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 'q()' to quit R.
> x <- array(list(41
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+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','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 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- '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
Connected Separate Learning Software Happiness Depression Belonging
1 41 38 13 12 14 12 53
2 39 32 16 11 18 11 86
3 30 35 19 15 11 14 66
4 31 33 15 6 12 12 67
5 34 37 14 13 16 21 76
6 35 29 13 10 18 12 78
7 39 31 19 12 14 22 53
8 34 36 15 14 14 11 80
9 36 35 14 12 15 10 74
10 37 38 15 6 15 13 76
11 38 31 16 10 17 10 79
12 36 34 16 12 19 8 54
13 38 35 16 12 10 15 67
14 39 38 16 11 16 14 54
15 33 37 17 15 18 10 87
16 32 33 15 12 14 14 58
17 36 32 15 10 14 14 75
18 38 38 20 12 17 11 88
19 39 38 18 11 14 10 64
20 32 32 16 12 16 13 57
21 32 33 16 11 18 7 66
22 31 31 16 12 11 14 68
23 39 38 19 13 14 12 54
24 37 39 16 11 12 14 56
25 39 32 17 9 17 11 86
26 41 32 17 13 9 9 80
27 36 35 16 10 16 11 76
28 33 37 15 14 14 15 69
29 33 33 16 12 15 14 78
30 34 33 14 10 11 13 67
31 31 28 15 12 16 9 80
32 27 32 12 8 13 15 54
33 37 31 14 10 17 10 71
34 34 37 16 12 15 11 84
35 34 30 14 12 14 13 74
36 32 33 7 7 16 8 71
37 29 31 10 6 9 20 63
38 36 33 14 12 15 12 71
39 29 31 16 10 17 10 76
40 35 33 16 10 13 10 69
41 37 32 16 10 15 9 74
42 34 33 14 12 16 14 75
43 38 32 20 15 16 8 54
44 35 33 14 10 12 14 52
45 38 28 14 10 12 11 69
46 37 35 11 12 11 13 68
47 38 39 14 13 15 9 65
48 33 34 15 11 15 11 75
49 36 38 16 11 17 15 74
50 38 32 14 12 13 11 75
51 32 38 16 14 16 10 72
52 32 30 14 10 14 14 67
53 32 33 12 12 11 18 63
54 34 38 16 13 12 14 62
55 32 32 9 5 12 11 63
56 37 32 14 6 15 12 76
57 39 34 16 12 16 13 74
58 29 34 16 12 15 9 67
59 37 36 15 11 12 10 73
60 35 34 16 10 12 15 70
61 30 28 12 7 8 20 53
62 38 34 16 12 13 12 77
63 34 35 16 14 11 12 77
64 31 35 14 11 14 14 52
65 34 31 16 12 15 13 54
66 35 37 17 13 10 11 80
67 36 35 18 14 11 17 66
68 30 27 18 11 12 12 73
69 39 40 12 12 15 13 63
70 35 37 16 12 15 14 69
71 38 36 10 8 14 13 67
72 31 38 14 11 16 15 54
73 34 39 18 14 15 13 81
74 38 41 18 14 15 10 69
75 34 27 16 12 13 11 84
76 39 30 17 9 12 19 80
77 37 37 16 13 17 13 70
78 34 31 16 11 13 17 69
79 28 31 13 12 15 13 77
80 37 27 16 12 13 9 54
81 33 36 16 12 15 11 79
82 37 38 20 12 16 10 30
83 35 37 16 12 15 9 71
84 37 33 15 12 16 12 73
85 32 34 15 11 15 12 72
86 33 31 16 10 14 13 77
87 38 39 14 9 15 13 75
88 33 34 16 12 14 12 69
89 29 32 16 12 13 15 54
90 33 33 15 12 7 22 70
91 31 36 12 9 17 13 73
92 36 32 17 15 13 15 54
93 35 41 16 12 15 13 77
94 32 28 15 12 14 15 82
95 29 30 13 12 13 10 80
96 39 36 16 10 16 11 80
97 37 35 16 13 12 16 69
98 35 31 16 9 14 11 78
99 37 34 16 12 17 11 81
100 32 36 14 10 15 10 76
101 38 36 16 14 17 10 76
102 37 35 16 11 12 16 73
103 36 37 20 15 16 12 85
104 32 28 15 11 11 11 66
105 33 39 16 11 15 16 79
106 40 32 13 12 9 19 68
107 38 35 17 12 16 11 76
108 41 39 16 12 15 16 71
109 36 35 16 11 10 15 54
110 43 42 12 7 10 24 46
111 30 34 16 12 15 14 82
112 31 33 16 14 11 15 74
113 32 41 17 11 13 11 88
114 32 33 13 11 14 15 38
115 37 34 12 10 18 12 76
116 37 32 18 13 16 10 86
117 33 40 14 13 14 14 54
118 34 40 14 8 14 13 70
119 33 35 13 11 14 9 69
120 38 36 16 12 14 15 90
121 33 37 13 11 12 15 54
122 31 27 16 13 14 14 76
123 38 39 13 12 15 11 89
124 37 38 16 14 15 8 76
125 33 31 15 13 15 11 73
126 31 33 16 15 13 11 79
127 39 32 15 10 17 8 90
128 44 39 17 11 17 10 74
129 33 36 15 9 19 11 81
130 35 33 12 11 15 13 72
131 32 33 16 10 13 11 71
132 28 32 10 11 9 20 66
133 40 37 16 8 15 10 77
134 27 30 12 11 15 15 65
135 37 38 14 12 15 12 74
136 32 29 15 12 16 14 82
137 28 22 13 9 11 23 54
138 34 35 15 11 14 14 63
139 30 35 11 10 11 16 54
140 35 34 12 8 15 11 64
141 31 35 8 9 13 12 69
142 32 34 16 8 15 10 54
143 30 34 15 9 16 14 84
144 30 35 17 15 14 12 86
145 31 23 16 11 15 12 77
146 40 31 10 8 16 11 89
147 32 27 18 13 16 12 76
148 36 36 13 12 11 13 60
149 32 31 16 12 12 11 75
150 35 32 13 9 9 19 73
151 38 39 10 7 16 12 85
152 42 37 15 13 13 17 79
153 34 38 16 9 16 9 71
154 35 39 16 6 12 12 72
155 35 34 14 8 9 19 69
156 33 31 10 8 13 18 78
157 36 32 17 15 13 15 54
158 32 37 13 6 14 14 69
159 33 36 15 9 19 11 81
160 34 32 16 11 13 9 84
161 32 35 12 8 12 18 84
162 34 36 13 8 13 16 69
Belonging_Final t
1 32 1
2 51 2
3 42 3
4 41 4
5 46 5
6 47 6
7 37 7
8 49 8
9 45 9
10 47 10
11 49 11
12 33 12
13 42 13
14 33 14
15 53 15
16 36 16
17 45 17
18 54 18
19 41 19
20 36 20
21 41 21
22 44 22
23 33 23
24 37 24
25 52 25
26 47 26
27 43 27
28 44 28
29 45 29
30 44 30
31 49 31
32 33 32
33 43 33
34 54 34
35 42 35
36 44 36
37 37 37
38 43 38
39 46 39
40 42 40
41 45 41
42 44 42
43 33 43
44 31 44
45 42 45
46 40 46
47 43 47
48 46 48
49 42 49
50 45 50
51 44 51
52 40 52
53 37 53
54 46 54
55 36 55
56 47 56
57 45 57
58 42 58
59 43 59
60 43 60
61 32 61
62 45 62
63 45 63
64 31 64
65 33 65
66 49 66
67 42 67
68 41 68
69 38 69
70 42 70
71 44 71
72 33 72
73 48 73
74 40 74
75 50 75
76 49 76
77 43 77
78 44 78
79 47 79
80 33 80
81 46 81
82 0 82
83 45 83
84 43 84
85 44 85
86 47 86
87 45 87
88 42 88
89 33 89
90 43 90
91 46 91
92 33 92
93 46 93
94 48 94
95 47 95
96 47 96
97 43 97
98 46 98
99 48 99
100 46 100
101 45 101
102 45 102
103 52 103
104 42 104
105 47 105
106 41 106
107 47 107
108 43 108
109 33 109
110 30 110
111 49 111
112 44 112
113 55 113
114 11 114
115 47 115
116 53 116
117 33 117
118 44 118
119 42 119
120 55 120
121 33 121
122 46 122
123 54 123
124 47 124
125 45 125
126 47 126
127 55 127
128 44 128
129 53 129
130 44 130
131 42 131
132 40 132
133 46 133
134 40 134
135 46 135
136 53 136
137 33 137
138 42 138
139 35 139
140 40 140
141 41 141
142 33 142
143 51 143
144 53 144
145 46 145
146 55 146
147 47 147
148 38 148
149 46 149
150 46 150
151 53 151
152 47 152
153 41 153
154 44 154
155 43 155
156 51 156
157 33 157
158 43 158
159 53 159
160 51 160
161 50 161
162 46 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Separate Learning Software
18.646799 0.342547 0.300043 -0.149104
Happiness Depression Belonging Belonging_Final
0.022260 -0.010015 0.047026 -0.032781
t
-0.007798
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.3632 -2.2754 -0.2136 2.2061 7.4368
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.646799 4.187023 4.453 1.62e-05 ***
Separate 0.342547 0.070524 4.857 2.92e-06 ***
Learning 0.300043 0.134014 2.239 0.0266 *
Software -0.149104 0.136957 -1.089 0.2780
Happiness 0.022260 0.128935 0.173 0.8632
Depression -0.010015 0.095599 -0.105 0.9167
Belonging 0.047026 0.075435 0.623 0.5340
Belonging_Final -0.032781 0.107857 -0.304 0.7616
t -0.007798 0.005477 -1.424 0.1565
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.11 on 153 degrees of freedom
Multiple R-squared: 0.1931, Adjusted R-squared: 0.1509
F-statistic: 4.576 on 8 and 153 DF, p-value: 5.111e-05
> 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,] 0.37373849 0.74747699 0.626261507
[2,] 0.95414819 0.09170363 0.045851813
[3,] 0.92074874 0.15850251 0.079251257
[4,] 0.89338294 0.21323412 0.106617058
[5,] 0.89745449 0.20509102 0.102545509
[6,] 0.87145284 0.25709431 0.128547156
[7,] 0.84426411 0.31147178 0.155735892
[8,] 0.80190302 0.39619396 0.198096982
[9,] 0.81420602 0.37158795 0.185793977
[10,] 0.81519678 0.36960644 0.184803220
[11,] 0.76324676 0.47350648 0.236753239
[12,] 0.73704186 0.52591628 0.262958141
[13,] 0.67495364 0.65009272 0.325046362
[14,] 0.68622434 0.62755132 0.313775658
[15,] 0.81329300 0.37341400 0.186706999
[16,] 0.81012236 0.37975528 0.189877641
[17,] 0.76842269 0.46315462 0.231577310
[18,] 0.76488267 0.47023465 0.235117326
[19,] 0.71409926 0.57180148 0.285900738
[20,] 0.66949817 0.66100365 0.330501826
[21,] 0.77921125 0.44157750 0.220788748
[22,] 0.80121470 0.39757061 0.198785305
[23,] 0.76075252 0.47849496 0.239247479
[24,] 0.71414036 0.57171928 0.285859640
[25,] 0.67387940 0.65224120 0.326120598
[26,] 0.65466843 0.69066314 0.345331570
[27,] 0.63757973 0.72484053 0.362420266
[28,] 0.72079147 0.55841706 0.279208528
[29,] 0.67337008 0.65325984 0.326629922
[30,] 0.65664614 0.68670772 0.343353862
[31,] 0.60750505 0.78498991 0.392494954
[32,] 0.58540676 0.82918647 0.414593236
[33,] 0.54091252 0.91817495 0.459087476
[34,] 0.64539984 0.70920032 0.354600160
[35,] 0.65844059 0.68311882 0.341559408
[36,] 0.64699263 0.70601473 0.353007367
[37,] 0.61240181 0.77519638 0.387598192
[38,] 0.56295223 0.87409554 0.437047771
[39,] 0.59214877 0.81570246 0.407851230
[40,] 0.62597877 0.74804247 0.374021233
[41,] 0.58199544 0.83600912 0.418004562
[42,] 0.53515785 0.92968429 0.464842146
[43,] 0.48800370 0.97600740 0.511996301
[44,] 0.44085320 0.88170639 0.559146805
[45,] 0.43950923 0.87901846 0.560490770
[46,] 0.47449148 0.94898296 0.525508522
[47,] 0.59723578 0.80552845 0.402764224
[48,] 0.55805130 0.88389740 0.441948701
[49,] 0.50887019 0.98225961 0.491129806
[50,] 0.47069467 0.94138934 0.529305332
[51,] 0.45788914 0.91577828 0.542110861
[52,] 0.41987887 0.83975774 0.580121130
[53,] 0.41769557 0.83539114 0.582304432
[54,] 0.37228071 0.74456142 0.627719288
[55,] 0.33238553 0.66477105 0.667614474
[56,] 0.29588326 0.59176652 0.704116739
[57,] 0.31845842 0.63691683 0.681541584
[58,] 0.33961811 0.67923622 0.660381891
[59,] 0.29835290 0.59670580 0.701647101
[60,] 0.33733680 0.67467361 0.662663197
[61,] 0.36711404 0.73422808 0.632885962
[62,] 0.35985513 0.71971025 0.640144873
[63,] 0.31773362 0.63546724 0.682266381
[64,] 0.28465635 0.56931271 0.715343647
[65,] 0.35071084 0.70142169 0.649289156
[66,] 0.31584940 0.63169879 0.684150604
[67,] 0.27542235 0.55084470 0.724577652
[68,] 0.33190769 0.66381537 0.668092314
[69,] 0.41043681 0.82087362 0.589563192
[70,] 0.39499637 0.78999274 0.605003630
[71,] 0.35048101 0.70096202 0.649518988
[72,] 0.30880010 0.61760020 0.691199900
[73,] 0.30184570 0.60369140 0.698154301
[74,] 0.28380787 0.56761575 0.716192127
[75,] 0.24784868 0.49569736 0.752151319
[76,] 0.22542322 0.45084644 0.774576778
[77,] 0.19798713 0.39597427 0.802012866
[78,] 0.22700677 0.45401354 0.772993228
[79,] 0.19623323 0.39246645 0.803766775
[80,] 0.21044557 0.42089114 0.789554430
[81,] 0.19924605 0.39849211 0.800753945
[82,] 0.18743295 0.37486590 0.812567052
[83,] 0.15918427 0.31836854 0.840815730
[84,] 0.16957070 0.33914140 0.830429302
[85,] 0.16673672 0.33347343 0.833263283
[86,] 0.15299065 0.30598131 0.847009345
[87,] 0.12769649 0.25539298 0.872303509
[88,] 0.11210136 0.22420271 0.887898643
[89,] 0.11364272 0.22728544 0.886357282
[90,] 0.10553844 0.21107687 0.894461565
[91,] 0.09177800 0.18355601 0.908221997
[92,] 0.07458893 0.14917786 0.925411072
[93,] 0.05901163 0.11802325 0.940988375
[94,] 0.07111166 0.14222332 0.928888338
[95,] 0.16544743 0.33089486 0.834552568
[96,] 0.15413426 0.30826852 0.845865741
[97,] 0.17926596 0.35853191 0.820734043
[98,] 0.16709204 0.33418409 0.832907957
[99,] 0.40108028 0.80216055 0.598919723
[100,] 0.45353039 0.90706077 0.546469613
[101,] 0.42743411 0.85486823 0.572565887
[102,] 0.53634281 0.92731438 0.463657191
[103,] 0.48750593 0.97501187 0.512494066
[104,] 0.47759648 0.95519296 0.522403522
[105,] 0.44621770 0.89243539 0.553782305
[106,] 0.41267834 0.82535667 0.587321664
[107,] 0.38391290 0.76782581 0.616087096
[108,] 0.33823361 0.67646721 0.661766394
[109,] 0.30430324 0.60860648 0.695696761
[110,] 0.26020932 0.52041864 0.739790682
[111,] 0.21974721 0.43949442 0.780252792
[112,] 0.18811488 0.37622976 0.811885120
[113,] 0.15445954 0.30891908 0.845540462
[114,] 0.12251832 0.24503665 0.877481677
[115,] 0.13148605 0.26297210 0.868513952
[116,] 0.14687268 0.29374536 0.853127320
[117,] 0.33479123 0.66958246 0.665208772
[118,] 0.29455413 0.58910826 0.705445870
[119,] 0.26596059 0.53192117 0.734039413
[120,] 0.22661983 0.45323966 0.773380172
[121,] 0.25411150 0.50822300 0.745888500
[122,] 0.36843872 0.73687743 0.631561285
[123,] 0.45378716 0.90757433 0.546212836
[124,] 0.42123043 0.84246086 0.578769572
[125,] 0.35235468 0.70470936 0.647645322
[126,] 0.29665248 0.59330497 0.703347517
[127,] 0.25173532 0.50347065 0.748264676
[128,] 0.26150443 0.52300886 0.738495569
[129,] 0.21950697 0.43901395 0.780493027
[130,] 0.40889779 0.81779559 0.591102206
[131,] 0.33809422 0.67618844 0.661905781
[132,] 0.36387718 0.72775436 0.636122819
[133,] 0.77619564 0.44760871 0.223804356
[134,] 0.68637544 0.62724913 0.313624563
[135,] 0.94509794 0.10980411 0.054902057
[136,] 0.89633998 0.20732004 0.103660018
[137,] 0.92185615 0.15628770 0.078143852
[138,] 0.98731414 0.02537173 0.012685863
[139,] 0.99416682 0.01166636 0.005833178
> postscript(file="/var/wessaorg/rcomp/tmp/1pl221351785284.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2mtji1351785284.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33scx1351785284.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4c77u1351785284.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5kcm01351785284.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
5.598057724 3.583826506 -5.908369794 -4.479341352 -1.756193869 1.648782542
7 8 9 10 11 12
4.506469475 -1.686583354 0.784353233 -0.428602894 3.123315155 0.988286659
13 14 15 16 17 18
2.607674136 2.611461374 -3.722641141 -2.256302584 1.291419591 -0.371205753
19 20 21 22 23 24
1.846811936 -2.190116649 -3.037909944 -2.965697302 2.104212067 0.473005096
25 26 27 28 29 30
3.219966832 6.100484721 -0.145438323 -2.479731497 -2.122725593 -0.249519796
31 32 33 34 35 36
-2.129616647 -6.363241469 3.074476676 -2.471097331 0.653794554 -0.899224692
37 38 39 40 41 42
-3.832822550 1.791131039 -5.615609432 -0.005806021 2.153215902 -0.335230749
43 44 45 46 47 48
3.229035365 1.126643994 5.378278567 3.210340153 2.207245532 -2.022360842
49 50 51 52 53 54
-0.773350900 4.139215270 -4.178643931 -1.238215179 -1.163161605 -1.639432551
55 56 57 58 59 60
-1.073765777 2.275407468 3.908896134 -5.870266548 1.430796911 -0.134305694
61 62 63 64 65 66
-1.740399307 2.863573068 -1.128447157 -3.297908793 0.568329336 -1.237003927
67 68 69 70 71 72
0.771676663 -3.361761023 3.457441074 -0.848309722 3.877734387 -4.326162202
73 74 75 76 77 78
-3.189529042 0.405195188 1.187480439 4.677985531 1.286598919 0.260377065
79 80 81 82 83 84
-5.045037835 5.059944906 -2.789167910 0.097444589 -0.792722188 2.733477331
85 86 87 88 89 90
-2.648308922 -1.166529823 1.558104948 -1.678077606 -4.522518609 -0.778165857
91 92 93 94 95 96
-3.700664875 2.648144645 -2.294245985 -0.660574077 -3.704327853 2.993086161
97 98 99 100 101 102
2.316021657 0.678103443 1.963276497 -3.208068365 2.718759289 1.934260283
103 104 105 106 107 108
-0.810737156 -0.149248646 -3.695908303 7.243157421 2.607678755 4.721673950
109 110 111 112 113 114
1.523474274 7.105199395 -4.882828997 -2.922916441 -5.785204022 -0.910116993
115 116 117 118 119 120
3.180109272 2.270971483 -2.327638686 -2.467203419 -1.057911451 2.355010301
121 122 123 124 125 126
-1.212436801 -1.044046848 2.202816974 1.303068188 -0.034805931 -2.886010096
127 128 129 130 131 132
4.644731578 7.215576396 -2.515767623 1.955285163 -2.380238149 -3.731787671
133 134 135 136 137 138
3.761393784 -4.767794301 1.792053192 -0.566242406 -1.145296402 -0.177607213
139 140 141 142 143 144
-2.838163267 1.468458709 -1.664828043 -2.485338261 -4.831317839 -4.875527433
145 146 147 148 149 150
0.117966659 7.436775043 -0.480955908 2.373734738 -1.291295099 2.067724626
151 152 153 154 155 156
2.718842520 7.008471290 -2.190115213 -1.801774595 1.062234508 1.037801606
157 158 159 160 161 162
3.155005205 -3.101553975 -2.281831980 0.001210389 -2.186158767 -0.288974678
> postscript(file="/var/wessaorg/rcomp/tmp/6llbb1351785284.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 5.598057724 NA
1 3.583826506 5.598057724
2 -5.908369794 3.583826506
3 -4.479341352 -5.908369794
4 -1.756193869 -4.479341352
5 1.648782542 -1.756193869
6 4.506469475 1.648782542
7 -1.686583354 4.506469475
8 0.784353233 -1.686583354
9 -0.428602894 0.784353233
10 3.123315155 -0.428602894
11 0.988286659 3.123315155
12 2.607674136 0.988286659
13 2.611461374 2.607674136
14 -3.722641141 2.611461374
15 -2.256302584 -3.722641141
16 1.291419591 -2.256302584
17 -0.371205753 1.291419591
18 1.846811936 -0.371205753
19 -2.190116649 1.846811936
20 -3.037909944 -2.190116649
21 -2.965697302 -3.037909944
22 2.104212067 -2.965697302
23 0.473005096 2.104212067
24 3.219966832 0.473005096
25 6.100484721 3.219966832
26 -0.145438323 6.100484721
27 -2.479731497 -0.145438323
28 -2.122725593 -2.479731497
29 -0.249519796 -2.122725593
30 -2.129616647 -0.249519796
31 -6.363241469 -2.129616647
32 3.074476676 -6.363241469
33 -2.471097331 3.074476676
34 0.653794554 -2.471097331
35 -0.899224692 0.653794554
36 -3.832822550 -0.899224692
37 1.791131039 -3.832822550
38 -5.615609432 1.791131039
39 -0.005806021 -5.615609432
40 2.153215902 -0.005806021
41 -0.335230749 2.153215902
42 3.229035365 -0.335230749
43 1.126643994 3.229035365
44 5.378278567 1.126643994
45 3.210340153 5.378278567
46 2.207245532 3.210340153
47 -2.022360842 2.207245532
48 -0.773350900 -2.022360842
49 4.139215270 -0.773350900
50 -4.178643931 4.139215270
51 -1.238215179 -4.178643931
52 -1.163161605 -1.238215179
53 -1.639432551 -1.163161605
54 -1.073765777 -1.639432551
55 2.275407468 -1.073765777
56 3.908896134 2.275407468
57 -5.870266548 3.908896134
58 1.430796911 -5.870266548
59 -0.134305694 1.430796911
60 -1.740399307 -0.134305694
61 2.863573068 -1.740399307
62 -1.128447157 2.863573068
63 -3.297908793 -1.128447157
64 0.568329336 -3.297908793
65 -1.237003927 0.568329336
66 0.771676663 -1.237003927
67 -3.361761023 0.771676663
68 3.457441074 -3.361761023
69 -0.848309722 3.457441074
70 3.877734387 -0.848309722
71 -4.326162202 3.877734387
72 -3.189529042 -4.326162202
73 0.405195188 -3.189529042
74 1.187480439 0.405195188
75 4.677985531 1.187480439
76 1.286598919 4.677985531
77 0.260377065 1.286598919
78 -5.045037835 0.260377065
79 5.059944906 -5.045037835
80 -2.789167910 5.059944906
81 0.097444589 -2.789167910
82 -0.792722188 0.097444589
83 2.733477331 -0.792722188
84 -2.648308922 2.733477331
85 -1.166529823 -2.648308922
86 1.558104948 -1.166529823
87 -1.678077606 1.558104948
88 -4.522518609 -1.678077606
89 -0.778165857 -4.522518609
90 -3.700664875 -0.778165857
91 2.648144645 -3.700664875
92 -2.294245985 2.648144645
93 -0.660574077 -2.294245985
94 -3.704327853 -0.660574077
95 2.993086161 -3.704327853
96 2.316021657 2.993086161
97 0.678103443 2.316021657
98 1.963276497 0.678103443
99 -3.208068365 1.963276497
100 2.718759289 -3.208068365
101 1.934260283 2.718759289
102 -0.810737156 1.934260283
103 -0.149248646 -0.810737156
104 -3.695908303 -0.149248646
105 7.243157421 -3.695908303
106 2.607678755 7.243157421
107 4.721673950 2.607678755
108 1.523474274 4.721673950
109 7.105199395 1.523474274
110 -4.882828997 7.105199395
111 -2.922916441 -4.882828997
112 -5.785204022 -2.922916441
113 -0.910116993 -5.785204022
114 3.180109272 -0.910116993
115 2.270971483 3.180109272
116 -2.327638686 2.270971483
117 -2.467203419 -2.327638686
118 -1.057911451 -2.467203419
119 2.355010301 -1.057911451
120 -1.212436801 2.355010301
121 -1.044046848 -1.212436801
122 2.202816974 -1.044046848
123 1.303068188 2.202816974
124 -0.034805931 1.303068188
125 -2.886010096 -0.034805931
126 4.644731578 -2.886010096
127 7.215576396 4.644731578
128 -2.515767623 7.215576396
129 1.955285163 -2.515767623
130 -2.380238149 1.955285163
131 -3.731787671 -2.380238149
132 3.761393784 -3.731787671
133 -4.767794301 3.761393784
134 1.792053192 -4.767794301
135 -0.566242406 1.792053192
136 -1.145296402 -0.566242406
137 -0.177607213 -1.145296402
138 -2.838163267 -0.177607213
139 1.468458709 -2.838163267
140 -1.664828043 1.468458709
141 -2.485338261 -1.664828043
142 -4.831317839 -2.485338261
143 -4.875527433 -4.831317839
144 0.117966659 -4.875527433
145 7.436775043 0.117966659
146 -0.480955908 7.436775043
147 2.373734738 -0.480955908
148 -1.291295099 2.373734738
149 2.067724626 -1.291295099
150 2.718842520 2.067724626
151 7.008471290 2.718842520
152 -2.190115213 7.008471290
153 -1.801774595 -2.190115213
154 1.062234508 -1.801774595
155 1.037801606 1.062234508
156 3.155005205 1.037801606
157 -3.101553975 3.155005205
158 -2.281831980 -3.101553975
159 0.001210389 -2.281831980
160 -2.186158767 0.001210389
161 -0.288974678 -2.186158767
162 NA -0.288974678
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.583826506 5.598057724
[2,] -5.908369794 3.583826506
[3,] -4.479341352 -5.908369794
[4,] -1.756193869 -4.479341352
[5,] 1.648782542 -1.756193869
[6,] 4.506469475 1.648782542
[7,] -1.686583354 4.506469475
[8,] 0.784353233 -1.686583354
[9,] -0.428602894 0.784353233
[10,] 3.123315155 -0.428602894
[11,] 0.988286659 3.123315155
[12,] 2.607674136 0.988286659
[13,] 2.611461374 2.607674136
[14,] -3.722641141 2.611461374
[15,] -2.256302584 -3.722641141
[16,] 1.291419591 -2.256302584
[17,] -0.371205753 1.291419591
[18,] 1.846811936 -0.371205753
[19,] -2.190116649 1.846811936
[20,] -3.037909944 -2.190116649
[21,] -2.965697302 -3.037909944
[22,] 2.104212067 -2.965697302
[23,] 0.473005096 2.104212067
[24,] 3.219966832 0.473005096
[25,] 6.100484721 3.219966832
[26,] -0.145438323 6.100484721
[27,] -2.479731497 -0.145438323
[28,] -2.122725593 -2.479731497
[29,] -0.249519796 -2.122725593
[30,] -2.129616647 -0.249519796
[31,] -6.363241469 -2.129616647
[32,] 3.074476676 -6.363241469
[33,] -2.471097331 3.074476676
[34,] 0.653794554 -2.471097331
[35,] -0.899224692 0.653794554
[36,] -3.832822550 -0.899224692
[37,] 1.791131039 -3.832822550
[38,] -5.615609432 1.791131039
[39,] -0.005806021 -5.615609432
[40,] 2.153215902 -0.005806021
[41,] -0.335230749 2.153215902
[42,] 3.229035365 -0.335230749
[43,] 1.126643994 3.229035365
[44,] 5.378278567 1.126643994
[45,] 3.210340153 5.378278567
[46,] 2.207245532 3.210340153
[47,] -2.022360842 2.207245532
[48,] -0.773350900 -2.022360842
[49,] 4.139215270 -0.773350900
[50,] -4.178643931 4.139215270
[51,] -1.238215179 -4.178643931
[52,] -1.163161605 -1.238215179
[53,] -1.639432551 -1.163161605
[54,] -1.073765777 -1.639432551
[55,] 2.275407468 -1.073765777
[56,] 3.908896134 2.275407468
[57,] -5.870266548 3.908896134
[58,] 1.430796911 -5.870266548
[59,] -0.134305694 1.430796911
[60,] -1.740399307 -0.134305694
[61,] 2.863573068 -1.740399307
[62,] -1.128447157 2.863573068
[63,] -3.297908793 -1.128447157
[64,] 0.568329336 -3.297908793
[65,] -1.237003927 0.568329336
[66,] 0.771676663 -1.237003927
[67,] -3.361761023 0.771676663
[68,] 3.457441074 -3.361761023
[69,] -0.848309722 3.457441074
[70,] 3.877734387 -0.848309722
[71,] -4.326162202 3.877734387
[72,] -3.189529042 -4.326162202
[73,] 0.405195188 -3.189529042
[74,] 1.187480439 0.405195188
[75,] 4.677985531 1.187480439
[76,] 1.286598919 4.677985531
[77,] 0.260377065 1.286598919
[78,] -5.045037835 0.260377065
[79,] 5.059944906 -5.045037835
[80,] -2.789167910 5.059944906
[81,] 0.097444589 -2.789167910
[82,] -0.792722188 0.097444589
[83,] 2.733477331 -0.792722188
[84,] -2.648308922 2.733477331
[85,] -1.166529823 -2.648308922
[86,] 1.558104948 -1.166529823
[87,] -1.678077606 1.558104948
[88,] -4.522518609 -1.678077606
[89,] -0.778165857 -4.522518609
[90,] -3.700664875 -0.778165857
[91,] 2.648144645 -3.700664875
[92,] -2.294245985 2.648144645
[93,] -0.660574077 -2.294245985
[94,] -3.704327853 -0.660574077
[95,] 2.993086161 -3.704327853
[96,] 2.316021657 2.993086161
[97,] 0.678103443 2.316021657
[98,] 1.963276497 0.678103443
[99,] -3.208068365 1.963276497
[100,] 2.718759289 -3.208068365
[101,] 1.934260283 2.718759289
[102,] -0.810737156 1.934260283
[103,] -0.149248646 -0.810737156
[104,] -3.695908303 -0.149248646
[105,] 7.243157421 -3.695908303
[106,] 2.607678755 7.243157421
[107,] 4.721673950 2.607678755
[108,] 1.523474274 4.721673950
[109,] 7.105199395 1.523474274
[110,] -4.882828997 7.105199395
[111,] -2.922916441 -4.882828997
[112,] -5.785204022 -2.922916441
[113,] -0.910116993 -5.785204022
[114,] 3.180109272 -0.910116993
[115,] 2.270971483 3.180109272
[116,] -2.327638686 2.270971483
[117,] -2.467203419 -2.327638686
[118,] -1.057911451 -2.467203419
[119,] 2.355010301 -1.057911451
[120,] -1.212436801 2.355010301
[121,] -1.044046848 -1.212436801
[122,] 2.202816974 -1.044046848
[123,] 1.303068188 2.202816974
[124,] -0.034805931 1.303068188
[125,] -2.886010096 -0.034805931
[126,] 4.644731578 -2.886010096
[127,] 7.215576396 4.644731578
[128,] -2.515767623 7.215576396
[129,] 1.955285163 -2.515767623
[130,] -2.380238149 1.955285163
[131,] -3.731787671 -2.380238149
[132,] 3.761393784 -3.731787671
[133,] -4.767794301 3.761393784
[134,] 1.792053192 -4.767794301
[135,] -0.566242406 1.792053192
[136,] -1.145296402 -0.566242406
[137,] -0.177607213 -1.145296402
[138,] -2.838163267 -0.177607213
[139,] 1.468458709 -2.838163267
[140,] -1.664828043 1.468458709
[141,] -2.485338261 -1.664828043
[142,] -4.831317839 -2.485338261
[143,] -4.875527433 -4.831317839
[144,] 0.117966659 -4.875527433
[145,] 7.436775043 0.117966659
[146,] -0.480955908 7.436775043
[147,] 2.373734738 -0.480955908
[148,] -1.291295099 2.373734738
[149,] 2.067724626 -1.291295099
[150,] 2.718842520 2.067724626
[151,] 7.008471290 2.718842520
[152,] -2.190115213 7.008471290
[153,] -1.801774595 -2.190115213
[154,] 1.062234508 -1.801774595
[155,] 1.037801606 1.062234508
[156,] 3.155005205 1.037801606
[157,] -3.101553975 3.155005205
[158,] -2.281831980 -3.101553975
[159,] 0.001210389 -2.281831980
[160,] -2.186158767 0.001210389
[161,] -0.288974678 -2.186158767
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.583826506 5.598057724
2 -5.908369794 3.583826506
3 -4.479341352 -5.908369794
4 -1.756193869 -4.479341352
5 1.648782542 -1.756193869
6 4.506469475 1.648782542
7 -1.686583354 4.506469475
8 0.784353233 -1.686583354
9 -0.428602894 0.784353233
10 3.123315155 -0.428602894
11 0.988286659 3.123315155
12 2.607674136 0.988286659
13 2.611461374 2.607674136
14 -3.722641141 2.611461374
15 -2.256302584 -3.722641141
16 1.291419591 -2.256302584
17 -0.371205753 1.291419591
18 1.846811936 -0.371205753
19 -2.190116649 1.846811936
20 -3.037909944 -2.190116649
21 -2.965697302 -3.037909944
22 2.104212067 -2.965697302
23 0.473005096 2.104212067
24 3.219966832 0.473005096
25 6.100484721 3.219966832
26 -0.145438323 6.100484721
27 -2.479731497 -0.145438323
28 -2.122725593 -2.479731497
29 -0.249519796 -2.122725593
30 -2.129616647 -0.249519796
31 -6.363241469 -2.129616647
32 3.074476676 -6.363241469
33 -2.471097331 3.074476676
34 0.653794554 -2.471097331
35 -0.899224692 0.653794554
36 -3.832822550 -0.899224692
37 1.791131039 -3.832822550
38 -5.615609432 1.791131039
39 -0.005806021 -5.615609432
40 2.153215902 -0.005806021
41 -0.335230749 2.153215902
42 3.229035365 -0.335230749
43 1.126643994 3.229035365
44 5.378278567 1.126643994
45 3.210340153 5.378278567
46 2.207245532 3.210340153
47 -2.022360842 2.207245532
48 -0.773350900 -2.022360842
49 4.139215270 -0.773350900
50 -4.178643931 4.139215270
51 -1.238215179 -4.178643931
52 -1.163161605 -1.238215179
53 -1.639432551 -1.163161605
54 -1.073765777 -1.639432551
55 2.275407468 -1.073765777
56 3.908896134 2.275407468
57 -5.870266548 3.908896134
58 1.430796911 -5.870266548
59 -0.134305694 1.430796911
60 -1.740399307 -0.134305694
61 2.863573068 -1.740399307
62 -1.128447157 2.863573068
63 -3.297908793 -1.128447157
64 0.568329336 -3.297908793
65 -1.237003927 0.568329336
66 0.771676663 -1.237003927
67 -3.361761023 0.771676663
68 3.457441074 -3.361761023
69 -0.848309722 3.457441074
70 3.877734387 -0.848309722
71 -4.326162202 3.877734387
72 -3.189529042 -4.326162202
73 0.405195188 -3.189529042
74 1.187480439 0.405195188
75 4.677985531 1.187480439
76 1.286598919 4.677985531
77 0.260377065 1.286598919
78 -5.045037835 0.260377065
79 5.059944906 -5.045037835
80 -2.789167910 5.059944906
81 0.097444589 -2.789167910
82 -0.792722188 0.097444589
83 2.733477331 -0.792722188
84 -2.648308922 2.733477331
85 -1.166529823 -2.648308922
86 1.558104948 -1.166529823
87 -1.678077606 1.558104948
88 -4.522518609 -1.678077606
89 -0.778165857 -4.522518609
90 -3.700664875 -0.778165857
91 2.648144645 -3.700664875
92 -2.294245985 2.648144645
93 -0.660574077 -2.294245985
94 -3.704327853 -0.660574077
95 2.993086161 -3.704327853
96 2.316021657 2.993086161
97 0.678103443 2.316021657
98 1.963276497 0.678103443
99 -3.208068365 1.963276497
100 2.718759289 -3.208068365
101 1.934260283 2.718759289
102 -0.810737156 1.934260283
103 -0.149248646 -0.810737156
104 -3.695908303 -0.149248646
105 7.243157421 -3.695908303
106 2.607678755 7.243157421
107 4.721673950 2.607678755
108 1.523474274 4.721673950
109 7.105199395 1.523474274
110 -4.882828997 7.105199395
111 -2.922916441 -4.882828997
112 -5.785204022 -2.922916441
113 -0.910116993 -5.785204022
114 3.180109272 -0.910116993
115 2.270971483 3.180109272
116 -2.327638686 2.270971483
117 -2.467203419 -2.327638686
118 -1.057911451 -2.467203419
119 2.355010301 -1.057911451
120 -1.212436801 2.355010301
121 -1.044046848 -1.212436801
122 2.202816974 -1.044046848
123 1.303068188 2.202816974
124 -0.034805931 1.303068188
125 -2.886010096 -0.034805931
126 4.644731578 -2.886010096
127 7.215576396 4.644731578
128 -2.515767623 7.215576396
129 1.955285163 -2.515767623
130 -2.380238149 1.955285163
131 -3.731787671 -2.380238149
132 3.761393784 -3.731787671
133 -4.767794301 3.761393784
134 1.792053192 -4.767794301
135 -0.566242406 1.792053192
136 -1.145296402 -0.566242406
137 -0.177607213 -1.145296402
138 -2.838163267 -0.177607213
139 1.468458709 -2.838163267
140 -1.664828043 1.468458709
141 -2.485338261 -1.664828043
142 -4.831317839 -2.485338261
143 -4.875527433 -4.831317839
144 0.117966659 -4.875527433
145 7.436775043 0.117966659
146 -0.480955908 7.436775043
147 2.373734738 -0.480955908
148 -1.291295099 2.373734738
149 2.067724626 -1.291295099
150 2.718842520 2.067724626
151 7.008471290 2.718842520
152 -2.190115213 7.008471290
153 -1.801774595 -2.190115213
154 1.062234508 -1.801774595
155 1.037801606 1.062234508
156 3.155005205 1.037801606
157 -3.101553975 3.155005205
158 -2.281831980 -3.101553975
159 0.001210389 -2.281831980
160 -2.186158767 0.001210389
161 -0.288974678 -2.186158767
> 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/7xbft1351785284.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8gp911351785284.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/917301351785284.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10lf1d1351785284.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11u2kr1351785284.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/12mf8h1351785284.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/13iky41351785284.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/14miyf1351785284.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/153k591351785284.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/16sc101351785284.tab")
+ }
>
> try(system("convert tmp/1pl221351785284.ps tmp/1pl221351785284.png",intern=TRUE))
character(0)
> try(system("convert tmp/2mtji1351785284.ps tmp/2mtji1351785284.png",intern=TRUE))
character(0)
> try(system("convert tmp/33scx1351785284.ps tmp/33scx1351785284.png",intern=TRUE))
character(0)
> try(system("convert tmp/4c77u1351785284.ps tmp/4c77u1351785284.png",intern=TRUE))
character(0)
> try(system("convert tmp/5kcm01351785284.ps tmp/5kcm01351785284.png",intern=TRUE))
character(0)
> try(system("convert tmp/6llbb1351785284.ps tmp/6llbb1351785284.png",intern=TRUE))
character(0)
> try(system("convert tmp/7xbft1351785284.ps tmp/7xbft1351785284.png",intern=TRUE))
character(0)
> try(system("convert tmp/8gp911351785284.ps tmp/8gp911351785284.png",intern=TRUE))
character(0)
> try(system("convert tmp/917301351785284.ps tmp/917301351785284.png",intern=TRUE))
character(0)
> try(system("convert tmp/10lf1d1351785284.ps tmp/10lf1d1351785284.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.229 0.843 9.072