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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+ ,0
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+ ,0
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+ ,0
+ ,6
+ ,0
+ ,19
+ ,21
+ ,0
+ ,1
+ ,28
+ ,28
+ ,14
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+ ,11
+ ,11
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+ ,8
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+ ,24
+ ,24
+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
+ ,16
+ ,0
+ ,19
+ ,0
+ ,16
+ ,0
+ ,17
+ ,20
+ ,0)
+ ,dim=c(12
+ ,154)
+ ,dimnames=list(c('M'
+ ,'CM'
+ ,'CM_M'
+ ,'D'
+ ,'D_M'
+ ,'PE'
+ ,'PE_M'
+ ,'PC'
+ ,'PC_M'
+ ,'PS'
+ ,'O'
+ ,'O_M')
+ ,1:154))
> y <- array(NA,dim=c(12,154),dimnames=list(c('M','CM','CM_M','D','D_M','PE','PE_M','PC','PC_M','PS','O','O_M'),1:154))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '10'
> #'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
PS M CM CM_M D D_M PE PE_M PC PC_M O O_M
1 24 1 24 24 14 14 11 11 12 12 26 26
2 25 0 25 0 11 0 7 0 8 0 23 0
3 30 0 17 0 6 0 17 0 8 0 25 0
4 19 1 18 18 12 12 10 10 8 8 23 23
5 22 0 18 0 8 0 12 0 9 0 19 0
6 22 0 16 0 10 0 12 0 7 0 29 0
7 25 0 20 0 10 0 11 0 4 0 25 0
8 23 0 16 0 11 0 11 0 11 0 21 0
9 17 0 18 0 16 0 12 0 7 0 22 0
10 21 0 17 0 11 0 13 0 7 0 25 0
11 19 1 23 23 13 13 14 14 12 12 24 24
12 19 0 30 0 12 0 16 0 10 0 18 0
13 15 0 23 0 8 0 11 0 10 0 22 0
14 16 0 18 0 12 0 10 0 8 0 15 0
15 23 1 15 15 11 11 11 11 8 8 22 22
16 27 1 12 12 4 4 15 15 4 4 28 28
17 22 0 21 0 9 0 9 0 9 0 20 0
18 14 1 15 15 8 8 11 11 8 8 12 12
19 22 1 20 20 8 8 17 17 7 7 24 24
20 23 0 31 0 14 0 17 0 11 0 20 0
21 23 0 27 0 15 0 11 0 9 0 21 0
22 19 0 21 0 9 0 14 0 13 0 21 0
23 18 1 31 31 14 14 10 10 8 8 23 23
24 20 1 19 19 11 11 11 11 8 8 28 28
25 23 0 16 0 8 0 15 0 9 0 24 0
26 25 0 20 0 9 0 15 0 6 0 24 0
27 19 1 21 21 9 9 13 13 9 9 24 24
28 24 1 22 22 9 9 16 16 9 9 23 23
29 22 0 17 0 9 0 13 0 6 0 23 0
30 26 0 25 0 16 0 18 0 16 0 24 0
31 29 0 26 0 11 0 18 0 5 0 18 0
32 32 0 25 0 8 0 12 0 7 0 25 0
33 25 0 17 0 9 0 17 0 9 0 21 0
34 29 1 32 32 16 16 9 9 6 6 26 26
35 28 1 33 33 11 11 9 9 6 6 22 22
36 17 1 13 13 16 16 12 12 5 5 22 22
37 28 0 32 0 12 0 18 0 12 0 22 0
38 29 1 25 25 12 12 12 12 7 7 23 23
39 26 1 29 29 14 14 18 18 10 10 30 30
40 25 0 22 0 9 0 14 0 9 0 23 0
41 14 1 18 18 10 10 15 15 8 8 17 17
42 25 0 17 0 9 0 16 0 5 0 23 0
43 26 1 20 20 10 10 10 10 8 8 23 23
44 20 1 15 15 12 12 11 11 8 8 25 25
45 18 0 20 0 14 0 14 0 10 0 24 0
46 32 1 33 33 14 14 9 9 6 6 24 24
47 25 0 29 0 10 0 12 0 8 0 23 0
48 25 0 23 0 14 0 17 0 7 0 21 0
49 23 1 26 26 16 16 5 5 4 4 24 24
50 21 1 18 18 9 9 12 12 8 8 24 24
51 20 0 20 0 10 0 12 0 8 0 28 0
52 15 0 11 0 6 0 6 0 4 0 16 0
53 30 1 28 28 8 8 24 24 20 20 20 20
54 24 0 26 0 13 0 12 0 8 0 29 0
55 26 0 22 0 10 0 12 0 8 0 27 0
56 24 1 17 17 8 8 14 14 6 6 22 22
57 22 1 12 12 7 7 7 7 4 4 28 28
58 14 0 14 0 15 0 13 0 8 0 16 0
59 24 1 17 17 9 9 12 12 9 9 25 25
60 24 1 21 21 10 10 13 13 6 6 24 24
61 24 0 19 0 12 0 14 0 7 0 28 0
62 24 1 18 18 13 13 8 8 9 9 24 24
63 19 1 10 10 10 10 11 11 5 5 23 23
64 31 1 29 29 11 11 9 9 5 5 30 30
65 22 1 31 31 8 8 11 11 8 8 24 24
66 27 1 19 19 9 9 13 13 8 8 21 21
67 19 1 9 9 13 13 10 10 6 6 25 25
68 25 0 20 0 11 0 11 0 8 0 25 0
69 20 0 28 0 8 0 12 0 7 0 22 0
70 21 0 19 0 9 0 9 0 7 0 23 0
71 27 0 30 0 9 0 15 0 9 0 26 0
72 23 0 29 0 15 0 18 0 11 0 23 0
73 25 0 26 0 9 0 15 0 6 0 25 0
74 20 0 23 0 10 0 12 0 8 0 21 0
75 22 0 21 0 12 0 14 0 9 0 24 0
76 23 1 19 19 12 12 10 10 8 8 29 29
77 25 0 28 0 11 0 13 0 6 0 22 0
78 25 0 23 0 14 0 13 0 10 0 27 0
79 17 0 18 0 6 0 11 0 8 0 26 0
80 19 1 21 21 12 12 13 13 8 8 22 22
81 25 0 20 0 8 0 16 0 10 0 24 0
82 19 1 23 23 14 14 8 8 5 5 27 27
83 20 1 21 21 11 11 16 16 7 7 24 24
84 26 0 21 0 10 0 11 0 5 0 24 0
85 23 1 15 15 14 14 9 9 8 8 29 29
86 27 0 28 0 12 0 16 0 14 0 22 0
87 17 1 19 19 10 10 12 12 7 7 21 21
88 17 1 26 26 14 14 14 14 8 8 24 24
89 17 1 16 16 11 11 9 9 5 5 23 23
90 22 0 22 0 10 0 15 0 6 0 20 0
91 21 1 19 19 9 9 11 11 10 10 27 27
92 32 0 31 0 10 0 21 0 12 0 26 0
93 21 1 31 31 16 16 14 14 9 9 25 25
94 21 0 29 0 13 0 18 0 12 0 21 0
95 18 1 19 19 9 9 12 12 7 7 21 21
96 18 0 22 0 10 0 13 0 8 0 19 0
97 23 0 23 0 10 0 15 0 10 0 21 0
98 19 1 15 15 7 7 12 12 6 6 21 21
99 20 0 20 0 9 0 19 0 10 0 16 0
100 21 0 18 0 8 0 15 0 10 0 22 0
101 20 1 23 23 14 14 11 11 10 10 29 29
102 17 0 25 0 14 0 11 0 5 0 15 0
103 18 0 21 0 8 0 10 0 7 0 17 0
104 19 0 24 0 9 0 13 0 10 0 15 0
105 22 0 25 0 14 0 15 0 11 0 21 0
106 15 1 17 17 14 14 12 12 6 6 21 21
107 14 0 13 0 8 0 12 0 7 0 19 0
108 18 0 28 0 8 0 16 0 12 0 24 0
109 24 1 21 21 8 8 9 9 11 11 20 20
110 35 0 25 0 7 0 18 0 11 0 17 0
111 29 0 9 0 6 0 8 0 11 0 23 0
112 21 0 16 0 8 0 13 0 5 0 24 0
113 20 1 17 17 11 11 9 9 6 6 19 19
114 22 1 25 25 14 14 15 15 9 9 24 24
115 13 1 20 20 11 11 8 8 4 4 13 13
116 26 0 29 0 11 0 7 0 4 0 22 0
117 17 0 14 0 11 0 12 0 7 0 16 0
118 25 0 22 0 14 0 14 0 11 0 19 0
119 20 0 15 0 8 0 6 0 6 0 25 0
120 19 1 19 19 20 20 8 8 7 7 25 25
121 21 1 20 20 11 11 17 17 8 8 23 23
122 22 0 15 0 8 0 10 0 4 0 24 0
123 24 0 20 0 11 0 11 0 8 0 26 0
124 21 0 18 0 10 0 14 0 9 0 26 0
125 26 0 33 0 14 0 11 0 8 0 25 0
126 24 0 22 0 11 0 13 0 11 0 18 0
127 16 0 16 0 9 0 12 0 8 0 21 0
128 23 1 17 17 9 9 11 11 5 5 26 26
129 18 0 16 0 8 0 9 0 4 0 23 0
130 16 1 21 21 10 10 12 12 8 8 23 23
131 26 1 26 26 13 13 20 20 10 10 22 22
132 19 0 18 0 13 0 12 0 6 0 20 0
133 21 0 18 0 12 0 13 0 9 0 13 0
134 21 0 17 0 8 0 12 0 9 0 24 0
135 22 0 22 0 13 0 12 0 13 0 15 0
136 23 0 30 0 14 0 9 0 9 0 14 0
137 29 0 30 0 12 0 15 0 10 0 22 0
138 21 0 24 0 14 0 24 0 20 0 10 0
139 21 1 21 21 15 15 7 7 5 5 24 24
140 23 0 21 0 13 0 17 0 11 0 22 0
141 27 0 29 0 16 0 11 0 6 0 24 0
142 25 0 31 0 9 0 17 0 9 0 19 0
143 21 0 20 0 9 0 11 0 7 0 20 0
144 10 0 16 0 9 0 12 0 9 0 13 0
145 20 0 22 0 8 0 14 0 10 0 20 0
146 26 0 20 0 7 0 11 0 9 0 22 0
147 24 0 28 0 16 0 16 0 8 0 24 0
148 29 0 38 0 11 0 21 0 7 0 29 0
149 19 0 22 0 9 0 14 0 6 0 12 0
150 24 0 20 0 11 0 20 0 13 0 20 0
151 19 0 17 0 9 0 13 0 6 0 21 0
152 24 1 28 28 14 14 11 11 8 8 24 24
153 22 0 22 0 13 0 15 0 10 0 22 0
154 17 0 31 0 16 0 19 0 16 0 20 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M CM CM_M D D_M
7.05980 -0.62509 0.29618 0.07064 -0.28347 -0.19329
PE PE_M PC PC_M O O_M
0.26080 -0.27411 -0.01770 0.11429 0.39223 0.13545
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.4591 -2.1628 -0.2324 2.1567 11.3523
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.05980 2.88598 2.446 0.015658 *
M -0.62509 5.01780 -0.125 0.901037
CM 0.29618 0.07776 3.809 0.000207 ***
CM_M 0.07064 0.11858 0.596 0.552312
D -0.28347 0.15572 -1.820 0.070804 .
D_M -0.19329 0.23927 -0.808 0.420535
PE 0.26080 0.13640 1.912 0.057885 .
PE_M -0.27411 0.22474 -1.220 0.224614
PC -0.01770 0.16192 -0.109 0.913109
PC_M 0.11429 0.28282 0.404 0.686726
O 0.39223 0.09401 4.172 5.22e-05 ***
O_M 0.13545 0.16631 0.814 0.416763
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.456 on 142 degrees of freedom
Multiple R-squared: 0.3902, Adjusted R-squared: 0.343
F-statistic: 8.261 on 11 and 142 DF, p-value: 4.335e-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,] 0.97114022 0.05771957 0.02885978
[2,] 0.94099178 0.11801643 0.05900822
[3,] 0.89645266 0.20709468 0.10354734
[4,] 0.83286085 0.33427830 0.16713915
[5,] 0.82146023 0.35707954 0.17853977
[6,] 0.80784521 0.38430959 0.19215479
[7,] 0.79035620 0.41928761 0.20964380
[8,] 0.74391249 0.51217502 0.25608751
[9,] 0.69225423 0.61549155 0.30774577
[10,] 0.71361946 0.57276108 0.28638054
[11,] 0.63821757 0.72356485 0.36178243
[12,] 0.56128262 0.87743476 0.43871738
[13,] 0.51091465 0.97817069 0.48908535
[14,] 0.48225410 0.96450820 0.51774590
[15,] 0.41457075 0.82914151 0.58542925
[16,] 0.42595428 0.85190857 0.57404572
[17,] 0.51770120 0.96459759 0.48229880
[18,] 0.65830291 0.68339419 0.34169709
[19,] 0.62077565 0.75844870 0.37922435
[20,] 0.69036259 0.61927482 0.30963741
[21,] 0.73098912 0.53802176 0.26901088
[22,] 0.69128590 0.61742820 0.30871410
[23,] 0.63958222 0.72083556 0.36041778
[24,] 0.73985112 0.52029776 0.26014888
[25,] 0.68812490 0.62375020 0.31187510
[26,] 0.63650072 0.72699856 0.36349928
[27,] 0.62921821 0.74156357 0.37078179
[28,] 0.59101454 0.81797092 0.40898546
[29,] 0.60371803 0.79256395 0.39628197
[30,] 0.54894192 0.90211615 0.45105808
[31,] 0.55857159 0.88285682 0.44142841
[32,] 0.64710222 0.70579556 0.35289778
[33,] 0.59714880 0.80570239 0.40285120
[34,] 0.56662667 0.86674666 0.43337333
[35,] 0.57325543 0.85348914 0.42674457
[36,] 0.52557762 0.94884477 0.47442238
[37,] 0.56338182 0.87323637 0.43661818
[38,] 0.51971853 0.96056294 0.48028147
[39,] 0.67460368 0.65079264 0.32539632
[40,] 0.63445608 0.73108784 0.36554392
[41,] 0.59355868 0.81288265 0.40644132
[42,] 0.57417260 0.85165481 0.42582740
[43,] 0.52342907 0.95314185 0.47657093
[44,] 0.48555764 0.97111527 0.51444236
[45,] 0.44598320 0.89196640 0.55401680
[46,] 0.40700760 0.81401521 0.59299240
[47,] 0.35919672 0.71839345 0.64080328
[48,] 0.36940334 0.73880667 0.63059666
[49,] 0.32613410 0.65226821 0.67386590
[50,] 0.36624343 0.73248685 0.63375657
[51,] 0.43550234 0.87100468 0.56449766
[52,] 0.54569938 0.90860124 0.45430062
[53,] 0.49749601 0.99499203 0.50250399
[54,] 0.47854570 0.95709140 0.52145430
[55,] 0.54817926 0.90364147 0.45182074
[56,] 0.49932848 0.99865697 0.50067152
[57,] 0.45334467 0.90668935 0.54665533
[58,] 0.41930442 0.83860885 0.58069558
[59,] 0.38326396 0.76652792 0.61673604
[60,] 0.35592247 0.71184494 0.64407753
[61,] 0.31238297 0.62476593 0.68761703
[62,] 0.27456907 0.54913814 0.72543093
[63,] 0.23746117 0.47492234 0.76253883
[64,] 0.20774205 0.41548411 0.79225795
[65,] 0.32193902 0.64387803 0.67806098
[66,] 0.29363788 0.58727576 0.70636212
[67,] 0.25513315 0.51026630 0.74486685
[68,] 0.25414186 0.50828372 0.74585814
[69,] 0.22942673 0.45885347 0.77057327
[70,] 0.22859855 0.45719709 0.77140145
[71,] 0.20305544 0.40611089 0.79694456
[72,] 0.18733675 0.37467351 0.81266325
[73,] 0.18031945 0.36063890 0.81968055
[74,] 0.22039296 0.44078592 0.77960704
[75,] 0.19823975 0.39647950 0.80176025
[76,] 0.16961741 0.33923482 0.83038259
[77,] 0.15683149 0.31366298 0.84316851
[78,] 0.15657467 0.31314934 0.84342533
[79,] 0.14523951 0.29047903 0.85476049
[80,] 0.14578108 0.29156216 0.85421892
[81,] 0.13055134 0.26110268 0.86944866
[82,] 0.12599001 0.25198001 0.87400999
[83,] 0.10123637 0.20247274 0.89876363
[84,] 0.08132906 0.16265812 0.91867094
[85,] 0.06566879 0.13133758 0.93433121
[86,] 0.05201297 0.10402595 0.94798703
[87,] 0.05984402 0.11968803 0.94015598
[88,] 0.04949821 0.09899642 0.95050179
[89,] 0.04271586 0.08543173 0.95728414
[90,] 0.03647168 0.07294336 0.96352832
[91,] 0.02720680 0.05441359 0.97279320
[92,] 0.02309519 0.04619039 0.97690481
[93,] 0.02922516 0.05845033 0.97077484
[94,] 0.12802811 0.25605623 0.87197189
[95,] 0.12992395 0.25984790 0.87007605
[96,] 0.59929145 0.80141711 0.40070855
[97,] 0.88324847 0.23350307 0.11675153
[98,] 0.85428711 0.29142579 0.14571289
[99,] 0.91336890 0.17326220 0.08663110
[100,] 0.88957229 0.22085542 0.11042771
[101,] 0.86524974 0.26950052 0.13475026
[102,] 0.84260528 0.31478944 0.15739472
[103,] 0.80034222 0.39931556 0.19965778
[104,] 0.81893736 0.36212528 0.18106264
[105,] 0.77415155 0.45169690 0.22584845
[106,] 0.72733747 0.54532506 0.27266253
[107,] 0.66704572 0.66590856 0.33295428
[108,] 0.62185902 0.75628197 0.37814098
[109,] 0.56551932 0.86896136 0.43448068
[110,] 0.50133220 0.99733560 0.49866780
[111,] 0.43820345 0.87640689 0.56179655
[112,] 0.41716359 0.83432718 0.58283641
[113,] 0.42172684 0.84345367 0.57827316
[114,] 0.34602443 0.69204887 0.65397557
[115,] 0.30999455 0.61998911 0.69000545
[116,] 0.26089044 0.52178088 0.73910956
[117,] 0.20448452 0.40896904 0.79551548
[118,] 0.15078045 0.30156091 0.84921955
[119,] 0.14108122 0.28216244 0.85891878
[120,] 0.09790674 0.19581349 0.90209326
[121,] 0.07187083 0.14374167 0.92812917
[122,] 0.05094699 0.10189398 0.94905301
[123,] 0.05630714 0.11261428 0.94369286
[124,] 0.09255202 0.18510403 0.90744798
[125,] 0.04601236 0.09202472 0.95398764
> postscript(file="/var/www/rcomp/tmp/13uhh1291573233.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/23uhh1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3v3gk1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4v3gk1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5v3gk1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 154
Frequency = 1
1 2 3 4 5 6
0.703639424 2.948479030 5.508094320 -1.092828824 1.453964228 -1.344489017
7 8 9 10 11 12
2.247433109 3.408461134 -2.490387995 -1.049059566 -3.311006778 -4.599573594
13 14 15 16 17 18
-7.925135274 -1.339319624 4.071867772 3.108501469 1.239061785 -1.081554583
19 20 21 22 23 24
-1.071453280 -1.356383899 1.248966973 -3.386372329 -5.907985183 -3.561531044
25 26 27 28 29 30
0.302747407 1.348398952 -4.207929638 0.992855748 0.150771469 2.246391317
31 32 33 34 35 36
5.691571119 6.991900432 2.945141785 4.275535886 2.635659003 1.492395866
37 38 39 40 41 42
1.752929150 6.462630135 -0.954873524 1.462177115 -3.813699289 2.350670500
43 44 45 46 47 48
4.220009428 -0.034429555 -3.902653371 7.010566674 0.176292607 2.550012223
49 50 51 52 53 54
1.671793466 -1.024179536 -4.119270055 -1.386716827 4.942153621 -1.438171986
55 56 57 58 59 60
1.680606660 3.141054199 -0.567675340 -2.478831227 1.718362550 1.558610398
61 62 63 64 65 66
0.204546568 3.733037770 1.191308846 2.978051972 -5.282921873 6.205363226
67 68 69 70 71 72
1.823137292 2.601703958 -4.719932161 -0.380685554 -0.344760437 -1.918061415
73 74 75 76 77 78
-0.820910463 -2.262163088 -0.783471203 -0.625764018 0.851974719 1.292904879
79 80 81 82 83 84
-6.615518563 -1.625685852 0.874931296 -3.820991767 -2.021302463 3.361189333
85 86 87 88 89 90
1.781733168 2.494646709 -3.234590626 -5.548336907 -2.559472683 -0.391550502
91 92 93 94 95 96
-3.180551626 3.130829808 -3.053202730 -3.682829536 -2.711350293 -3.442314403
97 98 99 100 101 102
-0.009162792 -1.100991342 -1.486120971 -1.487440566 -4.319409450 -2.159536362
103 104 105 106 107 108
-2.163903734 -1.713801336 -0.449943899 -2.497316088 -5.100541178 -8.459100560
109 110 111 112 113 114
2.179638535 11.352310984 10.062299708 -1.246453917 2.087855312 -0.264802180
115 116 117 118 119 120
-2.666614386 3.085195882 -0.369608112 4.483863326 -0.499208020 1.369035479
121 122 123 124 125 126
-0.210082071 0.814425296 1.209469068 -2.246342043 0.601783304 3.286490914
127 128 129 130 131 132
-4.472378637 0.563743735 -2.828718719 -6.120197826 3.916929700 -0.574026121
133 134 135 136 137 138
3.680450031 -1.211031134 3.326334933 3.344205143 4.092287028 0.972921134
139 140 141 142 143 144
0.959160331 0.537468035 3.710272023 -0.416895660 -0.021760337 -7.316799096
145 146 147 148 149 150
-2.626886954 3.662232405 -0.262148957 -2.924161423 -0.276340362 1.304178876
151 152 153 154
-2.064758751 0.678099750 -0.254811110 -7.222543831
> postscript(file="/var/www/rcomp/tmp/66ufn1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 0.703639424 NA
1 2.948479030 0.703639424
2 5.508094320 2.948479030
3 -1.092828824 5.508094320
4 1.453964228 -1.092828824
5 -1.344489017 1.453964228
6 2.247433109 -1.344489017
7 3.408461134 2.247433109
8 -2.490387995 3.408461134
9 -1.049059566 -2.490387995
10 -3.311006778 -1.049059566
11 -4.599573594 -3.311006778
12 -7.925135274 -4.599573594
13 -1.339319624 -7.925135274
14 4.071867772 -1.339319624
15 3.108501469 4.071867772
16 1.239061785 3.108501469
17 -1.081554583 1.239061785
18 -1.071453280 -1.081554583
19 -1.356383899 -1.071453280
20 1.248966973 -1.356383899
21 -3.386372329 1.248966973
22 -5.907985183 -3.386372329
23 -3.561531044 -5.907985183
24 0.302747407 -3.561531044
25 1.348398952 0.302747407
26 -4.207929638 1.348398952
27 0.992855748 -4.207929638
28 0.150771469 0.992855748
29 2.246391317 0.150771469
30 5.691571119 2.246391317
31 6.991900432 5.691571119
32 2.945141785 6.991900432
33 4.275535886 2.945141785
34 2.635659003 4.275535886
35 1.492395866 2.635659003
36 1.752929150 1.492395866
37 6.462630135 1.752929150
38 -0.954873524 6.462630135
39 1.462177115 -0.954873524
40 -3.813699289 1.462177115
41 2.350670500 -3.813699289
42 4.220009428 2.350670500
43 -0.034429555 4.220009428
44 -3.902653371 -0.034429555
45 7.010566674 -3.902653371
46 0.176292607 7.010566674
47 2.550012223 0.176292607
48 1.671793466 2.550012223
49 -1.024179536 1.671793466
50 -4.119270055 -1.024179536
51 -1.386716827 -4.119270055
52 4.942153621 -1.386716827
53 -1.438171986 4.942153621
54 1.680606660 -1.438171986
55 3.141054199 1.680606660
56 -0.567675340 3.141054199
57 -2.478831227 -0.567675340
58 1.718362550 -2.478831227
59 1.558610398 1.718362550
60 0.204546568 1.558610398
61 3.733037770 0.204546568
62 1.191308846 3.733037770
63 2.978051972 1.191308846
64 -5.282921873 2.978051972
65 6.205363226 -5.282921873
66 1.823137292 6.205363226
67 2.601703958 1.823137292
68 -4.719932161 2.601703958
69 -0.380685554 -4.719932161
70 -0.344760437 -0.380685554
71 -1.918061415 -0.344760437
72 -0.820910463 -1.918061415
73 -2.262163088 -0.820910463
74 -0.783471203 -2.262163088
75 -0.625764018 -0.783471203
76 0.851974719 -0.625764018
77 1.292904879 0.851974719
78 -6.615518563 1.292904879
79 -1.625685852 -6.615518563
80 0.874931296 -1.625685852
81 -3.820991767 0.874931296
82 -2.021302463 -3.820991767
83 3.361189333 -2.021302463
84 1.781733168 3.361189333
85 2.494646709 1.781733168
86 -3.234590626 2.494646709
87 -5.548336907 -3.234590626
88 -2.559472683 -5.548336907
89 -0.391550502 -2.559472683
90 -3.180551626 -0.391550502
91 3.130829808 -3.180551626
92 -3.053202730 3.130829808
93 -3.682829536 -3.053202730
94 -2.711350293 -3.682829536
95 -3.442314403 -2.711350293
96 -0.009162792 -3.442314403
97 -1.100991342 -0.009162792
98 -1.486120971 -1.100991342
99 -1.487440566 -1.486120971
100 -4.319409450 -1.487440566
101 -2.159536362 -4.319409450
102 -2.163903734 -2.159536362
103 -1.713801336 -2.163903734
104 -0.449943899 -1.713801336
105 -2.497316088 -0.449943899
106 -5.100541178 -2.497316088
107 -8.459100560 -5.100541178
108 2.179638535 -8.459100560
109 11.352310984 2.179638535
110 10.062299708 11.352310984
111 -1.246453917 10.062299708
112 2.087855312 -1.246453917
113 -0.264802180 2.087855312
114 -2.666614386 -0.264802180
115 3.085195882 -2.666614386
116 -0.369608112 3.085195882
117 4.483863326 -0.369608112
118 -0.499208020 4.483863326
119 1.369035479 -0.499208020
120 -0.210082071 1.369035479
121 0.814425296 -0.210082071
122 1.209469068 0.814425296
123 -2.246342043 1.209469068
124 0.601783304 -2.246342043
125 3.286490914 0.601783304
126 -4.472378637 3.286490914
127 0.563743735 -4.472378637
128 -2.828718719 0.563743735
129 -6.120197826 -2.828718719
130 3.916929700 -6.120197826
131 -0.574026121 3.916929700
132 3.680450031 -0.574026121
133 -1.211031134 3.680450031
134 3.326334933 -1.211031134
135 3.344205143 3.326334933
136 4.092287028 3.344205143
137 0.972921134 4.092287028
138 0.959160331 0.972921134
139 0.537468035 0.959160331
140 3.710272023 0.537468035
141 -0.416895660 3.710272023
142 -0.021760337 -0.416895660
143 -7.316799096 -0.021760337
144 -2.626886954 -7.316799096
145 3.662232405 -2.626886954
146 -0.262148957 3.662232405
147 -2.924161423 -0.262148957
148 -0.276340362 -2.924161423
149 1.304178876 -0.276340362
150 -2.064758751 1.304178876
151 0.678099750 -2.064758751
152 -0.254811110 0.678099750
153 -7.222543831 -0.254811110
154 NA -7.222543831
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.948479030 0.703639424
[2,] 5.508094320 2.948479030
[3,] -1.092828824 5.508094320
[4,] 1.453964228 -1.092828824
[5,] -1.344489017 1.453964228
[6,] 2.247433109 -1.344489017
[7,] 3.408461134 2.247433109
[8,] -2.490387995 3.408461134
[9,] -1.049059566 -2.490387995
[10,] -3.311006778 -1.049059566
[11,] -4.599573594 -3.311006778
[12,] -7.925135274 -4.599573594
[13,] -1.339319624 -7.925135274
[14,] 4.071867772 -1.339319624
[15,] 3.108501469 4.071867772
[16,] 1.239061785 3.108501469
[17,] -1.081554583 1.239061785
[18,] -1.071453280 -1.081554583
[19,] -1.356383899 -1.071453280
[20,] 1.248966973 -1.356383899
[21,] -3.386372329 1.248966973
[22,] -5.907985183 -3.386372329
[23,] -3.561531044 -5.907985183
[24,] 0.302747407 -3.561531044
[25,] 1.348398952 0.302747407
[26,] -4.207929638 1.348398952
[27,] 0.992855748 -4.207929638
[28,] 0.150771469 0.992855748
[29,] 2.246391317 0.150771469
[30,] 5.691571119 2.246391317
[31,] 6.991900432 5.691571119
[32,] 2.945141785 6.991900432
[33,] 4.275535886 2.945141785
[34,] 2.635659003 4.275535886
[35,] 1.492395866 2.635659003
[36,] 1.752929150 1.492395866
[37,] 6.462630135 1.752929150
[38,] -0.954873524 6.462630135
[39,] 1.462177115 -0.954873524
[40,] -3.813699289 1.462177115
[41,] 2.350670500 -3.813699289
[42,] 4.220009428 2.350670500
[43,] -0.034429555 4.220009428
[44,] -3.902653371 -0.034429555
[45,] 7.010566674 -3.902653371
[46,] 0.176292607 7.010566674
[47,] 2.550012223 0.176292607
[48,] 1.671793466 2.550012223
[49,] -1.024179536 1.671793466
[50,] -4.119270055 -1.024179536
[51,] -1.386716827 -4.119270055
[52,] 4.942153621 -1.386716827
[53,] -1.438171986 4.942153621
[54,] 1.680606660 -1.438171986
[55,] 3.141054199 1.680606660
[56,] -0.567675340 3.141054199
[57,] -2.478831227 -0.567675340
[58,] 1.718362550 -2.478831227
[59,] 1.558610398 1.718362550
[60,] 0.204546568 1.558610398
[61,] 3.733037770 0.204546568
[62,] 1.191308846 3.733037770
[63,] 2.978051972 1.191308846
[64,] -5.282921873 2.978051972
[65,] 6.205363226 -5.282921873
[66,] 1.823137292 6.205363226
[67,] 2.601703958 1.823137292
[68,] -4.719932161 2.601703958
[69,] -0.380685554 -4.719932161
[70,] -0.344760437 -0.380685554
[71,] -1.918061415 -0.344760437
[72,] -0.820910463 -1.918061415
[73,] -2.262163088 -0.820910463
[74,] -0.783471203 -2.262163088
[75,] -0.625764018 -0.783471203
[76,] 0.851974719 -0.625764018
[77,] 1.292904879 0.851974719
[78,] -6.615518563 1.292904879
[79,] -1.625685852 -6.615518563
[80,] 0.874931296 -1.625685852
[81,] -3.820991767 0.874931296
[82,] -2.021302463 -3.820991767
[83,] 3.361189333 -2.021302463
[84,] 1.781733168 3.361189333
[85,] 2.494646709 1.781733168
[86,] -3.234590626 2.494646709
[87,] -5.548336907 -3.234590626
[88,] -2.559472683 -5.548336907
[89,] -0.391550502 -2.559472683
[90,] -3.180551626 -0.391550502
[91,] 3.130829808 -3.180551626
[92,] -3.053202730 3.130829808
[93,] -3.682829536 -3.053202730
[94,] -2.711350293 -3.682829536
[95,] -3.442314403 -2.711350293
[96,] -0.009162792 -3.442314403
[97,] -1.100991342 -0.009162792
[98,] -1.486120971 -1.100991342
[99,] -1.487440566 -1.486120971
[100,] -4.319409450 -1.487440566
[101,] -2.159536362 -4.319409450
[102,] -2.163903734 -2.159536362
[103,] -1.713801336 -2.163903734
[104,] -0.449943899 -1.713801336
[105,] -2.497316088 -0.449943899
[106,] -5.100541178 -2.497316088
[107,] -8.459100560 -5.100541178
[108,] 2.179638535 -8.459100560
[109,] 11.352310984 2.179638535
[110,] 10.062299708 11.352310984
[111,] -1.246453917 10.062299708
[112,] 2.087855312 -1.246453917
[113,] -0.264802180 2.087855312
[114,] -2.666614386 -0.264802180
[115,] 3.085195882 -2.666614386
[116,] -0.369608112 3.085195882
[117,] 4.483863326 -0.369608112
[118,] -0.499208020 4.483863326
[119,] 1.369035479 -0.499208020
[120,] -0.210082071 1.369035479
[121,] 0.814425296 -0.210082071
[122,] 1.209469068 0.814425296
[123,] -2.246342043 1.209469068
[124,] 0.601783304 -2.246342043
[125,] 3.286490914 0.601783304
[126,] -4.472378637 3.286490914
[127,] 0.563743735 -4.472378637
[128,] -2.828718719 0.563743735
[129,] -6.120197826 -2.828718719
[130,] 3.916929700 -6.120197826
[131,] -0.574026121 3.916929700
[132,] 3.680450031 -0.574026121
[133,] -1.211031134 3.680450031
[134,] 3.326334933 -1.211031134
[135,] 3.344205143 3.326334933
[136,] 4.092287028 3.344205143
[137,] 0.972921134 4.092287028
[138,] 0.959160331 0.972921134
[139,] 0.537468035 0.959160331
[140,] 3.710272023 0.537468035
[141,] -0.416895660 3.710272023
[142,] -0.021760337 -0.416895660
[143,] -7.316799096 -0.021760337
[144,] -2.626886954 -7.316799096
[145,] 3.662232405 -2.626886954
[146,] -0.262148957 3.662232405
[147,] -2.924161423 -0.262148957
[148,] -0.276340362 -2.924161423
[149,] 1.304178876 -0.276340362
[150,] -2.064758751 1.304178876
[151,] 0.678099750 -2.064758751
[152,] -0.254811110 0.678099750
[153,] -7.222543831 -0.254811110
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.948479030 0.703639424
2 5.508094320 2.948479030
3 -1.092828824 5.508094320
4 1.453964228 -1.092828824
5 -1.344489017 1.453964228
6 2.247433109 -1.344489017
7 3.408461134 2.247433109
8 -2.490387995 3.408461134
9 -1.049059566 -2.490387995
10 -3.311006778 -1.049059566
11 -4.599573594 -3.311006778
12 -7.925135274 -4.599573594
13 -1.339319624 -7.925135274
14 4.071867772 -1.339319624
15 3.108501469 4.071867772
16 1.239061785 3.108501469
17 -1.081554583 1.239061785
18 -1.071453280 -1.081554583
19 -1.356383899 -1.071453280
20 1.248966973 -1.356383899
21 -3.386372329 1.248966973
22 -5.907985183 -3.386372329
23 -3.561531044 -5.907985183
24 0.302747407 -3.561531044
25 1.348398952 0.302747407
26 -4.207929638 1.348398952
27 0.992855748 -4.207929638
28 0.150771469 0.992855748
29 2.246391317 0.150771469
30 5.691571119 2.246391317
31 6.991900432 5.691571119
32 2.945141785 6.991900432
33 4.275535886 2.945141785
34 2.635659003 4.275535886
35 1.492395866 2.635659003
36 1.752929150 1.492395866
37 6.462630135 1.752929150
38 -0.954873524 6.462630135
39 1.462177115 -0.954873524
40 -3.813699289 1.462177115
41 2.350670500 -3.813699289
42 4.220009428 2.350670500
43 -0.034429555 4.220009428
44 -3.902653371 -0.034429555
45 7.010566674 -3.902653371
46 0.176292607 7.010566674
47 2.550012223 0.176292607
48 1.671793466 2.550012223
49 -1.024179536 1.671793466
50 -4.119270055 -1.024179536
51 -1.386716827 -4.119270055
52 4.942153621 -1.386716827
53 -1.438171986 4.942153621
54 1.680606660 -1.438171986
55 3.141054199 1.680606660
56 -0.567675340 3.141054199
57 -2.478831227 -0.567675340
58 1.718362550 -2.478831227
59 1.558610398 1.718362550
60 0.204546568 1.558610398
61 3.733037770 0.204546568
62 1.191308846 3.733037770
63 2.978051972 1.191308846
64 -5.282921873 2.978051972
65 6.205363226 -5.282921873
66 1.823137292 6.205363226
67 2.601703958 1.823137292
68 -4.719932161 2.601703958
69 -0.380685554 -4.719932161
70 -0.344760437 -0.380685554
71 -1.918061415 -0.344760437
72 -0.820910463 -1.918061415
73 -2.262163088 -0.820910463
74 -0.783471203 -2.262163088
75 -0.625764018 -0.783471203
76 0.851974719 -0.625764018
77 1.292904879 0.851974719
78 -6.615518563 1.292904879
79 -1.625685852 -6.615518563
80 0.874931296 -1.625685852
81 -3.820991767 0.874931296
82 -2.021302463 -3.820991767
83 3.361189333 -2.021302463
84 1.781733168 3.361189333
85 2.494646709 1.781733168
86 -3.234590626 2.494646709
87 -5.548336907 -3.234590626
88 -2.559472683 -5.548336907
89 -0.391550502 -2.559472683
90 -3.180551626 -0.391550502
91 3.130829808 -3.180551626
92 -3.053202730 3.130829808
93 -3.682829536 -3.053202730
94 -2.711350293 -3.682829536
95 -3.442314403 -2.711350293
96 -0.009162792 -3.442314403
97 -1.100991342 -0.009162792
98 -1.486120971 -1.100991342
99 -1.487440566 -1.486120971
100 -4.319409450 -1.487440566
101 -2.159536362 -4.319409450
102 -2.163903734 -2.159536362
103 -1.713801336 -2.163903734
104 -0.449943899 -1.713801336
105 -2.497316088 -0.449943899
106 -5.100541178 -2.497316088
107 -8.459100560 -5.100541178
108 2.179638535 -8.459100560
109 11.352310984 2.179638535
110 10.062299708 11.352310984
111 -1.246453917 10.062299708
112 2.087855312 -1.246453917
113 -0.264802180 2.087855312
114 -2.666614386 -0.264802180
115 3.085195882 -2.666614386
116 -0.369608112 3.085195882
117 4.483863326 -0.369608112
118 -0.499208020 4.483863326
119 1.369035479 -0.499208020
120 -0.210082071 1.369035479
121 0.814425296 -0.210082071
122 1.209469068 0.814425296
123 -2.246342043 1.209469068
124 0.601783304 -2.246342043
125 3.286490914 0.601783304
126 -4.472378637 3.286490914
127 0.563743735 -4.472378637
128 -2.828718719 0.563743735
129 -6.120197826 -2.828718719
130 3.916929700 -6.120197826
131 -0.574026121 3.916929700
132 3.680450031 -0.574026121
133 -1.211031134 3.680450031
134 3.326334933 -1.211031134
135 3.344205143 3.326334933
136 4.092287028 3.344205143
137 0.972921134 4.092287028
138 0.959160331 0.972921134
139 0.537468035 0.959160331
140 3.710272023 0.537468035
141 -0.416895660 3.710272023
142 -0.021760337 -0.416895660
143 -7.316799096 -0.021760337
144 -2.626886954 -7.316799096
145 3.662232405 -2.626886954
146 -0.262148957 3.662232405
147 -2.924161423 -0.262148957
148 -0.276340362 -2.924161423
149 1.304178876 -0.276340362
150 -2.064758751 1.304178876
151 0.678099750 -2.064758751
152 -0.254811110 0.678099750
153 -7.222543831 -0.254811110
> 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/7z3eq1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8z3eq1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9z3eq1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/109dwb1291573233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vduz1291573233.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/12gwt51291573233.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/13cn9w1291573233.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/14go711291573233.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/15m7md1291573233.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/16li321291573234.tab")
+ }
>
> try(system("convert tmp/13uhh1291573233.ps tmp/13uhh1291573233.png",intern=TRUE))
character(0)
> try(system("convert tmp/23uhh1291573233.ps tmp/23uhh1291573233.png",intern=TRUE))
character(0)
> try(system("convert tmp/3v3gk1291573233.ps tmp/3v3gk1291573233.png",intern=TRUE))
character(0)
> try(system("convert tmp/4v3gk1291573233.ps tmp/4v3gk1291573233.png",intern=TRUE))
character(0)
> try(system("convert tmp/5v3gk1291573233.ps tmp/5v3gk1291573233.png",intern=TRUE))
character(0)
> try(system("convert tmp/66ufn1291573233.ps tmp/66ufn1291573233.png",intern=TRUE))
character(0)
> try(system("convert tmp/7z3eq1291573233.ps tmp/7z3eq1291573233.png",intern=TRUE))
character(0)
> try(system("convert tmp/8z3eq1291573233.ps tmp/8z3eq1291573233.png",intern=TRUE))
character(0)
> try(system("convert tmp/9z3eq1291573233.ps tmp/9z3eq1291573233.png",intern=TRUE))
character(0)
> try(system("convert tmp/109dwb1291573233.ps tmp/109dwb1291573233.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.190 1.840 7.043