R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(9.492,8.641,9.793,9.603,9.238,9.535,10.295,9.941,9.984,9.563,8.872,9.302,9.215,8.834,9.998,9.604,9.507,9.718,10.095,9.583,9.883,9.365,8.919,9.449,9.769,9.321,9.939,9.336,10.195,9.464,10.010,10.213,9.563,9.890,9.305,9.391,9.928,8.686,9.843,9.627,10.074,9.503,10.119,10.000,9.313,9.866,9.172,9.241,9.659,8.904,9.755,9.080,9.435,8.971,10.063,9.793,9.454,9.759,8.820,9.403,9.676,8.642,9.402,9.610,9.294,9.448,10.319,9.548,9.801,9.596,8.923,9.746,9.829,9.125,9.782,9.441,9.162,9.915,10.444,10.209,9.985,9.842,9.429,10.132,9.849,9.172,10.313,9.819,9.955,10.048,10.082,10.541,10.208,10.233,9.439,9.963,10.158,9.225,10.474,9.757,10.490,10.281,10.444,10.640,10.695,10.786,9.832,9.747,10.411,9.511,10.402,9.701,10.540,10.112,10.915,11.183,10.384,10.834,9.886,10.216,10.943,9.867,10.203,10.837,10.573,10.647,11.502,10.656,10.866,10.835,9.945,10.331),dim=c(1,132),dimnames=list(c('births'),1:132))
> y <- array(NA,dim=c(1,132),dimnames=list(c('births'),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
> 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
births M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9.492 1 0 0 0 0 0 0 0 0 0 0 1
2 8.641 0 1 0 0 0 0 0 0 0 0 0 2
3 9.793 0 0 1 0 0 0 0 0 0 0 0 3
4 9.603 0 0 0 1 0 0 0 0 0 0 0 4
5 9.238 0 0 0 0 1 0 0 0 0 0 0 5
6 9.535 0 0 0 0 0 1 0 0 0 0 0 6
7 10.295 0 0 0 0 0 0 1 0 0 0 0 7
8 9.941 0 0 0 0 0 0 0 1 0 0 0 8
9 9.984 0 0 0 0 0 0 0 0 1 0 0 9
10 9.563 0 0 0 0 0 0 0 0 0 1 0 10
11 8.872 0 0 0 0 0 0 0 0 0 0 1 11
12 9.302 0 0 0 0 0 0 0 0 0 0 0 12
13 9.215 1 0 0 0 0 0 0 0 0 0 0 13
14 8.834 0 1 0 0 0 0 0 0 0 0 0 14
15 9.998 0 0 1 0 0 0 0 0 0 0 0 15
16 9.604 0 0 0 1 0 0 0 0 0 0 0 16
17 9.507 0 0 0 0 1 0 0 0 0 0 0 17
18 9.718 0 0 0 0 0 1 0 0 0 0 0 18
19 10.095 0 0 0 0 0 0 1 0 0 0 0 19
20 9.583 0 0 0 0 0 0 0 1 0 0 0 20
21 9.883 0 0 0 0 0 0 0 0 1 0 0 21
22 9.365 0 0 0 0 0 0 0 0 0 1 0 22
23 8.919 0 0 0 0 0 0 0 0 0 0 1 23
24 9.449 0 0 0 0 0 0 0 0 0 0 0 24
25 9.769 1 0 0 0 0 0 0 0 0 0 0 25
26 9.321 0 1 0 0 0 0 0 0 0 0 0 26
27 9.939 0 0 1 0 0 0 0 0 0 0 0 27
28 9.336 0 0 0 1 0 0 0 0 0 0 0 28
29 10.195 0 0 0 0 1 0 0 0 0 0 0 29
30 9.464 0 0 0 0 0 1 0 0 0 0 0 30
31 10.010 0 0 0 0 0 0 1 0 0 0 0 31
32 10.213 0 0 0 0 0 0 0 1 0 0 0 32
33 9.563 0 0 0 0 0 0 0 0 1 0 0 33
34 9.890 0 0 0 0 0 0 0 0 0 1 0 34
35 9.305 0 0 0 0 0 0 0 0 0 0 1 35
36 9.391 0 0 0 0 0 0 0 0 0 0 0 36
37 9.928 1 0 0 0 0 0 0 0 0 0 0 37
38 8.686 0 1 0 0 0 0 0 0 0 0 0 38
39 9.843 0 0 1 0 0 0 0 0 0 0 0 39
40 9.627 0 0 0 1 0 0 0 0 0 0 0 40
41 10.074 0 0 0 0 1 0 0 0 0 0 0 41
42 9.503 0 0 0 0 0 1 0 0 0 0 0 42
43 10.119 0 0 0 0 0 0 1 0 0 0 0 43
44 10.000 0 0 0 0 0 0 0 1 0 0 0 44
45 9.313 0 0 0 0 0 0 0 0 1 0 0 45
46 9.866 0 0 0 0 0 0 0 0 0 1 0 46
47 9.172 0 0 0 0 0 0 0 0 0 0 1 47
48 9.241 0 0 0 0 0 0 0 0 0 0 0 48
49 9.659 1 0 0 0 0 0 0 0 0 0 0 49
50 8.904 0 1 0 0 0 0 0 0 0 0 0 50
51 9.755 0 0 1 0 0 0 0 0 0 0 0 51
52 9.080 0 0 0 1 0 0 0 0 0 0 0 52
53 9.435 0 0 0 0 1 0 0 0 0 0 0 53
54 8.971 0 0 0 0 0 1 0 0 0 0 0 54
55 10.063 0 0 0 0 0 0 1 0 0 0 0 55
56 9.793 0 0 0 0 0 0 0 1 0 0 0 56
57 9.454 0 0 0 0 0 0 0 0 1 0 0 57
58 9.759 0 0 0 0 0 0 0 0 0 1 0 58
59 8.820 0 0 0 0 0 0 0 0 0 0 1 59
60 9.403 0 0 0 0 0 0 0 0 0 0 0 60
61 9.676 1 0 0 0 0 0 0 0 0 0 0 61
62 8.642 0 1 0 0 0 0 0 0 0 0 0 62
63 9.402 0 0 1 0 0 0 0 0 0 0 0 63
64 9.610 0 0 0 1 0 0 0 0 0 0 0 64
65 9.294 0 0 0 0 1 0 0 0 0 0 0 65
66 9.448 0 0 0 0 0 1 0 0 0 0 0 66
67 10.319 0 0 0 0 0 0 1 0 0 0 0 67
68 9.548 0 0 0 0 0 0 0 1 0 0 0 68
69 9.801 0 0 0 0 0 0 0 0 1 0 0 69
70 9.596 0 0 0 0 0 0 0 0 0 1 0 70
71 8.923 0 0 0 0 0 0 0 0 0 0 1 71
72 9.746 0 0 0 0 0 0 0 0 0 0 0 72
73 9.829 1 0 0 0 0 0 0 0 0 0 0 73
74 9.125 0 1 0 0 0 0 0 0 0 0 0 74
75 9.782 0 0 1 0 0 0 0 0 0 0 0 75
76 9.441 0 0 0 1 0 0 0 0 0 0 0 76
77 9.162 0 0 0 0 1 0 0 0 0 0 0 77
78 9.915 0 0 0 0 0 1 0 0 0 0 0 78
79 10.444 0 0 0 0 0 0 1 0 0 0 0 79
80 10.209 0 0 0 0 0 0 0 1 0 0 0 80
81 9.985 0 0 0 0 0 0 0 0 1 0 0 81
82 9.842 0 0 0 0 0 0 0 0 0 1 0 82
83 9.429 0 0 0 0 0 0 0 0 0 0 1 83
84 10.132 0 0 0 0 0 0 0 0 0 0 0 84
85 9.849 1 0 0 0 0 0 0 0 0 0 0 85
86 9.172 0 1 0 0 0 0 0 0 0 0 0 86
87 10.313 0 0 1 0 0 0 0 0 0 0 0 87
88 9.819 0 0 0 1 0 0 0 0 0 0 0 88
89 9.955 0 0 0 0 1 0 0 0 0 0 0 89
90 10.048 0 0 0 0 0 1 0 0 0 0 0 90
91 10.082 0 0 0 0 0 0 1 0 0 0 0 91
92 10.541 0 0 0 0 0 0 0 1 0 0 0 92
93 10.208 0 0 0 0 0 0 0 0 1 0 0 93
94 10.233 0 0 0 0 0 0 0 0 0 1 0 94
95 9.439 0 0 0 0 0 0 0 0 0 0 1 95
96 9.963 0 0 0 0 0 0 0 0 0 0 0 96
97 10.158 1 0 0 0 0 0 0 0 0 0 0 97
98 9.225 0 1 0 0 0 0 0 0 0 0 0 98
99 10.474 0 0 1 0 0 0 0 0 0 0 0 99
100 9.757 0 0 0 1 0 0 0 0 0 0 0 100
101 10.490 0 0 0 0 1 0 0 0 0 0 0 101
102 10.281 0 0 0 0 0 1 0 0 0 0 0 102
103 10.444 0 0 0 0 0 0 1 0 0 0 0 103
104 10.640 0 0 0 0 0 0 0 1 0 0 0 104
105 10.695 0 0 0 0 0 0 0 0 1 0 0 105
106 10.786 0 0 0 0 0 0 0 0 0 1 0 106
107 9.832 0 0 0 0 0 0 0 0 0 0 1 107
108 9.747 0 0 0 0 0 0 0 0 0 0 0 108
109 10.411 1 0 0 0 0 0 0 0 0 0 0 109
110 9.511 0 1 0 0 0 0 0 0 0 0 0 110
111 10.402 0 0 1 0 0 0 0 0 0 0 0 111
112 9.701 0 0 0 1 0 0 0 0 0 0 0 112
113 10.540 0 0 0 0 1 0 0 0 0 0 0 113
114 10.112 0 0 0 0 0 1 0 0 0 0 0 114
115 10.915 0 0 0 0 0 0 1 0 0 0 0 115
116 11.183 0 0 0 0 0 0 0 1 0 0 0 116
117 10.384 0 0 0 0 0 0 0 0 1 0 0 117
118 10.834 0 0 0 0 0 0 0 0 0 1 0 118
119 9.886 0 0 0 0 0 0 0 0 0 0 1 119
120 10.216 0 0 0 0 0 0 0 0 0 0 0 120
121 10.943 1 0 0 0 0 0 0 0 0 0 0 121
122 9.867 0 1 0 0 0 0 0 0 0 0 0 122
123 10.203 0 0 1 0 0 0 0 0 0 0 0 123
124 10.837 0 0 0 1 0 0 0 0 0 0 0 124
125 10.573 0 0 0 0 1 0 0 0 0 0 0 125
126 10.647 0 0 0 0 0 1 0 0 0 0 0 126
127 11.502 0 0 0 0 0 0 1 0 0 0 0 127
128 10.656 0 0 0 0 0 0 0 1 0 0 0 128
129 10.866 0 0 0 0 0 0 0 0 1 0 0 129
130 10.835 0 0 0 0 0 0 0 0 0 1 0 130
131 9.945 0 0 0 0 0 0 0 0 0 0 1 131
132 10.331 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
9.104055 0.276662 -0.550167 0.348186 0.022448 0.200074
M6 M7 M8 M9 M10 M11
0.116882 0.712508 0.523861 0.317941 0.348748 -0.389535
t
0.008556
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.80095 -0.19760 0.00218 0.19035 0.64955
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.1040545 0.1058761 85.988 < 2e-16 ***
M1 0.2766621 0.1315528 2.103 0.037568 *
M2 -0.5501667 0.1315130 -4.183 5.52e-05 ***
M3 0.3481864 0.1314769 2.648 0.009188 **
M4 0.0224485 0.1314446 0.171 0.864685
M5 0.2000742 0.1314162 1.522 0.130550
M6 0.1168818 0.1313915 0.890 0.375492
M7 0.7125076 0.1313706 5.424 3.11e-07 ***
M8 0.5238606 0.1313535 3.988 0.000115 ***
M9 0.3179409 0.1313402 2.421 0.017000 *
M10 0.3487485 0.1313307 2.655 0.009004 **
M11 -0.3895348 0.1313250 -2.966 0.003645 **
t 0.0085561 0.0007064 12.112 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.308 on 119 degrees of freedom
Multiple R-squared: 0.7238, Adjusted R-squared: 0.696
F-statistic: 25.99 on 12 and 119 DF, p-value: < 2.2e-16
> 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,] 0.15667665 0.31335329 0.8433234
[2,] 0.10069755 0.20139510 0.8993024
[3,] 0.04952535 0.09905069 0.9504747
[4,] 0.04424928 0.08849857 0.9557507
[5,] 0.06225672 0.12451345 0.9377433
[6,] 0.03594002 0.07188004 0.9640600
[7,] 0.02252129 0.04504257 0.9774787
[8,] 0.01132968 0.02265935 0.9886703
[9,] 0.00681365 0.01362730 0.9931863
[10,] 0.01427522 0.02855043 0.9857248
[11,] 0.04501900 0.09003800 0.9549810
[12,] 0.03308171 0.06616342 0.9669183
[13,] 0.04574154 0.09148307 0.9542585
[14,] 0.23179457 0.46358913 0.7682054
[15,] 0.22974342 0.45948684 0.7702566
[16,] 0.22177043 0.44354087 0.7782296
[17,] 0.24887721 0.49775442 0.7511228
[18,] 0.30909032 0.61818064 0.6909097
[19,] 0.31250093 0.62500185 0.6874991
[20,] 0.33583741 0.67167482 0.6641626
[21,] 0.29265155 0.58530310 0.7073485
[22,] 0.30314104 0.60628207 0.6968590
[23,] 0.34347285 0.68694571 0.6565271
[24,] 0.34537510 0.69075020 0.6546249
[25,] 0.34424841 0.68849683 0.6557516
[26,] 0.48265923 0.96531845 0.5173408
[27,] 0.47272501 0.94545002 0.5272750
[28,] 0.45071560 0.90143121 0.5492844
[29,] 0.43016454 0.86032907 0.5698355
[30,] 0.56811638 0.86376724 0.4318836
[31,] 0.55801463 0.88397074 0.4419854
[32,] 0.56764432 0.86471136 0.4323557
[33,] 0.54070513 0.91858973 0.4592949
[34,] 0.49809037 0.99618073 0.5019096
[35,] 0.48182739 0.96365477 0.5181726
[36,] 0.49922393 0.99844785 0.5007761
[37,] 0.58382240 0.83235521 0.4161776
[38,] 0.59700942 0.80598117 0.4029906
[39,] 0.72763251 0.54473498 0.2723675
[40,] 0.68965960 0.62068079 0.3103404
[41,] 0.64663305 0.70673389 0.3533669
[42,] 0.61726228 0.76547545 0.3827377
[43,] 0.57404278 0.85191443 0.4259572
[44,] 0.53922579 0.92154843 0.4607742
[45,] 0.49138468 0.98276935 0.5086153
[46,] 0.44130093 0.88260186 0.5586991
[47,] 0.40741696 0.81483392 0.5925830
[48,] 0.43514993 0.87029986 0.5648501
[49,] 0.43558003 0.87116007 0.5644200
[50,] 0.44049737 0.88099475 0.5595026
[51,] 0.39340318 0.78680636 0.6065968
[52,] 0.38998069 0.77996137 0.6100193
[53,] 0.44764513 0.89529026 0.5523549
[54,] 0.41018882 0.82037765 0.5898112
[55,] 0.39045452 0.78090904 0.6095455
[56,] 0.35138187 0.70276374 0.6486181
[57,] 0.38831843 0.77663687 0.6116816
[58,] 0.35557746 0.71115492 0.6444225
[59,] 0.34797540 0.69595081 0.6520246
[60,] 0.29880094 0.59760187 0.7011991
[61,] 0.25397705 0.50795410 0.7460230
[62,] 0.46577817 0.93155634 0.5342218
[63,] 0.49300741 0.98601482 0.5069926
[64,] 0.47984252 0.95968503 0.5201575
[65,] 0.46209109 0.92418217 0.5379089
[66,] 0.43347813 0.86695625 0.5665219
[67,] 0.44140883 0.88281767 0.5585912
[68,] 0.43174338 0.86348675 0.5682566
[69,] 0.62692604 0.74614793 0.3730740
[70,] 0.60364648 0.79270704 0.3963535
[71,] 0.55671742 0.88656515 0.4432826
[72,] 0.60171062 0.79657877 0.3982894
[73,] 0.56492219 0.87015561 0.4350778
[74,] 0.53494042 0.93011916 0.4650596
[75,] 0.51734869 0.96530262 0.4826513
[76,] 0.57727815 0.84544371 0.4227219
[77,] 0.57193013 0.85613973 0.4280699
[78,] 0.53302017 0.93395965 0.4669798
[79,] 0.51690477 0.96619046 0.4830952
[80,] 0.46291929 0.92583857 0.5370807
[81,] 0.43958940 0.87917879 0.5604106
[82,] 0.41221739 0.82443478 0.5877826
[83,] 0.36519924 0.73039848 0.6348008
[84,] 0.42099595 0.84199191 0.5790040
[85,] 0.37666067 0.75332133 0.6233393
[86,] 0.39406729 0.78813458 0.6059327
[87,] 0.37348544 0.74697087 0.6265146
[88,] 0.41301211 0.82602422 0.5869879
[89,] 0.35877716 0.71755431 0.6412228
[90,] 0.40129058 0.80258116 0.5987094
[91,] 0.42551325 0.85102651 0.5744867
[92,] 0.41072468 0.82144937 0.5892753
[93,] 0.33658916 0.67317833 0.6634108
[94,] 0.29588373 0.59176746 0.7041163
[95,] 0.22720426 0.45440853 0.7727957
[96,] 0.21949823 0.43899646 0.7805018
[97,] 0.56212112 0.87575776 0.4378789
[98,] 0.47002240 0.94004480 0.5299776
[99,] 0.44732777 0.89465553 0.5526722
[100,] 0.53025921 0.93948157 0.4697408
[101,] 0.84466474 0.31067053 0.1553353
> postscript(file="/var/www/rcomp/tmp/1uax21322601502.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/257oz1322601502.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3w6oe1322601502.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4ae3x1322601502.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5gzw81322601502.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 = 132
Frequency = 1
1 2 3 4 5
1.027273e-01 7.000000e-02 3.150909e-01 4.422727e-01 -1.089091e-01
6 7 8 9 10
2.627273e-01 4.185455e-01 2.446364e-01 4.850000e-01 2.463636e-02
11 12 13 14 15
6.336364e-02 9.527273e-02 -2.769455e-01 1.603273e-01 4.174182e-01
16 17 18 19 20
3.406000e-01 5.741818e-02 3.430545e-01 1.158727e-01 -2.160364e-01
21 22 23 24 25
2.813273e-01 -2.760364e-01 7.690909e-03 1.396000e-01 1.743818e-01
26 27 28 29 30
5.446545e-01 2.557455e-01 -3.007273e-02 6.427455e-01 -1.361818e-02
31 32 33 34 35
-7.180000e-02 3.112909e-01 -1.413455e-01 1.462909e-01 2.910182e-01
36 37 38 39 40
-2.107273e-02 2.307091e-01 -1.930182e-01 5.707273e-02 1.582545e-01
41 42 43 44 45
4.190727e-01 -7.729091e-02 -6.547273e-02 -4.381818e-03 -4.940182e-01
46 47 48 49 50
1.961818e-02 5.534545e-02 -2.737455e-01 -1.409636e-01 -7.769091e-02
51 52 53 54 55
-1.336000e-01 -4.914182e-01 -3.226000e-01 -7.119636e-01 -2.241455e-01
56 57 58 59 60
-3.140545e-01 -4.556909e-01 -1.900545e-01 -3.993273e-01 -2.144182e-01
61 62 63 64 65
-2.266364e-01 -4.423636e-01 -5.892727e-01 -6.409091e-02 -5.662727e-01
66 67 68 69 70
-3.376364e-01 -7.081818e-02 -6.617273e-01 -2.113636e-01 -4.557273e-01
71 72 73 74 75
-3.990000e-01 2.590909e-02 -1.763091e-01 -6.203636e-02 -3.119455e-01
76 77 78 79 80
-3.357636e-01 -8.009455e-01 2.669091e-02 -4.849091e-02 -1.034000e-01
81 82 83 84 85
-1.300364e-01 -3.124000e-01 4.327273e-03 3.092364e-01 -2.589818e-01
86 87 88 89 90
-1.177091e-01 1.163818e-01 -6.043636e-02 -1.106182e-01 5.701818e-02
91 92 93 94 95
-5.131636e-01 1.259273e-01 -9.709091e-03 -2.407273e-02 -8.834545e-02
96 97 98 99 100
3.756364e-02 -5.265455e-02 -1.673818e-01 1.747091e-01 -2.251091e-01
101 102 103 104 105
3.217091e-01 1.873455e-01 -2.538364e-01 1.222545e-01 3.746182e-01
106 107 108 109 110
4.262545e-01 2.019818e-01 -2.811091e-01 9.767273e-02 1.594545e-02
111 112 113 114 115
3.636364e-05 -3.837818e-01 2.690364e-01 -8.432727e-02 1.144909e-01
116 117 118 119 120
5.625818e-01 -3.905455e-02 3.715818e-01 1.533091e-01 8.521818e-02
121 122 123 124 125
5.270000e-01 2.692727e-01 -3.016364e-01 6.495455e-01 1.993636e-01
126 127 128 129 130
3.480000e-01 5.988182e-01 -6.709091e-02 3.402727e-01 2.699091e-01
131 132
1.096364e-01 9.754545e-02
> postscript(file="/var/www/rcomp/tmp/6hico1322601502.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 = 132
Frequency = 1
lag(myerror, k = 1) myerror
0 1.027273e-01 NA
1 7.000000e-02 1.027273e-01
2 3.150909e-01 7.000000e-02
3 4.422727e-01 3.150909e-01
4 -1.089091e-01 4.422727e-01
5 2.627273e-01 -1.089091e-01
6 4.185455e-01 2.627273e-01
7 2.446364e-01 4.185455e-01
8 4.850000e-01 2.446364e-01
9 2.463636e-02 4.850000e-01
10 6.336364e-02 2.463636e-02
11 9.527273e-02 6.336364e-02
12 -2.769455e-01 9.527273e-02
13 1.603273e-01 -2.769455e-01
14 4.174182e-01 1.603273e-01
15 3.406000e-01 4.174182e-01
16 5.741818e-02 3.406000e-01
17 3.430545e-01 5.741818e-02
18 1.158727e-01 3.430545e-01
19 -2.160364e-01 1.158727e-01
20 2.813273e-01 -2.160364e-01
21 -2.760364e-01 2.813273e-01
22 7.690909e-03 -2.760364e-01
23 1.396000e-01 7.690909e-03
24 1.743818e-01 1.396000e-01
25 5.446545e-01 1.743818e-01
26 2.557455e-01 5.446545e-01
27 -3.007273e-02 2.557455e-01
28 6.427455e-01 -3.007273e-02
29 -1.361818e-02 6.427455e-01
30 -7.180000e-02 -1.361818e-02
31 3.112909e-01 -7.180000e-02
32 -1.413455e-01 3.112909e-01
33 1.462909e-01 -1.413455e-01
34 2.910182e-01 1.462909e-01
35 -2.107273e-02 2.910182e-01
36 2.307091e-01 -2.107273e-02
37 -1.930182e-01 2.307091e-01
38 5.707273e-02 -1.930182e-01
39 1.582545e-01 5.707273e-02
40 4.190727e-01 1.582545e-01
41 -7.729091e-02 4.190727e-01
42 -6.547273e-02 -7.729091e-02
43 -4.381818e-03 -6.547273e-02
44 -4.940182e-01 -4.381818e-03
45 1.961818e-02 -4.940182e-01
46 5.534545e-02 1.961818e-02
47 -2.737455e-01 5.534545e-02
48 -1.409636e-01 -2.737455e-01
49 -7.769091e-02 -1.409636e-01
50 -1.336000e-01 -7.769091e-02
51 -4.914182e-01 -1.336000e-01
52 -3.226000e-01 -4.914182e-01
53 -7.119636e-01 -3.226000e-01
54 -2.241455e-01 -7.119636e-01
55 -3.140545e-01 -2.241455e-01
56 -4.556909e-01 -3.140545e-01
57 -1.900545e-01 -4.556909e-01
58 -3.993273e-01 -1.900545e-01
59 -2.144182e-01 -3.993273e-01
60 -2.266364e-01 -2.144182e-01
61 -4.423636e-01 -2.266364e-01
62 -5.892727e-01 -4.423636e-01
63 -6.409091e-02 -5.892727e-01
64 -5.662727e-01 -6.409091e-02
65 -3.376364e-01 -5.662727e-01
66 -7.081818e-02 -3.376364e-01
67 -6.617273e-01 -7.081818e-02
68 -2.113636e-01 -6.617273e-01
69 -4.557273e-01 -2.113636e-01
70 -3.990000e-01 -4.557273e-01
71 2.590909e-02 -3.990000e-01
72 -1.763091e-01 2.590909e-02
73 -6.203636e-02 -1.763091e-01
74 -3.119455e-01 -6.203636e-02
75 -3.357636e-01 -3.119455e-01
76 -8.009455e-01 -3.357636e-01
77 2.669091e-02 -8.009455e-01
78 -4.849091e-02 2.669091e-02
79 -1.034000e-01 -4.849091e-02
80 -1.300364e-01 -1.034000e-01
81 -3.124000e-01 -1.300364e-01
82 4.327273e-03 -3.124000e-01
83 3.092364e-01 4.327273e-03
84 -2.589818e-01 3.092364e-01
85 -1.177091e-01 -2.589818e-01
86 1.163818e-01 -1.177091e-01
87 -6.043636e-02 1.163818e-01
88 -1.106182e-01 -6.043636e-02
89 5.701818e-02 -1.106182e-01
90 -5.131636e-01 5.701818e-02
91 1.259273e-01 -5.131636e-01
92 -9.709091e-03 1.259273e-01
93 -2.407273e-02 -9.709091e-03
94 -8.834545e-02 -2.407273e-02
95 3.756364e-02 -8.834545e-02
96 -5.265455e-02 3.756364e-02
97 -1.673818e-01 -5.265455e-02
98 1.747091e-01 -1.673818e-01
99 -2.251091e-01 1.747091e-01
100 3.217091e-01 -2.251091e-01
101 1.873455e-01 3.217091e-01
102 -2.538364e-01 1.873455e-01
103 1.222545e-01 -2.538364e-01
104 3.746182e-01 1.222545e-01
105 4.262545e-01 3.746182e-01
106 2.019818e-01 4.262545e-01
107 -2.811091e-01 2.019818e-01
108 9.767273e-02 -2.811091e-01
109 1.594545e-02 9.767273e-02
110 3.636364e-05 1.594545e-02
111 -3.837818e-01 3.636364e-05
112 2.690364e-01 -3.837818e-01
113 -8.432727e-02 2.690364e-01
114 1.144909e-01 -8.432727e-02
115 5.625818e-01 1.144909e-01
116 -3.905455e-02 5.625818e-01
117 3.715818e-01 -3.905455e-02
118 1.533091e-01 3.715818e-01
119 8.521818e-02 1.533091e-01
120 5.270000e-01 8.521818e-02
121 2.692727e-01 5.270000e-01
122 -3.016364e-01 2.692727e-01
123 6.495455e-01 -3.016364e-01
124 1.993636e-01 6.495455e-01
125 3.480000e-01 1.993636e-01
126 5.988182e-01 3.480000e-01
127 -6.709091e-02 5.988182e-01
128 3.402727e-01 -6.709091e-02
129 2.699091e-01 3.402727e-01
130 1.096364e-01 2.699091e-01
131 9.754545e-02 1.096364e-01
132 NA 9.754545e-02
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 7.000000e-02 1.027273e-01
[2,] 3.150909e-01 7.000000e-02
[3,] 4.422727e-01 3.150909e-01
[4,] -1.089091e-01 4.422727e-01
[5,] 2.627273e-01 -1.089091e-01
[6,] 4.185455e-01 2.627273e-01
[7,] 2.446364e-01 4.185455e-01
[8,] 4.850000e-01 2.446364e-01
[9,] 2.463636e-02 4.850000e-01
[10,] 6.336364e-02 2.463636e-02
[11,] 9.527273e-02 6.336364e-02
[12,] -2.769455e-01 9.527273e-02
[13,] 1.603273e-01 -2.769455e-01
[14,] 4.174182e-01 1.603273e-01
[15,] 3.406000e-01 4.174182e-01
[16,] 5.741818e-02 3.406000e-01
[17,] 3.430545e-01 5.741818e-02
[18,] 1.158727e-01 3.430545e-01
[19,] -2.160364e-01 1.158727e-01
[20,] 2.813273e-01 -2.160364e-01
[21,] -2.760364e-01 2.813273e-01
[22,] 7.690909e-03 -2.760364e-01
[23,] 1.396000e-01 7.690909e-03
[24,] 1.743818e-01 1.396000e-01
[25,] 5.446545e-01 1.743818e-01
[26,] 2.557455e-01 5.446545e-01
[27,] -3.007273e-02 2.557455e-01
[28,] 6.427455e-01 -3.007273e-02
[29,] -1.361818e-02 6.427455e-01
[30,] -7.180000e-02 -1.361818e-02
[31,] 3.112909e-01 -7.180000e-02
[32,] -1.413455e-01 3.112909e-01
[33,] 1.462909e-01 -1.413455e-01
[34,] 2.910182e-01 1.462909e-01
[35,] -2.107273e-02 2.910182e-01
[36,] 2.307091e-01 -2.107273e-02
[37,] -1.930182e-01 2.307091e-01
[38,] 5.707273e-02 -1.930182e-01
[39,] 1.582545e-01 5.707273e-02
[40,] 4.190727e-01 1.582545e-01
[41,] -7.729091e-02 4.190727e-01
[42,] -6.547273e-02 -7.729091e-02
[43,] -4.381818e-03 -6.547273e-02
[44,] -4.940182e-01 -4.381818e-03
[45,] 1.961818e-02 -4.940182e-01
[46,] 5.534545e-02 1.961818e-02
[47,] -2.737455e-01 5.534545e-02
[48,] -1.409636e-01 -2.737455e-01
[49,] -7.769091e-02 -1.409636e-01
[50,] -1.336000e-01 -7.769091e-02
[51,] -4.914182e-01 -1.336000e-01
[52,] -3.226000e-01 -4.914182e-01
[53,] -7.119636e-01 -3.226000e-01
[54,] -2.241455e-01 -7.119636e-01
[55,] -3.140545e-01 -2.241455e-01
[56,] -4.556909e-01 -3.140545e-01
[57,] -1.900545e-01 -4.556909e-01
[58,] -3.993273e-01 -1.900545e-01
[59,] -2.144182e-01 -3.993273e-01
[60,] -2.266364e-01 -2.144182e-01
[61,] -4.423636e-01 -2.266364e-01
[62,] -5.892727e-01 -4.423636e-01
[63,] -6.409091e-02 -5.892727e-01
[64,] -5.662727e-01 -6.409091e-02
[65,] -3.376364e-01 -5.662727e-01
[66,] -7.081818e-02 -3.376364e-01
[67,] -6.617273e-01 -7.081818e-02
[68,] -2.113636e-01 -6.617273e-01
[69,] -4.557273e-01 -2.113636e-01
[70,] -3.990000e-01 -4.557273e-01
[71,] 2.590909e-02 -3.990000e-01
[72,] -1.763091e-01 2.590909e-02
[73,] -6.203636e-02 -1.763091e-01
[74,] -3.119455e-01 -6.203636e-02
[75,] -3.357636e-01 -3.119455e-01
[76,] -8.009455e-01 -3.357636e-01
[77,] 2.669091e-02 -8.009455e-01
[78,] -4.849091e-02 2.669091e-02
[79,] -1.034000e-01 -4.849091e-02
[80,] -1.300364e-01 -1.034000e-01
[81,] -3.124000e-01 -1.300364e-01
[82,] 4.327273e-03 -3.124000e-01
[83,] 3.092364e-01 4.327273e-03
[84,] -2.589818e-01 3.092364e-01
[85,] -1.177091e-01 -2.589818e-01
[86,] 1.163818e-01 -1.177091e-01
[87,] -6.043636e-02 1.163818e-01
[88,] -1.106182e-01 -6.043636e-02
[89,] 5.701818e-02 -1.106182e-01
[90,] -5.131636e-01 5.701818e-02
[91,] 1.259273e-01 -5.131636e-01
[92,] -9.709091e-03 1.259273e-01
[93,] -2.407273e-02 -9.709091e-03
[94,] -8.834545e-02 -2.407273e-02
[95,] 3.756364e-02 -8.834545e-02
[96,] -5.265455e-02 3.756364e-02
[97,] -1.673818e-01 -5.265455e-02
[98,] 1.747091e-01 -1.673818e-01
[99,] -2.251091e-01 1.747091e-01
[100,] 3.217091e-01 -2.251091e-01
[101,] 1.873455e-01 3.217091e-01
[102,] -2.538364e-01 1.873455e-01
[103,] 1.222545e-01 -2.538364e-01
[104,] 3.746182e-01 1.222545e-01
[105,] 4.262545e-01 3.746182e-01
[106,] 2.019818e-01 4.262545e-01
[107,] -2.811091e-01 2.019818e-01
[108,] 9.767273e-02 -2.811091e-01
[109,] 1.594545e-02 9.767273e-02
[110,] 3.636364e-05 1.594545e-02
[111,] -3.837818e-01 3.636364e-05
[112,] 2.690364e-01 -3.837818e-01
[113,] -8.432727e-02 2.690364e-01
[114,] 1.144909e-01 -8.432727e-02
[115,] 5.625818e-01 1.144909e-01
[116,] -3.905455e-02 5.625818e-01
[117,] 3.715818e-01 -3.905455e-02
[118,] 1.533091e-01 3.715818e-01
[119,] 8.521818e-02 1.533091e-01
[120,] 5.270000e-01 8.521818e-02
[121,] 2.692727e-01 5.270000e-01
[122,] -3.016364e-01 2.692727e-01
[123,] 6.495455e-01 -3.016364e-01
[124,] 1.993636e-01 6.495455e-01
[125,] 3.480000e-01 1.993636e-01
[126,] 5.988182e-01 3.480000e-01
[127,] -6.709091e-02 5.988182e-01
[128,] 3.402727e-01 -6.709091e-02
[129,] 2.699091e-01 3.402727e-01
[130,] 1.096364e-01 2.699091e-01
[131,] 9.754545e-02 1.096364e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 7.000000e-02 1.027273e-01
2 3.150909e-01 7.000000e-02
3 4.422727e-01 3.150909e-01
4 -1.089091e-01 4.422727e-01
5 2.627273e-01 -1.089091e-01
6 4.185455e-01 2.627273e-01
7 2.446364e-01 4.185455e-01
8 4.850000e-01 2.446364e-01
9 2.463636e-02 4.850000e-01
10 6.336364e-02 2.463636e-02
11 9.527273e-02 6.336364e-02
12 -2.769455e-01 9.527273e-02
13 1.603273e-01 -2.769455e-01
14 4.174182e-01 1.603273e-01
15 3.406000e-01 4.174182e-01
16 5.741818e-02 3.406000e-01
17 3.430545e-01 5.741818e-02
18 1.158727e-01 3.430545e-01
19 -2.160364e-01 1.158727e-01
20 2.813273e-01 -2.160364e-01
21 -2.760364e-01 2.813273e-01
22 7.690909e-03 -2.760364e-01
23 1.396000e-01 7.690909e-03
24 1.743818e-01 1.396000e-01
25 5.446545e-01 1.743818e-01
26 2.557455e-01 5.446545e-01
27 -3.007273e-02 2.557455e-01
28 6.427455e-01 -3.007273e-02
29 -1.361818e-02 6.427455e-01
30 -7.180000e-02 -1.361818e-02
31 3.112909e-01 -7.180000e-02
32 -1.413455e-01 3.112909e-01
33 1.462909e-01 -1.413455e-01
34 2.910182e-01 1.462909e-01
35 -2.107273e-02 2.910182e-01
36 2.307091e-01 -2.107273e-02
37 -1.930182e-01 2.307091e-01
38 5.707273e-02 -1.930182e-01
39 1.582545e-01 5.707273e-02
40 4.190727e-01 1.582545e-01
41 -7.729091e-02 4.190727e-01
42 -6.547273e-02 -7.729091e-02
43 -4.381818e-03 -6.547273e-02
44 -4.940182e-01 -4.381818e-03
45 1.961818e-02 -4.940182e-01
46 5.534545e-02 1.961818e-02
47 -2.737455e-01 5.534545e-02
48 -1.409636e-01 -2.737455e-01
49 -7.769091e-02 -1.409636e-01
50 -1.336000e-01 -7.769091e-02
51 -4.914182e-01 -1.336000e-01
52 -3.226000e-01 -4.914182e-01
53 -7.119636e-01 -3.226000e-01
54 -2.241455e-01 -7.119636e-01
55 -3.140545e-01 -2.241455e-01
56 -4.556909e-01 -3.140545e-01
57 -1.900545e-01 -4.556909e-01
58 -3.993273e-01 -1.900545e-01
59 -2.144182e-01 -3.993273e-01
60 -2.266364e-01 -2.144182e-01
61 -4.423636e-01 -2.266364e-01
62 -5.892727e-01 -4.423636e-01
63 -6.409091e-02 -5.892727e-01
64 -5.662727e-01 -6.409091e-02
65 -3.376364e-01 -5.662727e-01
66 -7.081818e-02 -3.376364e-01
67 -6.617273e-01 -7.081818e-02
68 -2.113636e-01 -6.617273e-01
69 -4.557273e-01 -2.113636e-01
70 -3.990000e-01 -4.557273e-01
71 2.590909e-02 -3.990000e-01
72 -1.763091e-01 2.590909e-02
73 -6.203636e-02 -1.763091e-01
74 -3.119455e-01 -6.203636e-02
75 -3.357636e-01 -3.119455e-01
76 -8.009455e-01 -3.357636e-01
77 2.669091e-02 -8.009455e-01
78 -4.849091e-02 2.669091e-02
79 -1.034000e-01 -4.849091e-02
80 -1.300364e-01 -1.034000e-01
81 -3.124000e-01 -1.300364e-01
82 4.327273e-03 -3.124000e-01
83 3.092364e-01 4.327273e-03
84 -2.589818e-01 3.092364e-01
85 -1.177091e-01 -2.589818e-01
86 1.163818e-01 -1.177091e-01
87 -6.043636e-02 1.163818e-01
88 -1.106182e-01 -6.043636e-02
89 5.701818e-02 -1.106182e-01
90 -5.131636e-01 5.701818e-02
91 1.259273e-01 -5.131636e-01
92 -9.709091e-03 1.259273e-01
93 -2.407273e-02 -9.709091e-03
94 -8.834545e-02 -2.407273e-02
95 3.756364e-02 -8.834545e-02
96 -5.265455e-02 3.756364e-02
97 -1.673818e-01 -5.265455e-02
98 1.747091e-01 -1.673818e-01
99 -2.251091e-01 1.747091e-01
100 3.217091e-01 -2.251091e-01
101 1.873455e-01 3.217091e-01
102 -2.538364e-01 1.873455e-01
103 1.222545e-01 -2.538364e-01
104 3.746182e-01 1.222545e-01
105 4.262545e-01 3.746182e-01
106 2.019818e-01 4.262545e-01
107 -2.811091e-01 2.019818e-01
108 9.767273e-02 -2.811091e-01
109 1.594545e-02 9.767273e-02
110 3.636364e-05 1.594545e-02
111 -3.837818e-01 3.636364e-05
112 2.690364e-01 -3.837818e-01
113 -8.432727e-02 2.690364e-01
114 1.144909e-01 -8.432727e-02
115 5.625818e-01 1.144909e-01
116 -3.905455e-02 5.625818e-01
117 3.715818e-01 -3.905455e-02
118 1.533091e-01 3.715818e-01
119 8.521818e-02 1.533091e-01
120 5.270000e-01 8.521818e-02
121 2.692727e-01 5.270000e-01
122 -3.016364e-01 2.692727e-01
123 6.495455e-01 -3.016364e-01
124 1.993636e-01 6.495455e-01
125 3.480000e-01 1.993636e-01
126 5.988182e-01 3.480000e-01
127 -6.709091e-02 5.988182e-01
128 3.402727e-01 -6.709091e-02
129 2.699091e-01 3.402727e-01
130 1.096364e-01 2.699091e-01
131 9.754545e-02 1.096364e-01
> 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/7k0qd1322601502.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8st5m1322601502.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/932o91322601502.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10ad961322601502.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jxlu1322601502.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/12bors1322601502.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/13oo151322601502.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/146dur1322601502.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/15dk551322601502.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/16ebsi1322601502.tab")
+ }
>
> try(system("convert tmp/1uax21322601502.ps tmp/1uax21322601502.png",intern=TRUE))
character(0)
> try(system("convert tmp/257oz1322601502.ps tmp/257oz1322601502.png",intern=TRUE))
character(0)
> try(system("convert tmp/3w6oe1322601502.ps tmp/3w6oe1322601502.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ae3x1322601502.ps tmp/4ae3x1322601502.png",intern=TRUE))
character(0)
> try(system("convert tmp/5gzw81322601502.ps tmp/5gzw81322601502.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hico1322601502.ps tmp/6hico1322601502.png",intern=TRUE))
character(0)
> try(system("convert tmp/7k0qd1322601502.ps tmp/7k0qd1322601502.png",intern=TRUE))
character(0)
> try(system("convert tmp/8st5m1322601502.ps tmp/8st5m1322601502.png",intern=TRUE))
character(0)
> try(system("convert tmp/932o91322601502.ps tmp/932o91322601502.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ad961322601502.ps tmp/10ad961322601502.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.950 0.310 5.245