.8835366
[133,] 0.09901646 0.1980329 0.9009835
[134,] 0.10245028 0.2049006 0.8975497
[135,] 0.08521786 0.1704357 0.9147821
[136,] 0.08534512 0.1706902 0.9146549
[137,] 0.19920900 0.3984180 0.8007910
[138,] 0.21279792 0.4255958 0.7872021
[139,] 0.27665881 0.5533176 0.7233412
[140,] 0.23691178 0.4738236 0.7630882
[141,] 0.23772872 0.4754574 0.7622713
[142,] 0.24080682 0.4816136 0.7591932
[143,] 0.23440394 0.4688079 0.7655961
[144,] 0.69138400 0.6172320 0.3086160
[145,] 0.64579758 0.7084048 0.3542024
[146,] 0.63651960 0.7269608 0.3634804
[147,] 0.58715146 0.8256971 0.4128485
[148,] 0.53859105 0.9228179 0.4614089
[149,] 0.54436763 0.9112647 0.4556324
[150,] 0.49102990 0.9820598 0.5089701
[151,] 0.45278276 0.9055655 0.5472172
[152,] 0.40206767 0.8041353 0.5979323
[153,] 0.38380162 0.7676032 0.6161984
[154,] 0.33663527 0.6732705 0.6633647
[155,] 0.35449834 0.7089967 0.6455017
[156,] 0.31146321 0.6229264 0.6885368
[157,] 0.33059453 0.6611891 0.6694055
[158,] 0.28330924 0.5666185 0.7166908
[159,] 0.35628417 0.7125683 0.6437158
[160,] 0.45117525 0.9023505 0.5488248
[161,] 0.38890722 0.7778144 0.6110928
[162,] 0.41757250 0.8351450 0.5824275
[163,] 0.41550943 0.8310189 0.5844906
[164,] 0.35061979 0.7012396 0.6493802
[165,] 0.29397233 0.5879447 0.7060277
[166,] 0.24487572 0.4897514 0.7551243
[167,] 0.23466749 0.4693350 0.7653325
[168,] 0.20356786 0.4071357 0.7964321
[169,] 0.15432157 0.3086431 0.8456784
[170,] 0.13666477 0.2733295 0.8633352
[171,] 0.09980845 0.1996169 0.9001915
[172,] 0.18434929 0.3686986 0.8156507
[173,] 0.15119724 0.3023945 0.8488028
[174,] 0.11311159 0.2262232 0.8868884
[175,] 0.07267000 0.1453400 0.9273300
[176,] 0.06546125 0.1309225 0.9345387
[177,] 0.23019866 0.4603973 0.7698013
[178,] 0.13580929 0.2716186 0.8641907
> postscript(file="/var/www/html/freestat/rcomp/tmp/1ashz1293394680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2ashz1293394680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3ljg21293394680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4ljg21293394680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5ljg21293394680.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 = 195
Frequency = 1
1 2 3 4 5
-11.299421574 -7.932344806 -21.881549879 -9.418742789 -11.443893593
6 7 8 9 10
-2.918056183 11.596721823 7.791127804 6.682351101 2.271993997
11 12 13 14 15
4.027551311 0.922553473 3.011722171 5.435297764 -3.948952475
16 17 18 19 20
-0.363493409 -14.945610033 14.460588249 11.644085571 -6.221215447
21 22 23 24 25
-0.451503098 -10.931489725 12.924590636 3.878785208 3.858475487
26 27 28 29 30
3.726693906 -5.179792688 -0.796474723 -17.299556926 -7.638719028
31 32 33 34 35
-11.212413241 0.838211779 -3.638751317 -20.017344336 -8.450746915
36 37 38 39 40
-14.153785724 -11.853615508 -5.110409538 -12.095832260 -5.281522083
41 42 43 44 45
-12.964561950 -13.166377497 -10.691629343 -30.839454644 5.493923806
46 47 48 49 50
-1.446914602 -7.084471490 6.669456570 8.783170124 -3.846964968
51 52 53 54 55
1.034354561 -0.444040232 -5.352200840 10.055449965 -12.665158223
56 57 58 59 60
-6.821866250 -1.540559354 -1.087147093 -4.594047854 -4.451082342
61 62 63 64 65
-3.604203091 -9.162850492 1.805922211 -1.967212717 -2.401267249
66 67 68 69 70
7.042664452 0.001982523 7.695644575 -3.313988470 6.855939642
71 72 73 74 75
-11.515592541 -6.286350915 9.348794836 -6.135389968 -2.818094739
76 77 78 79 80
6.552143054 2.206383052 -4.565859846 10.838290972 10.365775116
81 82 83 84 85
-4.764498308 -21.143613157 5.857987894 7.569721470 -5.412290591
86 87 88 89 90
-6.604782470 -1.438716980 6.766200551 -2.888028167 10.266217276
91 92 93 94 95
0.848548831 7.479799169 16.508578188 2.354165162 8.450372675
96 97 98 99 100
-10.938878856 8.177968380 5.878389507 -10.235351160 14.901914232
101 102 103 104 105
-9.325410484 -4.758317630 3.184984215 3.475044496 -3.210956602
106 107 108 109 110
7.088783053 -3.691958407 -4.626734097 9.613053512 -5.599131689
111 112 113 114 115
3.081791888 -6.701040246 -14.032799992 3.557422182 8.188612037
116 117 118 119 120
5.518043649 0.943166033 -4.241948307 8.103880906 9.336925501
121 122 123 124 125
-11.292630654 7.538146980 -23.154349520 7.703841018 21.458969083
126 127 128 129 130
-6.257098803 5.404690420 16.416022556 7.444519802 1.329961854
131 132 133 134 135
12.718248861 4.918092306 11.729121463 13.722118665 0.937115390
136 137 138 139 140
13.962227403 3.784209502 3.892852887 11.519659460 4.219186543
141 142 143 144 145
-2.601824359 12.223752323 1.329055560 5.661698948 17.551099172
146 147 148 149 150
13.044637025 17.087413623 0.845774387 -8.998083855 -12.826945122
151 152 153 154 155
-10.104103888 -32.338854728 -1.641484122 12.014614526 -5.987217145
156 157 158 159 160
-5.168912993 7.851160518 -0.633816348 -7.389300755 3.781036937
161 162 163 164 165
6.487254934 5.303822563 -13.396860671 10.074174531 -7.515761091
166 167 168 169 170
1.408594311 -2.258116897 -18.514564932 -6.209134454 -13.693344472
171 172 173 174 175
10.962202269 0.622300612 9.209342717 5.203891037 10.059800029
176 177 178 179 180
2.581564112 7.512357329 3.868999981 2.461048896 8.123070000
181 182 183 184 185
6.626385089 20.398014864 10.575815391 19.664624028 8.171672336
186 187 188 189 190
-3.640426243 4.802861580 -8.511398609 -16.780264514 1.448508338
191 192 193 194 195
-12.029706177 0.603144189 23.057112185 -9.177402007 -11.299421574
> postscript(file="/var/www/html/freestat/rcomp/tmp/6wty51293394680.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 = 195
Frequency = 1
lag(myerror, k = 1) myerror
0 -11.299421574 NA
1 -7.932344806 -11.299421574
2 -21.881549879 -7.932344806
3 -9.418742789 -21.881549879
4 -11.443893593 -9.418742789
5 -2.918056183 -11.443893593
6 11.596721823 -2.918056183
7 7.791127804 11.596721823
8 6.682351101 7.791127804
9 2.271993997 6.682351101
10 4.027551311 2.271993997
11 0.922553473 4.027551311
12 3.011722171 0.922553473
13 5.435297764 3.011722171
14 -3.948952475 5.435297764
15 -0.363493409 -3.948952475
16 -14.945610033 -0.363493409
17 14.460588249 -14.945610033
18 11.644085571 14.460588249
19 -6.221215447 11.644085571
20 -0.451503098 -6.221215447
21 -10.931489725 -0.451503098
22 12.924590636 -10.931489725
23 3.878785208 12.924590636
24 3.858475487 3.878785208
25 3.726693906 3.858475487
26 -5.179792688 3.726693906
27 -0.796474723 -5.179792688
28 -17.299556926 -0.796474723
29 -7.638719028 -17.299556926
30 -11.212413241 -7.638719028
31 0.838211779 -11.212413241
32 -3.638751317 0.838211779
33 -20.017344336 -3.638751317
34 -8.450746915 -20.017344336
35 -14.153785724 -8.450746915
36 -11.853615508 -14.153785724
37 -5.110409538 -11.853615508
38 -12.095832260 -5.110409538
39 -5.281522083 -12.095832260
40 -12.964561950 -5.281522083
41 -13.166377497 -12.964561950
42 -10.691629343 -13.166377497
43 -30.839454644 -10.691629343
44 5.493923806 -30.839454644
45 -1.446914602 5.493923806
46 -7.084471490 -1.446914602
47 6.669456570 -7.084471490
48 8.783170124 6.669456570
49 -3.846964968 8.783170124
50 1.034354561 -3.846964968
51 -0.444040232 1.034354561
52 -5.352200840 -0.444040232
53 10.055449965 -5.352200840
54 -12.665158223 10.055449965
55 -6.821866250 -12.665158223
56 -1.540559354 -6.821866250
57 -1.087147093 -1.540559354
58 -4.594047854 -1.087147093
59 -4.451082342 -4.594047854
60 -3.604203091 -4.451082342
61 -9.162850492 -3.604203091
62 1.805922211 -9.162850492
63 -1.967212717 1.805922211
64 -2.401267249 -1.967212717
65 7.042664452 -2.401267249
66 0.001982523 7.042664452
67 7.695644575 0.001982523
68 -3.313988470 7.695644575
69 6.855939642 -3.313988470
70 -11.515592541 6.855939642
71 -6.286350915 -11.515592541
72 9.348794836 -6.286350915
73 -6.135389968 9.348794836
74 -2.818094739 -6.135389968
75 6.552143054 -2.818094739
76 2.206383052 6.552143054
77 -4.565859846 2.206383052
78 10.838290972 -4.565859846
79 10.365775116 10.838290972
80 -4.764498308 10.365775116
81 -21.143613157 -4.764498308
82 5.857987894 -21.143613157
83 7.569721470 5.857987894
84 -5.412290591 7.569721470
85 -6.604782470 -5.412290591
86 -1.438716980 -6.604782470
87 6.766200551 -1.438716980
88 -2.888028167 6.766200551
89 10.266217276 -2.888028167
90 0.848548831 10.266217276
91 7.479799169 0.848548831
92 16.508578188 7.479799169
93 2.354165162 16.508578188
94 8.450372675 2.354165162
95 -10.938878856 8.450372675
96 8.177968380 -10.938878856
97 5.878389507 8.177968380
98 -10.235351160 5.878389507
99 14.901914232 -10.235351160
100 -9.325410484 14.901914232
101 -4.758317630 -9.325410484
102 3.184984215 -4.758317630
103 3.475044496 3.184984215
104 -3.210956602 3.475044496
105 7.088783053 -3.210956602
106 -3.691958407 7.088783053
107 -4.626734097 -3.691958407
108 9.613053512 -4.626734097
109 -5.599131689 9.613053512
110 3.081791888 -5.599131689
111 -6.701040246 3.081791888
112 -14.032799992 -6.701040246
113 3.557422182 -14.032799992
114 8.188612037 3.557422182
115 5.518043649 8.188612037
116 0.943166033 5.518043649
117 -4.241948307 0.943166033
118 8.103880906 -4.241948307
119 9.336925501 8.103880906
120 -11.292630654 9.336925501
121 7.538146980 -11.292630654
122 -23.154349520 7.538146980
123 7.703841018 -23.154349520
124 21.458969083 7.703841018
125 -6.257098803 21.458969083
126 5.404690420 -6.257098803
127 16.416022556 5.404690420
128 7.444519802 16.416022556
129 1.329961854 7.444519802
130 12.718248861 1.329961854
131 4.918092306 12.718248861
132 11.729121463 4.918092306
133 13.722118665 11.729121463
134 0.937115390 13.722118665
135 13.962227403 0.937115390
136 3.784209502 13.962227403
137 3.892852887 3.784209502
138 11.519659460 3.892852887
139 4.219186543 11.519659460
140 -2.601824359 4.219186543
141 12.223752323 -2.601824359
142 1.329055560 12.223752323
143 5.661698948 1.329055560
144 17.551099172 5.661698948
145 13.044637025 17.551099172
146 17.087413623 13.044637025
147 0.845774387 17.087413623
148 -8.998083855 0.845774387
149 -12.826945122 -8.998083855
150 -10.104103888 -12.826945122
151 -32.338854728 -10.104103888
152 -1.641484122 -32.338854728
153 12.014614526 -1.641484122
154 -5.987217145 12.014614526
155 -5.168912993 -5.987217145
156 7.851160518 -5.168912993
157 -0.633816348 7.851160518
158 -7.389300755 -0.633816348
159 3.781036937 -7.389300755
160 6.487254934 3.781036937
161 5.303822563 6.487254934
162 -13.396860671 5.303822563
163 10.074174531 -13.396860671
164 -7.515761091 10.074174531
165 1.408594311 -7.515761091
166 -2.258116897 1.408594311
167 -18.514564932 -2.258116897
168 -6.209134454 -18.514564932
169 -13.693344472 -6.209134454
170 10.962202269 -13.693344472
171 0.622300612 10.962202269
172 9.209342717 0.622300612
173 5.203891037 9.209342717
174 10.059800029 5.203891037
175 2.581564112 10.059800029
176 7.512357329 2.581564112
177 3.868999981 7.512357329
178 2.461048896 3.868999981
179 8.123070000 2.461048896
180 6.626385089 8.123070000
181 20.398014864 6.626385089
182 10.575815391 20.398014864
183 19.664624028 10.575815391
184 8.171672336 19.664624028
185 -3.640426243 8.171672336
186 4.802861580 -3.640426243
187 -8.511398609 4.802861580
188 -16.780264514 -8.511398609
189 1.448508338 -16.780264514
190 -12.029706177 1.448508338
191 0.603144189 -12.029706177
192 23.057112185 0.603144189
193 -9.177402007 23.057112185
194 -11.299421574 -9.177402007
195 NA -11.299421574
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -7.932344806 -11.299421574
[2,] -21.881549879 -7.932344806
[3,] -9.418742789 -21.881549879
[4,] -11.443893593 -9.418742789
[5,] -2.918056183 -11.443893593
[6,] 11.596721823 -2.918056183
[7,] 7.791127804 11.596721823
[8,] 6.682351101 7.791127804
[9,] 2.271993997 6.682351101
[10,] 4.027551311 2.271993997
[11,] 0.922553473 4.027551311
[12,] 3.011722171 0.922553473
[13,] 5.435297764 3.011722171
[14,] -3.948952475 5.435297764
[15,] -0.363493409 -3.948952475
[16,] -14.945610033 -0.363493409
[17,] 14.460588249 -14.945610033
[18,] 11.644085571 14.460588249
[19,] -6.221215447 11.644085571
[20,] -0.451503098 -6.221215447
[21,] -10.931489725 -0.451503098
[22,] 12.924590636 -10.931489725
[23,] 3.878785208 12.924590636
[24,] 3.858475487 3.878785208
[25,] 3.726693906 3.858475487
[26,] -5.179792688 3.726693906
[27,] -0.796474723 -5.179792688
[28,] -17.299556926 -0.796474723
[29,] -7.638719028 -17.299556926
[30,] -11.212413241 -7.638719028
[31,] 0.838211779 -11.212413241
[32,] -3.638751317 0.838211779
[33,] -20.017344336 -3.638751317
[34,] -8.450746915 -20.017344336
[35,] -14.153785724 -8.450746915
[36,] -11.853615508 -14.153785724
[37,] -5.110409538 -11.853615508
[38,] -12.095832260 -5.110409538
[39,] -5.281522083 -12.095832260
[40,] -12.964561950 -5.281522083
[41,] -13.166377497 -12.964561950
[42,] -10.691629343 -13.166377497
[43,] -30.839454644 -10.691629343
[44,] 5.493923806 -30.839454644
[45,] -1.446914602 5.493923806
[46,] -7.084471490 -1.446914602
[47,] 6.669456570 -7.084471490
[48,] 8.783170124 6.669456570
[49,] -3.846964968 8.783170124
[50,] 1.034354561 -3.846964968
[51,] -0.444040232 1.034354561
[52,] -5.352200840 -0.444040232
[53,] 10.055449965 -5.352200840
[54,] -12.665158223 10.055449965
[55,] -6.821866250 -12.665158223
[56,] -1.540559354 -6.821866250
[57,] -1.087147093 -1.540559354
[58,] -4.594047854 -1.087147093
[59,] -4.451082342 -4.594047854
[60,] -3.604203091 -4.451082342
[61,] -9.162850492 -3.604203091
[62,] 1.805922211 -9.162850492
[63,] -1.967212717 1.805922211
[64,] -2.401267249 -1.967212717
[65,] 7.042664452 -2.401267249
[66,] 0.001982523 7.042664452
[67,] 7.695644575 0.001982523
[68,] -3.313988470 7.695644575
[69,] 6.855939642 -3.313988470
[70,] -11.515592541 6.855939642
[71,] -6.286350915 -11.515592541
[72,] 9.348794836 -6.286350915
[73,] -6.135389968 9.348794836
[74,] -2.818094739 -6.135389968
[75,] 6.552143054 -2.818094739
[76,] 2.206383052 6.552143054
[77,] -4.565859846 2.206383052
[78,] 10.838290972 -4.565859846
[79,] 10.365775116 10.838290972
[80,] -4.764498308 10.365775116
[81,] -21.143613157 -4.764498308
[82,] 5.857987894 -21.143613157
[83,] 7.569721470 5.857987894
[84,] -5.412290591 7.569721470
[85,] -6.604782470 -5.412290591
[86,] -1.438716980 -6.604782470
[87,] 6.766200551 -1.438716980
[88,] -2.888028167 6.766200551
[89,] 10.266217276 -2.888028167
[90,] 0.848548831 10.266217276
[91,] 7.479799169 0.848548831
[92,] 16.508578188 7.479799169
[93,] 2.354165162 16.508578188
[94,] 8.450372675 2.354165162
[95,] -10.938878856 8.450372675
[96,] 8.177968380 -10.938878856
[97,] 5.878389507 8.177968380
[98,] -10.235351160 5.878389507
[99,] 14.901914232 -10.235351160
[100,] -9.325410484 14.901914232
[101,] -4.758317630 -9.325410484
[102,] 3.184984215 -4.758317630
[103,] 3.475044496 3.184984215
[104,] -3.210956602 3.475044496
[105,] 7.088783053 -3.210956602
[106,] -3.691958407 7.088783053
[107,] -4.626734097 -3.691958407
[108,] 9.613053512 -4.626734097
[109,] -5.599131689 9.613053512
[110,] 3.081791888 -5.599131689
[111,] -6.701040246 3.081791888
[112,] -14.032799992 -6.701040246
[113,] 3.557422182 -14.032799992
[114,] 8.188612037 3.557422182
[115,] 5.518043649 8.188612037
[116,] 0.943166033 5.518043649
[117,] -4.241948307 0.943166033
[118,] 8.103880906 -4.241948307
[119,] 9.336925501 8.103880906
[120,] -11.292630654 9.336925501
[121,] 7.538146980 -11.292630654
[122,] -23.154349520 7.538146980
[123,] 7.703841018 -23.154349520
[124,] 21.458969083 7.703841018
[125,] -6.257098803 21.458969083
[126,] 5.404690420 -6.257098803
[127,] 16.416022556 5.404690420
[128,] 7.444519802 16.416022556
[129,] 1.329961854 7.444519802
[130,] 12.718248861 1.329961854
[131,] 4.918092306 12.718248861
[132,] 11.729121463 4.918092306
[133,] 13.722118665 11.729121463
[134,] 0.937115390 13.722118665
[135,] 13.962227403 0.937115390
[136,] 3.784209502 13.962227403
[137,] 3.892852887 3.784209502
[138,] 11.519659460 3.892852887
[139,] 4.219186543 11.519659460
[140,] -2.601824359 4.219186543
[141,] 12.223752323 -2.601824359
[142,] 1.329055560 12.223752323
[143,] 5.661698948 1.329055560
[144,] 17.551099172 5.661698948
[145,] 13.044637025 17.551099172
[146,] 17.087413623 13.044637025
[147,] 0.845774387 17.087413623
[148,] -8.998083855 0.845774387
[149,] -12.826945122 -8.998083855
[150,] -10.104103888 -12.826945122
[151,] -32.338854728 -10.104103888
[152,] -1.641484122 -32.338854728
[153,] 12.014614526 -1.641484122
[154,] -5.987217145 12.014614526
[155,] -5.168912993 -5.987217145
[156,] 7.851160518 -5.168912993
[157,] -0.633816348 7.851160518
[158,] -7.389300755 -0.633816348
[159,] 3.781036937 -7.389300755
[160,] 6.487254934 3.781036937
[161,] 5.303822563 6.487254934
[162,] -13.396860671 5.303822563
[163,] 10.074174531 -13.396860671
[164,] -7.515761091 10.074174531
[165,] 1.408594311 -7.515761091
[166,] -2.258116897 1.408594311
[167,] -18.514564932 -2.258116897
[168,] -6.209134454 -18.514564932
[169,] -13.693344472 -6.209134454
[170,] 10.962202269 -13.693344472
[171,] 0.622300612 10.962202269
[172,] 9.209342717 0.622300612
[173,] 5.203891037 9.209342717
[174,] 10.059800029 5.203891037
[175,] 2.581564112 10.059800029
[176,] 7.512357329 2.581564112
[177,] 3.868999981 7.512357329
[178,] 2.461048896 3.868999981
[179,] 8.123070000 2.461048896
[180,] 6.626385089 8.123070000
[181,] 20.398014864 6.626385089
[182,] 10.575815391 20.398014864
[183,] 19.664624028 10.575815391
[184,] 8.171672336 19.664624028
[185,] -3.640426243 8.171672336
[186,] 4.802861580 -3.640426243
[187,] -8.511398609 4.802861580
[188,] -16.780264514 -8.511398609
[189,] 1.448508338 -16.780264514
[190,] -12.029706177 1.448508338
[191,] 0.603144189 -12.029706177
[192,] 23.057112185 0.603144189
[193,] -9.177402007 23.057112185
[194,] -11.299421574 -9.177402007
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -7.932344806 -11.299421574
2 -21.881549879 -7.932344806
3 -9.418742789 -21.881549879
4 -11.443893593 -9.418742789
5 -2.918056183 -11.443893593
6 11.596721823 -2.918056183
7 7.791127804 11.596721823
8 6.682351101 7.791127804
9 2.271993997 6.682351101
10 4.027551311 2.271993997
11 0.922553473 4.027551311
12 3.011722171 0.922553473
13 5.435297764 3.011722171
14 -3.948952475 5.435297764
15 -0.363493409 -3.948952475
16 -14.945610033 -0.363493409
17 14.460588249 -14.945610033
18 11.644085571 14.460588249
19 -6.221215447 11.644085571
20 -0.451503098 -6.221215447
21 -10.931489725 -0.451503098
22 12.924590636 -10.931489725
23 3.878785208 12.924590636
24 3.858475487 3.878785208
25 3.726693906 3.858475487
26 -5.179792688 3.726693906
27 -0.796474723 -5.179792688
28 -17.299556926 -0.796474723
29 -7.638719028 -17.299556926
30 -11.212413241 -7.638719028
31 0.838211779 -11.212413241
32 -3.638751317 0.838211779
33 -20.017344336 -3.638751317
34 -8.450746915 -20.017344336
35 -14.153785724 -8.450746915
36 -11.853615508 -14.153785724
37 -5.110409538 -11.853615508
38 -12.095832260 -5.110409538
39 -5.281522083 -12.095832260
40 -12.964561950 -5.281522083
41 -13.166377497 -12.964561950
42 -10.691629343 -13.166377497
43 -30.839454644 -10.691629343
44 5.493923806 -30.839454644
45 -1.446914602 5.493923806
46 -7.084471490 -1.446914602
47 6.669456570 -7.084471490
48 8.783170124 6.669456570
49 -3.846964968 8.783170124
50 1.034354561 -3.846964968
51 -0.444040232 1.034354561
52 -5.352200840 -0.444040232
53 10.055449965 -5.352200840
54 -12.665158223 10.055449965
55 -6.821866250 -12.665158223
56 -1.540559354 -6.821866250
57 -1.087147093 -1.540559354
58 -4.594047854 -1.087147093
59 -4.451082342 -4.594047854
60 -3.604203091 -4.451082342
61 -9.162850492 -3.604203091
62 1.805922211 -9.162850492
63 -1.967212717 1.805922211
64 -2.401267249 -1.967212717
65 7.042664452 -2.401267249
66 0.001982523 7.042664452
67 7.695644575 0.001982523
68 -3.313988470 7.695644575
69 6.855939642 -3.313988470
70 -11.515592541 6.855939642
71 -6.286350915 -11.515592541
72 9.348794836 -6.286350915
73 -6.135389968 9.348794836
74 -2.818094739 -6.135389968
75 6.552143054 -2.818094739
76 2.206383052 6.552143054
77 -4.565859846 2.206383052
78 10.838290972 -4.565859846
79 10.365775116 10.838290972
80 -4.764498308 10.365775116
81 -21.143613157 -4.764498308
82 5.857987894 -21.143613157
83 7.569721470 5.857987894
84 -5.412290591 7.569721470
85 -6.604782470 -5.412290591
86 -1.438716980 -6.604782470
87 6.766200551 -1.438716980
88 -2.888028167 6.766200551
89 10.266217276 -2.888028167
90 0.848548831 10.266217276
91 7.479799169 0.848548831
92 16.508578188 7.479799169
93 2.354165162 16.508578188
94 8.450372675 2.354165162
95 -10.938878856 8.450372675
96 8.177968380 -10.938878856
97 5.878389507 8.177968380
98 -10.235351160 5.878389507
99 14.901914232 -10.235351160
100 -9.325410484 14.901914232
101 -4.758317630 -9.325410484
102 3.184984215 -4.758317630
103 3.475044496 3.184984215
104 -3.210956602 3.475044496
105 7.088783053 -3.210956602
106 -3.691958407 7.088783053
107 -4.626734097 -3.691958407
108 9.613053512 -4.626734097
109 -5.599131689 9.613053512
110 3.081791888 -5.599131689
111 -6.701040246 3.081791888
112 -14.032799992 -6.701040246
113 3.557422182 -14.032799992
114 8.188612037 3.557422182
115 5.518043649 8.188612037
116 0.943166033 5.518043649
117 -4.241948307 0.943166033
118 8.103880906 -4.241948307
119 9.336925501 8.103880906
120 -11.292630654 9.336925501
121 7.538146980 -11.292630654
122 -23.154349520 7.538146980
123 7.703841018 -23.154349520
124 21.458969083 7.703841018
125 -6.257098803 21.458969083
126 5.404690420 -6.257098803
127 16.416022556 5.404690420
128 7.444519802 16.416022556
129 1.329961854 7.444519802
130 12.718248861 1.329961854
131 4.918092306 12.718248861
132 11.729121463 4.918092306
133 13.722118665 11.729121463
134 0.937115390 13.722118665
135 13.962227403 0.937115390
136 3.784209502 13.962227403
137 3.892852887 3.784209502
138 11.519659460 3.892852887
139 4.219186543 11.519659460
140 -2.601824359 4.219186543
141 12.223752323 -2.601824359
142 1.329055560 12.223752323
143 5.661698948 1.329055560
144 17.551099172 5.661698948
145 13.044637025 17.551099172
146 17.087413623 13.044637025
147 0.845774387 17.087413623
148 -8.998083855 0.845774387
149 -12.826945122 -8.998083855
150 -10.104103888 -12.826945122
151 -32.338854728 -10.104103888
152 -1.641484122 -32.338854728
153 12.014614526 -1.641484122
154 -5.987217145 12.014614526
155 -5.168912993 -5.987217145
156 7.851160518 -5.168912993
157 -0.633816348 7.851160518
158 -7.389300755 -0.633816348
159 3.781036937 -7.389300755
160 6.487254934 3.781036937
161 5.303822563 6.487254934
162 -13.396860671 5.303822563
163 10.074174531 -13.396860671
164 -7.515761091 10.074174531
165 1.408594311 -7.515761091
166 -2.258116897 1.408594311
167 -18.514564932 -2.258116897
168 -6.209134454 -18.514564932
169 -13.693344472 -6.209134454
170 10.962202269 -13.693344472
171 0.622300612 10.962202269
172 9.209342717 0.622300612
173 5.203891037 9.209342717
174 10.059800029 5.203891037
175 2.581564112 10.059800029
176 7.512357329 2.581564112
177 3.868999981 7.512357329
178 2.461048896 3.868999981
179 8.123070000 2.461048896
180 6.626385089 8.123070000
181 20.398014864 6.626385089
182 10.575815391 20.398014864
183 19.664624028 10.575815391
184 8.171672336 19.664624028
185 -3.640426243 8.171672336
186 4.802861580 -3.640426243
187 -8.511398609 4.802861580
188 -16.780264514 -8.511398609
189 1.448508338 -16.780264514
190 -12.029706177 1.448508338
191 0.603144189 -12.029706177
192 23.057112185 0.603144189
193 -9.177402007 23.057112185
194 -11.299421574 -9.177402007
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/7okf81293394680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8okf81293394680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9okf81293394680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10zbea1293394680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/112cdg1293394680.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/12out41293394680.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/13cdqg1293394680.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/14nn811293394680.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/freestat/rcomp/tmp/151e5s1293394680.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/freestat/rcomp/tmp/16nx4f1293394680.tab")
+ }
>
> try(system("convert tmp/1ashz1293394680.ps tmp/1ashz1293394680.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ashz1293394680.ps tmp/2ashz1293394680.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ljg21293394680.ps tmp/3ljg21293394680.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ljg21293394680.ps tmp/4ljg21293394680.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ljg21293394680.ps tmp/5ljg21293394680.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wty51293394680.ps tmp/6wty51293394680.png",intern=TRUE))
character(0)
> try(system("convert tmp/7okf81293394680.ps tmp/7okf81293394680.png",intern=TRUE))
character(0)
> try(system("convert tmp/8okf81293394680.ps tmp/8okf81293394680.png",intern=TRUE))
character(0)
> try(system("convert tmp/9okf81293394680.ps tmp/9okf81293394680.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zbea1293394680.ps tmp/10zbea1293394680.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.521 2.758 6.878