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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> x <- array(list(13
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+ ,2)
+ ,dim=c(9
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'FindingFriends'
+ ,'Findingfriends*G'
+ ,'KnowingPeople'
+ ,'Knowingpeople*G'
+ ,'Liked'
+ ,'Liked*G'
+ ,'Celebrity'
+ ,'Celebrity*G')
+ ,1:156))
> y <- array(NA,dim=c(9,156),dimnames=list(c('Popularity','FindingFriends','Findingfriends*G','KnowingPeople','Knowingpeople*G','Liked','Liked*G','Celebrity','Celebrity*G'),1:156))
> 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 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
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
Popularity FindingFriends Findingfriends*G KnowingPeople Knowingpeople*G
1 13 13 0 14 0
2 12 12 12 8 8
3 15 10 10 12 12
4 12 9 9 7 7
5 10 10 0 10 0
6 12 12 0 7 0
7 15 13 13 16 16
8 9 12 12 11 11
9 12 12 12 14 14
10 11 6 6 6 6
11 11 5 0 16 0
12 11 12 12 11 11
13 15 11 11 16 16
14 7 14 0 12 0
15 11 14 0 7 0
16 11 12 12 13 13
17 10 12 12 11 11
18 14 11 0 15 0
19 10 11 11 7 7
20 6 7 0 9 0
21 11 9 9 7 7
22 15 11 0 14 0
23 11 11 11 15 15
24 12 12 0 7 0
25 14 12 12 15 15
26 15 11 0 17 0
27 9 11 0 15 0
28 13 8 8 14 14
29 13 9 0 14 0
30 16 12 12 8 8
31 13 10 10 8 8
32 12 10 0 14 0
33 14 12 12 14 14
34 11 8 0 8 0
35 9 12 12 11 11
36 16 11 0 16 0
37 12 12 12 10 10
38 10 7 0 8 0
39 13 11 11 14 14
40 16 11 11 16 16
41 14 12 0 13 0
42 15 9 9 5 5
43 5 15 15 8 8
44 8 11 0 10 0
45 11 11 11 8 8
46 16 11 0 13 0
47 17 11 11 15 15
48 9 15 0 6 0
49 9 11 11 12 12
50 13 12 12 16 16
51 10 12 12 5 5
52 6 9 0 15 0
53 12 12 0 12 0
54 8 12 0 8 0
55 14 13 0 13 0
56 12 11 11 14 14
57 11 9 9 12 12
58 16 9 9 16 16
59 8 11 0 10 0
60 15 11 11 15 15
61 7 12 0 8 0
62 16 12 0 16 0
63 14 9 9 19 19
64 16 11 11 14 14
65 9 9 9 6 6
66 14 12 12 13 13
67 11 12 0 15 0
68 13 12 0 7 0
69 15 12 12 13 13
70 5 14 0 4 0
71 15 11 11 14 14
72 13 12 12 13 13
73 11 11 0 11 0
74 11 6 0 14 0
75 12 10 10 12 12
76 12 12 12 15 15
77 12 13 13 14 14
78 12 8 8 13 13
79 14 12 12 8 8
80 6 12 12 6 6
81 7 12 0 7 0
82 14 6 6 13 13
83 14 11 11 13 13
84 10 10 10 11 11
85 13 12 0 5 0
86 12 13 0 12 0
87 9 11 0 8 0
88 12 7 7 11 11
89 16 11 11 14 14
90 10 11 0 9 0
91 14 11 11 10 10
92 10 11 11 13 13
93 16 12 12 16 16
94 15 10 10 16 16
95 12 11 0 11 0
96 10 12 12 8 8
97 8 7 7 4 4
98 8 13 0 7 0
99 11 8 0 14 0
100 13 12 12 11 11
101 16 11 11 17 17
102 16 12 12 15 15
103 14 14 0 17 0
104 11 10 10 5 5
105 4 10 0 4 0
106 14 13 13 10 10
107 9 10 10 11 11
108 14 11 11 15 15
109 8 10 10 10 10
110 8 7 7 9 9
111 11 10 10 12 12
112 12 8 8 15 15
113 11 12 12 7 7
114 14 12 12 13 13
115 15 12 0 12 0
116 16 11 11 14 14
117 16 12 12 14 14
118 11 12 0 8 0
119 14 12 0 15 0
120 14 11 0 12 0
121 12 12 12 12 12
122 14 11 0 16 0
123 8 11 0 9 0
124 13 13 0 15 0
125 16 12 0 15 0
126 12 12 12 6 6
127 16 12 12 14 14
128 12 12 12 15 15
129 11 8 8 10 10
130 4 8 8 6 6
131 16 12 12 14 14
132 15 11 11 12 12
133 10 12 12 8 8
134 13 13 13 11 11
135 15 12 0 13 0
136 12 12 12 9 9
137 14 11 0 15 0
138 7 12 12 13 13
139 19 12 12 15 15
140 12 10 10 14 14
141 12 11 0 16 0
142 13 12 0 14 0
143 15 12 12 14 14
144 8 10 0 10 0
145 12 12 12 10 10
146 10 13 13 4 4
147 8 12 0 8 0
148 10 15 0 15 0
149 15 11 0 16 0
150 16 12 12 12 12
151 13 11 11 12 12
152 16 12 12 15 15
153 9 11 11 9 9
154 14 10 0 12 0
155 14 11 0 14 0
156 12 11 11 11 11
Liked Liked*G Celebrity Celebrity*G
1 13 0 3 0
2 13 13 5 5
3 16 16 6 6
4 12 12 6 6
5 11 0 5 0
6 12 0 3 0
7 18 18 8 8
8 11 11 4 4
9 14 14 4 4
10 9 9 4 4
11 14 0 6 0
12 12 12 6 6
13 11 11 5 5
14 12 0 4 0
15 13 0 6 0
16 11 11 4 4
17 12 12 6 6
18 16 0 6 0
19 9 9 4 4
20 11 0 4 0
21 13 13 2 2
22 15 0 7 0
23 10 10 5 5
24 11 0 4 0
25 13 13 6 6
26 16 0 6 0
27 15 0 7 0
28 14 14 5 5
29 14 0 6 0
30 14 14 4 4
31 8 8 4 4
32 13 0 7 0
33 15 15 7 7
34 13 0 4 0
35 11 11 4 4
36 15 0 6 0
37 15 15 6 6
38 9 0 5 0
39 13 13 6 6
40 16 16 7 7
41 13 0 6 0
42 11 11 3 3
43 12 12 3 3
44 12 0 4 0
45 12 12 6 6
46 14 0 7 0
47 14 14 5 5
48 8 0 4 0
49 13 13 5 5
50 16 16 6 6
51 13 13 6 6
52 11 0 6 0
53 14 0 5 0
54 13 0 4 0
55 13 0 5 0
56 13 13 5 5
57 12 12 4 4
58 16 16 6 6
59 15 0 2 0
60 15 15 8 8
61 12 0 3 0
62 14 0 6 0
63 12 12 6 6
64 15 15 6 6
65 12 12 5 5
66 13 13 5 5
67 12 0 6 0
68 12 0 5 0
69 13 13 6 6
70 5 0 2 0
71 13 13 5 5
72 13 13 5 5
73 14 0 5 0
74 17 0 6 0
75 13 13 6 6
76 13 13 6 6
77 12 12 5 5
78 13 13 5 5
79 14 14 4 4
80 11 11 2 2
81 12 0 4 0
82 12 12 6 6
83 16 16 6 6
84 12 12 5 5
85 12 0 3 0
86 12 0 6 0
87 10 0 4 0
88 15 15 5 5
89 15 15 8 8
90 12 0 4 0
91 16 16 6 6
92 15 15 6 6
93 16 16 7 7
94 13 13 6 6
95 12 0 5 0
96 11 11 4 4
97 13 13 6 6
98 10 0 3 0
99 15 0 5 0
100 13 13 6 6
101 16 16 7 7
102 15 15 7 7
103 18 0 6 0
104 13 13 3 3
105 10 0 2 0
106 16 16 8 8
107 13 13 3 3
108 15 15 8 8
109 14 14 3 3
110 15 15 4 4
111 14 14 5 5
112 13 13 7 7
113 13 13 6 6
114 15 15 6 6
115 16 0 7 0
116 14 14 6 6
117 14 14 6 6
118 16 0 6 0
119 14 0 6 0
120 12 0 4 0
121 13 13 4 4
122 12 0 5 0
123 12 0 4 0
124 14 0 6 0
125 14 0 6 0
126 14 14 5 5
127 16 16 8 8
128 13 13 6 6
129 14 14 5 5
130 4 4 4 4
131 16 16 8 8
132 13 13 6 6
133 16 16 4 4
134 15 15 6 6
135 14 0 6 0
136 13 13 4 4
137 14 0 6 0
138 12 12 3 3
139 15 15 6 6
140 14 14 5 5
141 13 0 4 0
142 14 0 6 0
143 16 16 4 4
144 6 0 4 0
145 13 13 4 4
146 13 13 6 6
147 14 0 5 0
148 15 0 6 0
149 14 0 6 0
150 15 15 8 8
151 13 13 7 7
152 16 16 7 7
153 12 12 4 4
154 15 0 6 0
155 12 0 6 0
156 14 14 2 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FindingFriends `Findingfriends*G` KnowingPeople
0.30372 0.17615 -0.15129 0.24048
`Knowingpeople*G` Liked `Liked*G` Celebrity
0.03251 0.21577 0.21952 0.70807
`Celebrity*G`
-0.16655
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.1181 -1.2447 -0.1155 1.2675 6.6949
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.30372 1.41562 0.215 0.8304
FindingFriends 0.17615 0.11409 1.544 0.1248
`Findingfriends*G` -0.15129 0.14219 -1.064 0.2891
KnowingPeople 0.24048 0.11080 2.170 0.0316 *
`Knowingpeople*G` 0.03251 0.13352 0.243 0.8080
Liked 0.21577 0.14089 1.531 0.1278
`Liked*G` 0.21952 0.17453 1.258 0.2105
Celebrity 0.70807 0.29282 2.418 0.0168 *
`Celebrity*G` -0.16655 0.34566 -0.482 0.6306
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.088 on 147 degrees of freedom
Multiple R-squared: 0.5206, Adjusted R-squared: 0.4946
F-statistic: 19.96 on 8 and 147 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.01488464 0.029769288 0.985115356
[2,] 0.27036717 0.540734344 0.729632828
[3,] 0.45273858 0.905477161 0.547261420
[4,] 0.38904456 0.778089116 0.610955442
[5,] 0.27631594 0.552631888 0.723684056
[6,] 0.23520048 0.470400960 0.764799520
[7,] 0.20434022 0.408680446 0.795659777
[8,] 0.17350408 0.347008150 0.826495925
[9,] 0.59254089 0.814918220 0.407459110
[10,] 0.51491502 0.970169959 0.485084979
[11,] 0.62809480 0.743810396 0.371905198
[12,] 0.55325497 0.893490062 0.446745031
[13,] 0.53531540 0.929369209 0.464684605
[14,] 0.46812658 0.936253159 0.531873421
[15,] 0.45005252 0.900105037 0.549947482
[16,] 0.57216440 0.855671201 0.427835601
[17,] 0.51524528 0.969509437 0.484754718
[18,] 0.47256789 0.945135781 0.527432109
[19,] 0.65662322 0.686753553 0.343376777
[20,] 0.79195759 0.416084828 0.208042414
[21,] 0.76910464 0.461790720 0.230895360
[22,] 0.72016071 0.559678582 0.279839291
[23,] 0.68266358 0.634672842 0.317336421
[24,] 0.68507643 0.629847141 0.314923570
[25,] 0.74562265 0.508754697 0.254377348
[26,] 0.72014397 0.559712067 0.279856034
[27,] 0.67931230 0.641375406 0.320687703
[28,] 0.62539091 0.749218177 0.374609089
[29,] 0.58095524 0.838089520 0.419044760
[30,] 0.57615308 0.847693840 0.423846920
[31,] 0.83629412 0.327411758 0.163705879
[32,] 0.94493960 0.110120798 0.055060399
[33,] 0.95487537 0.090249260 0.045124630
[34,] 0.94223099 0.115538023 0.057769012
[35,] 0.95981751 0.080364980 0.040182490
[36,] 0.97438791 0.051224179 0.025612089
[37,] 0.96617470 0.067650595 0.033825298
[38,] 0.97924737 0.041505252 0.020752626
[39,] 0.97977967 0.040440650 0.020220325
[40,] 0.97402741 0.051945189 0.025972595
[41,] 0.99686029 0.006279428 0.003139714
[42,] 0.99542980 0.009140406 0.004570203
[43,] 0.99650202 0.006995959 0.003497980
[44,] 0.99662855 0.006742899 0.003371449
[45,] 0.99539510 0.009209793 0.004604896
[46,] 0.99384359 0.012312822 0.006156411
[47,] 0.99173390 0.016532207 0.008266103
[48,] 0.99175995 0.016480091 0.008240045
[49,] 0.98881902 0.022361962 0.011180981
[50,] 0.98930088 0.021398249 0.010699124
[51,] 0.99162228 0.016755443 0.008377721
[52,] 0.98843981 0.023120376 0.011560188
[53,] 0.98727501 0.025449982 0.012724991
[54,] 0.98546400 0.029071993 0.014535997
[55,] 0.98312412 0.033751760 0.016875880
[56,] 0.98273001 0.034539983 0.017269991
[57,] 0.98624389 0.027512225 0.013756113
[58,] 0.98609339 0.027813227 0.013906613
[59,] 0.98326487 0.033470253 0.016735126
[60,] 0.98395283 0.032094334 0.016047167
[61,] 0.97874351 0.042512976 0.021256488
[62,] 0.97215567 0.055688651 0.027844325
[63,] 0.97004957 0.059900869 0.029950435
[64,] 0.96216950 0.075661008 0.037830504
[65,] 0.95795278 0.084094431 0.042047216
[66,] 0.94700875 0.105982499 0.052991250
[67,] 0.93550952 0.128980969 0.064490485
[68,] 0.94969818 0.100603637 0.050301818
[69,] 0.95235883 0.095282331 0.047641166
[70,] 0.95668396 0.086632077 0.043316039
[71,] 0.95643929 0.087121417 0.043560709
[72,] 0.94477522 0.110449566 0.055224783
[73,] 0.93749209 0.125015817 0.062507909
[74,] 0.99016413 0.019671735 0.009835867
[75,] 0.98691638 0.026167249 0.013083625
[76,] 0.98292916 0.034141684 0.017070842
[77,] 0.98011878 0.039762434 0.019881217
[78,] 0.97465337 0.050693261 0.025346631
[79,] 0.96918628 0.061627431 0.030813715
[80,] 0.96143736 0.077125282 0.038562641
[81,] 0.98270775 0.034584490 0.017292245
[82,] 0.97669616 0.046607679 0.023303840
[83,] 0.97223597 0.055528064 0.027764032
[84,] 0.96666986 0.066660282 0.033330141
[85,] 0.95603774 0.087924510 0.043962255
[86,] 0.95414638 0.091707235 0.045853617
[87,] 0.95158518 0.096829634 0.048414817
[88,] 0.94995051 0.100098977 0.050049488
[89,] 0.93584002 0.128319963 0.064159982
[90,] 0.91772783 0.164544334 0.082272167
[91,] 0.90137210 0.197255796 0.098627898
[92,] 0.90037957 0.199240863 0.099620432
[93,] 0.91363847 0.172723056 0.086361528
[94,] 0.92160394 0.156792128 0.078396064
[95,] 0.90508053 0.189838930 0.094919465
[96,] 0.89442921 0.211141580 0.105570790
[97,] 0.88931392 0.221372152 0.110686076
[98,] 0.90351400 0.192971993 0.096485997
[99,] 0.93085281 0.138294382 0.069147191
[100,] 0.92680253 0.146394944 0.073197472
[101,] 0.94418472 0.111630551 0.055815275
[102,] 0.92619334 0.147613322 0.073806661
[103,] 0.90446440 0.191071192 0.095535596
[104,] 0.88087163 0.238256738 0.119128369
[105,] 0.87670896 0.246582078 0.123291039
[106,] 0.88159441 0.236811177 0.118405589
[107,] 0.85506489 0.289870224 0.144935112
[108,] 0.82059256 0.358814877 0.179407439
[109,] 0.94144274 0.117114527 0.058557264
[110,] 0.92237837 0.155243260 0.077621630
[111,] 0.90378818 0.192423648 0.096211824
[112,] 0.88806694 0.223866111 0.111933055
[113,] 0.85561531 0.288769375 0.144384688
[114,] 0.85474207 0.290515852 0.145257926
[115,] 0.82553162 0.348936761 0.174468380
[116,] 0.77993769 0.440124623 0.220062311
[117,] 0.77191027 0.456179462 0.228089731
[118,] 0.77409091 0.451818188 0.225909094
[119,] 0.73397772 0.532044569 0.266022284
[120,] 0.68594519 0.628109612 0.314054806
[121,] 0.66486590 0.670268198 0.335134099
[122,] 0.76580937 0.468381260 0.234190630
[123,] 0.70446240 0.591075198 0.295537599
[124,] 0.73648132 0.527037365 0.263518683
[125,] 0.70159593 0.596808140 0.298404070
[126,] 0.62210704 0.755785915 0.377892957
[127,] 0.74690591 0.506188189 0.253094095
[128,] 0.96208042 0.075839164 0.037919582
[129,] 0.99156458 0.016870832 0.008435416
[130,] 0.98786284 0.024274311 0.012137155
[131,] 0.96717293 0.065654131 0.032827065
[132,] 0.91875317 0.162493658 0.081246829
[133,] 0.83347214 0.333055720 0.166527860
> postscript(file="/var/www/html/rcomp/tmp/1dk4h1290447455.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26b321290447455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/36b321290447455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/46b321290447455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5z3l51290447455.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 = 156
Frequency = 1
1 2 3 4 5 6
2.11047441 0.84780486 0.95821547 1.08912847 -0.38377769 3.18574142
7 8 9 10 11 12
-2.16191469 -1.55906481 -0.68386566 2.82558433 -1.30128261 -1.07739243
13 14 15 16 17 18
2.55934551 -4.07702162 -0.50653758 -0.10503544 -2.07739243 0.45077584
19 20 21 22 23 24
1.42829960 -2.90678250 1.81993880 1.19894884 -0.73238754 2.69343546
25 26 27 28 29 30
0.39538468 0.96981791 -5.04153013 -0.12594912 0.47508335 4.95404622
31 32 33 34 35 36
4.61545581 -1.19337060 -0.74371628 1.72601590 -1.55906481 2.42606316
37 38 39 40 41 42
-1.11025203 1.05715678 -0.30677013 0.29989135 1.40288462 6.69494971
43 44 45 46 47 48
-4.70844717 -2.06761972 -0.23357661 2.65519408 3.52648592 0.05276930
49 50 51 52 53 54
-3.21927651 -2.18344555 -0.87476219 -6.11809676 0.13566955 -1.97857607
55 56 57 58 59 60
1.93480888 -0.76524713 -0.19275211 0.89113411 -1.29877408 -0.53336470
61 62 63 64 65 66
-2.05473754 2.46568145 -0.18669529 1.82266660 -1.09636322 1.48287830
67 68 69 70 71 72
-1.86230702 2.76959693 1.94135530 -1.22668144 2.23475287 0.48287830
73 74 75 76 77 78
-0.44770350 -1.64377152 -0.73593961 -1.60461532 -0.37968527 -0.41768217
79 80 81 82 83 84
2.95404622 -2.11109226 -2.52233082 1.52579625 -0.33962972 -1.48614967
85 86 87 88 89 90
4.66669935 -0.31701812 -0.15512923 -0.71741493 0.73962062 0.17285924
91 92 93 94 95 96
0.47932622 -3.90434809 0.27503146 1.17211913 0.98382907 0.25989113
97 98 99 100 101 102
-2.47747746 -0.55887400 -0.85646269 0.48732593 0.02690603 0.98329841
103 104 105 106 107 108
-0.99015863 1.79952655 -2.60092089 -0.65343953 -1.83838533 -1.53336470
109 110 111 112 113 114
-3.00068165 -3.62992131 -1.62969826 -2.04669878 -0.42073282 0.07079203
115 116 117 118 119 120
1.28799249 2.25794824 2.23308835 -1.04201941 0.70616041 3.45142235
121 122 123 124 125 126
0.29738660 1.78143426 -1.82714076 -0.46998758 2.70616041 0.95849385
127 128 129 130 131 132
0.27947910 -1.60461532 -1.03400786 -2.04772725 0.27947910 2.23920050
133 134 135 136 137 138
-1.91651706 -0.40809723 2.18711834 1.11634254 0.88230840 -3.99879408
139 140 141 142 143 144
4.52482140 -1.17566889 0.27374022 -0.05336062 1.44557106 -0.59687402
145 146 147 148 149 150
0.84335723 -0.62663676 -2.90241460 -4.03804985 1.64182944 1.26073136
151 152 153 154 155 156
-0.30232249 0.54801677 -1.42351594 1.56412700 1.55431993 1.24299614
> postscript(file="/var/www/html/rcomp/tmp/6z3l51290447455.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 2.11047441 NA
1 0.84780486 2.11047441
2 0.95821547 0.84780486
3 1.08912847 0.95821547
4 -0.38377769 1.08912847
5 3.18574142 -0.38377769
6 -2.16191469 3.18574142
7 -1.55906481 -2.16191469
8 -0.68386566 -1.55906481
9 2.82558433 -0.68386566
10 -1.30128261 2.82558433
11 -1.07739243 -1.30128261
12 2.55934551 -1.07739243
13 -4.07702162 2.55934551
14 -0.50653758 -4.07702162
15 -0.10503544 -0.50653758
16 -2.07739243 -0.10503544
17 0.45077584 -2.07739243
18 1.42829960 0.45077584
19 -2.90678250 1.42829960
20 1.81993880 -2.90678250
21 1.19894884 1.81993880
22 -0.73238754 1.19894884
23 2.69343546 -0.73238754
24 0.39538468 2.69343546
25 0.96981791 0.39538468
26 -5.04153013 0.96981791
27 -0.12594912 -5.04153013
28 0.47508335 -0.12594912
29 4.95404622 0.47508335
30 4.61545581 4.95404622
31 -1.19337060 4.61545581
32 -0.74371628 -1.19337060
33 1.72601590 -0.74371628
34 -1.55906481 1.72601590
35 2.42606316 -1.55906481
36 -1.11025203 2.42606316
37 1.05715678 -1.11025203
38 -0.30677013 1.05715678
39 0.29989135 -0.30677013
40 1.40288462 0.29989135
41 6.69494971 1.40288462
42 -4.70844717 6.69494971
43 -2.06761972 -4.70844717
44 -0.23357661 -2.06761972
45 2.65519408 -0.23357661
46 3.52648592 2.65519408
47 0.05276930 3.52648592
48 -3.21927651 0.05276930
49 -2.18344555 -3.21927651
50 -0.87476219 -2.18344555
51 -6.11809676 -0.87476219
52 0.13566955 -6.11809676
53 -1.97857607 0.13566955
54 1.93480888 -1.97857607
55 -0.76524713 1.93480888
56 -0.19275211 -0.76524713
57 0.89113411 -0.19275211
58 -1.29877408 0.89113411
59 -0.53336470 -1.29877408
60 -2.05473754 -0.53336470
61 2.46568145 -2.05473754
62 -0.18669529 2.46568145
63 1.82266660 -0.18669529
64 -1.09636322 1.82266660
65 1.48287830 -1.09636322
66 -1.86230702 1.48287830
67 2.76959693 -1.86230702
68 1.94135530 2.76959693
69 -1.22668144 1.94135530
70 2.23475287 -1.22668144
71 0.48287830 2.23475287
72 -0.44770350 0.48287830
73 -1.64377152 -0.44770350
74 -0.73593961 -1.64377152
75 -1.60461532 -0.73593961
76 -0.37968527 -1.60461532
77 -0.41768217 -0.37968527
78 2.95404622 -0.41768217
79 -2.11109226 2.95404622
80 -2.52233082 -2.11109226
81 1.52579625 -2.52233082
82 -0.33962972 1.52579625
83 -1.48614967 -0.33962972
84 4.66669935 -1.48614967
85 -0.31701812 4.66669935
86 -0.15512923 -0.31701812
87 -0.71741493 -0.15512923
88 0.73962062 -0.71741493
89 0.17285924 0.73962062
90 0.47932622 0.17285924
91 -3.90434809 0.47932622
92 0.27503146 -3.90434809
93 1.17211913 0.27503146
94 0.98382907 1.17211913
95 0.25989113 0.98382907
96 -2.47747746 0.25989113
97 -0.55887400 -2.47747746
98 -0.85646269 -0.55887400
99 0.48732593 -0.85646269
100 0.02690603 0.48732593
101 0.98329841 0.02690603
102 -0.99015863 0.98329841
103 1.79952655 -0.99015863
104 -2.60092089 1.79952655
105 -0.65343953 -2.60092089
106 -1.83838533 -0.65343953
107 -1.53336470 -1.83838533
108 -3.00068165 -1.53336470
109 -3.62992131 -3.00068165
110 -1.62969826 -3.62992131
111 -2.04669878 -1.62969826
112 -0.42073282 -2.04669878
113 0.07079203 -0.42073282
114 1.28799249 0.07079203
115 2.25794824 1.28799249
116 2.23308835 2.25794824
117 -1.04201941 2.23308835
118 0.70616041 -1.04201941
119 3.45142235 0.70616041
120 0.29738660 3.45142235
121 1.78143426 0.29738660
122 -1.82714076 1.78143426
123 -0.46998758 -1.82714076
124 2.70616041 -0.46998758
125 0.95849385 2.70616041
126 0.27947910 0.95849385
127 -1.60461532 0.27947910
128 -1.03400786 -1.60461532
129 -2.04772725 -1.03400786
130 0.27947910 -2.04772725
131 2.23920050 0.27947910
132 -1.91651706 2.23920050
133 -0.40809723 -1.91651706
134 2.18711834 -0.40809723
135 1.11634254 2.18711834
136 0.88230840 1.11634254
137 -3.99879408 0.88230840
138 4.52482140 -3.99879408
139 -1.17566889 4.52482140
140 0.27374022 -1.17566889
141 -0.05336062 0.27374022
142 1.44557106 -0.05336062
143 -0.59687402 1.44557106
144 0.84335723 -0.59687402
145 -0.62663676 0.84335723
146 -2.90241460 -0.62663676
147 -4.03804985 -2.90241460
148 1.64182944 -4.03804985
149 1.26073136 1.64182944
150 -0.30232249 1.26073136
151 0.54801677 -0.30232249
152 -1.42351594 0.54801677
153 1.56412700 -1.42351594
154 1.55431993 1.56412700
155 1.24299614 1.55431993
156 NA 1.24299614
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.84780486 2.11047441
[2,] 0.95821547 0.84780486
[3,] 1.08912847 0.95821547
[4,] -0.38377769 1.08912847
[5,] 3.18574142 -0.38377769
[6,] -2.16191469 3.18574142
[7,] -1.55906481 -2.16191469
[8,] -0.68386566 -1.55906481
[9,] 2.82558433 -0.68386566
[10,] -1.30128261 2.82558433
[11,] -1.07739243 -1.30128261
[12,] 2.55934551 -1.07739243
[13,] -4.07702162 2.55934551
[14,] -0.50653758 -4.07702162
[15,] -0.10503544 -0.50653758
[16,] -2.07739243 -0.10503544
[17,] 0.45077584 -2.07739243
[18,] 1.42829960 0.45077584
[19,] -2.90678250 1.42829960
[20,] 1.81993880 -2.90678250
[21,] 1.19894884 1.81993880
[22,] -0.73238754 1.19894884
[23,] 2.69343546 -0.73238754
[24,] 0.39538468 2.69343546
[25,] 0.96981791 0.39538468
[26,] -5.04153013 0.96981791
[27,] -0.12594912 -5.04153013
[28,] 0.47508335 -0.12594912
[29,] 4.95404622 0.47508335
[30,] 4.61545581 4.95404622
[31,] -1.19337060 4.61545581
[32,] -0.74371628 -1.19337060
[33,] 1.72601590 -0.74371628
[34,] -1.55906481 1.72601590
[35,] 2.42606316 -1.55906481
[36,] -1.11025203 2.42606316
[37,] 1.05715678 -1.11025203
[38,] -0.30677013 1.05715678
[39,] 0.29989135 -0.30677013
[40,] 1.40288462 0.29989135
[41,] 6.69494971 1.40288462
[42,] -4.70844717 6.69494971
[43,] -2.06761972 -4.70844717
[44,] -0.23357661 -2.06761972
[45,] 2.65519408 -0.23357661
[46,] 3.52648592 2.65519408
[47,] 0.05276930 3.52648592
[48,] -3.21927651 0.05276930
[49,] -2.18344555 -3.21927651
[50,] -0.87476219 -2.18344555
[51,] -6.11809676 -0.87476219
[52,] 0.13566955 -6.11809676
[53,] -1.97857607 0.13566955
[54,] 1.93480888 -1.97857607
[55,] -0.76524713 1.93480888
[56,] -0.19275211 -0.76524713
[57,] 0.89113411 -0.19275211
[58,] -1.29877408 0.89113411
[59,] -0.53336470 -1.29877408
[60,] -2.05473754 -0.53336470
[61,] 2.46568145 -2.05473754
[62,] -0.18669529 2.46568145
[63,] 1.82266660 -0.18669529
[64,] -1.09636322 1.82266660
[65,] 1.48287830 -1.09636322
[66,] -1.86230702 1.48287830
[67,] 2.76959693 -1.86230702
[68,] 1.94135530 2.76959693
[69,] -1.22668144 1.94135530
[70,] 2.23475287 -1.22668144
[71,] 0.48287830 2.23475287
[72,] -0.44770350 0.48287830
[73,] -1.64377152 -0.44770350
[74,] -0.73593961 -1.64377152
[75,] -1.60461532 -0.73593961
[76,] -0.37968527 -1.60461532
[77,] -0.41768217 -0.37968527
[78,] 2.95404622 -0.41768217
[79,] -2.11109226 2.95404622
[80,] -2.52233082 -2.11109226
[81,] 1.52579625 -2.52233082
[82,] -0.33962972 1.52579625
[83,] -1.48614967 -0.33962972
[84,] 4.66669935 -1.48614967
[85,] -0.31701812 4.66669935
[86,] -0.15512923 -0.31701812
[87,] -0.71741493 -0.15512923
[88,] 0.73962062 -0.71741493
[89,] 0.17285924 0.73962062
[90,] 0.47932622 0.17285924
[91,] -3.90434809 0.47932622
[92,] 0.27503146 -3.90434809
[93,] 1.17211913 0.27503146
[94,] 0.98382907 1.17211913
[95,] 0.25989113 0.98382907
[96,] -2.47747746 0.25989113
[97,] -0.55887400 -2.47747746
[98,] -0.85646269 -0.55887400
[99,] 0.48732593 -0.85646269
[100,] 0.02690603 0.48732593
[101,] 0.98329841 0.02690603
[102,] -0.99015863 0.98329841
[103,] 1.79952655 -0.99015863
[104,] -2.60092089 1.79952655
[105,] -0.65343953 -2.60092089
[106,] -1.83838533 -0.65343953
[107,] -1.53336470 -1.83838533
[108,] -3.00068165 -1.53336470
[109,] -3.62992131 -3.00068165
[110,] -1.62969826 -3.62992131
[111,] -2.04669878 -1.62969826
[112,] -0.42073282 -2.04669878
[113,] 0.07079203 -0.42073282
[114,] 1.28799249 0.07079203
[115,] 2.25794824 1.28799249
[116,] 2.23308835 2.25794824
[117,] -1.04201941 2.23308835
[118,] 0.70616041 -1.04201941
[119,] 3.45142235 0.70616041
[120,] 0.29738660 3.45142235
[121,] 1.78143426 0.29738660
[122,] -1.82714076 1.78143426
[123,] -0.46998758 -1.82714076
[124,] 2.70616041 -0.46998758
[125,] 0.95849385 2.70616041
[126,] 0.27947910 0.95849385
[127,] -1.60461532 0.27947910
[128,] -1.03400786 -1.60461532
[129,] -2.04772725 -1.03400786
[130,] 0.27947910 -2.04772725
[131,] 2.23920050 0.27947910
[132,] -1.91651706 2.23920050
[133,] -0.40809723 -1.91651706
[134,] 2.18711834 -0.40809723
[135,] 1.11634254 2.18711834
[136,] 0.88230840 1.11634254
[137,] -3.99879408 0.88230840
[138,] 4.52482140 -3.99879408
[139,] -1.17566889 4.52482140
[140,] 0.27374022 -1.17566889
[141,] -0.05336062 0.27374022
[142,] 1.44557106 -0.05336062
[143,] -0.59687402 1.44557106
[144,] 0.84335723 -0.59687402
[145,] -0.62663676 0.84335723
[146,] -2.90241460 -0.62663676
[147,] -4.03804985 -2.90241460
[148,] 1.64182944 -4.03804985
[149,] 1.26073136 1.64182944
[150,] -0.30232249 1.26073136
[151,] 0.54801677 -0.30232249
[152,] -1.42351594 0.54801677
[153,] 1.56412700 -1.42351594
[154,] 1.55431993 1.56412700
[155,] 1.24299614 1.55431993
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.84780486 2.11047441
2 0.95821547 0.84780486
3 1.08912847 0.95821547
4 -0.38377769 1.08912847
5 3.18574142 -0.38377769
6 -2.16191469 3.18574142
7 -1.55906481 -2.16191469
8 -0.68386566 -1.55906481
9 2.82558433 -0.68386566
10 -1.30128261 2.82558433
11 -1.07739243 -1.30128261
12 2.55934551 -1.07739243
13 -4.07702162 2.55934551
14 -0.50653758 -4.07702162
15 -0.10503544 -0.50653758
16 -2.07739243 -0.10503544
17 0.45077584 -2.07739243
18 1.42829960 0.45077584
19 -2.90678250 1.42829960
20 1.81993880 -2.90678250
21 1.19894884 1.81993880
22 -0.73238754 1.19894884
23 2.69343546 -0.73238754
24 0.39538468 2.69343546
25 0.96981791 0.39538468
26 -5.04153013 0.96981791
27 -0.12594912 -5.04153013
28 0.47508335 -0.12594912
29 4.95404622 0.47508335
30 4.61545581 4.95404622
31 -1.19337060 4.61545581
32 -0.74371628 -1.19337060
33 1.72601590 -0.74371628
34 -1.55906481 1.72601590
35 2.42606316 -1.55906481
36 -1.11025203 2.42606316
37 1.05715678 -1.11025203
38 -0.30677013 1.05715678
39 0.29989135 -0.30677013
40 1.40288462 0.29989135
41 6.69494971 1.40288462
42 -4.70844717 6.69494971
43 -2.06761972 -4.70844717
44 -0.23357661 -2.06761972
45 2.65519408 -0.23357661
46 3.52648592 2.65519408
47 0.05276930 3.52648592
48 -3.21927651 0.05276930
49 -2.18344555 -3.21927651
50 -0.87476219 -2.18344555
51 -6.11809676 -0.87476219
52 0.13566955 -6.11809676
53 -1.97857607 0.13566955
54 1.93480888 -1.97857607
55 -0.76524713 1.93480888
56 -0.19275211 -0.76524713
57 0.89113411 -0.19275211
58 -1.29877408 0.89113411
59 -0.53336470 -1.29877408
60 -2.05473754 -0.53336470
61 2.46568145 -2.05473754
62 -0.18669529 2.46568145
63 1.82266660 -0.18669529
64 -1.09636322 1.82266660
65 1.48287830 -1.09636322
66 -1.86230702 1.48287830
67 2.76959693 -1.86230702
68 1.94135530 2.76959693
69 -1.22668144 1.94135530
70 2.23475287 -1.22668144
71 0.48287830 2.23475287
72 -0.44770350 0.48287830
73 -1.64377152 -0.44770350
74 -0.73593961 -1.64377152
75 -1.60461532 -0.73593961
76 -0.37968527 -1.60461532
77 -0.41768217 -0.37968527
78 2.95404622 -0.41768217
79 -2.11109226 2.95404622
80 -2.52233082 -2.11109226
81 1.52579625 -2.52233082
82 -0.33962972 1.52579625
83 -1.48614967 -0.33962972
84 4.66669935 -1.48614967
85 -0.31701812 4.66669935
86 -0.15512923 -0.31701812
87 -0.71741493 -0.15512923
88 0.73962062 -0.71741493
89 0.17285924 0.73962062
90 0.47932622 0.17285924
91 -3.90434809 0.47932622
92 0.27503146 -3.90434809
93 1.17211913 0.27503146
94 0.98382907 1.17211913
95 0.25989113 0.98382907
96 -2.47747746 0.25989113
97 -0.55887400 -2.47747746
98 -0.85646269 -0.55887400
99 0.48732593 -0.85646269
100 0.02690603 0.48732593
101 0.98329841 0.02690603
102 -0.99015863 0.98329841
103 1.79952655 -0.99015863
104 -2.60092089 1.79952655
105 -0.65343953 -2.60092089
106 -1.83838533 -0.65343953
107 -1.53336470 -1.83838533
108 -3.00068165 -1.53336470
109 -3.62992131 -3.00068165
110 -1.62969826 -3.62992131
111 -2.04669878 -1.62969826
112 -0.42073282 -2.04669878
113 0.07079203 -0.42073282
114 1.28799249 0.07079203
115 2.25794824 1.28799249
116 2.23308835 2.25794824
117 -1.04201941 2.23308835
118 0.70616041 -1.04201941
119 3.45142235 0.70616041
120 0.29738660 3.45142235
121 1.78143426 0.29738660
122 -1.82714076 1.78143426
123 -0.46998758 -1.82714076
124 2.70616041 -0.46998758
125 0.95849385 2.70616041
126 0.27947910 0.95849385
127 -1.60461532 0.27947910
128 -1.03400786 -1.60461532
129 -2.04772725 -1.03400786
130 0.27947910 -2.04772725
131 2.23920050 0.27947910
132 -1.91651706 2.23920050
133 -0.40809723 -1.91651706
134 2.18711834 -0.40809723
135 1.11634254 2.18711834
136 0.88230840 1.11634254
137 -3.99879408 0.88230840
138 4.52482140 -3.99879408
139 -1.17566889 4.52482140
140 0.27374022 -1.17566889
141 -0.05336062 0.27374022
142 1.44557106 -0.05336062
143 -0.59687402 1.44557106
144 0.84335723 -0.59687402
145 -0.62663676 0.84335723
146 -2.90241460 -0.62663676
147 -4.03804985 -2.90241460
148 1.64182944 -4.03804985
149 1.26073136 1.64182944
150 -0.30232249 1.26073136
151 0.54801677 -0.30232249
152 -1.42351594 0.54801677
153 1.56412700 -1.42351594
154 1.55431993 1.56412700
155 1.24299614 1.55431993
> 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/7rckq1290447455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8rckq1290447455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9kl1t1290447455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10kl1t1290447455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11nm0z1290447455.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/12r4yn1290447455.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/13g5vy1290447455.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/148wuj1290447455.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/15uxbp1290447455.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/168p9y1290447455.tab")
+ }
>
> try(system("convert tmp/1dk4h1290447455.ps tmp/1dk4h1290447455.png",intern=TRUE))
character(0)
> try(system("convert tmp/26b321290447455.ps tmp/26b321290447455.png",intern=TRUE))
character(0)
> try(system("convert tmp/36b321290447455.ps tmp/36b321290447455.png",intern=TRUE))
character(0)
> try(system("convert tmp/46b321290447455.ps tmp/46b321290447455.png",intern=TRUE))
character(0)
> try(system("convert tmp/5z3l51290447455.ps tmp/5z3l51290447455.png",intern=TRUE))
character(0)
> try(system("convert tmp/6z3l51290447455.ps tmp/6z3l51290447455.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rckq1290447455.ps tmp/7rckq1290447455.png",intern=TRUE))
character(0)
> try(system("convert tmp/8rckq1290447455.ps tmp/8rckq1290447455.png",intern=TRUE))
character(0)
> try(system("convert tmp/9kl1t1290447455.ps tmp/9kl1t1290447455.png",intern=TRUE))
character(0)
> try(system("convert tmp/10kl1t1290447455.ps tmp/10kl1t1290447455.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.260 1.755 9.376