R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(68
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+ ,dim=c(4
+ ,144)
+ ,dimnames=list(c('CompendiumViews'
+ ,'BloggedComputations'
+ ,'Hyperlinks'
+ ,'submittedfeedback')
+ ,1:144))
> y <- array(NA,dim=c(4,144),dimnames=list(c('CompendiumViews','BloggedComputations','Hyperlinks','submittedfeedback'),1:144))
> 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'
> 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
CompendiumViews BloggedComputations Hyperlinks submittedfeedback
1 68 13 13 20
2 17 26 27 28
3 1 0 0 0
4 114 37 37 40
5 95 47 39 60
6 148 80 99 60
7 56 21 21 44
8 26 36 33 52
9 63 35 36 60
10 96 40 44 52
11 74 35 33 24
12 65 46 47 64
13 40 20 19 26
14 173 24 41 48
15 28 19 22 36
16 55 15 17 40
17 58 48 46 64
18 25 0 0 20
19 103 38 31 79
20 29 12 20 16
21 31 10 10 52
22 43 51 55 52
23 74 4 6 44
24 99 24 17 29
25 25 39 33 40
26 69 19 33 28
27 62 23 32 49
28 25 39 37 60
29 38 37 44 52
30 57 20 22 28
31 52 20 15 56
32 91 41 18 35
33 48 26 25 12
34 52 0 7 32
35 35 31 35 48
36 0 0 0 0
37 31 8 14 48
38 107 35 31 31
39 242 3 9 64
40 41 47 59 72
41 57 42 62 36
42 32 11 12 56
43 17 10 23 28
44 36 26 31 52
45 29 27 57 44
46 22 0 23 44
47 21 15 14 55
48 41 32 31 36
49 64 13 17 48
50 71 24 24 44
51 28 10 11 66
52 36 14 16 40
53 45 24 32 44
54 22 29 36 48
55 27 40 37 68
56 38 22 25 24
57 26 27 30 32
58 41 8 10 44
59 21 27 16 52
60 28 0 3 56
61 36 0 0 68
62 58 17 17 32
63 65 7 9 34
64 29 18 22 36
65 21 7 5 34
66 19 24 23 56
67 55 18 16 64
68 119 39 53 52
69 34 17 23 48
70 25 0 0 40
71 113 39 51 36
72 46 20 25 10
73 28 29 51 48
74 63 27 46 25
75 52 23 16 68
76 35 0 0 36
77 32 31 25 32
78 45 19 34 36
79 42 12 14 43
80 28 23 32 17
81 32 33 24 52
82 32 21 16 56
83 27 17 19 40
84 69 27 27 48
85 30 14 24 40
86 48 12 12 48
87 57 21 43 68
88 36 14 13 44
89 20 14 19 40
90 54 22 24 40
91 26 25 27 28
92 58 36 26 40
93 35 10 14 44
94 28 16 26 20
95 8 12 15 22
96 96 20 30 56
97 50 38 33 52
98 15 13 14 2
99 65 12 11 52
100 33 11 12 30
101 7 8 8 3
102 17 22 22 20
103 55 14 12 48
104 32 7 6 32
105 22 14 10 36
106 41 2 1 45
107 50 35 31 40
108 7 5 5 8
109 0 0 0 0
110 26 34 35 32
111 22 12 15 28
112 26 34 36 44
113 37 30 27 56
114 29 21 36 13
115 0 0 0 0
116 0 0 0 0
117 42 28 29 52
118 51 16 19 51
119 77 12 16 52
120 32 14 15 48
121 63 7 1 3
122 50 41 36 48
123 18 21 22 24
124 37 28 16 37
125 23 1 1 32
126 19 10 10 8
127 39 31 31 44
128 38 7 22 48
129 55 26 22 56
130 22 1 0 8
131 7 0 0 0
132 21 12 10 25
133 5 0 0 4
134 21 17 9 12
135 1 5 0 0
136 22 4 0 6
137 0 0 0 0
138 31 6 7 48
139 25 0 2 52
140 0 0 0 0
141 4 0 0 0
142 20 15 16 12
143 29 0 25 28
144 33 12 6 40
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) BloggedComputations Hyperlinks
9.6344 0.1596 0.4719
submittedfeedback
0.5390
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-43.132 -14.108 -7.260 7.912 193.142
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.6344 5.5126 1.748 0.082704 .
BloggedComputations 0.1596 0.3699 0.431 0.666823
Hyperlinks 0.4719 0.3226 1.463 0.145720
submittedfeedback 0.5390 0.1457 3.700 0.000309 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 28.99 on 140 degrees of freedom
Multiple R-squared: 0.2576, Adjusted R-squared: 0.2417
F-statistic: 16.2 on 3 and 140 DF, p-value: 4.307e-09
> 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]
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[21,] 0.9352075 1.295850e-01 6.479248e-02
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[23,] 0.9645604 7.087915e-02 3.543958e-02
[24,] 0.9539548 9.209041e-02 4.604520e-02
[25,] 0.9378677 1.242647e-01 6.213233e-02
[26,] 0.9739071 5.218588e-02 2.609294e-02
[27,] 0.9659964 6.800724e-02 3.400362e-02
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[103,] 0.9999997 5.355398e-07 2.677699e-07
[104,] 0.9999995 1.036165e-06 5.180826e-07
[105,] 0.9999989 2.296572e-06 1.148286e-06
[106,] 0.9999987 2.562298e-06 1.281149e-06
[107,] 0.9999980 3.959417e-06 1.979709e-06
[108,] 0.9999955 8.971340e-06 4.485670e-06
[109,] 0.9999909 1.810047e-05 9.050237e-06
[110,] 0.9999824 3.520668e-05 1.760334e-05
[111,] 0.9999633 7.334829e-05 3.667415e-05
[112,] 0.9999262 1.475868e-04 7.379342e-05
[113,] 0.9999923 1.535004e-05 7.675022e-06
[114,] 0.9999816 3.676787e-05 1.838394e-05
[115,] 1.0000000 1.771239e-10 8.856193e-11
[116,] 1.0000000 9.066494e-10 4.533247e-10
[117,] 1.0000000 8.721642e-10 4.360821e-10
[118,] 1.0000000 4.655193e-09 2.327596e-09
[119,] 1.0000000 2.366033e-08 1.183016e-08
[120,] 0.9999999 1.054826e-07 5.274128e-08
[121,] 0.9999999 1.869443e-07 9.347213e-08
[122,] 0.9999995 9.452881e-07 4.726440e-07
[123,] 0.9999983 3.497098e-06 1.748549e-06
[124,] 0.9999991 1.802226e-06 9.011129e-07
[125,] 0.9999956 8.757431e-06 4.378716e-06
[126,] 0.9999804 3.921693e-05 1.960847e-05
[127,] 0.9998947 2.105914e-04 1.052957e-04
[128,] 0.9994706 1.058834e-03 5.294168e-04
[129,] 0.9981637 3.672550e-03 1.836275e-03
[130,] 0.9997821 4.357292e-04 2.178646e-04
[131,] 0.9978839 4.232135e-03 2.116067e-03
> postscript(file="/var/www/rcomp/tmp/117qj1322147561.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2zvag1322147561.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/31iuz1322147561.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/472sy1322147561.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/59lgi1322147561.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 144
Frequency = 1
1 2 3 4 5 6
39.3757925 -24.6175700 -8.6344354 59.4397048 27.1194580 46.5396309
7 8 9 10 11 12
9.3873386 -32.9814526 -1.5497032 31.1894232 30.2708322 -8.6521169
13 14 15 16 17 18
4.1931473 114.3147551 -14.4531523 13.3885671 -15.4994300 4.5850746
19 20 21 22 23 24
30.0894572 -0.6117019 -12.9785419 -28.7568924 37.1787847 61.8814476
25 26 27 28 29 30
-27.9919549 25.6683147 7.1822895 -40.6599828 -26.3317804 18.6994449
31 32 33 34 35 36
1.9099501 47.4622369 15.9505909 21.8135894 -21.9711296 -9.6344354
37 38 39 40 41 42
-12.3907841 60.4414296 193.1422400 -42.7865251 -7.9993025 -15.2380076
43 44 45 46 47 48
-20.1764517 -20.4416959 -35.5580946 -22.2048560 -27.2811471 -7.7748966
49 50 51 52 53 54
18.3955686 22.4928889 -23.9967694 -4.9799497 -7.2821868 -35.1238165
55 56 57 58 59 60
-43.1317776 0.1206920 -19.3489202 1.6528517 -28.5230278 -13.2354609
61 62 63 64 65 66
-10.2881015 20.3815655 31.6745800 -13.2935535 -10.4378822 -35.5035207
67 68 69 70 71 72
0.4450671 50.1020619 -15.0741332 -6.1954155 53.6702228 15.9862326
73 74 75 76 77 78
-36.2020834 13.8741000 -5.5090248 5.9606825 -11.6278930 -3.1157658
79 80 81 82 83 84
0.6659433 -9.5689265 -22.2556962 -18.7215332 -15.8743994 16.4423411
85 86 87 88 89 90
-14.7550253 4.9145897 -12.9307076 -5.7203943 -22.3956031 7.9681844
91 92 93 94 95 96
-15.4579713 8.7900326 -6.5538837 -7.2375018 -22.4864266 38.8316832
97 98 99 100 101 102
-9.3006502 -4.3936509 20.2303761 -0.2233705 -9.3033748 -17.3075566
103 104 105 106 107 108
11.5953921 1.1682824 -13.9925449 6.3183800 -1.4097909 -10.1040476
109 110 111 112 113 114
-9.6344354 -22.8255339 -11.7205736 -29.7657124 -20.3486512 -7.9811687
115 116 117 118 119 120
-9.6344354 -9.6344354 -13.8171246 2.3559299 29.8709538 -12.8202613
121 122 123 124 125 126
50.1594152 -9.0390019 -18.3040558 -4.5972591 -4.5147027 -1.2614637
127 128 129 130 131 132
-13.9274938 -9.0062610 0.6491662 7.8937698 -2.6344354 -8.7440778
133 134 135 136 137 138
-6.7905334 -2.0628688 -9.4324293 8.4930225 -9.6344354 -8.7683954
139 140 141 142 143 144
-13.6074784 -9.6344354 -5.6344354 -6.0468624 -7.5242329 -2.9419076
> postscript(file="/var/www/rcomp/tmp/62ic31322147561.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 144
Frequency = 1
lag(myerror, k = 1) myerror
0 39.3757925 NA
1 -24.6175700 39.3757925
2 -8.6344354 -24.6175700
3 59.4397048 -8.6344354
4 27.1194580 59.4397048
5 46.5396309 27.1194580
6 9.3873386 46.5396309
7 -32.9814526 9.3873386
8 -1.5497032 -32.9814526
9 31.1894232 -1.5497032
10 30.2708322 31.1894232
11 -8.6521169 30.2708322
12 4.1931473 -8.6521169
13 114.3147551 4.1931473
14 -14.4531523 114.3147551
15 13.3885671 -14.4531523
16 -15.4994300 13.3885671
17 4.5850746 -15.4994300
18 30.0894572 4.5850746
19 -0.6117019 30.0894572
20 -12.9785419 -0.6117019
21 -28.7568924 -12.9785419
22 37.1787847 -28.7568924
23 61.8814476 37.1787847
24 -27.9919549 61.8814476
25 25.6683147 -27.9919549
26 7.1822895 25.6683147
27 -40.6599828 7.1822895
28 -26.3317804 -40.6599828
29 18.6994449 -26.3317804
30 1.9099501 18.6994449
31 47.4622369 1.9099501
32 15.9505909 47.4622369
33 21.8135894 15.9505909
34 -21.9711296 21.8135894
35 -9.6344354 -21.9711296
36 -12.3907841 -9.6344354
37 60.4414296 -12.3907841
38 193.1422400 60.4414296
39 -42.7865251 193.1422400
40 -7.9993025 -42.7865251
41 -15.2380076 -7.9993025
42 -20.1764517 -15.2380076
43 -20.4416959 -20.1764517
44 -35.5580946 -20.4416959
45 -22.2048560 -35.5580946
46 -27.2811471 -22.2048560
47 -7.7748966 -27.2811471
48 18.3955686 -7.7748966
49 22.4928889 18.3955686
50 -23.9967694 22.4928889
51 -4.9799497 -23.9967694
52 -7.2821868 -4.9799497
53 -35.1238165 -7.2821868
54 -43.1317776 -35.1238165
55 0.1206920 -43.1317776
56 -19.3489202 0.1206920
57 1.6528517 -19.3489202
58 -28.5230278 1.6528517
59 -13.2354609 -28.5230278
60 -10.2881015 -13.2354609
61 20.3815655 -10.2881015
62 31.6745800 20.3815655
63 -13.2935535 31.6745800
64 -10.4378822 -13.2935535
65 -35.5035207 -10.4378822
66 0.4450671 -35.5035207
67 50.1020619 0.4450671
68 -15.0741332 50.1020619
69 -6.1954155 -15.0741332
70 53.6702228 -6.1954155
71 15.9862326 53.6702228
72 -36.2020834 15.9862326
73 13.8741000 -36.2020834
74 -5.5090248 13.8741000
75 5.9606825 -5.5090248
76 -11.6278930 5.9606825
77 -3.1157658 -11.6278930
78 0.6659433 -3.1157658
79 -9.5689265 0.6659433
80 -22.2556962 -9.5689265
81 -18.7215332 -22.2556962
82 -15.8743994 -18.7215332
83 16.4423411 -15.8743994
84 -14.7550253 16.4423411
85 4.9145897 -14.7550253
86 -12.9307076 4.9145897
87 -5.7203943 -12.9307076
88 -22.3956031 -5.7203943
89 7.9681844 -22.3956031
90 -15.4579713 7.9681844
91 8.7900326 -15.4579713
92 -6.5538837 8.7900326
93 -7.2375018 -6.5538837
94 -22.4864266 -7.2375018
95 38.8316832 -22.4864266
96 -9.3006502 38.8316832
97 -4.3936509 -9.3006502
98 20.2303761 -4.3936509
99 -0.2233705 20.2303761
100 -9.3033748 -0.2233705
101 -17.3075566 -9.3033748
102 11.5953921 -17.3075566
103 1.1682824 11.5953921
104 -13.9925449 1.1682824
105 6.3183800 -13.9925449
106 -1.4097909 6.3183800
107 -10.1040476 -1.4097909
108 -9.6344354 -10.1040476
109 -22.8255339 -9.6344354
110 -11.7205736 -22.8255339
111 -29.7657124 -11.7205736
112 -20.3486512 -29.7657124
113 -7.9811687 -20.3486512
114 -9.6344354 -7.9811687
115 -9.6344354 -9.6344354
116 -13.8171246 -9.6344354
117 2.3559299 -13.8171246
118 29.8709538 2.3559299
119 -12.8202613 29.8709538
120 50.1594152 -12.8202613
121 -9.0390019 50.1594152
122 -18.3040558 -9.0390019
123 -4.5972591 -18.3040558
124 -4.5147027 -4.5972591
125 -1.2614637 -4.5147027
126 -13.9274938 -1.2614637
127 -9.0062610 -13.9274938
128 0.6491662 -9.0062610
129 7.8937698 0.6491662
130 -2.6344354 7.8937698
131 -8.7440778 -2.6344354
132 -6.7905334 -8.7440778
133 -2.0628688 -6.7905334
134 -9.4324293 -2.0628688
135 8.4930225 -9.4324293
136 -9.6344354 8.4930225
137 -8.7683954 -9.6344354
138 -13.6074784 -8.7683954
139 -9.6344354 -13.6074784
140 -5.6344354 -9.6344354
141 -6.0468624 -5.6344354
142 -7.5242329 -6.0468624
143 -2.9419076 -7.5242329
144 NA -2.9419076
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -24.6175700 39.3757925
[2,] -8.6344354 -24.6175700
[3,] 59.4397048 -8.6344354
[4,] 27.1194580 59.4397048
[5,] 46.5396309 27.1194580
[6,] 9.3873386 46.5396309
[7,] -32.9814526 9.3873386
[8,] -1.5497032 -32.9814526
[9,] 31.1894232 -1.5497032
[10,] 30.2708322 31.1894232
[11,] -8.6521169 30.2708322
[12,] 4.1931473 -8.6521169
[13,] 114.3147551 4.1931473
[14,] -14.4531523 114.3147551
[15,] 13.3885671 -14.4531523
[16,] -15.4994300 13.3885671
[17,] 4.5850746 -15.4994300
[18,] 30.0894572 4.5850746
[19,] -0.6117019 30.0894572
[20,] -12.9785419 -0.6117019
[21,] -28.7568924 -12.9785419
[22,] 37.1787847 -28.7568924
[23,] 61.8814476 37.1787847
[24,] -27.9919549 61.8814476
[25,] 25.6683147 -27.9919549
[26,] 7.1822895 25.6683147
[27,] -40.6599828 7.1822895
[28,] -26.3317804 -40.6599828
[29,] 18.6994449 -26.3317804
[30,] 1.9099501 18.6994449
[31,] 47.4622369 1.9099501
[32,] 15.9505909 47.4622369
[33,] 21.8135894 15.9505909
[34,] -21.9711296 21.8135894
[35,] -9.6344354 -21.9711296
[36,] -12.3907841 -9.6344354
[37,] 60.4414296 -12.3907841
[38,] 193.1422400 60.4414296
[39,] -42.7865251 193.1422400
[40,] -7.9993025 -42.7865251
[41,] -15.2380076 -7.9993025
[42,] -20.1764517 -15.2380076
[43,] -20.4416959 -20.1764517
[44,] -35.5580946 -20.4416959
[45,] -22.2048560 -35.5580946
[46,] -27.2811471 -22.2048560
[47,] -7.7748966 -27.2811471
[48,] 18.3955686 -7.7748966
[49,] 22.4928889 18.3955686
[50,] -23.9967694 22.4928889
[51,] -4.9799497 -23.9967694
[52,] -7.2821868 -4.9799497
[53,] -35.1238165 -7.2821868
[54,] -43.1317776 -35.1238165
[55,] 0.1206920 -43.1317776
[56,] -19.3489202 0.1206920
[57,] 1.6528517 -19.3489202
[58,] -28.5230278 1.6528517
[59,] -13.2354609 -28.5230278
[60,] -10.2881015 -13.2354609
[61,] 20.3815655 -10.2881015
[62,] 31.6745800 20.3815655
[63,] -13.2935535 31.6745800
[64,] -10.4378822 -13.2935535
[65,] -35.5035207 -10.4378822
[66,] 0.4450671 -35.5035207
[67,] 50.1020619 0.4450671
[68,] -15.0741332 50.1020619
[69,] -6.1954155 -15.0741332
[70,] 53.6702228 -6.1954155
[71,] 15.9862326 53.6702228
[72,] -36.2020834 15.9862326
[73,] 13.8741000 -36.2020834
[74,] -5.5090248 13.8741000
[75,] 5.9606825 -5.5090248
[76,] -11.6278930 5.9606825
[77,] -3.1157658 -11.6278930
[78,] 0.6659433 -3.1157658
[79,] -9.5689265 0.6659433
[80,] -22.2556962 -9.5689265
[81,] -18.7215332 -22.2556962
[82,] -15.8743994 -18.7215332
[83,] 16.4423411 -15.8743994
[84,] -14.7550253 16.4423411
[85,] 4.9145897 -14.7550253
[86,] -12.9307076 4.9145897
[87,] -5.7203943 -12.9307076
[88,] -22.3956031 -5.7203943
[89,] 7.9681844 -22.3956031
[90,] -15.4579713 7.9681844
[91,] 8.7900326 -15.4579713
[92,] -6.5538837 8.7900326
[93,] -7.2375018 -6.5538837
[94,] -22.4864266 -7.2375018
[95,] 38.8316832 -22.4864266
[96,] -9.3006502 38.8316832
[97,] -4.3936509 -9.3006502
[98,] 20.2303761 -4.3936509
[99,] -0.2233705 20.2303761
[100,] -9.3033748 -0.2233705
[101,] -17.3075566 -9.3033748
[102,] 11.5953921 -17.3075566
[103,] 1.1682824 11.5953921
[104,] -13.9925449 1.1682824
[105,] 6.3183800 -13.9925449
[106,] -1.4097909 6.3183800
[107,] -10.1040476 -1.4097909
[108,] -9.6344354 -10.1040476
[109,] -22.8255339 -9.6344354
[110,] -11.7205736 -22.8255339
[111,] -29.7657124 -11.7205736
[112,] -20.3486512 -29.7657124
[113,] -7.9811687 -20.3486512
[114,] -9.6344354 -7.9811687
[115,] -9.6344354 -9.6344354
[116,] -13.8171246 -9.6344354
[117,] 2.3559299 -13.8171246
[118,] 29.8709538 2.3559299
[119,] -12.8202613 29.8709538
[120,] 50.1594152 -12.8202613
[121,] -9.0390019 50.1594152
[122,] -18.3040558 -9.0390019
[123,] -4.5972591 -18.3040558
[124,] -4.5147027 -4.5972591
[125,] -1.2614637 -4.5147027
[126,] -13.9274938 -1.2614637
[127,] -9.0062610 -13.9274938
[128,] 0.6491662 -9.0062610
[129,] 7.8937698 0.6491662
[130,] -2.6344354 7.8937698
[131,] -8.7440778 -2.6344354
[132,] -6.7905334 -8.7440778
[133,] -2.0628688 -6.7905334
[134,] -9.4324293 -2.0628688
[135,] 8.4930225 -9.4324293
[136,] -9.6344354 8.4930225
[137,] -8.7683954 -9.6344354
[138,] -13.6074784 -8.7683954
[139,] -9.6344354 -13.6074784
[140,] -5.6344354 -9.6344354
[141,] -6.0468624 -5.6344354
[142,] -7.5242329 -6.0468624
[143,] -2.9419076 -7.5242329
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -24.6175700 39.3757925
2 -8.6344354 -24.6175700
3 59.4397048 -8.6344354
4 27.1194580 59.4397048
5 46.5396309 27.1194580
6 9.3873386 46.5396309
7 -32.9814526 9.3873386
8 -1.5497032 -32.9814526
9 31.1894232 -1.5497032
10 30.2708322 31.1894232
11 -8.6521169 30.2708322
12 4.1931473 -8.6521169
13 114.3147551 4.1931473
14 -14.4531523 114.3147551
15 13.3885671 -14.4531523
16 -15.4994300 13.3885671
17 4.5850746 -15.4994300
18 30.0894572 4.5850746
19 -0.6117019 30.0894572
20 -12.9785419 -0.6117019
21 -28.7568924 -12.9785419
22 37.1787847 -28.7568924
23 61.8814476 37.1787847
24 -27.9919549 61.8814476
25 25.6683147 -27.9919549
26 7.1822895 25.6683147
27 -40.6599828 7.1822895
28 -26.3317804 -40.6599828
29 18.6994449 -26.3317804
30 1.9099501 18.6994449
31 47.4622369 1.9099501
32 15.9505909 47.4622369
33 21.8135894 15.9505909
34 -21.9711296 21.8135894
35 -9.6344354 -21.9711296
36 -12.3907841 -9.6344354
37 60.4414296 -12.3907841
38 193.1422400 60.4414296
39 -42.7865251 193.1422400
40 -7.9993025 -42.7865251
41 -15.2380076 -7.9993025
42 -20.1764517 -15.2380076
43 -20.4416959 -20.1764517
44 -35.5580946 -20.4416959
45 -22.2048560 -35.5580946
46 -27.2811471 -22.2048560
47 -7.7748966 -27.2811471
48 18.3955686 -7.7748966
49 22.4928889 18.3955686
50 -23.9967694 22.4928889
51 -4.9799497 -23.9967694
52 -7.2821868 -4.9799497
53 -35.1238165 -7.2821868
54 -43.1317776 -35.1238165
55 0.1206920 -43.1317776
56 -19.3489202 0.1206920
57 1.6528517 -19.3489202
58 -28.5230278 1.6528517
59 -13.2354609 -28.5230278
60 -10.2881015 -13.2354609
61 20.3815655 -10.2881015
62 31.6745800 20.3815655
63 -13.2935535 31.6745800
64 -10.4378822 -13.2935535
65 -35.5035207 -10.4378822
66 0.4450671 -35.5035207
67 50.1020619 0.4450671
68 -15.0741332 50.1020619
69 -6.1954155 -15.0741332
70 53.6702228 -6.1954155
71 15.9862326 53.6702228
72 -36.2020834 15.9862326
73 13.8741000 -36.2020834
74 -5.5090248 13.8741000
75 5.9606825 -5.5090248
76 -11.6278930 5.9606825
77 -3.1157658 -11.6278930
78 0.6659433 -3.1157658
79 -9.5689265 0.6659433
80 -22.2556962 -9.5689265
81 -18.7215332 -22.2556962
82 -15.8743994 -18.7215332
83 16.4423411 -15.8743994
84 -14.7550253 16.4423411
85 4.9145897 -14.7550253
86 -12.9307076 4.9145897
87 -5.7203943 -12.9307076
88 -22.3956031 -5.7203943
89 7.9681844 -22.3956031
90 -15.4579713 7.9681844
91 8.7900326 -15.4579713
92 -6.5538837 8.7900326
93 -7.2375018 -6.5538837
94 -22.4864266 -7.2375018
95 38.8316832 -22.4864266
96 -9.3006502 38.8316832
97 -4.3936509 -9.3006502
98 20.2303761 -4.3936509
99 -0.2233705 20.2303761
100 -9.3033748 -0.2233705
101 -17.3075566 -9.3033748
102 11.5953921 -17.3075566
103 1.1682824 11.5953921
104 -13.9925449 1.1682824
105 6.3183800 -13.9925449
106 -1.4097909 6.3183800
107 -10.1040476 -1.4097909
108 -9.6344354 -10.1040476
109 -22.8255339 -9.6344354
110 -11.7205736 -22.8255339
111 -29.7657124 -11.7205736
112 -20.3486512 -29.7657124
113 -7.9811687 -20.3486512
114 -9.6344354 -7.9811687
115 -9.6344354 -9.6344354
116 -13.8171246 -9.6344354
117 2.3559299 -13.8171246
118 29.8709538 2.3559299
119 -12.8202613 29.8709538
120 50.1594152 -12.8202613
121 -9.0390019 50.1594152
122 -18.3040558 -9.0390019
123 -4.5972591 -18.3040558
124 -4.5147027 -4.5972591
125 -1.2614637 -4.5147027
126 -13.9274938 -1.2614637
127 -9.0062610 -13.9274938
128 0.6491662 -9.0062610
129 7.8937698 0.6491662
130 -2.6344354 7.8937698
131 -8.7440778 -2.6344354
132 -6.7905334 -8.7440778
133 -2.0628688 -6.7905334
134 -9.4324293 -2.0628688
135 8.4930225 -9.4324293
136 -9.6344354 8.4930225
137 -8.7683954 -9.6344354
138 -13.6074784 -8.7683954
139 -9.6344354 -13.6074784
140 -5.6344354 -9.6344354
141 -6.0468624 -5.6344354
142 -7.5242329 -6.0468624
143 -2.9419076 -7.5242329
> 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/74a8b1322147561.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8fyzg1322147561.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/96qz51322147561.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10maln1322147561.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11sgn01322147561.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12ifzs1322147561.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/130fa21322147561.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14yn621322147561.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/15yfdd1322147562.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/16ddtj1322147562.tab")
+ }
>
> try(system("convert tmp/117qj1322147561.ps tmp/117qj1322147561.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zvag1322147561.ps tmp/2zvag1322147561.png",intern=TRUE))
character(0)
> try(system("convert tmp/31iuz1322147561.ps tmp/31iuz1322147561.png",intern=TRUE))
character(0)
> try(system("convert tmp/472sy1322147561.ps tmp/472sy1322147561.png",intern=TRUE))
character(0)
> try(system("convert tmp/59lgi1322147561.ps tmp/59lgi1322147561.png",intern=TRUE))
character(0)
> try(system("convert tmp/62ic31322147561.ps tmp/62ic31322147561.png",intern=TRUE))
character(0)
> try(system("convert tmp/74a8b1322147561.ps tmp/74a8b1322147561.png",intern=TRUE))
character(0)
> try(system("convert tmp/8fyzg1322147561.ps tmp/8fyzg1322147561.png",intern=TRUE))
character(0)
> try(system("convert tmp/96qz51322147561.ps tmp/96qz51322147561.png",intern=TRUE))
character(0)
> try(system("convert tmp/10maln1322147561.ps tmp/10maln1322147561.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.260 0.310 5.561