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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(14
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+ ,8)
+ ,dim=c(4
+ ,162)
+ ,dimnames=list(c('Happiness'
+ ,'Connected'
+ ,'Belonging'
+ ,'Software')
+ ,1:162))
> y <- array(NA,dim=c(4,162),dimnames=list(c('Happiness','Connected','Belonging','Software'),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 = '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
> 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 Belonging Software
1 14 41 53 12
2 18 39 86 11
3 11 30 66 15
4 12 31 67 6
5 16 34 76 13
6 18 35 78 10
7 14 39 53 12
8 14 34 80 14
9 15 36 74 12
10 15 37 76 6
11 17 38 79 10
12 19 36 54 12
13 10 38 67 12
14 16 39 54 11
15 18 33 87 15
16 14 32 58 12
17 14 36 75 10
18 17 38 88 12
19 14 39 64 11
20 16 32 57 12
21 18 32 66 11
22 11 31 68 12
23 14 39 54 13
24 12 37 56 11
25 17 39 86 9
26 9 41 80 13
27 16 36 76 10
28 14 33 69 14
29 15 33 78 12
30 11 34 67 10
31 16 31 80 12
32 13 27 54 8
33 17 37 71 10
34 15 34 84 12
35 14 34 74 12
36 16 32 71 7
37 9 29 63 6
38 15 36 71 12
39 17 29 76 10
40 13 35 69 10
41 15 37 74 10
42 16 34 75 12
43 16 38 54 15
44 12 35 52 10
45 12 38 69 10
46 11 37 68 12
47 15 38 65 13
48 15 33 75 11
49 17 36 74 11
50 13 38 75 12
51 16 32 72 14
52 14 32 67 10
53 11 32 63 12
54 12 34 62 13
55 12 32 63 5
56 15 37 76 6
57 16 39 74 12
58 15 29 67 12
59 12 37 73 11
60 12 35 70 10
61 8 30 53 7
62 13 38 77 12
63 11 34 77 14
64 14 31 52 11
65 15 34 54 12
66 10 35 80 13
67 11 36 66 14
68 12 30 73 11
69 15 39 63 12
70 15 35 69 12
71 14 38 67 8
72 16 31 54 11
73 15 34 81 14
74 15 38 69 14
75 13 34 84 12
76 12 39 80 9
77 17 37 70 13
78 13 34 69 11
79 15 28 77 12
80 13 37 54 12
81 15 33 79 12
82 16 37 30 12
83 15 35 71 12
84 16 37 73 12
85 15 32 72 11
86 14 33 77 10
87 15 38 75 9
88 14 33 69 12
89 13 29 54 12
90 7 33 70 12
91 17 31 73 9
92 13 36 54 15
93 15 35 77 12
94 14 32 82 12
95 13 29 80 12
96 16 39 80 10
97 12 37 69 13
98 14 35 78 9
99 17 37 81 12
100 15 32 76 10
101 17 38 76 14
102 12 37 73 11
103 16 36 85 15
104 11 32 66 11
105 15 33 79 11
106 9 40 68 12
107 16 38 76 12
108 15 41 71 12
109 10 36 54 11
110 10 43 46 7
111 15 30 82 12
112 11 31 74 14
113 13 32 88 11
114 14 32 38 11
115 18 37 76 10
116 16 37 86 13
117 14 33 54 13
118 14 34 70 8
119 14 33 69 11
120 14 38 90 12
121 12 33 54 11
122 14 31 76 13
123 15 38 89 12
124 15 37 76 14
125 15 33 73 13
126 13 31 79 15
127 17 39 90 10
128 17 44 74 11
129 19 33 81 9
130 15 35 72 11
131 13 32 71 10
132 9 28 66 11
133 15 40 77 8
134 15 27 65 11
135 15 37 74 12
136 16 32 82 12
137 11 28 54 9
138 14 34 63 11
139 11 30 54 10
140 15 35 64 8
141 13 31 69 9
142 15 32 54 8
143 16 30 84 9
144 14 30 86 15
145 15 31 77 11
146 16 40 89 8
147 16 32 76 13
148 11 36 60 12
149 12 32 75 12
150 9 35 73 9
151 16 38 85 7
152 13 42 79 13
153 16 34 71 9
154 12 35 72 6
155 9 35 69 8
156 13 33 78 8
157 13 36 54 15
158 14 32 69 6
159 19 33 81 9
160 13 34 84 11
161 12 32 84 8
162 13 34 69 8
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Belonging Software
6.32453 0.08112 0.05974 0.06147
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9210 -1.5113 0.4699 1.5011 5.7915
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.32453 2.22273 2.845 0.005024 **
Connected 0.08112 0.05256 1.543 0.124752
Belonging 0.05974 0.01652 3.616 0.000402 ***
Software 0.06147 0.08262 0.744 0.457992
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.24 on 158 degrees of freedom
Multiple R-squared: 0.09916, Adjusted R-squared: 0.08205
F-statistic: 5.797 on 3 and 158 DF, p-value: 0.000874
> 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,] 0.28358450 0.56716900 0.71641550
[2,] 0.23835207 0.47670413 0.76164793
[3,] 0.13702442 0.27404883 0.86297558
[4,] 0.11043546 0.22087091 0.88956454
[5,] 0.05975624 0.11951249 0.94024376
[6,] 0.64646667 0.70706665 0.35353333
[7,] 0.90818294 0.18363413 0.09181706
[8,] 0.88241821 0.23516358 0.11758179
[9,] 0.88449316 0.23101369 0.11550684
[10,] 0.84589948 0.30820104 0.15410052
[11,] 0.81090603 0.37818794 0.18909397
[12,] 0.75670502 0.48658996 0.24329498
[13,] 0.71025682 0.57948636 0.28974318
[14,] 0.72903811 0.54192377 0.27096189
[15,] 0.81132073 0.37735853 0.18867927
[16,] 0.85465206 0.29069589 0.14534794
[17,] 0.81614398 0.36771205 0.18385602
[18,] 0.80723717 0.38552566 0.19276283
[19,] 0.76627258 0.46745484 0.23372742
[20,] 0.96561236 0.06877527 0.03438764
[21,] 0.95492977 0.09014047 0.04507023
[22,] 0.93894465 0.12211070 0.06105535
[23,] 0.91902518 0.16194964 0.08097482
[24,] 0.93960436 0.12079129 0.06039564
[25,] 0.92512312 0.14975377 0.07487688
[26,] 0.90472074 0.19055851 0.09527926
[27,] 0.90431227 0.19137546 0.09568773
[28,] 0.87892757 0.24214486 0.12107243
[29,] 0.85133611 0.29732779 0.14866389
[30,] 0.83372621 0.33254758 0.16627379
[31,] 0.91266414 0.17467171 0.08733586
[32,] 0.89052914 0.21894173 0.10947086
[33,] 0.90064995 0.19870011 0.09935005
[34,] 0.88409109 0.23181781 0.11590891
[35,] 0.85747030 0.28505940 0.14252970
[36,] 0.83734378 0.32531243 0.16265622
[37,] 0.83492294 0.33015412 0.16507706
[38,] 0.81040482 0.37919036 0.18959518
[39,] 0.81488014 0.37023972 0.18511986
[40,] 0.85174268 0.29651465 0.14825732
[41,] 0.82427519 0.35144961 0.17572481
[42,] 0.79259600 0.41480799 0.20740400
[43,] 0.79609830 0.40780340 0.20390170
[44,] 0.78512062 0.42975875 0.21487938
[45,] 0.76609905 0.46780189 0.23390095
[46,] 0.72809990 0.54380019 0.27190010
[47,] 0.74510978 0.50978044 0.25489022
[48,] 0.72942523 0.54114955 0.27057477
[49,] 0.69787688 0.60424623 0.30212312
[50,] 0.65809845 0.68380309 0.34190155
[51,] 0.62581406 0.74837188 0.37418594
[52,] 0.59672962 0.80654075 0.40327038
[53,] 0.60992966 0.78014068 0.39007034
[54,] 0.60410420 0.79179159 0.39589580
[55,] 0.71274635 0.57450730 0.28725365
[56,] 0.70312128 0.59375743 0.29687872
[57,] 0.77122882 0.45754237 0.22877118
[58,] 0.74912567 0.50174867 0.25087433
[59,] 0.73958532 0.52082936 0.26041468
[60,] 0.85365650 0.29268699 0.14634350
[61,] 0.87387433 0.25225133 0.12612567
[62,] 0.86515731 0.26968538 0.13484269
[63,] 0.84397339 0.31205323 0.15602661
[64,] 0.82024966 0.35950068 0.17975034
[65,] 0.78855728 0.42288544 0.21144272
[66,] 0.82154864 0.35690272 0.17845136
[67,] 0.79031936 0.41936129 0.20968064
[68,] 0.75864000 0.48272000 0.24136000
[69,] 0.74617842 0.50764316 0.25382158
[70,] 0.76438433 0.47123135 0.23561567
[71,] 0.77945441 0.44109117 0.22054559
[72,] 0.74949261 0.50101478 0.25050739
[73,] 0.72027486 0.55945027 0.27972514
[74,] 0.68168266 0.63663468 0.31831734
[75,] 0.64242657 0.71514687 0.35757343
[76,] 0.75450925 0.49098149 0.24549075
[77,] 0.72454119 0.55091762 0.27545881
[78,] 0.70847749 0.58304502 0.29152251
[79,] 0.67914349 0.64171303 0.32085651
[80,] 0.63733566 0.72532868 0.36266434
[81,] 0.59707541 0.80584918 0.40292459
[82,] 0.55362274 0.89275451 0.44637726
[83,] 0.51594858 0.96810284 0.48405142
[84,] 0.83921964 0.32156072 0.16078036
[85,] 0.87007687 0.25984626 0.12992313
[86,] 0.84714547 0.30570905 0.15285453
[87,] 0.82018298 0.35963404 0.17981702
[88,] 0.79004524 0.41990953 0.20995476
[89,] 0.76551294 0.46897411 0.23448706
[90,] 0.73842009 0.52315983 0.26157991
[91,] 0.73154279 0.53691442 0.26845721
[92,] 0.69255453 0.61489094 0.30744547
[93,] 0.68956347 0.62087306 0.31043653
[94,] 0.65425934 0.69148133 0.34574066
[95,] 0.66338081 0.67323838 0.33661919
[96,] 0.66142125 0.67715750 0.33857875
[97,] 0.62450968 0.75098065 0.37549032
[98,] 0.62948750 0.74102501 0.37051250
[99,] 0.58676088 0.82647823 0.41323912
[100,] 0.76653158 0.46693683 0.23346842
[101,] 0.74383495 0.51233010 0.25616505
[102,] 0.70600628 0.58798744 0.29399372
[103,] 0.73186077 0.53627845 0.26813923
[104,] 0.76487102 0.47025796 0.23512898
[105,] 0.73119082 0.53761836 0.26880918
[106,] 0.75839442 0.48321116 0.24160558
[107,] 0.74440868 0.51118264 0.25559132
[108,] 0.75566522 0.48866956 0.24433478
[109,] 0.81233110 0.37533781 0.18766890
[110,] 0.77912451 0.44175098 0.22087549
[111,] 0.75987279 0.48025442 0.24012721
[112,] 0.71772796 0.56454407 0.28227204
[113,] 0.67329297 0.65341406 0.32670703
[114,] 0.65413018 0.69173963 0.34586982
[115,] 0.60611362 0.78777277 0.39388638
[116,] 0.55320902 0.89358195 0.44679098
[117,] 0.50556499 0.98887003 0.49443501
[118,] 0.45258626 0.90517252 0.54741374
[119,] 0.41063222 0.82126443 0.58936778
[120,] 0.37561224 0.75122448 0.62438776
[121,] 0.33642667 0.67285335 0.66357333
[122,] 0.34350268 0.68700536 0.65649732
[123,] 0.50675431 0.98649138 0.49324569
[124,] 0.46663059 0.93326118 0.53336941
[125,] 0.41103741 0.82207482 0.58896259
[126,] 0.55712215 0.88575571 0.44287785
[127,] 0.51241224 0.97517552 0.48758776
[128,] 0.48359828 0.96719657 0.51640172
[129,] 0.43871337 0.87742674 0.56128663
[130,] 0.40315548 0.80631097 0.59684452
[131,] 0.36275523 0.72551046 0.63724477
[132,] 0.31631105 0.63262210 0.68368895
[133,] 0.28132933 0.56265866 0.71867067
[134,] 0.26885814 0.53771627 0.73114186
[135,] 0.21636496 0.43272992 0.78363504
[136,] 0.26200409 0.52400818 0.73799591
[137,] 0.22178613 0.44357227 0.77821387
[138,] 0.19060384 0.38120769 0.80939616
[139,] 0.14644115 0.29288230 0.85355885
[140,] 0.12064681 0.24129362 0.87935319
[141,] 0.10626208 0.21252416 0.89373792
[142,] 0.07827504 0.15655007 0.92172496
[143,] 0.06380544 0.12761088 0.93619456
[144,] 0.13977297 0.27954594 0.86022703
[145,] 0.15216843 0.30433685 0.84783157
[146,] 0.13140469 0.26280939 0.86859531
[147,] 0.14565893 0.29131786 0.85434107
[148,] 0.11573308 0.23146617 0.88426692
[149,] 0.10137074 0.20274147 0.89862926
> postscript(file="/var/wessaorg/rcomp/tmp/1qfi51322132378.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2o1wd1322132378.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3603l1322132378.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/45p1t1322132378.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5e4t51322132378.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
0.44568551 2.69789702 -2.62307190 -1.21071854 1.57797145 3.56177374
7 8 9 10 11 12
0.60792105 -0.72246545 0.59668843 0.76489516 2.25867825 5.79153217
13 14 15 16 17 18
-4.14735180 2.60964701 2.87898886 0.87703450 -0.34011747 1.59806227
19 20 21 22 23 24
0.01222514 2.93677669 4.46056515 -2.63926960 0.48671071 -1.34760183
25 26 27 28 29 30
1.82083331 -6.22882169 1.60014035 0.01581638 0.60107299 -2.69994443
31 32 33 34 35 36
1.64382415 0.76746468 2.81773351 0.16150210 -0.24107603 2.40772680
37 38 39 40 41 42
-3.80951425 0.77591499 3.16796473 -0.90054657 0.63850695 1.69918178
43 44 45 46 47 48
2.44489219 -0.88492939 -2.14389988 -3.12597622 0.91066443 0.84176770
49 50 51 52 53 54
2.65815657 -1.62528930 1.91770759 0.46229111 -2.42167644 -1.58563794
55 56 57 58 59 60
-0.99139941 0.76489516 1.35333512 1.58270812 -2.36321901 -1.96028876
61 62 63 64 65 66
-4.35467830 -1.74477367 -3.54323889 1.37807354 1.95376771 -4.74211507
67 68 69 70 71 72
-3.04831037 -1.79539462 1.01049918 0.97651713 0.09852078 3.25858916
73 74 75 76 77 78
0.21779236 0.61022753 -1.83849790 -2.82071356 2.69307126 -0.88089695
79 80 81 82 83 84
1.06640402 -0.28958560 0.54133080 4.14422689 0.85703276 1.57531285
85 86 87 88 89 90
1.10211203 -0.21624853 0.55911514 0.13875267 0.35935655 -6.92098952
91 92 93 94 95 96
3.24642390 -0.39287227 0.49857964 -0.55677799 -1.19394031 1.11781829
97 98 99 100 101 102
-2.24718655 -0.37675811 2.09737535 0.92461142 2.19203222 -2.36321901
103 104 105 106 107 108
0.75511993 -2.53943485 0.60279895 -5.36932953 1.31496852 0.37032614
109 110 111 112 113 114
-3.14699968 -2.99101399 0.60545755 -3.12065902 -1.85376297 2.13334639
115 116 117 118 119 120
3.51902258 0.73719627 0.97341733 0.24376530 0.20022082 -1.52142210
121 122 123 124 125 126
-0.90364638 -0.17867525 -0.46167992 0.27314999 0.83831578 -1.48083810
127 128 129 130 131 132
1.52039642 2.00921442 4.60625087 0.85875872 -0.77667764 -4.21496377
133 134 135 136 137 138
0.33886338 1.92589618 0.51557066 1.44322201 -1.37512124 0.47755617
139 140 141 142 143 144
-1.59882492 1.52110065 -0.51460735 2.36187583 1.67037761 -0.81791564
145 146 147 148 149 150
0.88451886 0.62195713 1.74020698 -2.56692095 -2.13858268 -5.07804718
151 152 153 154 155 156
1.08462956 -2.25019727 2.12255497 -1.83390055 -4.77761028 -1.15305443
157 158 159 160 161 162
-0.39287227 0.58867932 4.60625087 -1.77702976 -2.43038978 -0.69649251
> postscript(file="/var/wessaorg/rcomp/tmp/6fv791322132378.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 0.44568551 NA
1 2.69789702 0.44568551
2 -2.62307190 2.69789702
3 -1.21071854 -2.62307190
4 1.57797145 -1.21071854
5 3.56177374 1.57797145
6 0.60792105 3.56177374
7 -0.72246545 0.60792105
8 0.59668843 -0.72246545
9 0.76489516 0.59668843
10 2.25867825 0.76489516
11 5.79153217 2.25867825
12 -4.14735180 5.79153217
13 2.60964701 -4.14735180
14 2.87898886 2.60964701
15 0.87703450 2.87898886
16 -0.34011747 0.87703450
17 1.59806227 -0.34011747
18 0.01222514 1.59806227
19 2.93677669 0.01222514
20 4.46056515 2.93677669
21 -2.63926960 4.46056515
22 0.48671071 -2.63926960
23 -1.34760183 0.48671071
24 1.82083331 -1.34760183
25 -6.22882169 1.82083331
26 1.60014035 -6.22882169
27 0.01581638 1.60014035
28 0.60107299 0.01581638
29 -2.69994443 0.60107299
30 1.64382415 -2.69994443
31 0.76746468 1.64382415
32 2.81773351 0.76746468
33 0.16150210 2.81773351
34 -0.24107603 0.16150210
35 2.40772680 -0.24107603
36 -3.80951425 2.40772680
37 0.77591499 -3.80951425
38 3.16796473 0.77591499
39 -0.90054657 3.16796473
40 0.63850695 -0.90054657
41 1.69918178 0.63850695
42 2.44489219 1.69918178
43 -0.88492939 2.44489219
44 -2.14389988 -0.88492939
45 -3.12597622 -2.14389988
46 0.91066443 -3.12597622
47 0.84176770 0.91066443
48 2.65815657 0.84176770
49 -1.62528930 2.65815657
50 1.91770759 -1.62528930
51 0.46229111 1.91770759
52 -2.42167644 0.46229111
53 -1.58563794 -2.42167644
54 -0.99139941 -1.58563794
55 0.76489516 -0.99139941
56 1.35333512 0.76489516
57 1.58270812 1.35333512
58 -2.36321901 1.58270812
59 -1.96028876 -2.36321901
60 -4.35467830 -1.96028876
61 -1.74477367 -4.35467830
62 -3.54323889 -1.74477367
63 1.37807354 -3.54323889
64 1.95376771 1.37807354
65 -4.74211507 1.95376771
66 -3.04831037 -4.74211507
67 -1.79539462 -3.04831037
68 1.01049918 -1.79539462
69 0.97651713 1.01049918
70 0.09852078 0.97651713
71 3.25858916 0.09852078
72 0.21779236 3.25858916
73 0.61022753 0.21779236
74 -1.83849790 0.61022753
75 -2.82071356 -1.83849790
76 2.69307126 -2.82071356
77 -0.88089695 2.69307126
78 1.06640402 -0.88089695
79 -0.28958560 1.06640402
80 0.54133080 -0.28958560
81 4.14422689 0.54133080
82 0.85703276 4.14422689
83 1.57531285 0.85703276
84 1.10211203 1.57531285
85 -0.21624853 1.10211203
86 0.55911514 -0.21624853
87 0.13875267 0.55911514
88 0.35935655 0.13875267
89 -6.92098952 0.35935655
90 3.24642390 -6.92098952
91 -0.39287227 3.24642390
92 0.49857964 -0.39287227
93 -0.55677799 0.49857964
94 -1.19394031 -0.55677799
95 1.11781829 -1.19394031
96 -2.24718655 1.11781829
97 -0.37675811 -2.24718655
98 2.09737535 -0.37675811
99 0.92461142 2.09737535
100 2.19203222 0.92461142
101 -2.36321901 2.19203222
102 0.75511993 -2.36321901
103 -2.53943485 0.75511993
104 0.60279895 -2.53943485
105 -5.36932953 0.60279895
106 1.31496852 -5.36932953
107 0.37032614 1.31496852
108 -3.14699968 0.37032614
109 -2.99101399 -3.14699968
110 0.60545755 -2.99101399
111 -3.12065902 0.60545755
112 -1.85376297 -3.12065902
113 2.13334639 -1.85376297
114 3.51902258 2.13334639
115 0.73719627 3.51902258
116 0.97341733 0.73719627
117 0.24376530 0.97341733
118 0.20022082 0.24376530
119 -1.52142210 0.20022082
120 -0.90364638 -1.52142210
121 -0.17867525 -0.90364638
122 -0.46167992 -0.17867525
123 0.27314999 -0.46167992
124 0.83831578 0.27314999
125 -1.48083810 0.83831578
126 1.52039642 -1.48083810
127 2.00921442 1.52039642
128 4.60625087 2.00921442
129 0.85875872 4.60625087
130 -0.77667764 0.85875872
131 -4.21496377 -0.77667764
132 0.33886338 -4.21496377
133 1.92589618 0.33886338
134 0.51557066 1.92589618
135 1.44322201 0.51557066
136 -1.37512124 1.44322201
137 0.47755617 -1.37512124
138 -1.59882492 0.47755617
139 1.52110065 -1.59882492
140 -0.51460735 1.52110065
141 2.36187583 -0.51460735
142 1.67037761 2.36187583
143 -0.81791564 1.67037761
144 0.88451886 -0.81791564
145 0.62195713 0.88451886
146 1.74020698 0.62195713
147 -2.56692095 1.74020698
148 -2.13858268 -2.56692095
149 -5.07804718 -2.13858268
150 1.08462956 -5.07804718
151 -2.25019727 1.08462956
152 2.12255497 -2.25019727
153 -1.83390055 2.12255497
154 -4.77761028 -1.83390055
155 -1.15305443 -4.77761028
156 -0.39287227 -1.15305443
157 0.58867932 -0.39287227
158 4.60625087 0.58867932
159 -1.77702976 4.60625087
160 -2.43038978 -1.77702976
161 -0.69649251 -2.43038978
162 NA -0.69649251
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.69789702 0.44568551
[2,] -2.62307190 2.69789702
[3,] -1.21071854 -2.62307190
[4,] 1.57797145 -1.21071854
[5,] 3.56177374 1.57797145
[6,] 0.60792105 3.56177374
[7,] -0.72246545 0.60792105
[8,] 0.59668843 -0.72246545
[9,] 0.76489516 0.59668843
[10,] 2.25867825 0.76489516
[11,] 5.79153217 2.25867825
[12,] -4.14735180 5.79153217
[13,] 2.60964701 -4.14735180
[14,] 2.87898886 2.60964701
[15,] 0.87703450 2.87898886
[16,] -0.34011747 0.87703450
[17,] 1.59806227 -0.34011747
[18,] 0.01222514 1.59806227
[19,] 2.93677669 0.01222514
[20,] 4.46056515 2.93677669
[21,] -2.63926960 4.46056515
[22,] 0.48671071 -2.63926960
[23,] -1.34760183 0.48671071
[24,] 1.82083331 -1.34760183
[25,] -6.22882169 1.82083331
[26,] 1.60014035 -6.22882169
[27,] 0.01581638 1.60014035
[28,] 0.60107299 0.01581638
[29,] -2.69994443 0.60107299
[30,] 1.64382415 -2.69994443
[31,] 0.76746468 1.64382415
[32,] 2.81773351 0.76746468
[33,] 0.16150210 2.81773351
[34,] -0.24107603 0.16150210
[35,] 2.40772680 -0.24107603
[36,] -3.80951425 2.40772680
[37,] 0.77591499 -3.80951425
[38,] 3.16796473 0.77591499
[39,] -0.90054657 3.16796473
[40,] 0.63850695 -0.90054657
[41,] 1.69918178 0.63850695
[42,] 2.44489219 1.69918178
[43,] -0.88492939 2.44489219
[44,] -2.14389988 -0.88492939
[45,] -3.12597622 -2.14389988
[46,] 0.91066443 -3.12597622
[47,] 0.84176770 0.91066443
[48,] 2.65815657 0.84176770
[49,] -1.62528930 2.65815657
[50,] 1.91770759 -1.62528930
[51,] 0.46229111 1.91770759
[52,] -2.42167644 0.46229111
[53,] -1.58563794 -2.42167644
[54,] -0.99139941 -1.58563794
[55,] 0.76489516 -0.99139941
[56,] 1.35333512 0.76489516
[57,] 1.58270812 1.35333512
[58,] -2.36321901 1.58270812
[59,] -1.96028876 -2.36321901
[60,] -4.35467830 -1.96028876
[61,] -1.74477367 -4.35467830
[62,] -3.54323889 -1.74477367
[63,] 1.37807354 -3.54323889
[64,] 1.95376771 1.37807354
[65,] -4.74211507 1.95376771
[66,] -3.04831037 -4.74211507
[67,] -1.79539462 -3.04831037
[68,] 1.01049918 -1.79539462
[69,] 0.97651713 1.01049918
[70,] 0.09852078 0.97651713
[71,] 3.25858916 0.09852078
[72,] 0.21779236 3.25858916
[73,] 0.61022753 0.21779236
[74,] -1.83849790 0.61022753
[75,] -2.82071356 -1.83849790
[76,] 2.69307126 -2.82071356
[77,] -0.88089695 2.69307126
[78,] 1.06640402 -0.88089695
[79,] -0.28958560 1.06640402
[80,] 0.54133080 -0.28958560
[81,] 4.14422689 0.54133080
[82,] 0.85703276 4.14422689
[83,] 1.57531285 0.85703276
[84,] 1.10211203 1.57531285
[85,] -0.21624853 1.10211203
[86,] 0.55911514 -0.21624853
[87,] 0.13875267 0.55911514
[88,] 0.35935655 0.13875267
[89,] -6.92098952 0.35935655
[90,] 3.24642390 -6.92098952
[91,] -0.39287227 3.24642390
[92,] 0.49857964 -0.39287227
[93,] -0.55677799 0.49857964
[94,] -1.19394031 -0.55677799
[95,] 1.11781829 -1.19394031
[96,] -2.24718655 1.11781829
[97,] -0.37675811 -2.24718655
[98,] 2.09737535 -0.37675811
[99,] 0.92461142 2.09737535
[100,] 2.19203222 0.92461142
[101,] -2.36321901 2.19203222
[102,] 0.75511993 -2.36321901
[103,] -2.53943485 0.75511993
[104,] 0.60279895 -2.53943485
[105,] -5.36932953 0.60279895
[106,] 1.31496852 -5.36932953
[107,] 0.37032614 1.31496852
[108,] -3.14699968 0.37032614
[109,] -2.99101399 -3.14699968
[110,] 0.60545755 -2.99101399
[111,] -3.12065902 0.60545755
[112,] -1.85376297 -3.12065902
[113,] 2.13334639 -1.85376297
[114,] 3.51902258 2.13334639
[115,] 0.73719627 3.51902258
[116,] 0.97341733 0.73719627
[117,] 0.24376530 0.97341733
[118,] 0.20022082 0.24376530
[119,] -1.52142210 0.20022082
[120,] -0.90364638 -1.52142210
[121,] -0.17867525 -0.90364638
[122,] -0.46167992 -0.17867525
[123,] 0.27314999 -0.46167992
[124,] 0.83831578 0.27314999
[125,] -1.48083810 0.83831578
[126,] 1.52039642 -1.48083810
[127,] 2.00921442 1.52039642
[128,] 4.60625087 2.00921442
[129,] 0.85875872 4.60625087
[130,] -0.77667764 0.85875872
[131,] -4.21496377 -0.77667764
[132,] 0.33886338 -4.21496377
[133,] 1.92589618 0.33886338
[134,] 0.51557066 1.92589618
[135,] 1.44322201 0.51557066
[136,] -1.37512124 1.44322201
[137,] 0.47755617 -1.37512124
[138,] -1.59882492 0.47755617
[139,] 1.52110065 -1.59882492
[140,] -0.51460735 1.52110065
[141,] 2.36187583 -0.51460735
[142,] 1.67037761 2.36187583
[143,] -0.81791564 1.67037761
[144,] 0.88451886 -0.81791564
[145,] 0.62195713 0.88451886
[146,] 1.74020698 0.62195713
[147,] -2.56692095 1.74020698
[148,] -2.13858268 -2.56692095
[149,] -5.07804718 -2.13858268
[150,] 1.08462956 -5.07804718
[151,] -2.25019727 1.08462956
[152,] 2.12255497 -2.25019727
[153,] -1.83390055 2.12255497
[154,] -4.77761028 -1.83390055
[155,] -1.15305443 -4.77761028
[156,] -0.39287227 -1.15305443
[157,] 0.58867932 -0.39287227
[158,] 4.60625087 0.58867932
[159,] -1.77702976 4.60625087
[160,] -2.43038978 -1.77702976
[161,] -0.69649251 -2.43038978
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.69789702 0.44568551
2 -2.62307190 2.69789702
3 -1.21071854 -2.62307190
4 1.57797145 -1.21071854
5 3.56177374 1.57797145
6 0.60792105 3.56177374
7 -0.72246545 0.60792105
8 0.59668843 -0.72246545
9 0.76489516 0.59668843
10 2.25867825 0.76489516
11 5.79153217 2.25867825
12 -4.14735180 5.79153217
13 2.60964701 -4.14735180
14 2.87898886 2.60964701
15 0.87703450 2.87898886
16 -0.34011747 0.87703450
17 1.59806227 -0.34011747
18 0.01222514 1.59806227
19 2.93677669 0.01222514
20 4.46056515 2.93677669
21 -2.63926960 4.46056515
22 0.48671071 -2.63926960
23 -1.34760183 0.48671071
24 1.82083331 -1.34760183
25 -6.22882169 1.82083331
26 1.60014035 -6.22882169
27 0.01581638 1.60014035
28 0.60107299 0.01581638
29 -2.69994443 0.60107299
30 1.64382415 -2.69994443
31 0.76746468 1.64382415
32 2.81773351 0.76746468
33 0.16150210 2.81773351
34 -0.24107603 0.16150210
35 2.40772680 -0.24107603
36 -3.80951425 2.40772680
37 0.77591499 -3.80951425
38 3.16796473 0.77591499
39 -0.90054657 3.16796473
40 0.63850695 -0.90054657
41 1.69918178 0.63850695
42 2.44489219 1.69918178
43 -0.88492939 2.44489219
44 -2.14389988 -0.88492939
45 -3.12597622 -2.14389988
46 0.91066443 -3.12597622
47 0.84176770 0.91066443
48 2.65815657 0.84176770
49 -1.62528930 2.65815657
50 1.91770759 -1.62528930
51 0.46229111 1.91770759
52 -2.42167644 0.46229111
53 -1.58563794 -2.42167644
54 -0.99139941 -1.58563794
55 0.76489516 -0.99139941
56 1.35333512 0.76489516
57 1.58270812 1.35333512
58 -2.36321901 1.58270812
59 -1.96028876 -2.36321901
60 -4.35467830 -1.96028876
61 -1.74477367 -4.35467830
62 -3.54323889 -1.74477367
63 1.37807354 -3.54323889
64 1.95376771 1.37807354
65 -4.74211507 1.95376771
66 -3.04831037 -4.74211507
67 -1.79539462 -3.04831037
68 1.01049918 -1.79539462
69 0.97651713 1.01049918
70 0.09852078 0.97651713
71 3.25858916 0.09852078
72 0.21779236 3.25858916
73 0.61022753 0.21779236
74 -1.83849790 0.61022753
75 -2.82071356 -1.83849790
76 2.69307126 -2.82071356
77 -0.88089695 2.69307126
78 1.06640402 -0.88089695
79 -0.28958560 1.06640402
80 0.54133080 -0.28958560
81 4.14422689 0.54133080
82 0.85703276 4.14422689
83 1.57531285 0.85703276
84 1.10211203 1.57531285
85 -0.21624853 1.10211203
86 0.55911514 -0.21624853
87 0.13875267 0.55911514
88 0.35935655 0.13875267
89 -6.92098952 0.35935655
90 3.24642390 -6.92098952
91 -0.39287227 3.24642390
92 0.49857964 -0.39287227
93 -0.55677799 0.49857964
94 -1.19394031 -0.55677799
95 1.11781829 -1.19394031
96 -2.24718655 1.11781829
97 -0.37675811 -2.24718655
98 2.09737535 -0.37675811
99 0.92461142 2.09737535
100 2.19203222 0.92461142
101 -2.36321901 2.19203222
102 0.75511993 -2.36321901
103 -2.53943485 0.75511993
104 0.60279895 -2.53943485
105 -5.36932953 0.60279895
106 1.31496852 -5.36932953
107 0.37032614 1.31496852
108 -3.14699968 0.37032614
109 -2.99101399 -3.14699968
110 0.60545755 -2.99101399
111 -3.12065902 0.60545755
112 -1.85376297 -3.12065902
113 2.13334639 -1.85376297
114 3.51902258 2.13334639
115 0.73719627 3.51902258
116 0.97341733 0.73719627
117 0.24376530 0.97341733
118 0.20022082 0.24376530
119 -1.52142210 0.20022082
120 -0.90364638 -1.52142210
121 -0.17867525 -0.90364638
122 -0.46167992 -0.17867525
123 0.27314999 -0.46167992
124 0.83831578 0.27314999
125 -1.48083810 0.83831578
126 1.52039642 -1.48083810
127 2.00921442 1.52039642
128 4.60625087 2.00921442
129 0.85875872 4.60625087
130 -0.77667764 0.85875872
131 -4.21496377 -0.77667764
132 0.33886338 -4.21496377
133 1.92589618 0.33886338
134 0.51557066 1.92589618
135 1.44322201 0.51557066
136 -1.37512124 1.44322201
137 0.47755617 -1.37512124
138 -1.59882492 0.47755617
139 1.52110065 -1.59882492
140 -0.51460735 1.52110065
141 2.36187583 -0.51460735
142 1.67037761 2.36187583
143 -0.81791564 1.67037761
144 0.88451886 -0.81791564
145 0.62195713 0.88451886
146 1.74020698 0.62195713
147 -2.56692095 1.74020698
148 -2.13858268 -2.56692095
149 -5.07804718 -2.13858268
150 1.08462956 -5.07804718
151 -2.25019727 1.08462956
152 2.12255497 -2.25019727
153 -1.83390055 2.12255497
154 -4.77761028 -1.83390055
155 -1.15305443 -4.77761028
156 -0.39287227 -1.15305443
157 0.58867932 -0.39287227
158 4.60625087 0.58867932
159 -1.77702976 4.60625087
160 -2.43038978 -1.77702976
161 -0.69649251 -2.43038978
> 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/7za291322132378.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8f6v51322132378.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9leb91322132378.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10f7nm1322132378.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/118q7e1322132378.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/12g97u1322132378.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/13e10t1322132378.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/14w83w1322132378.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/15i56x1322132378.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/16c23x1322132378.tab")
+ }
>
> try(system("convert tmp/1qfi51322132378.ps tmp/1qfi51322132378.png",intern=TRUE))
character(0)
> try(system("convert tmp/2o1wd1322132378.ps tmp/2o1wd1322132378.png",intern=TRUE))
character(0)
> try(system("convert tmp/3603l1322132378.ps tmp/3603l1322132378.png",intern=TRUE))
character(0)
> try(system("convert tmp/45p1t1322132378.ps tmp/45p1t1322132378.png",intern=TRUE))
character(0)
> try(system("convert tmp/5e4t51322132378.ps tmp/5e4t51322132378.png",intern=TRUE))
character(0)
> try(system("convert tmp/6fv791322132378.ps tmp/6fv791322132378.png",intern=TRUE))
character(0)
> try(system("convert tmp/7za291322132378.ps tmp/7za291322132378.png",intern=TRUE))
character(0)
> try(system("convert tmp/8f6v51322132378.ps tmp/8f6v51322132378.png",intern=TRUE))
character(0)
> try(system("convert tmp/9leb91322132378.ps tmp/9leb91322132378.png",intern=TRUE))
character(0)
> try(system("convert tmp/10f7nm1322132378.ps tmp/10f7nm1322132378.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.710 0.473 5.197