R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(14.458,13.594,17.814,20.235,21.811,21.439,21.393,19.831,20.468,21.080,21.600,17.390,17848,19592,21092,20889,25890,24965,22225,20977,22897,22785,22769,19637,20203,20450,23083,21738,26766,25280,22574,22729,21378,22902,24989,21116,15169,15846,20927,18273,22538,15596,14034,11366,14861,15149,13577,13026,13190,13196,15826,14733,16307,15703,14589,12043,15057,14053,12698,10888,10045,11549,13767,12424,13116,14211,12266,12602,15714,13742,12745,10491,10057,10900,11771,11992,11993,14504,11727,11477,13578,11555,11846,11397,10066,10269,14279,13870,13695,14420,11424,9704,12464,14301,13464,9893,11572,12380,16692,16052,16459,14761,13654,13480,18068,16560,14530,10650,11651,13735,13360,17818,20613,16231,13862,12004,17734,15034,12609,12320,10833,11350,13648,14890,16325,18045,15616,11926,16855,15083,12520,12355),dim=c(1,132),dimnames=list(c('Pas'),1:132))
> y <- array(NA,dim=c(1,132),dimnames=list(c('Pas'),1:132))
> 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 = 'Include Monthly 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.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
Pas M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 14.458 1 0 0 0 0 0 0 0 0 0 0 1
2 13.594 0 1 0 0 0 0 0 0 0 0 0 2
3 17.814 0 0 1 0 0 0 0 0 0 0 0 3
4 20.235 0 0 0 1 0 0 0 0 0 0 0 4
5 21.811 0 0 0 0 1 0 0 0 0 0 0 5
6 21.439 0 0 0 0 0 1 0 0 0 0 0 6
7 21.393 0 0 0 0 0 0 1 0 0 0 0 7
8 19.831 0 0 0 0 0 0 0 1 0 0 0 8
9 20.468 0 0 0 0 0 0 0 0 1 0 0 9
10 21.080 0 0 0 0 0 0 0 0 0 1 0 10
11 21.600 0 0 0 0 0 0 0 0 0 0 1 11
12 17.390 0 0 0 0 0 0 0 0 0 0 0 12
13 17848.000 1 0 0 0 0 0 0 0 0 0 0 13
14 19592.000 0 1 0 0 0 0 0 0 0 0 0 14
15 21092.000 0 0 1 0 0 0 0 0 0 0 0 15
16 20889.000 0 0 0 1 0 0 0 0 0 0 0 16
17 25890.000 0 0 0 0 1 0 0 0 0 0 0 17
18 24965.000 0 0 0 0 0 1 0 0 0 0 0 18
19 22225.000 0 0 0 0 0 0 1 0 0 0 0 19
20 20977.000 0 0 0 0 0 0 0 1 0 0 0 20
21 22897.000 0 0 0 0 0 0 0 0 1 0 0 21
22 22785.000 0 0 0 0 0 0 0 0 0 1 0 22
23 22769.000 0 0 0 0 0 0 0 0 0 0 1 23
24 19637.000 0 0 0 0 0 0 0 0 0 0 0 24
25 20203.000 1 0 0 0 0 0 0 0 0 0 0 25
26 20450.000 0 1 0 0 0 0 0 0 0 0 0 26
27 23083.000 0 0 1 0 0 0 0 0 0 0 0 27
28 21738.000 0 0 0 1 0 0 0 0 0 0 0 28
29 26766.000 0 0 0 0 1 0 0 0 0 0 0 29
30 25280.000 0 0 0 0 0 1 0 0 0 0 0 30
31 22574.000 0 0 0 0 0 0 1 0 0 0 0 31
32 22729.000 0 0 0 0 0 0 0 1 0 0 0 32
33 21378.000 0 0 0 0 0 0 0 0 1 0 0 33
34 22902.000 0 0 0 0 0 0 0 0 0 1 0 34
35 24989.000 0 0 0 0 0 0 0 0 0 0 1 35
36 21116.000 0 0 0 0 0 0 0 0 0 0 0 36
37 15169.000 1 0 0 0 0 0 0 0 0 0 0 37
38 15846.000 0 1 0 0 0 0 0 0 0 0 0 38
39 20927.000 0 0 1 0 0 0 0 0 0 0 0 39
40 18273.000 0 0 0 1 0 0 0 0 0 0 0 40
41 22538.000 0 0 0 0 1 0 0 0 0 0 0 41
42 15596.000 0 0 0 0 0 1 0 0 0 0 0 42
43 14034.000 0 0 0 0 0 0 1 0 0 0 0 43
44 11366.000 0 0 0 0 0 0 0 1 0 0 0 44
45 14861.000 0 0 0 0 0 0 0 0 1 0 0 45
46 15149.000 0 0 0 0 0 0 0 0 0 1 0 46
47 13577.000 0 0 0 0 0 0 0 0 0 0 1 47
48 13026.000 0 0 0 0 0 0 0 0 0 0 0 48
49 13190.000 1 0 0 0 0 0 0 0 0 0 0 49
50 13196.000 0 1 0 0 0 0 0 0 0 0 0 50
51 15826.000 0 0 1 0 0 0 0 0 0 0 0 51
52 14733.000 0 0 0 1 0 0 0 0 0 0 0 52
53 16307.000 0 0 0 0 1 0 0 0 0 0 0 53
54 15703.000 0 0 0 0 0 1 0 0 0 0 0 54
55 14589.000 0 0 0 0 0 0 1 0 0 0 0 55
56 12043.000 0 0 0 0 0 0 0 1 0 0 0 56
57 15057.000 0 0 0 0 0 0 0 0 1 0 0 57
58 14053.000 0 0 0 0 0 0 0 0 0 1 0 58
59 12698.000 0 0 0 0 0 0 0 0 0 0 1 59
60 10888.000 0 0 0 0 0 0 0 0 0 0 0 60
61 10045.000 1 0 0 0 0 0 0 0 0 0 0 61
62 11549.000 0 1 0 0 0 0 0 0 0 0 0 62
63 13767.000 0 0 1 0 0 0 0 0 0 0 0 63
64 12424.000 0 0 0 1 0 0 0 0 0 0 0 64
65 13116.000 0 0 0 0 1 0 0 0 0 0 0 65
66 14211.000 0 0 0 0 0 1 0 0 0 0 0 66
67 12266.000 0 0 0 0 0 0 1 0 0 0 0 67
68 12602.000 0 0 0 0 0 0 0 1 0 0 0 68
69 15714.000 0 0 0 0 0 0 0 0 1 0 0 69
70 13742.000 0 0 0 0 0 0 0 0 0 1 0 70
71 12745.000 0 0 0 0 0 0 0 0 0 0 1 71
72 10491.000 0 0 0 0 0 0 0 0 0 0 0 72
73 10057.000 1 0 0 0 0 0 0 0 0 0 0 73
74 10900.000 0 1 0 0 0 0 0 0 0 0 0 74
75 11771.000 0 0 1 0 0 0 0 0 0 0 0 75
76 11992.000 0 0 0 1 0 0 0 0 0 0 0 76
77 11993.000 0 0 0 0 1 0 0 0 0 0 0 77
78 14504.000 0 0 0 0 0 1 0 0 0 0 0 78
79 11727.000 0 0 0 0 0 0 1 0 0 0 0 79
80 11477.000 0 0 0 0 0 0 0 1 0 0 0 80
81 13578.000 0 0 0 0 0 0 0 0 1 0 0 81
82 11555.000 0 0 0 0 0 0 0 0 0 1 0 82
83 11846.000 0 0 0 0 0 0 0 0 0 0 1 83
84 11397.000 0 0 0 0 0 0 0 0 0 0 0 84
85 10066.000 1 0 0 0 0 0 0 0 0 0 0 85
86 10269.000 0 1 0 0 0 0 0 0 0 0 0 86
87 14279.000 0 0 1 0 0 0 0 0 0 0 0 87
88 13870.000 0 0 0 1 0 0 0 0 0 0 0 88
89 13695.000 0 0 0 0 1 0 0 0 0 0 0 89
90 14420.000 0 0 0 0 0 1 0 0 0 0 0 90
91 11424.000 0 0 0 0 0 0 1 0 0 0 0 91
92 9704.000 0 0 0 0 0 0 0 1 0 0 0 92
93 12464.000 0 0 0 0 0 0 0 0 1 0 0 93
94 14301.000 0 0 0 0 0 0 0 0 0 1 0 94
95 13464.000 0 0 0 0 0 0 0 0 0 0 1 95
96 9893.000 0 0 0 0 0 0 0 0 0 0 0 96
97 11572.000 1 0 0 0 0 0 0 0 0 0 0 97
98 12380.000 0 1 0 0 0 0 0 0 0 0 0 98
99 16692.000 0 0 1 0 0 0 0 0 0 0 0 99
100 16052.000 0 0 0 1 0 0 0 0 0 0 0 100
101 16459.000 0 0 0 0 1 0 0 0 0 0 0 101
102 14761.000 0 0 0 0 0 1 0 0 0 0 0 102
103 13654.000 0 0 0 0 0 0 1 0 0 0 0 103
104 13480.000 0 0 0 0 0 0 0 1 0 0 0 104
105 18068.000 0 0 0 0 0 0 0 0 1 0 0 105
106 16560.000 0 0 0 0 0 0 0 0 0 1 0 106
107 14530.000 0 0 0 0 0 0 0 0 0 0 1 107
108 10650.000 0 0 0 0 0 0 0 0 0 0 0 108
109 11651.000 1 0 0 0 0 0 0 0 0 0 0 109
110 13735.000 0 1 0 0 0 0 0 0 0 0 0 110
111 13360.000 0 0 1 0 0 0 0 0 0 0 0 111
112 17818.000 0 0 0 1 0 0 0 0 0 0 0 112
113 20613.000 0 0 0 0 1 0 0 0 0 0 0 113
114 16231.000 0 0 0 0 0 1 0 0 0 0 0 114
115 13862.000 0 0 0 0 0 0 1 0 0 0 0 115
116 12004.000 0 0 0 0 0 0 0 1 0 0 0 116
117 17734.000 0 0 0 0 0 0 0 0 1 0 0 117
118 15034.000 0 0 0 0 0 0 0 0 0 1 0 118
119 12609.000 0 0 0 0 0 0 0 0 0 0 1 119
120 12320.000 0 0 0 0 0 0 0 0 0 0 0 120
121 10833.000 1 0 0 0 0 0 0 0 0 0 0 121
122 11350.000 0 1 0 0 0 0 0 0 0 0 0 122
123 13648.000 0 0 1 0 0 0 0 0 0 0 0 123
124 14890.000 0 0 0 1 0 0 0 0 0 0 0 124
125 16325.000 0 0 0 0 1 0 0 0 0 0 0 125
126 18045.000 0 0 0 0 0 1 0 0 0 0 0 126
127 15616.000 0 0 0 0 0 0 1 0 0 0 0 127
128 11926.000 0 0 0 0 0 0 0 1 0 0 0 128
129 16855.000 0 0 0 0 0 0 0 0 1 0 0 129
130 15083.000 0 0 0 0 0 0 0 0 0 1 0 130
131 12520.000 0 0 0 0 0 0 0 0 0 0 1 131
132 12355.000 0 0 0 0 0 0 0 0 0 0 0 132
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
11516.706 -32.887 745.405 3028.250 2861.477 4766.354
M6 M7 M8 M9 M10 M11
3852.055 1868.785 620.104 3368.078 2685.140 1822.649
t
6.448
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-16293.5 -1692.2 -425.2 1921.6 11424.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11516.706 2087.765 5.516 2.05e-07 ***
M1 -32.887 2594.083 -0.013 0.9899
M2 745.405 2593.298 0.287 0.7743
M3 3028.250 2592.587 1.168 0.2451
M4 2861.477 2591.950 1.104 0.2718
M5 4766.354 2591.389 1.839 0.0684 .
M6 3852.055 2590.902 1.487 0.1397
M7 1868.785 2590.490 0.721 0.4721
M8 620.104 2590.153 0.239 0.8112
M9 3368.078 2589.891 1.300 0.1960
M10 2685.140 2589.704 1.037 0.3019
M11 1822.649 2589.591 0.704 0.4829
t 6.448 13.930 0.463 0.6443
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6073 on 119 degrees of freedom
Multiple R-squared: 0.06419, Adjusted R-squared: -0.03017
F-statistic: 0.6803 on 12 and 119 DF, p-value: 0.7675
> 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,] 0.044669470 8.933894e-02 9.553305e-01
[2,] 0.217795139 4.355903e-01 7.822049e-01
[3,] 0.206948425 4.138968e-01 7.930516e-01
[4,] 0.123002156 2.460043e-01 8.769978e-01
[5,] 0.071342655 1.426853e-01 9.286573e-01
[6,] 0.040840691 8.168138e-02 9.591593e-01
[7,] 0.022519168 4.503834e-02 9.774808e-01
[8,] 0.012456264 2.491253e-02 9.875437e-01
[9,] 0.009208815 1.841763e-02 9.907912e-01
[10,] 0.989571375 2.085725e-02 1.042862e-02
[11,] 0.999887004 2.259923e-04 1.129962e-04
[12,] 0.999988355 2.329065e-05 1.164533e-05
[13,] 0.999998133 3.734852e-06 1.867426e-06
[14,] 0.999999574 8.510633e-07 4.255317e-07
[15,] 0.999999914 1.717023e-07 8.585116e-08
[16,] 0.999999985 2.939966e-08 1.469983e-08
[17,] 0.999999999 2.146057e-09 1.073028e-09
[18,] 1.000000000 7.091809e-10 3.545905e-10
[19,] 1.000000000 1.092651e-10 5.463256e-11
[20,] 1.000000000 4.765389e-13 2.382695e-13
[21,] 1.000000000 1.913292e-15 9.566459e-16
[22,] 1.000000000 7.806620e-19 3.903310e-19
[23,] 1.000000000 3.695563e-21 1.847782e-21
[24,] 1.000000000 9.860938e-24 4.930469e-24
[25,] 1.000000000 4.368211e-25 2.184106e-25
[26,] 1.000000000 1.059926e-28 5.299631e-29
[27,] 1.000000000 1.301346e-29 6.506731e-30
[28,] 1.000000000 3.134129e-30 1.567064e-30
[29,] 1.000000000 8.668410e-31 4.334205e-31
[30,] 1.000000000 1.068221e-30 5.341104e-31
[31,] 1.000000000 1.057866e-30 5.289332e-31
[32,] 1.000000000 8.139121e-31 4.069561e-31
[33,] 1.000000000 4.524874e-31 2.262437e-31
[34,] 1.000000000 1.019848e-31 5.099242e-32
[35,] 1.000000000 6.086507e-32 3.043253e-32
[36,] 1.000000000 2.805630e-32 1.402815e-32
[37,] 1.000000000 5.374366e-32 2.687183e-32
[38,] 1.000000000 5.046705e-32 2.523352e-32
[39,] 1.000000000 1.022148e-31 5.110741e-32
[40,] 1.000000000 8.053888e-32 4.026944e-32
[41,] 1.000000000 1.636077e-31 8.180385e-32
[42,] 1.000000000 6.505645e-31 3.252823e-31
[43,] 1.000000000 1.968411e-30 9.842055e-31
[44,] 1.000000000 4.968320e-30 2.484160e-30
[45,] 1.000000000 1.314156e-29 6.570779e-30
[46,] 1.000000000 3.997811e-29 1.998906e-29
[47,] 1.000000000 1.123344e-28 5.616721e-29
[48,] 1.000000000 3.189406e-28 1.594703e-28
[49,] 1.000000000 1.182402e-27 5.912011e-28
[50,] 1.000000000 3.314362e-27 1.657181e-27
[51,] 1.000000000 1.562233e-26 7.811165e-27
[52,] 1.000000000 6.876571e-26 3.438285e-26
[53,] 1.000000000 1.131906e-25 5.659528e-26
[54,] 1.000000000 3.632008e-25 1.816004e-25
[55,] 1.000000000 1.512930e-24 7.564650e-25
[56,] 1.000000000 4.540388e-24 2.270194e-24
[57,] 1.000000000 1.735696e-23 8.678478e-24
[58,] 1.000000000 7.460379e-23 3.730190e-23
[59,] 1.000000000 3.275167e-22 1.637583e-22
[60,] 1.000000000 1.256882e-21 6.284410e-22
[61,] 1.000000000 3.240292e-21 1.620146e-21
[62,] 1.000000000 2.475378e-21 1.237689e-21
[63,] 1.000000000 1.296414e-20 6.482072e-21
[64,] 1.000000000 6.297274e-20 3.148637e-20
[65,] 1.000000000 2.542596e-19 1.271298e-19
[66,] 1.000000000 1.022082e-18 5.110409e-19
[67,] 1.000000000 2.082153e-18 1.041077e-18
[68,] 1.000000000 1.024845e-17 5.124224e-18
[69,] 1.000000000 3.739445e-17 1.869723e-17
[70,] 1.000000000 1.804955e-16 9.024777e-17
[71,] 1.000000000 7.094194e-16 3.547097e-16
[72,] 1.000000000 3.151412e-15 1.575706e-15
[73,] 1.000000000 1.183300e-14 5.916501e-15
[74,] 1.000000000 1.320855e-14 6.604277e-15
[75,] 1.000000000 5.241926e-14 2.620963e-14
[76,] 1.000000000 1.148480e-13 5.742398e-14
[77,] 1.000000000 2.471224e-13 1.235612e-13
[78,] 1.000000000 1.903604e-14 9.518021e-15
[79,] 1.000000000 7.258318e-14 3.629159e-14
[80,] 1.000000000 4.001303e-13 2.000652e-13
[81,] 1.000000000 8.347818e-13 4.173909e-13
[82,] 1.000000000 4.563017e-12 2.281508e-12
[83,] 1.000000000 2.323417e-11 1.161708e-11
[84,] 1.000000000 2.870066e-11 1.435033e-11
[85,] 1.000000000 1.489919e-10 7.449597e-11
[86,] 1.000000000 3.138523e-10 1.569261e-10
[87,] 1.000000000 3.365374e-10 1.682687e-10
[88,] 1.000000000 9.454252e-10 4.727126e-10
[89,] 0.999999998 4.988764e-09 2.494382e-09
[90,] 0.999999986 2.773341e-08 1.386670e-08
[91,] 0.999999933 1.349649e-07 6.748245e-08
[92,] 0.999999749 5.025880e-07 2.512940e-07
[93,] 0.999999517 9.658973e-07 4.829486e-07
[94,] 0.999997388 5.224562e-06 2.612281e-06
[95,] 0.999991301 1.739741e-05 8.698704e-06
[96,] 0.999958301 8.339701e-05 4.169850e-05
[97,] 0.999906803 1.863936e-04 9.319679e-05
[98,] 0.999992312 1.537563e-05 7.687813e-06
[99,] 0.999978453 4.309441e-05 2.154721e-05
[100,] 0.999993622 1.275572e-05 6.377861e-06
[101,] 0.999844893 3.102133e-04 1.551067e-04
> postscript(file="/var/www/html/freestat/rcomp/tmp/1fc491290769236.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2fc491290769236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/freestat/rcomp/tmp/3q3lu1290769236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4q3lu1290769236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5q3lu1290769236.ps",horizontal=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 = 132
Frequency = 1
1 2 3 4 5 6
-11475.80926 -12261.41290 -14546.48563 -14383.73926 -16293.48835 -15386.00835
7 8 9 10 11 12
-13409.23199 -12168.56108 -14922.34562 -14245.24381 -13388.68017 -11576.68926
13 14 15 16 17 18
6280.35968 7239.62005 6450.32732 6407.65268 9497.32759 9480.17959
19 20 21 22 23 24
8717.00195 8711.23486 7876.81332 8441.30314 9281.34677 7965.54768
25 26 27 28 29 30
8557.98662 8020.24699 8363.95426 7179.27962 10295.95453 9717.80653
31 32 33 34 35 36
8988.62890 10385.86181 6280.44026 8480.93008 11423.97372 9367.17462
37 38 39 40 41 42
3446.61357 3338.87393 6130.58120 3636.90657 5990.58148 -43.56652
43 44 45 46 47 48
371.25584 -1054.51125 -313.93280 650.55702 -65.39934 1199.80157
49 50 51 52 53 54
1390.24051 611.50087 952.20815 19.53351 -317.79158 -13.93958
55 56 57 58 59 60
848.88278 -454.88431 -195.30585 -522.81603 -1021.77240 -1015.57149
61 62 63 64 65 66
-1832.13255 -1112.87218 -1184.16491 -2366.83955 -3586.16464 -1583.31264
67 68 69 70 71 72
-1551.49027 26.74264 384.32109 -911.18909 -1052.14545 -1489.94455
73 74 75 76 77 78
-1897.50560 -1839.24524 -3257.53797 -2876.21260 -4786.53769 -1367.68569
79 80 81 82 83 84
-2167.86333 -1175.63042 -1829.05197 -3175.56215 -2028.51851 -661.31760
85 86 87 88 89 90
-1965.87866 -2547.61830 -826.91102 -1075.58566 -3161.91075 -1529.05875
91 92 93 94 95 96
-2548.23639 -3026.00348 -3020.42502 -506.93520 -487.89157 -2242.69066
97 98 99 100 101 102
-537.25172 -513.99135 1508.71592 1029.04128 -475.28381 -1265.43181
103 104 105 106 107 108
-395.60944 672.62347 2506.20192 1674.69174 500.73538 -1563.06372
109 110 111 112 113 114
-535.62477 763.63559 -1900.65714 2717.66823 3601.34314 127.19514
115 116 117 118 119 120
-264.98250 -880.74959 2094.82886 71.31868 -1497.63768 29.56323
121 122 123 124 125 126
-1430.99783 -1698.73747 -1690.03019 -287.70483 -764.02992 1863.82208
127 128 129 130 131 132
1411.64444 -1036.12265 1138.45581 42.94563 -1664.01074 -12.80983
> postscript(file="/var/www/html/freestat/rcomp/tmp/61c3x1290769236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 132
Frequency = 1
lag(myerror, k = 1) myerror
0 -11475.80926 NA
1 -12261.41290 -11475.80926
2 -14546.48563 -12261.41290
3 -14383.73926 -14546.48563
4 -16293.48835 -14383.73926
5 -15386.00835 -16293.48835
6 -13409.23199 -15386.00835
7 -12168.56108 -13409.23199
8 -14922.34562 -12168.56108
9 -14245.24381 -14922.34562
10 -13388.68017 -14245.24381
11 -11576.68926 -13388.68017
12 6280.35968 -11576.68926
13 7239.62005 6280.35968
14 6450.32732 7239.62005
15 6407.65268 6450.32732
16 9497.32759 6407.65268
17 9480.17959 9497.32759
18 8717.00195 9480.17959
19 8711.23486 8717.00195
20 7876.81332 8711.23486
21 8441.30314 7876.81332
22 9281.34677 8441.30314
23 7965.54768 9281.34677
24 8557.98662 7965.54768
25 8020.24699 8557.98662
26 8363.95426 8020.24699
27 7179.27962 8363.95426
28 10295.95453 7179.27962
29 9717.80653 10295.95453
30 8988.62890 9717.80653
31 10385.86181 8988.62890
32 6280.44026 10385.86181
33 8480.93008 6280.44026
34 11423.97372 8480.93008
35 9367.17462 11423.97372
36 3446.61357 9367.17462
37 3338.87393 3446.61357
38 6130.58120 3338.87393
39 3636.90657 6130.58120
40 5990.58148 3636.90657
41 -43.56652 5990.58148
42 371.25584 -43.56652
43 -1054.51125 371.25584
44 -313.93280 -1054.51125
45 650.55702 -313.93280
46 -65.39934 650.55702
47 1199.80157 -65.39934
48 1390.24051 1199.80157
49 611.50087 1390.24051
50 952.20815 611.50087
51 19.53351 952.20815
52 -317.79158 19.53351
53 -13.93958 -317.79158
54 848.88278 -13.93958
55 -454.88431 848.88278
56 -195.30585 -454.88431
57 -522.81603 -195.30585
58 -1021.77240 -522.81603
59 -1015.57149 -1021.77240
60 -1832.13255 -1015.57149
61 -1112.87218 -1832.13255
62 -1184.16491 -1112.87218
63 -2366.83955 -1184.16491
64 -3586.16464 -2366.83955
65 -1583.31264 -3586.16464
66 -1551.49027 -1583.31264
67 26.74264 -1551.49027
68 384.32109 26.74264
69 -911.18909 384.32109
70 -1052.14545 -911.18909
71 -1489.94455 -1052.14545
72 -1897.50560 -1489.94455
73 -1839.24524 -1897.50560
74 -3257.53797 -1839.24524
75 -2876.21260 -3257.53797
76 -4786.53769 -2876.21260
77 -1367.68569 -4786.53769
78 -2167.86333 -1367.68569
79 -1175.63042 -2167.86333
80 -1829.05197 -1175.63042
81 -3175.56215 -1829.05197
82 -2028.51851 -3175.56215
83 -661.31760 -2028.51851
84 -1965.87866 -661.31760
85 -2547.61830 -1965.87866
86 -826.91102 -2547.61830
87 -1075.58566 -826.91102
88 -3161.91075 -1075.58566
89 -1529.05875 -3161.91075
90 -2548.23639 -1529.05875
91 -3026.00348 -2548.23639
92 -3020.42502 -3026.00348
93 -506.93520 -3020.42502
94 -487.89157 -506.93520
95 -2242.69066 -487.89157
96 -537.25172 -2242.69066
97 -513.99135 -537.25172
98 1508.71592 -513.99135
99 1029.04128 1508.71592
100 -475.28381 1029.04128
101 -1265.43181 -475.28381
102 -395.60944 -1265.43181
103 672.62347 -395.60944
104 2506.20192 672.62347
105 1674.69174 2506.20192
106 500.73538 1674.69174
107 -1563.06372 500.73538
108 -535.62477 -1563.06372
109 763.63559 -535.62477
110 -1900.65714 763.63559
111 2717.66823 -1900.65714
112 3601.34314 2717.66823
113 127.19514 3601.34314
114 -264.98250 127.19514
115 -880.74959 -264.98250
116 2094.82886 -880.74959
117 71.31868 2094.82886
118 -1497.63768 71.31868
119 29.56323 -1497.63768
120 -1430.99783 29.56323
121 -1698.73747 -1430.99783
122 -1690.03019 -1698.73747
123 -287.70483 -1690.03019
124 -764.02992 -287.70483
125 1863.82208 -764.02992
126 1411.64444 1863.82208
127 -1036.12265 1411.64444
128 1138.45581 -1036.12265
129 42.94563 1138.45581
130 -1664.01074 42.94563
131 -12.80983 -1664.01074
132 NA -12.80983
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -12261.41290 -11475.80926
[2,] -14546.48563 -12261.41290
[3,] -14383.73926 -14546.48563
[4,] -16293.48835 -14383.73926
[5,] -15386.00835 -16293.48835
[6,] -13409.23199 -15386.00835
[7,] -12168.56108 -13409.23199
[8,] -14922.34562 -12168.56108
[9,] -14245.24381 -14922.34562
[10,] -13388.68017 -14245.24381
[11,] -11576.68926 -13388.68017
[12,] 6280.35968 -11576.68926
[13,] 7239.62005 6280.35968
[14,] 6450.32732 7239.62005
[15,] 6407.65268 6450.32732
[16,] 9497.32759 6407.65268
[17,] 9480.17959 9497.32759
[18,] 8717.00195 9480.17959
[19,] 8711.23486 8717.00195
[20,] 7876.81332 8711.23486
[21,] 8441.30314 7876.81332
[22,] 9281.34677 8441.30314
[23,] 7965.54768 9281.34677
[24,] 8557.98662 7965.54768
[25,] 8020.24699 8557.98662
[26,] 8363.95426 8020.24699
[27,] 7179.27962 8363.95426
[28,] 10295.95453 7179.27962
[29,] 9717.80653 10295.95453
[30,] 8988.62890 9717.80653
[31,] 10385.86181 8988.62890
[32,] 6280.44026 10385.86181
[33,] 8480.93008 6280.44026
[34,] 11423.97372 8480.93008
[35,] 9367.17462 11423.97372
[36,] 3446.61357 9367.17462
[37,] 3338.87393 3446.61357
[38,] 6130.58120 3338.87393
[39,] 3636.90657 6130.58120
[40,] 5990.58148 3636.90657
[41,] -43.56652 5990.58148
[42,] 371.25584 -43.56652
[43,] -1054.51125 371.25584
[44,] -313.93280 -1054.51125
[45,] 650.55702 -313.93280
[46,] -65.39934 650.55702
[47,] 1199.80157 -65.39934
[48,] 1390.24051 1199.80157
[49,] 611.50087 1390.24051
[50,] 952.20815 611.50087
[51,] 19.53351 952.20815
[52,] -317.79158 19.53351
[53,] -13.93958 -317.79158
[54,] 848.88278 -13.93958
[55,] -454.88431 848.88278
[56,] -195.30585 -454.88431
[57,] -522.81603 -195.30585
[58,] -1021.77240 -522.81603
[59,] -1015.57149 -1021.77240
[60,] -1832.13255 -1015.57149
[61,] -1112.87218 -1832.13255
[62,] -1184.16491 -1112.87218
[63,] -2366.83955 -1184.16491
[64,] -3586.16464 -2366.83955
[65,] -1583.31264 -3586.16464
[66,] -1551.49027 -1583.31264
[67,] 26.74264 -1551.49027
[68,] 384.32109 26.74264
[69,] -911.18909 384.32109
[70,] -1052.14545 -911.18909
[71,] -1489.94455 -1052.14545
[72,] -1897.50560 -1489.94455
[73,] -1839.24524 -1897.50560
[74,] -3257.53797 -1839.24524
[75,] -2876.21260 -3257.53797
[76,] -4786.53769 -2876.21260
[77,] -1367.68569 -4786.53769
[78,] -2167.86333 -1367.68569
[79,] -1175.63042 -2167.86333
[80,] -1829.05197 -1175.63042
[81,] -3175.56215 -1829.05197
[82,] -2028.51851 -3175.56215
[83,] -661.31760 -2028.51851
[84,] -1965.87866 -661.31760
[85,] -2547.61830 -1965.87866
[86,] -826.91102 -2547.61830
[87,] -1075.58566 -826.91102
[88,] -3161.91075 -1075.58566
[89,] -1529.05875 -3161.91075
[90,] -2548.23639 -1529.05875
[91,] -3026.00348 -2548.23639
[92,] -3020.42502 -3026.00348
[93,] -506.93520 -3020.42502
[94,] -487.89157 -506.93520
[95,] -2242.69066 -487.89157
[96,] -537.25172 -2242.69066
[97,] -513.99135 -537.25172
[98,] 1508.71592 -513.99135
[99,] 1029.04128 1508.71592
[100,] -475.28381 1029.04128
[101,] -1265.43181 -475.28381
[102,] -395.60944 -1265.43181
[103,] 672.62347 -395.60944
[104,] 2506.20192 672.62347
[105,] 1674.69174 2506.20192
[106,] 500.73538 1674.69174
[107,] -1563.06372 500.73538
[108,] -535.62477 -1563.06372
[109,] 763.63559 -535.62477
[110,] -1900.65714 763.63559
[111,] 2717.66823 -1900.65714
[112,] 3601.34314 2717.66823
[113,] 127.19514 3601.34314
[114,] -264.98250 127.19514
[115,] -880.74959 -264.98250
[116,] 2094.82886 -880.74959
[117,] 71.31868 2094.82886
[118,] -1497.63768 71.31868
[119,] 29.56323 -1497.63768
[120,] -1430.99783 29.56323
[121,] -1698.73747 -1430.99783
[122,] -1690.03019 -1698.73747
[123,] -287.70483 -1690.03019
[124,] -764.02992 -287.70483
[125,] 1863.82208 -764.02992
[126,] 1411.64444 1863.82208
[127,] -1036.12265 1411.64444
[128,] 1138.45581 -1036.12265
[129,] 42.94563 1138.45581
[130,] -1664.01074 42.94563
[131,] -12.80983 -1664.01074
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -12261.41290 -11475.80926
2 -14546.48563 -12261.41290
3 -14383.73926 -14546.48563
4 -16293.48835 -14383.73926
5 -15386.00835 -16293.48835
6 -13409.23199 -15386.00835
7 -12168.56108 -13409.23199
8 -14922.34562 -12168.56108
9 -14245.24381 -14922.34562
10 -13388.68017 -14245.24381
11 -11576.68926 -13388.68017
12 6280.35968 -11576.68926
13 7239.62005 6280.35968
14 6450.32732 7239.62005
15 6407.65268 6450.32732
16 9497.32759 6407.65268
17 9480.17959 9497.32759
18 8717.00195 9480.17959
19 8711.23486 8717.00195
20 7876.81332 8711.23486
21 8441.30314 7876.81332
22 9281.34677 8441.30314
23 7965.54768 9281.34677
24 8557.98662 7965.54768
25 8020.24699 8557.98662
26 8363.95426 8020.24699
27 7179.27962 8363.95426
28 10295.95453 7179.27962
29 9717.80653 10295.95453
30 8988.62890 9717.80653
31 10385.86181 8988.62890
32 6280.44026 10385.86181
33 8480.93008 6280.44026
34 11423.97372 8480.93008
35 9367.17462 11423.97372
36 3446.61357 9367.17462
37 3338.87393 3446.61357
38 6130.58120 3338.87393
39 3636.90657 6130.58120
40 5990.58148 3636.90657
41 -43.56652 5990.58148
42 371.25584 -43.56652
43 -1054.51125 371.25584
44 -313.93280 -1054.51125
45 650.55702 -313.93280
46 -65.39934 650.55702
47 1199.80157 -65.39934
48 1390.24051 1199.80157
49 611.50087 1390.24051
50 952.20815 611.50087
51 19.53351 952.20815
52 -317.79158 19.53351
53 -13.93958 -317.79158
54 848.88278 -13.93958
55 -454.88431 848.88278
56 -195.30585 -454.88431
57 -522.81603 -195.30585
58 -1021.77240 -522.81603
59 -1015.57149 -1021.77240
60 -1832.13255 -1015.57149
61 -1112.87218 -1832.13255
62 -1184.16491 -1112.87218
63 -2366.83955 -1184.16491
64 -3586.16464 -2366.83955
65 -1583.31264 -3586.16464
66 -1551.49027 -1583.31264
67 26.74264 -1551.49027
68 384.32109 26.74264
69 -911.18909 384.32109
70 -1052.14545 -911.18909
71 -1489.94455 -1052.14545
72 -1897.50560 -1489.94455
73 -1839.24524 -1897.50560
74 -3257.53797 -1839.24524
75 -2876.21260 -3257.53797
76 -4786.53769 -2876.21260
77 -1367.68569 -4786.53769
78 -2167.86333 -1367.68569
79 -1175.63042 -2167.86333
80 -1829.05197 -1175.63042
81 -3175.56215 -1829.05197
82 -2028.51851 -3175.56215
83 -661.31760 -2028.51851
84 -1965.87866 -661.31760
85 -2547.61830 -1965.87866
86 -826.91102 -2547.61830
87 -1075.58566 -826.91102
88 -3161.91075 -1075.58566
89 -1529.05875 -3161.91075
90 -2548.23639 -1529.05875
91 -3026.00348 -2548.23639
92 -3020.42502 -3026.00348
93 -506.93520 -3020.42502
94 -487.89157 -506.93520
95 -2242.69066 -487.89157
96 -537.25172 -2242.69066
97 -513.99135 -537.25172
98 1508.71592 -513.99135
99 1029.04128 1508.71592
100 -475.28381 1029.04128
101 -1265.43181 -475.28381
102 -395.60944 -1265.43181
103 672.62347 -395.60944
104 2506.20192 672.62347
105 1674.69174 2506.20192
106 500.73538 1674.69174
107 -1563.06372 500.73538
108 -535.62477 -1563.06372
109 763.63559 -535.62477
110 -1900.65714 763.63559
111 2717.66823 -1900.65714
112 3601.34314 2717.66823
113 127.19514 3601.34314
114 -264.98250 127.19514
115 -880.74959 -264.98250
116 2094.82886 -880.74959
117 71.31868 2094.82886
118 -1497.63768 71.31868
119 29.56323 -1497.63768
120 -1430.99783 29.56323
121 -1698.73747 -1430.99783
122 -1690.03019 -1698.73747
123 -287.70483 -1690.03019
124 -764.02992 -287.70483
125 1863.82208 -764.02992
126 1411.64444 1863.82208
127 -1036.12265 1411.64444
128 1138.45581 -1036.12265
129 42.94563 1138.45581
130 -1664.01074 42.94563
131 -12.80983 -1664.01074
> 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/html/freestat/rcomp/tmp/7b3k01290769236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8b3k01290769236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9b3k01290769236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/104dj31290769236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/freestat/rcomp/tmp/11pvi91290769236.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/html/freestat/rcomp/tmp/12tegx1290769236.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/html/freestat/rcomp/tmp/1376wo1290769236.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/html/freestat/rcomp/tmp/14s6ut1290769236.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/html/freestat/rcomp/tmp/15e7bz1290769236.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/html/freestat/rcomp/tmp/16hp951290769236.tab")
+ }
>
> try(system("convert tmp/1fc491290769236.ps tmp/1fc491290769236.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fc491290769236.ps tmp/2fc491290769236.png",intern=TRUE))
character(0)
> try(system("convert tmp/3q3lu1290769236.ps tmp/3q3lu1290769236.png",intern=TRUE))
character(0)
> try(system("convert tmp/4q3lu1290769236.ps tmp/4q3lu1290769236.png",intern=TRUE))
character(0)
> try(system("convert tmp/5q3lu1290769236.ps tmp/5q3lu1290769236.png",intern=TRUE))
character(0)
> try(system("convert tmp/61c3x1290769236.ps tmp/61c3x1290769236.png",intern=TRUE))
character(0)
> try(system("convert tmp/7b3k01290769236.ps tmp/7b3k01290769236.png",intern=TRUE))
character(0)
> try(system("convert tmp/8b3k01290769236.ps tmp/8b3k01290769236.png",intern=TRUE))
character(0)
> try(system("convert tmp/9b3k01290769236.ps tmp/9b3k01290769236.png",intern=TRUE))
character(0)
> try(system("convert tmp/104dj31290769236.ps tmp/104dj31290769236.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.112 2.705 5.460