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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,0,1,0,0,0,0,0,1,0,0,1,0,0,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,0,1,0,0,1,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,1,0,1,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,1,0,1,0,0,1,0,0,1,1,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0),dim=c(3,154),dimnames=list(c('T40','T20','Outcome'),1:154))
> y <- array(NA,dim=c(3,154),dimnames=list(c('T40','T20','Outcome'),1:154))
> 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
T40 T20 Outcome t
1 1 0 1 1
2 0 0 0 2
3 0 0 0 3
4 0 0 0 4
5 0 0 0 5
6 0 0 1 6
7 0 0 0 7
8 1 0 0 8
9 0 0 1 9
10 0 0 0 10
11 1 0 0 11
12 0 0 0 12
13 0 0 0 13
14 1 0 0 14
15 0 0 1 15
16 1 0 1 16
17 1 0 0 17
18 1 0 0 18
19 0 0 1 19
20 1 0 1 20
21 0 0 0 21
22 0 0 1 22
23 0 0 1 23
24 0 0 1 24
25 1 0 1 25
26 0 0 0 26
27 0 0 1 27
28 0 0 0 28
29 0 0 1 29
30 0 0 0 30
31 0 0 0 31
32 0 0 0 32
33 0 0 0 33
34 1 0 1 34
35 0 0 0 35
36 0 0 0 36
37 1 0 0 37
38 0 0 1 38
39 0 0 1 39
40 1 0 0 40
41 0 0 1 41
42 0 0 1 42
43 0 0 1 43
44 1 0 0 44
45 0 0 0 45
46 0 0 1 46
47 0 0 0 47
48 0 0 1 48
49 0 0 1 49
50 0 0 0 50
51 1 0 0 51
52 1 0 0 52
53 0 0 1 53
54 0 0 0 54
55 0 0 0 55
56 1 0 1 56
57 0 0 1 57
58 0 0 1 58
59 0 0 1 59
60 1 0 1 60
61 1 0 1 61
62 0 0 0 62
63 0 0 0 63
64 1 0 1 64
65 0 0 0 65
66 0 0 0 66
67 1 0 0 67
68 0 0 0 68
69 0 0 1 69
70 0 0 0 70
71 0 0 0 71
72 0 0 1 72
73 0 0 1 73
74 0 0 0 74
75 0 0 1 75
76 1 0 1 76
77 0 0 1 77
78 0 0 1 78
79 1 0 1 79
80 1 0 0 80
81 0 0 0 81
82 0 0 1 82
83 0 0 0 83
84 0 0 0 84
85 0 0 1 85
86 0 0 0 86
87 0 0 1 87
88 0 1 1 88
89 0 0 0 89
90 0 0 1 90
91 0 0 0 91
92 0 1 0 92
93 0 0 0 93
94 0 0 0 94
95 0 1 0 95
96 0 0 1 96
97 0 1 0 97
98 0 0 0 98
99 0 0 0 99
100 0 0 1 100
101 0 0 1 101
102 0 0 0 102
103 0 0 0 103
104 0 0 0 104
105 0 1 0 105
106 0 0 0 106
107 0 0 0 107
108 0 1 0 108
109 0 0 0 109
110 0 0 0 110
111 0 1 0 111
112 0 1 0 112
113 0 0 0 113
114 0 1 0 114
115 0 0 0 115
116 0 0 0 116
117 0 0 1 117
118 0 0 0 118
119 0 0 0 119
120 0 0 1 120
121 0 0 0 121
122 0 0 0 122
123 0 1 0 123
124 0 0 1 124
125 0 0 1 125
126 0 1 0 126
127 0 0 0 127
128 0 0 1 128
129 0 0 0 129
130 0 0 1 130
131 0 0 0 131
132 0 0 1 132
133 0 0 0 133
134 0 0 0 134
135 0 0 0 135
136 0 0 0 136
137 0 0 1 137
138 0 1 1 138
139 0 1 0 139
140 0 0 0 140
141 0 0 1 141
142 0 1 1 142
143 0 0 0 143
144 0 0 1 144
145 0 0 0 145
146 0 1 1 146
147 0 1 0 147
148 0 1 0 148
149 0 0 0 149
150 0 0 1 150
151 0 0 1 151
152 0 0 0 152
153 0 0 0 153
154 0 0 0 154
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) T20 Outcome t
0.350713 -0.036163 0.027423 -0.002687
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.36202 -0.22163 -0.07262 0.02198 0.86424
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3507133 0.0608569 5.763 4.54e-08 ***
T20 -0.0361628 0.0925494 -0.391 0.697
Outcome 0.0274233 0.0561358 0.489 0.626
t -0.0026869 0.0006505 -4.131 5.99e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.338 on 150 degrees of freedom
Multiple R-squared: 0.1243, Adjusted R-squared: 0.1068
F-statistic: 7.096 on 3 and 150 DF, p-value: 0.0001721
> 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,] 0.5097214 9.805572e-01 4.902786e-01
[2,] 0.9799586 4.008287e-02 2.004144e-02
[3,] 0.9769596 4.608086e-02 2.304043e-02
[4,] 0.9588752 8.224960e-02 4.112480e-02
[5,] 0.9914202 1.715960e-02 8.579798e-03
[6,] 0.9886113 2.277750e-02 1.138875e-02
[7,] 0.9835168 3.296634e-02 1.648317e-02
[8,] 0.9943048 1.139036e-02 5.695179e-03
[9,] 0.9940910 1.181804e-02 5.909021e-03
[10,] 0.9967468 6.506411e-03 3.253206e-03
[11,] 0.9977896 4.420763e-03 2.210381e-03
[12,] 0.9980789 3.842168e-03 1.921084e-03
[13,] 0.9990537 1.892693e-03 9.463463e-04
[14,] 0.9992684 1.463254e-03 7.316269e-04
[15,] 0.9996486 7.027600e-04 3.513800e-04
[16,] 0.9997617 4.766656e-04 2.383328e-04
[17,] 0.9997849 4.302337e-04 2.151168e-04
[18,] 0.9997711 4.577035e-04 2.288518e-04
[19,] 0.9999077 1.845028e-04 9.225139e-05
[20,] 0.9999158 1.684475e-04 8.422374e-05
[21,] 0.9999093 1.813130e-04 9.065650e-05
[22,] 0.9998912 2.176908e-04 1.088454e-04
[23,] 0.9998662 2.676240e-04 1.338120e-04
[24,] 0.9998203 3.593527e-04 1.796764e-04
[25,] 0.9997517 4.965256e-04 2.482628e-04
[26,] 0.9996540 6.919106e-04 3.459553e-04
[27,] 0.9995194 9.612155e-04 4.806077e-04
[28,] 0.9998892 2.216723e-04 1.108362e-04
[29,] 0.9998472 3.056634e-04 1.528317e-04
[30,] 0.9997904 4.192153e-04 2.096076e-04
[31,] 0.9999657 6.863427e-05 3.431714e-05
[32,] 0.9999584 8.310411e-05 4.155205e-05
[33,] 0.9999480 1.040654e-04 5.203270e-05
[34,] 0.9999915 1.692967e-05 8.464833e-06
[35,] 0.9999894 2.114760e-05 1.057380e-05
[36,] 0.9999866 2.689163e-05 1.344582e-05
[37,] 0.9999828 3.440548e-05 1.720274e-05
[38,] 0.9999975 5.035128e-06 2.517564e-06
[39,] 0.9999967 6.559818e-06 3.279909e-06
[40,] 0.9999958 8.406030e-06 4.203015e-06
[41,] 0.9999944 1.122278e-05 5.611390e-06
[42,] 0.9999928 1.431097e-05 7.155484e-06
[43,] 0.9999910 1.798077e-05 8.990387e-06
[44,] 0.9999880 2.397480e-05 1.198740e-05
[45,] 0.9999987 2.587981e-06 1.293990e-06
[46,] 0.9999999 2.149567e-07 1.074784e-07
[47,] 0.9999999 2.724782e-07 1.362391e-07
[48,] 0.9999998 3.692735e-07 1.846367e-07
[49,] 0.9999997 5.086956e-07 2.543478e-07
[50,] 1.0000000 3.499839e-08 1.749920e-08
[51,] 1.0000000 4.518766e-08 2.259383e-08
[52,] 1.0000000 5.731238e-08 2.865619e-08
[53,] 1.0000000 7.085482e-08 3.542741e-08
[54,] 1.0000000 3.404062e-09 1.702031e-09
[55,] 1.0000000 8.101088e-11 4.050544e-11
[56,] 1.0000000 1.231892e-10 6.159458e-11
[57,] 1.0000000 1.913028e-10 9.565138e-11
[58,] 1.0000000 1.386862e-12 6.934312e-13
[59,] 1.0000000 2.331489e-12 1.165744e-12
[60,] 1.0000000 3.969728e-12 1.984864e-12
[61,] 1.0000000 2.441659e-15 1.220830e-15
[62,] 1.0000000 4.748212e-15 2.374106e-15
[63,] 1.0000000 8.392889e-15 4.196444e-15
[64,] 1.0000000 1.661687e-14 8.308436e-15
[65,] 1.0000000 3.306385e-14 1.653193e-14
[66,] 1.0000000 5.852148e-14 2.926074e-14
[67,] 1.0000000 1.020823e-13 5.104117e-14
[68,] 1.0000000 2.014107e-13 1.007054e-13
[69,] 1.0000000 3.419177e-13 1.709588e-13
[70,] 1.0000000 1.932757e-17 9.663785e-18
[71,] 1.0000000 4.170605e-17 2.085302e-17
[72,] 1.0000000 8.823829e-17 4.411915e-17
[73,] 1.0000000 4.342575e-25 2.171288e-25
[74,] 1.0000000 0.000000e+00 0.000000e+00
[75,] 1.0000000 0.000000e+00 0.000000e+00
[76,] 1.0000000 0.000000e+00 0.000000e+00
[77,] 1.0000000 0.000000e+00 0.000000e+00
[78,] 1.0000000 0.000000e+00 0.000000e+00
[79,] 1.0000000 0.000000e+00 0.000000e+00
[80,] 1.0000000 0.000000e+00 0.000000e+00
[81,] 1.0000000 0.000000e+00 0.000000e+00
[82,] 1.0000000 0.000000e+00 0.000000e+00
[83,] 1.0000000 0.000000e+00 0.000000e+00
[84,] 1.0000000 0.000000e+00 0.000000e+00
[85,] 1.0000000 0.000000e+00 0.000000e+00
[86,] 1.0000000 0.000000e+00 0.000000e+00
[87,] 1.0000000 0.000000e+00 0.000000e+00
[88,] 1.0000000 0.000000e+00 0.000000e+00
[89,] 1.0000000 0.000000e+00 0.000000e+00
[90,] 1.0000000 0.000000e+00 0.000000e+00
[91,] 1.0000000 0.000000e+00 0.000000e+00
[92,] 1.0000000 0.000000e+00 0.000000e+00
[93,] 1.0000000 0.000000e+00 0.000000e+00
[94,] 1.0000000 0.000000e+00 0.000000e+00
[95,] 1.0000000 0.000000e+00 0.000000e+00
[96,] 1.0000000 0.000000e+00 0.000000e+00
[97,] 1.0000000 0.000000e+00 0.000000e+00
[98,] 1.0000000 0.000000e+00 0.000000e+00
[99,] 1.0000000 0.000000e+00 0.000000e+00
[100,] 1.0000000 0.000000e+00 0.000000e+00
[101,] 1.0000000 0.000000e+00 0.000000e+00
[102,] 1.0000000 0.000000e+00 0.000000e+00
[103,] 1.0000000 0.000000e+00 0.000000e+00
[104,] 1.0000000 0.000000e+00 0.000000e+00
[105,] 1.0000000 0.000000e+00 0.000000e+00
[106,] 1.0000000 0.000000e+00 0.000000e+00
[107,] 1.0000000 0.000000e+00 0.000000e+00
[108,] 1.0000000 0.000000e+00 0.000000e+00
[109,] 1.0000000 0.000000e+00 0.000000e+00
[110,] 1.0000000 0.000000e+00 0.000000e+00
[111,] 1.0000000 0.000000e+00 0.000000e+00
[112,] 1.0000000 0.000000e+00 0.000000e+00
[113,] 1.0000000 0.000000e+00 0.000000e+00
[114,] 1.0000000 0.000000e+00 0.000000e+00
[115,] 1.0000000 0.000000e+00 0.000000e+00
[116,] 1.0000000 0.000000e+00 0.000000e+00
[117,] 1.0000000 0.000000e+00 0.000000e+00
[118,] 1.0000000 0.000000e+00 0.000000e+00
[119,] 1.0000000 0.000000e+00 0.000000e+00
[120,] 1.0000000 0.000000e+00 0.000000e+00
[121,] 1.0000000 0.000000e+00 0.000000e+00
[122,] 1.0000000 0.000000e+00 0.000000e+00
[123,] 1.0000000 0.000000e+00 0.000000e+00
[124,] 1.0000000 0.000000e+00 0.000000e+00
[125,] 1.0000000 0.000000e+00 0.000000e+00
[126,] 1.0000000 0.000000e+00 0.000000e+00
[127,] 1.0000000 0.000000e+00 0.000000e+00
[128,] 1.0000000 0.000000e+00 0.000000e+00
[129,] 1.0000000 0.000000e+00 0.000000e+00
[130,] 1.0000000 0.000000e+00 0.000000e+00
[131,] 1.0000000 0.000000e+00 0.000000e+00
[132,] 1.0000000 0.000000e+00 0.000000e+00
[133,] 1.0000000 0.000000e+00 0.000000e+00
[134,] 1.0000000 0.000000e+00 0.000000e+00
[135,] 1.0000000 0.000000e+00 0.000000e+00
[136,] 1.0000000 0.000000e+00 0.000000e+00
[137,] 1.0000000 0.000000e+00 0.000000e+00
[138,] 1.0000000 0.000000e+00 0.000000e+00
[139,] 1.0000000 0.000000e+00 0.000000e+00
[140,] 1.0000000 0.000000e+00 0.000000e+00
[141,] 1.0000000 0.000000e+00 0.000000e+00
> postscript(file="/var/fisher/rcomp/tmp/17qi21356041101.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2wbgy1356041101.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3xw941356041101.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/42ssn1356041101.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5h0dn1356041101.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 = 154
Frequency = 1
1 2 3 4 5
0.6245502964 -0.3453394936 -0.3426526151 -0.3399657365 -0.3372788579
6 7 8 9 10
-0.3620153108 -0.3319051008 0.6707817777 -0.3539546752 -0.3238444652
11 12 13 14 15
0.6788424134 -0.3184707080 -0.3157838295 0.6869030491 -0.3378334038
16 17 18 19 20
0.6648534747 0.6949636848 0.6976505633 -0.3270858896 0.6756009890
21 22 23 24 25
-0.2942888010 -0.3190252539 -0.3163383754 -0.3136514968 0.6890353818
26 27 28 29 30
-0.2808544082 -0.3055908611 -0.2754806511 -0.3002171040 -0.2701068940
31 32 33 34 35
-0.2674200154 -0.2647331369 -0.2620462583 0.7132172888 -0.2566725012
36 37 38 39 40
-0.2539856226 0.7487012559 -0.2760351970 -0.2733483184 0.7567618916
41 42 43 44 45
-0.2679745613 -0.2652876827 -0.2626008042 0.7675094058 -0.2298037156
46 47 48 49 50
-0.2545401685 -0.2244299585 -0.2491664114 -0.2464795328 -0.2163693228
51 52 53 54 55
0.7863175558 0.7890044343 -0.2357320186 -0.2056218086 -0.2029349300
56 57 58 59 60
0.7723286171 -0.2249845044 -0.2222976258 -0.2196107472 0.7830761313
61 62 63 64 65
0.7857630099 -0.1841267801 -0.1814399015 0.7938236456 -0.1760661444
66 67 68 69 70
-0.1733792659 0.8293076127 -0.1680055088 -0.1927419617 -0.1626317516
71 72 73 74 75
-0.1599448731 -0.1846813260 -0.1819944474 -0.1518842374 -0.1766206903
76 77 78 79 80
0.8260661883 -0.1712469332 -0.1685600546 0.8341268239 0.8642370339
81 82 83 84 85
-0.1330760875 -0.1578125404 -0.1277023304 -0.1250154518 -0.1497519047
86 87 88 89 90
-0.1196416947 -0.1443781476 -0.1055284316 -0.1115810590 -0.1363175119
91 92 93 94 95
-0.1062073019 -0.0673575859 -0.1008335448 -0.0981466662 -0.0592969503
96 97 98 99 100
-0.1201962406 -0.0539231931 -0.0873991520 -0.0847122734 -0.1094487263
101 102 103 104 105
-0.1067618478 -0.0766516378 -0.0739647592 -0.0712778806 -0.0324281647
106 107 108 109 110
-0.0659041235 -0.0632172450 -0.0243675290 -0.0578434879 -0.0551566093
111 112 113 114 115
-0.0163068933 -0.0136200148 -0.0470959736 -0.0082462577 -0.0417222165
116 117 118 119 120
-0.0390353379 -0.0637717908 -0.0336615808 -0.0309747023 -0.0557111552
121 122 123 124 125
-0.0256009451 -0.0229140666 0.0159356494 -0.0449636409 -0.0422767624
126 127 128 129 130
0.0239962850 -0.0094796738 -0.0342161267 -0.0041059167 -0.0288423696
131 132 133 134 135
0.0012678404 -0.0234686125 0.0066415976 0.0093284761 0.0120153547
136 137 138 139 140
0.0147022332 -0.0100342197 0.0288154963 0.0589257063 0.0254497475
141 142 143 144 145
0.0007132946 0.0395630105 0.0335103831 0.0087739302 0.0388841403
146 147 148 149 150
0.0503105248 0.0804207348 0.0831076133 0.0496316545 0.0248952016
151 152 153 154
0.0275820802 0.0576922902 0.0603791687 0.0630660473
> postscript(file="/var/fisher/rcomp/tmp/6lwb31356041101.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 = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 0.6245502964 NA
1 -0.3453394936 0.6245502964
2 -0.3426526151 -0.3453394936
3 -0.3399657365 -0.3426526151
4 -0.3372788579 -0.3399657365
5 -0.3620153108 -0.3372788579
6 -0.3319051008 -0.3620153108
7 0.6707817777 -0.3319051008
8 -0.3539546752 0.6707817777
9 -0.3238444652 -0.3539546752
10 0.6788424134 -0.3238444652
11 -0.3184707080 0.6788424134
12 -0.3157838295 -0.3184707080
13 0.6869030491 -0.3157838295
14 -0.3378334038 0.6869030491
15 0.6648534747 -0.3378334038
16 0.6949636848 0.6648534747
17 0.6976505633 0.6949636848
18 -0.3270858896 0.6976505633
19 0.6756009890 -0.3270858896
20 -0.2942888010 0.6756009890
21 -0.3190252539 -0.2942888010
22 -0.3163383754 -0.3190252539
23 -0.3136514968 -0.3163383754
24 0.6890353818 -0.3136514968
25 -0.2808544082 0.6890353818
26 -0.3055908611 -0.2808544082
27 -0.2754806511 -0.3055908611
28 -0.3002171040 -0.2754806511
29 -0.2701068940 -0.3002171040
30 -0.2674200154 -0.2701068940
31 -0.2647331369 -0.2674200154
32 -0.2620462583 -0.2647331369
33 0.7132172888 -0.2620462583
34 -0.2566725012 0.7132172888
35 -0.2539856226 -0.2566725012
36 0.7487012559 -0.2539856226
37 -0.2760351970 0.7487012559
38 -0.2733483184 -0.2760351970
39 0.7567618916 -0.2733483184
40 -0.2679745613 0.7567618916
41 -0.2652876827 -0.2679745613
42 -0.2626008042 -0.2652876827
43 0.7675094058 -0.2626008042
44 -0.2298037156 0.7675094058
45 -0.2545401685 -0.2298037156
46 -0.2244299585 -0.2545401685
47 -0.2491664114 -0.2244299585
48 -0.2464795328 -0.2491664114
49 -0.2163693228 -0.2464795328
50 0.7863175558 -0.2163693228
51 0.7890044343 0.7863175558
52 -0.2357320186 0.7890044343
53 -0.2056218086 -0.2357320186
54 -0.2029349300 -0.2056218086
55 0.7723286171 -0.2029349300
56 -0.2249845044 0.7723286171
57 -0.2222976258 -0.2249845044
58 -0.2196107472 -0.2222976258
59 0.7830761313 -0.2196107472
60 0.7857630099 0.7830761313
61 -0.1841267801 0.7857630099
62 -0.1814399015 -0.1841267801
63 0.7938236456 -0.1814399015
64 -0.1760661444 0.7938236456
65 -0.1733792659 -0.1760661444
66 0.8293076127 -0.1733792659
67 -0.1680055088 0.8293076127
68 -0.1927419617 -0.1680055088
69 -0.1626317516 -0.1927419617
70 -0.1599448731 -0.1626317516
71 -0.1846813260 -0.1599448731
72 -0.1819944474 -0.1846813260
73 -0.1518842374 -0.1819944474
74 -0.1766206903 -0.1518842374
75 0.8260661883 -0.1766206903
76 -0.1712469332 0.8260661883
77 -0.1685600546 -0.1712469332
78 0.8341268239 -0.1685600546
79 0.8642370339 0.8341268239
80 -0.1330760875 0.8642370339
81 -0.1578125404 -0.1330760875
82 -0.1277023304 -0.1578125404
83 -0.1250154518 -0.1277023304
84 -0.1497519047 -0.1250154518
85 -0.1196416947 -0.1497519047
86 -0.1443781476 -0.1196416947
87 -0.1055284316 -0.1443781476
88 -0.1115810590 -0.1055284316
89 -0.1363175119 -0.1115810590
90 -0.1062073019 -0.1363175119
91 -0.0673575859 -0.1062073019
92 -0.1008335448 -0.0673575859
93 -0.0981466662 -0.1008335448
94 -0.0592969503 -0.0981466662
95 -0.1201962406 -0.0592969503
96 -0.0539231931 -0.1201962406
97 -0.0873991520 -0.0539231931
98 -0.0847122734 -0.0873991520
99 -0.1094487263 -0.0847122734
100 -0.1067618478 -0.1094487263
101 -0.0766516378 -0.1067618478
102 -0.0739647592 -0.0766516378
103 -0.0712778806 -0.0739647592
104 -0.0324281647 -0.0712778806
105 -0.0659041235 -0.0324281647
106 -0.0632172450 -0.0659041235
107 -0.0243675290 -0.0632172450
108 -0.0578434879 -0.0243675290
109 -0.0551566093 -0.0578434879
110 -0.0163068933 -0.0551566093
111 -0.0136200148 -0.0163068933
112 -0.0470959736 -0.0136200148
113 -0.0082462577 -0.0470959736
114 -0.0417222165 -0.0082462577
115 -0.0390353379 -0.0417222165
116 -0.0637717908 -0.0390353379
117 -0.0336615808 -0.0637717908
118 -0.0309747023 -0.0336615808
119 -0.0557111552 -0.0309747023
120 -0.0256009451 -0.0557111552
121 -0.0229140666 -0.0256009451
122 0.0159356494 -0.0229140666
123 -0.0449636409 0.0159356494
124 -0.0422767624 -0.0449636409
125 0.0239962850 -0.0422767624
126 -0.0094796738 0.0239962850
127 -0.0342161267 -0.0094796738
128 -0.0041059167 -0.0342161267
129 -0.0288423696 -0.0041059167
130 0.0012678404 -0.0288423696
131 -0.0234686125 0.0012678404
132 0.0066415976 -0.0234686125
133 0.0093284761 0.0066415976
134 0.0120153547 0.0093284761
135 0.0147022332 0.0120153547
136 -0.0100342197 0.0147022332
137 0.0288154963 -0.0100342197
138 0.0589257063 0.0288154963
139 0.0254497475 0.0589257063
140 0.0007132946 0.0254497475
141 0.0395630105 0.0007132946
142 0.0335103831 0.0395630105
143 0.0087739302 0.0335103831
144 0.0388841403 0.0087739302
145 0.0503105248 0.0388841403
146 0.0804207348 0.0503105248
147 0.0831076133 0.0804207348
148 0.0496316545 0.0831076133
149 0.0248952016 0.0496316545
150 0.0275820802 0.0248952016
151 0.0576922902 0.0275820802
152 0.0603791687 0.0576922902
153 0.0630660473 0.0603791687
154 NA 0.0630660473
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.3453394936 0.6245502964
[2,] -0.3426526151 -0.3453394936
[3,] -0.3399657365 -0.3426526151
[4,] -0.3372788579 -0.3399657365
[5,] -0.3620153108 -0.3372788579
[6,] -0.3319051008 -0.3620153108
[7,] 0.6707817777 -0.3319051008
[8,] -0.3539546752 0.6707817777
[9,] -0.3238444652 -0.3539546752
[10,] 0.6788424134 -0.3238444652
[11,] -0.3184707080 0.6788424134
[12,] -0.3157838295 -0.3184707080
[13,] 0.6869030491 -0.3157838295
[14,] -0.3378334038 0.6869030491
[15,] 0.6648534747 -0.3378334038
[16,] 0.6949636848 0.6648534747
[17,] 0.6976505633 0.6949636848
[18,] -0.3270858896 0.6976505633
[19,] 0.6756009890 -0.3270858896
[20,] -0.2942888010 0.6756009890
[21,] -0.3190252539 -0.2942888010
[22,] -0.3163383754 -0.3190252539
[23,] -0.3136514968 -0.3163383754
[24,] 0.6890353818 -0.3136514968
[25,] -0.2808544082 0.6890353818
[26,] -0.3055908611 -0.2808544082
[27,] -0.2754806511 -0.3055908611
[28,] -0.3002171040 -0.2754806511
[29,] -0.2701068940 -0.3002171040
[30,] -0.2674200154 -0.2701068940
[31,] -0.2647331369 -0.2674200154
[32,] -0.2620462583 -0.2647331369
[33,] 0.7132172888 -0.2620462583
[34,] -0.2566725012 0.7132172888
[35,] -0.2539856226 -0.2566725012
[36,] 0.7487012559 -0.2539856226
[37,] -0.2760351970 0.7487012559
[38,] -0.2733483184 -0.2760351970
[39,] 0.7567618916 -0.2733483184
[40,] -0.2679745613 0.7567618916
[41,] -0.2652876827 -0.2679745613
[42,] -0.2626008042 -0.2652876827
[43,] 0.7675094058 -0.2626008042
[44,] -0.2298037156 0.7675094058
[45,] -0.2545401685 -0.2298037156
[46,] -0.2244299585 -0.2545401685
[47,] -0.2491664114 -0.2244299585
[48,] -0.2464795328 -0.2491664114
[49,] -0.2163693228 -0.2464795328
[50,] 0.7863175558 -0.2163693228
[51,] 0.7890044343 0.7863175558
[52,] -0.2357320186 0.7890044343
[53,] -0.2056218086 -0.2357320186
[54,] -0.2029349300 -0.2056218086
[55,] 0.7723286171 -0.2029349300
[56,] -0.2249845044 0.7723286171
[57,] -0.2222976258 -0.2249845044
[58,] -0.2196107472 -0.2222976258
[59,] 0.7830761313 -0.2196107472
[60,] 0.7857630099 0.7830761313
[61,] -0.1841267801 0.7857630099
[62,] -0.1814399015 -0.1841267801
[63,] 0.7938236456 -0.1814399015
[64,] -0.1760661444 0.7938236456
[65,] -0.1733792659 -0.1760661444
[66,] 0.8293076127 -0.1733792659
[67,] -0.1680055088 0.8293076127
[68,] -0.1927419617 -0.1680055088
[69,] -0.1626317516 -0.1927419617
[70,] -0.1599448731 -0.1626317516
[71,] -0.1846813260 -0.1599448731
[72,] -0.1819944474 -0.1846813260
[73,] -0.1518842374 -0.1819944474
[74,] -0.1766206903 -0.1518842374
[75,] 0.8260661883 -0.1766206903
[76,] -0.1712469332 0.8260661883
[77,] -0.1685600546 -0.1712469332
[78,] 0.8341268239 -0.1685600546
[79,] 0.8642370339 0.8341268239
[80,] -0.1330760875 0.8642370339
[81,] -0.1578125404 -0.1330760875
[82,] -0.1277023304 -0.1578125404
[83,] -0.1250154518 -0.1277023304
[84,] -0.1497519047 -0.1250154518
[85,] -0.1196416947 -0.1497519047
[86,] -0.1443781476 -0.1196416947
[87,] -0.1055284316 -0.1443781476
[88,] -0.1115810590 -0.1055284316
[89,] -0.1363175119 -0.1115810590
[90,] -0.1062073019 -0.1363175119
[91,] -0.0673575859 -0.1062073019
[92,] -0.1008335448 -0.0673575859
[93,] -0.0981466662 -0.1008335448
[94,] -0.0592969503 -0.0981466662
[95,] -0.1201962406 -0.0592969503
[96,] -0.0539231931 -0.1201962406
[97,] -0.0873991520 -0.0539231931
[98,] -0.0847122734 -0.0873991520
[99,] -0.1094487263 -0.0847122734
[100,] -0.1067618478 -0.1094487263
[101,] -0.0766516378 -0.1067618478
[102,] -0.0739647592 -0.0766516378
[103,] -0.0712778806 -0.0739647592
[104,] -0.0324281647 -0.0712778806
[105,] -0.0659041235 -0.0324281647
[106,] -0.0632172450 -0.0659041235
[107,] -0.0243675290 -0.0632172450
[108,] -0.0578434879 -0.0243675290
[109,] -0.0551566093 -0.0578434879
[110,] -0.0163068933 -0.0551566093
[111,] -0.0136200148 -0.0163068933
[112,] -0.0470959736 -0.0136200148
[113,] -0.0082462577 -0.0470959736
[114,] -0.0417222165 -0.0082462577
[115,] -0.0390353379 -0.0417222165
[116,] -0.0637717908 -0.0390353379
[117,] -0.0336615808 -0.0637717908
[118,] -0.0309747023 -0.0336615808
[119,] -0.0557111552 -0.0309747023
[120,] -0.0256009451 -0.0557111552
[121,] -0.0229140666 -0.0256009451
[122,] 0.0159356494 -0.0229140666
[123,] -0.0449636409 0.0159356494
[124,] -0.0422767624 -0.0449636409
[125,] 0.0239962850 -0.0422767624
[126,] -0.0094796738 0.0239962850
[127,] -0.0342161267 -0.0094796738
[128,] -0.0041059167 -0.0342161267
[129,] -0.0288423696 -0.0041059167
[130,] 0.0012678404 -0.0288423696
[131,] -0.0234686125 0.0012678404
[132,] 0.0066415976 -0.0234686125
[133,] 0.0093284761 0.0066415976
[134,] 0.0120153547 0.0093284761
[135,] 0.0147022332 0.0120153547
[136,] -0.0100342197 0.0147022332
[137,] 0.0288154963 -0.0100342197
[138,] 0.0589257063 0.0288154963
[139,] 0.0254497475 0.0589257063
[140,] 0.0007132946 0.0254497475
[141,] 0.0395630105 0.0007132946
[142,] 0.0335103831 0.0395630105
[143,] 0.0087739302 0.0335103831
[144,] 0.0388841403 0.0087739302
[145,] 0.0503105248 0.0388841403
[146,] 0.0804207348 0.0503105248
[147,] 0.0831076133 0.0804207348
[148,] 0.0496316545 0.0831076133
[149,] 0.0248952016 0.0496316545
[150,] 0.0275820802 0.0248952016
[151,] 0.0576922902 0.0275820802
[152,] 0.0603791687 0.0576922902
[153,] 0.0630660473 0.0603791687
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.3453394936 0.6245502964
2 -0.3426526151 -0.3453394936
3 -0.3399657365 -0.3426526151
4 -0.3372788579 -0.3399657365
5 -0.3620153108 -0.3372788579
6 -0.3319051008 -0.3620153108
7 0.6707817777 -0.3319051008
8 -0.3539546752 0.6707817777
9 -0.3238444652 -0.3539546752
10 0.6788424134 -0.3238444652
11 -0.3184707080 0.6788424134
12 -0.3157838295 -0.3184707080
13 0.6869030491 -0.3157838295
14 -0.3378334038 0.6869030491
15 0.6648534747 -0.3378334038
16 0.6949636848 0.6648534747
17 0.6976505633 0.6949636848
18 -0.3270858896 0.6976505633
19 0.6756009890 -0.3270858896
20 -0.2942888010 0.6756009890
21 -0.3190252539 -0.2942888010
22 -0.3163383754 -0.3190252539
23 -0.3136514968 -0.3163383754
24 0.6890353818 -0.3136514968
25 -0.2808544082 0.6890353818
26 -0.3055908611 -0.2808544082
27 -0.2754806511 -0.3055908611
28 -0.3002171040 -0.2754806511
29 -0.2701068940 -0.3002171040
30 -0.2674200154 -0.2701068940
31 -0.2647331369 -0.2674200154
32 -0.2620462583 -0.2647331369
33 0.7132172888 -0.2620462583
34 -0.2566725012 0.7132172888
35 -0.2539856226 -0.2566725012
36 0.7487012559 -0.2539856226
37 -0.2760351970 0.7487012559
38 -0.2733483184 -0.2760351970
39 0.7567618916 -0.2733483184
40 -0.2679745613 0.7567618916
41 -0.2652876827 -0.2679745613
42 -0.2626008042 -0.2652876827
43 0.7675094058 -0.2626008042
44 -0.2298037156 0.7675094058
45 -0.2545401685 -0.2298037156
46 -0.2244299585 -0.2545401685
47 -0.2491664114 -0.2244299585
48 -0.2464795328 -0.2491664114
49 -0.2163693228 -0.2464795328
50 0.7863175558 -0.2163693228
51 0.7890044343 0.7863175558
52 -0.2357320186 0.7890044343
53 -0.2056218086 -0.2357320186
54 -0.2029349300 -0.2056218086
55 0.7723286171 -0.2029349300
56 -0.2249845044 0.7723286171
57 -0.2222976258 -0.2249845044
58 -0.2196107472 -0.2222976258
59 0.7830761313 -0.2196107472
60 0.7857630099 0.7830761313
61 -0.1841267801 0.7857630099
62 -0.1814399015 -0.1841267801
63 0.7938236456 -0.1814399015
64 -0.1760661444 0.7938236456
65 -0.1733792659 -0.1760661444
66 0.8293076127 -0.1733792659
67 -0.1680055088 0.8293076127
68 -0.1927419617 -0.1680055088
69 -0.1626317516 -0.1927419617
70 -0.1599448731 -0.1626317516
71 -0.1846813260 -0.1599448731
72 -0.1819944474 -0.1846813260
73 -0.1518842374 -0.1819944474
74 -0.1766206903 -0.1518842374
75 0.8260661883 -0.1766206903
76 -0.1712469332 0.8260661883
77 -0.1685600546 -0.1712469332
78 0.8341268239 -0.1685600546
79 0.8642370339 0.8341268239
80 -0.1330760875 0.8642370339
81 -0.1578125404 -0.1330760875
82 -0.1277023304 -0.1578125404
83 -0.1250154518 -0.1277023304
84 -0.1497519047 -0.1250154518
85 -0.1196416947 -0.1497519047
86 -0.1443781476 -0.1196416947
87 -0.1055284316 -0.1443781476
88 -0.1115810590 -0.1055284316
89 -0.1363175119 -0.1115810590
90 -0.1062073019 -0.1363175119
91 -0.0673575859 -0.1062073019
92 -0.1008335448 -0.0673575859
93 -0.0981466662 -0.1008335448
94 -0.0592969503 -0.0981466662
95 -0.1201962406 -0.0592969503
96 -0.0539231931 -0.1201962406
97 -0.0873991520 -0.0539231931
98 -0.0847122734 -0.0873991520
99 -0.1094487263 -0.0847122734
100 -0.1067618478 -0.1094487263
101 -0.0766516378 -0.1067618478
102 -0.0739647592 -0.0766516378
103 -0.0712778806 -0.0739647592
104 -0.0324281647 -0.0712778806
105 -0.0659041235 -0.0324281647
106 -0.0632172450 -0.0659041235
107 -0.0243675290 -0.0632172450
108 -0.0578434879 -0.0243675290
109 -0.0551566093 -0.0578434879
110 -0.0163068933 -0.0551566093
111 -0.0136200148 -0.0163068933
112 -0.0470959736 -0.0136200148
113 -0.0082462577 -0.0470959736
114 -0.0417222165 -0.0082462577
115 -0.0390353379 -0.0417222165
116 -0.0637717908 -0.0390353379
117 -0.0336615808 -0.0637717908
118 -0.0309747023 -0.0336615808
119 -0.0557111552 -0.0309747023
120 -0.0256009451 -0.0557111552
121 -0.0229140666 -0.0256009451
122 0.0159356494 -0.0229140666
123 -0.0449636409 0.0159356494
124 -0.0422767624 -0.0449636409
125 0.0239962850 -0.0422767624
126 -0.0094796738 0.0239962850
127 -0.0342161267 -0.0094796738
128 -0.0041059167 -0.0342161267
129 -0.0288423696 -0.0041059167
130 0.0012678404 -0.0288423696
131 -0.0234686125 0.0012678404
132 0.0066415976 -0.0234686125
133 0.0093284761 0.0066415976
134 0.0120153547 0.0093284761
135 0.0147022332 0.0120153547
136 -0.0100342197 0.0147022332
137 0.0288154963 -0.0100342197
138 0.0589257063 0.0288154963
139 0.0254497475 0.0589257063
140 0.0007132946 0.0254497475
141 0.0395630105 0.0007132946
142 0.0335103831 0.0395630105
143 0.0087739302 0.0335103831
144 0.0388841403 0.0087739302
145 0.0503105248 0.0388841403
146 0.0804207348 0.0503105248
147 0.0831076133 0.0804207348
148 0.0496316545 0.0831076133
149 0.0248952016 0.0496316545
150 0.0275820802 0.0248952016
151 0.0576922902 0.0275820802
152 0.0603791687 0.0576922902
153 0.0630660473 0.0603791687
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/72fa71356041101.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8vduc1356041101.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/92puj1356041101.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/107w901356041101.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11gd8k1356041101.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12cmeq1356041101.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/134pr71356041101.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14i2jk1356041101.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15xzy41356041101.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/166ihg1356041101.tab")
+ }
>
> try(system("convert tmp/17qi21356041101.ps tmp/17qi21356041101.png",intern=TRUE))
character(0)
> try(system("convert tmp/2wbgy1356041101.ps tmp/2wbgy1356041101.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xw941356041101.ps tmp/3xw941356041101.png",intern=TRUE))
character(0)
> try(system("convert tmp/42ssn1356041101.ps tmp/42ssn1356041101.png",intern=TRUE))
character(0)
> try(system("convert tmp/5h0dn1356041101.ps tmp/5h0dn1356041101.png",intern=TRUE))
character(0)
> try(system("convert tmp/6lwb31356041101.ps tmp/6lwb31356041101.png",intern=TRUE))
character(0)
> try(system("convert tmp/72fa71356041101.ps tmp/72fa71356041101.png",intern=TRUE))
character(0)
> try(system("convert tmp/8vduc1356041101.ps tmp/8vduc1356041101.png",intern=TRUE))
character(0)
> try(system("convert tmp/92puj1356041101.ps tmp/92puj1356041101.png",intern=TRUE))
character(0)
> try(system("convert tmp/107w901356041101.ps tmp/107w901356041101.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.605 1.996 10.594