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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+ ,1
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+ ,0
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+ ,1
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+ ,1
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+ ,16
+ ,16
+ ,19
+ ,19
+ ,16
+ ,16
+ ,17
+ ,20
+ ,20)
+ ,dim=c(12
+ ,158)
+ ,dimnames=list(c('G'
+ ,'CM'
+ ,'CM*G'
+ ,'D'
+ ,'D*G'
+ ,'PE'
+ ,'PE*G'
+ ,'PC'
+ ,'PC*G'
+ ,'PS'
+ ,'O'
+ ,'O*G')
+ ,1:158))
> y <- array(NA,dim=c(12,158),dimnames=list(c('G','CM','CM*G','D','D*G','PE','PE*G','PC','PC*G','PS','O','O*G'),1:158))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '10'
> 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
PS G CM CM*G D D*G PE PE*G PC PC*G O O*G
1 24 0 24 0 14 0 11 0 12 0 26 0
2 25 1 25 25 11 11 7 7 8 8 23 23
3 30 1 17 17 6 6 17 17 8 8 25 25
4 19 0 18 0 12 0 10 0 8 0 23 0
5 22 1 18 18 8 8 12 12 9 9 19 19
6 22 1 16 16 10 10 12 12 7 7 29 29
7 25 1 20 20 10 10 11 11 4 4 25 25
8 23 1 16 16 11 11 11 11 11 11 21 21
9 17 1 18 18 16 16 12 12 7 7 22 22
10 21 1 17 17 11 11 13 13 7 7 25 25
11 19 0 23 0 13 0 14 0 12 0 24 0
12 19 1 30 30 12 12 16 16 10 10 18 18
13 15 1 23 23 8 8 11 11 10 10 22 22
14 16 1 18 18 12 12 10 10 8 8 15 15
15 23 0 15 0 11 0 11 0 8 0 22 0
16 27 0 12 0 4 0 15 0 4 0 28 0
17 22 1 21 21 9 9 9 9 9 9 20 20
18 14 0 15 0 8 0 11 0 8 0 12 0
19 22 0 20 0 8 0 17 0 7 0 24 0
20 23 1 31 31 14 14 17 17 11 11 20 20
21 23 1 27 27 15 15 11 11 9 9 21 21
22 21 0 34 0 16 0 18 0 11 0 20 0
23 19 1 21 21 9 9 14 14 13 13 21 21
24 18 0 31 0 14 0 10 0 8 0 23 0
25 20 0 19 0 11 0 11 0 8 0 28 0
26 23 1 16 16 8 8 15 15 9 9 24 24
27 25 1 20 20 9 9 15 15 6 6 24 24
28 19 0 21 0 9 0 13 0 9 0 24 0
29 24 0 22 0 9 0 16 0 9 0 23 0
30 22 1 17 17 9 9 13 13 6 6 23 23
31 25 0 24 0 10 0 9 0 6 0 29 0
32 26 1 25 25 16 16 18 18 16 16 24 24
33 29 1 26 26 11 11 18 18 5 5 18 18
34 32 1 25 25 8 8 12 12 7 7 25 25
35 25 1 17 17 9 9 17 17 9 9 21 21
36 29 0 32 0 16 0 9 0 6 0 26 0
37 28 0 33 0 11 0 9 0 6 0 22 0
38 17 0 13 0 16 0 12 0 5 0 22 0
39 28 1 32 32 12 12 18 18 12 12 22 22
40 29 0 25 0 12 0 12 0 7 0 23 0
41 26 0 29 0 14 0 18 0 10 0 30 0
42 25 1 22 22 9 9 14 14 9 9 23 23
43 14 0 18 0 10 0 15 0 8 0 17 0
44 25 1 17 17 9 9 16 16 5 5 23 23
45 26 0 20 0 10 0 10 0 8 0 23 0
46 20 0 15 0 12 0 11 0 8 0 25 0
47 18 1 20 20 14 14 14 14 10 10 24 24
48 32 0 33 0 14 0 9 0 6 0 24 0
49 25 1 29 29 10 10 12 12 8 8 23 23
50 25 1 23 23 14 14 17 17 7 7 21 21
51 23 0 26 0 16 0 5 0 4 0 24 0
52 21 0 18 0 9 0 12 0 8 0 24 0
53 20 1 20 20 10 10 12 12 8 8 28 28
54 15 1 11 11 6 6 6 6 4 4 16 16
55 30 0 28 0 8 0 24 0 20 0 20 0
56 24 1 26 26 13 13 12 12 8 8 29 29
57 26 1 22 22 10 10 12 12 8 8 27 27
58 24 0 17 0 8 0 14 0 6 0 22 0
59 22 0 12 0 7 0 7 0 4 0 28 0
60 14 1 14 14 15 15 13 13 8 8 16 16
61 24 0 17 0 9 0 12 0 9 0 25 0
62 24 0 21 0 10 0 13 0 6 0 24 0
63 24 1 19 19 12 12 14 14 7 7 28 28
64 24 0 18 0 13 0 8 0 9 0 24 0
65 19 0 10 0 10 0 11 0 5 0 23 0
66 31 0 29 0 11 0 9 0 5 0 30 0
67 22 0 31 0 8 0 11 0 8 0 24 0
68 27 0 19 0 9 0 13 0 8 0 21 0
69 19 0 9 0 13 0 10 0 6 0 25 0
70 25 1 20 20 11 11 11 11 8 8 25 25
71 20 1 28 28 8 8 12 12 7 7 22 22
72 21 1 19 19 9 9 9 9 7 7 23 23
73 27 1 30 30 9 9 15 15 9 9 26 26
74 23 1 29 29 15 15 18 18 11 11 23 23
75 25 1 26 26 9 9 15 15 6 6 25 25
76 20 1 23 23 10 10 12 12 8 8 21 21
77 22 1 21 21 12 12 14 14 9 9 24 24
78 23 0 19 0 12 0 10 0 8 0 29 0
79 25 1 28 28 11 11 13 13 6 6 22 22
80 25 1 23 23 14 14 13 13 10 10 27 27
81 17 1 18 18 6 6 11 11 8 8 26 26
82 19 0 21 0 12 0 13 0 8 0 22 0
83 25 1 20 20 8 8 16 16 10 10 24 24
84 19 0 23 0 14 0 8 0 5 0 27 0
85 20 0 21 0 11 0 16 0 7 0 24 0
86 26 1 21 21 10 10 11 11 5 5 24 24
87 23 0 15 0 14 0 9 0 8 0 29 0
88 27 1 28 28 12 12 16 16 14 14 22 22
89 17 0 19 0 10 0 12 0 7 0 21 0
90 17 0 26 0 14 0 14 0 8 0 24 0
91 19 1 10 10 5 5 8 8 6 6 24 24
92 17 0 16 0 11 0 9 0 5 0 23 0
93 22 1 22 22 10 10 15 15 6 6 20 20
94 21 0 19 0 9 0 11 0 10 0 27 0
95 32 1 31 31 10 10 21 21 12 12 26 26
96 21 0 31 0 16 0 14 0 9 0 25 0
97 21 1 29 29 13 13 18 18 12 12 21 21
98 18 0 19 0 9 0 12 0 7 0 21 0
99 18 1 22 22 10 10 13 13 8 8 19 19
100 23 1 23 23 10 10 15 15 10 10 21 21
101 19 0 15 0 7 0 12 0 6 0 21 0
102 20 1 20 20 9 9 19 19 10 10 16 16
103 21 1 18 18 8 8 15 15 10 10 22 22
104 20 0 23 0 14 0 11 0 10 0 29 0
105 17 1 25 25 14 14 11 11 5 5 15 15
106 18 1 21 21 8 8 10 10 7 7 17 17
107 19 1 24 24 9 9 13 13 10 10 15 15
108 22 1 25 25 14 14 15 15 11 11 21 21
109 15 0 17 0 14 0 12 0 6 0 21 0
110 14 1 13 13 8 8 12 12 7 7 19 19
111 18 1 28 28 8 8 16 16 12 12 24 24
112 24 0 21 0 8 0 9 0 11 0 20 0
113 35 1 25 25 7 7 18 18 11 11 17 17
114 29 1 9 9 6 6 8 8 11 11 23 23
115 21 1 16 16 8 8 13 13 5 5 24 24
116 25 1 19 19 6 6 17 17 8 8 14 14
117 20 0 17 0 11 0 9 0 6 0 19 0
118 22 0 25 0 14 0 15 0 9 0 24 0
119 13 0 20 0 11 0 8 0 4 0 13 0
120 26 1 29 29 11 11 7 7 4 4 22 22
121 17 1 14 14 11 11 12 12 7 7 16 16
122 25 1 22 22 14 14 14 14 11 11 19 19
123 20 1 15 15 8 8 6 6 6 6 25 25
124 19 0 19 0 20 0 8 0 7 0 25 0
125 21 0 20 0 11 0 17 0 8 0 23 0
126 22 1 15 15 8 8 10 10 4 4 24 24
127 24 1 20 20 11 11 11 11 8 8 26 26
128 21 1 18 18 10 10 14 14 9 9 26 26
129 26 1 33 33 14 14 11 11 8 8 25 25
130 24 1 22 22 11 11 13 13 11 11 18 18
131 16 1 16 16 9 9 12 12 8 8 21 21
132 23 0 17 0 9 0 11 0 5 0 26 0
133 18 1 16 16 8 8 9 9 4 4 23 23
134 16 0 21 0 10 0 12 0 8 0 23 0
135 26 0 26 0 13 0 20 0 10 0 22 0
136 19 1 18 18 13 13 12 12 6 6 20 20
137 21 1 18 18 12 12 13 13 9 9 13 13
138 21 0 17 0 8 0 12 0 9 0 24 0
139 22 1 22 22 13 13 12 12 13 13 15 15
140 23 1 30 30 14 14 9 9 9 9 14 14
141 29 1 30 30 12 12 15 15 10 10 22 22
142 21 1 24 24 14 14 24 24 20 20 10 10
143 21 0 21 0 15 0 7 0 5 0 24 0
144 23 1 21 21 13 13 17 17 11 11 22 22
145 27 1 29 29 16 16 11 11 6 6 24 24
146 25 1 31 31 9 9 17 17 9 9 19 19
147 21 1 20 20 9 9 11 11 7 7 20 20
148 10 1 16 16 9 9 12 12 9 9 13 13
149 20 1 22 22 8 8 14 14 10 10 20 20
150 26 1 20 20 7 7 11 11 9 9 22 22
151 24 1 28 28 16 16 16 16 8 8 24 24
152 29 1 38 38 11 11 21 21 7 7 29 29
153 19 1 22 22 9 9 14 14 6 6 12 12
154 24 1 20 20 11 11 20 20 13 13 20 20
155 19 1 17 17 9 9 13 13 6 6 21 21
156 24 0 28 0 14 0 11 0 8 0 24 0
157 22 1 22 22 13 13 15 15 10 10 22 22
158 17 1 31 31 16 16 19 19 16 16 20 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) G CM `CM*G` D `D*G`
6.64886 1.00061 0.35783 -0.06059 -0.47406 0.16007
PE `PE*G` PC `PC*G` O `O*G`
-0.01925 0.30781 0.09405 -0.12802 0.52629 -0.15640
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.5470 -2.2064 -0.1587 2.1496 11.0089
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.64886 4.02720 1.651 0.10089
G 1.00061 4.89243 0.205 0.83823
CM 0.35783 0.08666 4.129 6.11e-05 ***
`CM*G` -0.06059 0.11579 -0.523 0.60155
D -0.47406 0.17262 -2.746 0.00679 **
`D*G` 0.16007 0.22949 0.698 0.48658
PE -0.01925 0.17193 -0.112 0.91098
`PE*G` 0.30781 0.21743 1.416 0.15899
PC 0.09405 0.22813 0.412 0.68074
`PC*G` -0.12802 0.27878 -0.459 0.64676
O 0.52629 0.13132 4.008 9.74e-05 ***
`O*G` -0.15640 0.15990 -0.978 0.32964
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.429 on 146 degrees of freedom
Multiple R-squared: 0.3888, Adjusted R-squared: 0.3428
F-statistic: 8.443 on 11 and 146 DF, p-value: 2.117e-11
> 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,] 0.97308895 0.05382210 0.02691105
[2,] 0.94455560 0.11088880 0.05544440
[3,] 0.90215658 0.19568683 0.09784342
[4,] 0.84104472 0.31791056 0.15895528
[5,] 0.83062848 0.33874303 0.16937152
[6,] 0.81809768 0.36380464 0.18190232
[7,] 0.80192702 0.39614596 0.19807298
[8,] 0.76679792 0.46640415 0.23320208
[9,] 0.72127961 0.55744079 0.27872039
[10,] 0.68552575 0.62894850 0.31447425
[11,] 0.67743775 0.64512450 0.32256225
[12,] 0.60126300 0.79747401 0.39873700
[13,] 0.52488388 0.95023224 0.47511612
[14,] 0.46716543 0.93433085 0.53283457
[15,] 0.43074954 0.86149907 0.56925046
[16,] 0.36643088 0.73286177 0.63356912
[17,] 0.34403091 0.68806181 0.65596909
[18,] 0.35773109 0.71546218 0.64226891
[19,] 0.44538520 0.89077040 0.55461480
[20,] 0.59056329 0.81887341 0.40943671
[21,] 0.55154870 0.89690260 0.44845130
[22,] 0.60387537 0.79224927 0.39612463
[23,] 0.64705866 0.70588267 0.35294134
[24,] 0.61167480 0.77665041 0.38832520
[25,] 0.55771213 0.88457573 0.44228787
[26,] 0.67328152 0.65343697 0.32671848
[27,] 0.61810962 0.76378075 0.38189038
[28,] 0.56421907 0.87156186 0.43578093
[29,] 0.56915674 0.86168652 0.43084326
[30,] 0.52762438 0.94475123 0.47237562
[31,] 0.55971101 0.88057798 0.44028899
[32,] 0.50361946 0.99276108 0.49638054
[33,] 0.51177832 0.97644335 0.48822168
[34,] 0.61503958 0.76992084 0.38496042
[35,] 0.56505952 0.86988097 0.43494048
[36,] 0.53323844 0.93352312 0.46676156
[37,] 0.52612620 0.94774760 0.47387380
[38,] 0.47611490 0.95222980 0.52388510
[39,] 0.51209433 0.97581135 0.48790567
[40,] 0.47028700 0.94057401 0.52971300
[41,] 0.63525490 0.72949020 0.36474510
[42,] 0.59238162 0.81523677 0.40761838
[43,] 0.55216728 0.89566543 0.44783272
[44,] 0.53537733 0.92924533 0.46462267
[45,] 0.48726501 0.97453003 0.51273499
[46,] 0.45044194 0.90088388 0.54955806
[47,] 0.41174497 0.82348994 0.58825503
[48,] 0.37506966 0.75013932 0.62493034
[49,] 0.32985305 0.65970610 0.67014695
[50,] 0.33806965 0.67613929 0.66193035
[51,] 0.29641438 0.59282875 0.70358562
[52,] 0.33560437 0.67120873 0.66439563
[53,] 0.39528143 0.79056287 0.60471857
[54,] 0.50695064 0.98609873 0.49304936
[55,] 0.45919554 0.91839109 0.54080446
[56,] 0.44303828 0.88607656 0.55696172
[57,] 0.51575929 0.96848142 0.48424071
[58,] 0.46730265 0.93460531 0.53269735
[59,] 0.42244063 0.84488126 0.57755937
[60,] 0.38877734 0.77755467 0.61122266
[61,] 0.35392291 0.70784582 0.64607709
[62,] 0.32840862 0.65681724 0.67159138
[63,] 0.28675667 0.57351335 0.71324333
[64,] 0.25127410 0.50254819 0.74872590
[65,] 0.21599557 0.43199113 0.78400443
[66,] 0.18990911 0.37981821 0.81009089
[67,] 0.29874296 0.59748593 0.70125704
[68,] 0.26958489 0.53916979 0.73041511
[69,] 0.23266714 0.46533429 0.76733286
[70,] 0.22982403 0.45964806 0.77017597
[71,] 0.20438792 0.40877584 0.79561208
[72,] 0.20382072 0.40764145 0.79617928
[73,] 0.18027645 0.36055290 0.81972355
[74,] 0.16687936 0.33375872 0.83312064
[75,] 0.16035071 0.32070142 0.83964929
[76,] 0.19436421 0.38872842 0.80563579
[77,] 0.16494352 0.32988704 0.83505648
[78,] 0.14706860 0.29413721 0.85293140
[79,] 0.12311399 0.24622799 0.87688601
[80,] 0.11318354 0.22636708 0.88681646
[81,] 0.11049532 0.22099065 0.88950468
[82,] 0.10091123 0.20182247 0.89908877
[83,] 0.10181721 0.20363442 0.89818279
[84,] 0.09026334 0.18052668 0.90973666
[85,] 0.08857095 0.17714190 0.91142905
[86,] 0.06986744 0.13973487 0.93013256
[87,] 0.05524287 0.11048574 0.94475713
[88,] 0.04412078 0.08824155 0.95587922
[89,] 0.03481224 0.06962448 0.96518776
[90,] 0.03978221 0.07956442 0.96021779
[91,] 0.03275431 0.06550861 0.96724569
[92,] 0.02907449 0.05814899 0.97092551
[93,] 0.02572654 0.05145308 0.97427346
[94,] 0.01887723 0.03775446 0.98112277
[95,] 0.01578867 0.03157734 0.98421133
[96,] 0.02187581 0.04375161 0.97812419
[97,] 0.11296817 0.22593635 0.88703183
[98,] 0.11019866 0.22039732 0.88980134
[99,] 0.47013475 0.94026949 0.52986525
[100,] 0.79742657 0.40514685 0.20257343
[101,] 0.75635837 0.48728326 0.24364163
[102,] 0.81821525 0.36356949 0.18178475
[103,] 0.82710985 0.34578031 0.17289015
[104,] 0.79177761 0.41644478 0.20822239
[105,] 0.77157159 0.45685682 0.22842841
[106,] 0.74238460 0.51523081 0.25761540
[107,] 0.68827641 0.62344718 0.31172359
[108,] 0.70915267 0.58169466 0.29084733
[109,] 0.65365409 0.69269182 0.34634591
[110,] 0.69116428 0.61767143 0.30883572
[111,] 0.64133271 0.71733458 0.35866729
[112,] 0.59514003 0.80971994 0.40485997
[113,] 0.53888378 0.92223244 0.46111622
[114,] 0.47440499 0.94880999 0.52559501
[115,] 0.41265924 0.82531848 0.58734076
[116,] 0.39225628 0.78451257 0.60774372
[117,] 0.39871739 0.79743478 0.60128261
[118,] 0.32483441 0.64966883 0.67516559
[119,] 0.29428063 0.58856127 0.70571937
[120,] 0.24640540 0.49281081 0.75359460
[121,] 0.19261140 0.38522280 0.80738860
[122,] 0.14153789 0.28307579 0.85846211
[123,] 0.13352012 0.26704023 0.86647988
[124,] 0.08885038 0.17770077 0.91114962
[125,] 0.06487165 0.12974330 0.93512835
[126,] 0.04580752 0.09161504 0.95419248
[127,] 0.05115131 0.10230262 0.94884869
[128,] 0.08612639 0.17225277 0.91387361
[129,] 0.04245465 0.08490930 0.95754535
> postscript(file="/var/www/html/freestat/rcomp/tmp/1tc251290766840.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/2tc251290766840.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3tc251290766840.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4llj81290766840.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5llj81290766840.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 158
Frequency = 1
1 2 3 4 5 6
0.79966460 3.11771681 5.30030204 -1.06563940 1.32716052 -1.21724770
7 8 9 10 11 12
2.26003903 3.48030491 -2.33853505 -1.00948171 -3.20622105 -4.73411693
13 14 15 16 17 18
-7.94618710 -1.39415618 4.07933641 3.12987452 1.24521807 -1.07994456
19 20 21 22 23 24
-0.97500917 -1.39775779 1.39871375 -1.44393416 -3.43161123 -5.76931751
25 26 27 28 29 30
-3.50973489 0.20649365 1.22963728 -4.12390545 1.10231928 0.06836235
31 32 33 34 35 36
-1.14964677 2.41533880 5.39390698 6.95920466 2.75580755 4.41095997
37 38 39 40 41 42
2.78797360 1.46673688 1.68264466 6.56210652 -0.77176196 1.39550632
43 44 45 46 47 48
-3.75974595 2.16872190 4.27057039 -0.02547291 -3.77600062 7.15758312
49 50 51 52 53 54
0.17198180 2.47439606 1.72161869 -0.97561220 -4.00234231 -1.54894073
55 56 57 58 59 60
5.17958091 -1.21369678 1.77307489 3.18735630 -0.60197352 -2.49880573
61 62 63 64 65 66
1.76187341 1.63232208 0.31179238 3.74956894 1.15030121 3.10302449
67 68 69 70 71 72
-5.12073692 6.26468475 1.76443244 2.70988575 -4.82282944 -0.33791471
73 74 75 76 77 78
-0.38063736 -1.88753130 -0.92368472 -2.30480281 -0.73518145 -0.58121703
79 80 81 82 83 84
0.79661501 1.51116358 -6.63547733 -1.55507711 0.76294695 -3.76817829
85 86 87 88 89 90
-1.92990370 3.36665872 1.77898066 2.51664321 -3.18645178 -5.42943263
91 92 93 94 95 96
-1.03402761 -2.56113215 -0.57127615 -3.11968068 3.00665474 -2.89080659
97 98 99 100 101 102
-3.74175847 -2.66051566 -3.55633660 -0.10255010 -1.08326400 -1.82959274
103 104 105 106 107 108
-1.61423136 -4.23326829 -2.23729637 -2.31557837 -1.91729960 -0.40710521
109 110 111 112 113 114
-2.48047925 -5.25457775 -8.54702907 2.24206599 11.00886809 10.11691499
115 116 117 118 119 120
-1.35224651 3.77465324 2.09214635 -0.14640100 -2.65474677 3.16280027
121 122 123 124 125 126
-0.50016813 4.51295457 -0.37102661 1.37200294 -0.12058100 0.77670327
127 128 129 130 131 132
1.33999235 -2.21123230 0.78775799 3.22943913 -4.53812529 0.59254558
133 134 135 136 137 138
-2.86208274 -6.04875091 4.07650629 -0.57468016 3.51391893 -1.18589941
139 140 141 142 143 144
3.32358515 3.35938145 4.07486815 0.66761156 0.98116628 0.52084723
145 146 147 148 149 150
3.90665337 -0.66573903 -0.10258998 -7.54501370 -2.77483827 3.59757364
151 152 153 154 155 156
-0.17097321 -3.03952490 -0.63755943 1.13214648 -2.19185085 0.79714005
157 158
-0.23323788 -7.17707488
> postscript(file="/var/www/html/freestat/rcomp/tmp/6llj81290766840.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 = 158
Frequency = 1
lag(myerror, k = 1) myerror
0 0.79966460 NA
1 3.11771681 0.79966460
2 5.30030204 3.11771681
3 -1.06563940 5.30030204
4 1.32716052 -1.06563940
5 -1.21724770 1.32716052
6 2.26003903 -1.21724770
7 3.48030491 2.26003903
8 -2.33853505 3.48030491
9 -1.00948171 -2.33853505
10 -3.20622105 -1.00948171
11 -4.73411693 -3.20622105
12 -7.94618710 -4.73411693
13 -1.39415618 -7.94618710
14 4.07933641 -1.39415618
15 3.12987452 4.07933641
16 1.24521807 3.12987452
17 -1.07994456 1.24521807
18 -0.97500917 -1.07994456
19 -1.39775779 -0.97500917
20 1.39871375 -1.39775779
21 -1.44393416 1.39871375
22 -3.43161123 -1.44393416
23 -5.76931751 -3.43161123
24 -3.50973489 -5.76931751
25 0.20649365 -3.50973489
26 1.22963728 0.20649365
27 -4.12390545 1.22963728
28 1.10231928 -4.12390545
29 0.06836235 1.10231928
30 -1.14964677 0.06836235
31 2.41533880 -1.14964677
32 5.39390698 2.41533880
33 6.95920466 5.39390698
34 2.75580755 6.95920466
35 4.41095997 2.75580755
36 2.78797360 4.41095997
37 1.46673688 2.78797360
38 1.68264466 1.46673688
39 6.56210652 1.68264466
40 -0.77176196 6.56210652
41 1.39550632 -0.77176196
42 -3.75974595 1.39550632
43 2.16872190 -3.75974595
44 4.27057039 2.16872190
45 -0.02547291 4.27057039
46 -3.77600062 -0.02547291
47 7.15758312 -3.77600062
48 0.17198180 7.15758312
49 2.47439606 0.17198180
50 1.72161869 2.47439606
51 -0.97561220 1.72161869
52 -4.00234231 -0.97561220
53 -1.54894073 -4.00234231
54 5.17958091 -1.54894073
55 -1.21369678 5.17958091
56 1.77307489 -1.21369678
57 3.18735630 1.77307489
58 -0.60197352 3.18735630
59 -2.49880573 -0.60197352
60 1.76187341 -2.49880573
61 1.63232208 1.76187341
62 0.31179238 1.63232208
63 3.74956894 0.31179238
64 1.15030121 3.74956894
65 3.10302449 1.15030121
66 -5.12073692 3.10302449
67 6.26468475 -5.12073692
68 1.76443244 6.26468475
69 2.70988575 1.76443244
70 -4.82282944 2.70988575
71 -0.33791471 -4.82282944
72 -0.38063736 -0.33791471
73 -1.88753130 -0.38063736
74 -0.92368472 -1.88753130
75 -2.30480281 -0.92368472
76 -0.73518145 -2.30480281
77 -0.58121703 -0.73518145
78 0.79661501 -0.58121703
79 1.51116358 0.79661501
80 -6.63547733 1.51116358
81 -1.55507711 -6.63547733
82 0.76294695 -1.55507711
83 -3.76817829 0.76294695
84 -1.92990370 -3.76817829
85 3.36665872 -1.92990370
86 1.77898066 3.36665872
87 2.51664321 1.77898066
88 -3.18645178 2.51664321
89 -5.42943263 -3.18645178
90 -1.03402761 -5.42943263
91 -2.56113215 -1.03402761
92 -0.57127615 -2.56113215
93 -3.11968068 -0.57127615
94 3.00665474 -3.11968068
95 -2.89080659 3.00665474
96 -3.74175847 -2.89080659
97 -2.66051566 -3.74175847
98 -3.55633660 -2.66051566
99 -0.10255010 -3.55633660
100 -1.08326400 -0.10255010
101 -1.82959274 -1.08326400
102 -1.61423136 -1.82959274
103 -4.23326829 -1.61423136
104 -2.23729637 -4.23326829
105 -2.31557837 -2.23729637
106 -1.91729960 -2.31557837
107 -0.40710521 -1.91729960
108 -2.48047925 -0.40710521
109 -5.25457775 -2.48047925
110 -8.54702907 -5.25457775
111 2.24206599 -8.54702907
112 11.00886809 2.24206599
113 10.11691499 11.00886809
114 -1.35224651 10.11691499
115 3.77465324 -1.35224651
116 2.09214635 3.77465324
117 -0.14640100 2.09214635
118 -2.65474677 -0.14640100
119 3.16280027 -2.65474677
120 -0.50016813 3.16280027
121 4.51295457 -0.50016813
122 -0.37102661 4.51295457
123 1.37200294 -0.37102661
124 -0.12058100 1.37200294
125 0.77670327 -0.12058100
126 1.33999235 0.77670327
127 -2.21123230 1.33999235
128 0.78775799 -2.21123230
129 3.22943913 0.78775799
130 -4.53812529 3.22943913
131 0.59254558 -4.53812529
132 -2.86208274 0.59254558
133 -6.04875091 -2.86208274
134 4.07650629 -6.04875091
135 -0.57468016 4.07650629
136 3.51391893 -0.57468016
137 -1.18589941 3.51391893
138 3.32358515 -1.18589941
139 3.35938145 3.32358515
140 4.07486815 3.35938145
141 0.66761156 4.07486815
142 0.98116628 0.66761156
143 0.52084723 0.98116628
144 3.90665337 0.52084723
145 -0.66573903 3.90665337
146 -0.10258998 -0.66573903
147 -7.54501370 -0.10258998
148 -2.77483827 -7.54501370
149 3.59757364 -2.77483827
150 -0.17097321 3.59757364
151 -3.03952490 -0.17097321
152 -0.63755943 -3.03952490
153 1.13214648 -0.63755943
154 -2.19185085 1.13214648
155 0.79714005 -2.19185085
156 -0.23323788 0.79714005
157 -7.17707488 -0.23323788
158 NA -7.17707488
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.11771681 0.79966460
[2,] 5.30030204 3.11771681
[3,] -1.06563940 5.30030204
[4,] 1.32716052 -1.06563940
[5,] -1.21724770 1.32716052
[6,] 2.26003903 -1.21724770
[7,] 3.48030491 2.26003903
[8,] -2.33853505 3.48030491
[9,] -1.00948171 -2.33853505
[10,] -3.20622105 -1.00948171
[11,] -4.73411693 -3.20622105
[12,] -7.94618710 -4.73411693
[13,] -1.39415618 -7.94618710
[14,] 4.07933641 -1.39415618
[15,] 3.12987452 4.07933641
[16,] 1.24521807 3.12987452
[17,] -1.07994456 1.24521807
[18,] -0.97500917 -1.07994456
[19,] -1.39775779 -0.97500917
[20,] 1.39871375 -1.39775779
[21,] -1.44393416 1.39871375
[22,] -3.43161123 -1.44393416
[23,] -5.76931751 -3.43161123
[24,] -3.50973489 -5.76931751
[25,] 0.20649365 -3.50973489
[26,] 1.22963728 0.20649365
[27,] -4.12390545 1.22963728
[28,] 1.10231928 -4.12390545
[29,] 0.06836235 1.10231928
[30,] -1.14964677 0.06836235
[31,] 2.41533880 -1.14964677
[32,] 5.39390698 2.41533880
[33,] 6.95920466 5.39390698
[34,] 2.75580755 6.95920466
[35,] 4.41095997 2.75580755
[36,] 2.78797360 4.41095997
[37,] 1.46673688 2.78797360
[38,] 1.68264466 1.46673688
[39,] 6.56210652 1.68264466
[40,] -0.77176196 6.56210652
[41,] 1.39550632 -0.77176196
[42,] -3.75974595 1.39550632
[43,] 2.16872190 -3.75974595
[44,] 4.27057039 2.16872190
[45,] -0.02547291 4.27057039
[46,] -3.77600062 -0.02547291
[47,] 7.15758312 -3.77600062
[48,] 0.17198180 7.15758312
[49,] 2.47439606 0.17198180
[50,] 1.72161869 2.47439606
[51,] -0.97561220 1.72161869
[52,] -4.00234231 -0.97561220
[53,] -1.54894073 -4.00234231
[54,] 5.17958091 -1.54894073
[55,] -1.21369678 5.17958091
[56,] 1.77307489 -1.21369678
[57,] 3.18735630 1.77307489
[58,] -0.60197352 3.18735630
[59,] -2.49880573 -0.60197352
[60,] 1.76187341 -2.49880573
[61,] 1.63232208 1.76187341
[62,] 0.31179238 1.63232208
[63,] 3.74956894 0.31179238
[64,] 1.15030121 3.74956894
[65,] 3.10302449 1.15030121
[66,] -5.12073692 3.10302449
[67,] 6.26468475 -5.12073692
[68,] 1.76443244 6.26468475
[69,] 2.70988575 1.76443244
[70,] -4.82282944 2.70988575
[71,] -0.33791471 -4.82282944
[72,] -0.38063736 -0.33791471
[73,] -1.88753130 -0.38063736
[74,] -0.92368472 -1.88753130
[75,] -2.30480281 -0.92368472
[76,] -0.73518145 -2.30480281
[77,] -0.58121703 -0.73518145
[78,] 0.79661501 -0.58121703
[79,] 1.51116358 0.79661501
[80,] -6.63547733 1.51116358
[81,] -1.55507711 -6.63547733
[82,] 0.76294695 -1.55507711
[83,] -3.76817829 0.76294695
[84,] -1.92990370 -3.76817829
[85,] 3.36665872 -1.92990370
[86,] 1.77898066 3.36665872
[87,] 2.51664321 1.77898066
[88,] -3.18645178 2.51664321
[89,] -5.42943263 -3.18645178
[90,] -1.03402761 -5.42943263
[91,] -2.56113215 -1.03402761
[92,] -0.57127615 -2.56113215
[93,] -3.11968068 -0.57127615
[94,] 3.00665474 -3.11968068
[95,] -2.89080659 3.00665474
[96,] -3.74175847 -2.89080659
[97,] -2.66051566 -3.74175847
[98,] -3.55633660 -2.66051566
[99,] -0.10255010 -3.55633660
[100,] -1.08326400 -0.10255010
[101,] -1.82959274 -1.08326400
[102,] -1.61423136 -1.82959274
[103,] -4.23326829 -1.61423136
[104,] -2.23729637 -4.23326829
[105,] -2.31557837 -2.23729637
[106,] -1.91729960 -2.31557837
[107,] -0.40710521 -1.91729960
[108,] -2.48047925 -0.40710521
[109,] -5.25457775 -2.48047925
[110,] -8.54702907 -5.25457775
[111,] 2.24206599 -8.54702907
[112,] 11.00886809 2.24206599
[113,] 10.11691499 11.00886809
[114,] -1.35224651 10.11691499
[115,] 3.77465324 -1.35224651
[116,] 2.09214635 3.77465324
[117,] -0.14640100 2.09214635
[118,] -2.65474677 -0.14640100
[119,] 3.16280027 -2.65474677
[120,] -0.50016813 3.16280027
[121,] 4.51295457 -0.50016813
[122,] -0.37102661 4.51295457
[123,] 1.37200294 -0.37102661
[124,] -0.12058100 1.37200294
[125,] 0.77670327 -0.12058100
[126,] 1.33999235 0.77670327
[127,] -2.21123230 1.33999235
[128,] 0.78775799 -2.21123230
[129,] 3.22943913 0.78775799
[130,] -4.53812529 3.22943913
[131,] 0.59254558 -4.53812529
[132,] -2.86208274 0.59254558
[133,] -6.04875091 -2.86208274
[134,] 4.07650629 -6.04875091
[135,] -0.57468016 4.07650629
[136,] 3.51391893 -0.57468016
[137,] -1.18589941 3.51391893
[138,] 3.32358515 -1.18589941
[139,] 3.35938145 3.32358515
[140,] 4.07486815 3.35938145
[141,] 0.66761156 4.07486815
[142,] 0.98116628 0.66761156
[143,] 0.52084723 0.98116628
[144,] 3.90665337 0.52084723
[145,] -0.66573903 3.90665337
[146,] -0.10258998 -0.66573903
[147,] -7.54501370 -0.10258998
[148,] -2.77483827 -7.54501370
[149,] 3.59757364 -2.77483827
[150,] -0.17097321 3.59757364
[151,] -3.03952490 -0.17097321
[152,] -0.63755943 -3.03952490
[153,] 1.13214648 -0.63755943
[154,] -2.19185085 1.13214648
[155,] 0.79714005 -2.19185085
[156,] -0.23323788 0.79714005
[157,] -7.17707488 -0.23323788
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.11771681 0.79966460
2 5.30030204 3.11771681
3 -1.06563940 5.30030204
4 1.32716052 -1.06563940
5 -1.21724770 1.32716052
6 2.26003903 -1.21724770
7 3.48030491 2.26003903
8 -2.33853505 3.48030491
9 -1.00948171 -2.33853505
10 -3.20622105 -1.00948171
11 -4.73411693 -3.20622105
12 -7.94618710 -4.73411693
13 -1.39415618 -7.94618710
14 4.07933641 -1.39415618
15 3.12987452 4.07933641
16 1.24521807 3.12987452
17 -1.07994456 1.24521807
18 -0.97500917 -1.07994456
19 -1.39775779 -0.97500917
20 1.39871375 -1.39775779
21 -1.44393416 1.39871375
22 -3.43161123 -1.44393416
23 -5.76931751 -3.43161123
24 -3.50973489 -5.76931751
25 0.20649365 -3.50973489
26 1.22963728 0.20649365
27 -4.12390545 1.22963728
28 1.10231928 -4.12390545
29 0.06836235 1.10231928
30 -1.14964677 0.06836235
31 2.41533880 -1.14964677
32 5.39390698 2.41533880
33 6.95920466 5.39390698
34 2.75580755 6.95920466
35 4.41095997 2.75580755
36 2.78797360 4.41095997
37 1.46673688 2.78797360
38 1.68264466 1.46673688
39 6.56210652 1.68264466
40 -0.77176196 6.56210652
41 1.39550632 -0.77176196
42 -3.75974595 1.39550632
43 2.16872190 -3.75974595
44 4.27057039 2.16872190
45 -0.02547291 4.27057039
46 -3.77600062 -0.02547291
47 7.15758312 -3.77600062
48 0.17198180 7.15758312
49 2.47439606 0.17198180
50 1.72161869 2.47439606
51 -0.97561220 1.72161869
52 -4.00234231 -0.97561220
53 -1.54894073 -4.00234231
54 5.17958091 -1.54894073
55 -1.21369678 5.17958091
56 1.77307489 -1.21369678
57 3.18735630 1.77307489
58 -0.60197352 3.18735630
59 -2.49880573 -0.60197352
60 1.76187341 -2.49880573
61 1.63232208 1.76187341
62 0.31179238 1.63232208
63 3.74956894 0.31179238
64 1.15030121 3.74956894
65 3.10302449 1.15030121
66 -5.12073692 3.10302449
67 6.26468475 -5.12073692
68 1.76443244 6.26468475
69 2.70988575 1.76443244
70 -4.82282944 2.70988575
71 -0.33791471 -4.82282944
72 -0.38063736 -0.33791471
73 -1.88753130 -0.38063736
74 -0.92368472 -1.88753130
75 -2.30480281 -0.92368472
76 -0.73518145 -2.30480281
77 -0.58121703 -0.73518145
78 0.79661501 -0.58121703
79 1.51116358 0.79661501
80 -6.63547733 1.51116358
81 -1.55507711 -6.63547733
82 0.76294695 -1.55507711
83 -3.76817829 0.76294695
84 -1.92990370 -3.76817829
85 3.36665872 -1.92990370
86 1.77898066 3.36665872
87 2.51664321 1.77898066
88 -3.18645178 2.51664321
89 -5.42943263 -3.18645178
90 -1.03402761 -5.42943263
91 -2.56113215 -1.03402761
92 -0.57127615 -2.56113215
93 -3.11968068 -0.57127615
94 3.00665474 -3.11968068
95 -2.89080659 3.00665474
96 -3.74175847 -2.89080659
97 -2.66051566 -3.74175847
98 -3.55633660 -2.66051566
99 -0.10255010 -3.55633660
100 -1.08326400 -0.10255010
101 -1.82959274 -1.08326400
102 -1.61423136 -1.82959274
103 -4.23326829 -1.61423136
104 -2.23729637 -4.23326829
105 -2.31557837 -2.23729637
106 -1.91729960 -2.31557837
107 -0.40710521 -1.91729960
108 -2.48047925 -0.40710521
109 -5.25457775 -2.48047925
110 -8.54702907 -5.25457775
111 2.24206599 -8.54702907
112 11.00886809 2.24206599
113 10.11691499 11.00886809
114 -1.35224651 10.11691499
115 3.77465324 -1.35224651
116 2.09214635 3.77465324
117 -0.14640100 2.09214635
118 -2.65474677 -0.14640100
119 3.16280027 -2.65474677
120 -0.50016813 3.16280027
121 4.51295457 -0.50016813
122 -0.37102661 4.51295457
123 1.37200294 -0.37102661
124 -0.12058100 1.37200294
125 0.77670327 -0.12058100
126 1.33999235 0.77670327
127 -2.21123230 1.33999235
128 0.78775799 -2.21123230
129 3.22943913 0.78775799
130 -4.53812529 3.22943913
131 0.59254558 -4.53812529
132 -2.86208274 0.59254558
133 -6.04875091 -2.86208274
134 4.07650629 -6.04875091
135 -0.57468016 4.07650629
136 3.51391893 -0.57468016
137 -1.18589941 3.51391893
138 3.32358515 -1.18589941
139 3.35938145 3.32358515
140 4.07486815 3.35938145
141 0.66761156 4.07486815
142 0.98116628 0.66761156
143 0.52084723 0.98116628
144 3.90665337 0.52084723
145 -0.66573903 3.90665337
146 -0.10258998 -0.66573903
147 -7.54501370 -0.10258998
148 -2.77483827 -7.54501370
149 3.59757364 -2.77483827
150 -0.17097321 3.59757364
151 -3.03952490 -0.17097321
152 -0.63755943 -3.03952490
153 1.13214648 -0.63755943
154 -2.19185085 1.13214648
155 0.79714005 -2.19185085
156 -0.23323788 0.79714005
157 -7.17707488 -0.23323788
> 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/7wcib1290766840.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/8pm0e1290766840.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/9pm0e1290766840.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/10pm0e1290766840.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/11a4y21290766840.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/12e5f81290766840.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/13l6u21290766840.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14dxtm1290766840.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/15hy9a1290766840.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/16vp711290766840.tab")
+ }
>
> try(system("convert tmp/1tc251290766840.ps tmp/1tc251290766840.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tc251290766840.ps tmp/2tc251290766840.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tc251290766840.ps tmp/3tc251290766840.png",intern=TRUE))
character(0)
> try(system("convert tmp/4llj81290766840.ps tmp/4llj81290766840.png",intern=TRUE))
character(0)
> try(system("convert tmp/5llj81290766840.ps tmp/5llj81290766840.png",intern=TRUE))
character(0)
> try(system("convert tmp/6llj81290766840.ps tmp/6llj81290766840.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wcib1290766840.ps tmp/7wcib1290766840.png",intern=TRUE))
character(0)
> try(system("convert tmp/8pm0e1290766840.ps tmp/8pm0e1290766840.png",intern=TRUE))
character(0)
> try(system("convert tmp/9pm0e1290766840.ps tmp/9pm0e1290766840.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pm0e1290766840.ps tmp/10pm0e1290766840.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.563 2.652 6.954