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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+ ,dim=c(6
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression')
+ ,1:162))
> y <- array(NA,dim=c(6,162),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression'),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 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '3'
> #'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
Learning Connected Separate Software Happiness Depression t
1 13 41 38 12 14 12 1
2 16 39 32 11 18 11 2
3 19 30 35 15 11 14 3
4 15 31 33 6 12 12 4
5 14 34 37 13 16 21 5
6 13 35 29 10 18 12 6
7 19 39 31 12 14 22 7
8 15 34 36 14 14 11 8
9 14 36 35 12 15 10 9
10 15 37 38 6 15 13 10
11 16 38 31 10 17 10 11
12 16 36 34 12 19 8 12
13 16 38 35 12 10 15 13
14 16 39 38 11 16 14 14
15 17 33 37 15 18 10 15
16 15 32 33 12 14 14 16
17 15 36 32 10 14 14 17
18 20 38 38 12 17 11 18
19 18 39 38 11 14 10 19
20 16 32 32 12 16 13 20
21 16 32 33 11 18 7 21
22 16 31 31 12 11 14 22
23 19 39 38 13 14 12 23
24 16 37 39 11 12 14 24
25 17 39 32 9 17 11 25
26 17 41 32 13 9 9 26
27 16 36 35 10 16 11 27
28 15 33 37 14 14 15 28
29 16 33 33 12 15 14 29
30 14 34 33 10 11 13 30
31 15 31 28 12 16 9 31
32 12 27 32 8 13 15 32
33 14 37 31 10 17 10 33
34 16 34 37 12 15 11 34
35 14 34 30 12 14 13 35
36 7 32 33 7 16 8 36
37 10 29 31 6 9 20 37
38 14 36 33 12 15 12 38
39 16 29 31 10 17 10 39
40 16 35 33 10 13 10 40
41 16 37 32 10 15 9 41
42 14 34 33 12 16 14 42
43 20 38 32 15 16 8 43
44 14 35 33 10 12 14 44
45 14 38 28 10 12 11 45
46 11 37 35 12 11 13 46
47 14 38 39 13 15 9 47
48 15 33 34 11 15 11 48
49 16 36 38 11 17 15 49
50 14 38 32 12 13 11 50
51 16 32 38 14 16 10 51
52 14 32 30 10 14 14 52
53 12 32 33 12 11 18 53
54 16 34 38 13 12 14 54
55 9 32 32 5 12 11 55
56 14 37 32 6 15 12 56
57 16 39 34 12 16 13 57
58 16 29 34 12 15 9 58
59 15 37 36 11 12 10 59
60 16 35 34 10 12 15 60
61 12 30 28 7 8 20 61
62 16 38 34 12 13 12 62
63 16 34 35 14 11 12 63
64 14 31 35 11 14 14 64
65 16 34 31 12 15 13 65
66 17 35 37 13 10 11 66
67 18 36 35 14 11 17 67
68 18 30 27 11 12 12 68
69 12 39 40 12 15 13 69
70 16 35 37 12 15 14 70
71 10 38 36 8 14 13 71
72 14 31 38 11 16 15 72
73 18 34 39 14 15 13 73
74 18 38 41 14 15 10 74
75 16 34 27 12 13 11 75
76 17 39 30 9 12 19 76
77 16 37 37 13 17 13 77
78 16 34 31 11 13 17 78
79 13 28 31 12 15 13 79
80 16 37 27 12 13 9 80
81 16 33 36 12 15 11 81
82 20 37 38 12 16 10 82
83 16 35 37 12 15 9 83
84 15 37 33 12 16 12 84
85 15 32 34 11 15 12 85
86 16 33 31 10 14 13 86
87 14 38 39 9 15 13 87
88 16 33 34 12 14 12 88
89 16 29 32 12 13 15 89
90 15 33 33 12 7 22 90
91 12 31 36 9 17 13 91
92 17 36 32 15 13 15 92
93 16 35 41 12 15 13 93
94 15 32 28 12 14 15 94
95 13 29 30 12 13 10 95
96 16 39 36 10 16 11 96
97 16 37 35 13 12 16 97
98 16 35 31 9 14 11 98
99 16 37 34 12 17 11 99
100 14 32 36 10 15 10 100
101 16 38 36 14 17 10 101
102 16 37 35 11 12 16 102
103 20 36 37 15 16 12 103
104 15 32 28 11 11 11 104
105 16 33 39 11 15 16 105
106 13 40 32 12 9 19 106
107 17 38 35 12 16 11 107
108 16 41 39 12 15 16 108
109 16 36 35 11 10 15 109
110 12 43 42 7 10 24 110
111 16 30 34 12 15 14 111
112 16 31 33 14 11 15 112
113 17 32 41 11 13 11 113
114 13 32 33 11 14 15 114
115 12 37 34 10 18 12 115
116 18 37 32 13 16 10 116
117 14 33 40 13 14 14 117
118 14 34 40 8 14 13 118
119 13 33 35 11 14 9 119
120 16 38 36 12 14 15 120
121 13 33 37 11 12 15 121
122 16 31 27 13 14 14 122
123 13 38 39 12 15 11 123
124 16 37 38 14 15 8 124
125 15 33 31 13 15 11 125
126 16 31 33 15 13 11 126
127 15 39 32 10 17 8 127
128 17 44 39 11 17 10 128
129 15 33 36 9 19 11 129
130 12 35 33 11 15 13 130
131 16 32 33 10 13 11 131
132 10 28 32 11 9 20 132
133 16 40 37 8 15 10 133
134 12 27 30 11 15 15 134
135 14 37 38 12 15 12 135
136 15 32 29 12 16 14 136
137 13 28 22 9 11 23 137
138 15 34 35 11 14 14 138
139 11 30 35 10 11 16 139
140 12 35 34 8 15 11 140
141 8 31 35 9 13 12 141
142 16 32 34 8 15 10 142
143 15 30 34 9 16 14 143
144 17 30 35 15 14 12 144
145 16 31 23 11 15 12 145
146 10 40 31 8 16 11 146
147 18 32 27 13 16 12 147
148 13 36 36 12 11 13 148
149 16 32 31 12 12 11 149
150 13 35 32 9 9 19 150
151 10 38 39 7 16 12 151
152 15 42 37 13 13 17 152
153 16 34 38 9 16 9 153
154 16 35 39 6 12 12 154
155 14 35 34 8 9 19 155
156 10 33 31 8 13 18 156
157 17 36 32 15 13 15 157
158 13 32 37 6 14 14 158
159 15 33 36 9 19 11 159
160 16 34 32 11 13 9 160
161 12 32 35 8 12 18 161
162 13 34 36 8 13 16 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Software Happiness Depression
6.349742 0.108770 -0.019653 0.534476 0.060830 -0.073008
t
-0.003861
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.1734 -1.0880 0.2509 1.1607 4.1436
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.349742 2.415661 2.629 0.00944 **
Connected 0.108770 0.046833 2.323 0.02151 *
Separate -0.019653 0.044373 -0.443 0.65845
Software 0.534476 0.069055 7.740 1.2e-12 ***
Happiness 0.060830 0.074729 0.814 0.41689
Depression -0.073008 0.055147 -1.324 0.18749
t -0.003861 0.003171 -1.217 0.22532
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.84 on 155 degrees of freedom
Multiple R-squared: 0.36, Adjusted R-squared: 0.3352
F-statistic: 14.53 on 6 and 155 DF, p-value: 4.028e-13
> 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,] 0.85774756 0.28450489 0.1422524
[2,] 0.77302175 0.45395650 0.2269782
[3,] 0.78270508 0.43458984 0.2172949
[4,] 0.77677800 0.44644399 0.2232220
[5,] 0.70910462 0.58179075 0.2908954
[6,] 0.63754506 0.72490987 0.3624549
[7,] 0.65844658 0.68310683 0.3415534
[8,] 0.60196688 0.79606623 0.3980331
[9,] 0.83723156 0.32553688 0.1627684
[10,] 0.80627292 0.38745415 0.1937271
[11,] 0.75798650 0.48402701 0.2420135
[12,] 0.69199984 0.61600033 0.3080002
[13,] 0.64665439 0.70669122 0.3533456
[14,] 0.60759493 0.78481013 0.3924051
[15,] 0.58738760 0.82522480 0.4126124
[16,] 0.53358702 0.93282597 0.4664130
[17,] 0.47868730 0.95737459 0.5213127
[18,] 0.42839806 0.85679612 0.5716019
[19,] 0.47790082 0.95580164 0.5220992
[20,] 0.42057218 0.84114436 0.5794278
[21,] 0.43612976 0.87225953 0.5638702
[22,] 0.38431518 0.76863035 0.6156848
[23,] 0.38937502 0.77875004 0.6106250
[24,] 0.38017403 0.76034806 0.6198260
[25,] 0.32378464 0.64756929 0.6762154
[26,] 0.30955832 0.61911665 0.6904417
[27,] 0.82365342 0.35269316 0.1763466
[28,] 0.80468621 0.39062757 0.1953138
[29,] 0.78884859 0.42230282 0.2111514
[30,] 0.82419721 0.35160558 0.1758028
[31,] 0.80994999 0.38010002 0.1900500
[32,] 0.78289808 0.43420384 0.2171019
[33,] 0.76020269 0.47959462 0.2397973
[34,] 0.77538146 0.44923707 0.2246185
[35,] 0.73583664 0.52832672 0.2641634
[36,] 0.70256356 0.59487288 0.2974364
[37,] 0.87083022 0.25833956 0.1291698
[38,] 0.88084936 0.23830127 0.1191506
[39,] 0.85843309 0.28313382 0.1415669
[40,] 0.84351879 0.31296243 0.1564812
[41,] 0.83645304 0.32709393 0.1635470
[42,] 0.80700548 0.38598903 0.1929945
[43,] 0.77432688 0.45134624 0.2256731
[44,] 0.79067493 0.41865015 0.2093251
[45,] 0.76357838 0.47284323 0.2364216
[46,] 0.78593832 0.42812337 0.2140617
[47,] 0.77668714 0.44662571 0.2233129
[48,] 0.74045259 0.51909483 0.2595474
[49,] 0.72889022 0.54221956 0.2711098
[50,] 0.69344383 0.61311234 0.3065562
[51,] 0.70220332 0.59559336 0.2977967
[52,] 0.66716698 0.66566603 0.3328330
[53,] 0.62611168 0.74777664 0.3738883
[54,] 0.58591269 0.82817462 0.4140873
[55,] 0.54334629 0.91330741 0.4566537
[56,] 0.50524019 0.98951962 0.4947598
[57,] 0.48150061 0.96300122 0.5184994
[58,] 0.47724061 0.95448121 0.5227594
[59,] 0.60076819 0.79846362 0.3992318
[60,] 0.73221338 0.53557325 0.2677866
[61,] 0.69900322 0.60199357 0.3009968
[62,] 0.80415875 0.39168249 0.1958412
[63,] 0.77454372 0.45091256 0.2254563
[64,] 0.77031683 0.45936635 0.2296832
[65,] 0.74809235 0.50381531 0.2519077
[66,] 0.71099741 0.57800517 0.2890026
[67,] 0.76636045 0.46727910 0.2336395
[68,] 0.73067342 0.53865316 0.2693266
[69,] 0.71009972 0.57980056 0.2899003
[70,] 0.71488298 0.57023404 0.2851170
[71,] 0.67568176 0.64863648 0.3243182
[72,] 0.63929355 0.72141290 0.3607065
[73,] 0.78090759 0.43818481 0.2190924
[74,] 0.74602962 0.50794077 0.2539704
[75,] 0.71944359 0.56111282 0.2805564
[76,] 0.67963750 0.64072500 0.3203625
[77,] 0.66700769 0.66598462 0.3329923
[78,] 0.62449912 0.75100177 0.3755009
[79,] 0.58292106 0.83415788 0.4170789
[80,] 0.55629164 0.88741672 0.4437084
[81,] 0.52282247 0.95435506 0.4771775
[82,] 0.51449059 0.97101882 0.4855094
[83,] 0.47139772 0.94279544 0.5286023
[84,] 0.42914445 0.85828890 0.5708556
[85,] 0.38469524 0.76939048 0.6153048
[86,] 0.40570159 0.81140318 0.5942984
[87,] 0.36858501 0.73717002 0.6314150
[88,] 0.32727527 0.65455054 0.6727247
[89,] 0.32244676 0.64489352 0.6775532
[90,] 0.28092508 0.56185016 0.7190749
[91,] 0.24607366 0.49214732 0.7539263
[92,] 0.22477650 0.44955301 0.7752235
[93,] 0.20710192 0.41420385 0.7928981
[94,] 0.25463630 0.50927260 0.7453637
[95,] 0.21767748 0.43535497 0.7823225
[96,] 0.21393356 0.42786712 0.7860664
[97,] 0.22503707 0.45007415 0.7749629
[98,] 0.20335543 0.40671086 0.7966446
[99,] 0.18188441 0.36376882 0.8181156
[100,] 0.17550966 0.35101933 0.8244903
[101,] 0.16799800 0.33599600 0.8320020
[102,] 0.15853186 0.31706373 0.8414681
[103,] 0.13876712 0.27753423 0.8612329
[104,] 0.18620543 0.37241087 0.8137946
[105,] 0.16296624 0.32593248 0.8370338
[106,] 0.18320797 0.36641595 0.8167920
[107,] 0.18588859 0.37177719 0.8141114
[108,] 0.16187210 0.32374419 0.8381279
[109,] 0.15973525 0.31947050 0.8402648
[110,] 0.14920280 0.29840561 0.8507972
[111,] 0.15371889 0.30743779 0.8462811
[112,] 0.13007885 0.26015769 0.8699212
[113,] 0.11410211 0.22820423 0.8858979
[114,] 0.11296872 0.22593744 0.8870313
[115,] 0.09007812 0.18015625 0.9099219
[116,] 0.07102603 0.14205206 0.9289740
[117,] 0.05420680 0.10841360 0.9457932
[118,] 0.04047159 0.08094318 0.9595284
[119,] 0.04281571 0.08563142 0.9571843
[120,] 0.04076073 0.08152146 0.9592393
[121,] 0.04088671 0.08177342 0.9591133
[122,] 0.04492421 0.08984841 0.9550758
[123,] 0.04959757 0.09919515 0.9504024
[124,] 0.10182390 0.20364780 0.8981761
[125,] 0.10197362 0.20394724 0.8980264
[126,] 0.08028783 0.16057566 0.9197122
[127,] 0.05960237 0.11920473 0.9403976
[128,] 0.04996499 0.09992998 0.9500350
[129,] 0.04869841 0.09739683 0.9513016
[130,] 0.04326902 0.08653804 0.9567310
[131,] 0.02987984 0.05975969 0.9701202
[132,] 0.39938379 0.79876758 0.6006162
[133,] 0.39698573 0.79397146 0.6030143
[134,] 0.35558063 0.71116126 0.6444194
[135,] 0.32535160 0.65070321 0.6746484
[136,] 0.28306253 0.56612507 0.7169375
[137,] 0.26435885 0.52871771 0.7356411
[138,] 0.40280711 0.80561423 0.5971929
[139,] 0.73433323 0.53133355 0.2656668
[140,] 0.69094244 0.61811511 0.3090576
[141,] 0.56345932 0.87308136 0.4365407
[142,] 0.78909693 0.42180615 0.2109031
[143,] 0.83826751 0.32346498 0.1617325
> postscript(file="/var/wessaorg/rcomp/tmp/10sri1322166028.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2f5wq1322166028.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3m9l81322166028.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4tlm21322166028.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5l8be1322166028.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
-3.44785766 -0.12622971 2.42245334 2.88167222 -1.68974186 -2.12718353
7 8 9 10 11 12
3.38535156 -1.84070883 -2.13892850 2.24100035 0.51993004 -0.53633597
13 14 15 16 17 18
0.32816329 0.37870202 -0.53606461 -0.36326892 0.25480909 3.68858500
19 20 21 22 23 24
2.22763313 0.43785211 0.43613416 0.91184781 2.32014005 0.89782160
25 26 27 28 29 30
2.09234538 0.08138685 1.01169162 -1.34304147 0.51731875 -0.34832746
31 32 33 34 35 36
-0.78155684 -0.50556149 -1.28636669 0.28744156 -1.63942615 -6.17338621
37 38 39 40 41 42
-1.04614115 -1.92026217 1.60695792 1.24082456 0.81282354 -1.60209359
43 44 45 46 47 48
1.90555823 -0.39087124 -1.03061218 -4.64251275 -2.73863587 -0.07422463
49 50 51 52 53 54
0.85231129 -2.06247672 -0.61252388 -0.21429640 -2.74590506 0.25134507
55 56 57 58 59 60
-2.58839317 1.22765913 -0.14138977 0.71897025 -0.31804969 1.76355973
61 62 63 64 65 66
0.40513698 0.09616531 -0.39253155 -0.49540751 0.43521526 1.07188473
67 68 69 70 71 72
1.77041037 3.44722139 -3.91631189 0.53667681 -3.67970166 -0.45421436
73 74 75 76 77 78
1.55437631 0.94343944 0.37085144 3.13814246 -0.38298286 1.43357049
79 80 81 82 83 84
-1.85811561 -0.08217187 0.55800631 4.03225538 0.22182479 -0.91227480
85 86 87 88 89 90
0.25039556 1.75483928 -0.15427739 0.67956151 1.35904955 0.82351864
91 92 93 94 95 96
-1.55806443 0.00581338 0.63107642 -0.08740165 -2.02213356 0.97141591
97 98 99 100 101 102
0.17809592 1.97208597 0.03144969 -0.26392879 -1.17225167 1.26635028
103 104 105 106 107 108
2.74503344 0.37613811 1.60913649 -2.23643997 1.03404777 0.21608157
109 110 111 112 113 114
1.45079659 -0.37418515 1.17985157 0.30266506 2.54471847 -1.37744673
115 116 117 118 119 120
-2.82565139 1.51111926 -1.47902020 1.01544076 -1.86565470 0.51758077
121 122 123 124 125 126
-1.25891890 0.50232811 -2.76473887 -0.95973685 -0.90487038 -0.59145404
127 128 129 130 131 132
-0.26737325 0.94175113 1.10342215 -2.84883344 1.99145726 -3.22333926
133 134 135 136 137 138
2.08191473 -1.87617448 -1.55628707 -0.10027101 0.76574446 0.46396631
139 140 141 142 143 144
-2.23411105 -1.33316286 -5.21437607 2.92786076 1.84598778 0.63829173
145 146 147 148 149 150
1.37461351 -3.97364026 2.22239705 -2.12030824 1.01351970 0.08070402
151 152 153 154 155 156
-2.97208572 -1.10193697 2.16308728 4.14360205 1.67378961 -2.48009776
157 158 159 160 161 162
0.25675221 1.47040387 1.21924007 1.18572927 -0.21258085 0.38654706
> postscript(file="/var/wessaorg/rcomp/tmp/6npma1322166028.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 -3.44785766 NA
1 -0.12622971 -3.44785766
2 2.42245334 -0.12622971
3 2.88167222 2.42245334
4 -1.68974186 2.88167222
5 -2.12718353 -1.68974186
6 3.38535156 -2.12718353
7 -1.84070883 3.38535156
8 -2.13892850 -1.84070883
9 2.24100035 -2.13892850
10 0.51993004 2.24100035
11 -0.53633597 0.51993004
12 0.32816329 -0.53633597
13 0.37870202 0.32816329
14 -0.53606461 0.37870202
15 -0.36326892 -0.53606461
16 0.25480909 -0.36326892
17 3.68858500 0.25480909
18 2.22763313 3.68858500
19 0.43785211 2.22763313
20 0.43613416 0.43785211
21 0.91184781 0.43613416
22 2.32014005 0.91184781
23 0.89782160 2.32014005
24 2.09234538 0.89782160
25 0.08138685 2.09234538
26 1.01169162 0.08138685
27 -1.34304147 1.01169162
28 0.51731875 -1.34304147
29 -0.34832746 0.51731875
30 -0.78155684 -0.34832746
31 -0.50556149 -0.78155684
32 -1.28636669 -0.50556149
33 0.28744156 -1.28636669
34 -1.63942615 0.28744156
35 -6.17338621 -1.63942615
36 -1.04614115 -6.17338621
37 -1.92026217 -1.04614115
38 1.60695792 -1.92026217
39 1.24082456 1.60695792
40 0.81282354 1.24082456
41 -1.60209359 0.81282354
42 1.90555823 -1.60209359
43 -0.39087124 1.90555823
44 -1.03061218 -0.39087124
45 -4.64251275 -1.03061218
46 -2.73863587 -4.64251275
47 -0.07422463 -2.73863587
48 0.85231129 -0.07422463
49 -2.06247672 0.85231129
50 -0.61252388 -2.06247672
51 -0.21429640 -0.61252388
52 -2.74590506 -0.21429640
53 0.25134507 -2.74590506
54 -2.58839317 0.25134507
55 1.22765913 -2.58839317
56 -0.14138977 1.22765913
57 0.71897025 -0.14138977
58 -0.31804969 0.71897025
59 1.76355973 -0.31804969
60 0.40513698 1.76355973
61 0.09616531 0.40513698
62 -0.39253155 0.09616531
63 -0.49540751 -0.39253155
64 0.43521526 -0.49540751
65 1.07188473 0.43521526
66 1.77041037 1.07188473
67 3.44722139 1.77041037
68 -3.91631189 3.44722139
69 0.53667681 -3.91631189
70 -3.67970166 0.53667681
71 -0.45421436 -3.67970166
72 1.55437631 -0.45421436
73 0.94343944 1.55437631
74 0.37085144 0.94343944
75 3.13814246 0.37085144
76 -0.38298286 3.13814246
77 1.43357049 -0.38298286
78 -1.85811561 1.43357049
79 -0.08217187 -1.85811561
80 0.55800631 -0.08217187
81 4.03225538 0.55800631
82 0.22182479 4.03225538
83 -0.91227480 0.22182479
84 0.25039556 -0.91227480
85 1.75483928 0.25039556
86 -0.15427739 1.75483928
87 0.67956151 -0.15427739
88 1.35904955 0.67956151
89 0.82351864 1.35904955
90 -1.55806443 0.82351864
91 0.00581338 -1.55806443
92 0.63107642 0.00581338
93 -0.08740165 0.63107642
94 -2.02213356 -0.08740165
95 0.97141591 -2.02213356
96 0.17809592 0.97141591
97 1.97208597 0.17809592
98 0.03144969 1.97208597
99 -0.26392879 0.03144969
100 -1.17225167 -0.26392879
101 1.26635028 -1.17225167
102 2.74503344 1.26635028
103 0.37613811 2.74503344
104 1.60913649 0.37613811
105 -2.23643997 1.60913649
106 1.03404777 -2.23643997
107 0.21608157 1.03404777
108 1.45079659 0.21608157
109 -0.37418515 1.45079659
110 1.17985157 -0.37418515
111 0.30266506 1.17985157
112 2.54471847 0.30266506
113 -1.37744673 2.54471847
114 -2.82565139 -1.37744673
115 1.51111926 -2.82565139
116 -1.47902020 1.51111926
117 1.01544076 -1.47902020
118 -1.86565470 1.01544076
119 0.51758077 -1.86565470
120 -1.25891890 0.51758077
121 0.50232811 -1.25891890
122 -2.76473887 0.50232811
123 -0.95973685 -2.76473887
124 -0.90487038 -0.95973685
125 -0.59145404 -0.90487038
126 -0.26737325 -0.59145404
127 0.94175113 -0.26737325
128 1.10342215 0.94175113
129 -2.84883344 1.10342215
130 1.99145726 -2.84883344
131 -3.22333926 1.99145726
132 2.08191473 -3.22333926
133 -1.87617448 2.08191473
134 -1.55628707 -1.87617448
135 -0.10027101 -1.55628707
136 0.76574446 -0.10027101
137 0.46396631 0.76574446
138 -2.23411105 0.46396631
139 -1.33316286 -2.23411105
140 -5.21437607 -1.33316286
141 2.92786076 -5.21437607
142 1.84598778 2.92786076
143 0.63829173 1.84598778
144 1.37461351 0.63829173
145 -3.97364026 1.37461351
146 2.22239705 -3.97364026
147 -2.12030824 2.22239705
148 1.01351970 -2.12030824
149 0.08070402 1.01351970
150 -2.97208572 0.08070402
151 -1.10193697 -2.97208572
152 2.16308728 -1.10193697
153 4.14360205 2.16308728
154 1.67378961 4.14360205
155 -2.48009776 1.67378961
156 0.25675221 -2.48009776
157 1.47040387 0.25675221
158 1.21924007 1.47040387
159 1.18572927 1.21924007
160 -0.21258085 1.18572927
161 0.38654706 -0.21258085
162 NA 0.38654706
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.12622971 -3.44785766
[2,] 2.42245334 -0.12622971
[3,] 2.88167222 2.42245334
[4,] -1.68974186 2.88167222
[5,] -2.12718353 -1.68974186
[6,] 3.38535156 -2.12718353
[7,] -1.84070883 3.38535156
[8,] -2.13892850 -1.84070883
[9,] 2.24100035 -2.13892850
[10,] 0.51993004 2.24100035
[11,] -0.53633597 0.51993004
[12,] 0.32816329 -0.53633597
[13,] 0.37870202 0.32816329
[14,] -0.53606461 0.37870202
[15,] -0.36326892 -0.53606461
[16,] 0.25480909 -0.36326892
[17,] 3.68858500 0.25480909
[18,] 2.22763313 3.68858500
[19,] 0.43785211 2.22763313
[20,] 0.43613416 0.43785211
[21,] 0.91184781 0.43613416
[22,] 2.32014005 0.91184781
[23,] 0.89782160 2.32014005
[24,] 2.09234538 0.89782160
[25,] 0.08138685 2.09234538
[26,] 1.01169162 0.08138685
[27,] -1.34304147 1.01169162
[28,] 0.51731875 -1.34304147
[29,] -0.34832746 0.51731875
[30,] -0.78155684 -0.34832746
[31,] -0.50556149 -0.78155684
[32,] -1.28636669 -0.50556149
[33,] 0.28744156 -1.28636669
[34,] -1.63942615 0.28744156
[35,] -6.17338621 -1.63942615
[36,] -1.04614115 -6.17338621
[37,] -1.92026217 -1.04614115
[38,] 1.60695792 -1.92026217
[39,] 1.24082456 1.60695792
[40,] 0.81282354 1.24082456
[41,] -1.60209359 0.81282354
[42,] 1.90555823 -1.60209359
[43,] -0.39087124 1.90555823
[44,] -1.03061218 -0.39087124
[45,] -4.64251275 -1.03061218
[46,] -2.73863587 -4.64251275
[47,] -0.07422463 -2.73863587
[48,] 0.85231129 -0.07422463
[49,] -2.06247672 0.85231129
[50,] -0.61252388 -2.06247672
[51,] -0.21429640 -0.61252388
[52,] -2.74590506 -0.21429640
[53,] 0.25134507 -2.74590506
[54,] -2.58839317 0.25134507
[55,] 1.22765913 -2.58839317
[56,] -0.14138977 1.22765913
[57,] 0.71897025 -0.14138977
[58,] -0.31804969 0.71897025
[59,] 1.76355973 -0.31804969
[60,] 0.40513698 1.76355973
[61,] 0.09616531 0.40513698
[62,] -0.39253155 0.09616531
[63,] -0.49540751 -0.39253155
[64,] 0.43521526 -0.49540751
[65,] 1.07188473 0.43521526
[66,] 1.77041037 1.07188473
[67,] 3.44722139 1.77041037
[68,] -3.91631189 3.44722139
[69,] 0.53667681 -3.91631189
[70,] -3.67970166 0.53667681
[71,] -0.45421436 -3.67970166
[72,] 1.55437631 -0.45421436
[73,] 0.94343944 1.55437631
[74,] 0.37085144 0.94343944
[75,] 3.13814246 0.37085144
[76,] -0.38298286 3.13814246
[77,] 1.43357049 -0.38298286
[78,] -1.85811561 1.43357049
[79,] -0.08217187 -1.85811561
[80,] 0.55800631 -0.08217187
[81,] 4.03225538 0.55800631
[82,] 0.22182479 4.03225538
[83,] -0.91227480 0.22182479
[84,] 0.25039556 -0.91227480
[85,] 1.75483928 0.25039556
[86,] -0.15427739 1.75483928
[87,] 0.67956151 -0.15427739
[88,] 1.35904955 0.67956151
[89,] 0.82351864 1.35904955
[90,] -1.55806443 0.82351864
[91,] 0.00581338 -1.55806443
[92,] 0.63107642 0.00581338
[93,] -0.08740165 0.63107642
[94,] -2.02213356 -0.08740165
[95,] 0.97141591 -2.02213356
[96,] 0.17809592 0.97141591
[97,] 1.97208597 0.17809592
[98,] 0.03144969 1.97208597
[99,] -0.26392879 0.03144969
[100,] -1.17225167 -0.26392879
[101,] 1.26635028 -1.17225167
[102,] 2.74503344 1.26635028
[103,] 0.37613811 2.74503344
[104,] 1.60913649 0.37613811
[105,] -2.23643997 1.60913649
[106,] 1.03404777 -2.23643997
[107,] 0.21608157 1.03404777
[108,] 1.45079659 0.21608157
[109,] -0.37418515 1.45079659
[110,] 1.17985157 -0.37418515
[111,] 0.30266506 1.17985157
[112,] 2.54471847 0.30266506
[113,] -1.37744673 2.54471847
[114,] -2.82565139 -1.37744673
[115,] 1.51111926 -2.82565139
[116,] -1.47902020 1.51111926
[117,] 1.01544076 -1.47902020
[118,] -1.86565470 1.01544076
[119,] 0.51758077 -1.86565470
[120,] -1.25891890 0.51758077
[121,] 0.50232811 -1.25891890
[122,] -2.76473887 0.50232811
[123,] -0.95973685 -2.76473887
[124,] -0.90487038 -0.95973685
[125,] -0.59145404 -0.90487038
[126,] -0.26737325 -0.59145404
[127,] 0.94175113 -0.26737325
[128,] 1.10342215 0.94175113
[129,] -2.84883344 1.10342215
[130,] 1.99145726 -2.84883344
[131,] -3.22333926 1.99145726
[132,] 2.08191473 -3.22333926
[133,] -1.87617448 2.08191473
[134,] -1.55628707 -1.87617448
[135,] -0.10027101 -1.55628707
[136,] 0.76574446 -0.10027101
[137,] 0.46396631 0.76574446
[138,] -2.23411105 0.46396631
[139,] -1.33316286 -2.23411105
[140,] -5.21437607 -1.33316286
[141,] 2.92786076 -5.21437607
[142,] 1.84598778 2.92786076
[143,] 0.63829173 1.84598778
[144,] 1.37461351 0.63829173
[145,] -3.97364026 1.37461351
[146,] 2.22239705 -3.97364026
[147,] -2.12030824 2.22239705
[148,] 1.01351970 -2.12030824
[149,] 0.08070402 1.01351970
[150,] -2.97208572 0.08070402
[151,] -1.10193697 -2.97208572
[152,] 2.16308728 -1.10193697
[153,] 4.14360205 2.16308728
[154,] 1.67378961 4.14360205
[155,] -2.48009776 1.67378961
[156,] 0.25675221 -2.48009776
[157,] 1.47040387 0.25675221
[158,] 1.21924007 1.47040387
[159,] 1.18572927 1.21924007
[160,] -0.21258085 1.18572927
[161,] 0.38654706 -0.21258085
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.12622971 -3.44785766
2 2.42245334 -0.12622971
3 2.88167222 2.42245334
4 -1.68974186 2.88167222
5 -2.12718353 -1.68974186
6 3.38535156 -2.12718353
7 -1.84070883 3.38535156
8 -2.13892850 -1.84070883
9 2.24100035 -2.13892850
10 0.51993004 2.24100035
11 -0.53633597 0.51993004
12 0.32816329 -0.53633597
13 0.37870202 0.32816329
14 -0.53606461 0.37870202
15 -0.36326892 -0.53606461
16 0.25480909 -0.36326892
17 3.68858500 0.25480909
18 2.22763313 3.68858500
19 0.43785211 2.22763313
20 0.43613416 0.43785211
21 0.91184781 0.43613416
22 2.32014005 0.91184781
23 0.89782160 2.32014005
24 2.09234538 0.89782160
25 0.08138685 2.09234538
26 1.01169162 0.08138685
27 -1.34304147 1.01169162
28 0.51731875 -1.34304147
29 -0.34832746 0.51731875
30 -0.78155684 -0.34832746
31 -0.50556149 -0.78155684
32 -1.28636669 -0.50556149
33 0.28744156 -1.28636669
34 -1.63942615 0.28744156
35 -6.17338621 -1.63942615
36 -1.04614115 -6.17338621
37 -1.92026217 -1.04614115
38 1.60695792 -1.92026217
39 1.24082456 1.60695792
40 0.81282354 1.24082456
41 -1.60209359 0.81282354
42 1.90555823 -1.60209359
43 -0.39087124 1.90555823
44 -1.03061218 -0.39087124
45 -4.64251275 -1.03061218
46 -2.73863587 -4.64251275
47 -0.07422463 -2.73863587
48 0.85231129 -0.07422463
49 -2.06247672 0.85231129
50 -0.61252388 -2.06247672
51 -0.21429640 -0.61252388
52 -2.74590506 -0.21429640
53 0.25134507 -2.74590506
54 -2.58839317 0.25134507
55 1.22765913 -2.58839317
56 -0.14138977 1.22765913
57 0.71897025 -0.14138977
58 -0.31804969 0.71897025
59 1.76355973 -0.31804969
60 0.40513698 1.76355973
61 0.09616531 0.40513698
62 -0.39253155 0.09616531
63 -0.49540751 -0.39253155
64 0.43521526 -0.49540751
65 1.07188473 0.43521526
66 1.77041037 1.07188473
67 3.44722139 1.77041037
68 -3.91631189 3.44722139
69 0.53667681 -3.91631189
70 -3.67970166 0.53667681
71 -0.45421436 -3.67970166
72 1.55437631 -0.45421436
73 0.94343944 1.55437631
74 0.37085144 0.94343944
75 3.13814246 0.37085144
76 -0.38298286 3.13814246
77 1.43357049 -0.38298286
78 -1.85811561 1.43357049
79 -0.08217187 -1.85811561
80 0.55800631 -0.08217187
81 4.03225538 0.55800631
82 0.22182479 4.03225538
83 -0.91227480 0.22182479
84 0.25039556 -0.91227480
85 1.75483928 0.25039556
86 -0.15427739 1.75483928
87 0.67956151 -0.15427739
88 1.35904955 0.67956151
89 0.82351864 1.35904955
90 -1.55806443 0.82351864
91 0.00581338 -1.55806443
92 0.63107642 0.00581338
93 -0.08740165 0.63107642
94 -2.02213356 -0.08740165
95 0.97141591 -2.02213356
96 0.17809592 0.97141591
97 1.97208597 0.17809592
98 0.03144969 1.97208597
99 -0.26392879 0.03144969
100 -1.17225167 -0.26392879
101 1.26635028 -1.17225167
102 2.74503344 1.26635028
103 0.37613811 2.74503344
104 1.60913649 0.37613811
105 -2.23643997 1.60913649
106 1.03404777 -2.23643997
107 0.21608157 1.03404777
108 1.45079659 0.21608157
109 -0.37418515 1.45079659
110 1.17985157 -0.37418515
111 0.30266506 1.17985157
112 2.54471847 0.30266506
113 -1.37744673 2.54471847
114 -2.82565139 -1.37744673
115 1.51111926 -2.82565139
116 -1.47902020 1.51111926
117 1.01544076 -1.47902020
118 -1.86565470 1.01544076
119 0.51758077 -1.86565470
120 -1.25891890 0.51758077
121 0.50232811 -1.25891890
122 -2.76473887 0.50232811
123 -0.95973685 -2.76473887
124 -0.90487038 -0.95973685
125 -0.59145404 -0.90487038
126 -0.26737325 -0.59145404
127 0.94175113 -0.26737325
128 1.10342215 0.94175113
129 -2.84883344 1.10342215
130 1.99145726 -2.84883344
131 -3.22333926 1.99145726
132 2.08191473 -3.22333926
133 -1.87617448 2.08191473
134 -1.55628707 -1.87617448
135 -0.10027101 -1.55628707
136 0.76574446 -0.10027101
137 0.46396631 0.76574446
138 -2.23411105 0.46396631
139 -1.33316286 -2.23411105
140 -5.21437607 -1.33316286
141 2.92786076 -5.21437607
142 1.84598778 2.92786076
143 0.63829173 1.84598778
144 1.37461351 0.63829173
145 -3.97364026 1.37461351
146 2.22239705 -3.97364026
147 -2.12030824 2.22239705
148 1.01351970 -2.12030824
149 0.08070402 1.01351970
150 -2.97208572 0.08070402
151 -1.10193697 -2.97208572
152 2.16308728 -1.10193697
153 4.14360205 2.16308728
154 1.67378961 4.14360205
155 -2.48009776 1.67378961
156 0.25675221 -2.48009776
157 1.47040387 0.25675221
158 1.21924007 1.47040387
159 1.18572927 1.21924007
160 -0.21258085 1.18572927
161 0.38654706 -0.21258085
> 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/7u0wh1322166028.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/87gl71322166028.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/98cqo1322166028.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10hme41322166028.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11w0cp1322166028.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/12mzs71322166028.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/1383mv1322166028.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/14t95n1322166028.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/15915g1322166028.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/16hqoq1322166028.tab")
+ }
>
> try(system("convert tmp/10sri1322166028.ps tmp/10sri1322166028.png",intern=TRUE))
character(0)
> try(system("convert tmp/2f5wq1322166028.ps tmp/2f5wq1322166028.png",intern=TRUE))
character(0)
> try(system("convert tmp/3m9l81322166028.ps tmp/3m9l81322166028.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tlm21322166028.ps tmp/4tlm21322166028.png",intern=TRUE))
character(0)
> try(system("convert tmp/5l8be1322166028.ps tmp/5l8be1322166028.png",intern=TRUE))
character(0)
> try(system("convert tmp/6npma1322166028.ps tmp/6npma1322166028.png",intern=TRUE))
character(0)
> try(system("convert tmp/7u0wh1322166028.ps tmp/7u0wh1322166028.png",intern=TRUE))
character(0)
> try(system("convert tmp/87gl71322166028.ps tmp/87gl71322166028.png",intern=TRUE))
character(0)
> try(system("convert tmp/98cqo1322166028.ps tmp/98cqo1322166028.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hme41322166028.ps tmp/10hme41322166028.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.167 0.559 5.765