R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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+ ,9
+ ,9
+ ,25
+ ,19
+ ,19
+ ,20
+ ,20
+ ,9
+ ,9
+ ,11
+ ,11
+ ,7
+ ,7
+ ,21
+ ,20
+ ,20
+ ,16
+ ,16
+ ,9
+ ,9
+ ,12
+ ,12
+ ,9
+ ,9
+ ,10
+ ,13
+ ,13
+ ,22
+ ,22
+ ,8
+ ,8
+ ,14
+ ,14
+ ,10
+ ,10
+ ,20
+ ,20
+ ,20
+ ,20
+ ,20
+ ,7
+ ,7
+ ,11
+ ,11
+ ,9
+ ,9
+ ,26
+ ,22
+ ,22
+ ,28
+ ,28
+ ,16
+ ,16
+ ,16
+ ,16
+ ,8
+ ,8
+ ,24
+ ,24
+ ,24
+ ,38
+ ,38
+ ,11
+ ,11
+ ,21
+ ,21
+ ,7
+ ,7
+ ,29
+ ,29
+ ,29
+ ,22
+ ,22
+ ,9
+ ,9
+ ,14
+ ,14
+ ,6
+ ,6
+ ,19
+ ,12
+ ,12
+ ,20
+ ,20
+ ,11
+ ,11
+ ,20
+ ,20
+ ,13
+ ,13
+ ,24
+ ,20
+ ,20
+ ,17
+ ,17
+ ,9
+ ,9
+ ,13
+ ,13
+ ,6
+ ,6
+ ,19
+ ,21
+ ,21
+ ,28
+ ,0
+ ,14
+ ,0
+ ,11
+ ,0
+ ,8
+ ,0
+ ,24
+ ,24
+ ,0
+ ,22
+ ,22
+ ,13
+ ,13
+ ,15
+ ,15
+ ,10
+ ,10
+ ,22
+ ,22
+ ,22
+ ,31
+ ,31
+ ,16
+ ,16
+ ,19
+ ,19
+ ,16
+ ,16
+ ,17
+ ,20
+ ,20)
+ ,dim=c(11
+ ,158)
+ ,dimnames=list(c('CM'
+ ,'CM*G'
+ ,'D'
+ ,'D*G'
+ ,'PE'
+ ,'PE*G'
+ ,'PC'
+ ,'PC*G'
+ ,'O'
+ ,'PS'
+ ,'PS*G')
+ ,1:158))
> y <- array(NA,dim=c(11,158),dimnames=list(c('CM','CM*G','D','D*G','PE','PE*G','PC','PC*G','O','PS','PS*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 = '9'
> 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
O CM CM*G D D*G PE PE*G PC PC*G PS PS*G
1 24 24 0 14 0 11 0 12 0 26 0
2 25 25 25 11 11 7 7 8 8 23 23
3 30 17 17 6 6 17 17 8 8 25 25
4 19 18 0 12 0 10 0 8 0 23 0
5 22 18 18 8 8 12 12 9 9 19 19
6 22 16 16 10 10 12 12 7 7 29 29
7 25 20 20 10 10 11 11 4 4 25 25
8 23 16 16 11 11 11 11 11 11 21 21
9 17 18 18 16 16 12 12 7 7 22 22
10 21 17 17 11 11 13 13 7 7 25 25
11 19 23 0 13 0 14 0 12 0 24 0
12 19 30 30 12 12 16 16 10 10 18 18
13 15 23 23 8 8 11 11 10 10 22 22
14 16 18 18 12 12 10 10 8 8 15 15
15 23 15 0 11 0 11 0 8 0 22 0
16 27 12 0 4 0 15 0 4 0 28 0
17 22 21 21 9 9 9 9 9 9 20 20
18 14 15 0 8 0 11 0 8 0 12 0
19 22 20 0 8 0 17 0 7 0 24 0
20 23 31 31 14 14 17 17 11 11 20 20
21 23 27 27 15 15 11 11 9 9 21 21
22 21 34 0 16 0 18 0 11 0 20 0
23 19 21 21 9 9 14 14 13 13 21 21
24 18 31 0 14 0 10 0 8 0 23 0
25 20 19 0 11 0 11 0 8 0 28 0
26 23 16 16 8 8 15 15 9 9 24 24
27 25 20 20 9 9 15 15 6 6 24 24
28 19 21 0 9 0 13 0 9 0 24 0
29 24 22 0 9 0 16 0 9 0 23 0
30 22 17 17 9 9 13 13 6 6 23 23
31 25 24 0 10 0 9 0 6 0 29 0
32 26 25 25 16 16 18 18 16 16 24 24
33 29 26 26 11 11 18 18 5 5 18 18
34 32 25 25 8 8 12 12 7 7 25 25
35 25 17 17 9 9 17 17 9 9 21 21
36 29 32 0 16 0 9 0 6 0 26 0
37 28 33 0 11 0 9 0 6 0 22 0
38 17 13 0 16 0 12 0 5 0 22 0
39 28 32 32 12 12 18 18 12 12 22 22
40 29 25 0 12 0 12 0 7 0 23 0
41 26 29 0 14 0 18 0 10 0 30 0
42 25 22 22 9 9 14 14 9 9 23 23
43 14 18 0 10 0 15 0 8 0 17 0
44 25 17 17 9 9 16 16 5 5 23 23
45 26 20 0 10 0 10 0 8 0 23 0
46 20 15 0 12 0 11 0 8 0 25 0
47 18 20 20 14 14 14 14 10 10 24 24
48 32 33 0 14 0 9 0 6 0 24 0
49 25 29 29 10 10 12 12 8 8 23 23
50 25 23 23 14 14 17 17 7 7 21 21
51 23 26 0 16 0 5 0 4 0 24 0
52 21 18 0 9 0 12 0 8 0 24 0
53 20 20 20 10 10 12 12 8 8 28 28
54 15 11 11 6 6 6 6 4 4 16 16
55 30 28 0 8 0 24 0 20 0 20 0
56 24 26 26 13 13 12 12 8 8 29 29
57 26 22 22 10 10 12 12 8 8 27 27
58 24 17 0 8 0 14 0 6 0 22 0
59 22 12 0 7 0 7 0 4 0 28 0
60 14 14 14 15 15 13 13 8 8 16 16
61 24 17 0 9 0 12 0 9 0 25 0
62 24 21 0 10 0 13 0 6 0 24 0
63 24 19 19 12 12 14 14 7 7 28 28
64 24 18 0 13 0 8 0 9 0 24 0
65 19 10 0 10 0 11 0 5 0 23 0
66 31 29 0 11 0 9 0 5 0 30 0
67 22 31 0 8 0 11 0 8 0 24 0
68 27 19 0 9 0 13 0 8 0 21 0
69 19 9 0 13 0 10 0 6 0 25 0
70 25 20 20 11 11 11 11 8 8 25 25
71 20 28 28 8 8 12 12 7 7 22 22
72 21 19 19 9 9 9 9 7 7 23 23
73 27 30 30 9 9 15 15 9 9 26 26
74 23 29 29 15 15 18 18 11 11 23 23
75 25 26 26 9 9 15 15 6 6 25 25
76 20 23 23 10 10 12 12 8 8 21 21
77 22 21 21 12 12 14 14 9 9 24 24
78 23 19 0 12 0 10 0 8 0 29 0
79 25 28 28 11 11 13 13 6 6 22 22
80 25 23 23 14 14 13 13 10 10 27 27
81 17 18 18 6 6 11 11 8 8 26 26
82 19 21 0 12 0 13 0 8 0 22 0
83 25 20 20 8 8 16 16 10 10 24 24
84 19 23 0 14 0 8 0 5 0 27 0
85 20 21 0 11 0 16 0 7 0 24 0
86 26 21 21 10 10 11 11 5 5 24 24
87 23 15 0 14 0 9 0 8 0 29 0
88 27 28 28 12 12 16 16 14 14 22 22
89 17 19 0 10 0 12 0 7 0 21 0
90 17 26 0 14 0 14 0 8 0 24 0
91 19 10 10 5 5 8 8 6 6 24 24
92 17 16 0 11 0 9 0 5 0 23 0
93 22 22 22 10 10 15 15 6 6 20 20
94 21 19 0 9 0 11 0 10 0 27 0
95 32 31 31 10 10 21 21 12 12 26 26
96 21 31 0 16 0 14 0 9 0 25 0
97 21 29 29 13 13 18 18 12 12 21 21
98 18 19 0 9 0 12 0 7 0 21 0
99 18 22 22 10 10 13 13 8 8 19 19
100 23 23 23 10 10 15 15 10 10 21 21
101 19 15 0 7 0 12 0 6 0 21 0
102 20 20 20 9 9 19 19 10 10 16 16
103 21 18 18 8 8 15 15 10 10 22 22
104 20 23 0 14 0 11 0 10 0 29 0
105 17 25 25 14 14 11 11 5 5 15 15
106 18 21 21 8 8 10 10 7 7 17 17
107 19 24 24 9 9 13 13 10 10 15 15
108 22 25 25 14 14 15 15 11 11 21 21
109 15 17 0 14 0 12 0 6 0 21 0
110 14 13 13 8 8 12 12 7 7 19 19
111 18 28 28 8 8 16 16 12 12 24 24
112 24 21 0 8 0 9 0 11 0 20 0
113 35 25 25 7 7 18 18 11 11 17 17
114 29 9 9 6 6 8 8 11 11 23 23
115 21 16 16 8 8 13 13 5 5 24 24
116 25 19 19 6 6 17 17 8 8 14 14
117 20 17 0 11 0 9 0 6 0 19 0
118 22 25 0 14 0 15 0 9 0 24 0
119 13 20 0 11 0 8 0 4 0 13 0
120 26 29 29 11 11 7 7 4 4 22 22
121 17 14 14 11 11 12 12 7 7 16 16
122 25 22 22 14 14 14 14 11 11 19 19
123 20 15 15 8 8 6 6 6 6 25 25
124 19 19 0 20 0 8 0 7 0 25 0
125 21 20 0 11 0 17 0 8 0 23 0
126 22 15 15 8 8 10 10 4 4 24 24
127 24 20 20 11 11 11 11 8 8 26 26
128 21 18 18 10 10 14 14 9 9 26 26
129 26 33 33 14 14 11 11 8 8 25 25
130 24 22 22 11 11 13 13 11 11 18 18
131 16 16 16 9 9 12 12 8 8 21 21
132 23 17 0 9 0 11 0 5 0 26 0
133 18 16 16 8 8 9 9 4 4 23 23
134 16 21 0 10 0 12 0 8 0 23 0
135 26 26 0 13 0 20 0 10 0 22 0
136 19 18 18 13 13 12 12 6 6 20 20
137 21 18 18 12 12 13 13 9 9 13 13
138 21 17 0 8 0 12 0 9 0 24 0
139 22 22 22 13 13 12 12 13 13 15 15
140 23 30 30 14 14 9 9 9 9 14 14
141 29 30 30 12 12 15 15 10 10 22 22
142 21 24 24 14 14 24 24 20 20 10 10
143 21 21 0 15 0 7 0 5 0 24 0
144 23 21 21 13 13 17 17 11 11 22 22
145 27 29 29 16 16 11 11 6 6 24 24
146 25 31 31 9 9 17 17 9 9 19 19
147 21 20 20 9 9 11 11 7 7 20 20
148 10 16 16 9 9 12 12 9 9 13 13
149 20 22 22 8 8 14 14 10 10 20 20
150 26 20 20 7 7 11 11 9 9 22 22
151 24 28 28 16 16 16 16 8 8 24 24
152 29 38 38 11 11 21 21 7 7 29 29
153 19 22 22 9 9 14 14 6 6 12 12
154 24 20 20 11 11 20 20 13 13 20 20
155 19 17 17 9 9 13 13 6 6 21 21
156 24 28 0 14 0 11 0 8 0 24 0
157 22 22 22 13 13 15 15 10 10 22 22
158 17 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) CM `CM*G` D `D*G` PE
7.32685 0.35698 -0.05923 -0.48387 0.17523 -0.03101
`PE*G` PC `PC*G` PS `PS*G`
0.32328 0.09272 -0.12310 0.50976 -0.13201
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.5727 -2.1746 -0.1635 2.0747 11.0413
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.32685 2.27928 3.215 0.00161 **
CM 0.35698 0.08628 4.137 5.89e-05 ***
`CM*G` -0.05923 0.11522 -0.514 0.60798
D -0.48387 0.16529 -2.927 0.00396 **
`D*G` 0.17523 0.21648 0.809 0.41955
PE -0.03101 0.16151 -0.192 0.84799
`PE*G` 0.32328 0.20318 1.591 0.11373
PC 0.09272 0.22729 0.408 0.68390
`PC*G` -0.12310 0.27683 -0.445 0.65721
PS 0.50976 0.10316 4.941 2.09e-06 ***
`PS*G` -0.13201 0.10618 -1.243 0.21574
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.418 on 147 degrees of freedom
Multiple R-squared: 0.3886, Adjusted R-squared: 0.347
F-statistic: 9.344 on 10 and 147 DF, p-value: 6.792e-12
> 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,] 0.97356349 0.05287303 0.02643651
[2,] 0.94557443 0.10885114 0.05442557
[3,] 0.90353384 0.19293232 0.09646616
[4,] 0.84742259 0.30515482 0.15257741
[5,] 0.77335016 0.45329969 0.22664984
[6,] 0.79383855 0.41232289 0.20616145
[7,] 0.76087261 0.47825477 0.23912739
[8,] 0.74259968 0.51480064 0.25740032
[9,] 0.70767212 0.58465577 0.29232788
[10,] 0.66430963 0.67138073 0.33569037
[11,] 0.65554451 0.68891099 0.34445549
[12,] 0.64425977 0.71148046 0.35574023
[13,] 0.56820266 0.86359468 0.43179734
[14,] 0.49457636 0.98915273 0.50542364
[15,] 0.44163929 0.88327857 0.55836071
[16,] 0.41006605 0.82013210 0.58993395
[17,] 0.34364760 0.68729520 0.65635240
[18,] 0.31532175 0.63064350 0.68467825
[19,] 0.31063333 0.62126667 0.68936667
[20,] 0.40056325 0.80112651 0.59943675
[21,] 0.54665882 0.90668237 0.45334118
[22,] 0.50848216 0.98303569 0.49151784
[23,] 0.56429388 0.87141224 0.43570612
[24,] 0.61842143 0.76315714 0.38157857
[25,] 0.58162598 0.83674804 0.41837402
[26,] 0.53054382 0.93891235 0.46945618
[27,] 0.65236821 0.69526358 0.34763179
[28,] 0.59835942 0.80328117 0.40164058
[29,] 0.54473253 0.91053495 0.45526747
[30,] 0.54533824 0.90932353 0.45466176
[31,] 0.50464891 0.99070217 0.49535109
[32,] 0.54367146 0.91265707 0.45632854
[33,] 0.48807144 0.97614287 0.51192856
[34,] 0.49175150 0.98350300 0.50824850
[35,] 0.60032645 0.79934709 0.39967355
[36,] 0.54965077 0.90069847 0.45034923
[37,] 0.51888130 0.96223741 0.48111870
[38,] 0.50933114 0.98133772 0.49066886
[39,] 0.45958759 0.91917517 0.54041241
[40,] 0.49193800 0.98387600 0.50806200
[41,] 0.45614179 0.91228359 0.54385821
[42,] 0.61852710 0.76294580 0.38147290
[43,] 0.57551074 0.84897853 0.42448926
[44,] 0.53554985 0.92890031 0.46445015
[45,] 0.51958553 0.96082894 0.48041447
[46,] 0.47173924 0.94347848 0.52826076
[47,] 0.43596402 0.87192804 0.56403598
[48,] 0.39815748 0.79631497 0.60184252
[49,] 0.36236571 0.72473142 0.63763429
[50,] 0.31785358 0.63570716 0.68214642
[51,] 0.32664796 0.65329592 0.67335204
[52,] 0.28601180 0.57202359 0.71398820
[53,] 0.32647863 0.65295726 0.67352137
[54,] 0.38574377 0.77148754 0.61425623
[55,] 0.49902639 0.99805278 0.50097361
[56,] 0.45154536 0.90309073 0.54845464
[57,] 0.43602216 0.87204432 0.56397784
[58,] 0.51050164 0.97899672 0.48949836
[59,] 0.46236033 0.92472066 0.53763967
[60,] 0.41750625 0.83501249 0.58249375
[61,] 0.38404699 0.76809397 0.61595301
[62,] 0.34962931 0.69925863 0.65037069
[63,] 0.32481418 0.64962836 0.67518582
[64,] 0.28369082 0.56738164 0.71630918
[65,] 0.24937070 0.49874141 0.75062930
[66,] 0.21451991 0.42903981 0.78548009
[67,] 0.18927444 0.37854888 0.81072556
[68,] 0.30003870 0.60007741 0.69996130
[69,] 0.27108357 0.54216714 0.72891643
[70,] 0.23428483 0.46856966 0.76571517
[71,] 0.23260538 0.46521076 0.76739462
[72,] 0.20812586 0.41625171 0.79187414
[73,] 0.20762590 0.41525180 0.79237410
[74,] 0.18397393 0.36794786 0.81602607
[75,] 0.17130322 0.34260645 0.82869678
[76,] 0.16405690 0.32811380 0.83594310
[77,] 0.19958384 0.39916767 0.80041616
[78,] 0.16987315 0.33974631 0.83012685
[79,] 0.15118034 0.30236067 0.84881966
[80,] 0.12735509 0.25471018 0.87264491
[81,] 0.11727096 0.23454192 0.88272904
[82,] 0.11381211 0.22762421 0.88618789
[83,] 0.10203351 0.20406702 0.89796649
[84,] 0.10404270 0.20808541 0.89595730
[85,] 0.09253417 0.18506833 0.90746583
[86,] 0.09146912 0.18293825 0.90853088
[87,] 0.07243668 0.14487336 0.92756332
[88,] 0.05723830 0.11447659 0.94276170
[89,] 0.04632573 0.09265145 0.95367427
[90,] 0.03683498 0.07366997 0.96316502
[91,] 0.03893009 0.07786019 0.96106991
[92,] 0.03194432 0.06388863 0.96805568
[93,] 0.02760402 0.05520803 0.97239598
[94,] 0.02354690 0.04709381 0.97645310
[95,] 0.01735871 0.03471743 0.98264129
[96,] 0.01590833 0.03181666 0.98409167
[97,] 0.02190828 0.04381656 0.97809172
[98,] 0.11570823 0.23141646 0.88429177
[99,] 0.10625911 0.21251823 0.89374089
[100,] 0.45709473 0.91418947 0.54290527
[101,] 0.78565030 0.42869941 0.21434970
[102,] 0.74434187 0.51131626 0.25565813
[103,] 0.82195654 0.35608691 0.17804346
[104,] 0.79992250 0.40015499 0.20007750
[105,] 0.75516734 0.48966533 0.24483266
[106,] 0.76423389 0.47153221 0.23576611
[107,] 0.73572130 0.52855740 0.26427870
[108,] 0.68194338 0.63611324 0.31805662
[109,] 0.70268591 0.59462819 0.29731409
[110,] 0.64761368 0.70477263 0.35238632
[111,] 0.58837700 0.82324600 0.41162300
[112,] 0.54754567 0.90490866 0.45245433
[113,] 0.50074009 0.99851982 0.49925991
[114,] 0.44477966 0.88955933 0.55522034
[115,] 0.38439928 0.76879856 0.61560072
[116,] 0.32868667 0.65737333 0.67131333
[117,] 0.30965184 0.61930367 0.69034816
[118,] 0.31311513 0.62623026 0.68688487
[119,] 0.38216315 0.76432629 0.61783685
[120,] 0.35228337 0.70456675 0.64771663
[121,] 0.30223889 0.60447779 0.69776111
[122,] 0.24382922 0.48765844 0.75617078
[123,] 0.18622515 0.37245029 0.81377485
[124,] 0.17978052 0.35956104 0.82021948
[125,] 0.12614620 0.25229239 0.87385380
[126,] 0.09699338 0.19398676 0.90300662
[127,] 0.07291445 0.14582891 0.92708555
[128,] 0.08441511 0.16883023 0.91558489
[129,] 0.14343692 0.28687384 0.85656308
[130,] 0.08226356 0.16452712 0.91773644
[131,] 0.04442000 0.08883999 0.95558000
> postscript(file="/var/www/html/rcomp/tmp/16xiu1290536540.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/rcomp/tmp/26xiu1290536540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3y6zx1290536540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4y6zx1290536540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5y6zx1290536540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
0.85446196 3.13317751 5.29380213 -1.10222848 1.37155513 -1.25393573
7 8 9 10 11 12
2.26720922 3.49047629 -2.35338549 -1.02432253 -3.15986657 -4.72788472
13 14 15 16 17 18
-7.92780435 -1.32874274 4.02561665 3.14588696 1.28599964 -1.32838373
19 20 21 22 23 24
-0.95159544 -1.42576775 1.38899711 -1.37922930 -3.43160792 -5.77522993
25 26 27 28 29 30
-3.46085897 0.20148811 1.22799122 -4.13420942 1.11160915 0.08353795
31 32 33 34 35 36
-1.11595798 2.32660545 5.41806558 6.96004379 2.76107889 4.46067914
37 38 39 40 41 42
2.72341022 1.46808989 1.64181802 6.55366554 -0.56693529 1.39363812
43 44 45 46 47 48
-3.85633790 2.17634748 4.21608142 -0.01979667 -3.81507147 7.15548777
49 50 51 52 53 54
0.17218280 2.45698686 1.68347291 -1.00155939 -4.03681307 -1.42644906
55 56 57 58 59 60
5.24327860 -1.27517062 1.74543593 3.13854831 -0.65061850 -2.46640666
61 62 63 64 65 66
1.75293590 1.62782971 0.26328463 3.71712741 1.09506209 3.16597531
67 68 69 70 71 72
-5.15717047 6.20175290 1.76038178 2.69734364 -4.79995660 -0.31250165
73 74 75 76 77 78
-0.41389275 -1.94715711 -0.93626474 -2.28581050 -0.76046335 -0.51776579
79 80 81 82 83 84
0.80329471 1.45069538 -6.62806937 -1.57036878 0.74858835 -3.74228508
85 86 87 88 89 90
-1.88799082 3.37758450 1.84687040 2.47811568 -3.25266977 -5.37604125
91 92 93 94 95 96
-0.98312947 -2.62497449 -0.54787476 -3.10427956 2.93448527 -2.79569135
97 98 99 100 101 102
-3.77854583 -2.73653539 -3.52482984 -0.10187476 -1.18362436 -1.79758800
103 104 105 106 107 108
-1.60813675 -4.13238853 -2.17913113 -2.24240344 -1.85721062 -0.43246989
109 110 111 112 113 114
-2.51052394 -5.20044133 -8.57266781 2.11146339 11.04128840 10.15288195
115 116 117 118 119 120
-1.33547013 3.85355852 1.96436015 -0.08077353 -2.89358407 3.19842316
121 122 123 124 125 126
-0.43904158 4.50855589 -0.33919279 1.42289663 -0.08296346 0.80872040
127 128 129 130 131 132
1.31959290 -2.23997782 0.75248073 3.25267991 -4.51018721 0.58306148
133 134 135 136 137 138
-2.81900804 -6.07887244 4.16024015 -0.55415782 3.58031908 -1.22117007
139 140 141 142 143 144
3.35622022 3.41591024 4.05338401 0.66347256 0.95380556 0.48760668
145 146 147 148 149 150
3.87774974 -0.65193182 -0.06154367 -7.45780590 -2.75136706 3.62644005
151 152 153 154 155 156
-0.22510701 -3.12626725 -0.54222996 1.10752917 -2.16096058 0.81696152
157 158
-0.25597622 -7.24116850
> postscript(file="/var/www/html/rcomp/tmp/69fyi1290536540.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 0.85446196 NA
1 3.13317751 0.85446196
2 5.29380213 3.13317751
3 -1.10222848 5.29380213
4 1.37155513 -1.10222848
5 -1.25393573 1.37155513
6 2.26720922 -1.25393573
7 3.49047629 2.26720922
8 -2.35338549 3.49047629
9 -1.02432253 -2.35338549
10 -3.15986657 -1.02432253
11 -4.72788472 -3.15986657
12 -7.92780435 -4.72788472
13 -1.32874274 -7.92780435
14 4.02561665 -1.32874274
15 3.14588696 4.02561665
16 1.28599964 3.14588696
17 -1.32838373 1.28599964
18 -0.95159544 -1.32838373
19 -1.42576775 -0.95159544
20 1.38899711 -1.42576775
21 -1.37922930 1.38899711
22 -3.43160792 -1.37922930
23 -5.77522993 -3.43160792
24 -3.46085897 -5.77522993
25 0.20148811 -3.46085897
26 1.22799122 0.20148811
27 -4.13420942 1.22799122
28 1.11160915 -4.13420942
29 0.08353795 1.11160915
30 -1.11595798 0.08353795
31 2.32660545 -1.11595798
32 5.41806558 2.32660545
33 6.96004379 5.41806558
34 2.76107889 6.96004379
35 4.46067914 2.76107889
36 2.72341022 4.46067914
37 1.46808989 2.72341022
38 1.64181802 1.46808989
39 6.55366554 1.64181802
40 -0.56693529 6.55366554
41 1.39363812 -0.56693529
42 -3.85633790 1.39363812
43 2.17634748 -3.85633790
44 4.21608142 2.17634748
45 -0.01979667 4.21608142
46 -3.81507147 -0.01979667
47 7.15548777 -3.81507147
48 0.17218280 7.15548777
49 2.45698686 0.17218280
50 1.68347291 2.45698686
51 -1.00155939 1.68347291
52 -4.03681307 -1.00155939
53 -1.42644906 -4.03681307
54 5.24327860 -1.42644906
55 -1.27517062 5.24327860
56 1.74543593 -1.27517062
57 3.13854831 1.74543593
58 -0.65061850 3.13854831
59 -2.46640666 -0.65061850
60 1.75293590 -2.46640666
61 1.62782971 1.75293590
62 0.26328463 1.62782971
63 3.71712741 0.26328463
64 1.09506209 3.71712741
65 3.16597531 1.09506209
66 -5.15717047 3.16597531
67 6.20175290 -5.15717047
68 1.76038178 6.20175290
69 2.69734364 1.76038178
70 -4.79995660 2.69734364
71 -0.31250165 -4.79995660
72 -0.41389275 -0.31250165
73 -1.94715711 -0.41389275
74 -0.93626474 -1.94715711
75 -2.28581050 -0.93626474
76 -0.76046335 -2.28581050
77 -0.51776579 -0.76046335
78 0.80329471 -0.51776579
79 1.45069538 0.80329471
80 -6.62806937 1.45069538
81 -1.57036878 -6.62806937
82 0.74858835 -1.57036878
83 -3.74228508 0.74858835
84 -1.88799082 -3.74228508
85 3.37758450 -1.88799082
86 1.84687040 3.37758450
87 2.47811568 1.84687040
88 -3.25266977 2.47811568
89 -5.37604125 -3.25266977
90 -0.98312947 -5.37604125
91 -2.62497449 -0.98312947
92 -0.54787476 -2.62497449
93 -3.10427956 -0.54787476
94 2.93448527 -3.10427956
95 -2.79569135 2.93448527
96 -3.77854583 -2.79569135
97 -2.73653539 -3.77854583
98 -3.52482984 -2.73653539
99 -0.10187476 -3.52482984
100 -1.18362436 -0.10187476
101 -1.79758800 -1.18362436
102 -1.60813675 -1.79758800
103 -4.13238853 -1.60813675
104 -2.17913113 -4.13238853
105 -2.24240344 -2.17913113
106 -1.85721062 -2.24240344
107 -0.43246989 -1.85721062
108 -2.51052394 -0.43246989
109 -5.20044133 -2.51052394
110 -8.57266781 -5.20044133
111 2.11146339 -8.57266781
112 11.04128840 2.11146339
113 10.15288195 11.04128840
114 -1.33547013 10.15288195
115 3.85355852 -1.33547013
116 1.96436015 3.85355852
117 -0.08077353 1.96436015
118 -2.89358407 -0.08077353
119 3.19842316 -2.89358407
120 -0.43904158 3.19842316
121 4.50855589 -0.43904158
122 -0.33919279 4.50855589
123 1.42289663 -0.33919279
124 -0.08296346 1.42289663
125 0.80872040 -0.08296346
126 1.31959290 0.80872040
127 -2.23997782 1.31959290
128 0.75248073 -2.23997782
129 3.25267991 0.75248073
130 -4.51018721 3.25267991
131 0.58306148 -4.51018721
132 -2.81900804 0.58306148
133 -6.07887244 -2.81900804
134 4.16024015 -6.07887244
135 -0.55415782 4.16024015
136 3.58031908 -0.55415782
137 -1.22117007 3.58031908
138 3.35622022 -1.22117007
139 3.41591024 3.35622022
140 4.05338401 3.41591024
141 0.66347256 4.05338401
142 0.95380556 0.66347256
143 0.48760668 0.95380556
144 3.87774974 0.48760668
145 -0.65193182 3.87774974
146 -0.06154367 -0.65193182
147 -7.45780590 -0.06154367
148 -2.75136706 -7.45780590
149 3.62644005 -2.75136706
150 -0.22510701 3.62644005
151 -3.12626725 -0.22510701
152 -0.54222996 -3.12626725
153 1.10752917 -0.54222996
154 -2.16096058 1.10752917
155 0.81696152 -2.16096058
156 -0.25597622 0.81696152
157 -7.24116850 -0.25597622
158 NA -7.24116850
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.13317751 0.85446196
[2,] 5.29380213 3.13317751
[3,] -1.10222848 5.29380213
[4,] 1.37155513 -1.10222848
[5,] -1.25393573 1.37155513
[6,] 2.26720922 -1.25393573
[7,] 3.49047629 2.26720922
[8,] -2.35338549 3.49047629
[9,] -1.02432253 -2.35338549
[10,] -3.15986657 -1.02432253
[11,] -4.72788472 -3.15986657
[12,] -7.92780435 -4.72788472
[13,] -1.32874274 -7.92780435
[14,] 4.02561665 -1.32874274
[15,] 3.14588696 4.02561665
[16,] 1.28599964 3.14588696
[17,] -1.32838373 1.28599964
[18,] -0.95159544 -1.32838373
[19,] -1.42576775 -0.95159544
[20,] 1.38899711 -1.42576775
[21,] -1.37922930 1.38899711
[22,] -3.43160792 -1.37922930
[23,] -5.77522993 -3.43160792
[24,] -3.46085897 -5.77522993
[25,] 0.20148811 -3.46085897
[26,] 1.22799122 0.20148811
[27,] -4.13420942 1.22799122
[28,] 1.11160915 -4.13420942
[29,] 0.08353795 1.11160915
[30,] -1.11595798 0.08353795
[31,] 2.32660545 -1.11595798
[32,] 5.41806558 2.32660545
[33,] 6.96004379 5.41806558
[34,] 2.76107889 6.96004379
[35,] 4.46067914 2.76107889
[36,] 2.72341022 4.46067914
[37,] 1.46808989 2.72341022
[38,] 1.64181802 1.46808989
[39,] 6.55366554 1.64181802
[40,] -0.56693529 6.55366554
[41,] 1.39363812 -0.56693529
[42,] -3.85633790 1.39363812
[43,] 2.17634748 -3.85633790
[44,] 4.21608142 2.17634748
[45,] -0.01979667 4.21608142
[46,] -3.81507147 -0.01979667
[47,] 7.15548777 -3.81507147
[48,] 0.17218280 7.15548777
[49,] 2.45698686 0.17218280
[50,] 1.68347291 2.45698686
[51,] -1.00155939 1.68347291
[52,] -4.03681307 -1.00155939
[53,] -1.42644906 -4.03681307
[54,] 5.24327860 -1.42644906
[55,] -1.27517062 5.24327860
[56,] 1.74543593 -1.27517062
[57,] 3.13854831 1.74543593
[58,] -0.65061850 3.13854831
[59,] -2.46640666 -0.65061850
[60,] 1.75293590 -2.46640666
[61,] 1.62782971 1.75293590
[62,] 0.26328463 1.62782971
[63,] 3.71712741 0.26328463
[64,] 1.09506209 3.71712741
[65,] 3.16597531 1.09506209
[66,] -5.15717047 3.16597531
[67,] 6.20175290 -5.15717047
[68,] 1.76038178 6.20175290
[69,] 2.69734364 1.76038178
[70,] -4.79995660 2.69734364
[71,] -0.31250165 -4.79995660
[72,] -0.41389275 -0.31250165
[73,] -1.94715711 -0.41389275
[74,] -0.93626474 -1.94715711
[75,] -2.28581050 -0.93626474
[76,] -0.76046335 -2.28581050
[77,] -0.51776579 -0.76046335
[78,] 0.80329471 -0.51776579
[79,] 1.45069538 0.80329471
[80,] -6.62806937 1.45069538
[81,] -1.57036878 -6.62806937
[82,] 0.74858835 -1.57036878
[83,] -3.74228508 0.74858835
[84,] -1.88799082 -3.74228508
[85,] 3.37758450 -1.88799082
[86,] 1.84687040 3.37758450
[87,] 2.47811568 1.84687040
[88,] -3.25266977 2.47811568
[89,] -5.37604125 -3.25266977
[90,] -0.98312947 -5.37604125
[91,] -2.62497449 -0.98312947
[92,] -0.54787476 -2.62497449
[93,] -3.10427956 -0.54787476
[94,] 2.93448527 -3.10427956
[95,] -2.79569135 2.93448527
[96,] -3.77854583 -2.79569135
[97,] -2.73653539 -3.77854583
[98,] -3.52482984 -2.73653539
[99,] -0.10187476 -3.52482984
[100,] -1.18362436 -0.10187476
[101,] -1.79758800 -1.18362436
[102,] -1.60813675 -1.79758800
[103,] -4.13238853 -1.60813675
[104,] -2.17913113 -4.13238853
[105,] -2.24240344 -2.17913113
[106,] -1.85721062 -2.24240344
[107,] -0.43246989 -1.85721062
[108,] -2.51052394 -0.43246989
[109,] -5.20044133 -2.51052394
[110,] -8.57266781 -5.20044133
[111,] 2.11146339 -8.57266781
[112,] 11.04128840 2.11146339
[113,] 10.15288195 11.04128840
[114,] -1.33547013 10.15288195
[115,] 3.85355852 -1.33547013
[116,] 1.96436015 3.85355852
[117,] -0.08077353 1.96436015
[118,] -2.89358407 -0.08077353
[119,] 3.19842316 -2.89358407
[120,] -0.43904158 3.19842316
[121,] 4.50855589 -0.43904158
[122,] -0.33919279 4.50855589
[123,] 1.42289663 -0.33919279
[124,] -0.08296346 1.42289663
[125,] 0.80872040 -0.08296346
[126,] 1.31959290 0.80872040
[127,] -2.23997782 1.31959290
[128,] 0.75248073 -2.23997782
[129,] 3.25267991 0.75248073
[130,] -4.51018721 3.25267991
[131,] 0.58306148 -4.51018721
[132,] -2.81900804 0.58306148
[133,] -6.07887244 -2.81900804
[134,] 4.16024015 -6.07887244
[135,] -0.55415782 4.16024015
[136,] 3.58031908 -0.55415782
[137,] -1.22117007 3.58031908
[138,] 3.35622022 -1.22117007
[139,] 3.41591024 3.35622022
[140,] 4.05338401 3.41591024
[141,] 0.66347256 4.05338401
[142,] 0.95380556 0.66347256
[143,] 0.48760668 0.95380556
[144,] 3.87774974 0.48760668
[145,] -0.65193182 3.87774974
[146,] -0.06154367 -0.65193182
[147,] -7.45780590 -0.06154367
[148,] -2.75136706 -7.45780590
[149,] 3.62644005 -2.75136706
[150,] -0.22510701 3.62644005
[151,] -3.12626725 -0.22510701
[152,] -0.54222996 -3.12626725
[153,] 1.10752917 -0.54222996
[154,] -2.16096058 1.10752917
[155,] 0.81696152 -2.16096058
[156,] -0.25597622 0.81696152
[157,] -7.24116850 -0.25597622
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.13317751 0.85446196
2 5.29380213 3.13317751
3 -1.10222848 5.29380213
4 1.37155513 -1.10222848
5 -1.25393573 1.37155513
6 2.26720922 -1.25393573
7 3.49047629 2.26720922
8 -2.35338549 3.49047629
9 -1.02432253 -2.35338549
10 -3.15986657 -1.02432253
11 -4.72788472 -3.15986657
12 -7.92780435 -4.72788472
13 -1.32874274 -7.92780435
14 4.02561665 -1.32874274
15 3.14588696 4.02561665
16 1.28599964 3.14588696
17 -1.32838373 1.28599964
18 -0.95159544 -1.32838373
19 -1.42576775 -0.95159544
20 1.38899711 -1.42576775
21 -1.37922930 1.38899711
22 -3.43160792 -1.37922930
23 -5.77522993 -3.43160792
24 -3.46085897 -5.77522993
25 0.20148811 -3.46085897
26 1.22799122 0.20148811
27 -4.13420942 1.22799122
28 1.11160915 -4.13420942
29 0.08353795 1.11160915
30 -1.11595798 0.08353795
31 2.32660545 -1.11595798
32 5.41806558 2.32660545
33 6.96004379 5.41806558
34 2.76107889 6.96004379
35 4.46067914 2.76107889
36 2.72341022 4.46067914
37 1.46808989 2.72341022
38 1.64181802 1.46808989
39 6.55366554 1.64181802
40 -0.56693529 6.55366554
41 1.39363812 -0.56693529
42 -3.85633790 1.39363812
43 2.17634748 -3.85633790
44 4.21608142 2.17634748
45 -0.01979667 4.21608142
46 -3.81507147 -0.01979667
47 7.15548777 -3.81507147
48 0.17218280 7.15548777
49 2.45698686 0.17218280
50 1.68347291 2.45698686
51 -1.00155939 1.68347291
52 -4.03681307 -1.00155939
53 -1.42644906 -4.03681307
54 5.24327860 -1.42644906
55 -1.27517062 5.24327860
56 1.74543593 -1.27517062
57 3.13854831 1.74543593
58 -0.65061850 3.13854831
59 -2.46640666 -0.65061850
60 1.75293590 -2.46640666
61 1.62782971 1.75293590
62 0.26328463 1.62782971
63 3.71712741 0.26328463
64 1.09506209 3.71712741
65 3.16597531 1.09506209
66 -5.15717047 3.16597531
67 6.20175290 -5.15717047
68 1.76038178 6.20175290
69 2.69734364 1.76038178
70 -4.79995660 2.69734364
71 -0.31250165 -4.79995660
72 -0.41389275 -0.31250165
73 -1.94715711 -0.41389275
74 -0.93626474 -1.94715711
75 -2.28581050 -0.93626474
76 -0.76046335 -2.28581050
77 -0.51776579 -0.76046335
78 0.80329471 -0.51776579
79 1.45069538 0.80329471
80 -6.62806937 1.45069538
81 -1.57036878 -6.62806937
82 0.74858835 -1.57036878
83 -3.74228508 0.74858835
84 -1.88799082 -3.74228508
85 3.37758450 -1.88799082
86 1.84687040 3.37758450
87 2.47811568 1.84687040
88 -3.25266977 2.47811568
89 -5.37604125 -3.25266977
90 -0.98312947 -5.37604125
91 -2.62497449 -0.98312947
92 -0.54787476 -2.62497449
93 -3.10427956 -0.54787476
94 2.93448527 -3.10427956
95 -2.79569135 2.93448527
96 -3.77854583 -2.79569135
97 -2.73653539 -3.77854583
98 -3.52482984 -2.73653539
99 -0.10187476 -3.52482984
100 -1.18362436 -0.10187476
101 -1.79758800 -1.18362436
102 -1.60813675 -1.79758800
103 -4.13238853 -1.60813675
104 -2.17913113 -4.13238853
105 -2.24240344 -2.17913113
106 -1.85721062 -2.24240344
107 -0.43246989 -1.85721062
108 -2.51052394 -0.43246989
109 -5.20044133 -2.51052394
110 -8.57266781 -5.20044133
111 2.11146339 -8.57266781
112 11.04128840 2.11146339
113 10.15288195 11.04128840
114 -1.33547013 10.15288195
115 3.85355852 -1.33547013
116 1.96436015 3.85355852
117 -0.08077353 1.96436015
118 -2.89358407 -0.08077353
119 3.19842316 -2.89358407
120 -0.43904158 3.19842316
121 4.50855589 -0.43904158
122 -0.33919279 4.50855589
123 1.42289663 -0.33919279
124 -0.08296346 1.42289663
125 0.80872040 -0.08296346
126 1.31959290 0.80872040
127 -2.23997782 1.31959290
128 0.75248073 -2.23997782
129 3.25267991 0.75248073
130 -4.51018721 3.25267991
131 0.58306148 -4.51018721
132 -2.81900804 0.58306148
133 -6.07887244 -2.81900804
134 4.16024015 -6.07887244
135 -0.55415782 4.16024015
136 3.58031908 -0.55415782
137 -1.22117007 3.58031908
138 3.35622022 -1.22117007
139 3.41591024 3.35622022
140 4.05338401 3.41591024
141 0.66347256 4.05338401
142 0.95380556 0.66347256
143 0.48760668 0.95380556
144 3.87774974 0.48760668
145 -0.65193182 3.87774974
146 -0.06154367 -0.65193182
147 -7.45780590 -0.06154367
148 -2.75136706 -7.45780590
149 3.62644005 -2.75136706
150 -0.22510701 3.62644005
151 -3.12626725 -0.22510701
152 -0.54222996 -3.12626725
153 1.10752917 -0.54222996
154 -2.16096058 1.10752917
155 0.81696152 -2.16096058
156 -0.25597622 0.81696152
157 -7.24116850 -0.25597622
> 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/rcomp/tmp/717yk1290536540.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/rcomp/tmp/817yk1290536540.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/rcomp/tmp/917yk1290536540.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/rcomp/tmp/10ugx51290536540.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/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11gzdb1290536540.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/121hcz1290536540.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13f9sq1290536540.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/rcomp/tmp/14j98w1290536540.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/rcomp/tmp/15mspj1290536540.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/rcomp/tmp/16pan71290536540.tab")
+ }
>
> try(system("convert tmp/16xiu1290536540.ps tmp/16xiu1290536540.png",intern=TRUE))
character(0)
> try(system("convert tmp/26xiu1290536540.ps tmp/26xiu1290536540.png",intern=TRUE))
character(0)
> try(system("convert tmp/3y6zx1290536540.ps tmp/3y6zx1290536540.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y6zx1290536540.ps tmp/4y6zx1290536540.png",intern=TRUE))
character(0)
> try(system("convert tmp/5y6zx1290536540.ps tmp/5y6zx1290536540.png",intern=TRUE))
character(0)
> try(system("convert tmp/69fyi1290536540.ps tmp/69fyi1290536540.png",intern=TRUE))
character(0)
> try(system("convert tmp/717yk1290536540.ps tmp/717yk1290536540.png",intern=TRUE))
character(0)
> try(system("convert tmp/817yk1290536540.ps tmp/817yk1290536540.png",intern=TRUE))
character(0)
> try(system("convert tmp/917yk1290536540.ps tmp/917yk1290536540.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ugx51290536540.ps tmp/10ugx51290536540.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.435 1.726 9.937