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(41
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+ ,69)
+ ,dim=c(7
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging')
+ ,1:162))
> y <- array(NA,dim=c(7,162),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging'),1:162))
> 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 = '5'
> #'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
Happiness Connected Separate Learning Software Depression Belonging
1 14 41 38 13 12 12 53
2 18 39 32 16 11 11 86
3 11 30 35 19 15 14 66
4 12 31 33 15 6 12 67
5 16 34 37 14 13 21 76
6 18 35 29 13 10 12 78
7 14 39 31 19 12 22 53
8 14 34 36 15 14 11 80
9 15 36 35 14 12 10 74
10 15 37 38 15 6 13 76
11 17 38 31 16 10 10 79
12 19 36 34 16 12 8 54
13 10 38 35 16 12 15 67
14 16 39 38 16 11 14 54
15 18 33 37 17 15 10 87
16 14 32 33 15 12 14 58
17 14 36 32 15 10 14 75
18 17 38 38 20 12 11 88
19 14 39 38 18 11 10 64
20 16 32 32 16 12 13 57
21 18 32 33 16 11 7 66
22 11 31 31 16 12 14 68
23 14 39 38 19 13 12 54
24 12 37 39 16 11 14 56
25 17 39 32 17 9 11 86
26 9 41 32 17 13 9 80
27 16 36 35 16 10 11 76
28 14 33 37 15 14 15 69
29 15 33 33 16 12 14 78
30 11 34 33 14 10 13 67
31 16 31 28 15 12 9 80
32 13 27 32 12 8 15 54
33 17 37 31 14 10 10 71
34 15 34 37 16 12 11 84
35 14 34 30 14 12 13 74
36 16 32 33 7 7 8 71
37 9 29 31 10 6 20 63
38 15 36 33 14 12 12 71
39 17 29 31 16 10 10 76
40 13 35 33 16 10 10 69
41 15 37 32 16 10 9 74
42 16 34 33 14 12 14 75
43 16 38 32 20 15 8 54
44 12 35 33 14 10 14 52
45 12 38 28 14 10 11 69
46 11 37 35 11 12 13 68
47 15 38 39 14 13 9 65
48 15 33 34 15 11 11 75
49 17 36 38 16 11 15 74
50 13 38 32 14 12 11 75
51 16 32 38 16 14 10 72
52 14 32 30 14 10 14 67
53 11 32 33 12 12 18 63
54 12 34 38 16 13 14 62
55 12 32 32 9 5 11 63
56 15 37 32 14 6 12 76
57 16 39 34 16 12 13 74
58 15 29 34 16 12 9 67
59 12 37 36 15 11 10 73
60 12 35 34 16 10 15 70
61 8 30 28 12 7 20 53
62 13 38 34 16 12 12 77
63 11 34 35 16 14 12 77
64 14 31 35 14 11 14 52
65 15 34 31 16 12 13 54
66 10 35 37 17 13 11 80
67 11 36 35 18 14 17 66
68 12 30 27 18 11 12 73
69 15 39 40 12 12 13 63
70 15 35 37 16 12 14 69
71 14 38 36 10 8 13 67
72 16 31 38 14 11 15 54
73 15 34 39 18 14 13 81
74 15 38 41 18 14 10 69
75 13 34 27 16 12 11 84
76 12 39 30 17 9 19 80
77 17 37 37 16 13 13 70
78 13 34 31 16 11 17 69
79 15 28 31 13 12 13 77
80 13 37 27 16 12 9 54
81 15 33 36 16 12 11 79
82 16 37 38 20 12 10 30
83 15 35 37 16 12 9 71
84 16 37 33 15 12 12 73
85 15 32 34 15 11 12 72
86 14 33 31 16 10 13 77
87 15 38 39 14 9 13 75
88 14 33 34 16 12 12 69
89 13 29 32 16 12 15 54
90 7 33 33 15 12 22 70
91 17 31 36 12 9 13 73
92 13 36 32 17 15 15 54
93 15 35 41 16 12 13 77
94 14 32 28 15 12 15 82
95 13 29 30 13 12 10 80
96 16 39 36 16 10 11 80
97 12 37 35 16 13 16 69
98 14 35 31 16 9 11 78
99 17 37 34 16 12 11 81
100 15 32 36 14 10 10 76
101 17 38 36 16 14 10 76
102 12 37 35 16 11 16 73
103 16 36 37 20 15 12 85
104 11 32 28 15 11 11 66
105 15 33 39 16 11 16 79
106 9 40 32 13 12 19 68
107 16 38 35 17 12 11 76
108 15 41 39 16 12 16 71
109 10 36 35 16 11 15 54
110 10 43 42 12 7 24 46
111 15 30 34 16 12 14 82
112 11 31 33 16 14 15 74
113 13 32 41 17 11 11 88
114 14 32 33 13 11 15 38
115 18 37 34 12 10 12 76
116 16 37 32 18 13 10 86
117 14 33 40 14 13 14 54
118 14 34 40 14 8 13 70
119 14 33 35 13 11 9 69
120 14 38 36 16 12 15 90
121 12 33 37 13 11 15 54
122 14 31 27 16 13 14 76
123 15 38 39 13 12 11 89
124 15 37 38 16 14 8 76
125 15 33 31 15 13 11 73
126 13 31 33 16 15 11 79
127 17 39 32 15 10 8 90
128 17 44 39 17 11 10 74
129 19 33 36 15 9 11 81
130 15 35 33 12 11 13 72
131 13 32 33 16 10 11 71
132 9 28 32 10 11 20 66
133 15 40 37 16 8 10 77
134 15 27 30 12 11 15 65
135 15 37 38 14 12 12 74
136 16 32 29 15 12 14 82
137 11 28 22 13 9 23 54
138 14 34 35 15 11 14 63
139 11 30 35 11 10 16 54
140 15 35 34 12 8 11 64
141 13 31 35 8 9 12 69
142 15 32 34 16 8 10 54
143 16 30 34 15 9 14 84
144 14 30 35 17 15 12 86
145 15 31 23 16 11 12 77
146 16 40 31 10 8 11 89
147 16 32 27 18 13 12 76
148 11 36 36 13 12 13 60
149 12 32 31 16 12 11 75
150 9 35 32 13 9 19 73
151 16 38 39 10 7 12 85
152 13 42 37 15 13 17 79
153 16 34 38 16 9 9 71
154 12 35 39 16 6 12 72
155 9 35 34 14 8 19 69
156 13 33 31 10 8 18 78
157 13 36 32 17 15 15 54
158 14 32 37 13 6 14 69
159 19 33 36 15 9 11 81
160 13 34 32 16 11 9 84
161 12 32 35 12 8 18 84
162 13 34 36 13 8 16 69
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Software Depression
12.84615 0.01434 0.07185 0.06959 -0.04592 -0.35599
Belonging
0.03269
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7305 -1.3665 0.2614 1.1389 4.6220
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.84615 2.55329 5.031 1.33e-06 ***
Connected 0.01434 0.05018 0.286 0.7755
Separate 0.07185 0.04660 1.542 0.1252
Learning 0.06959 0.08430 0.826 0.4103
Software -0.04592 0.08570 -0.536 0.5929
Depression -0.35599 0.05172 -6.882 1.37e-10 ***
Belonging 0.03269 0.01491 2.192 0.0299 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.944 on 155 degrees of freedom
Multiple R-squared: 0.3341, Adjusted R-squared: 0.3084
F-statistic: 12.96 on 6 and 155 DF, p-value: 7.52e-12
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.05917471 0.1183494211 0.9408252895
[2,] 0.01894939 0.0378987766 0.9810506117
[3,] 0.88561411 0.2287717855 0.1143858928
[4,] 0.98336555 0.0332688988 0.0166344494
[5,] 0.98384034 0.0323193266 0.0161596633
[6,] 0.98905812 0.0218837511 0.0109418756
[7,] 0.98102556 0.0379488753 0.0189744377
[8,] 0.97418851 0.0516229776 0.0258114888
[9,] 0.96211849 0.0757630164 0.0378815082
[10,] 0.94955789 0.1008842118 0.0504421059
[11,] 0.94966799 0.1006640222 0.0503320111
[12,] 0.94897615 0.1020476967 0.0510238483
[13,] 0.97013224 0.0597355226 0.0298677613
[14,] 0.95684211 0.0863157809 0.0431578904
[15,] 0.94684395 0.1063121004 0.0531560502
[16,] 0.92921236 0.1415752740 0.0707876370
[17,] 0.99954478 0.0009104431 0.0004552216
[18,] 0.99926038 0.0014792457 0.0007396228
[19,] 0.99878604 0.0024279121 0.0012139561
[20,] 0.99812211 0.0037557762 0.0018778881
[21,] 0.99910558 0.0017888372 0.0008944186
[22,] 0.99857961 0.0028407863 0.0014203931
[23,] 0.99780383 0.0043923334 0.0021961667
[24,] 0.99743275 0.0051344926 0.0025672463
[25,] 0.99610768 0.0077846347 0.0038923173
[26,] 0.99446673 0.0110665360 0.0055332680
[27,] 0.99193887 0.0161222534 0.0080611267
[28,] 0.99407469 0.0118506158 0.0059253079
[29,] 0.99156819 0.0168636195 0.0084318097
[30,] 0.99088339 0.0182332272 0.0091166136
[31,] 0.99105068 0.0178986371 0.0089493186
[32,] 0.98770814 0.0245837241 0.0122918620
[33,] 0.98735086 0.0252982820 0.0126491410
[34,] 0.98294311 0.0341137822 0.0170568911
[35,] 0.97881692 0.0423661545 0.0211830773
[36,] 0.98233644 0.0353271130 0.0176635565
[37,] 0.98702903 0.0259419464 0.0129709732
[38,] 0.98230367 0.0353926681 0.0176963340
[39,] 0.97602087 0.0479582600 0.0239791300
[40,] 0.98328730 0.0334254077 0.0167127038
[41,] 0.98179852 0.0364029553 0.0182014776
[42,] 0.97604087 0.0479182542 0.0239591271
[43,] 0.96902601 0.0619479790 0.0309739895
[44,] 0.96182300 0.0763540077 0.0381770038
[45,] 0.95924946 0.0815010714 0.0407505357
[46,] 0.95925053 0.0814989322 0.0407494661
[47,] 0.94779230 0.1044154015 0.0522077008
[48,] 0.94365814 0.1126837168 0.0563418584
[49,] 0.92890068 0.1421986398 0.0710993199
[50,] 0.95313793 0.0937241366 0.0468620683
[51,] 0.94884036 0.1023192799 0.0511596399
[52,] 0.95611013 0.0877797483 0.0438898742
[53,] 0.95376982 0.0924603589 0.0462301795
[54,] 0.97562539 0.0487492243 0.0243746122
[55,] 0.97042857 0.0591428543 0.0295714272
[56,] 0.96804187 0.0639162611 0.0319581306
[57,] 0.99489928 0.0102014304 0.0051007152
[58,] 0.99436102 0.0112779652 0.0056389826
[59,] 0.99482133 0.0103573430 0.0051786715
[60,] 0.99340506 0.0131898800 0.0065949400
[61,] 0.99186232 0.0162753666 0.0081376833
[62,] 0.98890482 0.0221903590 0.0110951795
[63,] 0.99301854 0.0139629281 0.0069814641
[64,] 0.99049091 0.0190181842 0.0095090921
[65,] 0.98741758 0.0251648499 0.0125824249
[66,] 0.98657702 0.0268459654 0.0134229827
[67,] 0.98206663 0.0358667414 0.0179333707
[68,] 0.98690360 0.0261928013 0.0130964007
[69,] 0.98298379 0.0340324127 0.0170162063
[70,] 0.97997050 0.0400590043 0.0200295022
[71,] 0.97884502 0.0423099630 0.0211549815
[72,] 0.97215477 0.0556904589 0.0278452294
[73,] 0.97009838 0.0598032403 0.0299016202
[74,] 0.96235927 0.0752814624 0.0376407312
[75,] 0.95904917 0.0819016515 0.0409508257
[76,] 0.94897065 0.1020587077 0.0510293538
[77,] 0.93548178 0.1290364384 0.0645182192
[78,] 0.92002461 0.1599507894 0.0799753947
[79,] 0.90137985 0.1972402928 0.0986201464
[80,] 0.88239204 0.2352159177 0.1176079588
[81,] 0.92707121 0.1458575895 0.0729287948
[82,] 0.94673407 0.1065318511 0.0532659256
[83,] 0.93394584 0.1321083287 0.0660541644
[84,] 0.91891882 0.1621623697 0.0810811848
[85,] 0.90294565 0.1941086975 0.0970543488
[86,] 0.90250434 0.1949913288 0.0974956644
[87,] 0.88233750 0.2353250019 0.1176625010
[88,] 0.86196162 0.2760767551 0.1380383776
[89,] 0.84323300 0.3135340039 0.1567670020
[90,] 0.83985117 0.3202976502 0.1601488251
[91,] 0.80929602 0.3814079641 0.1907039821
[92,] 0.80220613 0.3955877318 0.1977938659
[93,] 0.77862973 0.4427405361 0.2213702681
[94,] 0.75309354 0.4938129201 0.2469064600
[95,] 0.82391289 0.3521742114 0.1760871057
[96,] 0.82572319 0.3485536132 0.1742768066
[97,] 0.85646989 0.2870602275 0.1435301138
[98,] 0.83292033 0.3341593449 0.1670796725
[99,] 0.83786511 0.3242697739 0.1621348870
[100,] 0.87084462 0.2583107648 0.1291553824
[101,] 0.84743935 0.3051212922 0.1525606461
[102,] 0.83495051 0.3300989717 0.1650494859
[103,] 0.83241194 0.3351761219 0.1675880610
[104,] 0.84423745 0.3115250926 0.1557625463
[105,] 0.85259315 0.2948136987 0.1474068493
[106,] 0.91270276 0.1745944730 0.0872972365
[107,] 0.88967311 0.2206537767 0.1103268883
[108,] 0.88932471 0.2213505839 0.1106752919
[109,] 0.86254944 0.2749011298 0.1374505649
[110,] 0.84409955 0.3118009002 0.1559004501
[111,] 0.80824842 0.3835031614 0.1917515807
[112,] 0.77043730 0.4591254073 0.2295627036
[113,] 0.72804466 0.5439106782 0.2719553391
[114,] 0.67972994 0.6405401289 0.3202700644
[115,] 0.63694830 0.7261034050 0.3630517025
[116,] 0.58264564 0.8347087256 0.4173543628
[117,] 0.57948702 0.8410259613 0.4205129806
[118,] 0.52577955 0.9484409083 0.4742204541
[119,] 0.51985527 0.9602894549 0.4801447275
[120,] 0.67444422 0.6511115667 0.3255557833
[121,] 0.63736073 0.7252785380 0.3626392690
[122,] 0.63203663 0.7359267304 0.3679633652
[123,] 0.62942528 0.7411494440 0.3705747220
[124,] 0.56814843 0.8637031311 0.4318515656
[125,] 0.56655218 0.8668956338 0.4334478169
[126,] 0.51920491 0.9615901882 0.4807950941
[127,] 0.51677997 0.9664400617 0.4832200309
[128,] 0.51039445 0.9792111092 0.4896055546
[129,] 0.46894617 0.9378923443 0.5310538278
[130,] 0.39915975 0.7983194988 0.6008402506
[131,] 0.33887803 0.6777560655 0.6611219672
[132,] 0.29058123 0.5811624666 0.7094187667
[133,] 0.24176920 0.4835383965 0.7582308017
[134,] 0.23339982 0.4667996340 0.7666001830
[135,] 0.19108284 0.3821656802 0.8089171599
[136,] 0.15206887 0.3041377362 0.8479311319
[137,] 0.12100001 0.2420000122 0.8789999939
[138,] 0.20766897 0.4153379427 0.7923310286
[139,] 0.50703853 0.9859229401 0.4929614700
[140,] 0.55103413 0.8979317348 0.4489658674
[141,] 0.43446365 0.8689272982 0.5655363509
[142,] 0.35336434 0.7067286844 0.6466356578
[143,] 0.21913438 0.4382687520 0.7808656240
> postscript(file="/var/wessaorg/rcomp/tmp/1mj901322129983.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/28txi1322129983.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39j6e1322129983.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4y7le1322129983.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5nd601322129983.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 = 162
Frequency = 1
1 2 3 4 5 6
0.02132883 2.79181906 -2.59813780 -2.34831133 4.62197307 3.84506499
7 8 9 10 11 12
3.69526863 -1.02044185 -0.15937918 0.26820626 1.70490696 3.71502838
13 14 15 16 17 18
-3.31851893 2.47459210 2.24399018 0.91899694 0.28600637 1.07721035
19 20 21 22 23 24
-1.41539054 2.59795734 2.05010601 -2.31940700 -0.35432279 -1.63395543
25 26 27 28 29 30
1.63039006 -6.73047248 0.90021227 0.70552457 1.18135382 -2.78207713
31 32 33 34 35 36
0.79359456 0.57437912 2.11991682 -0.38446642 0.29651948 0.68533327
37 38 39 40 41 42
-1.84933798 0.79435422 1.93200976 -2.06892872 -0.54516514 2.40425896
43 44 45 46 47 48
0.68943761 -0.95014715 -2.25750550 -2.70086881 -0.49136664 0.16327776
49 50 51 52 53 54
3.21988130 -1.64919664 0.70043440 0.81814480 -0.61171036 -1.62336455
55 56 57 58 59 60
-2.14439628 0.41293467 1.79822695 -0.25352967 -3.32839664 -1.39354245
61 62 63 64 65 66
-2.41452998 -1.64147630 -3.56414026 1.00941779 1.73919044 -5.29173832
67 68 69 70 71 72
-1.59253777 -2.07815777 1.00502196 1.15943363 0.13154733 3.08447171
73 74 75 76 77 78
0.23450230 -0.64228657 -1.66593311 -0.18190967 2.78800389 0.62692939
79 80 81 82 83 84
1.28223367 -1.44035174 -0.13484683 1.63132628 -0.68586265 1.64505118
85 86 87 88 89 90
0.63165793 -0.09007453 0.42204680 -0.30829953 0.45099946 -3.63968799
91 92 93 94 95 96
2.94253403 0.41879011 0.25455079 0.84979518 -1.82626324 0.65460133
97 98 99 100 101 102
-0.96764879 -0.90932484 1.88613488 -0.33108535 1.62736808 -1.19022658
103 104 105 106 107 108
0.76953562 -3.09709376 1.38360057 -2.53160092 0.89377744 1.57629516
109 110 111 112 113 114
-2.91084750 0.04586249 1.02177362 -2.21140645 -2.88945629 2.02196233
115 116 117 118 119 120
3.59208600 0.41715957 0.64793864 -0.47494267 -1.28524536 -0.14214015
121 122 123 124 125 126
-0.80275842 0.75243961 -0.54017434 -1.21397019 0.53604441 -1.75285661
127 128 129 130 131 132
0.61679740 1.18380169 3.73162220 1.22526129 -1.73529944 -1.77532068
133 134 135 136 137 138
-0.78135435 2.49629793 0.32269243 2.42195681 2.10277853 0.53726779
139 140 141 142 143 144
-1.16678221 0.56516907 -0.93248160 0.30068119 1.88824264 -0.82463216
145 146 147 148 149 150
1.20336176 1.03108468 1.88694417 -2.63608612 -2.63049948 -2.76108975
151 152 153 154 155 156
0.92574445 -0.08432486 0.11886916 -3.06980640 -2.88956539 0.98289026
157 158 159 160 161 162
0.41879011 0.13572274 3.73162220 -2.78308760 -0.62548488 -0.01729523
> postscript(file="/var/wessaorg/rcomp/tmp/6km6i1322129983.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 0.02132883 NA
1 2.79181906 0.02132883
2 -2.59813780 2.79181906
3 -2.34831133 -2.59813780
4 4.62197307 -2.34831133
5 3.84506499 4.62197307
6 3.69526863 3.84506499
7 -1.02044185 3.69526863
8 -0.15937918 -1.02044185
9 0.26820626 -0.15937918
10 1.70490696 0.26820626
11 3.71502838 1.70490696
12 -3.31851893 3.71502838
13 2.47459210 -3.31851893
14 2.24399018 2.47459210
15 0.91899694 2.24399018
16 0.28600637 0.91899694
17 1.07721035 0.28600637
18 -1.41539054 1.07721035
19 2.59795734 -1.41539054
20 2.05010601 2.59795734
21 -2.31940700 2.05010601
22 -0.35432279 -2.31940700
23 -1.63395543 -0.35432279
24 1.63039006 -1.63395543
25 -6.73047248 1.63039006
26 0.90021227 -6.73047248
27 0.70552457 0.90021227
28 1.18135382 0.70552457
29 -2.78207713 1.18135382
30 0.79359456 -2.78207713
31 0.57437912 0.79359456
32 2.11991682 0.57437912
33 -0.38446642 2.11991682
34 0.29651948 -0.38446642
35 0.68533327 0.29651948
36 -1.84933798 0.68533327
37 0.79435422 -1.84933798
38 1.93200976 0.79435422
39 -2.06892872 1.93200976
40 -0.54516514 -2.06892872
41 2.40425896 -0.54516514
42 0.68943761 2.40425896
43 -0.95014715 0.68943761
44 -2.25750550 -0.95014715
45 -2.70086881 -2.25750550
46 -0.49136664 -2.70086881
47 0.16327776 -0.49136664
48 3.21988130 0.16327776
49 -1.64919664 3.21988130
50 0.70043440 -1.64919664
51 0.81814480 0.70043440
52 -0.61171036 0.81814480
53 -1.62336455 -0.61171036
54 -2.14439628 -1.62336455
55 0.41293467 -2.14439628
56 1.79822695 0.41293467
57 -0.25352967 1.79822695
58 -3.32839664 -0.25352967
59 -1.39354245 -3.32839664
60 -2.41452998 -1.39354245
61 -1.64147630 -2.41452998
62 -3.56414026 -1.64147630
63 1.00941779 -3.56414026
64 1.73919044 1.00941779
65 -5.29173832 1.73919044
66 -1.59253777 -5.29173832
67 -2.07815777 -1.59253777
68 1.00502196 -2.07815777
69 1.15943363 1.00502196
70 0.13154733 1.15943363
71 3.08447171 0.13154733
72 0.23450230 3.08447171
73 -0.64228657 0.23450230
74 -1.66593311 -0.64228657
75 -0.18190967 -1.66593311
76 2.78800389 -0.18190967
77 0.62692939 2.78800389
78 1.28223367 0.62692939
79 -1.44035174 1.28223367
80 -0.13484683 -1.44035174
81 1.63132628 -0.13484683
82 -0.68586265 1.63132628
83 1.64505118 -0.68586265
84 0.63165793 1.64505118
85 -0.09007453 0.63165793
86 0.42204680 -0.09007453
87 -0.30829953 0.42204680
88 0.45099946 -0.30829953
89 -3.63968799 0.45099946
90 2.94253403 -3.63968799
91 0.41879011 2.94253403
92 0.25455079 0.41879011
93 0.84979518 0.25455079
94 -1.82626324 0.84979518
95 0.65460133 -1.82626324
96 -0.96764879 0.65460133
97 -0.90932484 -0.96764879
98 1.88613488 -0.90932484
99 -0.33108535 1.88613488
100 1.62736808 -0.33108535
101 -1.19022658 1.62736808
102 0.76953562 -1.19022658
103 -3.09709376 0.76953562
104 1.38360057 -3.09709376
105 -2.53160092 1.38360057
106 0.89377744 -2.53160092
107 1.57629516 0.89377744
108 -2.91084750 1.57629516
109 0.04586249 -2.91084750
110 1.02177362 0.04586249
111 -2.21140645 1.02177362
112 -2.88945629 -2.21140645
113 2.02196233 -2.88945629
114 3.59208600 2.02196233
115 0.41715957 3.59208600
116 0.64793864 0.41715957
117 -0.47494267 0.64793864
118 -1.28524536 -0.47494267
119 -0.14214015 -1.28524536
120 -0.80275842 -0.14214015
121 0.75243961 -0.80275842
122 -0.54017434 0.75243961
123 -1.21397019 -0.54017434
124 0.53604441 -1.21397019
125 -1.75285661 0.53604441
126 0.61679740 -1.75285661
127 1.18380169 0.61679740
128 3.73162220 1.18380169
129 1.22526129 3.73162220
130 -1.73529944 1.22526129
131 -1.77532068 -1.73529944
132 -0.78135435 -1.77532068
133 2.49629793 -0.78135435
134 0.32269243 2.49629793
135 2.42195681 0.32269243
136 2.10277853 2.42195681
137 0.53726779 2.10277853
138 -1.16678221 0.53726779
139 0.56516907 -1.16678221
140 -0.93248160 0.56516907
141 0.30068119 -0.93248160
142 1.88824264 0.30068119
143 -0.82463216 1.88824264
144 1.20336176 -0.82463216
145 1.03108468 1.20336176
146 1.88694417 1.03108468
147 -2.63608612 1.88694417
148 -2.63049948 -2.63608612
149 -2.76108975 -2.63049948
150 0.92574445 -2.76108975
151 -0.08432486 0.92574445
152 0.11886916 -0.08432486
153 -3.06980640 0.11886916
154 -2.88956539 -3.06980640
155 0.98289026 -2.88956539
156 0.41879011 0.98289026
157 0.13572274 0.41879011
158 3.73162220 0.13572274
159 -2.78308760 3.73162220
160 -0.62548488 -2.78308760
161 -0.01729523 -0.62548488
162 NA -0.01729523
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.79181906 0.02132883
[2,] -2.59813780 2.79181906
[3,] -2.34831133 -2.59813780
[4,] 4.62197307 -2.34831133
[5,] 3.84506499 4.62197307
[6,] 3.69526863 3.84506499
[7,] -1.02044185 3.69526863
[8,] -0.15937918 -1.02044185
[9,] 0.26820626 -0.15937918
[10,] 1.70490696 0.26820626
[11,] 3.71502838 1.70490696
[12,] -3.31851893 3.71502838
[13,] 2.47459210 -3.31851893
[14,] 2.24399018 2.47459210
[15,] 0.91899694 2.24399018
[16,] 0.28600637 0.91899694
[17,] 1.07721035 0.28600637
[18,] -1.41539054 1.07721035
[19,] 2.59795734 -1.41539054
[20,] 2.05010601 2.59795734
[21,] -2.31940700 2.05010601
[22,] -0.35432279 -2.31940700
[23,] -1.63395543 -0.35432279
[24,] 1.63039006 -1.63395543
[25,] -6.73047248 1.63039006
[26,] 0.90021227 -6.73047248
[27,] 0.70552457 0.90021227
[28,] 1.18135382 0.70552457
[29,] -2.78207713 1.18135382
[30,] 0.79359456 -2.78207713
[31,] 0.57437912 0.79359456
[32,] 2.11991682 0.57437912
[33,] -0.38446642 2.11991682
[34,] 0.29651948 -0.38446642
[35,] 0.68533327 0.29651948
[36,] -1.84933798 0.68533327
[37,] 0.79435422 -1.84933798
[38,] 1.93200976 0.79435422
[39,] -2.06892872 1.93200976
[40,] -0.54516514 -2.06892872
[41,] 2.40425896 -0.54516514
[42,] 0.68943761 2.40425896
[43,] -0.95014715 0.68943761
[44,] -2.25750550 -0.95014715
[45,] -2.70086881 -2.25750550
[46,] -0.49136664 -2.70086881
[47,] 0.16327776 -0.49136664
[48,] 3.21988130 0.16327776
[49,] -1.64919664 3.21988130
[50,] 0.70043440 -1.64919664
[51,] 0.81814480 0.70043440
[52,] -0.61171036 0.81814480
[53,] -1.62336455 -0.61171036
[54,] -2.14439628 -1.62336455
[55,] 0.41293467 -2.14439628
[56,] 1.79822695 0.41293467
[57,] -0.25352967 1.79822695
[58,] -3.32839664 -0.25352967
[59,] -1.39354245 -3.32839664
[60,] -2.41452998 -1.39354245
[61,] -1.64147630 -2.41452998
[62,] -3.56414026 -1.64147630
[63,] 1.00941779 -3.56414026
[64,] 1.73919044 1.00941779
[65,] -5.29173832 1.73919044
[66,] -1.59253777 -5.29173832
[67,] -2.07815777 -1.59253777
[68,] 1.00502196 -2.07815777
[69,] 1.15943363 1.00502196
[70,] 0.13154733 1.15943363
[71,] 3.08447171 0.13154733
[72,] 0.23450230 3.08447171
[73,] -0.64228657 0.23450230
[74,] -1.66593311 -0.64228657
[75,] -0.18190967 -1.66593311
[76,] 2.78800389 -0.18190967
[77,] 0.62692939 2.78800389
[78,] 1.28223367 0.62692939
[79,] -1.44035174 1.28223367
[80,] -0.13484683 -1.44035174
[81,] 1.63132628 -0.13484683
[82,] -0.68586265 1.63132628
[83,] 1.64505118 -0.68586265
[84,] 0.63165793 1.64505118
[85,] -0.09007453 0.63165793
[86,] 0.42204680 -0.09007453
[87,] -0.30829953 0.42204680
[88,] 0.45099946 -0.30829953
[89,] -3.63968799 0.45099946
[90,] 2.94253403 -3.63968799
[91,] 0.41879011 2.94253403
[92,] 0.25455079 0.41879011
[93,] 0.84979518 0.25455079
[94,] -1.82626324 0.84979518
[95,] 0.65460133 -1.82626324
[96,] -0.96764879 0.65460133
[97,] -0.90932484 -0.96764879
[98,] 1.88613488 -0.90932484
[99,] -0.33108535 1.88613488
[100,] 1.62736808 -0.33108535
[101,] -1.19022658 1.62736808
[102,] 0.76953562 -1.19022658
[103,] -3.09709376 0.76953562
[104,] 1.38360057 -3.09709376
[105,] -2.53160092 1.38360057
[106,] 0.89377744 -2.53160092
[107,] 1.57629516 0.89377744
[108,] -2.91084750 1.57629516
[109,] 0.04586249 -2.91084750
[110,] 1.02177362 0.04586249
[111,] -2.21140645 1.02177362
[112,] -2.88945629 -2.21140645
[113,] 2.02196233 -2.88945629
[114,] 3.59208600 2.02196233
[115,] 0.41715957 3.59208600
[116,] 0.64793864 0.41715957
[117,] -0.47494267 0.64793864
[118,] -1.28524536 -0.47494267
[119,] -0.14214015 -1.28524536
[120,] -0.80275842 -0.14214015
[121,] 0.75243961 -0.80275842
[122,] -0.54017434 0.75243961
[123,] -1.21397019 -0.54017434
[124,] 0.53604441 -1.21397019
[125,] -1.75285661 0.53604441
[126,] 0.61679740 -1.75285661
[127,] 1.18380169 0.61679740
[128,] 3.73162220 1.18380169
[129,] 1.22526129 3.73162220
[130,] -1.73529944 1.22526129
[131,] -1.77532068 -1.73529944
[132,] -0.78135435 -1.77532068
[133,] 2.49629793 -0.78135435
[134,] 0.32269243 2.49629793
[135,] 2.42195681 0.32269243
[136,] 2.10277853 2.42195681
[137,] 0.53726779 2.10277853
[138,] -1.16678221 0.53726779
[139,] 0.56516907 -1.16678221
[140,] -0.93248160 0.56516907
[141,] 0.30068119 -0.93248160
[142,] 1.88824264 0.30068119
[143,] -0.82463216 1.88824264
[144,] 1.20336176 -0.82463216
[145,] 1.03108468 1.20336176
[146,] 1.88694417 1.03108468
[147,] -2.63608612 1.88694417
[148,] -2.63049948 -2.63608612
[149,] -2.76108975 -2.63049948
[150,] 0.92574445 -2.76108975
[151,] -0.08432486 0.92574445
[152,] 0.11886916 -0.08432486
[153,] -3.06980640 0.11886916
[154,] -2.88956539 -3.06980640
[155,] 0.98289026 -2.88956539
[156,] 0.41879011 0.98289026
[157,] 0.13572274 0.41879011
[158,] 3.73162220 0.13572274
[159,] -2.78308760 3.73162220
[160,] -0.62548488 -2.78308760
[161,] -0.01729523 -0.62548488
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.79181906 0.02132883
2 -2.59813780 2.79181906
3 -2.34831133 -2.59813780
4 4.62197307 -2.34831133
5 3.84506499 4.62197307
6 3.69526863 3.84506499
7 -1.02044185 3.69526863
8 -0.15937918 -1.02044185
9 0.26820626 -0.15937918
10 1.70490696 0.26820626
11 3.71502838 1.70490696
12 -3.31851893 3.71502838
13 2.47459210 -3.31851893
14 2.24399018 2.47459210
15 0.91899694 2.24399018
16 0.28600637 0.91899694
17 1.07721035 0.28600637
18 -1.41539054 1.07721035
19 2.59795734 -1.41539054
20 2.05010601 2.59795734
21 -2.31940700 2.05010601
22 -0.35432279 -2.31940700
23 -1.63395543 -0.35432279
24 1.63039006 -1.63395543
25 -6.73047248 1.63039006
26 0.90021227 -6.73047248
27 0.70552457 0.90021227
28 1.18135382 0.70552457
29 -2.78207713 1.18135382
30 0.79359456 -2.78207713
31 0.57437912 0.79359456
32 2.11991682 0.57437912
33 -0.38446642 2.11991682
34 0.29651948 -0.38446642
35 0.68533327 0.29651948
36 -1.84933798 0.68533327
37 0.79435422 -1.84933798
38 1.93200976 0.79435422
39 -2.06892872 1.93200976
40 -0.54516514 -2.06892872
41 2.40425896 -0.54516514
42 0.68943761 2.40425896
43 -0.95014715 0.68943761
44 -2.25750550 -0.95014715
45 -2.70086881 -2.25750550
46 -0.49136664 -2.70086881
47 0.16327776 -0.49136664
48 3.21988130 0.16327776
49 -1.64919664 3.21988130
50 0.70043440 -1.64919664
51 0.81814480 0.70043440
52 -0.61171036 0.81814480
53 -1.62336455 -0.61171036
54 -2.14439628 -1.62336455
55 0.41293467 -2.14439628
56 1.79822695 0.41293467
57 -0.25352967 1.79822695
58 -3.32839664 -0.25352967
59 -1.39354245 -3.32839664
60 -2.41452998 -1.39354245
61 -1.64147630 -2.41452998
62 -3.56414026 -1.64147630
63 1.00941779 -3.56414026
64 1.73919044 1.00941779
65 -5.29173832 1.73919044
66 -1.59253777 -5.29173832
67 -2.07815777 -1.59253777
68 1.00502196 -2.07815777
69 1.15943363 1.00502196
70 0.13154733 1.15943363
71 3.08447171 0.13154733
72 0.23450230 3.08447171
73 -0.64228657 0.23450230
74 -1.66593311 -0.64228657
75 -0.18190967 -1.66593311
76 2.78800389 -0.18190967
77 0.62692939 2.78800389
78 1.28223367 0.62692939
79 -1.44035174 1.28223367
80 -0.13484683 -1.44035174
81 1.63132628 -0.13484683
82 -0.68586265 1.63132628
83 1.64505118 -0.68586265
84 0.63165793 1.64505118
85 -0.09007453 0.63165793
86 0.42204680 -0.09007453
87 -0.30829953 0.42204680
88 0.45099946 -0.30829953
89 -3.63968799 0.45099946
90 2.94253403 -3.63968799
91 0.41879011 2.94253403
92 0.25455079 0.41879011
93 0.84979518 0.25455079
94 -1.82626324 0.84979518
95 0.65460133 -1.82626324
96 -0.96764879 0.65460133
97 -0.90932484 -0.96764879
98 1.88613488 -0.90932484
99 -0.33108535 1.88613488
100 1.62736808 -0.33108535
101 -1.19022658 1.62736808
102 0.76953562 -1.19022658
103 -3.09709376 0.76953562
104 1.38360057 -3.09709376
105 -2.53160092 1.38360057
106 0.89377744 -2.53160092
107 1.57629516 0.89377744
108 -2.91084750 1.57629516
109 0.04586249 -2.91084750
110 1.02177362 0.04586249
111 -2.21140645 1.02177362
112 -2.88945629 -2.21140645
113 2.02196233 -2.88945629
114 3.59208600 2.02196233
115 0.41715957 3.59208600
116 0.64793864 0.41715957
117 -0.47494267 0.64793864
118 -1.28524536 -0.47494267
119 -0.14214015 -1.28524536
120 -0.80275842 -0.14214015
121 0.75243961 -0.80275842
122 -0.54017434 0.75243961
123 -1.21397019 -0.54017434
124 0.53604441 -1.21397019
125 -1.75285661 0.53604441
126 0.61679740 -1.75285661
127 1.18380169 0.61679740
128 3.73162220 1.18380169
129 1.22526129 3.73162220
130 -1.73529944 1.22526129
131 -1.77532068 -1.73529944
132 -0.78135435 -1.77532068
133 2.49629793 -0.78135435
134 0.32269243 2.49629793
135 2.42195681 0.32269243
136 2.10277853 2.42195681
137 0.53726779 2.10277853
138 -1.16678221 0.53726779
139 0.56516907 -1.16678221
140 -0.93248160 0.56516907
141 0.30068119 -0.93248160
142 1.88824264 0.30068119
143 -0.82463216 1.88824264
144 1.20336176 -0.82463216
145 1.03108468 1.20336176
146 1.88694417 1.03108468
147 -2.63608612 1.88694417
148 -2.63049948 -2.63608612
149 -2.76108975 -2.63049948
150 0.92574445 -2.76108975
151 -0.08432486 0.92574445
152 0.11886916 -0.08432486
153 -3.06980640 0.11886916
154 -2.88956539 -3.06980640
155 0.98289026 -2.88956539
156 0.41879011 0.98289026
157 0.13572274 0.41879011
158 3.73162220 0.13572274
159 -2.78308760 3.73162220
160 -0.62548488 -2.78308760
161 -0.01729523 -0.62548488
> 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/7nngo1322129983.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/88dsv1322129983.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9w2v51322129983.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10wtn61322129983.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11cukb1322129983.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/1216qp1322129983.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/13jxq41322129983.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/14jxn71322129983.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/15k3ws1322129983.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/16gpnd1322129983.tab")
+ }
>
> try(system("convert tmp/1mj901322129983.ps tmp/1mj901322129983.png",intern=TRUE))
character(0)
> try(system("convert tmp/28txi1322129983.ps tmp/28txi1322129983.png",intern=TRUE))
character(0)
> try(system("convert tmp/39j6e1322129983.ps tmp/39j6e1322129983.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y7le1322129983.ps tmp/4y7le1322129983.png",intern=TRUE))
character(0)
> try(system("convert tmp/5nd601322129983.ps tmp/5nd601322129983.png",intern=TRUE))
character(0)
> try(system("convert tmp/6km6i1322129983.ps tmp/6km6i1322129983.png",intern=TRUE))
character(0)
> try(system("convert tmp/7nngo1322129983.ps tmp/7nngo1322129983.png",intern=TRUE))
character(0)
> try(system("convert tmp/88dsv1322129983.ps tmp/88dsv1322129983.png",intern=TRUE))
character(0)
> try(system("convert tmp/9w2v51322129983.ps tmp/9w2v51322129983.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wtn61322129983.ps tmp/10wtn61322129983.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.298 0.517 5.868