R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(1845
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+ ,dim=c(9
+ ,144)
+ ,dimnames=list(c('Pageview'
+ ,'TimeRfc'
+ ,'CRSCompeVi'
+ ,'NrCompeVi'
+ ,'BloComp'
+ ,'RevCompe'
+ ,'NrSubmFeedbPR'
+ ,'CompWrNrRev'
+ ,'CompWRNrBl')
+ ,1:144))
> y <- array(NA,dim=c(9,144),dimnames=list(c('Pageview','TimeRfc','CRSCompeVi','NrCompeVi','BloComp','RevCompe','NrSubmFeedbPR','CompWrNrRev','CompWRNrBl'),1:144))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '7'
> #'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
NrSubmFeedbPR Pageview TimeRfc CRSCompeVi NrCompeVi BloComp RevCompe
1 82 1845 162687 595 115 48 21
2 80 1796 201906 545 76 58 20
3 0 192 7215 72 1 0 0
4 84 2444 146367 679 155 67 27
5 124 3567 257045 1201 125 83 31
6 140 6917 524450 1967 278 136 36
7 88 1840 188294 595 89 65 23
8 115 1740 195674 496 59 86 30
9 109 2078 177020 670 87 62 30
10 108 3097 325899 1039 130 71 27
11 63 1946 121844 634 158 50 24
12 118 2370 203938 743 120 88 30
13 71 1883 107394 681 87 50 22
14 112 3198 220751 1086 264 79 28
15 63 1490 172905 419 51 56 18
16 86 1573 156326 474 85 54 22
17 148 1807 145178 442 100 81 37
18 54 1309 89171 373 72 13 15
19 134 2820 172624 899 147 74 34
20 57 776 39790 242 49 18 18
21 59 1162 87927 399 40 31 15
22 113 2818 241285 850 99 99 30
23 96 1760 195820 642 127 38 25
24 96 2315 146946 717 164 59 34
25 78 1994 159763 619 41 54 21
26 80 1806 207078 657 160 63 21
27 93 2152 212394 691 92 66 25
28 109 1457 201536 366 59 90 31
29 115 3000 394662 994 89 72 31
30 79 2236 217892 929 90 61 20
31 103 1685 182286 490 76 61 28
32 71 1626 181740 553 116 61 22
33 66 2257 137978 738 92 53 17
34 100 3373 255929 1028 361 118 25
35 100 2571 236489 844 85 73 25
36 0 1 0 0 0 0 0
37 121 2142 230761 1000 63 54 31
38 51 1878 132807 629 138 54 14
39 119 2190 157118 532 270 46 35
40 136 2186 253254 811 64 83 34
41 84 2532 269329 837 96 106 22
42 136 1823 161273 682 62 44 34
43 84 1095 107181 400 35 27 23
44 92 2162 195891 804 59 64 24
45 103 1365 139667 419 56 71 26
46 85 1244 171101 334 41 44 23
47 106 756 81407 216 49 23 35
48 96 2417 247563 786 121 78 24
49 124 2327 239807 752 113 60 31
50 106 2786 172743 964 190 73 30
51 82 658 48188 205 37 12 22
52 87 2012 169355 506 52 104 23
53 97 2602 315622 830 89 83 27
54 107 2071 241518 694 73 57 30
55 126 1911 195583 691 49 67 33
56 43 1775 159913 547 77 44 12
57 96 1918 220241 538 58 53 26
58 100 1046 101694 329 75 26 26
59 91 1190 157258 427 32 67 23
60 136 2890 202536 972 59 36 38
61 128 1836 173505 541 71 56 32
62 83 2254 150518 836 91 52 21
63 74 1392 141491 376 87 54 22
64 96 1325 125612 467 48 57 26
65 102 1317 166049 430 63 27 28
66 122 1525 124197 483 41 58 33
67 144 2335 195043 504 86 76 36
68 90 2897 138708 887 152 93 25
69 97 1118 116552 271 49 59 25
70 78 340 31970 101 40 5 21
71 72 2977 258158 1097 135 57 19
72 45 1449 151184 469 83 42 12
73 120 1550 135926 528 62 88 30
74 59 1684 119629 475 91 53 21
75 150 2728 171518 698 95 81 39
76 117 1574 108949 425 82 35 32
77 123 2413 183471 709 112 102 28
78 114 2563 159966 824 70 71 29
79 75 1079 93786 336 78 28 21
80 114 1235 84971 395 105 34 31
81 94 980 88882 234 49 54 26
82 116 2246 304603 830 60 49 29
83 86 1076 75101 334 49 30 23
84 90 1637 145043 524 132 57 25
85 87 1208 95827 393 49 54 22
86 99 1865 173924 574 71 38 26
87 132 2726 241957 672 102 63 33
88 96 1208 115367 284 74 58 24
89 91 1419 118408 450 49 46 24
90 77 1609 164078 653 74 46 21
91 104 1864 158931 684 59 51 28
92 100 2412 184139 706 91 87 28
93 94 1238 152856 417 68 39 25
94 60 1462 144014 549 81 28 15
95 46 973 62535 394 33 26 13
96 135 2319 245196 730 166 52 36
97 99 1890 199841 571 97 96 27
98 2 223 19349 67 15 13 1
99 96 2526 247280 877 105 43 24
100 109 2072 159408 856 61 42 31
101 15 778 72128 306 11 30 4
102 68 1194 104253 382 45 59 21
103 102 1424 151090 435 89 73 27
104 84 1328 137382 336 67 39 23
105 46 839 87448 227 27 36 12
106 59 596 27676 194 59 2 16
107 116 1671 165507 410 127 102 29
108 29 1167 132148 273 48 30 26
109 0 0 0 0 0 0 0
110 91 1106 95778 343 58 46 25
111 76 1148 109001 376 57 25 21
112 86 1485 158833 495 60 59 24
113 84 1526 147690 448 77 60 21
114 65 962 89887 313 71 36 21
115 0 78 3616 14 5 0 0
116 0 0 0 0 0 0 0
117 84 1184 199005 410 70 45 23
118 114 1671 160930 606 76 79 33
119 132 2142 177948 593 124 30 32
120 92 1015 136061 312 56 43 23
121 3 778 43410 292 63 7 1
122 109 1856 184277 547 92 80 29
123 81 1056 108858 302 58 32 20
124 121 2234 141744 632 64 81 33
125 48 731 60493 174 29 3 12
126 8 285 19764 75 19 10 2
127 80 1872 177559 572 64 47 21
128 107 1181 140281 389 79 35 28
129 140 1725 164249 562 104 54 35
130 8 256 11796 79 22 1 2
131 0 98 10674 33 7 0 0
132 56 1435 151322 487 37 46 18
133 4 41 6836 11 5 0 1
134 70 1930 174712 664 48 51 21
135 0 42 5118 6 1 5 0
136 14 528 40248 183 34 8 4
137 0 0 0 0 0 0 0
138 104 1121 127628 342 53 38 29
139 89 1305 88837 269 44 21 26
140 0 81 7131 27 0 0 0
141 12 262 9056 99 18 0 4
142 60 1099 87957 305 52 18 19
143 84 1290 144470 327 56 53 22
144 88 1248 111408 459 50 17 22
CompWrNrRev CompWRNrBl
1 6200 37
2 10265 43
3 603 0
4 8874 54
5 20323 86
6 26258 181
7 10165 42
8 8247 59
9 8683 46
10 16957 77
11 8058 49
12 20488 79
13 7945 37
14 13448 92
15 5389 31
16 6185 28
17 24369 103
18 70 2
19 17327 48
20 3878 25
21 3149 16
22 20517 106
23 2570 35
24 5162 33
25 5299 45
26 7233 64
27 15657 73
28 15329 78
29 14881 63
30 16318 69
31 9556 36
32 10462 41
33 7192 59
34 4362 33
35 14349 76
36 0 0
37 10881 27
38 8022 44
39 13073 43
40 26641 104
41 14426 120
42 15604 44
43 9184 71
44 5989 78
45 11270 106
46 13958 61
47 7162 53
48 13275 51
49 21224 46
50 10615 55
51 2102 14
52 12396 44
53 18717 113
54 9724 55
55 9863 46
56 8374 39
57 8030 51
58 7509 31
59 14146 36
60 7768 47
61 13823 53
62 7230 38
63 10170 52
64 7573 37
65 5753 11
66 9791 45
67 19365 59
68 9422 82
69 12310 49
70 1283 6
71 6372 81
72 5413 56
73 10837 105
74 3394 46
75 12964 46
76 3495 2
77 11580 51
78 9970 95
79 4911 18
80 10138 55
81 14697 48
82 8464 48
83 4204 39
84 10226 40
85 3456 36
86 8895 60
87 22557 114
88 6900 39
89 8620 45
90 7820 59
91 12112 59
92 13178 93
93 7028 35
94 6616 47
95 9570 36
96 14612 59
97 11219 79
98 786 14
99 11252 42
100 9289 41
101 593 8
102 6562 41
103 8208 24
104 7488 22
105 4574 18
106 522 1
107 12840 53
108 1350 6
109 0 0
110 10623 49
111 5322 33
112 7987 50
113 10566 64
114 1900 53
115 0 0
116 0 0
117 10698 48
118 14884 90
119 6852 46
120 6873 29
121 4 1
122 9188 64
123 5141 29
124 4260 27
125 443 4
126 2416 10
127 9831 47
128 5953 44
129 9435 51
130 0 0
131 0 0
132 7642 38
133 0 0
134 6837 57
135 0 0
136 775 6
137 0 0
138 8191 22
139 1661 34
140 0 0
141 548 10
142 3080 16
143 13400 93
144 8181 22
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pageview TimeRfc CRSCompeVi NrCompeVi BloComp
-9.729e-01 -2.468e-03 3.159e-06 1.046e-02 -1.419e-02 4.410e-02
RevCompe CompWrNrRev CompWRNrBl
3.404e+00 8.218e-04 -4.819e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-60.375 -2.677 0.973 4.445 19.604
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.729e-01 1.850e+00 -0.526 0.599890
Pageview -2.468e-03 3.829e-03 -0.644 0.520359
TimeRfc 3.159e-06 2.201e-05 0.144 0.886095
CRSCompeVi 1.046e-02 9.860e-03 1.061 0.290791
NrCompeVi -1.419e-02 2.112e-02 -0.672 0.502915
BloComp 4.410e-02 5.043e-02 0.874 0.383432
RevCompe 3.404e+00 1.117e-01 30.463 < 2e-16 ***
CompWrNrRev 8.218e-04 2.381e-04 3.452 0.000744 ***
CompWRNrBl -4.819e-02 4.589e-02 -1.050 0.295541
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.481 on 135 degrees of freedom
Multiple R-squared: 0.952, Adjusted R-squared: 0.9491
F-statistic: 334.3 on 8 and 135 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,] 0.5234385291 0.9531229418 0.4765614709
[2,] 0.4115985305 0.8231970611 0.5884014695
[3,] 0.6932731701 0.6134536599 0.3067268299
[4,] 0.6480693124 0.7038613752 0.3519306876
[5,] 0.6389180529 0.7221638943 0.3610819471
[6,] 0.6382532087 0.7234935827 0.3617467913
[7,] 0.6973454147 0.6053091706 0.3026545853
[8,] 0.6971286972 0.6057426057 0.3028713028
[9,] 0.6199749775 0.7600500449 0.3800250225
[10,] 0.5974231868 0.8051536263 0.4025768132
[11,] 0.5412346572 0.9175306856 0.4587653428
[12,] 0.4852738517 0.9705477034 0.5147261483
[13,] 0.7292805611 0.5414388777 0.2707194389
[14,] 0.6811389358 0.6377221284 0.3188610642
[15,] 0.6138491709 0.7723016583 0.3861508291
[16,] 0.6146872939 0.7706254121 0.3853127061
[17,] 0.6286735111 0.7426529778 0.3713264889
[18,] 0.6831256728 0.6337486545 0.3168743272
[19,] 0.6651491261 0.6697017477 0.3348508739
[20,] 0.6032320755 0.7935358490 0.3967679245
[21,] 0.6660844551 0.6678310897 0.3339155449
[22,] 0.6153359576 0.7693280848 0.3846640424
[23,] 0.6310850482 0.7378299036 0.3689149518
[24,] 0.5785530426 0.8428939149 0.4214469574
[25,] 0.5175777313 0.9648445374 0.4824222687
[26,] 0.4674731253 0.9349462507 0.5325268747
[27,] 0.4204412639 0.8408825278 0.5795587361
[28,] 0.3857665757 0.7715331513 0.6142334243
[29,] 0.3320286892 0.6640573784 0.6679713108
[30,] 0.2917413356 0.5834826712 0.7082586644
[31,] 0.3106267142 0.6212534285 0.6893732858
[32,] 0.2732495717 0.5464991434 0.7267504283
[33,] 0.2474318521 0.4948637041 0.7525681479
[34,] 0.2500884496 0.5001768992 0.7499115504
[35,] 0.2097460084 0.4194920168 0.7902539916
[36,] 0.2663564243 0.5327128487 0.7336435757
[37,] 0.2238603201 0.4477206401 0.7761396799
[38,] 0.1880903259 0.3761806518 0.8119096741
[39,] 0.1740763280 0.3481526561 0.8259236720
[40,] 0.1865544749 0.3731089499 0.8134455251
[41,] 0.1550380641 0.3100761282 0.8449619359
[42,] 0.1672506010 0.3345012020 0.8327493990
[43,] 0.1384224126 0.2768448253 0.8615775874
[44,] 0.1203006322 0.2406012644 0.8796993678
[45,] 0.1052034711 0.2104069423 0.8947965289
[46,] 0.0855722930 0.1711445859 0.9144277070
[47,] 0.0861294615 0.1722589229 0.9138705385
[48,] 0.0672974366 0.1345948731 0.9327025634
[49,] 0.0526155047 0.1052310094 0.9473844953
[50,] 0.0573057653 0.1146115307 0.9426942347
[51,] 0.0455152954 0.0910305907 0.9544847046
[52,] 0.0435811483 0.0871622966 0.9564188517
[53,] 0.0329779228 0.0659558455 0.9670220772
[54,] 0.0250164865 0.0500329729 0.9749835135
[55,] 0.0189651846 0.0379303692 0.9810348154
[56,] 0.0191228405 0.0382456810 0.9808771595
[57,] 0.0165406565 0.0330813130 0.9834593435
[58,] 0.0127159737 0.0254319475 0.9872840263
[59,] 0.0123032965 0.0246065931 0.9876967035
[60,] 0.0092768059 0.0185536118 0.9907231941
[61,] 0.0066577601 0.0133155203 0.9933422399
[62,] 0.0086497719 0.0172995437 0.9913502281
[63,] 0.0201637120 0.0403274239 0.9798362880
[64,] 0.0174485912 0.0348971824 0.9825514088
[65,] 0.0141383799 0.0282767598 0.9858616201
[66,] 0.0253704989 0.0507409978 0.9746295011
[67,] 0.0230764923 0.0461529847 0.9769235077
[68,] 0.0171493289 0.0342986579 0.9828506711
[69,] 0.0134944428 0.0269888856 0.9865055572
[70,] 0.0105144390 0.0210288780 0.9894855610
[71,] 0.0164918841 0.0329837682 0.9835081159
[72,] 0.0135056899 0.0270113798 0.9864943101
[73,] 0.0151434952 0.0302869905 0.9848565048
[74,] 0.0170013102 0.0340026205 0.9829986898
[75,] 0.0142062845 0.0284125691 0.9857937155
[76,] 0.0123795632 0.0247591265 0.9876204368
[77,] 0.0126684178 0.0253368356 0.9873315822
[78,] 0.0095100296 0.0190200592 0.9904899704
[79,] 0.0067027113 0.0134054227 0.9932972887
[80,] 0.0046824290 0.0093648580 0.9953175710
[81,] 0.0044677898 0.0089355796 0.9955322102
[82,] 0.0036779872 0.0073559745 0.9963220128
[83,] 0.0026717364 0.0053434728 0.9973282636
[84,] 0.0031708942 0.0063417884 0.9968291058
[85,] 0.0038260557 0.0076521114 0.9961739443
[86,] 0.0026552378 0.0053104755 0.9973447622
[87,] 0.0017589731 0.0035179463 0.9982410269
[88,] 0.0012314375 0.0024628749 0.9987685625
[89,] 0.0010956157 0.0021912313 0.9989043843
[90,] 0.0008853302 0.0017706604 0.9991146698
[91,] 0.0008171182 0.0016342364 0.9991828818
[92,] 0.0005329108 0.0010658216 0.9994670892
[93,] 0.0003249561 0.0006499122 0.9996750439
[94,] 0.0002360239 0.0004720477 0.9997639761
[95,] 0.0001619934 0.0003239869 0.9998380066
[96,] 0.0001122164 0.0002244329 0.9998877836
[97,] 0.9999078175 0.0001843650 0.0000921825
[98,] 0.9998285848 0.0003428303 0.0001714152
[99,] 0.9997714292 0.0004571417 0.0002285708
[100,] 0.9995561142 0.0008877716 0.0004438858
[101,] 0.9991686199 0.0016627603 0.0008313801
[102,] 0.9985444542 0.0029110917 0.0014555458
[103,] 0.9985702606 0.0028594789 0.0014297394
[104,] 0.9973579165 0.0052841670 0.0026420835
[105,] 0.9954487856 0.0091024287 0.0045512144
[106,] 0.9946351275 0.0107297450 0.0053648725
[107,] 0.9948015080 0.0103969840 0.0051984920
[108,] 0.9962444097 0.0075111806 0.0037555903
[109,] 0.9947685090 0.0104629819 0.0052314910
[110,] 0.9939225591 0.0121548818 0.0060774409
[111,] 0.9889003148 0.0221993705 0.0110996852
[112,] 0.9869709663 0.0260580675 0.0130290337
[113,] 0.9884245667 0.0231508667 0.0115754333
[114,] 0.9902870613 0.0194258773 0.0097129387
[115,] 0.9800498670 0.0399002659 0.0199501330
[116,] 0.9891967520 0.0216064960 0.0108032480
[117,] 0.9760167410 0.0479665181 0.0239832590
[118,] 0.9854825205 0.0290349591 0.0145174795
[119,] 0.9680056405 0.0639887190 0.0319943595
[120,] 0.9197369139 0.1605261723 0.0802630861
[121,] 0.9019243017 0.1961513967 0.0980756983
> postscript(file="/var/wessaorg/rcomp/tmp/1fy9n1324581649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2nsmk1324581649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3tfd31324581649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4essd1324581649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5gjs81324581649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 144
Frequency = 1
1 2 3 4 5 6
5.51790092 3.15384349 0.18965322 -13.90072047 0.45053735 -1.62081246
7 8 9 10 11 12
0.48158365 5.46529191 -0.99028995 1.31402531 -24.14895165 -0.90694634
13 14 15 16 17 18
-11.43599695 7.15538634 -3.22288112 5.61682235 5.20423228 3.45439796
19 20 21 22 23 24
3.16093715 -6.11338107 4.72034372 -5.54406471 8.59556118 -23.92445307
25 26 27 28 29 30
1.45514507 3.06244118 -4.65631080 -8.37613110 -4.88091562 -4.48109092
31 32 33 34 35 36
-0.59858865 -12.91574006 2.42976368 10.57286979 2.51298011 0.97536051
37 38 39 40 41 42
1.43388766 -2.93490334 -6.67562809 -2.26770484 -2.64512783 6.34577107
43 44 45 46 47 48
0.05187720 4.44804850 7.53619080 -3.16176648 -16.45288356 2.07613996
49 50 51 52 53 54
0.31628525 -5.48152995 6.36515750 -3.08538589 -9.51181642 -3.86214772
55 56 57 58 59 60
3.37927463 -4.56484981 1.23595316 6.54043789 -0.72510612 -0.90440436
61 62 63 64 65 66
8.11655766 3.73030416 -7.84785579 0.19659691 1.40732455 1.12170306
67 68 69 70 71 72
7.11828052 -2.41671774 2.77921732 6.76154387 1.43340140 0.89974594
73 74 75 76 77 78
9.89345059 -14.31001386 6.46372662 4.99844087 16.66636096 7.71927845
79 80 81 82 83 84
0.05431523 2.42071291 -5.27978031 8.21822827 5.41301747 -3.13096798
85 86 87 88 89 90
8.87155533 4.44393352 5.21676313 9.63527891 2.46069488 -1.43992984
91 92 93 94 95 96
-1.90394308 -5.23239654 3.25135172 4.07392415 -5.99782634 1.65647114
97 98 99 100 101 102
-2.13147611 -0.97382057 3.93959226 -6.52388107 -0.41800303 -9.25966996
103 104 105 106 107 108
2.01929455 0.15778961 1.45456370 5.23850107 6.88901880 -60.37476005
109 110 111 112 113 114
0.97289291 -1.85035271 0.97770237 -2.63080684 4.96028958 -6.27267437
115 116 117 118 119 120
1.07849360 0.97289291 -2.77289170 -10.36628454 19.60360837 8.15080414
121 122 123 124 125 126
-0.07130661 2.85836629 9.58997622 3.24740694 8.03110794 0.34746321
127 128 129 130 131 132
0.59662569 7.88209168 13.50749095 2.20215338 0.93524617 -12.27319566
133 134 135 136 137 138
1.60481402 -7.67448596 0.79130754 0.40264879 0.97289291 -1.53863852
139 140 141 142 143 144
1.57839021 0.86790392 -0.77173196 -6.26627700 1.32927686 6.31933057
> postscript(file="/var/wessaorg/rcomp/tmp/60gok1324581649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 144
Frequency = 1
lag(myerror, k = 1) myerror
0 5.51790092 NA
1 3.15384349 5.51790092
2 0.18965322 3.15384349
3 -13.90072047 0.18965322
4 0.45053735 -13.90072047
5 -1.62081246 0.45053735
6 0.48158365 -1.62081246
7 5.46529191 0.48158365
8 -0.99028995 5.46529191
9 1.31402531 -0.99028995
10 -24.14895165 1.31402531
11 -0.90694634 -24.14895165
12 -11.43599695 -0.90694634
13 7.15538634 -11.43599695
14 -3.22288112 7.15538634
15 5.61682235 -3.22288112
16 5.20423228 5.61682235
17 3.45439796 5.20423228
18 3.16093715 3.45439796
19 -6.11338107 3.16093715
20 4.72034372 -6.11338107
21 -5.54406471 4.72034372
22 8.59556118 -5.54406471
23 -23.92445307 8.59556118
24 1.45514507 -23.92445307
25 3.06244118 1.45514507
26 -4.65631080 3.06244118
27 -8.37613110 -4.65631080
28 -4.88091562 -8.37613110
29 -4.48109092 -4.88091562
30 -0.59858865 -4.48109092
31 -12.91574006 -0.59858865
32 2.42976368 -12.91574006
33 10.57286979 2.42976368
34 2.51298011 10.57286979
35 0.97536051 2.51298011
36 1.43388766 0.97536051
37 -2.93490334 1.43388766
38 -6.67562809 -2.93490334
39 -2.26770484 -6.67562809
40 -2.64512783 -2.26770484
41 6.34577107 -2.64512783
42 0.05187720 6.34577107
43 4.44804850 0.05187720
44 7.53619080 4.44804850
45 -3.16176648 7.53619080
46 -16.45288356 -3.16176648
47 2.07613996 -16.45288356
48 0.31628525 2.07613996
49 -5.48152995 0.31628525
50 6.36515750 -5.48152995
51 -3.08538589 6.36515750
52 -9.51181642 -3.08538589
53 -3.86214772 -9.51181642
54 3.37927463 -3.86214772
55 -4.56484981 3.37927463
56 1.23595316 -4.56484981
57 6.54043789 1.23595316
58 -0.72510612 6.54043789
59 -0.90440436 -0.72510612
60 8.11655766 -0.90440436
61 3.73030416 8.11655766
62 -7.84785579 3.73030416
63 0.19659691 -7.84785579
64 1.40732455 0.19659691
65 1.12170306 1.40732455
66 7.11828052 1.12170306
67 -2.41671774 7.11828052
68 2.77921732 -2.41671774
69 6.76154387 2.77921732
70 1.43340140 6.76154387
71 0.89974594 1.43340140
72 9.89345059 0.89974594
73 -14.31001386 9.89345059
74 6.46372662 -14.31001386
75 4.99844087 6.46372662
76 16.66636096 4.99844087
77 7.71927845 16.66636096
78 0.05431523 7.71927845
79 2.42071291 0.05431523
80 -5.27978031 2.42071291
81 8.21822827 -5.27978031
82 5.41301747 8.21822827
83 -3.13096798 5.41301747
84 8.87155533 -3.13096798
85 4.44393352 8.87155533
86 5.21676313 4.44393352
87 9.63527891 5.21676313
88 2.46069488 9.63527891
89 -1.43992984 2.46069488
90 -1.90394308 -1.43992984
91 -5.23239654 -1.90394308
92 3.25135172 -5.23239654
93 4.07392415 3.25135172
94 -5.99782634 4.07392415
95 1.65647114 -5.99782634
96 -2.13147611 1.65647114
97 -0.97382057 -2.13147611
98 3.93959226 -0.97382057
99 -6.52388107 3.93959226
100 -0.41800303 -6.52388107
101 -9.25966996 -0.41800303
102 2.01929455 -9.25966996
103 0.15778961 2.01929455
104 1.45456370 0.15778961
105 5.23850107 1.45456370
106 6.88901880 5.23850107
107 -60.37476005 6.88901880
108 0.97289291 -60.37476005
109 -1.85035271 0.97289291
110 0.97770237 -1.85035271
111 -2.63080684 0.97770237
112 4.96028958 -2.63080684
113 -6.27267437 4.96028958
114 1.07849360 -6.27267437
115 0.97289291 1.07849360
116 -2.77289170 0.97289291
117 -10.36628454 -2.77289170
118 19.60360837 -10.36628454
119 8.15080414 19.60360837
120 -0.07130661 8.15080414
121 2.85836629 -0.07130661
122 9.58997622 2.85836629
123 3.24740694 9.58997622
124 8.03110794 3.24740694
125 0.34746321 8.03110794
126 0.59662569 0.34746321
127 7.88209168 0.59662569
128 13.50749095 7.88209168
129 2.20215338 13.50749095
130 0.93524617 2.20215338
131 -12.27319566 0.93524617
132 1.60481402 -12.27319566
133 -7.67448596 1.60481402
134 0.79130754 -7.67448596
135 0.40264879 0.79130754
136 0.97289291 0.40264879
137 -1.53863852 0.97289291
138 1.57839021 -1.53863852
139 0.86790392 1.57839021
140 -0.77173196 0.86790392
141 -6.26627700 -0.77173196
142 1.32927686 -6.26627700
143 6.31933057 1.32927686
144 NA 6.31933057
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.15384349 5.51790092
[2,] 0.18965322 3.15384349
[3,] -13.90072047 0.18965322
[4,] 0.45053735 -13.90072047
[5,] -1.62081246 0.45053735
[6,] 0.48158365 -1.62081246
[7,] 5.46529191 0.48158365
[8,] -0.99028995 5.46529191
[9,] 1.31402531 -0.99028995
[10,] -24.14895165 1.31402531
[11,] -0.90694634 -24.14895165
[12,] -11.43599695 -0.90694634
[13,] 7.15538634 -11.43599695
[14,] -3.22288112 7.15538634
[15,] 5.61682235 -3.22288112
[16,] 5.20423228 5.61682235
[17,] 3.45439796 5.20423228
[18,] 3.16093715 3.45439796
[19,] -6.11338107 3.16093715
[20,] 4.72034372 -6.11338107
[21,] -5.54406471 4.72034372
[22,] 8.59556118 -5.54406471
[23,] -23.92445307 8.59556118
[24,] 1.45514507 -23.92445307
[25,] 3.06244118 1.45514507
[26,] -4.65631080 3.06244118
[27,] -8.37613110 -4.65631080
[28,] -4.88091562 -8.37613110
[29,] -4.48109092 -4.88091562
[30,] -0.59858865 -4.48109092
[31,] -12.91574006 -0.59858865
[32,] 2.42976368 -12.91574006
[33,] 10.57286979 2.42976368
[34,] 2.51298011 10.57286979
[35,] 0.97536051 2.51298011
[36,] 1.43388766 0.97536051
[37,] -2.93490334 1.43388766
[38,] -6.67562809 -2.93490334
[39,] -2.26770484 -6.67562809
[40,] -2.64512783 -2.26770484
[41,] 6.34577107 -2.64512783
[42,] 0.05187720 6.34577107
[43,] 4.44804850 0.05187720
[44,] 7.53619080 4.44804850
[45,] -3.16176648 7.53619080
[46,] -16.45288356 -3.16176648
[47,] 2.07613996 -16.45288356
[48,] 0.31628525 2.07613996
[49,] -5.48152995 0.31628525
[50,] 6.36515750 -5.48152995
[51,] -3.08538589 6.36515750
[52,] -9.51181642 -3.08538589
[53,] -3.86214772 -9.51181642
[54,] 3.37927463 -3.86214772
[55,] -4.56484981 3.37927463
[56,] 1.23595316 -4.56484981
[57,] 6.54043789 1.23595316
[58,] -0.72510612 6.54043789
[59,] -0.90440436 -0.72510612
[60,] 8.11655766 -0.90440436
[61,] 3.73030416 8.11655766
[62,] -7.84785579 3.73030416
[63,] 0.19659691 -7.84785579
[64,] 1.40732455 0.19659691
[65,] 1.12170306 1.40732455
[66,] 7.11828052 1.12170306
[67,] -2.41671774 7.11828052
[68,] 2.77921732 -2.41671774
[69,] 6.76154387 2.77921732
[70,] 1.43340140 6.76154387
[71,] 0.89974594 1.43340140
[72,] 9.89345059 0.89974594
[73,] -14.31001386 9.89345059
[74,] 6.46372662 -14.31001386
[75,] 4.99844087 6.46372662
[76,] 16.66636096 4.99844087
[77,] 7.71927845 16.66636096
[78,] 0.05431523 7.71927845
[79,] 2.42071291 0.05431523
[80,] -5.27978031 2.42071291
[81,] 8.21822827 -5.27978031
[82,] 5.41301747 8.21822827
[83,] -3.13096798 5.41301747
[84,] 8.87155533 -3.13096798
[85,] 4.44393352 8.87155533
[86,] 5.21676313 4.44393352
[87,] 9.63527891 5.21676313
[88,] 2.46069488 9.63527891
[89,] -1.43992984 2.46069488
[90,] -1.90394308 -1.43992984
[91,] -5.23239654 -1.90394308
[92,] 3.25135172 -5.23239654
[93,] 4.07392415 3.25135172
[94,] -5.99782634 4.07392415
[95,] 1.65647114 -5.99782634
[96,] -2.13147611 1.65647114
[97,] -0.97382057 -2.13147611
[98,] 3.93959226 -0.97382057
[99,] -6.52388107 3.93959226
[100,] -0.41800303 -6.52388107
[101,] -9.25966996 -0.41800303
[102,] 2.01929455 -9.25966996
[103,] 0.15778961 2.01929455
[104,] 1.45456370 0.15778961
[105,] 5.23850107 1.45456370
[106,] 6.88901880 5.23850107
[107,] -60.37476005 6.88901880
[108,] 0.97289291 -60.37476005
[109,] -1.85035271 0.97289291
[110,] 0.97770237 -1.85035271
[111,] -2.63080684 0.97770237
[112,] 4.96028958 -2.63080684
[113,] -6.27267437 4.96028958
[114,] 1.07849360 -6.27267437
[115,] 0.97289291 1.07849360
[116,] -2.77289170 0.97289291
[117,] -10.36628454 -2.77289170
[118,] 19.60360837 -10.36628454
[119,] 8.15080414 19.60360837
[120,] -0.07130661 8.15080414
[121,] 2.85836629 -0.07130661
[122,] 9.58997622 2.85836629
[123,] 3.24740694 9.58997622
[124,] 8.03110794 3.24740694
[125,] 0.34746321 8.03110794
[126,] 0.59662569 0.34746321
[127,] 7.88209168 0.59662569
[128,] 13.50749095 7.88209168
[129,] 2.20215338 13.50749095
[130,] 0.93524617 2.20215338
[131,] -12.27319566 0.93524617
[132,] 1.60481402 -12.27319566
[133,] -7.67448596 1.60481402
[134,] 0.79130754 -7.67448596
[135,] 0.40264879 0.79130754
[136,] 0.97289291 0.40264879
[137,] -1.53863852 0.97289291
[138,] 1.57839021 -1.53863852
[139,] 0.86790392 1.57839021
[140,] -0.77173196 0.86790392
[141,] -6.26627700 -0.77173196
[142,] 1.32927686 -6.26627700
[143,] 6.31933057 1.32927686
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.15384349 5.51790092
2 0.18965322 3.15384349
3 -13.90072047 0.18965322
4 0.45053735 -13.90072047
5 -1.62081246 0.45053735
6 0.48158365 -1.62081246
7 5.46529191 0.48158365
8 -0.99028995 5.46529191
9 1.31402531 -0.99028995
10 -24.14895165 1.31402531
11 -0.90694634 -24.14895165
12 -11.43599695 -0.90694634
13 7.15538634 -11.43599695
14 -3.22288112 7.15538634
15 5.61682235 -3.22288112
16 5.20423228 5.61682235
17 3.45439796 5.20423228
18 3.16093715 3.45439796
19 -6.11338107 3.16093715
20 4.72034372 -6.11338107
21 -5.54406471 4.72034372
22 8.59556118 -5.54406471
23 -23.92445307 8.59556118
24 1.45514507 -23.92445307
25 3.06244118 1.45514507
26 -4.65631080 3.06244118
27 -8.37613110 -4.65631080
28 -4.88091562 -8.37613110
29 -4.48109092 -4.88091562
30 -0.59858865 -4.48109092
31 -12.91574006 -0.59858865
32 2.42976368 -12.91574006
33 10.57286979 2.42976368
34 2.51298011 10.57286979
35 0.97536051 2.51298011
36 1.43388766 0.97536051
37 -2.93490334 1.43388766
38 -6.67562809 -2.93490334
39 -2.26770484 -6.67562809
40 -2.64512783 -2.26770484
41 6.34577107 -2.64512783
42 0.05187720 6.34577107
43 4.44804850 0.05187720
44 7.53619080 4.44804850
45 -3.16176648 7.53619080
46 -16.45288356 -3.16176648
47 2.07613996 -16.45288356
48 0.31628525 2.07613996
49 -5.48152995 0.31628525
50 6.36515750 -5.48152995
51 -3.08538589 6.36515750
52 -9.51181642 -3.08538589
53 -3.86214772 -9.51181642
54 3.37927463 -3.86214772
55 -4.56484981 3.37927463
56 1.23595316 -4.56484981
57 6.54043789 1.23595316
58 -0.72510612 6.54043789
59 -0.90440436 -0.72510612
60 8.11655766 -0.90440436
61 3.73030416 8.11655766
62 -7.84785579 3.73030416
63 0.19659691 -7.84785579
64 1.40732455 0.19659691
65 1.12170306 1.40732455
66 7.11828052 1.12170306
67 -2.41671774 7.11828052
68 2.77921732 -2.41671774
69 6.76154387 2.77921732
70 1.43340140 6.76154387
71 0.89974594 1.43340140
72 9.89345059 0.89974594
73 -14.31001386 9.89345059
74 6.46372662 -14.31001386
75 4.99844087 6.46372662
76 16.66636096 4.99844087
77 7.71927845 16.66636096
78 0.05431523 7.71927845
79 2.42071291 0.05431523
80 -5.27978031 2.42071291
81 8.21822827 -5.27978031
82 5.41301747 8.21822827
83 -3.13096798 5.41301747
84 8.87155533 -3.13096798
85 4.44393352 8.87155533
86 5.21676313 4.44393352
87 9.63527891 5.21676313
88 2.46069488 9.63527891
89 -1.43992984 2.46069488
90 -1.90394308 -1.43992984
91 -5.23239654 -1.90394308
92 3.25135172 -5.23239654
93 4.07392415 3.25135172
94 -5.99782634 4.07392415
95 1.65647114 -5.99782634
96 -2.13147611 1.65647114
97 -0.97382057 -2.13147611
98 3.93959226 -0.97382057
99 -6.52388107 3.93959226
100 -0.41800303 -6.52388107
101 -9.25966996 -0.41800303
102 2.01929455 -9.25966996
103 0.15778961 2.01929455
104 1.45456370 0.15778961
105 5.23850107 1.45456370
106 6.88901880 5.23850107
107 -60.37476005 6.88901880
108 0.97289291 -60.37476005
109 -1.85035271 0.97289291
110 0.97770237 -1.85035271
111 -2.63080684 0.97770237
112 4.96028958 -2.63080684
113 -6.27267437 4.96028958
114 1.07849360 -6.27267437
115 0.97289291 1.07849360
116 -2.77289170 0.97289291
117 -10.36628454 -2.77289170
118 19.60360837 -10.36628454
119 8.15080414 19.60360837
120 -0.07130661 8.15080414
121 2.85836629 -0.07130661
122 9.58997622 2.85836629
123 3.24740694 9.58997622
124 8.03110794 3.24740694
125 0.34746321 8.03110794
126 0.59662569 0.34746321
127 7.88209168 0.59662569
128 13.50749095 7.88209168
129 2.20215338 13.50749095
130 0.93524617 2.20215338
131 -12.27319566 0.93524617
132 1.60481402 -12.27319566
133 -7.67448596 1.60481402
134 0.79130754 -7.67448596
135 0.40264879 0.79130754
136 0.97289291 0.40264879
137 -1.53863852 0.97289291
138 1.57839021 -1.53863852
139 0.86790392 1.57839021
140 -0.77173196 0.86790392
141 -6.26627700 -0.77173196
142 1.32927686 -6.26627700
143 6.31933057 1.32927686
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7a01r1324581649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/888wo1324581649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/965cu1324581649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10yyes1324581649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11esvs1324581649.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12ko511324581649.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/138xa91324581649.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14eyto1324581649.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15yug71324581649.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/163cva1324581649.tab")
+ }
>
> try(system("convert tmp/1fy9n1324581649.ps tmp/1fy9n1324581649.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nsmk1324581649.ps tmp/2nsmk1324581649.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tfd31324581649.ps tmp/3tfd31324581649.png",intern=TRUE))
character(0)
> try(system("convert tmp/4essd1324581649.ps tmp/4essd1324581649.png",intern=TRUE))
character(0)
> try(system("convert tmp/5gjs81324581649.ps tmp/5gjs81324581649.png",intern=TRUE))
character(0)
> try(system("convert tmp/60gok1324581649.ps tmp/60gok1324581649.png",intern=TRUE))
character(0)
> try(system("convert tmp/7a01r1324581649.ps tmp/7a01r1324581649.png",intern=TRUE))
character(0)
> try(system("convert tmp/888wo1324581649.ps tmp/888wo1324581649.png",intern=TRUE))
character(0)
> try(system("convert tmp/965cu1324581649.ps tmp/965cu1324581649.png",intern=TRUE))
character(0)
> try(system("convert tmp/10yyes1324581649.ps tmp/10yyes1324581649.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.337 0.936 6.290