R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(1
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+ ,1
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+ ,1
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+ ,1
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+ ,16
+ ,19
+ ,19
+ ,16
+ ,16
+ ,17
+ ,17)
+ ,dim=c(12
+ ,159)
+ ,dimnames=list(c('Browser'
+ ,'Organization'
+ ,'CM'
+ ,'CM_B'
+ ,'DA'
+ ,'DA_B'
+ ,'PE'
+ ,'PE_B'
+ ,'PC'
+ ,'PC_B'
+ ,'PS'
+ ,'PS_B')
+ ,1:159))
> y <- array(NA,dim=c(12,159),dimnames=list(c('Browser','Organization','CM','CM_B','DA','DA_B','PE','PE_B','PC','PC_B','PS','PS_B'),1:159))
> 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 = '2'
> #'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
Organization Browser CM CM_B DA DA_B PE PE_B PC PC_B PS PS_B
1 26 1 24 24 14 14 11 11 12 12 24 24
2 23 1 25 25 11 11 7 7 8 8 25 25
3 25 0 17 0 6 0 17 0 8 0 30 0
4 23 1 18 18 12 12 10 10 8 8 19 19
5 19 1 18 18 8 8 12 12 9 9 22 22
6 29 0 16 0 10 0 12 0 7 0 22 0
7 25 1 20 20 10 10 11 11 4 4 25 25
8 21 1 16 16 11 11 11 11 11 11 23 23
9 22 1 18 18 16 16 12 12 7 7 17 17
10 25 1 17 17 11 11 13 13 7 7 21 21
11 24 1 23 23 13 13 14 14 12 12 19 19
12 18 1 30 30 12 12 16 16 10 10 19 19
13 22 1 23 23 8 8 11 11 10 10 15 15
14 15 1 18 18 12 12 10 10 8 8 16 16
15 22 1 15 15 11 11 11 11 8 8 23 23
16 28 1 12 12 4 4 15 15 4 4 27 27
17 20 1 21 21 9 9 9 9 9 9 22 22
18 12 1 15 15 8 8 11 11 8 8 14 14
19 24 1 20 20 8 8 17 17 7 7 22 22
20 20 1 31 31 14 14 17 17 11 11 23 23
21 21 1 27 27 15 15 11 11 9 9 23 23
22 20 1 34 34 16 16 18 18 11 11 21 21
23 21 1 21 21 9 9 14 14 13 13 19 19
24 23 1 31 31 14 14 10 10 8 8 18 18
25 28 1 19 19 11 11 11 11 8 8 20 20
26 24 1 16 16 8 8 15 15 9 9 23 23
27 24 1 20 20 9 9 15 15 6 6 25 25
28 24 1 21 21 9 9 13 13 9 9 19 19
29 23 1 22 22 9 9 16 16 9 9 24 24
30 23 1 17 17 9 9 13 13 6 6 22 22
31 29 1 24 24 10 10 9 9 6 6 25 25
32 24 1 25 25 16 16 18 18 16 16 26 26
33 18 1 26 26 11 11 18 18 5 5 29 29
34 25 1 25 25 8 8 12 12 7 7 32 32
35 21 1 17 17 9 9 17 17 9 9 25 25
36 26 1 32 32 16 16 9 9 6 6 29 29
37 22 1 33 33 11 11 9 9 6 6 28 28
38 22 1 13 13 16 16 12 12 5 5 17 17
39 22 0 32 0 12 0 18 0 12 0 28 0
40 23 1 25 25 12 12 12 12 7 7 29 29
41 30 1 29 29 14 14 18 18 10 10 26 26
42 23 1 22 22 9 9 14 14 9 9 25 25
43 17 1 18 18 10 10 15 15 8 8 14 14
44 23 1 17 17 9 9 16 16 5 5 25 25
45 23 1 20 20 10 10 10 10 8 8 26 26
46 25 1 15 15 12 12 11 11 8 8 20 20
47 24 1 20 20 14 14 14 14 10 10 18 18
48 24 1 33 33 14 14 9 9 6 6 32 32
49 23 1 29 29 10 10 12 12 8 8 25 25
50 21 1 23 23 14 14 17 17 7 7 25 25
51 24 1 26 26 16 16 5 5 4 4 23 23
52 24 1 18 18 9 9 12 12 8 8 21 21
53 28 1 20 20 10 10 12 12 8 8 20 20
54 16 1 11 11 6 6 6 6 4 4 15 15
55 20 1 28 28 8 8 24 24 20 20 30 30
56 29 1 26 26 13 13 12 12 8 8 24 24
57 27 1 22 22 10 10 12 12 8 8 26 26
58 22 1 17 17 8 8 14 14 6 6 24 24
59 28 1 12 12 7 7 7 7 4 4 22 22
60 16 1 14 14 15 15 13 13 8 8 14 14
61 25 1 17 17 9 9 12 12 9 9 24 24
62 24 1 21 21 10 10 13 13 6 6 24 24
63 28 0 19 0 12 0 14 0 7 0 24 0
64 24 1 18 18 13 13 8 8 9 9 24 24
65 23 1 10 10 10 10 11 11 5 5 19 19
66 30 1 29 29 11 11 9 9 5 5 31 31
67 24 1 31 31 8 8 11 11 8 8 22 22
68 21 1 19 19 9 9 13 13 8 8 27 27
69 25 1 9 9 13 13 10 10 6 6 19 19
70 25 0 20 0 11 0 11 0 8 0 25 0
71 22 1 28 28 8 8 12 12 7 7 20 20
72 23 1 19 19 9 9 9 9 7 7 21 21
73 26 1 30 30 9 9 15 15 9 9 27 27
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 21 1 23 23 10 10 12 12 8 8 20 20
77 25 1 13 13 14 14 13 13 6 6 21 21
78 24 1 21 21 12 12 14 14 9 9 22 22
79 29 1 19 19 12 12 10 10 8 8 23 23
80 22 1 28 28 11 11 13 13 6 6 25 25
81 27 1 23 23 14 14 13 13 10 10 25 25
82 26 0 18 0 6 0 11 0 8 0 17 0
83 22 1 21 21 12 12 13 13 8 8 19 19
84 24 1 20 20 8 8 16 16 10 10 25 25
85 27 0 23 0 14 0 8 0 5 0 19 0
86 24 1 21 21 11 11 16 16 7 7 20 20
87 24 1 21 21 10 10 11 11 5 5 26 26
88 29 1 15 15 14 14 9 9 8 8 23 23
89 22 1 28 28 12 12 16 16 14 14 27 27
90 21 0 19 0 10 0 12 0 7 0 17 0
91 24 1 26 26 14 14 14 14 8 8 17 17
92 24 1 10 10 5 5 8 8 6 6 19 19
93 23 0 16 0 11 0 9 0 5 0 17 0
94 20 1 22 22 10 10 15 15 6 6 22 22
95 27 1 19 19 9 9 11 11 10 10 21 21
96 26 1 31 31 10 10 21 21 12 12 32 32
97 25 1 31 31 16 16 14 14 9 9 21 21
98 21 1 29 29 13 13 18 18 12 12 21 21
99 21 1 19 19 9 9 12 12 7 7 18 18
100 19 1 22 22 10 10 13 13 8 8 18 18
101 21 1 23 23 10 10 15 15 10 10 23 23
102 21 1 15 15 7 7 12 12 6 6 19 19
103 16 1 20 20 9 9 19 19 10 10 20 20
104 22 1 18 18 8 8 15 15 10 10 21 21
105 29 1 23 23 14 14 11 11 10 10 20 20
106 15 0 25 0 14 0 11 0 5 0 17 0
107 17 1 21 21 8 8 10 10 7 7 18 18
108 15 1 24 24 9 9 13 13 10 10 19 19
109 21 1 25 25 14 14 15 15 11 11 22 22
110 21 0 17 0 14 0 12 0 6 0 15 0
111 19 1 13 13 8 8 12 12 7 7 14 14
112 24 1 28 28 8 8 16 16 12 12 18 18
113 20 1 21 21 8 8 9 9 11 11 24 24
114 17 0 25 0 7 0 18 0 11 0 35 0
115 23 1 9 9 6 6 8 8 11 11 29 29
116 24 1 16 16 8 8 13 13 5 5 21 21
117 14 1 19 19 6 6 17 17 8 8 25 25
118 19 1 17 17 11 11 9 9 6 6 20 20
119 24 1 25 25 14 14 15 15 9 9 22 22
120 13 1 20 20 11 11 8 8 4 4 13 13
121 22 1 29 29 11 11 7 7 4 4 26 26
122 16 1 14 14 11 11 12 12 7 7 17 17
123 19 0 22 0 14 0 14 0 11 0 25 0
124 25 1 15 15 8 8 6 6 6 6 20 20
125 25 1 19 19 20 20 8 8 7 7 19 19
126 23 1 20 20 11 11 17 17 8 8 21 21
127 24 0 15 0 8 0 10 0 4 0 22 0
128 26 1 20 20 11 11 11 11 8 8 24 24
129 26 1 18 18 10 10 14 14 9 9 21 21
130 25 1 33 33 14 14 11 11 8 8 26 26
131 18 1 22 22 11 11 13 13 11 11 24 24
132 21 1 16 16 9 9 12 12 8 8 16 16
133 26 1 17 17 9 9 11 11 5 5 23 23
134 23 1 16 16 8 8 9 9 4 4 18 18
135 23 1 21 21 10 10 12 12 8 8 16 16
136 22 1 26 26 13 13 20 20 10 10 26 26
137 20 1 18 18 13 13 12 12 6 6 19 19
138 13 1 18 18 12 12 13 13 9 9 21 21
139 24 1 17 17 8 8 12 12 9 9 21 21
140 15 1 22 22 13 13 12 12 13 13 22 22
141 14 1 30 30 14 14 9 9 9 9 23 23
142 22 0 30 0 12 0 15 0 10 0 29 0
143 10 1 24 24 14 14 24 24 20 20 21 21
144 24 1 21 21 15 15 7 7 5 5 21 21
145 22 1 21 21 13 13 17 17 11 11 23 23
146 24 1 29 29 16 16 11 11 6 6 27 27
147 19 1 31 31 9 9 17 17 9 9 25 25
148 20 0 20 0 9 0 11 0 7 0 21 0
149 13 1 16 16 9 9 12 12 9 9 10 10
150 20 1 22 22 8 8 14 14 10 10 20 20
151 22 1 20 20 7 7 11 11 9 9 26 26
152 24 1 28 28 16 16 16 16 8 8 24 24
153 29 1 38 38 11 11 21 21 7 7 29 29
154 12 1 22 22 9 9 14 14 6 6 19 19
155 20 1 20 20 11 11 20 20 13 13 24 24
156 21 1 17 17 9 9 13 13 6 6 19 19
157 24 1 28 28 14 14 11 11 8 8 24 24
158 22 1 22 22 13 13 15 15 10 10 22 22
159 20 1 31 31 16 16 19 19 16 16 17 17
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Browser CM CM_B DA DA_B
29.54946 -15.21818 -0.38008 0.31736 0.02094 0.20754
PE PE_B PC PC_B PS PS_B
-0.54164 0.41074 0.27593 -0.50948 0.25113 0.22172
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.244 -1.776 0.242 2.390 7.231
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.54946 6.41144 4.609 8.72e-06 ***
Browser -15.21818 6.76030 -2.251 0.0259 *
CM -0.38008 0.28349 -1.341 0.1821
CM_B 0.31736 0.29079 1.091 0.2769
DA 0.02094 0.42217 0.050 0.9605
DA_B 0.20754 0.43807 0.474 0.6364
PE -0.54164 0.62507 -0.867 0.3876
PE_B 0.41074 0.63394 0.648 0.5181
PC 0.27593 0.74480 0.370 0.7116
PC_B -0.50948 0.75668 -0.673 0.5018
PS 0.25113 0.28976 0.867 0.3875
PS_B 0.22172 0.30117 0.736 0.4628
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.485 on 147 degrees of freedom
Multiple R-squared: 0.2589, Adjusted R-squared: 0.2035
F-statistic: 4.669 on 11 and 147 DF, p-value: 4.207e-06
> 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,] 0.910952404 0.178095192 0.0890476
[2,] 0.871980473 0.256039053 0.1280195
[3,] 0.804268671 0.391462658 0.1957313
[4,] 0.833764165 0.332471671 0.1662358
[5,] 0.757143770 0.485712460 0.2428562
[6,] 0.781648336 0.436703327 0.2183517
[7,] 0.723861243 0.552277513 0.2761388
[8,] 0.652161982 0.695676035 0.3478380
[9,] 0.572663309 0.854673382 0.4273367
[10,] 0.586142800 0.827714399 0.4138572
[11,] 0.741411063 0.517177874 0.2585889
[12,] 0.677637474 0.644725052 0.3223625
[13,] 0.608004512 0.783990975 0.3919955
[14,] 0.596970966 0.806058068 0.4030290
[15,] 0.525835847 0.948328305 0.4741642
[16,] 0.452699710 0.905399420 0.5473003
[17,] 0.477398229 0.954796459 0.5226018
[18,] 0.410270669 0.820541339 0.5897293
[19,] 0.662138434 0.675723131 0.3378616
[20,] 0.616638439 0.766723123 0.3833616
[21,] 0.574059994 0.851880012 0.4259400
[22,] 0.511436431 0.977127138 0.4885636
[23,] 0.488347417 0.976694833 0.5116526
[24,] 0.426191383 0.852382765 0.5738086
[25,] 0.385503168 0.771006336 0.6144968
[26,] 0.353624898 0.707249796 0.6463751
[27,] 0.516638596 0.966722808 0.4833614
[28,] 0.457579603 0.915159206 0.5424204
[29,] 0.413692045 0.827384089 0.5863080
[30,] 0.359976524 0.719953047 0.6400235
[31,] 0.315213168 0.630426337 0.6847868
[32,] 0.293093310 0.586186620 0.7069067
[33,] 0.278522132 0.557044264 0.7214779
[34,] 0.267062340 0.534124681 0.7329377
[35,] 0.223858909 0.447717819 0.7761411
[36,] 0.211085706 0.422171411 0.7889143
[37,] 0.179031618 0.358063237 0.8209684
[38,] 0.156219524 0.312439047 0.8437805
[39,] 0.248674090 0.497348180 0.7513259
[40,] 0.271914854 0.543829709 0.7280851
[41,] 0.264838102 0.529676204 0.7351619
[42,] 0.334204333 0.668408665 0.6657957
[43,] 0.320777331 0.641554661 0.6792227
[44,] 0.280632365 0.561264731 0.7193676
[45,] 0.303334480 0.606668960 0.6966655
[46,] 0.326888374 0.653776748 0.6731116
[47,] 0.292748479 0.585496957 0.7072515
[48,] 0.251390077 0.502780155 0.7486099
[49,] 0.288273738 0.576547476 0.7117263
[50,] 0.248001795 0.496003590 0.7519982
[51,] 0.210610363 0.421220726 0.7893896
[52,] 0.201194471 0.402388942 0.7988055
[53,] 0.184919449 0.369838898 0.8150806
[54,] 0.187077140 0.374154281 0.8129229
[55,] 0.165430478 0.330860955 0.8345695
[56,] 0.136373421 0.272746843 0.8636266
[57,] 0.114023617 0.228047234 0.8859764
[58,] 0.092595492 0.185190984 0.9074045
[59,] 0.084912735 0.169825470 0.9150873
[60,] 0.068709508 0.137419016 0.9312905
[61,] 0.057316653 0.114633305 0.9426833
[62,] 0.044906768 0.089813536 0.9550932
[63,] 0.036281207 0.072562414 0.9637188
[64,] 0.029364230 0.058728459 0.9706358
[65,] 0.043475610 0.086951221 0.9565244
[66,] 0.035738900 0.071477799 0.9642611
[67,] 0.033931928 0.067863856 0.9660681
[68,] 0.027703230 0.055406460 0.9722968
[69,] 0.021115741 0.042231482 0.9788843
[70,] 0.016843225 0.033686450 0.9831568
[71,] 0.013907534 0.027815067 0.9860925
[72,] 0.012352020 0.024704040 0.9876480
[73,] 0.009121198 0.018242396 0.9908788
[74,] 0.012316132 0.024632265 0.9876839
[75,] 0.009442541 0.018885082 0.9905575
[76,] 0.011188552 0.022377104 0.9888114
[77,] 0.011749920 0.023499840 0.9882501
[78,] 0.010555940 0.021111880 0.9894441
[79,] 0.009030362 0.018060724 0.9909696
[80,] 0.007431583 0.014863166 0.9925684
[81,] 0.013773918 0.027547836 0.9862261
[82,] 0.011011590 0.022023181 0.9889884
[83,] 0.010180474 0.020360948 0.9898195
[84,] 0.007571800 0.015143600 0.9924282
[85,] 0.005485987 0.010971974 0.9945140
[86,] 0.004059027 0.008118053 0.9959410
[87,] 0.002908874 0.005817749 0.9970911
[88,] 0.002003864 0.004007729 0.9979961
[89,] 0.002154030 0.004308061 0.9978460
[90,] 0.001622749 0.003245498 0.9983773
[91,] 0.007481118 0.014962235 0.9925189
[92,] 0.011732978 0.023465956 0.9882670
[93,] 0.011148229 0.022296457 0.9888518
[94,] 0.014124797 0.028249594 0.9858752
[95,] 0.010530756 0.021061513 0.9894692
[96,] 0.007509778 0.015019557 0.9924902
[97,] 0.005305573 0.010611146 0.9946944
[98,] 0.014817316 0.029634632 0.9851827
[99,] 0.012447941 0.024895883 0.9875521
[100,] 0.015713163 0.031426325 0.9842868
[101,] 0.012450179 0.024900358 0.9875498
[102,] 0.009549474 0.019098948 0.9904505
[103,] 0.039032795 0.078065590 0.9609672
[104,] 0.036310259 0.072620518 0.9636897
[105,] 0.030152752 0.060305505 0.9698472
[106,] 0.055531860 0.111063720 0.9444681
[107,] 0.052428408 0.104856817 0.9475716
[108,] 0.064426618 0.128853236 0.9355734
[109,] 0.058813923 0.117627847 0.9411861
[110,] 0.056200244 0.112400488 0.9437998
[111,] 0.048543557 0.097087113 0.9514564
[112,] 0.035681778 0.071363557 0.9643182
[113,] 0.025476865 0.050953731 0.9745231
[114,] 0.024979645 0.049959290 0.9750204
[115,] 0.038802894 0.077605788 0.9611971
[116,] 0.029962005 0.059924009 0.9700380
[117,] 0.024569263 0.049138525 0.9754307
[118,] 0.019292273 0.038584546 0.9807077
[119,] 0.016171755 0.032343510 0.9838282
[120,] 0.011676189 0.023352379 0.9883238
[121,] 0.017125436 0.034250872 0.9828746
[122,] 0.010684701 0.021369403 0.9893153
[123,] 0.006361123 0.012722245 0.9936389
[124,] 0.021476093 0.042952185 0.9785239
[125,] 0.032536665 0.065073329 0.9674633
[126,] 0.025646548 0.051293095 0.9743535
[127,] 0.089798709 0.179597418 0.9102013
[128,] 0.051863244 0.103726489 0.9481368
[129,] 0.157261793 0.314523586 0.8427382
[130,] 0.100360520 0.200721040 0.8996395
> postscript(file="/var/www/rcomp/tmp/10x061290179379.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2b6h81290179379.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3b6h81290179379.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4b6h81290179379.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5b6h81290179379.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 = 159
Frequency = 1
1 2 3 4 5 6
2.869399611 -1.313099793 1.252689949 1.249176430 -2.760101466 4.365675664
7 8 9 10 11 12
0.191157071 -1.707638988 0.309203534 2.628378037 3.792123458 -1.745640052
13 14 15 16 17 18
3.966134643 -5.332265356 -1.471021081 3.638159981 -2.193115880 -6.529901734
19 20 21 22 23 24
2.552740561 -1.666837709 -2.398719827 -0.859026269 1.814157061 2.080463355
25 26 27 28 29 30
6.198428618 2.034301514 0.410346733 3.749044280 0.840204920 0.378935361
31 32 33 34 35 36
4.647353730 1.379969570 -7.402541744 -1.516674897 -1.815361686 -1.113163662
37 38 39 40 41 42
-3.435180214 -0.471516969 3.768492152 -3.012042954 6.686505748 0.105551209
43 44 45 46 47 48
-1.275094311 -0.880474468 -1.478383858 2.719055566 3.381219868 -4.012035870
49 50 51 52 53 54
-0.179224309 -3.048538448 -1.643092553 2.250716631 6.620533543 -4.385397547
55 56 57 58 59 60
-0.775805012 5.420015113 2.908862857 -1.207388061 4.269775076 -3.930194608
61 62 63 64 65 66
2.002988619 0.455639801 5.045053469 -0.371816727 0.634597864 2.661817015
67 68 69 70 71 72
2.690842297 -3.392776440 1.989082876 0.294095315 1.345726573 0.687184969
73 74 75 76 77 78
2.792529187 1.110135468 1.786683958 -0.191297844 1.458488775 1.775941849
79 80 81 82 83 84
5.420488349 -1.806634408 3.128518828 2.647695272 0.830046503 1.703941082
85 86 87 88 89 90
4.081205344 2.744823718 -0.985419722 4.581733243 -0.719695403 -1.238405277
91 92 93 94 95 96
3.763303684 2.617857318 -1.472647355 -2.274130879 5.649645163 1.748568940
97 98 99 100 101 102
2.962097025 0.746357154 0.498444642 -1.177414752 -0.750048451 -0.001889518
103 104 105 106 107 108
-3.767575336 1.339005806 7.230981545 -5.031447094 -3.409429021 -4.829234033
109 110 111 112 113 114
-0.832123170 -1.304119167 0.241999937 5.982799364 -2.443233641 -5.269411507
115 116 117 118 119 120
-2.234109133 1.783993722 -8.238024314 -3.655924245 1.700770683 -6.755793099
121 122 123 124 125 126
-3.469273342 -4.799280110 -4.211418857 2.511373258 0.988692711 1.573701669
127 128 129 130 131 132
-1.228027217 2.369740536 4.517589110 0.553987679 -4.542353528 1.489534579
133 134 135 136 137 138
2.410728576 1.445396917 3.574667366 -1.011380324 -2.184610312 -9.070274513
139 140 141 142 143 144
2.650028402 -6.717405526 -9.243870619 1.684133709 -8.141911261 -0.287165817
145 146 147 148 149 150
-0.065584851 -2.093825826 -2.937241493 -3.383564403 -3.439795919 -0.068150459
151 152 153 154 155 156
-1.428485594 0.383618097 5.209940315 -8.757991584 -1.284389717 -0.202506425
157 158 159
0.186078800 -0.025363288 2.142881925
> postscript(file="/var/www/rcomp/tmp/6mxht1290179379.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 2.869399611 NA
1 -1.313099793 2.869399611
2 1.252689949 -1.313099793
3 1.249176430 1.252689949
4 -2.760101466 1.249176430
5 4.365675664 -2.760101466
6 0.191157071 4.365675664
7 -1.707638988 0.191157071
8 0.309203534 -1.707638988
9 2.628378037 0.309203534
10 3.792123458 2.628378037
11 -1.745640052 3.792123458
12 3.966134643 -1.745640052
13 -5.332265356 3.966134643
14 -1.471021081 -5.332265356
15 3.638159981 -1.471021081
16 -2.193115880 3.638159981
17 -6.529901734 -2.193115880
18 2.552740561 -6.529901734
19 -1.666837709 2.552740561
20 -2.398719827 -1.666837709
21 -0.859026269 -2.398719827
22 1.814157061 -0.859026269
23 2.080463355 1.814157061
24 6.198428618 2.080463355
25 2.034301514 6.198428618
26 0.410346733 2.034301514
27 3.749044280 0.410346733
28 0.840204920 3.749044280
29 0.378935361 0.840204920
30 4.647353730 0.378935361
31 1.379969570 4.647353730
32 -7.402541744 1.379969570
33 -1.516674897 -7.402541744
34 -1.815361686 -1.516674897
35 -1.113163662 -1.815361686
36 -3.435180214 -1.113163662
37 -0.471516969 -3.435180214
38 3.768492152 -0.471516969
39 -3.012042954 3.768492152
40 6.686505748 -3.012042954
41 0.105551209 6.686505748
42 -1.275094311 0.105551209
43 -0.880474468 -1.275094311
44 -1.478383858 -0.880474468
45 2.719055566 -1.478383858
46 3.381219868 2.719055566
47 -4.012035870 3.381219868
48 -0.179224309 -4.012035870
49 -3.048538448 -0.179224309
50 -1.643092553 -3.048538448
51 2.250716631 -1.643092553
52 6.620533543 2.250716631
53 -4.385397547 6.620533543
54 -0.775805012 -4.385397547
55 5.420015113 -0.775805012
56 2.908862857 5.420015113
57 -1.207388061 2.908862857
58 4.269775076 -1.207388061
59 -3.930194608 4.269775076
60 2.002988619 -3.930194608
61 0.455639801 2.002988619
62 5.045053469 0.455639801
63 -0.371816727 5.045053469
64 0.634597864 -0.371816727
65 2.661817015 0.634597864
66 2.690842297 2.661817015
67 -3.392776440 2.690842297
68 1.989082876 -3.392776440
69 0.294095315 1.989082876
70 1.345726573 0.294095315
71 0.687184969 1.345726573
72 2.792529187 0.687184969
73 1.110135468 2.792529187
74 1.786683958 1.110135468
75 -0.191297844 1.786683958
76 1.458488775 -0.191297844
77 1.775941849 1.458488775
78 5.420488349 1.775941849
79 -1.806634408 5.420488349
80 3.128518828 -1.806634408
81 2.647695272 3.128518828
82 0.830046503 2.647695272
83 1.703941082 0.830046503
84 4.081205344 1.703941082
85 2.744823718 4.081205344
86 -0.985419722 2.744823718
87 4.581733243 -0.985419722
88 -0.719695403 4.581733243
89 -1.238405277 -0.719695403
90 3.763303684 -1.238405277
91 2.617857318 3.763303684
92 -1.472647355 2.617857318
93 -2.274130879 -1.472647355
94 5.649645163 -2.274130879
95 1.748568940 5.649645163
96 2.962097025 1.748568940
97 0.746357154 2.962097025
98 0.498444642 0.746357154
99 -1.177414752 0.498444642
100 -0.750048451 -1.177414752
101 -0.001889518 -0.750048451
102 -3.767575336 -0.001889518
103 1.339005806 -3.767575336
104 7.230981545 1.339005806
105 -5.031447094 7.230981545
106 -3.409429021 -5.031447094
107 -4.829234033 -3.409429021
108 -0.832123170 -4.829234033
109 -1.304119167 -0.832123170
110 0.241999937 -1.304119167
111 5.982799364 0.241999937
112 -2.443233641 5.982799364
113 -5.269411507 -2.443233641
114 -2.234109133 -5.269411507
115 1.783993722 -2.234109133
116 -8.238024314 1.783993722
117 -3.655924245 -8.238024314
118 1.700770683 -3.655924245
119 -6.755793099 1.700770683
120 -3.469273342 -6.755793099
121 -4.799280110 -3.469273342
122 -4.211418857 -4.799280110
123 2.511373258 -4.211418857
124 0.988692711 2.511373258
125 1.573701669 0.988692711
126 -1.228027217 1.573701669
127 2.369740536 -1.228027217
128 4.517589110 2.369740536
129 0.553987679 4.517589110
130 -4.542353528 0.553987679
131 1.489534579 -4.542353528
132 2.410728576 1.489534579
133 1.445396917 2.410728576
134 3.574667366 1.445396917
135 -1.011380324 3.574667366
136 -2.184610312 -1.011380324
137 -9.070274513 -2.184610312
138 2.650028402 -9.070274513
139 -6.717405526 2.650028402
140 -9.243870619 -6.717405526
141 1.684133709 -9.243870619
142 -8.141911261 1.684133709
143 -0.287165817 -8.141911261
144 -0.065584851 -0.287165817
145 -2.093825826 -0.065584851
146 -2.937241493 -2.093825826
147 -3.383564403 -2.937241493
148 -3.439795919 -3.383564403
149 -0.068150459 -3.439795919
150 -1.428485594 -0.068150459
151 0.383618097 -1.428485594
152 5.209940315 0.383618097
153 -8.757991584 5.209940315
154 -1.284389717 -8.757991584
155 -0.202506425 -1.284389717
156 0.186078800 -0.202506425
157 -0.025363288 0.186078800
158 2.142881925 -0.025363288
159 NA 2.142881925
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.313099793 2.869399611
[2,] 1.252689949 -1.313099793
[3,] 1.249176430 1.252689949
[4,] -2.760101466 1.249176430
[5,] 4.365675664 -2.760101466
[6,] 0.191157071 4.365675664
[7,] -1.707638988 0.191157071
[8,] 0.309203534 -1.707638988
[9,] 2.628378037 0.309203534
[10,] 3.792123458 2.628378037
[11,] -1.745640052 3.792123458
[12,] 3.966134643 -1.745640052
[13,] -5.332265356 3.966134643
[14,] -1.471021081 -5.332265356
[15,] 3.638159981 -1.471021081
[16,] -2.193115880 3.638159981
[17,] -6.529901734 -2.193115880
[18,] 2.552740561 -6.529901734
[19,] -1.666837709 2.552740561
[20,] -2.398719827 -1.666837709
[21,] -0.859026269 -2.398719827
[22,] 1.814157061 -0.859026269
[23,] 2.080463355 1.814157061
[24,] 6.198428618 2.080463355
[25,] 2.034301514 6.198428618
[26,] 0.410346733 2.034301514
[27,] 3.749044280 0.410346733
[28,] 0.840204920 3.749044280
[29,] 0.378935361 0.840204920
[30,] 4.647353730 0.378935361
[31,] 1.379969570 4.647353730
[32,] -7.402541744 1.379969570
[33,] -1.516674897 -7.402541744
[34,] -1.815361686 -1.516674897
[35,] -1.113163662 -1.815361686
[36,] -3.435180214 -1.113163662
[37,] -0.471516969 -3.435180214
[38,] 3.768492152 -0.471516969
[39,] -3.012042954 3.768492152
[40,] 6.686505748 -3.012042954
[41,] 0.105551209 6.686505748
[42,] -1.275094311 0.105551209
[43,] -0.880474468 -1.275094311
[44,] -1.478383858 -0.880474468
[45,] 2.719055566 -1.478383858
[46,] 3.381219868 2.719055566
[47,] -4.012035870 3.381219868
[48,] -0.179224309 -4.012035870
[49,] -3.048538448 -0.179224309
[50,] -1.643092553 -3.048538448
[51,] 2.250716631 -1.643092553
[52,] 6.620533543 2.250716631
[53,] -4.385397547 6.620533543
[54,] -0.775805012 -4.385397547
[55,] 5.420015113 -0.775805012
[56,] 2.908862857 5.420015113
[57,] -1.207388061 2.908862857
[58,] 4.269775076 -1.207388061
[59,] -3.930194608 4.269775076
[60,] 2.002988619 -3.930194608
[61,] 0.455639801 2.002988619
[62,] 5.045053469 0.455639801
[63,] -0.371816727 5.045053469
[64,] 0.634597864 -0.371816727
[65,] 2.661817015 0.634597864
[66,] 2.690842297 2.661817015
[67,] -3.392776440 2.690842297
[68,] 1.989082876 -3.392776440
[69,] 0.294095315 1.989082876
[70,] 1.345726573 0.294095315
[71,] 0.687184969 1.345726573
[72,] 2.792529187 0.687184969
[73,] 1.110135468 2.792529187
[74,] 1.786683958 1.110135468
[75,] -0.191297844 1.786683958
[76,] 1.458488775 -0.191297844
[77,] 1.775941849 1.458488775
[78,] 5.420488349 1.775941849
[79,] -1.806634408 5.420488349
[80,] 3.128518828 -1.806634408
[81,] 2.647695272 3.128518828
[82,] 0.830046503 2.647695272
[83,] 1.703941082 0.830046503
[84,] 4.081205344 1.703941082
[85,] 2.744823718 4.081205344
[86,] -0.985419722 2.744823718
[87,] 4.581733243 -0.985419722
[88,] -0.719695403 4.581733243
[89,] -1.238405277 -0.719695403
[90,] 3.763303684 -1.238405277
[91,] 2.617857318 3.763303684
[92,] -1.472647355 2.617857318
[93,] -2.274130879 -1.472647355
[94,] 5.649645163 -2.274130879
[95,] 1.748568940 5.649645163
[96,] 2.962097025 1.748568940
[97,] 0.746357154 2.962097025
[98,] 0.498444642 0.746357154
[99,] -1.177414752 0.498444642
[100,] -0.750048451 -1.177414752
[101,] -0.001889518 -0.750048451
[102,] -3.767575336 -0.001889518
[103,] 1.339005806 -3.767575336
[104,] 7.230981545 1.339005806
[105,] -5.031447094 7.230981545
[106,] -3.409429021 -5.031447094
[107,] -4.829234033 -3.409429021
[108,] -0.832123170 -4.829234033
[109,] -1.304119167 -0.832123170
[110,] 0.241999937 -1.304119167
[111,] 5.982799364 0.241999937
[112,] -2.443233641 5.982799364
[113,] -5.269411507 -2.443233641
[114,] -2.234109133 -5.269411507
[115,] 1.783993722 -2.234109133
[116,] -8.238024314 1.783993722
[117,] -3.655924245 -8.238024314
[118,] 1.700770683 -3.655924245
[119,] -6.755793099 1.700770683
[120,] -3.469273342 -6.755793099
[121,] -4.799280110 -3.469273342
[122,] -4.211418857 -4.799280110
[123,] 2.511373258 -4.211418857
[124,] 0.988692711 2.511373258
[125,] 1.573701669 0.988692711
[126,] -1.228027217 1.573701669
[127,] 2.369740536 -1.228027217
[128,] 4.517589110 2.369740536
[129,] 0.553987679 4.517589110
[130,] -4.542353528 0.553987679
[131,] 1.489534579 -4.542353528
[132,] 2.410728576 1.489534579
[133,] 1.445396917 2.410728576
[134,] 3.574667366 1.445396917
[135,] -1.011380324 3.574667366
[136,] -2.184610312 -1.011380324
[137,] -9.070274513 -2.184610312
[138,] 2.650028402 -9.070274513
[139,] -6.717405526 2.650028402
[140,] -9.243870619 -6.717405526
[141,] 1.684133709 -9.243870619
[142,] -8.141911261 1.684133709
[143,] -0.287165817 -8.141911261
[144,] -0.065584851 -0.287165817
[145,] -2.093825826 -0.065584851
[146,] -2.937241493 -2.093825826
[147,] -3.383564403 -2.937241493
[148,] -3.439795919 -3.383564403
[149,] -0.068150459 -3.439795919
[150,] -1.428485594 -0.068150459
[151,] 0.383618097 -1.428485594
[152,] 5.209940315 0.383618097
[153,] -8.757991584 5.209940315
[154,] -1.284389717 -8.757991584
[155,] -0.202506425 -1.284389717
[156,] 0.186078800 -0.202506425
[157,] -0.025363288 0.186078800
[158,] 2.142881925 -0.025363288
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.313099793 2.869399611
2 1.252689949 -1.313099793
3 1.249176430 1.252689949
4 -2.760101466 1.249176430
5 4.365675664 -2.760101466
6 0.191157071 4.365675664
7 -1.707638988 0.191157071
8 0.309203534 -1.707638988
9 2.628378037 0.309203534
10 3.792123458 2.628378037
11 -1.745640052 3.792123458
12 3.966134643 -1.745640052
13 -5.332265356 3.966134643
14 -1.471021081 -5.332265356
15 3.638159981 -1.471021081
16 -2.193115880 3.638159981
17 -6.529901734 -2.193115880
18 2.552740561 -6.529901734
19 -1.666837709 2.552740561
20 -2.398719827 -1.666837709
21 -0.859026269 -2.398719827
22 1.814157061 -0.859026269
23 2.080463355 1.814157061
24 6.198428618 2.080463355
25 2.034301514 6.198428618
26 0.410346733 2.034301514
27 3.749044280 0.410346733
28 0.840204920 3.749044280
29 0.378935361 0.840204920
30 4.647353730 0.378935361
31 1.379969570 4.647353730
32 -7.402541744 1.379969570
33 -1.516674897 -7.402541744
34 -1.815361686 -1.516674897
35 -1.113163662 -1.815361686
36 -3.435180214 -1.113163662
37 -0.471516969 -3.435180214
38 3.768492152 -0.471516969
39 -3.012042954 3.768492152
40 6.686505748 -3.012042954
41 0.105551209 6.686505748
42 -1.275094311 0.105551209
43 -0.880474468 -1.275094311
44 -1.478383858 -0.880474468
45 2.719055566 -1.478383858
46 3.381219868 2.719055566
47 -4.012035870 3.381219868
48 -0.179224309 -4.012035870
49 -3.048538448 -0.179224309
50 -1.643092553 -3.048538448
51 2.250716631 -1.643092553
52 6.620533543 2.250716631
53 -4.385397547 6.620533543
54 -0.775805012 -4.385397547
55 5.420015113 -0.775805012
56 2.908862857 5.420015113
57 -1.207388061 2.908862857
58 4.269775076 -1.207388061
59 -3.930194608 4.269775076
60 2.002988619 -3.930194608
61 0.455639801 2.002988619
62 5.045053469 0.455639801
63 -0.371816727 5.045053469
64 0.634597864 -0.371816727
65 2.661817015 0.634597864
66 2.690842297 2.661817015
67 -3.392776440 2.690842297
68 1.989082876 -3.392776440
69 0.294095315 1.989082876
70 1.345726573 0.294095315
71 0.687184969 1.345726573
72 2.792529187 0.687184969
73 1.110135468 2.792529187
74 1.786683958 1.110135468
75 -0.191297844 1.786683958
76 1.458488775 -0.191297844
77 1.775941849 1.458488775
78 5.420488349 1.775941849
79 -1.806634408 5.420488349
80 3.128518828 -1.806634408
81 2.647695272 3.128518828
82 0.830046503 2.647695272
83 1.703941082 0.830046503
84 4.081205344 1.703941082
85 2.744823718 4.081205344
86 -0.985419722 2.744823718
87 4.581733243 -0.985419722
88 -0.719695403 4.581733243
89 -1.238405277 -0.719695403
90 3.763303684 -1.238405277
91 2.617857318 3.763303684
92 -1.472647355 2.617857318
93 -2.274130879 -1.472647355
94 5.649645163 -2.274130879
95 1.748568940 5.649645163
96 2.962097025 1.748568940
97 0.746357154 2.962097025
98 0.498444642 0.746357154
99 -1.177414752 0.498444642
100 -0.750048451 -1.177414752
101 -0.001889518 -0.750048451
102 -3.767575336 -0.001889518
103 1.339005806 -3.767575336
104 7.230981545 1.339005806
105 -5.031447094 7.230981545
106 -3.409429021 -5.031447094
107 -4.829234033 -3.409429021
108 -0.832123170 -4.829234033
109 -1.304119167 -0.832123170
110 0.241999937 -1.304119167
111 5.982799364 0.241999937
112 -2.443233641 5.982799364
113 -5.269411507 -2.443233641
114 -2.234109133 -5.269411507
115 1.783993722 -2.234109133
116 -8.238024314 1.783993722
117 -3.655924245 -8.238024314
118 1.700770683 -3.655924245
119 -6.755793099 1.700770683
120 -3.469273342 -6.755793099
121 -4.799280110 -3.469273342
122 -4.211418857 -4.799280110
123 2.511373258 -4.211418857
124 0.988692711 2.511373258
125 1.573701669 0.988692711
126 -1.228027217 1.573701669
127 2.369740536 -1.228027217
128 4.517589110 2.369740536
129 0.553987679 4.517589110
130 -4.542353528 0.553987679
131 1.489534579 -4.542353528
132 2.410728576 1.489534579
133 1.445396917 2.410728576
134 3.574667366 1.445396917
135 -1.011380324 3.574667366
136 -2.184610312 -1.011380324
137 -9.070274513 -2.184610312
138 2.650028402 -9.070274513
139 -6.717405526 2.650028402
140 -9.243870619 -6.717405526
141 1.684133709 -9.243870619
142 -8.141911261 1.684133709
143 -0.287165817 -8.141911261
144 -0.065584851 -0.287165817
145 -2.093825826 -0.065584851
146 -2.937241493 -2.093825826
147 -3.383564403 -2.937241493
148 -3.439795919 -3.383564403
149 -0.068150459 -3.439795919
150 -1.428485594 -0.068150459
151 0.383618097 -1.428485594
152 5.209940315 0.383618097
153 -8.757991584 5.209940315
154 -1.284389717 -8.757991584
155 -0.202506425 -1.284389717
156 0.186078800 -0.202506425
157 -0.025363288 0.186078800
158 2.142881925 -0.025363288
> 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/7wogw1290179379.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8wogw1290179379.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9wogw1290179379.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10pyfh1290179379.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11tge51290179379.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/12ezut1290179379.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/13sra21290179379.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/14v9881290179379.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/15zapv1290179379.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/16i6fl1290179380.tab")
+ }
>
> try(system("convert tmp/10x061290179379.ps tmp/10x061290179379.png",intern=TRUE))
character(0)
> try(system("convert tmp/2b6h81290179379.ps tmp/2b6h81290179379.png",intern=TRUE))
character(0)
> try(system("convert tmp/3b6h81290179379.ps tmp/3b6h81290179379.png",intern=TRUE))
character(0)
> try(system("convert tmp/4b6h81290179379.ps tmp/4b6h81290179379.png",intern=TRUE))
character(0)
> try(system("convert tmp/5b6h81290179379.ps tmp/5b6h81290179379.png",intern=TRUE))
character(0)
> try(system("convert tmp/6mxht1290179379.ps tmp/6mxht1290179379.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wogw1290179379.ps tmp/7wogw1290179379.png",intern=TRUE))
character(0)
> try(system("convert tmp/8wogw1290179379.ps tmp/8wogw1290179379.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wogw1290179379.ps tmp/9wogw1290179379.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pyfh1290179379.ps tmp/10pyfh1290179379.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.400 2.090 8.513