R version 2.15.2 (2012-10-26) -- "Trick or Treat"
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
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Type 'q()' to quit R.
> x <- array(list(14
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+ ,46)
+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('happiness'
+ ,'depression'
+ ,'connected'
+ ,'separate'
+ ,'learning'
+ ,'software'
+ ,'belonging'
+ ,'belonging_final
')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('happiness','depression','connected','separate','learning','software','belonging','belonging_final
'),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'
> 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
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
happiness depression connected separate learning software belonging
1 14 12 41 38 13 12 53
2 18 11 39 32 16 11 86
3 11 14 30 35 19 15 66
4 12 12 31 33 15 6 67
5 16 21 34 37 14 13 76
6 18 12 35 29 13 10 78
7 14 22 39 31 19 12 53
8 14 11 34 36 15 14 80
9 15 10 36 35 14 12 74
10 15 13 37 38 15 6 76
11 17 10 38 31 16 10 79
12 19 8 36 34 16 12 54
13 10 15 38 35 16 12 67
14 16 14 39 38 16 11 54
15 18 10 33 37 17 15 87
16 14 14 32 33 15 12 58
17 14 14 36 32 15 10 75
18 17 11 38 38 20 12 88
19 14 10 39 38 18 11 64
20 16 13 32 32 16 12 57
21 18 7 32 33 16 11 66
22 11 14 31 31 16 12 68
23 14 12 39 38 19 13 54
24 12 14 37 39 16 11 56
25 17 11 39 32 17 9 86
26 9 9 41 32 17 13 80
27 16 11 36 35 16 10 76
28 14 15 33 37 15 14 69
29 15 14 33 33 16 12 78
30 11 13 34 33 14 10 67
31 16 9 31 28 15 12 80
32 13 15 27 32 12 8 54
33 17 10 37 31 14 10 71
34 15 11 34 37 16 12 84
35 14 13 34 30 14 12 74
36 16 8 32 33 7 7 71
37 9 20 29 31 10 6 63
38 15 12 36 33 14 12 71
39 17 10 29 31 16 10 76
40 13 10 35 33 16 10 69
41 15 9 37 32 16 10 74
42 16 14 34 33 14 12 75
43 16 8 38 32 20 15 54
44 12 14 35 33 14 10 52
45 12 11 38 28 14 10 69
46 11 13 37 35 11 12 68
47 15 9 38 39 14 13 65
48 15 11 33 34 15 11 75
49 17 15 36 38 16 11 74
50 13 11 38 32 14 12 75
51 16 10 32 38 16 14 72
52 14 14 32 30 14 10 67
53 11 18 32 33 12 12 63
54 12 14 34 38 16 13 62
55 12 11 32 32 9 5 63
56 15 12 37 32 14 6 76
57 16 13 39 34 16 12 74
58 15 9 29 34 16 12 67
59 12 10 37 36 15 11 73
60 12 15 35 34 16 10 70
61 8 20 30 28 12 7 53
62 13 12 38 34 16 12 77
63 11 12 34 35 16 14 77
64 14 14 31 35 14 11 52
65 15 13 34 31 16 12 54
66 10 11 35 37 17 13 80
67 11 17 36 35 18 14 66
68 12 12 30 27 18 11 73
69 15 13 39 40 12 12 63
70 15 14 35 37 16 12 69
71 14 13 38 36 10 8 67
72 16 15 31 38 14 11 54
73 15 13 34 39 18 14 81
74 15 10 38 41 18 14 69
75 13 11 34 27 16 12 84
76 12 19 39 30 17 9 80
77 17 13 37 37 16 13 70
78 13 17 34 31 16 11 69
79 15 13 28 31 13 12 77
80 13 9 37 27 16 12 54
81 15 11 33 36 16 12 79
82 16 10 37 38 20 12 30
83 15 9 35 37 16 12 71
84 16 12 37 33 15 12 73
85 15 12 32 34 15 11 72
86 14 13 33 31 16 10 77
87 15 13 38 39 14 9 75
88 14 12 33 34 16 12 69
89 13 15 29 32 16 12 54
90 7 22 33 33 15 12 70
91 17 13 31 36 12 9 73
92 13 15 36 32 17 15 54
93 15 13 35 41 16 12 77
94 14 15 32 28 15 12 82
95 13 10 29 30 13 12 80
96 16 11 39 36 16 10 80
97 12 16 37 35 16 13 69
98 14 11 35 31 16 9 78
99 17 11 37 34 16 12 81
100 15 10 32 36 14 10 76
101 17 10 38 36 16 14 76
102 12 16 37 35 16 11 73
103 16 12 36 37 20 15 85
104 11 11 32 28 15 11 66
105 15 16 33 39 16 11 79
106 9 19 40 32 13 12 68
107 16 11 38 35 17 12 76
108 15 16 41 39 16 12 71
109 10 15 36 35 16 11 54
110 10 24 43 42 12 7 46
111 15 14 30 34 16 12 82
112 11 15 31 33 16 14 74
113 13 11 32 41 17 11 88
114 14 15 32 33 13 11 38
115 18 12 37 34 12 10 76
116 16 10 37 32 18 13 86
117 14 14 33 40 14 13 54
118 14 13 34 40 14 8 70
119 14 9 33 35 13 11 69
120 14 15 38 36 16 12 90
121 12 15 33 37 13 11 54
122 14 14 31 27 16 13 76
123 15 11 38 39 13 12 89
124 15 8 37 38 16 14 76
125 15 11 33 31 15 13 73
126 13 11 31 33 16 15 79
127 17 8 39 32 15 10 90
128 17 10 44 39 17 11 74
129 19 11 33 36 15 9 81
130 15 13 35 33 12 11 72
131 13 11 32 33 16 10 71
132 9 20 28 32 10 11 66
133 15 10 40 37 16 8 77
134 15 15 27 30 12 11 65
135 15 12 37 38 14 12 74
136 16 14 32 29 15 12 82
137 11 23 28 22 13 9 54
138 14 14 34 35 15 11 63
139 11 16 30 35 11 10 54
140 15 11 35 34 12 8 64
141 13 12 31 35 8 9 69
142 15 10 32 34 16 8 54
143 16 14 30 34 15 9 84
144 14 12 30 35 17 15 86
145 15 12 31 23 16 11 77
146 16 11 40 31 10 8 89
147 16 12 32 27 18 13 76
148 11 13 36 36 13 12 60
149 12 11 32 31 16 12 75
150 9 19 35 32 13 9 73
151 16 12 38 39 10 7 85
152 13 17 42 37 15 13 79
153 16 9 34 38 16 9 71
154 12 12 35 39 16 6 72
155 9 19 35 34 14 8 69
156 13 18 33 31 10 8 78
157 13 15 36 32 17 15 54
158 14 14 32 37 13 6 69
159 19 11 33 36 15 9 81
160 13 9 34 32 16 11 84
161 12 18 32 35 12 8 84
162 13 16 34 36 13 8 69
belonging_final\r
1 32
2 51
3 42
4 41
5 46
6 47
7 37
8 49
9 45
10 47
11 49
12 33
13 42
14 33
15 53
16 36
17 45
18 54
19 41
20 36
21 41
22 44
23 33
24 37
25 52
26 47
27 43
28 44
29 45
30 44
31 49
32 33
33 43
34 54
35 42
36 44
37 37
38 43
39 46
40 42
41 45
42 44
43 33
44 31
45 42
46 40
47 43
48 46
49 42
50 45
51 44
52 40
53 37
54 46
55 36
56 47
57 45
58 42
59 43
60 43
61 32
62 45
63 45
64 31
65 33
66 49
67 42
68 41
69 38
70 42
71 44
72 33
73 48
74 40
75 50
76 49
77 43
78 44
79 47
80 33
81 46
82 0
83 45
84 43
85 44
86 47
87 45
88 42
89 33
90 43
91 46
92 33
93 46
94 48
95 47
96 47
97 43
98 46
99 48
100 46
101 45
102 45
103 52
104 42
105 47
106 41
107 47
108 43
109 33
110 30
111 49
112 44
113 55
114 11
115 47
116 53
117 33
118 44
119 42
120 55
121 33
122 46
123 54
124 47
125 45
126 47
127 55
128 44
129 53
130 44
131 42
132 40
133 46
134 40
135 46
136 53
137 33
138 42
139 35
140 40
141 41
142 33
143 51
144 53
145 46
146 55
147 47
148 38
149 46
150 46
151 53
152 47
153 41
154 44
155 43
156 51
157 33
158 43
159 53
160 51
161 50
162 46
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) depression connected
12.65475 -0.35118 0.01376
separate learning software
0.07391 0.06620 -0.04622
belonging `belonging_final\\r`
0.05851 -0.03930
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7815 -1.2879 0.2204 1.0695 4.5528
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.65475 2.57972 4.905 2.35e-06 ***
depression -0.35118 0.05248 -6.691 3.87e-10 ***
connected 0.01376 0.05030 0.274 0.785
separate 0.07391 0.04684 1.578 0.117
learning 0.06620 0.08468 0.782 0.436
software -0.04622 0.08588 -0.538 0.591
belonging 0.05851 0.04675 1.252 0.213
`belonging_final\\r` -0.03930 0.06741 -0.583 0.561
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.948 on 154 degrees of freedom
Multiple R-squared: 0.3356, Adjusted R-squared: 0.3054
F-statistic: 11.11 on 7 and 154 DF, p-value: 2.444e-11
> 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,] 0.05944293 0.1188858563 0.9405570718
[2,] 0.88568239 0.2286352156 0.1143176078
[3,] 0.98388450 0.0322310040 0.0161155020
[4,] 0.97454093 0.0509181339 0.0254590669
[5,] 0.98564854 0.0287029173 0.0143514586
[6,] 0.97470177 0.0505964627 0.0252982313
[7,] 0.97572316 0.0485536882 0.0242768441
[8,] 0.96148155 0.0770369045 0.0385184522
[9,] 0.94206040 0.1158792052 0.0579396026
[10,] 0.94289294 0.1142141238 0.0571070619
[11,] 0.94980953 0.1003809444 0.0501904722
[12,] 0.95174882 0.0965023549 0.0482511774
[13,] 0.94733763 0.1053247385 0.0526623692
[14,] 0.92649797 0.1470040624 0.0735020312
[15,] 0.90405660 0.1918867913 0.0959433956
[16,] 0.99968755 0.0006249078 0.0003124539
[17,] 0.99945897 0.0010820558 0.0005410279
[18,] 0.99909508 0.0018098439 0.0009049219
[19,] 0.99855850 0.0028830088 0.0014415044
[20,] 0.99900138 0.0019972373 0.0009986187
[21,] 0.99842546 0.0031490733 0.0015745366
[22,] 0.99766789 0.0046642270 0.0023321135
[23,] 0.99729374 0.0054125162 0.0027062581
[24,] 0.99589121 0.0082175854 0.0041087927
[25,] 0.99471946 0.0105610710 0.0052805355
[26,] 0.99228059 0.0154388195 0.0077194097
[27,] 0.99501200 0.0099760060 0.0049880030
[28,] 0.99281721 0.0143655735 0.0071827867
[29,] 0.99207855 0.0158429000 0.0079214500
[30,] 0.99222660 0.0155468064 0.0077734032
[31,] 0.98920438 0.0215912363 0.0107956182
[32,] 0.98838228 0.0232354488 0.0116177244
[33,] 0.98421352 0.0315729561 0.0157864781
[34,] 0.98072840 0.0385432058 0.0192716029
[35,] 0.98342232 0.0331553632 0.0165776816
[36,] 0.98827809 0.0234438167 0.0117219084
[37,] 0.98384055 0.0323189000 0.0161594500
[38,] 0.97795384 0.0440923116 0.0220461558
[39,] 0.98238888 0.0352222424 0.0176111212
[40,] 0.98084981 0.0383003762 0.0191501881
[41,] 0.97472311 0.0505537719 0.0252768860
[42,] 0.96717990 0.0656402050 0.0328201025
[43,] 0.96056997 0.0788600555 0.0394300277
[44,] 0.95192128 0.0961574360 0.0480787180
[45,] 0.95362757 0.0927448535 0.0463724268
[46,] 0.94146552 0.1170689559 0.0585344780
[47,] 0.93678971 0.1264205860 0.0632102930
[48,] 0.92046211 0.1590757733 0.0795378867
[49,] 0.95022523 0.0995495430 0.0497747715
[50,] 0.94568522 0.1086295631 0.0543147816
[51,] 0.95363281 0.0927343886 0.0463671943
[52,] 0.95268165 0.0946367036 0.0473183518
[53,] 0.97649090 0.0470181937 0.0235090969
[54,] 0.97110485 0.0577903040 0.0288951520
[55,] 0.96886306 0.0622738766 0.0311369383
[56,] 0.99498526 0.0100294876 0.0050147438
[57,] 0.99431383 0.0113723400 0.0056861700
[58,] 0.99504472 0.0099105654 0.0049552827
[59,] 0.99363695 0.0127260986 0.0063630493
[60,] 0.99211902 0.0157619528 0.0078809764
[61,] 0.98925893 0.0214821414 0.0107410707
[62,] 0.99327506 0.0134498781 0.0067249391
[63,] 0.99078076 0.0184384759 0.0092192379
[64,] 0.98786717 0.0242656575 0.0121328287
[65,] 0.98723445 0.0255311051 0.0127655526
[66,] 0.98287839 0.0342432103 0.0171216051
[67,] 0.98775629 0.0244874181 0.0122437091
[68,] 0.98424808 0.0315038397 0.0157519198
[69,] 0.98131994 0.0373601278 0.0186800639
[70,] 0.97938613 0.0412277497 0.0206138748
[71,] 0.97284562 0.0543087626 0.0271543813
[72,] 0.96721641 0.0655671868 0.0327835934
[73,] 0.95861647 0.0827670573 0.0413835286
[74,] 0.95453793 0.0909241350 0.0454620675
[75,] 0.94352603 0.1129479388 0.0564739694
[76,] 0.92884309 0.1423138179 0.0711569090
[77,] 0.91203509 0.1759298123 0.0879649061
[78,] 0.89190093 0.2161981435 0.1080990718
[79,] 0.87146265 0.2570747043 0.1285373521
[80,] 0.92006315 0.1598736990 0.0799368495
[81,] 0.94152590 0.1169482008 0.0584741004
[82,] 0.92779056 0.1444188867 0.0722094434
[83,] 0.91153650 0.1769269985 0.0884634992
[84,] 0.89361078 0.2127784317 0.1063892159
[85,] 0.89402833 0.2119433374 0.1059716687
[86,] 0.87200679 0.2559864105 0.1279932053
[87,] 0.84993771 0.3001245827 0.1500622913
[88,] 0.83066827 0.3386634691 0.1693317346
[89,] 0.82532380 0.3493523944 0.1746761972
[90,] 0.79291257 0.4141748631 0.2070874316
[91,] 0.78407778 0.4318444419 0.2159222210
[92,] 0.75885975 0.4822804904 0.2411402452
[93,] 0.73181700 0.5363660070 0.2681830035
[94,] 0.80397966 0.3920406743 0.1960203371
[95,] 0.80770771 0.3845845715 0.1922922858
[96,] 0.84029049 0.3194190214 0.1597095107
[97,] 0.81481122 0.3703775666 0.1851887833
[98,] 0.82043731 0.3591253801 0.1795626900
[99,] 0.85656724 0.2868655119 0.1434327560
[100,] 0.82984643 0.3403071390 0.1701535695
[101,] 0.81894347 0.3621130554 0.1810565277
[102,] 0.81479319 0.3704136198 0.1852068099
[103,] 0.82474998 0.3505000477 0.1752500239
[104,] 0.91418639 0.1716272295 0.0858136148
[105,] 0.95494039 0.0901192235 0.0450596118
[106,] 0.94075895 0.1184821070 0.0592410535
[107,] 0.94494956 0.1101008712 0.0550504356
[108,] 0.92785243 0.1442951457 0.0721475729
[109,] 0.91430550 0.1713890044 0.0856945022
[110,] 0.89006495 0.2198701044 0.1099350522
[111,] 0.86425164 0.2714967140 0.1357483570
[112,] 0.83284833 0.3343033447 0.1671516723
[113,] 0.79494288 0.4101142415 0.2050571208
[114,] 0.76437601 0.4712479890 0.2356239945
[115,] 0.71718623 0.5656275366 0.2828137683
[116,] 0.69471333 0.6105733413 0.3052866707
[117,] 0.64488395 0.7102321057 0.3551160529
[118,] 0.67423113 0.6515377456 0.3257688728
[119,] 0.73578750 0.5284250047 0.2642125024
[120,] 0.70839703 0.5832059389 0.2916029694
[121,] 0.68156108 0.6368778436 0.3184389218
[122,] 0.65453709 0.6909258231 0.3454629115
[123,] 0.59588521 0.8082295806 0.4041147903
[124,] 0.60764967 0.7847006618 0.3923503309
[125,] 0.55938008 0.8812398382 0.4406199191
[126,] 0.52411511 0.9517697886 0.4758848943
[127,] 0.55316811 0.8936637870 0.4468318935
[128,] 0.48235942 0.9647188347 0.5176405826
[129,] 0.41027851 0.8205570253 0.5897214874
[130,] 0.34471643 0.6894328562 0.6552835719
[131,] 0.27634917 0.5526983451 0.7236508275
[132,] 0.23403529 0.4680705894 0.7659647053
[133,] 0.24342258 0.4868451516 0.7565774242
[134,] 0.20375890 0.4075177926 0.7962411037
[135,] 0.19478796 0.3895759157 0.8052120421
[136,] 0.19235088 0.3847017516 0.8076491242
[137,] 0.32151016 0.6430203187 0.6784898406
[138,] 0.76888889 0.4622222269 0.2311111135
[139,] 0.82066483 0.3586703402 0.1793351701
[140,] 0.70486583 0.5902683367 0.2951341683
[141,] 0.57801331 0.8439733865 0.4219866932
> postscript(file="/var/wessaorg/rcomp/tmp/17xv61352149748.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2iiog1352149748.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3gk1v1352149748.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4xr071352149748.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5uhf01352149748.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
0.03738346 2.72831569 -2.51326596 -2.33055066 4.55281821 3.81951358
7 8 9 10 11 12
3.89347054 -1.02122612 -0.15841146 0.27774319 1.74955010 3.77930065
13 14 15 16 17 18
-3.27074118 2.50326556 2.22890779 0.96542771 0.25092196 1.08093559
19 20 21 22 23 24
-1.30453768 2.68046859 2.12316057 -2.20985924 -0.30525745 -1.50293079
25 26 27 28 29 30
1.60899129 -6.78149146 0.77232416 0.77045576 1.06900101 -2.65167996
31 32 33 34 35 36
0.81654245 0.58917999 2.12796244 -0.29128862 0.17430915 0.71059868
37 38 39 40 41 42
-1.93792655 0.78870293 1.93101914 -2.04701136 -0.52644722 2.32385740
43 44 45 46 47 48
0.77346732 -0.94754802 -2.23516461 -2.76548874 -0.33856229 0.17634860
49 50 51 52 53 54
3.07926259 -1.67152612 0.71265794 0.79154668 -0.68449450 -1.29263639
55 56 57 58 59 60
-2.23310517 0.43621921 1.79537951 -0.18008904 -3.37859103 -1.38420624
61 62 63 64 65 66
-2.42754587 -1.71757385 -3.64400873 1.00589015 1.78447797 -5.28750473
67 68 69 70 71 72
-1.52229876 -2.19188964 0.98518301 1.15451555 0.24389283 3.09692847
73 74 75 76 77 78
0.16301130 -0.70571381 -1.70939235 -0.20056031 2.80281835 0.69768556
79 80 81 82 83 84
1.27014518 -1.36589964 -0.22549210 1.01471927 -0.60051775 1.59172679
85 86 87 88 89 90
0.63821765 -0.08967782 0.37481864 -0.29859904 0.48174101 -3.66585403
91 92 93 94 95 96
2.98159162 0.45786720 0.19682059 0.75356589 -1.89878835 0.58030319
97 98 99 100 101 102
-0.93729631 -0.96359797 1.82887221 -0.34742740 1.58317737 -1.18516193
103 104 105 106 107 108
0.76912472 -2.99704837 1.30178511 -2.57101922 0.92825152 1.54878045
109 110 111 112 113 114
-2.88253439 0.09451093 0.95954578 -2.26512379 -2.86655820 1.59040747
115 116 117 118 119 120
3.60565249 0.44330074 0.66283242 -0.43701799 -1.27369362 -0.17943975
121 122 123 124 125 126
-0.79048722 0.74252719 -0.58811775 -1.17464631 0.56823011 -1.79828621
127 128 129 130 131 132
0.61791405 1.15175214 3.86015696 1.22061626 -1.77156377 -1.82461648
133 134 135 136 137 138
-0.81475871 2.50716265 0.34776719 2.52498603 2.10402497 0.68713704
139 140 141 142 143 144
-1.08541720 0.61655494 -0.99337655 0.35184927 1.84867927 -0.82110855
145 146 147 148 149 150
1.18485948 1.02867215 1.93330953 -2.56855621 -2.60813709 -2.73689672
151 152 153 154 155 156
0.92530902 -0.16442969 0.04347164 -3.06989534 -2.88099955 1.06968744
157 158 159 160 161 162
0.45786720 0.15639110 3.86015696 -2.78822880 -0.73494843 0.11549067
> postscript(file="/var/wessaorg/rcomp/tmp/6nsud1352149748.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 0.03738346 NA
1 2.72831569 0.03738346
2 -2.51326596 2.72831569
3 -2.33055066 -2.51326596
4 4.55281821 -2.33055066
5 3.81951358 4.55281821
6 3.89347054 3.81951358
7 -1.02122612 3.89347054
8 -0.15841146 -1.02122612
9 0.27774319 -0.15841146
10 1.74955010 0.27774319
11 3.77930065 1.74955010
12 -3.27074118 3.77930065
13 2.50326556 -3.27074118
14 2.22890779 2.50326556
15 0.96542771 2.22890779
16 0.25092196 0.96542771
17 1.08093559 0.25092196
18 -1.30453768 1.08093559
19 2.68046859 -1.30453768
20 2.12316057 2.68046859
21 -2.20985924 2.12316057
22 -0.30525745 -2.20985924
23 -1.50293079 -0.30525745
24 1.60899129 -1.50293079
25 -6.78149146 1.60899129
26 0.77232416 -6.78149146
27 0.77045576 0.77232416
28 1.06900101 0.77045576
29 -2.65167996 1.06900101
30 0.81654245 -2.65167996
31 0.58917999 0.81654245
32 2.12796244 0.58917999
33 -0.29128862 2.12796244
34 0.17430915 -0.29128862
35 0.71059868 0.17430915
36 -1.93792655 0.71059868
37 0.78870293 -1.93792655
38 1.93101914 0.78870293
39 -2.04701136 1.93101914
40 -0.52644722 -2.04701136
41 2.32385740 -0.52644722
42 0.77346732 2.32385740
43 -0.94754802 0.77346732
44 -2.23516461 -0.94754802
45 -2.76548874 -2.23516461
46 -0.33856229 -2.76548874
47 0.17634860 -0.33856229
48 3.07926259 0.17634860
49 -1.67152612 3.07926259
50 0.71265794 -1.67152612
51 0.79154668 0.71265794
52 -0.68449450 0.79154668
53 -1.29263639 -0.68449450
54 -2.23310517 -1.29263639
55 0.43621921 -2.23310517
56 1.79537951 0.43621921
57 -0.18008904 1.79537951
58 -3.37859103 -0.18008904
59 -1.38420624 -3.37859103
60 -2.42754587 -1.38420624
61 -1.71757385 -2.42754587
62 -3.64400873 -1.71757385
63 1.00589015 -3.64400873
64 1.78447797 1.00589015
65 -5.28750473 1.78447797
66 -1.52229876 -5.28750473
67 -2.19188964 -1.52229876
68 0.98518301 -2.19188964
69 1.15451555 0.98518301
70 0.24389283 1.15451555
71 3.09692847 0.24389283
72 0.16301130 3.09692847
73 -0.70571381 0.16301130
74 -1.70939235 -0.70571381
75 -0.20056031 -1.70939235
76 2.80281835 -0.20056031
77 0.69768556 2.80281835
78 1.27014518 0.69768556
79 -1.36589964 1.27014518
80 -0.22549210 -1.36589964
81 1.01471927 -0.22549210
82 -0.60051775 1.01471927
83 1.59172679 -0.60051775
84 0.63821765 1.59172679
85 -0.08967782 0.63821765
86 0.37481864 -0.08967782
87 -0.29859904 0.37481864
88 0.48174101 -0.29859904
89 -3.66585403 0.48174101
90 2.98159162 -3.66585403
91 0.45786720 2.98159162
92 0.19682059 0.45786720
93 0.75356589 0.19682059
94 -1.89878835 0.75356589
95 0.58030319 -1.89878835
96 -0.93729631 0.58030319
97 -0.96359797 -0.93729631
98 1.82887221 -0.96359797
99 -0.34742740 1.82887221
100 1.58317737 -0.34742740
101 -1.18516193 1.58317737
102 0.76912472 -1.18516193
103 -2.99704837 0.76912472
104 1.30178511 -2.99704837
105 -2.57101922 1.30178511
106 0.92825152 -2.57101922
107 1.54878045 0.92825152
108 -2.88253439 1.54878045
109 0.09451093 -2.88253439
110 0.95954578 0.09451093
111 -2.26512379 0.95954578
112 -2.86655820 -2.26512379
113 1.59040747 -2.86655820
114 3.60565249 1.59040747
115 0.44330074 3.60565249
116 0.66283242 0.44330074
117 -0.43701799 0.66283242
118 -1.27369362 -0.43701799
119 -0.17943975 -1.27369362
120 -0.79048722 -0.17943975
121 0.74252719 -0.79048722
122 -0.58811775 0.74252719
123 -1.17464631 -0.58811775
124 0.56823011 -1.17464631
125 -1.79828621 0.56823011
126 0.61791405 -1.79828621
127 1.15175214 0.61791405
128 3.86015696 1.15175214
129 1.22061626 3.86015696
130 -1.77156377 1.22061626
131 -1.82461648 -1.77156377
132 -0.81475871 -1.82461648
133 2.50716265 -0.81475871
134 0.34776719 2.50716265
135 2.52498603 0.34776719
136 2.10402497 2.52498603
137 0.68713704 2.10402497
138 -1.08541720 0.68713704
139 0.61655494 -1.08541720
140 -0.99337655 0.61655494
141 0.35184927 -0.99337655
142 1.84867927 0.35184927
143 -0.82110855 1.84867927
144 1.18485948 -0.82110855
145 1.02867215 1.18485948
146 1.93330953 1.02867215
147 -2.56855621 1.93330953
148 -2.60813709 -2.56855621
149 -2.73689672 -2.60813709
150 0.92530902 -2.73689672
151 -0.16442969 0.92530902
152 0.04347164 -0.16442969
153 -3.06989534 0.04347164
154 -2.88099955 -3.06989534
155 1.06968744 -2.88099955
156 0.45786720 1.06968744
157 0.15639110 0.45786720
158 3.86015696 0.15639110
159 -2.78822880 3.86015696
160 -0.73494843 -2.78822880
161 0.11549067 -0.73494843
162 NA 0.11549067
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.72831569 0.03738346
[2,] -2.51326596 2.72831569
[3,] -2.33055066 -2.51326596
[4,] 4.55281821 -2.33055066
[5,] 3.81951358 4.55281821
[6,] 3.89347054 3.81951358
[7,] -1.02122612 3.89347054
[8,] -0.15841146 -1.02122612
[9,] 0.27774319 -0.15841146
[10,] 1.74955010 0.27774319
[11,] 3.77930065 1.74955010
[12,] -3.27074118 3.77930065
[13,] 2.50326556 -3.27074118
[14,] 2.22890779 2.50326556
[15,] 0.96542771 2.22890779
[16,] 0.25092196 0.96542771
[17,] 1.08093559 0.25092196
[18,] -1.30453768 1.08093559
[19,] 2.68046859 -1.30453768
[20,] 2.12316057 2.68046859
[21,] -2.20985924 2.12316057
[22,] -0.30525745 -2.20985924
[23,] -1.50293079 -0.30525745
[24,] 1.60899129 -1.50293079
[25,] -6.78149146 1.60899129
[26,] 0.77232416 -6.78149146
[27,] 0.77045576 0.77232416
[28,] 1.06900101 0.77045576
[29,] -2.65167996 1.06900101
[30,] 0.81654245 -2.65167996
[31,] 0.58917999 0.81654245
[32,] 2.12796244 0.58917999
[33,] -0.29128862 2.12796244
[34,] 0.17430915 -0.29128862
[35,] 0.71059868 0.17430915
[36,] -1.93792655 0.71059868
[37,] 0.78870293 -1.93792655
[38,] 1.93101914 0.78870293
[39,] -2.04701136 1.93101914
[40,] -0.52644722 -2.04701136
[41,] 2.32385740 -0.52644722
[42,] 0.77346732 2.32385740
[43,] -0.94754802 0.77346732
[44,] -2.23516461 -0.94754802
[45,] -2.76548874 -2.23516461
[46,] -0.33856229 -2.76548874
[47,] 0.17634860 -0.33856229
[48,] 3.07926259 0.17634860
[49,] -1.67152612 3.07926259
[50,] 0.71265794 -1.67152612
[51,] 0.79154668 0.71265794
[52,] -0.68449450 0.79154668
[53,] -1.29263639 -0.68449450
[54,] -2.23310517 -1.29263639
[55,] 0.43621921 -2.23310517
[56,] 1.79537951 0.43621921
[57,] -0.18008904 1.79537951
[58,] -3.37859103 -0.18008904
[59,] -1.38420624 -3.37859103
[60,] -2.42754587 -1.38420624
[61,] -1.71757385 -2.42754587
[62,] -3.64400873 -1.71757385
[63,] 1.00589015 -3.64400873
[64,] 1.78447797 1.00589015
[65,] -5.28750473 1.78447797
[66,] -1.52229876 -5.28750473
[67,] -2.19188964 -1.52229876
[68,] 0.98518301 -2.19188964
[69,] 1.15451555 0.98518301
[70,] 0.24389283 1.15451555
[71,] 3.09692847 0.24389283
[72,] 0.16301130 3.09692847
[73,] -0.70571381 0.16301130
[74,] -1.70939235 -0.70571381
[75,] -0.20056031 -1.70939235
[76,] 2.80281835 -0.20056031
[77,] 0.69768556 2.80281835
[78,] 1.27014518 0.69768556
[79,] -1.36589964 1.27014518
[80,] -0.22549210 -1.36589964
[81,] 1.01471927 -0.22549210
[82,] -0.60051775 1.01471927
[83,] 1.59172679 -0.60051775
[84,] 0.63821765 1.59172679
[85,] -0.08967782 0.63821765
[86,] 0.37481864 -0.08967782
[87,] -0.29859904 0.37481864
[88,] 0.48174101 -0.29859904
[89,] -3.66585403 0.48174101
[90,] 2.98159162 -3.66585403
[91,] 0.45786720 2.98159162
[92,] 0.19682059 0.45786720
[93,] 0.75356589 0.19682059
[94,] -1.89878835 0.75356589
[95,] 0.58030319 -1.89878835
[96,] -0.93729631 0.58030319
[97,] -0.96359797 -0.93729631
[98,] 1.82887221 -0.96359797
[99,] -0.34742740 1.82887221
[100,] 1.58317737 -0.34742740
[101,] -1.18516193 1.58317737
[102,] 0.76912472 -1.18516193
[103,] -2.99704837 0.76912472
[104,] 1.30178511 -2.99704837
[105,] -2.57101922 1.30178511
[106,] 0.92825152 -2.57101922
[107,] 1.54878045 0.92825152
[108,] -2.88253439 1.54878045
[109,] 0.09451093 -2.88253439
[110,] 0.95954578 0.09451093
[111,] -2.26512379 0.95954578
[112,] -2.86655820 -2.26512379
[113,] 1.59040747 -2.86655820
[114,] 3.60565249 1.59040747
[115,] 0.44330074 3.60565249
[116,] 0.66283242 0.44330074
[117,] -0.43701799 0.66283242
[118,] -1.27369362 -0.43701799
[119,] -0.17943975 -1.27369362
[120,] -0.79048722 -0.17943975
[121,] 0.74252719 -0.79048722
[122,] -0.58811775 0.74252719
[123,] -1.17464631 -0.58811775
[124,] 0.56823011 -1.17464631
[125,] -1.79828621 0.56823011
[126,] 0.61791405 -1.79828621
[127,] 1.15175214 0.61791405
[128,] 3.86015696 1.15175214
[129,] 1.22061626 3.86015696
[130,] -1.77156377 1.22061626
[131,] -1.82461648 -1.77156377
[132,] -0.81475871 -1.82461648
[133,] 2.50716265 -0.81475871
[134,] 0.34776719 2.50716265
[135,] 2.52498603 0.34776719
[136,] 2.10402497 2.52498603
[137,] 0.68713704 2.10402497
[138,] -1.08541720 0.68713704
[139,] 0.61655494 -1.08541720
[140,] -0.99337655 0.61655494
[141,] 0.35184927 -0.99337655
[142,] 1.84867927 0.35184927
[143,] -0.82110855 1.84867927
[144,] 1.18485948 -0.82110855
[145,] 1.02867215 1.18485948
[146,] 1.93330953 1.02867215
[147,] -2.56855621 1.93330953
[148,] -2.60813709 -2.56855621
[149,] -2.73689672 -2.60813709
[150,] 0.92530902 -2.73689672
[151,] -0.16442969 0.92530902
[152,] 0.04347164 -0.16442969
[153,] -3.06989534 0.04347164
[154,] -2.88099955 -3.06989534
[155,] 1.06968744 -2.88099955
[156,] 0.45786720 1.06968744
[157,] 0.15639110 0.45786720
[158,] 3.86015696 0.15639110
[159,] -2.78822880 3.86015696
[160,] -0.73494843 -2.78822880
[161,] 0.11549067 -0.73494843
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.72831569 0.03738346
2 -2.51326596 2.72831569
3 -2.33055066 -2.51326596
4 4.55281821 -2.33055066
5 3.81951358 4.55281821
6 3.89347054 3.81951358
7 -1.02122612 3.89347054
8 -0.15841146 -1.02122612
9 0.27774319 -0.15841146
10 1.74955010 0.27774319
11 3.77930065 1.74955010
12 -3.27074118 3.77930065
13 2.50326556 -3.27074118
14 2.22890779 2.50326556
15 0.96542771 2.22890779
16 0.25092196 0.96542771
17 1.08093559 0.25092196
18 -1.30453768 1.08093559
19 2.68046859 -1.30453768
20 2.12316057 2.68046859
21 -2.20985924 2.12316057
22 -0.30525745 -2.20985924
23 -1.50293079 -0.30525745
24 1.60899129 -1.50293079
25 -6.78149146 1.60899129
26 0.77232416 -6.78149146
27 0.77045576 0.77232416
28 1.06900101 0.77045576
29 -2.65167996 1.06900101
30 0.81654245 -2.65167996
31 0.58917999 0.81654245
32 2.12796244 0.58917999
33 -0.29128862 2.12796244
34 0.17430915 -0.29128862
35 0.71059868 0.17430915
36 -1.93792655 0.71059868
37 0.78870293 -1.93792655
38 1.93101914 0.78870293
39 -2.04701136 1.93101914
40 -0.52644722 -2.04701136
41 2.32385740 -0.52644722
42 0.77346732 2.32385740
43 -0.94754802 0.77346732
44 -2.23516461 -0.94754802
45 -2.76548874 -2.23516461
46 -0.33856229 -2.76548874
47 0.17634860 -0.33856229
48 3.07926259 0.17634860
49 -1.67152612 3.07926259
50 0.71265794 -1.67152612
51 0.79154668 0.71265794
52 -0.68449450 0.79154668
53 -1.29263639 -0.68449450
54 -2.23310517 -1.29263639
55 0.43621921 -2.23310517
56 1.79537951 0.43621921
57 -0.18008904 1.79537951
58 -3.37859103 -0.18008904
59 -1.38420624 -3.37859103
60 -2.42754587 -1.38420624
61 -1.71757385 -2.42754587
62 -3.64400873 -1.71757385
63 1.00589015 -3.64400873
64 1.78447797 1.00589015
65 -5.28750473 1.78447797
66 -1.52229876 -5.28750473
67 -2.19188964 -1.52229876
68 0.98518301 -2.19188964
69 1.15451555 0.98518301
70 0.24389283 1.15451555
71 3.09692847 0.24389283
72 0.16301130 3.09692847
73 -0.70571381 0.16301130
74 -1.70939235 -0.70571381
75 -0.20056031 -1.70939235
76 2.80281835 -0.20056031
77 0.69768556 2.80281835
78 1.27014518 0.69768556
79 -1.36589964 1.27014518
80 -0.22549210 -1.36589964
81 1.01471927 -0.22549210
82 -0.60051775 1.01471927
83 1.59172679 -0.60051775
84 0.63821765 1.59172679
85 -0.08967782 0.63821765
86 0.37481864 -0.08967782
87 -0.29859904 0.37481864
88 0.48174101 -0.29859904
89 -3.66585403 0.48174101
90 2.98159162 -3.66585403
91 0.45786720 2.98159162
92 0.19682059 0.45786720
93 0.75356589 0.19682059
94 -1.89878835 0.75356589
95 0.58030319 -1.89878835
96 -0.93729631 0.58030319
97 -0.96359797 -0.93729631
98 1.82887221 -0.96359797
99 -0.34742740 1.82887221
100 1.58317737 -0.34742740
101 -1.18516193 1.58317737
102 0.76912472 -1.18516193
103 -2.99704837 0.76912472
104 1.30178511 -2.99704837
105 -2.57101922 1.30178511
106 0.92825152 -2.57101922
107 1.54878045 0.92825152
108 -2.88253439 1.54878045
109 0.09451093 -2.88253439
110 0.95954578 0.09451093
111 -2.26512379 0.95954578
112 -2.86655820 -2.26512379
113 1.59040747 -2.86655820
114 3.60565249 1.59040747
115 0.44330074 3.60565249
116 0.66283242 0.44330074
117 -0.43701799 0.66283242
118 -1.27369362 -0.43701799
119 -0.17943975 -1.27369362
120 -0.79048722 -0.17943975
121 0.74252719 -0.79048722
122 -0.58811775 0.74252719
123 -1.17464631 -0.58811775
124 0.56823011 -1.17464631
125 -1.79828621 0.56823011
126 0.61791405 -1.79828621
127 1.15175214 0.61791405
128 3.86015696 1.15175214
129 1.22061626 3.86015696
130 -1.77156377 1.22061626
131 -1.82461648 -1.77156377
132 -0.81475871 -1.82461648
133 2.50716265 -0.81475871
134 0.34776719 2.50716265
135 2.52498603 0.34776719
136 2.10402497 2.52498603
137 0.68713704 2.10402497
138 -1.08541720 0.68713704
139 0.61655494 -1.08541720
140 -0.99337655 0.61655494
141 0.35184927 -0.99337655
142 1.84867927 0.35184927
143 -0.82110855 1.84867927
144 1.18485948 -0.82110855
145 1.02867215 1.18485948
146 1.93330953 1.02867215
147 -2.56855621 1.93330953
148 -2.60813709 -2.56855621
149 -2.73689672 -2.60813709
150 0.92530902 -2.73689672
151 -0.16442969 0.92530902
152 0.04347164 -0.16442969
153 -3.06989534 0.04347164
154 -2.88099955 -3.06989534
155 1.06968744 -2.88099955
156 0.45786720 1.06968744
157 0.15639110 0.45786720
158 3.86015696 0.15639110
159 -2.78822880 3.86015696
160 -0.73494843 -2.78822880
161 0.11549067 -0.73494843
> 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/70rp51352149748.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8cqaf1352149748.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9pksu1352149748.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10vdpl1352149748.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111bbj1352149749.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/12jaq41352149749.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/13485l1352149749.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/14cayu1352149749.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/15dk9u1352149749.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/16bu1i1352149749.tab")
+ }
>
> try(system("convert tmp/17xv61352149748.ps tmp/17xv61352149748.png",intern=TRUE))
character(0)
> try(system("convert tmp/2iiog1352149748.ps tmp/2iiog1352149748.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gk1v1352149748.ps tmp/3gk1v1352149748.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xr071352149748.ps tmp/4xr071352149748.png",intern=TRUE))
character(0)
> try(system("convert tmp/5uhf01352149748.ps tmp/5uhf01352149748.png",intern=TRUE))
character(0)
> try(system("convert tmp/6nsud1352149748.ps tmp/6nsud1352149748.png",intern=TRUE))
character(0)
> try(system("convert tmp/70rp51352149748.ps tmp/70rp51352149748.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cqaf1352149748.ps tmp/8cqaf1352149748.png",intern=TRUE))
character(0)
> try(system("convert tmp/9pksu1352149748.ps tmp/9pksu1352149748.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vdpl1352149748.ps tmp/10vdpl1352149748.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.952 1.439 13.384