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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+ ,84
+ ,51
+ ,34
+ ,32
+ ,160
+ ,10
+ ,12
+ ,8
+ ,12
+ ,18
+ ,84
+ ,50
+ ,32
+ ,35
+ ,161
+ ,11
+ ,13
+ ,8
+ ,13
+ ,16
+ ,69
+ ,46
+ ,34
+ ,36
+ ,162)
+ ,dim=c(10
+ ,162)
+ ,dimnames=list(c('month'
+ ,'learning'
+ ,'software'
+ ,'happiness'
+ ,'depression'
+ ,'belonging'
+ ,'belonging_final'
+ ,'connected'
+ ,'separate'
+ ,'t_')
+ ,1:162))
> y <- array(NA,dim=c(10,162),dimnames=list(c('month','learning','software','happiness','depression','belonging','belonging_final','connected','separate','t_'),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 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'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 month software happiness depression belonging belonging_final
1 13 9 12 14 12 53 32
2 16 9 11 18 11 86 51
3 19 9 15 11 14 66 42
4 15 9 6 12 12 67 41
5 14 9 13 16 21 76 46
6 13 9 10 18 12 78 47
7 19 9 12 14 22 53 37
8 15 9 14 14 11 80 49
9 14 9 12 15 10 74 45
10 15 9 6 15 13 76 47
11 16 9 10 17 10 79 49
12 16 9 12 19 8 54 33
13 16 9 12 10 15 67 42
14 16 9 11 16 14 54 33
15 17 9 15 18 10 87 53
16 15 9 12 14 14 58 36
17 15 9 10 14 14 75 45
18 20 9 12 17 11 88 54
19 18 9 11 14 10 64 41
20 16 9 12 16 13 57 36
21 16 9 11 18 7 66 41
22 16 9 12 11 14 68 44
23 19 9 13 14 12 54 33
24 16 9 11 12 14 56 37
25 17 9 9 17 11 86 52
26 17 9 13 9 9 80 47
27 16 9 10 16 11 76 43
28 15 9 14 14 15 69 44
29 16 9 12 15 14 78 45
30 14 9 10 11 13 67 44
31 15 9 12 16 9 80 49
32 12 9 8 13 15 54 33
33 14 9 10 17 10 71 43
34 16 9 12 15 11 84 54
35 14 9 12 14 13 74 42
36 7 9 7 16 8 71 44
37 10 9 6 9 20 63 37
38 14 9 12 15 12 71 43
39 16 9 10 17 10 76 46
40 16 9 10 13 10 69 42
41 16 9 10 15 9 74 45
42 14 9 12 16 14 75 44
43 20 9 15 16 8 54 33
44 14 9 10 12 14 52 31
45 14 9 10 12 11 69 42
46 11 9 12 11 13 68 40
47 14 9 13 15 9 65 43
48 15 9 11 15 11 75 46
49 16 9 11 17 15 74 42
50 14 9 12 13 11 75 45
51 16 9 14 16 10 72 44
52 14 9 10 14 14 67 40
53 12 9 12 11 18 63 37
54 16 9 13 12 14 62 46
55 9 9 5 12 11 63 36
56 14 9 6 15 12 76 47
57 16 9 12 16 13 74 45
58 16 9 12 15 9 67 42
59 15 9 11 12 10 73 43
60 16 9 10 12 15 70 43
61 12 9 7 8 20 53 32
62 16 9 12 13 12 77 45
63 16 9 14 11 12 77 45
64 14 9 11 14 14 52 31
65 16 9 12 15 13 54 33
66 17 10 13 10 11 80 49
67 18 10 14 11 17 66 42
68 18 10 11 12 12 73 41
69 12 10 12 15 13 63 38
70 16 10 12 15 14 69 42
71 10 10 8 14 13 67 44
72 14 10 11 16 15 54 33
73 18 10 14 15 13 81 48
74 18 10 14 15 10 69 40
75 16 10 12 13 11 84 50
76 17 10 9 12 19 80 49
77 16 10 13 17 13 70 43
78 16 10 11 13 17 69 44
79 13 10 12 15 13 77 47
80 16 10 12 13 9 54 33
81 16 10 12 15 11 79 46
82 20 10 12 16 10 30 0
83 16 10 12 15 9 71 45
84 15 10 12 16 12 73 43
85 15 10 11 15 12 72 44
86 16 10 10 14 13 77 47
87 14 10 9 15 13 75 45
88 16 10 12 14 12 69 42
89 16 10 12 13 15 54 33
90 15 10 12 7 22 70 43
91 12 10 9 17 13 73 46
92 17 10 15 13 15 54 33
93 16 10 12 15 13 77 46
94 15 10 12 14 15 82 48
95 13 10 12 13 10 80 47
96 16 10 10 16 11 80 47
97 16 10 13 12 16 69 43
98 16 10 9 14 11 78 46
99 16 10 12 17 11 81 48
100 14 10 10 15 10 76 46
101 16 10 14 17 10 76 45
102 16 10 11 12 16 73 45
103 20 10 15 16 12 85 52
104 15 10 11 11 11 66 42
105 16 10 11 15 16 79 47
106 13 10 12 9 19 68 41
107 17 10 12 16 11 76 47
108 16 10 12 15 16 71 43
109 16 10 11 10 15 54 33
110 12 10 7 10 24 46 30
111 16 10 12 15 14 82 49
112 16 10 14 11 15 74 44
113 17 10 11 13 11 88 55
114 13 10 11 14 15 38 11
115 12 10 10 18 12 76 47
116 18 10 13 16 10 86 53
117 14 10 13 14 14 54 33
118 14 10 8 14 13 70 44
119 13 10 11 14 9 69 42
120 16 10 12 14 15 90 55
121 13 10 11 12 15 54 33
122 16 10 13 14 14 76 46
123 13 10 12 15 11 89 54
124 16 10 14 15 8 76 47
125 15 10 13 15 11 73 45
126 16 10 15 13 11 79 47
127 15 10 10 17 8 90 55
128 17 10 11 17 10 74 44
129 15 10 9 19 11 81 53
130 12 10 11 15 13 72 44
131 16 10 10 13 11 71 42
132 10 10 11 9 20 66 40
133 16 10 8 15 10 77 46
134 12 10 11 15 15 65 40
135 14 10 12 15 12 74 46
136 15 10 12 16 14 82 53
137 13 10 9 11 23 54 33
138 15 10 11 14 14 63 42
139 11 10 10 11 16 54 35
140 12 10 8 15 11 64 40
141 8 10 9 13 12 69 41
142 16 10 8 15 10 54 33
143 15 10 9 16 14 84 51
144 17 10 15 14 12 86 53
145 16 10 11 15 12 77 46
146 10 10 8 16 11 89 55
147 18 10 13 16 12 76 47
148 13 10 12 11 13 60 38
149 16 10 12 12 11 75 46
150 13 10 9 9 19 73 46
151 10 10 7 16 12 85 53
152 15 10 13 13 17 79 47
153 16 10 9 16 9 71 41
154 16 9 6 12 12 72 44
155 14 10 8 9 19 69 43
156 10 10 8 13 18 78 51
157 17 10 15 13 15 54 33
158 13 10 6 14 14 69 43
159 15 10 9 19 11 81 53
160 16 10 11 13 9 84 51
161 12 10 8 12 18 84 50
162 13 11 8 13 16 69 46
connected separate t_
1 41 38 1
2 39 32 2
3 30 35 3
4 31 33 4
5 34 37 5
6 35 29 6
7 39 31 7
8 34 36 8
9 36 35 9
10 37 38 10
11 38 31 11
12 36 34 12
13 38 35 13
14 39 38 14
15 33 37 15
16 32 33 16
17 36 32 17
18 38 38 18
19 39 38 19
20 32 32 20
21 32 33 21
22 31 31 22
23 39 38 23
24 37 39 24
25 39 32 25
26 41 32 26
27 36 35 27
28 33 37 28
29 33 33 29
30 34 33 30
31 31 28 31
32 27 32 32
33 37 31 33
34 34 37 34
35 34 30 35
36 32 33 36
37 29 31 37
38 36 33 38
39 29 31 39
40 35 33 40
41 37 32 41
42 34 33 42
43 38 32 43
44 35 33 44
45 38 28 45
46 37 35 46
47 38 39 47
48 33 34 48
49 36 38 49
50 38 32 50
51 32 38 51
52 32 30 52
53 32 33 53
54 34 38 54
55 32 32 55
56 37 32 56
57 39 34 57
58 29 34 58
59 37 36 59
60 35 34 60
61 30 28 61
62 38 34 62
63 34 35 63
64 31 35 64
65 34 31 65
66 35 37 66
67 36 35 67
68 30 27 68
69 39 40 69
70 35 37 70
71 38 36 71
72 31 38 72
73 34 39 73
74 38 41 74
75 34 27 75
76 39 30 76
77 37 37 77
78 34 31 78
79 28 31 79
80 37 27 80
81 33 36 81
82 37 38 82
83 35 37 83
84 37 33 84
85 32 34 85
86 33 31 86
87 38 39 87
88 33 34 88
89 29 32 89
90 33 33 90
91 31 36 91
92 36 32 92
93 35 41 93
94 32 28 94
95 29 30 95
96 39 36 96
97 37 35 97
98 35 31 98
99 37 34 99
100 32 36 100
101 38 36 101
102 37 35 102
103 36 37 103
104 32 28 104
105 33 39 105
106 40 32 106
107 38 35 107
108 41 39 108
109 36 35 109
110 43 42 110
111 30 34 111
112 31 33 112
113 32 41 113
114 32 33 114
115 37 34 115
116 37 32 116
117 33 40 117
118 34 40 118
119 33 35 119
120 38 36 120
121 33 37 121
122 31 27 122
123 38 39 123
124 37 38 124
125 33 31 125
126 31 33 126
127 39 32 127
128 44 39 128
129 33 36 129
130 35 33 130
131 32 33 131
132 28 32 132
133 40 37 133
134 27 30 134
135 37 38 135
136 32 29 136
137 28 22 137
138 34 35 138
139 30 35 139
140 35 34 140
141 31 35 141
142 32 34 142
143 30 34 143
144 30 35 144
145 31 23 145
146 40 31 146
147 32 27 147
148 36 36 148
149 32 31 149
150 35 32 150
151 38 39 151
152 42 37 152
153 34 38 153
154 35 39 154
155 35 34 155
156 33 31 156
157 36 32 157
158 32 37 158
159 33 36 159
160 34 32 160
161 32 35 161
162 34 36 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month software happiness
2.489904 0.426092 0.516844 0.045758
depression belonging belonging_final connected
-0.070940 0.038589 -0.050854 0.104520
separate t_
-0.016644 -0.008084
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.1138 -1.1526 0.1658 1.1535 4.5715
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.489904 4.955614 0.502 0.6161
month 0.426092 0.544893 0.782 0.4354
software 0.516844 0.071623 7.216 2.37e-11 ***
happiness 0.045758 0.076974 0.594 0.5531
depression -0.070940 0.057365 -1.237 0.2181
belonging 0.038589 0.044998 0.858 0.3925
belonging_final -0.050854 0.064311 -0.791 0.4303
connected 0.104520 0.047309 2.209 0.0286 *
separate -0.016644 0.045099 -0.369 0.7126
t_ -0.008084 0.005915 -1.367 0.1737
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.849 on 152 degrees of freedom
Multiple R-squared: 0.3663, Adjusted R-squared: 0.3287
F-statistic: 9.761 on 9 and 152 DF, p-value: 1.034e-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,] 0.73553351 0.52893299 0.2644665
[2,] 0.71000539 0.57998921 0.2899946
[3,] 0.58805061 0.82389879 0.4119494
[4,] 0.47489277 0.94978554 0.5251072
[5,] 0.39640164 0.79280328 0.6035984
[6,] 0.55169189 0.89661622 0.4483081
[7,] 0.46578713 0.93157426 0.5342129
[8,] 0.37712479 0.75424958 0.6228752
[9,] 0.30654460 0.61308921 0.6934554
[10,] 0.29863711 0.59727422 0.7013629
[11,] 0.42513993 0.85027986 0.5748601
[12,] 0.49766050 0.99532099 0.5023395
[13,] 0.44333394 0.88666787 0.5566661
[14,] 0.37502694 0.75005388 0.6249731
[15,] 0.34614488 0.69228977 0.6538551
[16,] 0.44944097 0.89888195 0.5505590
[17,] 0.39182538 0.78365076 0.6081746
[18,] 0.50141122 0.99717756 0.4985888
[19,] 0.45039364 0.90078728 0.5496064
[20,] 0.41292535 0.82585070 0.5870747
[21,] 0.40077305 0.80154610 0.5992269
[22,] 0.36909382 0.73818764 0.6309062
[23,] 0.32044609 0.64089218 0.6795539
[24,] 0.86413638 0.27172724 0.1358636
[25,] 0.83728205 0.32543590 0.1627180
[26,] 0.81874510 0.36250980 0.1812549
[27,] 0.84910007 0.30179986 0.1508999
[28,] 0.83698360 0.32603280 0.1630164
[29,] 0.80993033 0.38013935 0.1900697
[30,] 0.78163012 0.43673976 0.2183699
[31,] 0.81158264 0.37683471 0.1884174
[32,] 0.77229437 0.45541125 0.2277056
[33,] 0.74307676 0.51384647 0.2569232
[34,] 0.87699313 0.24601374 0.1230069
[35,] 0.89953199 0.20093602 0.1004680
[36,] 0.87766606 0.24466787 0.1223339
[37,] 0.87461438 0.25077124 0.1253856
[38,] 0.86702305 0.26595389 0.1329769
[39,] 0.83966656 0.32066687 0.1603334
[40,] 0.80970315 0.38059370 0.1902968
[41,] 0.82215507 0.35568986 0.1778449
[42,] 0.79215284 0.41569432 0.2078472
[43,] 0.81161670 0.37676661 0.1883833
[44,] 0.79073850 0.41852300 0.2092615
[45,] 0.75443938 0.49112125 0.2455606
[46,] 0.73918640 0.52162721 0.2608136
[47,] 0.71070079 0.57859842 0.2892992
[48,] 0.71395559 0.57208881 0.2860444
[49,] 0.68000558 0.63998884 0.3199944
[50,] 0.64585667 0.70828666 0.3541433
[51,] 0.62068697 0.75862607 0.3793130
[52,] 0.58647795 0.82704411 0.4135221
[53,] 0.55027151 0.89945697 0.4497285
[54,] 0.50264929 0.99470142 0.4973507
[55,] 0.47759068 0.95518137 0.5224093
[56,] 0.51551772 0.96896455 0.4844823
[57,] 0.73774056 0.52451888 0.2622594
[58,] 0.69795424 0.60409153 0.3020458
[59,] 0.82894017 0.34211965 0.1710598
[60,] 0.79910857 0.40178287 0.2008914
[61,] 0.78749011 0.42501978 0.2125099
[62,] 0.76559495 0.46881010 0.2344051
[63,] 0.72825608 0.54348785 0.2717439
[64,] 0.76004033 0.47991934 0.2399597
[65,] 0.72423498 0.55153003 0.2757650
[66,] 0.70104780 0.59790440 0.2989522
[67,] 0.71832550 0.56334901 0.2816745
[68,] 0.67705147 0.64589707 0.3229485
[69,] 0.63700228 0.72599544 0.3629977
[70,] 0.75941369 0.48117262 0.2405863
[71,] 0.72203990 0.55592019 0.2779601
[72,] 0.69500932 0.60998136 0.3049907
[73,] 0.65247196 0.69505607 0.3475280
[74,] 0.63544390 0.72911221 0.3645561
[75,] 0.59065504 0.81868991 0.4093450
[76,] 0.54698824 0.90602352 0.4530118
[77,] 0.52051294 0.95897412 0.4794871
[78,] 0.48206657 0.96413315 0.5179334
[79,] 0.47244756 0.94489512 0.5275524
[80,] 0.42948047 0.85896095 0.5705195
[81,] 0.38716108 0.77432216 0.6128389
[82,] 0.34388193 0.68776387 0.6561181
[83,] 0.36949955 0.73899910 0.6305004
[84,] 0.33364226 0.66728453 0.6663577
[85,] 0.29210207 0.58420413 0.7078979
[86,] 0.28739474 0.57478948 0.7126053
[87,] 0.24715836 0.49431673 0.7528416
[88,] 0.21354456 0.42708913 0.7864554
[89,] 0.19066693 0.38133387 0.8093331
[90,] 0.17407371 0.34814742 0.8259263
[91,] 0.21747691 0.43495381 0.7825231
[92,] 0.18380462 0.36760924 0.8161954
[93,] 0.17881013 0.35762026 0.8211899
[94,] 0.18657775 0.37315550 0.8134223
[95,] 0.16820834 0.33641667 0.8317917
[96,] 0.14709880 0.29419761 0.8529012
[97,] 0.14553352 0.29106703 0.8544665
[98,] 0.13980407 0.27960814 0.8601959
[99,] 0.12772169 0.25544338 0.8722783
[100,] 0.10927737 0.21855475 0.8907226
[101,] 0.15708793 0.31417585 0.8429121
[102,] 0.13989177 0.27978355 0.8601082
[103,] 0.15562210 0.31124420 0.8443779
[104,] 0.16582976 0.33165953 0.8341702
[105,] 0.13972415 0.27944830 0.8602758
[106,] 0.14508694 0.29017388 0.8549131
[107,] 0.12899495 0.25798990 0.8710050
[108,] 0.14605007 0.29210015 0.8539499
[109,] 0.11862537 0.23725073 0.8813746
[110,] 0.10513172 0.21026344 0.8948683
[111,] 0.09731849 0.19463697 0.9026815
[112,] 0.07514022 0.15028044 0.9248598
[113,] 0.05706902 0.11413805 0.9429310
[114,] 0.04280688 0.08561377 0.9571931
[115,] 0.03122418 0.06244835 0.9687758
[116,] 0.03167138 0.06334275 0.9683286
[117,] 0.03403036 0.06806073 0.9659696
[118,] 0.03238921 0.06477842 0.9676108
[119,] 0.04107039 0.08214078 0.9589296
[120,] 0.04022806 0.08045613 0.9597719
[121,] 0.12017635 0.24035270 0.8798237
[122,] 0.12589221 0.25178443 0.8741078
[123,] 0.10077755 0.20155509 0.8992225
[124,] 0.07983242 0.15966484 0.9201676
[125,] 0.05810117 0.11620234 0.9418988
[126,] 0.06214186 0.12428372 0.9378581
[127,] 0.05511692 0.11023385 0.9448831
[128,] 0.03676953 0.07353906 0.9632305
[129,] 0.53183456 0.93633088 0.4681654
[130,] 0.47447109 0.94894218 0.5255289
[131,] 0.41222335 0.82444670 0.5877766
[132,] 0.31989397 0.63978794 0.6801060
[133,] 0.23880345 0.47760691 0.7611965
[134,] 0.23957952 0.47915905 0.7604205
[135,] 0.29017879 0.58035758 0.7098212
[136,] 0.64845858 0.70308283 0.3515414
[137,] 0.59859460 0.80281080 0.4014054
> postscript(file="/var/wessaorg/rcomp/tmp/1vmor1352147316.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/290ha1352147316.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/309801352147316.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4v3551352147316.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/55q0j1352147316.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
-3.37882687 -0.30591030 2.47263419 2.71742058 -1.77698820 -2.21234655
7 8 9 10 11 12
3.72587561 -1.90589928 -2.17839191 2.11351620 0.51480072 -0.33416124
13 14 15 16 17 18
0.34595412 0.51478005 -0.56566756 -0.24775525 0.16096265 3.63212220
19 20 21 22 23 24
2.38389964 0.64407085 0.57545741 1.03019635 2.50348678 1.13057651
25 26 27 28 29 30
2.01032507 -0.05655799 0.84710528 -1.16908205 0.39296494 -0.18406493
31 32 33 34 35 36
-0.73926663 -0.42658644 -1.19923902 0.40877762 -1.73637131 -6.11383362
37 38 39 40 41 42
-1.18431923 -1.82130109 1.64504357 1.30903492 0.88859570 -1.58730524
43 44 45 46 47 48
2.26086196 -0.23249175 -0.93062935 -4.61068291 -2.35584409 0.03385524
49 50 51 52 53 54
0.82237232 -1.98204807 -0.42396327 -0.11685711 -2.66970018 0.86248373
55 56 57 58 59 60
-2.64546147 1.31458186 0.04650521 0.97935125 -0.27106084 1.90008443
61 62 63 64 65 66
0.51578276 0.14201228 -0.35735010 -0.22779800 0.79113693 0.74889077
67 68 69 70 71 72
1.66647212 2.99761645 -4.06844991 0.35060268 -3.75043854 -0.53532860
73 74 75 76 77 78
1.25009229 0.71680245 0.03579110 2.83851708 -0.46888217 1.34281627
79 80 81 82 83 84
-2.07024804 -0.08607342 0.23663000 3.29480081 0.17638282 -1.10297686
85 86 87 88 89 90
0.09639724 1.54318563 -0.39161924 0.55910308 1.33170572 0.60059395
91 92 93 94 95 96
-1.64105812 0.07378683 0.42688024 -0.37145386 -2.29913695 0.73096639
97 98 99 100 101 102
0.13970999 1.71667619 -0.13620972 -0.42673536 -1.25551601 1.16116962
103 104 105 106 107 108
2.66580757 0.39204824 1.40298015 -2.34720929 1.02843782 0.17952505
109 110 111 112 113 114
1.46579905 -0.27926344 1.00904101 0.17068701 2.40181087 -1.79338632
115 116 117 118 119 120
-2.80590194 1.48723215 -1.36040295 0.99840852 -1.86961845 0.39203608
121 122 123 124 125 126
-1.18185718 0.48482237 -2.87555967 -0.88042602 -0.82705092 -0.64863590
127 128 129 130 131 132
-0.32263705 0.96242451 1.27097148 -2.79907978 1.92592519 -3.26867189
133 134 135 136 137 138
2.01562407 -1.77192869 -1.44772988 0.07654219 0.86738569 0.76569484
139 140 141 142 143 144
-2.02082092 -1.18764547 -5.24131648 3.10105589 1.79703770 0.69487131
145 146 147 148 149 150
1.41164121 -3.95934890 2.39986595 -1.88401830 1.09928235 0.14295104
151 152 153 154 155 156
-2.93630029 -1.06226508 2.16479569 4.57145060 1.73529856 -2.29195040
157 158 159 160 161 162
0.59925849 1.57324426 1.51349686 1.23198717 -0.31706336 0.26031766
> postscript(file="/var/wessaorg/rcomp/tmp/633ko1352147316.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 -3.37882687 NA
1 -0.30591030 -3.37882687
2 2.47263419 -0.30591030
3 2.71742058 2.47263419
4 -1.77698820 2.71742058
5 -2.21234655 -1.77698820
6 3.72587561 -2.21234655
7 -1.90589928 3.72587561
8 -2.17839191 -1.90589928
9 2.11351620 -2.17839191
10 0.51480072 2.11351620
11 -0.33416124 0.51480072
12 0.34595412 -0.33416124
13 0.51478005 0.34595412
14 -0.56566756 0.51478005
15 -0.24775525 -0.56566756
16 0.16096265 -0.24775525
17 3.63212220 0.16096265
18 2.38389964 3.63212220
19 0.64407085 2.38389964
20 0.57545741 0.64407085
21 1.03019635 0.57545741
22 2.50348678 1.03019635
23 1.13057651 2.50348678
24 2.01032507 1.13057651
25 -0.05655799 2.01032507
26 0.84710528 -0.05655799
27 -1.16908205 0.84710528
28 0.39296494 -1.16908205
29 -0.18406493 0.39296494
30 -0.73926663 -0.18406493
31 -0.42658644 -0.73926663
32 -1.19923902 -0.42658644
33 0.40877762 -1.19923902
34 -1.73637131 0.40877762
35 -6.11383362 -1.73637131
36 -1.18431923 -6.11383362
37 -1.82130109 -1.18431923
38 1.64504357 -1.82130109
39 1.30903492 1.64504357
40 0.88859570 1.30903492
41 -1.58730524 0.88859570
42 2.26086196 -1.58730524
43 -0.23249175 2.26086196
44 -0.93062935 -0.23249175
45 -4.61068291 -0.93062935
46 -2.35584409 -4.61068291
47 0.03385524 -2.35584409
48 0.82237232 0.03385524
49 -1.98204807 0.82237232
50 -0.42396327 -1.98204807
51 -0.11685711 -0.42396327
52 -2.66970018 -0.11685711
53 0.86248373 -2.66970018
54 -2.64546147 0.86248373
55 1.31458186 -2.64546147
56 0.04650521 1.31458186
57 0.97935125 0.04650521
58 -0.27106084 0.97935125
59 1.90008443 -0.27106084
60 0.51578276 1.90008443
61 0.14201228 0.51578276
62 -0.35735010 0.14201228
63 -0.22779800 -0.35735010
64 0.79113693 -0.22779800
65 0.74889077 0.79113693
66 1.66647212 0.74889077
67 2.99761645 1.66647212
68 -4.06844991 2.99761645
69 0.35060268 -4.06844991
70 -3.75043854 0.35060268
71 -0.53532860 -3.75043854
72 1.25009229 -0.53532860
73 0.71680245 1.25009229
74 0.03579110 0.71680245
75 2.83851708 0.03579110
76 -0.46888217 2.83851708
77 1.34281627 -0.46888217
78 -2.07024804 1.34281627
79 -0.08607342 -2.07024804
80 0.23663000 -0.08607342
81 3.29480081 0.23663000
82 0.17638282 3.29480081
83 -1.10297686 0.17638282
84 0.09639724 -1.10297686
85 1.54318563 0.09639724
86 -0.39161924 1.54318563
87 0.55910308 -0.39161924
88 1.33170572 0.55910308
89 0.60059395 1.33170572
90 -1.64105812 0.60059395
91 0.07378683 -1.64105812
92 0.42688024 0.07378683
93 -0.37145386 0.42688024
94 -2.29913695 -0.37145386
95 0.73096639 -2.29913695
96 0.13970999 0.73096639
97 1.71667619 0.13970999
98 -0.13620972 1.71667619
99 -0.42673536 -0.13620972
100 -1.25551601 -0.42673536
101 1.16116962 -1.25551601
102 2.66580757 1.16116962
103 0.39204824 2.66580757
104 1.40298015 0.39204824
105 -2.34720929 1.40298015
106 1.02843782 -2.34720929
107 0.17952505 1.02843782
108 1.46579905 0.17952505
109 -0.27926344 1.46579905
110 1.00904101 -0.27926344
111 0.17068701 1.00904101
112 2.40181087 0.17068701
113 -1.79338632 2.40181087
114 -2.80590194 -1.79338632
115 1.48723215 -2.80590194
116 -1.36040295 1.48723215
117 0.99840852 -1.36040295
118 -1.86961845 0.99840852
119 0.39203608 -1.86961845
120 -1.18185718 0.39203608
121 0.48482237 -1.18185718
122 -2.87555967 0.48482237
123 -0.88042602 -2.87555967
124 -0.82705092 -0.88042602
125 -0.64863590 -0.82705092
126 -0.32263705 -0.64863590
127 0.96242451 -0.32263705
128 1.27097148 0.96242451
129 -2.79907978 1.27097148
130 1.92592519 -2.79907978
131 -3.26867189 1.92592519
132 2.01562407 -3.26867189
133 -1.77192869 2.01562407
134 -1.44772988 -1.77192869
135 0.07654219 -1.44772988
136 0.86738569 0.07654219
137 0.76569484 0.86738569
138 -2.02082092 0.76569484
139 -1.18764547 -2.02082092
140 -5.24131648 -1.18764547
141 3.10105589 -5.24131648
142 1.79703770 3.10105589
143 0.69487131 1.79703770
144 1.41164121 0.69487131
145 -3.95934890 1.41164121
146 2.39986595 -3.95934890
147 -1.88401830 2.39986595
148 1.09928235 -1.88401830
149 0.14295104 1.09928235
150 -2.93630029 0.14295104
151 -1.06226508 -2.93630029
152 2.16479569 -1.06226508
153 4.57145060 2.16479569
154 1.73529856 4.57145060
155 -2.29195040 1.73529856
156 0.59925849 -2.29195040
157 1.57324426 0.59925849
158 1.51349686 1.57324426
159 1.23198717 1.51349686
160 -0.31706336 1.23198717
161 0.26031766 -0.31706336
162 NA 0.26031766
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.30591030 -3.37882687
[2,] 2.47263419 -0.30591030
[3,] 2.71742058 2.47263419
[4,] -1.77698820 2.71742058
[5,] -2.21234655 -1.77698820
[6,] 3.72587561 -2.21234655
[7,] -1.90589928 3.72587561
[8,] -2.17839191 -1.90589928
[9,] 2.11351620 -2.17839191
[10,] 0.51480072 2.11351620
[11,] -0.33416124 0.51480072
[12,] 0.34595412 -0.33416124
[13,] 0.51478005 0.34595412
[14,] -0.56566756 0.51478005
[15,] -0.24775525 -0.56566756
[16,] 0.16096265 -0.24775525
[17,] 3.63212220 0.16096265
[18,] 2.38389964 3.63212220
[19,] 0.64407085 2.38389964
[20,] 0.57545741 0.64407085
[21,] 1.03019635 0.57545741
[22,] 2.50348678 1.03019635
[23,] 1.13057651 2.50348678
[24,] 2.01032507 1.13057651
[25,] -0.05655799 2.01032507
[26,] 0.84710528 -0.05655799
[27,] -1.16908205 0.84710528
[28,] 0.39296494 -1.16908205
[29,] -0.18406493 0.39296494
[30,] -0.73926663 -0.18406493
[31,] -0.42658644 -0.73926663
[32,] -1.19923902 -0.42658644
[33,] 0.40877762 -1.19923902
[34,] -1.73637131 0.40877762
[35,] -6.11383362 -1.73637131
[36,] -1.18431923 -6.11383362
[37,] -1.82130109 -1.18431923
[38,] 1.64504357 -1.82130109
[39,] 1.30903492 1.64504357
[40,] 0.88859570 1.30903492
[41,] -1.58730524 0.88859570
[42,] 2.26086196 -1.58730524
[43,] -0.23249175 2.26086196
[44,] -0.93062935 -0.23249175
[45,] -4.61068291 -0.93062935
[46,] -2.35584409 -4.61068291
[47,] 0.03385524 -2.35584409
[48,] 0.82237232 0.03385524
[49,] -1.98204807 0.82237232
[50,] -0.42396327 -1.98204807
[51,] -0.11685711 -0.42396327
[52,] -2.66970018 -0.11685711
[53,] 0.86248373 -2.66970018
[54,] -2.64546147 0.86248373
[55,] 1.31458186 -2.64546147
[56,] 0.04650521 1.31458186
[57,] 0.97935125 0.04650521
[58,] -0.27106084 0.97935125
[59,] 1.90008443 -0.27106084
[60,] 0.51578276 1.90008443
[61,] 0.14201228 0.51578276
[62,] -0.35735010 0.14201228
[63,] -0.22779800 -0.35735010
[64,] 0.79113693 -0.22779800
[65,] 0.74889077 0.79113693
[66,] 1.66647212 0.74889077
[67,] 2.99761645 1.66647212
[68,] -4.06844991 2.99761645
[69,] 0.35060268 -4.06844991
[70,] -3.75043854 0.35060268
[71,] -0.53532860 -3.75043854
[72,] 1.25009229 -0.53532860
[73,] 0.71680245 1.25009229
[74,] 0.03579110 0.71680245
[75,] 2.83851708 0.03579110
[76,] -0.46888217 2.83851708
[77,] 1.34281627 -0.46888217
[78,] -2.07024804 1.34281627
[79,] -0.08607342 -2.07024804
[80,] 0.23663000 -0.08607342
[81,] 3.29480081 0.23663000
[82,] 0.17638282 3.29480081
[83,] -1.10297686 0.17638282
[84,] 0.09639724 -1.10297686
[85,] 1.54318563 0.09639724
[86,] -0.39161924 1.54318563
[87,] 0.55910308 -0.39161924
[88,] 1.33170572 0.55910308
[89,] 0.60059395 1.33170572
[90,] -1.64105812 0.60059395
[91,] 0.07378683 -1.64105812
[92,] 0.42688024 0.07378683
[93,] -0.37145386 0.42688024
[94,] -2.29913695 -0.37145386
[95,] 0.73096639 -2.29913695
[96,] 0.13970999 0.73096639
[97,] 1.71667619 0.13970999
[98,] -0.13620972 1.71667619
[99,] -0.42673536 -0.13620972
[100,] -1.25551601 -0.42673536
[101,] 1.16116962 -1.25551601
[102,] 2.66580757 1.16116962
[103,] 0.39204824 2.66580757
[104,] 1.40298015 0.39204824
[105,] -2.34720929 1.40298015
[106,] 1.02843782 -2.34720929
[107,] 0.17952505 1.02843782
[108,] 1.46579905 0.17952505
[109,] -0.27926344 1.46579905
[110,] 1.00904101 -0.27926344
[111,] 0.17068701 1.00904101
[112,] 2.40181087 0.17068701
[113,] -1.79338632 2.40181087
[114,] -2.80590194 -1.79338632
[115,] 1.48723215 -2.80590194
[116,] -1.36040295 1.48723215
[117,] 0.99840852 -1.36040295
[118,] -1.86961845 0.99840852
[119,] 0.39203608 -1.86961845
[120,] -1.18185718 0.39203608
[121,] 0.48482237 -1.18185718
[122,] -2.87555967 0.48482237
[123,] -0.88042602 -2.87555967
[124,] -0.82705092 -0.88042602
[125,] -0.64863590 -0.82705092
[126,] -0.32263705 -0.64863590
[127,] 0.96242451 -0.32263705
[128,] 1.27097148 0.96242451
[129,] -2.79907978 1.27097148
[130,] 1.92592519 -2.79907978
[131,] -3.26867189 1.92592519
[132,] 2.01562407 -3.26867189
[133,] -1.77192869 2.01562407
[134,] -1.44772988 -1.77192869
[135,] 0.07654219 -1.44772988
[136,] 0.86738569 0.07654219
[137,] 0.76569484 0.86738569
[138,] -2.02082092 0.76569484
[139,] -1.18764547 -2.02082092
[140,] -5.24131648 -1.18764547
[141,] 3.10105589 -5.24131648
[142,] 1.79703770 3.10105589
[143,] 0.69487131 1.79703770
[144,] 1.41164121 0.69487131
[145,] -3.95934890 1.41164121
[146,] 2.39986595 -3.95934890
[147,] -1.88401830 2.39986595
[148,] 1.09928235 -1.88401830
[149,] 0.14295104 1.09928235
[150,] -2.93630029 0.14295104
[151,] -1.06226508 -2.93630029
[152,] 2.16479569 -1.06226508
[153,] 4.57145060 2.16479569
[154,] 1.73529856 4.57145060
[155,] -2.29195040 1.73529856
[156,] 0.59925849 -2.29195040
[157,] 1.57324426 0.59925849
[158,] 1.51349686 1.57324426
[159,] 1.23198717 1.51349686
[160,] -0.31706336 1.23198717
[161,] 0.26031766 -0.31706336
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.30591030 -3.37882687
2 2.47263419 -0.30591030
3 2.71742058 2.47263419
4 -1.77698820 2.71742058
5 -2.21234655 -1.77698820
6 3.72587561 -2.21234655
7 -1.90589928 3.72587561
8 -2.17839191 -1.90589928
9 2.11351620 -2.17839191
10 0.51480072 2.11351620
11 -0.33416124 0.51480072
12 0.34595412 -0.33416124
13 0.51478005 0.34595412
14 -0.56566756 0.51478005
15 -0.24775525 -0.56566756
16 0.16096265 -0.24775525
17 3.63212220 0.16096265
18 2.38389964 3.63212220
19 0.64407085 2.38389964
20 0.57545741 0.64407085
21 1.03019635 0.57545741
22 2.50348678 1.03019635
23 1.13057651 2.50348678
24 2.01032507 1.13057651
25 -0.05655799 2.01032507
26 0.84710528 -0.05655799
27 -1.16908205 0.84710528
28 0.39296494 -1.16908205
29 -0.18406493 0.39296494
30 -0.73926663 -0.18406493
31 -0.42658644 -0.73926663
32 -1.19923902 -0.42658644
33 0.40877762 -1.19923902
34 -1.73637131 0.40877762
35 -6.11383362 -1.73637131
36 -1.18431923 -6.11383362
37 -1.82130109 -1.18431923
38 1.64504357 -1.82130109
39 1.30903492 1.64504357
40 0.88859570 1.30903492
41 -1.58730524 0.88859570
42 2.26086196 -1.58730524
43 -0.23249175 2.26086196
44 -0.93062935 -0.23249175
45 -4.61068291 -0.93062935
46 -2.35584409 -4.61068291
47 0.03385524 -2.35584409
48 0.82237232 0.03385524
49 -1.98204807 0.82237232
50 -0.42396327 -1.98204807
51 -0.11685711 -0.42396327
52 -2.66970018 -0.11685711
53 0.86248373 -2.66970018
54 -2.64546147 0.86248373
55 1.31458186 -2.64546147
56 0.04650521 1.31458186
57 0.97935125 0.04650521
58 -0.27106084 0.97935125
59 1.90008443 -0.27106084
60 0.51578276 1.90008443
61 0.14201228 0.51578276
62 -0.35735010 0.14201228
63 -0.22779800 -0.35735010
64 0.79113693 -0.22779800
65 0.74889077 0.79113693
66 1.66647212 0.74889077
67 2.99761645 1.66647212
68 -4.06844991 2.99761645
69 0.35060268 -4.06844991
70 -3.75043854 0.35060268
71 -0.53532860 -3.75043854
72 1.25009229 -0.53532860
73 0.71680245 1.25009229
74 0.03579110 0.71680245
75 2.83851708 0.03579110
76 -0.46888217 2.83851708
77 1.34281627 -0.46888217
78 -2.07024804 1.34281627
79 -0.08607342 -2.07024804
80 0.23663000 -0.08607342
81 3.29480081 0.23663000
82 0.17638282 3.29480081
83 -1.10297686 0.17638282
84 0.09639724 -1.10297686
85 1.54318563 0.09639724
86 -0.39161924 1.54318563
87 0.55910308 -0.39161924
88 1.33170572 0.55910308
89 0.60059395 1.33170572
90 -1.64105812 0.60059395
91 0.07378683 -1.64105812
92 0.42688024 0.07378683
93 -0.37145386 0.42688024
94 -2.29913695 -0.37145386
95 0.73096639 -2.29913695
96 0.13970999 0.73096639
97 1.71667619 0.13970999
98 -0.13620972 1.71667619
99 -0.42673536 -0.13620972
100 -1.25551601 -0.42673536
101 1.16116962 -1.25551601
102 2.66580757 1.16116962
103 0.39204824 2.66580757
104 1.40298015 0.39204824
105 -2.34720929 1.40298015
106 1.02843782 -2.34720929
107 0.17952505 1.02843782
108 1.46579905 0.17952505
109 -0.27926344 1.46579905
110 1.00904101 -0.27926344
111 0.17068701 1.00904101
112 2.40181087 0.17068701
113 -1.79338632 2.40181087
114 -2.80590194 -1.79338632
115 1.48723215 -2.80590194
116 -1.36040295 1.48723215
117 0.99840852 -1.36040295
118 -1.86961845 0.99840852
119 0.39203608 -1.86961845
120 -1.18185718 0.39203608
121 0.48482237 -1.18185718
122 -2.87555967 0.48482237
123 -0.88042602 -2.87555967
124 -0.82705092 -0.88042602
125 -0.64863590 -0.82705092
126 -0.32263705 -0.64863590
127 0.96242451 -0.32263705
128 1.27097148 0.96242451
129 -2.79907978 1.27097148
130 1.92592519 -2.79907978
131 -3.26867189 1.92592519
132 2.01562407 -3.26867189
133 -1.77192869 2.01562407
134 -1.44772988 -1.77192869
135 0.07654219 -1.44772988
136 0.86738569 0.07654219
137 0.76569484 0.86738569
138 -2.02082092 0.76569484
139 -1.18764547 -2.02082092
140 -5.24131648 -1.18764547
141 3.10105589 -5.24131648
142 1.79703770 3.10105589
143 0.69487131 1.79703770
144 1.41164121 0.69487131
145 -3.95934890 1.41164121
146 2.39986595 -3.95934890
147 -1.88401830 2.39986595
148 1.09928235 -1.88401830
149 0.14295104 1.09928235
150 -2.93630029 0.14295104
151 -1.06226508 -2.93630029
152 2.16479569 -1.06226508
153 4.57145060 2.16479569
154 1.73529856 4.57145060
155 -2.29195040 1.73529856
156 0.59925849 -2.29195040
157 1.57324426 0.59925849
158 1.51349686 1.57324426
159 1.23198717 1.51349686
160 -0.31706336 1.23198717
161 0.26031766 -0.31706336
> 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/7epnz1352147316.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/81o241352147316.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9fasf1352147316.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10h09n1352147316.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111fhl1352147316.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/123uwh1352147316.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/130eab1352147316.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/14p1591352147316.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/1532u21352147316.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/16xtui1352147316.tab")
+ }
>
> try(system("convert tmp/1vmor1352147316.ps tmp/1vmor1352147316.png",intern=TRUE))
character(0)
> try(system("convert tmp/290ha1352147316.ps tmp/290ha1352147316.png",intern=TRUE))
character(0)
> try(system("convert tmp/309801352147316.ps tmp/309801352147316.png",intern=TRUE))
character(0)
> try(system("convert tmp/4v3551352147316.ps tmp/4v3551352147316.png",intern=TRUE))
character(0)
> try(system("convert tmp/55q0j1352147316.ps tmp/55q0j1352147316.png",intern=TRUE))
character(0)
> try(system("convert tmp/633ko1352147316.ps tmp/633ko1352147316.png",intern=TRUE))
character(0)
> try(system("convert tmp/7epnz1352147316.ps tmp/7epnz1352147316.png",intern=TRUE))
character(0)
> try(system("convert tmp/81o241352147316.ps tmp/81o241352147316.png",intern=TRUE))
character(0)
> try(system("convert tmp/9fasf1352147316.ps tmp/9fasf1352147316.png",intern=TRUE))
character(0)
> try(system("convert tmp/10h09n1352147316.ps tmp/10h09n1352147316.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.924 1.151 9.062