R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(13
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+ ,16)
+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('percieved_competence'
+ ,'gender'
+ ,'age'
+ ,'connected'
+ ,'seperate'
+ ,'software'
+ ,'happiness'
+ ,'depression')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('percieved_competence','gender','age','connected','seperate','software','happiness','depression'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '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
> 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
percieved_competence gender age connected seperate software happiness
1 13 2 7 41 38 12 14
2 16 2 5 39 32 11 18
3 19 2 5 30 35 15 11
4 15 1 5 31 33 6 12
5 14 2 8 34 37 13 16
6 13 2 6 35 29 10 18
7 19 2 5 39 31 12 14
8 15 2 6 34 36 14 14
9 14 2 5 36 35 12 15
10 15 2 4 37 38 6 15
11 16 1 6 38 31 10 17
12 16 2 5 36 34 12 19
13 16 1 5 38 35 12 10
14 16 2 6 39 38 11 16
15 17 2 7 33 37 15 18
16 15 1 6 32 33 12 14
17 15 1 7 36 32 10 14
18 20 2 6 38 38 12 17
19 18 1 8 39 38 11 14
20 16 2 7 32 32 12 16
21 16 1 5 32 33 11 18
22 16 2 5 31 31 12 11
23 19 2 7 39 38 13 14
24 16 2 7 37 39 11 12
25 17 1 5 39 32 9 17
26 17 2 4 41 32 13 9
27 16 1 10 36 35 10 16
28 15 2 6 33 37 14 14
29 16 2 5 33 33 12 15
30 14 1 5 34 33 10 11
31 15 2 5 31 28 12 16
32 12 1 5 27 32 8 13
33 14 2 6 37 31 10 17
34 16 2 5 34 37 12 15
35 14 1 5 34 30 12 14
36 7 1 5 32 33 7 16
37 10 1 5 29 31 6 9
38 14 1 5 36 33 12 15
39 16 2 5 29 31 10 17
40 16 1 5 35 33 10 13
41 16 1 5 37 32 10 15
42 14 2 7 34 33 12 16
43 20 1 5 38 32 15 16
44 14 1 6 35 33 10 12
45 14 2 7 38 28 10 12
46 11 2 7 37 35 12 11
47 14 2 5 38 39 13 15
48 15 2 5 33 34 11 15
49 16 2 4 36 38 11 17
50 14 1 5 38 32 12 13
51 16 2 4 32 38 14 16
52 14 1 5 32 30 10 14
53 12 1 5 32 33 12 11
54 16 2 7 34 38 13 12
55 9 1 5 32 32 5 12
56 14 2 5 37 32 6 15
57 16 2 6 39 34 12 16
58 16 2 4 29 34 12 15
59 15 1 6 37 36 11 12
60 16 2 6 35 34 10 12
61 12 1 5 30 28 7 8
62 16 1 7 38 34 12 13
63 16 2 6 34 35 14 11
64 14 2 8 31 35 11 14
65 16 2 7 34 31 12 15
66 17 1 5 35 37 13 10
67 18 2 6 36 35 14 11
68 18 1 6 30 27 11 12
69 12 2 5 39 40 12 15
70 16 1 5 35 37 12 15
71 10 1 5 38 36 8 14
72 14 2 5 31 38 11 16
73 18 2 4 34 39 14 15
74 18 1 6 38 41 14 15
75 16 1 6 34 27 12 13
76 17 2 6 39 30 9 12
77 16 2 6 37 37 13 17
78 16 2 7 34 31 11 13
79 13 1 5 28 31 12 15
80 16 1 7 37 27 12 13
81 16 1 6 33 36 12 15
82 20 1 5 37 38 12 16
83 16 2 5 35 37 12 15
84 15 1 4 37 33 12 16
85 15 2 8 32 34 11 15
86 16 2 8 33 31 10 14
87 14 1 5 38 39 9 15
88 16 2 5 33 34 12 14
89 16 2 6 29 32 12 13
90 15 2 4 33 33 12 7
91 12 2 5 31 36 9 17
92 17 2 5 36 32 15 13
93 16 2 5 35 41 12 15
94 15 2 5 32 28 12 14
95 13 2 6 29 30 12 13
96 16 2 6 39 36 10 16
97 16 2 5 37 35 13 12
98 16 2 6 35 31 9 14
99 16 1 5 37 34 12 17
100 14 1 7 32 36 10 15
101 16 2 5 38 36 14 17
102 16 1 6 37 35 11 12
103 20 2 6 36 37 15 16
104 15 1 6 32 28 11 11
105 16 2 4 33 39 11 15
106 13 1 5 40 32 12 9
107 17 2 5 38 35 12 16
108 16 1 7 41 39 12 15
109 16 1 6 36 35 11 10
110 12 2 9 43 42 7 10
111 16 2 6 30 34 12 15
112 16 2 6 31 33 14 11
113 17 2 5 32 41 11 13
114 13 1 6 32 33 11 14
115 12 2 5 37 34 10 18
116 18 1 8 37 32 13 16
117 14 2 7 33 40 13 14
118 14 2 5 34 40 8 14
119 13 2 7 33 35 11 14
120 16 2 6 38 36 12 14
121 13 2 6 33 37 11 12
122 16 2 9 31 27 13 14
123 13 2 7 38 39 12 15
124 16 2 6 37 38 14 15
125 15 2 5 33 31 13 15
126 16 2 5 31 33 15 13
127 15 1 6 39 32 10 17
128 17 2 6 44 39 11 17
129 15 2 7 33 36 9 19
130 12 2 5 35 33 11 15
131 16 1 5 32 33 10 13
132 10 1 5 28 32 11 9
133 16 2 6 40 37 8 15
134 12 1 4 27 30 11 15
135 14 1 5 37 38 12 15
136 15 2 7 32 29 12 16
137 13 1 5 28 22 9 11
138 15 1 7 34 35 11 14
139 11 2 7 30 35 10 11
140 12 2 6 35 34 8 15
141 8 1 5 31 35 9 13
142 16 2 8 32 34 8 15
143 15 1 5 30 34 9 16
144 17 2 5 30 35 15 14
145 16 1 5 31 23 11 15
146 10 2 6 40 31 8 16
147 18 2 4 32 27 13 16
148 13 1 5 36 36 12 11
149 16 1 5 32 31 12 12
150 13 1 7 35 32 9 9
151 10 2 6 38 39 7 16
152 15 2 7 42 37 13 13
153 16 1 10 34 38 9 16
154 16 2 6 35 39 6 12
155 14 2 8 35 34 8 9
156 10 2 4 33 31 8 13
157 17 2 5 36 32 15 13
158 13 2 6 32 37 6 14
159 15 2 7 33 36 9 19
160 16 2 7 34 32 11 13
161 12 2 6 32 35 8 12
162 13 2 6 34 36 8 13
depression t
1 12 1
2 11 2
3 14 3
4 12 4
5 21 5
6 12 6
7 22 7
8 11 8
9 10 9
10 13 10
11 10 11
12 8 12
13 15 13
14 14 14
15 10 15
16 14 16
17 14 17
18 11 18
19 10 19
20 13 20
21 7 21
22 14 22
23 12 23
24 14 24
25 11 25
26 9 26
27 11 27
28 15 28
29 14 29
30 13 30
31 9 31
32 15 32
33 10 33
34 11 34
35 13 35
36 8 36
37 20 37
38 12 38
39 10 39
40 10 40
41 9 41
42 14 42
43 8 43
44 14 44
45 11 45
46 13 46
47 9 47
48 11 48
49 15 49
50 11 50
51 10 51
52 14 52
53 18 53
54 14 54
55 11 55
56 12 56
57 13 57
58 9 58
59 10 59
60 15 60
61 20 61
62 12 62
63 12 63
64 14 64
65 13 65
66 11 66
67 17 67
68 12 68
69 13 69
70 14 70
71 13 71
72 15 72
73 13 73
74 10 74
75 11 75
76 19 76
77 13 77
78 17 78
79 13 79
80 9 80
81 11 81
82 10 82
83 9 83
84 12 84
85 12 85
86 13 86
87 13 87
88 12 88
89 15 89
90 22 90
91 13 91
92 15 92
93 13 93
94 15 94
95 10 95
96 11 96
97 16 97
98 11 98
99 11 99
100 10 100
101 10 101
102 16 102
103 12 103
104 11 104
105 16 105
106 19 106
107 11 107
108 16 108
109 15 109
110 24 110
111 14 111
112 15 112
113 11 113
114 15 114
115 12 115
116 10 116
117 14 117
118 13 118
119 9 119
120 15 120
121 15 121
122 14 122
123 11 123
124 8 124
125 11 125
126 11 126
127 8 127
128 10 128
129 11 129
130 13 130
131 11 131
132 20 132
133 10 133
134 15 134
135 12 135
136 14 136
137 23 137
138 14 138
139 16 139
140 11 140
141 12 141
142 10 142
143 14 143
144 12 144
145 12 145
146 11 146
147 12 147
148 13 148
149 11 149
150 19 150
151 12 151
152 17 152
153 9 153
154 12 154
155 19 155
156 18 156
157 15 157
158 14 158
159 11 159
160 9 160
161 18 161
162 16 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) gender age connected seperate software
6.04208 0.14504 0.14099 0.10225 -0.02631 0.53286
happiness depression t
0.05087 -0.08022 -0.00436
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.0411 -1.1661 0.1906 1.1344 4.2758
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.042076 2.473965 2.442 0.0157 *
gender 0.145036 0.322662 0.449 0.6537
age 0.140985 0.128192 1.100 0.2731
connected 0.102250 0.047382 2.158 0.0325 *
seperate -0.026305 0.045461 -0.579 0.5637
software 0.532858 0.070429 7.566 3.35e-12 ***
happiness 0.050866 0.076943 0.661 0.5095
depression -0.080217 0.056287 -1.425 0.1562
t -0.004360 0.003204 -1.361 0.1756
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.843 on 153 degrees of freedom
Multiple R-squared: 0.3661, Adjusted R-squared: 0.333
F-statistic: 11.05 on 8 and 153 DF, p-value: 3.025e-12
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.20015918 0.4003184 0.79984082
[2,] 0.28122755 0.5624551 0.71877245
[3,] 0.35659119 0.7131824 0.64340881
[4,] 0.36339217 0.7267843 0.63660783
[5,] 0.36478964 0.7295793 0.63521036
[6,] 0.33912445 0.6782489 0.66087555
[7,] 0.70023888 0.5995222 0.29976112
[8,] 0.80364021 0.3927196 0.19635979
[9,] 0.73646478 0.5270704 0.26353522
[10,] 0.68305717 0.6338857 0.31694283
[11,] 0.63986448 0.7202710 0.36013552
[12,] 0.61163485 0.7767303 0.38836515
[13,] 0.56229450 0.8754110 0.43770550
[14,] 0.50814443 0.9837111 0.49185557
[15,] 0.45912780 0.9182556 0.54087220
[16,] 0.40476451 0.8095290 0.59523549
[17,] 0.51692360 0.9661528 0.48307640
[18,] 0.46307874 0.9261575 0.53692126
[19,] 0.50674701 0.9865060 0.49325299
[20,] 0.44565377 0.8913075 0.55434623
[21,] 0.46549134 0.9309827 0.53450866
[22,] 0.43675693 0.8735139 0.56324307
[23,] 0.37735576 0.7547115 0.62264424
[24,] 0.38323557 0.7664711 0.61676443
[25,] 0.87249097 0.2550181 0.12750903
[26,] 0.85424364 0.2915127 0.14575636
[27,] 0.84386819 0.3122636 0.15613181
[28,] 0.86983170 0.2603366 0.13016830
[29,] 0.85859886 0.2828023 0.14140114
[30,] 0.83604865 0.3279027 0.16395135
[31,] 0.81829518 0.3634096 0.18170482
[32,] 0.82538334 0.3492333 0.17461666
[33,] 0.78987163 0.4202567 0.21012837
[34,] 0.75401725 0.4919655 0.24598275
[35,] 0.89111992 0.2177602 0.10888008
[36,] 0.90396263 0.1920747 0.09603737
[37,] 0.88564114 0.2287177 0.11435886
[38,] 0.86718109 0.2656378 0.13281891
[39,] 0.86363030 0.2727394 0.13636970
[40,] 0.83452496 0.3309501 0.16547504
[41,] 0.80197196 0.3960561 0.19802804
[42,] 0.82133073 0.3573385 0.17866927
[43,] 0.80559244 0.3888151 0.19440756
[44,] 0.81648665 0.3670267 0.18351335
[45,] 0.81336408 0.3732718 0.18663592
[46,] 0.78108014 0.4378397 0.21891986
[47,] 0.76637710 0.4672458 0.23362290
[48,] 0.73281040 0.5343792 0.26718960
[49,] 0.74463614 0.5107277 0.25536386
[50,] 0.71133399 0.5773320 0.28866601
[51,] 0.67590724 0.6481855 0.32409276
[52,] 0.63999587 0.7200083 0.36000413
[53,] 0.61170664 0.7765867 0.38829336
[54,] 0.57948321 0.8410336 0.42051679
[55,] 0.55428255 0.8914349 0.44571745
[56,] 0.54370626 0.9125875 0.45629374
[57,] 0.66989928 0.6602014 0.33010072
[58,] 0.79090968 0.4181806 0.20909032
[59,] 0.76104648 0.4779070 0.23895352
[60,] 0.84018784 0.3196243 0.15981216
[61,] 0.81318914 0.3736217 0.18681086
[62,] 0.80995362 0.3800928 0.19004638
[63,] 0.78947457 0.4210509 0.21052543
[64,] 0.75520540 0.4895892 0.24479460
[65,] 0.80406227 0.3918755 0.19593773
[66,] 0.77197483 0.4560503 0.22802517
[67,] 0.74774599 0.5045080 0.25225401
[68,] 0.74878046 0.5024391 0.25121954
[69,] 0.71328448 0.5734310 0.28671552
[70,] 0.67886772 0.6422646 0.32113228
[71,] 0.82130857 0.3573829 0.17869143
[72,] 0.78960588 0.4207882 0.21039412
[73,] 0.76176988 0.4764602 0.23823012
[74,] 0.72933466 0.5413307 0.27066534
[75,] 0.70724599 0.5855080 0.29275401
[76,] 0.66515497 0.6696901 0.33484503
[77,] 0.62463857 0.7507229 0.37536143
[78,] 0.59260158 0.8147968 0.40739842
[79,] 0.56577933 0.8684413 0.43422067
[80,] 0.55355224 0.8928955 0.44644776
[81,] 0.51023301 0.9795340 0.48976699
[82,] 0.46927669 0.9385534 0.53072331
[83,] 0.42334032 0.8466806 0.57665968
[84,] 0.45761320 0.9152264 0.54238680
[85,] 0.41830418 0.8366084 0.58169582
[86,] 0.37593189 0.7518638 0.62406811
[87,] 0.36651726 0.7330345 0.63348274
[88,] 0.32321854 0.6464371 0.67678146
[89,] 0.28902166 0.5780433 0.71097834
[90,] 0.26148596 0.5229719 0.73851404
[91,] 0.24454146 0.4890829 0.75545854
[92,] 0.29258235 0.5851647 0.70741765
[93,] 0.25177306 0.5035461 0.74822694
[94,] 0.27227337 0.5445467 0.72772663
[95,] 0.27416081 0.5483216 0.72583919
[96,] 0.26139651 0.5227930 0.73860349
[97,] 0.23610731 0.4722146 0.76389269
[98,] 0.23775586 0.4755117 0.76224414
[99,] 0.21416918 0.4283384 0.78583082
[100,] 0.19846660 0.3969332 0.80153340
[101,] 0.17176480 0.3435296 0.82823520
[102,] 0.25281390 0.5056278 0.74718610
[103,] 0.22238138 0.4447628 0.77761862
[104,] 0.23327879 0.4665576 0.76672121
[105,] 0.21728212 0.4345642 0.78271788
[106,] 0.19131950 0.3826390 0.80868050
[107,] 0.20902944 0.4180589 0.79097056
[108,] 0.20737194 0.4147439 0.79262806
[109,] 0.21448814 0.4289763 0.78551186
[110,] 0.18386132 0.3677226 0.81613868
[111,] 0.15098001 0.3019600 0.84901999
[112,] 0.16030628 0.3206126 0.83969372
[113,] 0.13073434 0.2614687 0.86926566
[114,] 0.10511866 0.2102373 0.89488134
[115,] 0.08170348 0.1634070 0.91829652
[116,] 0.06184653 0.1236931 0.93815347
[117,] 0.06980687 0.1396137 0.93019313
[118,] 0.05885216 0.1177043 0.94114784
[119,] 0.05730869 0.1146174 0.94269131
[120,] 0.07233652 0.1446730 0.92766348
[121,] 0.07676355 0.1535271 0.92323645
[122,] 0.18439213 0.3687843 0.81560787
[123,] 0.16224600 0.3244920 0.83775400
[124,] 0.14716614 0.2943323 0.85283386
[125,] 0.11367064 0.2273413 0.88632936
[126,] 0.08847746 0.1769549 0.91152254
[127,] 0.07598846 0.1519769 0.92401154
[128,] 0.11940168 0.2388034 0.88059832
[129,] 0.08880493 0.1776099 0.91119507
[130,] 0.49669435 0.9933887 0.50330565
[131,] 0.43027815 0.8605563 0.56972185
[132,] 0.41167928 0.8233586 0.58832072
[133,] 0.65640402 0.6871920 0.34359598
[134,] 0.70414998 0.5917000 0.29585002
[135,] 0.62279246 0.7544151 0.37720754
[136,] 0.54263950 0.9147210 0.45736050
[137,] 0.63742389 0.7251522 0.36257611
[138,] 0.51962782 0.9607444 0.48037218
[139,] 0.41638777 0.8327755 0.58361223
> postscript(file="/var/www/rcomp/tmp/1d0v31322148407.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/22jvr1322148407.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3guzb1322148407.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4cfvi1322148407.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5nr4l1322148407.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
-3.65116381 -0.06898907 2.39982071 2.97878216 -1.99789919 -2.24937333
7 8 9 10 11 12
3.47950128 -1.96245101 -2.11327801 2.44653547 0.55375601 -0.49040241
13 14 15 16 17 18
0.50011567 0.34256371 -0.76090037 -0.35058079 0.14320489 3.63787833
19 20 21 22 23 24
2.00829470 0.17258302 0.58007848 0.87376700 2.11640117 0.67945094
25 26 27 28 29 30
2.29291385 0.20378435 0.50038303 -1.42582416 0.54893312 -0.21495545
31 32 33 34 35 36
-0.82132500 -0.39237038 -1.39310725 0.33305376 -1.49038676 -6.04113582
37 38 39 40 41 42
-0.93110777 -1.73397405 1.59204050 1.38401175 0.97561634 -1.82947189
43 44 45 46 47 48
2.08671057 -0.36779806 -1.32838853 -4.89205690 -2.65994730 -0.04971177
49 50 51 52 53 54
1.11324030 -1.89094277 -0.41783331 -0.06582990 -2.57480319 0.02498337
55 56 57 58 59 60
-2.47476508 1.26806721 -0.18824754 0.85058291 -0.28170726 1.66345015
61 62 63 64 65 66
0.51037696 0.01223648 -0.51613284 -0.88058151 0.18884966 1.23683741
67 68 69 70 71 72
1.69789288 3.39697130 -3.78624214 0.77345635 -3.45315646 -0.36534428
73 74 75 76 77 78
1.79141232 1.06179718 0.35454947 3.07271689 -0.40135296 1.20099101
79 80 81 82 83 84
-1.70960030 -0.23181990 0.61797641 4.27584816 0.28401602 -0.54553911
85 86 87 88 89 90
-0.12887477 1.35826088 0.11179712 0.72291866 1.23420143 1.00455398
91 92 93 94 95 96
-1.48069602 0.07393999 0.75370743 -0.06585124 -2.19333414 0.93969131
97 98 99 100 101 102
0.26920699 1.86047661 0.27409978 -0.35241606 -1.05778998 1.36077600
103 104 105 106 107 108
2.71919303 0.34639035 1.87241375 -2.00607336 1.13886584 0.25671497
109 110 111 112 113 114
1.51506264 -0.72679496 1.09853529 0.19230515 2.62181767 -1.31021173
115 116 117 118 119 120
-2.70610673 1.31044660 -1.64719957 1.12095593 -2.10537487 0.50346894
121 122 123 124 125 126
-1.32002336 -0.04483874 -2.91725442 -1.00233264 -0.85861432 -0.56112734
127 128 129 130 131 132
-0.17684714 0.98293886 0.93635163 -2.76255209 2.16775203 -3.05263152
133 134 135 136 137 138
2.06143769 -1.55957003 -1.28176402 -0.32034008 0.91075105 0.42133963
139 140 141 142 143 144
-2.46444423 -1.39548927 -5.02070999 2.55779426 2.07179049 0.70156695
145 146 147 148 149 150
1.51361637 -4.01036201 2.40467336 -1.89176049 1.17877306 0.01362895
151 152 153 154 155 156
-2.96054218 -1.20224525 1.70560118 4.09561255 1.33487725 -2.25492005
157 158 159 160 161 162
0.35734891 1.42589465 1.06715576 0.94309067 -0.25675159 0.35811295
> postscript(file="/var/www/rcomp/tmp/6ajyb1322148407.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 -3.65116381 NA
1 -0.06898907 -3.65116381
2 2.39982071 -0.06898907
3 2.97878216 2.39982071
4 -1.99789919 2.97878216
5 -2.24937333 -1.99789919
6 3.47950128 -2.24937333
7 -1.96245101 3.47950128
8 -2.11327801 -1.96245101
9 2.44653547 -2.11327801
10 0.55375601 2.44653547
11 -0.49040241 0.55375601
12 0.50011567 -0.49040241
13 0.34256371 0.50011567
14 -0.76090037 0.34256371
15 -0.35058079 -0.76090037
16 0.14320489 -0.35058079
17 3.63787833 0.14320489
18 2.00829470 3.63787833
19 0.17258302 2.00829470
20 0.58007848 0.17258302
21 0.87376700 0.58007848
22 2.11640117 0.87376700
23 0.67945094 2.11640117
24 2.29291385 0.67945094
25 0.20378435 2.29291385
26 0.50038303 0.20378435
27 -1.42582416 0.50038303
28 0.54893312 -1.42582416
29 -0.21495545 0.54893312
30 -0.82132500 -0.21495545
31 -0.39237038 -0.82132500
32 -1.39310725 -0.39237038
33 0.33305376 -1.39310725
34 -1.49038676 0.33305376
35 -6.04113582 -1.49038676
36 -0.93110777 -6.04113582
37 -1.73397405 -0.93110777
38 1.59204050 -1.73397405
39 1.38401175 1.59204050
40 0.97561634 1.38401175
41 -1.82947189 0.97561634
42 2.08671057 -1.82947189
43 -0.36779806 2.08671057
44 -1.32838853 -0.36779806
45 -4.89205690 -1.32838853
46 -2.65994730 -4.89205690
47 -0.04971177 -2.65994730
48 1.11324030 -0.04971177
49 -1.89094277 1.11324030
50 -0.41783331 -1.89094277
51 -0.06582990 -0.41783331
52 -2.57480319 -0.06582990
53 0.02498337 -2.57480319
54 -2.47476508 0.02498337
55 1.26806721 -2.47476508
56 -0.18824754 1.26806721
57 0.85058291 -0.18824754
58 -0.28170726 0.85058291
59 1.66345015 -0.28170726
60 0.51037696 1.66345015
61 0.01223648 0.51037696
62 -0.51613284 0.01223648
63 -0.88058151 -0.51613284
64 0.18884966 -0.88058151
65 1.23683741 0.18884966
66 1.69789288 1.23683741
67 3.39697130 1.69789288
68 -3.78624214 3.39697130
69 0.77345635 -3.78624214
70 -3.45315646 0.77345635
71 -0.36534428 -3.45315646
72 1.79141232 -0.36534428
73 1.06179718 1.79141232
74 0.35454947 1.06179718
75 3.07271689 0.35454947
76 -0.40135296 3.07271689
77 1.20099101 -0.40135296
78 -1.70960030 1.20099101
79 -0.23181990 -1.70960030
80 0.61797641 -0.23181990
81 4.27584816 0.61797641
82 0.28401602 4.27584816
83 -0.54553911 0.28401602
84 -0.12887477 -0.54553911
85 1.35826088 -0.12887477
86 0.11179712 1.35826088
87 0.72291866 0.11179712
88 1.23420143 0.72291866
89 1.00455398 1.23420143
90 -1.48069602 1.00455398
91 0.07393999 -1.48069602
92 0.75370743 0.07393999
93 -0.06585124 0.75370743
94 -2.19333414 -0.06585124
95 0.93969131 -2.19333414
96 0.26920699 0.93969131
97 1.86047661 0.26920699
98 0.27409978 1.86047661
99 -0.35241606 0.27409978
100 -1.05778998 -0.35241606
101 1.36077600 -1.05778998
102 2.71919303 1.36077600
103 0.34639035 2.71919303
104 1.87241375 0.34639035
105 -2.00607336 1.87241375
106 1.13886584 -2.00607336
107 0.25671497 1.13886584
108 1.51506264 0.25671497
109 -0.72679496 1.51506264
110 1.09853529 -0.72679496
111 0.19230515 1.09853529
112 2.62181767 0.19230515
113 -1.31021173 2.62181767
114 -2.70610673 -1.31021173
115 1.31044660 -2.70610673
116 -1.64719957 1.31044660
117 1.12095593 -1.64719957
118 -2.10537487 1.12095593
119 0.50346894 -2.10537487
120 -1.32002336 0.50346894
121 -0.04483874 -1.32002336
122 -2.91725442 -0.04483874
123 -1.00233264 -2.91725442
124 -0.85861432 -1.00233264
125 -0.56112734 -0.85861432
126 -0.17684714 -0.56112734
127 0.98293886 -0.17684714
128 0.93635163 0.98293886
129 -2.76255209 0.93635163
130 2.16775203 -2.76255209
131 -3.05263152 2.16775203
132 2.06143769 -3.05263152
133 -1.55957003 2.06143769
134 -1.28176402 -1.55957003
135 -0.32034008 -1.28176402
136 0.91075105 -0.32034008
137 0.42133963 0.91075105
138 -2.46444423 0.42133963
139 -1.39548927 -2.46444423
140 -5.02070999 -1.39548927
141 2.55779426 -5.02070999
142 2.07179049 2.55779426
143 0.70156695 2.07179049
144 1.51361637 0.70156695
145 -4.01036201 1.51361637
146 2.40467336 -4.01036201
147 -1.89176049 2.40467336
148 1.17877306 -1.89176049
149 0.01362895 1.17877306
150 -2.96054218 0.01362895
151 -1.20224525 -2.96054218
152 1.70560118 -1.20224525
153 4.09561255 1.70560118
154 1.33487725 4.09561255
155 -2.25492005 1.33487725
156 0.35734891 -2.25492005
157 1.42589465 0.35734891
158 1.06715576 1.42589465
159 0.94309067 1.06715576
160 -0.25675159 0.94309067
161 0.35811295 -0.25675159
162 NA 0.35811295
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.06898907 -3.65116381
[2,] 2.39982071 -0.06898907
[3,] 2.97878216 2.39982071
[4,] -1.99789919 2.97878216
[5,] -2.24937333 -1.99789919
[6,] 3.47950128 -2.24937333
[7,] -1.96245101 3.47950128
[8,] -2.11327801 -1.96245101
[9,] 2.44653547 -2.11327801
[10,] 0.55375601 2.44653547
[11,] -0.49040241 0.55375601
[12,] 0.50011567 -0.49040241
[13,] 0.34256371 0.50011567
[14,] -0.76090037 0.34256371
[15,] -0.35058079 -0.76090037
[16,] 0.14320489 -0.35058079
[17,] 3.63787833 0.14320489
[18,] 2.00829470 3.63787833
[19,] 0.17258302 2.00829470
[20,] 0.58007848 0.17258302
[21,] 0.87376700 0.58007848
[22,] 2.11640117 0.87376700
[23,] 0.67945094 2.11640117
[24,] 2.29291385 0.67945094
[25,] 0.20378435 2.29291385
[26,] 0.50038303 0.20378435
[27,] -1.42582416 0.50038303
[28,] 0.54893312 -1.42582416
[29,] -0.21495545 0.54893312
[30,] -0.82132500 -0.21495545
[31,] -0.39237038 -0.82132500
[32,] -1.39310725 -0.39237038
[33,] 0.33305376 -1.39310725
[34,] -1.49038676 0.33305376
[35,] -6.04113582 -1.49038676
[36,] -0.93110777 -6.04113582
[37,] -1.73397405 -0.93110777
[38,] 1.59204050 -1.73397405
[39,] 1.38401175 1.59204050
[40,] 0.97561634 1.38401175
[41,] -1.82947189 0.97561634
[42,] 2.08671057 -1.82947189
[43,] -0.36779806 2.08671057
[44,] -1.32838853 -0.36779806
[45,] -4.89205690 -1.32838853
[46,] -2.65994730 -4.89205690
[47,] -0.04971177 -2.65994730
[48,] 1.11324030 -0.04971177
[49,] -1.89094277 1.11324030
[50,] -0.41783331 -1.89094277
[51,] -0.06582990 -0.41783331
[52,] -2.57480319 -0.06582990
[53,] 0.02498337 -2.57480319
[54,] -2.47476508 0.02498337
[55,] 1.26806721 -2.47476508
[56,] -0.18824754 1.26806721
[57,] 0.85058291 -0.18824754
[58,] -0.28170726 0.85058291
[59,] 1.66345015 -0.28170726
[60,] 0.51037696 1.66345015
[61,] 0.01223648 0.51037696
[62,] -0.51613284 0.01223648
[63,] -0.88058151 -0.51613284
[64,] 0.18884966 -0.88058151
[65,] 1.23683741 0.18884966
[66,] 1.69789288 1.23683741
[67,] 3.39697130 1.69789288
[68,] -3.78624214 3.39697130
[69,] 0.77345635 -3.78624214
[70,] -3.45315646 0.77345635
[71,] -0.36534428 -3.45315646
[72,] 1.79141232 -0.36534428
[73,] 1.06179718 1.79141232
[74,] 0.35454947 1.06179718
[75,] 3.07271689 0.35454947
[76,] -0.40135296 3.07271689
[77,] 1.20099101 -0.40135296
[78,] -1.70960030 1.20099101
[79,] -0.23181990 -1.70960030
[80,] 0.61797641 -0.23181990
[81,] 4.27584816 0.61797641
[82,] 0.28401602 4.27584816
[83,] -0.54553911 0.28401602
[84,] -0.12887477 -0.54553911
[85,] 1.35826088 -0.12887477
[86,] 0.11179712 1.35826088
[87,] 0.72291866 0.11179712
[88,] 1.23420143 0.72291866
[89,] 1.00455398 1.23420143
[90,] -1.48069602 1.00455398
[91,] 0.07393999 -1.48069602
[92,] 0.75370743 0.07393999
[93,] -0.06585124 0.75370743
[94,] -2.19333414 -0.06585124
[95,] 0.93969131 -2.19333414
[96,] 0.26920699 0.93969131
[97,] 1.86047661 0.26920699
[98,] 0.27409978 1.86047661
[99,] -0.35241606 0.27409978
[100,] -1.05778998 -0.35241606
[101,] 1.36077600 -1.05778998
[102,] 2.71919303 1.36077600
[103,] 0.34639035 2.71919303
[104,] 1.87241375 0.34639035
[105,] -2.00607336 1.87241375
[106,] 1.13886584 -2.00607336
[107,] 0.25671497 1.13886584
[108,] 1.51506264 0.25671497
[109,] -0.72679496 1.51506264
[110,] 1.09853529 -0.72679496
[111,] 0.19230515 1.09853529
[112,] 2.62181767 0.19230515
[113,] -1.31021173 2.62181767
[114,] -2.70610673 -1.31021173
[115,] 1.31044660 -2.70610673
[116,] -1.64719957 1.31044660
[117,] 1.12095593 -1.64719957
[118,] -2.10537487 1.12095593
[119,] 0.50346894 -2.10537487
[120,] -1.32002336 0.50346894
[121,] -0.04483874 -1.32002336
[122,] -2.91725442 -0.04483874
[123,] -1.00233264 -2.91725442
[124,] -0.85861432 -1.00233264
[125,] -0.56112734 -0.85861432
[126,] -0.17684714 -0.56112734
[127,] 0.98293886 -0.17684714
[128,] 0.93635163 0.98293886
[129,] -2.76255209 0.93635163
[130,] 2.16775203 -2.76255209
[131,] -3.05263152 2.16775203
[132,] 2.06143769 -3.05263152
[133,] -1.55957003 2.06143769
[134,] -1.28176402 -1.55957003
[135,] -0.32034008 -1.28176402
[136,] 0.91075105 -0.32034008
[137,] 0.42133963 0.91075105
[138,] -2.46444423 0.42133963
[139,] -1.39548927 -2.46444423
[140,] -5.02070999 -1.39548927
[141,] 2.55779426 -5.02070999
[142,] 2.07179049 2.55779426
[143,] 0.70156695 2.07179049
[144,] 1.51361637 0.70156695
[145,] -4.01036201 1.51361637
[146,] 2.40467336 -4.01036201
[147,] -1.89176049 2.40467336
[148,] 1.17877306 -1.89176049
[149,] 0.01362895 1.17877306
[150,] -2.96054218 0.01362895
[151,] -1.20224525 -2.96054218
[152,] 1.70560118 -1.20224525
[153,] 4.09561255 1.70560118
[154,] 1.33487725 4.09561255
[155,] -2.25492005 1.33487725
[156,] 0.35734891 -2.25492005
[157,] 1.42589465 0.35734891
[158,] 1.06715576 1.42589465
[159,] 0.94309067 1.06715576
[160,] -0.25675159 0.94309067
[161,] 0.35811295 -0.25675159
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.06898907 -3.65116381
2 2.39982071 -0.06898907
3 2.97878216 2.39982071
4 -1.99789919 2.97878216
5 -2.24937333 -1.99789919
6 3.47950128 -2.24937333
7 -1.96245101 3.47950128
8 -2.11327801 -1.96245101
9 2.44653547 -2.11327801
10 0.55375601 2.44653547
11 -0.49040241 0.55375601
12 0.50011567 -0.49040241
13 0.34256371 0.50011567
14 -0.76090037 0.34256371
15 -0.35058079 -0.76090037
16 0.14320489 -0.35058079
17 3.63787833 0.14320489
18 2.00829470 3.63787833
19 0.17258302 2.00829470
20 0.58007848 0.17258302
21 0.87376700 0.58007848
22 2.11640117 0.87376700
23 0.67945094 2.11640117
24 2.29291385 0.67945094
25 0.20378435 2.29291385
26 0.50038303 0.20378435
27 -1.42582416 0.50038303
28 0.54893312 -1.42582416
29 -0.21495545 0.54893312
30 -0.82132500 -0.21495545
31 -0.39237038 -0.82132500
32 -1.39310725 -0.39237038
33 0.33305376 -1.39310725
34 -1.49038676 0.33305376
35 -6.04113582 -1.49038676
36 -0.93110777 -6.04113582
37 -1.73397405 -0.93110777
38 1.59204050 -1.73397405
39 1.38401175 1.59204050
40 0.97561634 1.38401175
41 -1.82947189 0.97561634
42 2.08671057 -1.82947189
43 -0.36779806 2.08671057
44 -1.32838853 -0.36779806
45 -4.89205690 -1.32838853
46 -2.65994730 -4.89205690
47 -0.04971177 -2.65994730
48 1.11324030 -0.04971177
49 -1.89094277 1.11324030
50 -0.41783331 -1.89094277
51 -0.06582990 -0.41783331
52 -2.57480319 -0.06582990
53 0.02498337 -2.57480319
54 -2.47476508 0.02498337
55 1.26806721 -2.47476508
56 -0.18824754 1.26806721
57 0.85058291 -0.18824754
58 -0.28170726 0.85058291
59 1.66345015 -0.28170726
60 0.51037696 1.66345015
61 0.01223648 0.51037696
62 -0.51613284 0.01223648
63 -0.88058151 -0.51613284
64 0.18884966 -0.88058151
65 1.23683741 0.18884966
66 1.69789288 1.23683741
67 3.39697130 1.69789288
68 -3.78624214 3.39697130
69 0.77345635 -3.78624214
70 -3.45315646 0.77345635
71 -0.36534428 -3.45315646
72 1.79141232 -0.36534428
73 1.06179718 1.79141232
74 0.35454947 1.06179718
75 3.07271689 0.35454947
76 -0.40135296 3.07271689
77 1.20099101 -0.40135296
78 -1.70960030 1.20099101
79 -0.23181990 -1.70960030
80 0.61797641 -0.23181990
81 4.27584816 0.61797641
82 0.28401602 4.27584816
83 -0.54553911 0.28401602
84 -0.12887477 -0.54553911
85 1.35826088 -0.12887477
86 0.11179712 1.35826088
87 0.72291866 0.11179712
88 1.23420143 0.72291866
89 1.00455398 1.23420143
90 -1.48069602 1.00455398
91 0.07393999 -1.48069602
92 0.75370743 0.07393999
93 -0.06585124 0.75370743
94 -2.19333414 -0.06585124
95 0.93969131 -2.19333414
96 0.26920699 0.93969131
97 1.86047661 0.26920699
98 0.27409978 1.86047661
99 -0.35241606 0.27409978
100 -1.05778998 -0.35241606
101 1.36077600 -1.05778998
102 2.71919303 1.36077600
103 0.34639035 2.71919303
104 1.87241375 0.34639035
105 -2.00607336 1.87241375
106 1.13886584 -2.00607336
107 0.25671497 1.13886584
108 1.51506264 0.25671497
109 -0.72679496 1.51506264
110 1.09853529 -0.72679496
111 0.19230515 1.09853529
112 2.62181767 0.19230515
113 -1.31021173 2.62181767
114 -2.70610673 -1.31021173
115 1.31044660 -2.70610673
116 -1.64719957 1.31044660
117 1.12095593 -1.64719957
118 -2.10537487 1.12095593
119 0.50346894 -2.10537487
120 -1.32002336 0.50346894
121 -0.04483874 -1.32002336
122 -2.91725442 -0.04483874
123 -1.00233264 -2.91725442
124 -0.85861432 -1.00233264
125 -0.56112734 -0.85861432
126 -0.17684714 -0.56112734
127 0.98293886 -0.17684714
128 0.93635163 0.98293886
129 -2.76255209 0.93635163
130 2.16775203 -2.76255209
131 -3.05263152 2.16775203
132 2.06143769 -3.05263152
133 -1.55957003 2.06143769
134 -1.28176402 -1.55957003
135 -0.32034008 -1.28176402
136 0.91075105 -0.32034008
137 0.42133963 0.91075105
138 -2.46444423 0.42133963
139 -1.39548927 -2.46444423
140 -5.02070999 -1.39548927
141 2.55779426 -5.02070999
142 2.07179049 2.55779426
143 0.70156695 2.07179049
144 1.51361637 0.70156695
145 -4.01036201 1.51361637
146 2.40467336 -4.01036201
147 -1.89176049 2.40467336
148 1.17877306 -1.89176049
149 0.01362895 1.17877306
150 -2.96054218 0.01362895
151 -1.20224525 -2.96054218
152 1.70560118 -1.20224525
153 4.09561255 1.70560118
154 1.33487725 4.09561255
155 -2.25492005 1.33487725
156 0.35734891 -2.25492005
157 1.42589465 0.35734891
158 1.06715576 1.42589465
159 0.94309067 1.06715576
160 -0.25675159 0.94309067
161 0.35811295 -0.25675159
> 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/www/rcomp/tmp/7utcg1322148407.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8tj271322148407.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9kxbn1322148407.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10g7ew1322148407.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/rcomp/tmp/111d8e1322148407.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/www/rcomp/tmp/12s3wg1322148407.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/www/rcomp/tmp/13v0251322148407.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/www/rcomp/tmp/14k0he1322148407.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/www/rcomp/tmp/15uuvo1322148407.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/www/rcomp/tmp/16dywy1322148407.tab")
+ }
>
> try(system("convert tmp/1d0v31322148407.ps tmp/1d0v31322148407.png",intern=TRUE))
character(0)
> try(system("convert tmp/22jvr1322148407.ps tmp/22jvr1322148407.png",intern=TRUE))
character(0)
> try(system("convert tmp/3guzb1322148407.ps tmp/3guzb1322148407.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cfvi1322148407.ps tmp/4cfvi1322148407.png",intern=TRUE))
character(0)
> try(system("convert tmp/5nr4l1322148407.ps tmp/5nr4l1322148407.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ajyb1322148407.ps tmp/6ajyb1322148407.png",intern=TRUE))
character(0)
> try(system("convert tmp/7utcg1322148407.ps tmp/7utcg1322148407.png",intern=TRUE))
character(0)
> try(system("convert tmp/8tj271322148407.ps tmp/8tj271322148407.png",intern=TRUE))
character(0)
> try(system("convert tmp/9kxbn1322148407.ps tmp/9kxbn1322148407.png",intern=TRUE))
character(0)
> try(system("convert tmp/10g7ew1322148407.ps tmp/10g7ew1322148407.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.970 0.290 5.198