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.
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+ ,dim=c(5
+ ,144)
+ ,dimnames=list(c('month'
+ ,'CompendiumViews'
+ ,'BloggedComputations'
+ ,'includedhyperlinks'
+ ,'submittedFeedbackMessages')
+ ,1:144))
> y <- array(NA,dim=c(5,144),dimnames=list(c('month','CompendiumViews','BloggedComputations','includedhyperlinks','submittedFeedbackMessages'),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'
> #'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
month CompendiumViews BloggedComputations includedhyperlinks
1 9 68 13 13
2 9 17 26 27
3 9 1 0 0
4 9 114 37 37
5 9 95 47 39
6 9 148 80 99
7 9 56 21 21
8 9 26 36 33
9 9 63 35 36
10 9 96 40 44
11 9 74 35 33
12 9 65 46 47
13 9 40 20 19
14 9 173 24 41
15 9 28 19 22
16 9 55 15 17
17 9 58 48 46
18 9 25 0 0
19 9 103 38 31
20 9 29 12 20
21 9 31 10 10
22 9 43 51 55
23 9 74 4 6
24 9 99 24 17
25 9 25 39 33
26 9 69 19 33
27 9 62 23 32
28 9 25 39 37
29 9 38 37 44
30 9 57 20 22
31 9 52 20 15
32 9 91 41 18
33 9 48 26 25
34 9 52 0 7
35 9 35 31 35
36 9 0 0 0
37 9 31 8 14
38 9 107 35 31
39 9 242 3 9
40 9 41 47 59
41 9 57 42 62
42 9 32 11 12
43 9 17 10 23
44 9 36 26 31
45 9 29 27 57
46 9 22 0 23
47 9 21 15 14
48 9 41 32 31
49 10 64 13 17
50 10 71 24 24
51 10 28 10 11
52 10 36 14 16
53 10 45 24 32
54 10 22 29 36
55 10 27 40 37
56 10 38 22 25
57 10 26 27 30
58 10 41 8 10
59 10 21 27 16
60 10 28 0 3
61 10 36 0 0
62 10 58 17 17
63 10 65 7 9
64 10 29 18 22
65 10 21 7 5
66 10 19 24 23
67 10 55 18 16
68 10 119 39 53
69 10 34 17 23
70 10 25 0 0
71 10 113 39 51
72 10 46 20 25
73 10 28 29 51
74 10 63 27 46
75 10 52 23 16
76 10 35 0 0
77 10 32 31 25
78 10 45 19 34
79 10 42 12 14
80 10 28 23 32
81 10 32 33 24
82 10 32 21 16
83 10 27 17 19
84 10 69 27 27
85 10 30 14 24
86 10 48 12 12
87 10 57 21 43
88 10 36 14 13
89 10 20 14 19
90 10 54 22 24
91 10 26 25 27
92 10 58 36 26
93 10 35 10 14
94 10 28 16 26
95 10 8 12 15
96 10 96 20 30
97 11 50 38 33
98 11 15 13 14
99 11 65 12 11
100 11 33 11 12
101 11 7 8 8
102 11 17 22 22
103 11 55 14 12
104 11 32 7 6
105 11 22 14 10
106 11 41 2 1
107 11 50 35 31
108 11 7 5 5
109 11 0 0 0
110 11 26 34 35
111 11 22 12 15
112 11 26 34 36
113 11 37 30 27
114 11 29 21 36
115 11 0 0 0
116 11 0 0 0
117 11 42 28 29
118 11 51 16 19
119 11 77 12 16
120 11 32 14 15
121 11 63 7 1
122 11 50 41 36
123 11 18 21 22
124 11 37 28 16
125 11 23 1 1
126 11 19 10 10
127 11 39 31 31
128 11 38 7 22
129 11 55 26 22
130 11 22 1 0
131 11 7 0 0
132 11 21 12 10
133 11 5 0 0
134 11 21 17 9
135 11 1 5 0
136 11 22 4 0
137 11 0 0 0
138 11 31 6 7
139 11 25 0 2
140 11 0 0 0
141 11 4 0 0
142 11 20 15 16
143 11 29 0 25
144 11 33 12 6
submittedFeedbackMessages
1 20
2 28
3 0
4 40
5 60
6 60
7 44
8 52
9 60
10 52
11 24
12 64
13 26
14 48
15 36
16 40
17 64
18 20
19 79
20 16
21 52
22 52
23 44
24 29
25 40
26 28
27 49
28 60
29 52
30 28
31 56
32 35
33 12
34 32
35 48
36 0
37 48
38 31
39 64
40 72
41 36
42 56
43 28
44 52
45 44
46 44
47 55
48 36
49 48
50 44
51 66
52 40
53 44
54 48
55 68
56 24
57 32
58 44
59 52
60 56
61 68
62 32
63 34
64 36
65 34
66 56
67 64
68 52
69 48
70 40
71 36
72 10
73 48
74 25
75 68
76 36
77 32
78 36
79 43
80 17
81 52
82 56
83 40
84 48
85 40
86 48
87 68
88 44
89 40
90 40
91 28
92 40
93 44
94 20
95 22
96 56
97 52
98 2
99 52
100 30
101 3
102 20
103 48
104 32
105 36
106 45
107 40
108 8
109 0
110 32
111 28
112 44
113 56
114 13
115 0
116 0
117 52
118 51
119 52
120 48
121 3
122 48
123 24
124 37
125 32
126 8
127 44
128 48
129 56
130 8
131 0
132 25
133 4
134 12
135 0
136 6
137 0
138 48
139 52
140 0
141 0
142 12
143 28
144 40
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CompendiumViews
10.710523 -0.005468
BloggedComputations includedhyperlinks
0.001721 -0.014034
submittedFeedbackMessages
-0.005593
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.71052 -0.51339 -0.01096 0.56306 1.26606
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.710523 0.140435 76.267 <2e-16 ***
CompendiumViews -0.005468 0.002130 -2.567 0.0113 *
BloggedComputations 0.001721 0.009329 0.184 0.8539
includedhyperlinks -0.014034 0.008191 -1.713 0.0889 .
submittedFeedbackMessages -0.005593 0.003847 -1.454 0.1482
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7307 on 139 degrees of freedom
Multiple R-squared: 0.2269, Adjusted R-squared: 0.2046
F-statistic: 10.2 on 4 and 139 DF, p-value: 2.874e-07
> 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,] 6.820032e-45 1.364006e-44 1.000000e+00
[2,] 8.580364e-61 1.716073e-60 1.000000e+00
[3,] 1.389794e-76 2.779588e-76 1.000000e+00
[4,] 4.270006e-91 8.540012e-91 1.000000e+00
[5,] 6.044544e-101 1.208909e-100 1.000000e+00
[6,] 4.253362e-120 8.506725e-120 1.000000e+00
[7,] 6.030514e-130 1.206103e-129 1.000000e+00
[8,] 7.918616e-145 1.583723e-144 1.000000e+00
[9,] 0.000000e+00 0.000000e+00 1.000000e+00
[10,] 3.001405e-174 6.002809e-174 1.000000e+00
[11,] 2.069280e-188 4.138559e-188 1.000000e+00
[12,] 2.875899e-202 5.751799e-202 1.000000e+00
[13,] 9.391962e-231 1.878392e-230 1.000000e+00
[14,] 1.242165e-238 2.484330e-238 1.000000e+00
[15,] 3.462896e-245 6.925791e-245 1.000000e+00
[16,] 1.396386e-261 2.792771e-261 1.000000e+00
[17,] 8.168795e-272 1.633759e-271 1.000000e+00
[18,] 4.291300e-299 8.582599e-299 1.000000e+00
[19,] 9.431664e-318 1.886333e-317 1.000000e+00
[20,] 0.000000e+00 0.000000e+00 1.000000e+00
[21,] 0.000000e+00 0.000000e+00 1.000000e+00
[22,] 0.000000e+00 0.000000e+00 1.000000e+00
[23,] 0.000000e+00 0.000000e+00 1.000000e+00
[24,] 0.000000e+00 0.000000e+00 1.000000e+00
[25,] 0.000000e+00 0.000000e+00 1.000000e+00
[26,] 0.000000e+00 0.000000e+00 1.000000e+00
[27,] 0.000000e+00 0.000000e+00 1.000000e+00
[28,] 0.000000e+00 0.000000e+00 1.000000e+00
[29,] 0.000000e+00 0.000000e+00 1.000000e+00
[30,] 0.000000e+00 0.000000e+00 1.000000e+00
[31,] 0.000000e+00 0.000000e+00 1.000000e+00
[32,] 0.000000e+00 0.000000e+00 1.000000e+00
[33,] 0.000000e+00 0.000000e+00 1.000000e+00
[34,] 0.000000e+00 0.000000e+00 1.000000e+00
[35,] 0.000000e+00 0.000000e+00 1.000000e+00
[36,] 0.000000e+00 0.000000e+00 1.000000e+00
[37,] 0.000000e+00 0.000000e+00 1.000000e+00
[38,] 0.000000e+00 0.000000e+00 1.000000e+00
[39,] 0.000000e+00 0.000000e+00 1.000000e+00
[40,] 0.000000e+00 0.000000e+00 1.000000e+00
[41,] 0.000000e+00 0.000000e+00 1.000000e+00
[42,] 5.917547e-13 1.183509e-12 1.000000e+00
[43,] 4.341781e-08 8.683562e-08 1.000000e+00
[44,] 3.540490e-06 7.080980e-06 9.999965e-01
[45,] 9.933890e-05 1.986778e-04 9.999007e-01
[46,] 9.790959e-04 1.958192e-03 9.990209e-01
[47,] 4.258785e-03 8.517570e-03 9.957412e-01
[48,] 9.482759e-03 1.896552e-02 9.905172e-01
[49,] 3.106903e-02 6.213807e-02 9.689310e-01
[50,] 6.407250e-02 1.281450e-01 9.359275e-01
[51,] 9.549680e-02 1.909936e-01 9.045032e-01
[52,] 1.311636e-01 2.623271e-01 8.688364e-01
[53,] 1.465305e-01 2.930610e-01 8.534695e-01
[54,] 1.474069e-01 2.948138e-01 8.525931e-01
[55,] 2.108836e-01 4.217673e-01 7.891164e-01
[56,] 2.678033e-01 5.356065e-01 7.321967e-01
[57,] 3.212823e-01 6.425646e-01 6.787177e-01
[58,] 3.780117e-01 7.560233e-01 6.219883e-01
[59,] 4.015973e-01 8.031946e-01 5.984027e-01
[60,] 4.132210e-01 8.264421e-01 5.867790e-01
[61,] 4.947944e-01 9.895888e-01 5.052056e-01
[62,] 5.176480e-01 9.647040e-01 4.823520e-01
[63,] 5.685533e-01 8.628934e-01 4.314467e-01
[64,] 6.413332e-01 7.173335e-01 3.586668e-01
[65,] 7.160531e-01 5.678937e-01 2.839469e-01
[66,] 7.146569e-01 5.706863e-01 2.853431e-01
[67,] 7.353096e-01 5.293808e-01 2.646904e-01
[68,] 7.513648e-01 4.972703e-01 2.486352e-01
[69,] 7.991630e-01 4.016740e-01 2.008370e-01
[70,] 8.454634e-01 3.090733e-01 1.545366e-01
[71,] 8.507736e-01 2.984529e-01 1.492264e-01
[72,] 8.746179e-01 2.507642e-01 1.253821e-01
[73,] 8.995165e-01 2.009671e-01 1.004835e-01
[74,] 9.244272e-01 1.511456e-01 7.557278e-02
[75,] 9.457251e-01 1.085497e-01 5.427486e-02
[76,] 9.626449e-01 7.471018e-02 3.735509e-02
[77,] 9.718641e-01 5.627171e-02 2.813585e-02
[78,] 9.785074e-01 4.298520e-02 2.149260e-02
[79,] 9.878379e-01 2.432417e-02 1.216209e-02
[80,] 9.873708e-01 2.525841e-02 1.262920e-02
[81,] 9.949829e-01 1.003425e-02 5.017125e-03
[82,] 9.984391e-01 3.121729e-03 1.560864e-03
[83,] 9.994595e-01 1.081098e-03 5.405490e-04
[84,] 9.999089e-01 1.821252e-04 9.106258e-05
[85,] 9.999953e-01 9.459478e-06 4.729739e-06
[86,] 9.999999e-01 1.816466e-07 9.082332e-08
[87,] 1.000000e+00 1.020958e-09 5.104790e-10
[88,] 1.000000e+00 5.024616e-17 2.512308e-17
[89,] 1.000000e+00 0.000000e+00 0.000000e+00
[90,] 1.000000e+00 0.000000e+00 0.000000e+00
[91,] 1.000000e+00 0.000000e+00 0.000000e+00
[92,] 1.000000e+00 0.000000e+00 0.000000e+00
[93,] 1.000000e+00 0.000000e+00 0.000000e+00
[94,] 1.000000e+00 0.000000e+00 0.000000e+00
[95,] 1.000000e+00 0.000000e+00 0.000000e+00
[96,] 1.000000e+00 0.000000e+00 0.000000e+00
[97,] 1.000000e+00 0.000000e+00 0.000000e+00
[98,] 1.000000e+00 0.000000e+00 0.000000e+00
[99,] 1.000000e+00 0.000000e+00 0.000000e+00
[100,] 1.000000e+00 0.000000e+00 0.000000e+00
[101,] 1.000000e+00 0.000000e+00 0.000000e+00
[102,] 1.000000e+00 0.000000e+00 0.000000e+00
[103,] 1.000000e+00 0.000000e+00 0.000000e+00
[104,] 1.000000e+00 0.000000e+00 0.000000e+00
[105,] 1.000000e+00 0.000000e+00 0.000000e+00
[106,] 1.000000e+00 0.000000e+00 0.000000e+00
[107,] 1.000000e+00 0.000000e+00 0.000000e+00
[108,] 1.000000e+00 0.000000e+00 0.000000e+00
[109,] 1.000000e+00 0.000000e+00 0.000000e+00
[110,] 1.000000e+00 0.000000e+00 0.000000e+00
[111,] 1.000000e+00 3.306975e-309 1.653488e-309
[112,] 1.000000e+00 4.765652e-287 2.382826e-287
[113,] 1.000000e+00 9.913397e-281 4.956699e-281
[114,] 1.000000e+00 5.994543e-270 2.997272e-270
[115,] 1.000000e+00 3.662207e-247 1.831104e-247
[116,] 1.000000e+00 1.715660e-246 8.578300e-247
[117,] 1.000000e+00 2.078970e-229 1.039485e-229
[118,] 1.000000e+00 9.021512e-201 4.510756e-201
[119,] 1.000000e+00 1.870132e-191 9.350661e-192
[120,] 1.000000e+00 4.327947e-176 2.163973e-176
[121,] 1.000000e+00 0.000000e+00 0.000000e+00
[122,] 1.000000e+00 1.972554e-150 9.862770e-151
[123,] 1.000000e+00 4.289463e-130 2.144732e-130
[124,] 1.000000e+00 7.135486e-125 3.567743e-125
[125,] 1.000000e+00 1.278950e-99 6.394751e-100
[126,] 1.000000e+00 4.658893e-90 2.329446e-90
[127,] 1.000000e+00 2.060070e-74 1.030035e-74
[128,] 1.000000e+00 1.451633e-61 7.258163e-62
[129,] 1.000000e+00 3.621999e-44 1.810999e-44
> postscript(file="/var/www/rcomp/tmp/16b301322154884.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2qfy91322154884.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3exdw1322154884.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4dwjd1322154884.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/521kc1322154884.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
-1.066726477 -1.126764677 -1.705054841 -0.407789476 -0.388966560 0.686134122
7 8 9 10 11 12
-0.899601869 -0.876313053 -0.585408055 -0.346024479 -0.768717590 -0.416650995
13 14 15 16 17 18
-1.114125174 0.038110062 -1.079989294 -0.973254782 -0.472407088 -1.461944832
19 20 21 22 23 24
-0.335731283 -1.202408289 -1.127014255 -0.500407357 -0.982427111 -0.809658015
25 26 27 28 29 30
-0.954064716 -0.746148858 -0.687887493 -0.786060483 -0.658033244 -0.967870902
31 32 33 34 35 36
-0.936841653 -0.835070515 -1.074803677 -1.148934667 -0.812795448 -1.710523326
37 38 39 40 41 42
-1.089807437 -0.577173290 0.091969218 -0.336455624 -0.399610361 -1.072824684
43 44 45 46 47 48
-1.155364654 -0.832485959 -0.552336944 -1.021317954 -1.117386877 -0.904963340
49 50 51 52 53 54
0.124150366 0.219365443 -0.051078783 -0.089469339 0.189460554 0.133590949
55 56 57 58 59 60
0.267901919 -0.055484230 -0.014792763 -0.113633722 -0.126751315 -0.202076510
61 62 63 64 65 66
-0.133312200 -0.005038112 -0.050636621 -0.072799691 -0.347387810 -0.011910406
67 68 69 70 71 72
0.141787051 0.907781968 0.037418139 -0.350078457 0.757409031 -0.086600579
73 74 75 76 77 78
0.376918825 0.372939374 0.139149283 -0.317766884 -0.059038647 0.181388521
79 80 81 82 83 84
-0.064505168 -0.052802175 0.035351028 -0.033898002 -0.101745662 0.267741789
85 86 87 88 89 90
-0.010004532 -0.031796595 0.548864483 -0.109199458 -0.134861703 0.107470162
91 92 93 94 95 96
-0.075827196 0.133317384 -0.093749008 -0.108181183 -0.353858880 0.514288645
97 98 99 100 101 102
1.251488346 0.556798557 1.069506457 0.787217514 0.443042799 0.765200921
103 104 105 106 107 108
1.003040564 0.715613350 0.727391812 0.775976122 1.161462946 0.434069352
109 110 111 112 113 114
0.289476674 1.043331736 0.756259820 1.124486025 1.132333473 0.989873128
115 116 117 118 119 120
0.289476674 0.289476674 1.168813786 1.092745597 1.205300597 0.919368807
121 122 123 124 125 126
0.652757813 1.266055112 0.794763799 0.875123543 0.606551370 0.561257904
127 128 129 130 131 132
1.130567358 1.062468790 1.167478347 0.452808771 0.327756067 0.663839056
133 134 135 136 137 138
0.339192373 0.568485861 0.286339571 0.436458781 0.289476674 0.815393547
139 140 141 142 143 144
0.745110296 0.289476674 0.311350613 0.664700862 0.955537269 0.757222797
> postscript(file="/var/www/rcomp/tmp/64pil1322154884.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 -1.066726477 NA
1 -1.126764677 -1.066726477
2 -1.705054841 -1.126764677
3 -0.407789476 -1.705054841
4 -0.388966560 -0.407789476
5 0.686134122 -0.388966560
6 -0.899601869 0.686134122
7 -0.876313053 -0.899601869
8 -0.585408055 -0.876313053
9 -0.346024479 -0.585408055
10 -0.768717590 -0.346024479
11 -0.416650995 -0.768717590
12 -1.114125174 -0.416650995
13 0.038110062 -1.114125174
14 -1.079989294 0.038110062
15 -0.973254782 -1.079989294
16 -0.472407088 -0.973254782
17 -1.461944832 -0.472407088
18 -0.335731283 -1.461944832
19 -1.202408289 -0.335731283
20 -1.127014255 -1.202408289
21 -0.500407357 -1.127014255
22 -0.982427111 -0.500407357
23 -0.809658015 -0.982427111
24 -0.954064716 -0.809658015
25 -0.746148858 -0.954064716
26 -0.687887493 -0.746148858
27 -0.786060483 -0.687887493
28 -0.658033244 -0.786060483
29 -0.967870902 -0.658033244
30 -0.936841653 -0.967870902
31 -0.835070515 -0.936841653
32 -1.074803677 -0.835070515
33 -1.148934667 -1.074803677
34 -0.812795448 -1.148934667
35 -1.710523326 -0.812795448
36 -1.089807437 -1.710523326
37 -0.577173290 -1.089807437
38 0.091969218 -0.577173290
39 -0.336455624 0.091969218
40 -0.399610361 -0.336455624
41 -1.072824684 -0.399610361
42 -1.155364654 -1.072824684
43 -0.832485959 -1.155364654
44 -0.552336944 -0.832485959
45 -1.021317954 -0.552336944
46 -1.117386877 -1.021317954
47 -0.904963340 -1.117386877
48 0.124150366 -0.904963340
49 0.219365443 0.124150366
50 -0.051078783 0.219365443
51 -0.089469339 -0.051078783
52 0.189460554 -0.089469339
53 0.133590949 0.189460554
54 0.267901919 0.133590949
55 -0.055484230 0.267901919
56 -0.014792763 -0.055484230
57 -0.113633722 -0.014792763
58 -0.126751315 -0.113633722
59 -0.202076510 -0.126751315
60 -0.133312200 -0.202076510
61 -0.005038112 -0.133312200
62 -0.050636621 -0.005038112
63 -0.072799691 -0.050636621
64 -0.347387810 -0.072799691
65 -0.011910406 -0.347387810
66 0.141787051 -0.011910406
67 0.907781968 0.141787051
68 0.037418139 0.907781968
69 -0.350078457 0.037418139
70 0.757409031 -0.350078457
71 -0.086600579 0.757409031
72 0.376918825 -0.086600579
73 0.372939374 0.376918825
74 0.139149283 0.372939374
75 -0.317766884 0.139149283
76 -0.059038647 -0.317766884
77 0.181388521 -0.059038647
78 -0.064505168 0.181388521
79 -0.052802175 -0.064505168
80 0.035351028 -0.052802175
81 -0.033898002 0.035351028
82 -0.101745662 -0.033898002
83 0.267741789 -0.101745662
84 -0.010004532 0.267741789
85 -0.031796595 -0.010004532
86 0.548864483 -0.031796595
87 -0.109199458 0.548864483
88 -0.134861703 -0.109199458
89 0.107470162 -0.134861703
90 -0.075827196 0.107470162
91 0.133317384 -0.075827196
92 -0.093749008 0.133317384
93 -0.108181183 -0.093749008
94 -0.353858880 -0.108181183
95 0.514288645 -0.353858880
96 1.251488346 0.514288645
97 0.556798557 1.251488346
98 1.069506457 0.556798557
99 0.787217514 1.069506457
100 0.443042799 0.787217514
101 0.765200921 0.443042799
102 1.003040564 0.765200921
103 0.715613350 1.003040564
104 0.727391812 0.715613350
105 0.775976122 0.727391812
106 1.161462946 0.775976122
107 0.434069352 1.161462946
108 0.289476674 0.434069352
109 1.043331736 0.289476674
110 0.756259820 1.043331736
111 1.124486025 0.756259820
112 1.132333473 1.124486025
113 0.989873128 1.132333473
114 0.289476674 0.989873128
115 0.289476674 0.289476674
116 1.168813786 0.289476674
117 1.092745597 1.168813786
118 1.205300597 1.092745597
119 0.919368807 1.205300597
120 0.652757813 0.919368807
121 1.266055112 0.652757813
122 0.794763799 1.266055112
123 0.875123543 0.794763799
124 0.606551370 0.875123543
125 0.561257904 0.606551370
126 1.130567358 0.561257904
127 1.062468790 1.130567358
128 1.167478347 1.062468790
129 0.452808771 1.167478347
130 0.327756067 0.452808771
131 0.663839056 0.327756067
132 0.339192373 0.663839056
133 0.568485861 0.339192373
134 0.286339571 0.568485861
135 0.436458781 0.286339571
136 0.289476674 0.436458781
137 0.815393547 0.289476674
138 0.745110296 0.815393547
139 0.289476674 0.745110296
140 0.311350613 0.289476674
141 0.664700862 0.311350613
142 0.955537269 0.664700862
143 0.757222797 0.955537269
144 NA 0.757222797
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.126764677 -1.066726477
[2,] -1.705054841 -1.126764677
[3,] -0.407789476 -1.705054841
[4,] -0.388966560 -0.407789476
[5,] 0.686134122 -0.388966560
[6,] -0.899601869 0.686134122
[7,] -0.876313053 -0.899601869
[8,] -0.585408055 -0.876313053
[9,] -0.346024479 -0.585408055
[10,] -0.768717590 -0.346024479
[11,] -0.416650995 -0.768717590
[12,] -1.114125174 -0.416650995
[13,] 0.038110062 -1.114125174
[14,] -1.079989294 0.038110062
[15,] -0.973254782 -1.079989294
[16,] -0.472407088 -0.973254782
[17,] -1.461944832 -0.472407088
[18,] -0.335731283 -1.461944832
[19,] -1.202408289 -0.335731283
[20,] -1.127014255 -1.202408289
[21,] -0.500407357 -1.127014255
[22,] -0.982427111 -0.500407357
[23,] -0.809658015 -0.982427111
[24,] -0.954064716 -0.809658015
[25,] -0.746148858 -0.954064716
[26,] -0.687887493 -0.746148858
[27,] -0.786060483 -0.687887493
[28,] -0.658033244 -0.786060483
[29,] -0.967870902 -0.658033244
[30,] -0.936841653 -0.967870902
[31,] -0.835070515 -0.936841653
[32,] -1.074803677 -0.835070515
[33,] -1.148934667 -1.074803677
[34,] -0.812795448 -1.148934667
[35,] -1.710523326 -0.812795448
[36,] -1.089807437 -1.710523326
[37,] -0.577173290 -1.089807437
[38,] 0.091969218 -0.577173290
[39,] -0.336455624 0.091969218
[40,] -0.399610361 -0.336455624
[41,] -1.072824684 -0.399610361
[42,] -1.155364654 -1.072824684
[43,] -0.832485959 -1.155364654
[44,] -0.552336944 -0.832485959
[45,] -1.021317954 -0.552336944
[46,] -1.117386877 -1.021317954
[47,] -0.904963340 -1.117386877
[48,] 0.124150366 -0.904963340
[49,] 0.219365443 0.124150366
[50,] -0.051078783 0.219365443
[51,] -0.089469339 -0.051078783
[52,] 0.189460554 -0.089469339
[53,] 0.133590949 0.189460554
[54,] 0.267901919 0.133590949
[55,] -0.055484230 0.267901919
[56,] -0.014792763 -0.055484230
[57,] -0.113633722 -0.014792763
[58,] -0.126751315 -0.113633722
[59,] -0.202076510 -0.126751315
[60,] -0.133312200 -0.202076510
[61,] -0.005038112 -0.133312200
[62,] -0.050636621 -0.005038112
[63,] -0.072799691 -0.050636621
[64,] -0.347387810 -0.072799691
[65,] -0.011910406 -0.347387810
[66,] 0.141787051 -0.011910406
[67,] 0.907781968 0.141787051
[68,] 0.037418139 0.907781968
[69,] -0.350078457 0.037418139
[70,] 0.757409031 -0.350078457
[71,] -0.086600579 0.757409031
[72,] 0.376918825 -0.086600579
[73,] 0.372939374 0.376918825
[74,] 0.139149283 0.372939374
[75,] -0.317766884 0.139149283
[76,] -0.059038647 -0.317766884
[77,] 0.181388521 -0.059038647
[78,] -0.064505168 0.181388521
[79,] -0.052802175 -0.064505168
[80,] 0.035351028 -0.052802175
[81,] -0.033898002 0.035351028
[82,] -0.101745662 -0.033898002
[83,] 0.267741789 -0.101745662
[84,] -0.010004532 0.267741789
[85,] -0.031796595 -0.010004532
[86,] 0.548864483 -0.031796595
[87,] -0.109199458 0.548864483
[88,] -0.134861703 -0.109199458
[89,] 0.107470162 -0.134861703
[90,] -0.075827196 0.107470162
[91,] 0.133317384 -0.075827196
[92,] -0.093749008 0.133317384
[93,] -0.108181183 -0.093749008
[94,] -0.353858880 -0.108181183
[95,] 0.514288645 -0.353858880
[96,] 1.251488346 0.514288645
[97,] 0.556798557 1.251488346
[98,] 1.069506457 0.556798557
[99,] 0.787217514 1.069506457
[100,] 0.443042799 0.787217514
[101,] 0.765200921 0.443042799
[102,] 1.003040564 0.765200921
[103,] 0.715613350 1.003040564
[104,] 0.727391812 0.715613350
[105,] 0.775976122 0.727391812
[106,] 1.161462946 0.775976122
[107,] 0.434069352 1.161462946
[108,] 0.289476674 0.434069352
[109,] 1.043331736 0.289476674
[110,] 0.756259820 1.043331736
[111,] 1.124486025 0.756259820
[112,] 1.132333473 1.124486025
[113,] 0.989873128 1.132333473
[114,] 0.289476674 0.989873128
[115,] 0.289476674 0.289476674
[116,] 1.168813786 0.289476674
[117,] 1.092745597 1.168813786
[118,] 1.205300597 1.092745597
[119,] 0.919368807 1.205300597
[120,] 0.652757813 0.919368807
[121,] 1.266055112 0.652757813
[122,] 0.794763799 1.266055112
[123,] 0.875123543 0.794763799
[124,] 0.606551370 0.875123543
[125,] 0.561257904 0.606551370
[126,] 1.130567358 0.561257904
[127,] 1.062468790 1.130567358
[128,] 1.167478347 1.062468790
[129,] 0.452808771 1.167478347
[130,] 0.327756067 0.452808771
[131,] 0.663839056 0.327756067
[132,] 0.339192373 0.663839056
[133,] 0.568485861 0.339192373
[134,] 0.286339571 0.568485861
[135,] 0.436458781 0.286339571
[136,] 0.289476674 0.436458781
[137,] 0.815393547 0.289476674
[138,] 0.745110296 0.815393547
[139,] 0.289476674 0.745110296
[140,] 0.311350613 0.289476674
[141,] 0.664700862 0.311350613
[142,] 0.955537269 0.664700862
[143,] 0.757222797 0.955537269
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.126764677 -1.066726477
2 -1.705054841 -1.126764677
3 -0.407789476 -1.705054841
4 -0.388966560 -0.407789476
5 0.686134122 -0.388966560
6 -0.899601869 0.686134122
7 -0.876313053 -0.899601869
8 -0.585408055 -0.876313053
9 -0.346024479 -0.585408055
10 -0.768717590 -0.346024479
11 -0.416650995 -0.768717590
12 -1.114125174 -0.416650995
13 0.038110062 -1.114125174
14 -1.079989294 0.038110062
15 -0.973254782 -1.079989294
16 -0.472407088 -0.973254782
17 -1.461944832 -0.472407088
18 -0.335731283 -1.461944832
19 -1.202408289 -0.335731283
20 -1.127014255 -1.202408289
21 -0.500407357 -1.127014255
22 -0.982427111 -0.500407357
23 -0.809658015 -0.982427111
24 -0.954064716 -0.809658015
25 -0.746148858 -0.954064716
26 -0.687887493 -0.746148858
27 -0.786060483 -0.687887493
28 -0.658033244 -0.786060483
29 -0.967870902 -0.658033244
30 -0.936841653 -0.967870902
31 -0.835070515 -0.936841653
32 -1.074803677 -0.835070515
33 -1.148934667 -1.074803677
34 -0.812795448 -1.148934667
35 -1.710523326 -0.812795448
36 -1.089807437 -1.710523326
37 -0.577173290 -1.089807437
38 0.091969218 -0.577173290
39 -0.336455624 0.091969218
40 -0.399610361 -0.336455624
41 -1.072824684 -0.399610361
42 -1.155364654 -1.072824684
43 -0.832485959 -1.155364654
44 -0.552336944 -0.832485959
45 -1.021317954 -0.552336944
46 -1.117386877 -1.021317954
47 -0.904963340 -1.117386877
48 0.124150366 -0.904963340
49 0.219365443 0.124150366
50 -0.051078783 0.219365443
51 -0.089469339 -0.051078783
52 0.189460554 -0.089469339
53 0.133590949 0.189460554
54 0.267901919 0.133590949
55 -0.055484230 0.267901919
56 -0.014792763 -0.055484230
57 -0.113633722 -0.014792763
58 -0.126751315 -0.113633722
59 -0.202076510 -0.126751315
60 -0.133312200 -0.202076510
61 -0.005038112 -0.133312200
62 -0.050636621 -0.005038112
63 -0.072799691 -0.050636621
64 -0.347387810 -0.072799691
65 -0.011910406 -0.347387810
66 0.141787051 -0.011910406
67 0.907781968 0.141787051
68 0.037418139 0.907781968
69 -0.350078457 0.037418139
70 0.757409031 -0.350078457
71 -0.086600579 0.757409031
72 0.376918825 -0.086600579
73 0.372939374 0.376918825
74 0.139149283 0.372939374
75 -0.317766884 0.139149283
76 -0.059038647 -0.317766884
77 0.181388521 -0.059038647
78 -0.064505168 0.181388521
79 -0.052802175 -0.064505168
80 0.035351028 -0.052802175
81 -0.033898002 0.035351028
82 -0.101745662 -0.033898002
83 0.267741789 -0.101745662
84 -0.010004532 0.267741789
85 -0.031796595 -0.010004532
86 0.548864483 -0.031796595
87 -0.109199458 0.548864483
88 -0.134861703 -0.109199458
89 0.107470162 -0.134861703
90 -0.075827196 0.107470162
91 0.133317384 -0.075827196
92 -0.093749008 0.133317384
93 -0.108181183 -0.093749008
94 -0.353858880 -0.108181183
95 0.514288645 -0.353858880
96 1.251488346 0.514288645
97 0.556798557 1.251488346
98 1.069506457 0.556798557
99 0.787217514 1.069506457
100 0.443042799 0.787217514
101 0.765200921 0.443042799
102 1.003040564 0.765200921
103 0.715613350 1.003040564
104 0.727391812 0.715613350
105 0.775976122 0.727391812
106 1.161462946 0.775976122
107 0.434069352 1.161462946
108 0.289476674 0.434069352
109 1.043331736 0.289476674
110 0.756259820 1.043331736
111 1.124486025 0.756259820
112 1.132333473 1.124486025
113 0.989873128 1.132333473
114 0.289476674 0.989873128
115 0.289476674 0.289476674
116 1.168813786 0.289476674
117 1.092745597 1.168813786
118 1.205300597 1.092745597
119 0.919368807 1.205300597
120 0.652757813 0.919368807
121 1.266055112 0.652757813
122 0.794763799 1.266055112
123 0.875123543 0.794763799
124 0.606551370 0.875123543
125 0.561257904 0.606551370
126 1.130567358 0.561257904
127 1.062468790 1.130567358
128 1.167478347 1.062468790
129 0.452808771 1.167478347
130 0.327756067 0.452808771
131 0.663839056 0.327756067
132 0.339192373 0.663839056
133 0.568485861 0.339192373
134 0.286339571 0.568485861
135 0.436458781 0.286339571
136 0.289476674 0.436458781
137 0.815393547 0.289476674
138 0.745110296 0.815393547
139 0.289476674 0.745110296
140 0.311350613 0.289476674
141 0.664700862 0.311350613
142 0.955537269 0.664700862
143 0.757222797 0.955537269
> 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/7e4481322154884.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/81av81322154884.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9my9d1322154884.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10e7ms1322154884.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1140ph1322154884.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/12x8vz1322154884.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/13agce1322154884.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/145vzj1322154884.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/15z4r11322154884.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/16fma11322154884.tab")
+ }
>
> try(system("convert tmp/16b301322154884.ps tmp/16b301322154884.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qfy91322154884.ps tmp/2qfy91322154884.png",intern=TRUE))
character(0)
> try(system("convert tmp/3exdw1322154884.ps tmp/3exdw1322154884.png",intern=TRUE))
character(0)
> try(system("convert tmp/4dwjd1322154884.ps tmp/4dwjd1322154884.png",intern=TRUE))
character(0)
> try(system("convert tmp/521kc1322154884.ps tmp/521kc1322154884.png",intern=TRUE))
character(0)
> try(system("convert tmp/64pil1322154884.ps tmp/64pil1322154884.png",intern=TRUE))
character(0)
> try(system("convert tmp/7e4481322154884.ps tmp/7e4481322154884.png",intern=TRUE))
character(0)
> try(system("convert tmp/81av81322154884.ps tmp/81av81322154884.png",intern=TRUE))
character(0)
> try(system("convert tmp/9my9d1322154884.ps tmp/9my9d1322154884.png",intern=TRUE))
character(0)
> try(system("convert tmp/10e7ms1322154884.ps tmp/10e7ms1322154884.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.690 0.390 6.112