R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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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(32
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+ ,dim=c(7
+ ,142)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging')
+ ,1:142))
> y <- array(NA,dim=c(7,142),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging'),1:142))
> 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'
> 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
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Learning Connected Separate Software Happiness Depression Belonging t
1 16 32 33 11 18 7 66 1
2 16 31 31 12 11 14 68 2
3 19 39 38 13 14 12 54 3
4 16 37 39 11 12 14 56 4
5 17 39 32 9 17 11 86 5
6 17 41 32 13 9 9 80 6
7 16 36 35 10 16 11 76 7
8 15 33 37 14 14 15 69 8
9 16 33 33 12 15 14 78 9
10 14 34 33 10 11 13 67 10
11 15 31 28 12 16 9 80 11
12 12 27 32 8 13 15 54 12
13 14 37 31 10 17 10 71 13
14 16 34 37 12 15 11 84 14
15 14 34 30 12 14 13 74 15
16 7 32 33 7 16 8 71 16
17 10 29 31 6 9 20 63 17
18 14 36 33 12 15 12 71 18
19 16 29 31 10 17 10 76 19
20 16 35 33 10 13 10 69 20
21 16 37 32 10 15 9 74 21
22 14 34 33 12 16 14 75 22
23 20 38 32 15 16 8 54 23
24 14 35 33 10 12 14 52 24
25 14 38 28 10 12 11 69 25
26 11 37 35 12 11 13 68 26
27 14 38 39 13 15 9 65 27
28 15 33 34 11 15 11 75 28
29 16 36 38 11 17 15 74 29
30 14 38 32 12 13 11 75 30
31 16 32 38 14 16 10 72 31
32 14 32 30 10 14 14 67 32
33 12 32 33 12 11 18 63 33
34 16 34 38 13 12 14 62 34
35 9 32 32 5 12 11 63 35
36 14 37 32 6 15 12 76 36
37 16 39 34 12 16 13 74 37
38 16 29 34 12 15 9 67 38
39 15 37 36 11 12 10 73 39
40 16 35 34 10 12 15 70 40
41 12 30 28 7 8 20 53 41
42 16 38 34 12 13 12 77 42
43 16 34 35 14 11 12 77 43
44 14 31 35 11 14 14 52 44
45 16 34 31 12 15 13 54 45
46 17 35 37 13 10 11 80 46
47 18 36 35 14 11 17 66 47
48 18 30 27 11 12 12 73 48
49 12 39 40 12 15 13 63 49
50 16 35 37 12 15 14 69 50
51 10 38 36 8 14 13 67 51
52 14 31 38 11 16 15 54 52
53 18 34 39 14 15 13 81 53
54 18 38 41 14 15 10 69 54
55 16 34 27 12 13 11 84 55
56 17 39 30 9 12 19 80 56
57 16 37 37 13 17 13 70 57
58 16 34 31 11 13 17 69 58
59 13 28 31 12 15 13 77 59
60 16 37 27 12 13 9 54 60
61 16 33 36 12 15 11 79 61
62 20 37 38 12 16 10 30 62
63 16 35 37 12 15 9 71 63
64 15 37 33 12 16 12 73 64
65 15 32 34 11 15 12 72 65
66 16 33 31 10 14 13 77 66
67 14 38 39 9 15 13 75 67
68 16 33 34 12 14 12 69 68
69 16 29 32 12 13 15 54 69
70 15 33 33 12 7 22 70 70
71 12 31 36 9 17 13 73 71
72 17 36 32 15 13 15 54 72
73 16 35 41 12 15 13 77 73
74 15 32 28 12 14 15 82 74
75 13 29 30 12 13 10 80 75
76 16 39 36 10 16 11 80 76
77 16 37 35 13 12 16 69 77
78 16 35 31 9 14 11 78 78
79 16 37 34 12 17 11 81 79
80 14 32 36 10 15 10 76 80
81 16 38 36 14 17 10 76 81
82 16 37 35 11 12 16 73 82
83 20 36 37 15 16 12 85 83
84 15 32 28 11 11 11 66 84
85 16 33 39 11 15 16 79 85
86 13 40 32 12 9 19 68 86
87 17 38 35 12 16 11 76 87
88 16 41 39 12 15 16 71 88
89 16 36 35 11 10 15 54 89
90 12 43 42 7 10 24 46 90
91 16 30 34 12 15 14 82 91
92 16 31 33 14 11 15 74 92
93 17 32 41 11 13 11 88 93
94 13 32 33 11 14 15 38 94
95 12 37 34 10 18 12 76 95
96 18 37 32 13 16 10 86 96
97 14 33 40 13 14 14 54 97
98 14 34 40 8 14 13 70 98
99 13 33 35 11 14 9 69 99
100 16 38 36 12 14 15 90 100
101 13 33 37 11 12 15 54 101
102 16 31 27 13 14 14 76 102
103 13 38 39 12 15 11 89 103
104 16 37 38 14 15 8 76 104
105 15 33 31 13 15 11 73 105
106 16 31 33 15 13 11 79 106
107 15 39 32 10 17 8 90 107
108 17 44 39 11 17 10 74 108
109 15 33 36 9 19 11 81 109
110 12 35 33 11 15 13 72 110
111 16 32 33 10 13 11 71 111
112 10 28 32 11 9 20 66 112
113 16 40 37 8 15 10 77 113
114 12 27 30 11 15 15 65 114
115 14 37 38 12 15 12 74 115
116 15 32 29 12 16 14 82 116
117 13 28 22 9 11 23 54 117
118 15 34 35 11 14 14 63 118
119 11 30 35 10 11 16 54 119
120 12 35 34 8 15 11 64 120
121 8 31 35 9 13 12 69 121
122 16 32 34 8 15 10 54 122
123 15 30 34 9 16 14 84 123
124 17 30 35 15 14 12 86 124
125 16 31 23 11 15 12 77 125
126 10 40 31 8 16 11 89 126
127 18 32 27 13 16 12 76 127
128 13 36 36 12 11 13 60 128
129 16 32 31 12 12 11 75 129
130 13 35 32 9 9 19 73 130
131 10 38 39 7 16 12 85 131
132 15 42 37 13 13 17 79 132
133 16 34 38 9 16 9 71 133
134 16 35 39 6 12 12 72 134
135 14 35 34 8 9 19 69 135
136 10 33 31 8 13 18 78 136
137 17 36 32 15 13 15 54 137
138 13 32 37 6 14 14 69 138
139 15 33 36 9 19 11 81 139
140 16 34 32 11 13 9 84 140
141 12 32 35 8 12 18 84 141
142 13 34 36 8 13 16 69 142
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Software Happiness Depression
5.708987 0.105960 -0.026002 0.575965 0.070893 -0.082961
Belonging t
0.003033 -0.001739
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.9315 -1.1536 0.2057 1.1058 4.3002
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.708987 2.768472 2.062 0.0411 *
Connected 0.105960 0.049787 2.128 0.0351 *
Separate -0.026002 0.046659 -0.557 0.5783
Software 0.575965 0.074183 7.764 1.89e-12 ***
Happiness 0.070893 0.084953 0.835 0.4055
Depression -0.082961 0.063030 -1.316 0.1903
Belonging 0.003033 0.015782 0.192 0.8479
t -0.001739 0.003932 -0.442 0.6590
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.82 on 134 degrees of freedom
Multiple R-squared: 0.3915, Adjusted R-squared: 0.3598
F-statistic: 12.32 on 7 and 134 DF, p-value: 4.184e-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,] 0.05300644 0.10601287 0.9469936
[2,] 0.01583512 0.03167024 0.9841649
[3,] 0.04646792 0.09293584 0.9535321
[4,] 0.05096336 0.10192672 0.9490366
[5,] 0.02341275 0.04682550 0.9765872
[6,] 0.29784000 0.59567999 0.7021600
[7,] 0.21454137 0.42908273 0.7854586
[8,] 0.15189159 0.30378318 0.8481084
[9,] 0.60378866 0.79242268 0.3962113
[10,] 0.76120902 0.47758197 0.2387910
[11,] 0.75617932 0.48764136 0.2438207
[12,] 0.71155370 0.57689260 0.2884463
[13,] 0.69961167 0.60077666 0.3003883
[14,] 0.62969398 0.74061204 0.3703060
[15,] 0.56278344 0.87443313 0.4372166
[16,] 0.73304236 0.53391528 0.2669576
[17,] 0.70250841 0.59498318 0.2974916
[18,] 0.71537030 0.56925940 0.2846297
[19,] 0.71080412 0.57839177 0.2891959
[20,] 0.67375422 0.65249156 0.3262458
[21,] 0.63536790 0.72926421 0.3646321
[22,] 0.59425752 0.81148497 0.4057425
[23,] 0.58829985 0.82340029 0.4117001
[24,] 0.58574878 0.82850245 0.4142512
[25,] 0.57229588 0.85540825 0.4277041
[26,] 0.61890610 0.76218779 0.3810939
[27,] 0.56074831 0.87850338 0.4392517
[28,] 0.59008002 0.81983997 0.4099200
[29,] 0.55654743 0.88690514 0.4434526
[30,] 0.61126292 0.77747416 0.3887371
[31,] 0.58865733 0.82268535 0.4113427
[32,] 0.53900841 0.92198318 0.4609916
[33,] 0.49242158 0.98484316 0.5075784
[34,] 0.43941220 0.87882440 0.5605878
[35,] 0.38729459 0.77458918 0.6127054
[36,] 0.38507084 0.77014168 0.6149292
[37,] 0.37218570 0.74437139 0.6278143
[38,] 0.50301370 0.99397259 0.4969863
[39,] 0.64938601 0.70122798 0.3506140
[40,] 0.60673433 0.78653133 0.3932657
[41,] 0.70482624 0.59034752 0.2951738
[42,] 0.66323551 0.67352898 0.3367645
[43,] 0.64622983 0.70754035 0.3537702
[44,] 0.61805201 0.76389597 0.3819480
[45,] 0.56903895 0.86192211 0.4309611
[46,] 0.63450207 0.73099586 0.3654979
[47,] 0.58992321 0.82015358 0.4100768
[48,] 0.55631747 0.88736505 0.4436825
[49,] 0.58465093 0.83069813 0.4153491
[50,] 0.53648197 0.92703605 0.4635180
[51,] 0.49119603 0.98239207 0.5088040
[52,] 0.69293377 0.61413246 0.3070662
[53,] 0.64826842 0.70346317 0.3517316
[54,] 0.62263419 0.75473162 0.3773658
[55,] 0.57430171 0.85139658 0.4256983
[56,] 0.55824361 0.88351277 0.4417564
[57,] 0.50974250 0.98051499 0.4902575
[58,] 0.46177804 0.92355609 0.5382220
[59,] 0.43035731 0.86071462 0.5696427
[60,] 0.38945125 0.77890251 0.6105487
[61,] 0.37693608 0.75387215 0.6230639
[62,] 0.35013878 0.70027756 0.6498612
[63,] 0.31018253 0.62036505 0.6898175
[64,] 0.27238529 0.54477059 0.7276147
[65,] 0.30060441 0.60120881 0.6993956
[66,] 0.26677047 0.53354095 0.7332295
[67,] 0.22984084 0.45968169 0.7701592
[68,] 0.22667258 0.45334516 0.7733274
[69,] 0.19134621 0.38269243 0.8086538
[70,] 0.16231146 0.32462292 0.8376885
[71,] 0.15058458 0.30116916 0.8494154
[72,] 0.13410446 0.26820893 0.8658955
[73,] 0.16262458 0.32524916 0.8373754
[74,] 0.13322244 0.26644487 0.8667776
[75,] 0.12847905 0.25695810 0.8715210
[76,] 0.14606349 0.29212699 0.8539365
[77,] 0.12729448 0.25458895 0.8727055
[78,] 0.11025404 0.22050808 0.8897460
[79,] 0.10640995 0.21281991 0.8935900
[80,] 0.10054005 0.20108011 0.8994599
[81,] 0.08953529 0.17907059 0.9104647
[82,] 0.07493834 0.14987668 0.9250617
[83,] 0.10681613 0.21363226 0.8931839
[84,] 0.09272547 0.18545095 0.9072745
[85,] 0.11380703 0.22761405 0.8861930
[86,] 0.11414218 0.22828436 0.8858578
[87,] 0.09843863 0.19687726 0.9015614
[88,] 0.09707297 0.19414595 0.9029270
[89,] 0.09186070 0.18372139 0.9081393
[90,] 0.10281825 0.20563650 0.8971818
[91,] 0.08346294 0.16692588 0.9165371
[92,] 0.07281814 0.14563628 0.9271819
[93,] 0.07371805 0.14743610 0.9262819
[94,] 0.05760477 0.11520954 0.9423952
[95,] 0.04470767 0.08941534 0.9552923
[96,] 0.03374715 0.06749430 0.9662528
[97,] 0.02412967 0.04825935 0.9758703
[98,] 0.02449912 0.04899825 0.9755009
[99,] 0.02373296 0.04746593 0.9762670
[100,] 0.02481328 0.04962656 0.9751867
[101,] 0.02934011 0.05868023 0.9706599
[102,] 0.03157134 0.06314268 0.9684287
[103,] 0.07492238 0.14984475 0.9250776
[104,] 0.07646278 0.15292556 0.9235372
[105,] 0.06004385 0.12008771 0.9399561
[106,] 0.04536899 0.09073799 0.9546310
[107,] 0.03465715 0.06931430 0.9653429
[108,] 0.03145648 0.06291297 0.9685435
[109,] 0.03071021 0.06142041 0.9692898
[110,] 0.02093864 0.04187729 0.9790614
[111,] 0.39839834 0.79679668 0.6016017
[112,] 0.35572231 0.71144463 0.6442777
[113,] 0.31516254 0.63032508 0.6848375
[114,] 0.24673755 0.49347510 0.7532624
[115,] 0.20102456 0.40204912 0.7989754
[116,] 0.20154715 0.40309430 0.7984528
[117,] 0.30701254 0.61402509 0.6929875
[118,] 0.68418223 0.63163555 0.3158178
[119,] 0.58792125 0.82415750 0.4120787
[120,] 0.44601292 0.89202583 0.5539871
[121,] 0.69193527 0.61612946 0.3080647
> postscript(file="/var/fisher/rcomp/tmp/1pk4f1351787090.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2h1kt1351787090.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3nzpf1351787090.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4twle1351787090.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5npdv1351787090.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 = 142
Frequency = 1
1 2 3 4 5
0.5289553156 1.0795990914 2.5035691283 1.1968018488 2.2621985144
6 7 8 9 10
0.1675795984 1.1868194692 -1.2505572311 0.6179530299 -0.1003623054
11 12 13 14 15
-0.7884232948 -0.1656729948 -1.1514025374 0.3576140338 -1.5555138193
16 17 18 19 20
-5.9315173474 -0.5718883105 -1.8289659322 1.6915435314 1.4143322013
21 22 23 24 25
0.9382373601 -1.5271938788 1.8627369129 -0.1244126364 -0.8710066156
26 27 28 29 30
-4.4933771057 -2.6758735959 0.0131765787 0.9941346811 -1.9993260747
31 32 33 34 35
-0.6442899319 -0.0579091854 -2.5734388857 0.3707202670 -2.2158279027
36 37 38 39 40
1.5109995663 -0.0848336501 0.7367822704 -0.2037450262 1.9577792436
41 42 43 44 45
0.8111408656 0.1504403547 -0.4081239160 -0.3315420803 0.5124252623
46 47 48 49 50
1.0979350485 1.8350815137 3.4855294725 -3.8036983178 0.6086367541
51 52 53 54 55
-3.4356465732 -0.3045161962 1.5005292578 0.9179464812 0.3106808823
56 57 58 59 60
3.3352360577 -0.3948552598 1.5391321862 -1.8972308965 -0.0734340278
61 62 63 64 65
0.5344695089 4.1591382792 0.2103721536 -0.9318914873 0.2755389675
66 67 68 69 70
1.8079670001 -0.0009399937 0.6788235176 1.4176696416 0.9791297322
71 72 73 74 75
-1.4659920694 -0.0467263022 0.6454152453 -0.1513400589 -2.1175645148
76 77 78 79 80
1.0028001884 0.1943031246 2.0239253922 -0.0579234968 -0.2484618654
81 82 83 84 85
-1.3281275821 1.3427960264 2.5468244104 0.3713789001 1.6449810170
86 87 88 89 90
-2.1453694901 0.9620889473 0.2508198912 1.5773817622 0.0941899171
91 92 93 94 95
1.0922986032 0.2009441921 2.5165398503 -1.2771323268 -2.8509364774
96 97 98 99 100
1.3164375216 -1.4792823293 1.1648325744 -1.9141842200 0.4418656461
101 102 103 104 105
-1.1736541779 0.3365815674 -2.8746164193 -1.1543033458 -1.0767908815
106 107 108 109 110
-0.8394709524 -0.3974052627 0.8950337470 1.0561972107 -2.9071263620
111 112 113 114 115
1.9673540117 -3.1636475556 2.1361462404 -1.9433450129 -1.6453339855
116 117 118 119 120
-0.2770482712 0.8804528319 0.4459004678 -2.1466576650 -1.2774972692
121 122 123 124 125
-5.1923006181 2.9912290823 1.7988821117 0.3406306526 1.1846528213
126 127 128 129 130
-4.0215863915 1.9663880349 -2.1597746117 0.8534830591 0.1736742842
131 132 133 134 135
-2.9218993883 -1.2061099051 2.1210647273 4.3001640180 1.8224700669
136 137 138 139 140
-2.4357082687 0.0663078527 1.6062302432 1.1083668207 0.9985467185
141 142
-0.1643515175 0.4601502450
> postscript(file="/var/fisher/rcomp/tmp/65ur01351787090.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 = 142
Frequency = 1
lag(myerror, k = 1) myerror
0 0.5289553156 NA
1 1.0795990914 0.5289553156
2 2.5035691283 1.0795990914
3 1.1968018488 2.5035691283
4 2.2621985144 1.1968018488
5 0.1675795984 2.2621985144
6 1.1868194692 0.1675795984
7 -1.2505572311 1.1868194692
8 0.6179530299 -1.2505572311
9 -0.1003623054 0.6179530299
10 -0.7884232948 -0.1003623054
11 -0.1656729948 -0.7884232948
12 -1.1514025374 -0.1656729948
13 0.3576140338 -1.1514025374
14 -1.5555138193 0.3576140338
15 -5.9315173474 -1.5555138193
16 -0.5718883105 -5.9315173474
17 -1.8289659322 -0.5718883105
18 1.6915435314 -1.8289659322
19 1.4143322013 1.6915435314
20 0.9382373601 1.4143322013
21 -1.5271938788 0.9382373601
22 1.8627369129 -1.5271938788
23 -0.1244126364 1.8627369129
24 -0.8710066156 -0.1244126364
25 -4.4933771057 -0.8710066156
26 -2.6758735959 -4.4933771057
27 0.0131765787 -2.6758735959
28 0.9941346811 0.0131765787
29 -1.9993260747 0.9941346811
30 -0.6442899319 -1.9993260747
31 -0.0579091854 -0.6442899319
32 -2.5734388857 -0.0579091854
33 0.3707202670 -2.5734388857
34 -2.2158279027 0.3707202670
35 1.5109995663 -2.2158279027
36 -0.0848336501 1.5109995663
37 0.7367822704 -0.0848336501
38 -0.2037450262 0.7367822704
39 1.9577792436 -0.2037450262
40 0.8111408656 1.9577792436
41 0.1504403547 0.8111408656
42 -0.4081239160 0.1504403547
43 -0.3315420803 -0.4081239160
44 0.5124252623 -0.3315420803
45 1.0979350485 0.5124252623
46 1.8350815137 1.0979350485
47 3.4855294725 1.8350815137
48 -3.8036983178 3.4855294725
49 0.6086367541 -3.8036983178
50 -3.4356465732 0.6086367541
51 -0.3045161962 -3.4356465732
52 1.5005292578 -0.3045161962
53 0.9179464812 1.5005292578
54 0.3106808823 0.9179464812
55 3.3352360577 0.3106808823
56 -0.3948552598 3.3352360577
57 1.5391321862 -0.3948552598
58 -1.8972308965 1.5391321862
59 -0.0734340278 -1.8972308965
60 0.5344695089 -0.0734340278
61 4.1591382792 0.5344695089
62 0.2103721536 4.1591382792
63 -0.9318914873 0.2103721536
64 0.2755389675 -0.9318914873
65 1.8079670001 0.2755389675
66 -0.0009399937 1.8079670001
67 0.6788235176 -0.0009399937
68 1.4176696416 0.6788235176
69 0.9791297322 1.4176696416
70 -1.4659920694 0.9791297322
71 -0.0467263022 -1.4659920694
72 0.6454152453 -0.0467263022
73 -0.1513400589 0.6454152453
74 -2.1175645148 -0.1513400589
75 1.0028001884 -2.1175645148
76 0.1943031246 1.0028001884
77 2.0239253922 0.1943031246
78 -0.0579234968 2.0239253922
79 -0.2484618654 -0.0579234968
80 -1.3281275821 -0.2484618654
81 1.3427960264 -1.3281275821
82 2.5468244104 1.3427960264
83 0.3713789001 2.5468244104
84 1.6449810170 0.3713789001
85 -2.1453694901 1.6449810170
86 0.9620889473 -2.1453694901
87 0.2508198912 0.9620889473
88 1.5773817622 0.2508198912
89 0.0941899171 1.5773817622
90 1.0922986032 0.0941899171
91 0.2009441921 1.0922986032
92 2.5165398503 0.2009441921
93 -1.2771323268 2.5165398503
94 -2.8509364774 -1.2771323268
95 1.3164375216 -2.8509364774
96 -1.4792823293 1.3164375216
97 1.1648325744 -1.4792823293
98 -1.9141842200 1.1648325744
99 0.4418656461 -1.9141842200
100 -1.1736541779 0.4418656461
101 0.3365815674 -1.1736541779
102 -2.8746164193 0.3365815674
103 -1.1543033458 -2.8746164193
104 -1.0767908815 -1.1543033458
105 -0.8394709524 -1.0767908815
106 -0.3974052627 -0.8394709524
107 0.8950337470 -0.3974052627
108 1.0561972107 0.8950337470
109 -2.9071263620 1.0561972107
110 1.9673540117 -2.9071263620
111 -3.1636475556 1.9673540117
112 2.1361462404 -3.1636475556
113 -1.9433450129 2.1361462404
114 -1.6453339855 -1.9433450129
115 -0.2770482712 -1.6453339855
116 0.8804528319 -0.2770482712
117 0.4459004678 0.8804528319
118 -2.1466576650 0.4459004678
119 -1.2774972692 -2.1466576650
120 -5.1923006181 -1.2774972692
121 2.9912290823 -5.1923006181
122 1.7988821117 2.9912290823
123 0.3406306526 1.7988821117
124 1.1846528213 0.3406306526
125 -4.0215863915 1.1846528213
126 1.9663880349 -4.0215863915
127 -2.1597746117 1.9663880349
128 0.8534830591 -2.1597746117
129 0.1736742842 0.8534830591
130 -2.9218993883 0.1736742842
131 -1.2061099051 -2.9218993883
132 2.1210647273 -1.2061099051
133 4.3001640180 2.1210647273
134 1.8224700669 4.3001640180
135 -2.4357082687 1.8224700669
136 0.0663078527 -2.4357082687
137 1.6062302432 0.0663078527
138 1.1083668207 1.6062302432
139 0.9985467185 1.1083668207
140 -0.1643515175 0.9985467185
141 0.4601502450 -0.1643515175
142 NA 0.4601502450
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.0795990914 0.5289553156
[2,] 2.5035691283 1.0795990914
[3,] 1.1968018488 2.5035691283
[4,] 2.2621985144 1.1968018488
[5,] 0.1675795984 2.2621985144
[6,] 1.1868194692 0.1675795984
[7,] -1.2505572311 1.1868194692
[8,] 0.6179530299 -1.2505572311
[9,] -0.1003623054 0.6179530299
[10,] -0.7884232948 -0.1003623054
[11,] -0.1656729948 -0.7884232948
[12,] -1.1514025374 -0.1656729948
[13,] 0.3576140338 -1.1514025374
[14,] -1.5555138193 0.3576140338
[15,] -5.9315173474 -1.5555138193
[16,] -0.5718883105 -5.9315173474
[17,] -1.8289659322 -0.5718883105
[18,] 1.6915435314 -1.8289659322
[19,] 1.4143322013 1.6915435314
[20,] 0.9382373601 1.4143322013
[21,] -1.5271938788 0.9382373601
[22,] 1.8627369129 -1.5271938788
[23,] -0.1244126364 1.8627369129
[24,] -0.8710066156 -0.1244126364
[25,] -4.4933771057 -0.8710066156
[26,] -2.6758735959 -4.4933771057
[27,] 0.0131765787 -2.6758735959
[28,] 0.9941346811 0.0131765787
[29,] -1.9993260747 0.9941346811
[30,] -0.6442899319 -1.9993260747
[31,] -0.0579091854 -0.6442899319
[32,] -2.5734388857 -0.0579091854
[33,] 0.3707202670 -2.5734388857
[34,] -2.2158279027 0.3707202670
[35,] 1.5109995663 -2.2158279027
[36,] -0.0848336501 1.5109995663
[37,] 0.7367822704 -0.0848336501
[38,] -0.2037450262 0.7367822704
[39,] 1.9577792436 -0.2037450262
[40,] 0.8111408656 1.9577792436
[41,] 0.1504403547 0.8111408656
[42,] -0.4081239160 0.1504403547
[43,] -0.3315420803 -0.4081239160
[44,] 0.5124252623 -0.3315420803
[45,] 1.0979350485 0.5124252623
[46,] 1.8350815137 1.0979350485
[47,] 3.4855294725 1.8350815137
[48,] -3.8036983178 3.4855294725
[49,] 0.6086367541 -3.8036983178
[50,] -3.4356465732 0.6086367541
[51,] -0.3045161962 -3.4356465732
[52,] 1.5005292578 -0.3045161962
[53,] 0.9179464812 1.5005292578
[54,] 0.3106808823 0.9179464812
[55,] 3.3352360577 0.3106808823
[56,] -0.3948552598 3.3352360577
[57,] 1.5391321862 -0.3948552598
[58,] -1.8972308965 1.5391321862
[59,] -0.0734340278 -1.8972308965
[60,] 0.5344695089 -0.0734340278
[61,] 4.1591382792 0.5344695089
[62,] 0.2103721536 4.1591382792
[63,] -0.9318914873 0.2103721536
[64,] 0.2755389675 -0.9318914873
[65,] 1.8079670001 0.2755389675
[66,] -0.0009399937 1.8079670001
[67,] 0.6788235176 -0.0009399937
[68,] 1.4176696416 0.6788235176
[69,] 0.9791297322 1.4176696416
[70,] -1.4659920694 0.9791297322
[71,] -0.0467263022 -1.4659920694
[72,] 0.6454152453 -0.0467263022
[73,] -0.1513400589 0.6454152453
[74,] -2.1175645148 -0.1513400589
[75,] 1.0028001884 -2.1175645148
[76,] 0.1943031246 1.0028001884
[77,] 2.0239253922 0.1943031246
[78,] -0.0579234968 2.0239253922
[79,] -0.2484618654 -0.0579234968
[80,] -1.3281275821 -0.2484618654
[81,] 1.3427960264 -1.3281275821
[82,] 2.5468244104 1.3427960264
[83,] 0.3713789001 2.5468244104
[84,] 1.6449810170 0.3713789001
[85,] -2.1453694901 1.6449810170
[86,] 0.9620889473 -2.1453694901
[87,] 0.2508198912 0.9620889473
[88,] 1.5773817622 0.2508198912
[89,] 0.0941899171 1.5773817622
[90,] 1.0922986032 0.0941899171
[91,] 0.2009441921 1.0922986032
[92,] 2.5165398503 0.2009441921
[93,] -1.2771323268 2.5165398503
[94,] -2.8509364774 -1.2771323268
[95,] 1.3164375216 -2.8509364774
[96,] -1.4792823293 1.3164375216
[97,] 1.1648325744 -1.4792823293
[98,] -1.9141842200 1.1648325744
[99,] 0.4418656461 -1.9141842200
[100,] -1.1736541779 0.4418656461
[101,] 0.3365815674 -1.1736541779
[102,] -2.8746164193 0.3365815674
[103,] -1.1543033458 -2.8746164193
[104,] -1.0767908815 -1.1543033458
[105,] -0.8394709524 -1.0767908815
[106,] -0.3974052627 -0.8394709524
[107,] 0.8950337470 -0.3974052627
[108,] 1.0561972107 0.8950337470
[109,] -2.9071263620 1.0561972107
[110,] 1.9673540117 -2.9071263620
[111,] -3.1636475556 1.9673540117
[112,] 2.1361462404 -3.1636475556
[113,] -1.9433450129 2.1361462404
[114,] -1.6453339855 -1.9433450129
[115,] -0.2770482712 -1.6453339855
[116,] 0.8804528319 -0.2770482712
[117,] 0.4459004678 0.8804528319
[118,] -2.1466576650 0.4459004678
[119,] -1.2774972692 -2.1466576650
[120,] -5.1923006181 -1.2774972692
[121,] 2.9912290823 -5.1923006181
[122,] 1.7988821117 2.9912290823
[123,] 0.3406306526 1.7988821117
[124,] 1.1846528213 0.3406306526
[125,] -4.0215863915 1.1846528213
[126,] 1.9663880349 -4.0215863915
[127,] -2.1597746117 1.9663880349
[128,] 0.8534830591 -2.1597746117
[129,] 0.1736742842 0.8534830591
[130,] -2.9218993883 0.1736742842
[131,] -1.2061099051 -2.9218993883
[132,] 2.1210647273 -1.2061099051
[133,] 4.3001640180 2.1210647273
[134,] 1.8224700669 4.3001640180
[135,] -2.4357082687 1.8224700669
[136,] 0.0663078527 -2.4357082687
[137,] 1.6062302432 0.0663078527
[138,] 1.1083668207 1.6062302432
[139,] 0.9985467185 1.1083668207
[140,] -0.1643515175 0.9985467185
[141,] 0.4601502450 -0.1643515175
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.0795990914 0.5289553156
2 2.5035691283 1.0795990914
3 1.1968018488 2.5035691283
4 2.2621985144 1.1968018488
5 0.1675795984 2.2621985144
6 1.1868194692 0.1675795984
7 -1.2505572311 1.1868194692
8 0.6179530299 -1.2505572311
9 -0.1003623054 0.6179530299
10 -0.7884232948 -0.1003623054
11 -0.1656729948 -0.7884232948
12 -1.1514025374 -0.1656729948
13 0.3576140338 -1.1514025374
14 -1.5555138193 0.3576140338
15 -5.9315173474 -1.5555138193
16 -0.5718883105 -5.9315173474
17 -1.8289659322 -0.5718883105
18 1.6915435314 -1.8289659322
19 1.4143322013 1.6915435314
20 0.9382373601 1.4143322013
21 -1.5271938788 0.9382373601
22 1.8627369129 -1.5271938788
23 -0.1244126364 1.8627369129
24 -0.8710066156 -0.1244126364
25 -4.4933771057 -0.8710066156
26 -2.6758735959 -4.4933771057
27 0.0131765787 -2.6758735959
28 0.9941346811 0.0131765787
29 -1.9993260747 0.9941346811
30 -0.6442899319 -1.9993260747
31 -0.0579091854 -0.6442899319
32 -2.5734388857 -0.0579091854
33 0.3707202670 -2.5734388857
34 -2.2158279027 0.3707202670
35 1.5109995663 -2.2158279027
36 -0.0848336501 1.5109995663
37 0.7367822704 -0.0848336501
38 -0.2037450262 0.7367822704
39 1.9577792436 -0.2037450262
40 0.8111408656 1.9577792436
41 0.1504403547 0.8111408656
42 -0.4081239160 0.1504403547
43 -0.3315420803 -0.4081239160
44 0.5124252623 -0.3315420803
45 1.0979350485 0.5124252623
46 1.8350815137 1.0979350485
47 3.4855294725 1.8350815137
48 -3.8036983178 3.4855294725
49 0.6086367541 -3.8036983178
50 -3.4356465732 0.6086367541
51 -0.3045161962 -3.4356465732
52 1.5005292578 -0.3045161962
53 0.9179464812 1.5005292578
54 0.3106808823 0.9179464812
55 3.3352360577 0.3106808823
56 -0.3948552598 3.3352360577
57 1.5391321862 -0.3948552598
58 -1.8972308965 1.5391321862
59 -0.0734340278 -1.8972308965
60 0.5344695089 -0.0734340278
61 4.1591382792 0.5344695089
62 0.2103721536 4.1591382792
63 -0.9318914873 0.2103721536
64 0.2755389675 -0.9318914873
65 1.8079670001 0.2755389675
66 -0.0009399937 1.8079670001
67 0.6788235176 -0.0009399937
68 1.4176696416 0.6788235176
69 0.9791297322 1.4176696416
70 -1.4659920694 0.9791297322
71 -0.0467263022 -1.4659920694
72 0.6454152453 -0.0467263022
73 -0.1513400589 0.6454152453
74 -2.1175645148 -0.1513400589
75 1.0028001884 -2.1175645148
76 0.1943031246 1.0028001884
77 2.0239253922 0.1943031246
78 -0.0579234968 2.0239253922
79 -0.2484618654 -0.0579234968
80 -1.3281275821 -0.2484618654
81 1.3427960264 -1.3281275821
82 2.5468244104 1.3427960264
83 0.3713789001 2.5468244104
84 1.6449810170 0.3713789001
85 -2.1453694901 1.6449810170
86 0.9620889473 -2.1453694901
87 0.2508198912 0.9620889473
88 1.5773817622 0.2508198912
89 0.0941899171 1.5773817622
90 1.0922986032 0.0941899171
91 0.2009441921 1.0922986032
92 2.5165398503 0.2009441921
93 -1.2771323268 2.5165398503
94 -2.8509364774 -1.2771323268
95 1.3164375216 -2.8509364774
96 -1.4792823293 1.3164375216
97 1.1648325744 -1.4792823293
98 -1.9141842200 1.1648325744
99 0.4418656461 -1.9141842200
100 -1.1736541779 0.4418656461
101 0.3365815674 -1.1736541779
102 -2.8746164193 0.3365815674
103 -1.1543033458 -2.8746164193
104 -1.0767908815 -1.1543033458
105 -0.8394709524 -1.0767908815
106 -0.3974052627 -0.8394709524
107 0.8950337470 -0.3974052627
108 1.0561972107 0.8950337470
109 -2.9071263620 1.0561972107
110 1.9673540117 -2.9071263620
111 -3.1636475556 1.9673540117
112 2.1361462404 -3.1636475556
113 -1.9433450129 2.1361462404
114 -1.6453339855 -1.9433450129
115 -0.2770482712 -1.6453339855
116 0.8804528319 -0.2770482712
117 0.4459004678 0.8804528319
118 -2.1466576650 0.4459004678
119 -1.2774972692 -2.1466576650
120 -5.1923006181 -1.2774972692
121 2.9912290823 -5.1923006181
122 1.7988821117 2.9912290823
123 0.3406306526 1.7988821117
124 1.1846528213 0.3406306526
125 -4.0215863915 1.1846528213
126 1.9663880349 -4.0215863915
127 -2.1597746117 1.9663880349
128 0.8534830591 -2.1597746117
129 0.1736742842 0.8534830591
130 -2.9218993883 0.1736742842
131 -1.2061099051 -2.9218993883
132 2.1210647273 -1.2061099051
133 4.3001640180 2.1210647273
134 1.8224700669 4.3001640180
135 -2.4357082687 1.8224700669
136 0.0663078527 -2.4357082687
137 1.6062302432 0.0663078527
138 1.1083668207 1.6062302432
139 0.9985467185 1.1083668207
140 -0.1643515175 0.9985467185
141 0.4601502450 -0.1643515175
> 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/fisher/rcomp/tmp/7g0381351787090.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8fx6g1351787090.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/916pu1351787090.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10xxwe1351787090.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11jxr71351787090.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/fisher/rcomp/tmp/12ulz51351787090.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/fisher/rcomp/tmp/13215b1351787090.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/fisher/rcomp/tmp/141ulw1351787090.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/fisher/rcomp/tmp/15o5811351787090.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/fisher/rcomp/tmp/16rf2t1351787090.tab")
+ }
>
> try(system("convert tmp/1pk4f1351787090.ps tmp/1pk4f1351787090.png",intern=TRUE))
character(0)
> try(system("convert tmp/2h1kt1351787090.ps tmp/2h1kt1351787090.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nzpf1351787090.ps tmp/3nzpf1351787090.png",intern=TRUE))
character(0)
> try(system("convert tmp/4twle1351787090.ps tmp/4twle1351787090.png",intern=TRUE))
character(0)
> try(system("convert tmp/5npdv1351787090.ps tmp/5npdv1351787090.png",intern=TRUE))
character(0)
> try(system("convert tmp/65ur01351787090.ps tmp/65ur01351787090.png",intern=TRUE))
character(0)
> try(system("convert tmp/7g0381351787090.ps tmp/7g0381351787090.png",intern=TRUE))
character(0)
> try(system("convert tmp/8fx6g1351787090.ps tmp/8fx6g1351787090.png",intern=TRUE))
character(0)
> try(system("convert tmp/916pu1351787090.ps tmp/916pu1351787090.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xxwe1351787090.ps tmp/10xxwe1351787090.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.582 1.109 8.698