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(69
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+ ,58
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+ ,13)
+ ,dim=c(7
+ ,151)
+ ,dimnames=list(c('Anxiety'
+ ,'Concern'
+ ,'Doubts'
+ ,'Pexpectations'
+ ,'Pcriticism'
+ ,'Standards'
+ ,'Organization')
+ ,1:151))
> y <- array(NA,dim=c(7,151),dimnames=list(c('Anxiety','Concern','Doubts','Pexpectations','Pcriticism','Standards','Organization'),1:151))
> 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
Anxiety Concern Doubts Pexpectations Pcriticism Standards Organization
1 69 26 9 15 6 25 25
2 53 20 9 15 6 25 24
3 43 21 9 14 13 19 21
4 60 31 14 10 8 18 23
5 49 21 8 10 7 18 17
6 62 18 8 12 9 22 19
7 45 26 11 18 5 29 18
8 50 22 10 12 8 26 27
9 75 22 9 14 9 25 23
10 82 29 15 18 11 23 23
11 60 15 14 9 8 23 29
12 59 16 11 11 11 23 21
13 21 24 14 11 12 24 26
14 62 17 6 17 8 30 25
15 54 19 20 8 7 19 25
16 47 22 9 16 9 24 23
17 59 31 10 21 12 32 26
18 37 28 8 24 20 30 20
19 43 38 11 21 7 29 29
20 48 26 14 14 8 17 24
21 79 25 11 7 8 25 23
22 62 25 16 18 16 26 24
23 16 29 14 18 10 26 30
24 38 28 11 13 6 25 22
25 58 15 11 11 8 23 22
26 60 18 12 13 9 21 13
27 67 21 9 13 9 19 24
28 55 25 7 18 11 35 17
29 47 23 13 14 12 19 24
30 59 23 10 12 8 20 21
31 49 19 9 9 7 21 23
32 47 18 9 12 8 21 24
33 57 18 13 8 9 24 24
34 39 26 16 5 4 23 24
35 49 18 12 10 8 19 23
36 26 18 6 11 8 17 26
37 53 28 14 11 8 24 24
38 75 17 14 12 6 15 21
39 65 29 10 12 8 25 23
40 49 12 4 15 4 27 28
41 48 25 12 12 7 29 23
42 45 28 12 16 14 27 22
43 31 20 14 14 10 18 24
44 61 17 9 17 9 25 21
45 49 17 9 13 6 22 23
46 69 20 10 10 8 26 23
47 54 31 14 17 11 23 20
48 80 21 10 12 8 16 23
49 57 19 9 13 8 27 21
50 34 23 14 13 10 25 27
51 69 15 8 11 8 14 12
52 44 24 9 13 10 19 15
53 70 28 8 12 7 20 22
54 51 16 9 12 8 16 21
55 66 19 9 12 7 18 21
56 18 21 9 9 9 22 20
57 74 21 15 7 5 21 24
58 59 20 8 17 7 22 24
59 48 16 10 12 7 22 29
60 55 25 8 12 7 32 25
61 44 30 14 9 9 23 14
62 56 29 11 9 5 31 30
63 65 22 10 13 8 18 19
64 77 19 12 10 8 23 29
65 46 33 14 11 8 26 25
66 70 17 9 12 9 24 25
67 39 9 13 10 6 19 25
68 55 14 15 13 8 14 16
69 44 15 8 6 6 20 25
70 45 12 7 7 4 22 28
71 45 21 10 13 6 24 24
72 49 20 10 11 4 25 25
73 65 29 13 18 12 21 21
74 45 33 11 9 6 28 22
75 48 15 12 11 8 20 25
76 41 19 9 11 10 21 27
77 40 23 10 15 10 23 21
78 64 20 11 8 4 13 13
79 56 20 11 11 8 24 26
80 52 18 10 14 9 21 26
81 41 31 16 14 9 21 25
82 42 18 16 12 7 17 22
83 54 13 8 12 7 14 19
84 40 9 6 8 11 29 23
85 40 20 11 11 8 25 25
86 51 18 12 10 8 16 15
87 48 23 14 17 7 25 21
88 80 17 9 16 5 25 23
89 38 17 11 13 7 21 25
90 57 16 8 15 9 23 24
91 28 31 8 11 8 22 24
92 51 15 7 12 6 19 21
93 46 28 16 16 8 24 24
94 58 26 13 20 10 26 22
95 67 20 8 16 10 25 24
96 72 19 11 11 8 20 28
97 26 25 14 15 11 22 21
98 54 18 10 15 8 14 17
99 53 20 10 12 8 20 28
100 64 33 14 9 6 32 24
101 47 24 14 24 20 21 10
102 43 22 10 15 6 22 20
103 66 32 12 18 12 28 22
104 54 31 9 17 9 25 19
105 62 13 16 12 5 17 22
106 52 18 8 15 10 21 22
107 64 17 9 11 5 23 26
108 55 29 16 11 6 27 24
109 57 22 13 15 10 22 22
110 74 18 13 12 6 19 20
111 32 22 8 14 10 20 20
112 38 25 14 11 5 17 15
113 66 20 11 20 13 24 20
114 37 20 9 11 7 21 20
115 26 17 8 12 9 21 24
116 64 21 13 17 11 23 22
117 28 26 13 12 8 24 29
118 66 10 10 11 5 19 23
119 65 15 8 10 4 22 24
120 48 20 7 11 9 26 22
121 44 14 11 12 7 17 16
122 64 16 11 9 5 17 23
123 39 23 14 8 5 19 27
124 50 11 6 6 4 15 16
125 66 19 10 12 7 17 21
126 48 30 9 15 9 27 26
127 70 21 12 13 8 19 22
128 66 20 11 17 8 21 23
129 61 22 14 14 11 25 19
130 31 30 12 16 10 19 18
131 61 25 14 15 9 22 24
132 54 28 8 16 12 18 24
133 34 23 14 11 10 20 29
134 62 23 8 11 10 15 22
135 47 21 11 16 7 20 24
136 52 30 12 15 10 29 22
137 37 22 9 14 6 19 12
138 46 32 16 9 6 29 26
139 38 22 11 13 11 24 18
140 63 15 11 11 8 23 22
141 34 21 12 14 9 22 24
142 46 27 15 11 9 23 21
143 40 22 13 12 13 22 15
144 30 9 6 8 11 29 23
145 35 29 11 7 4 26 22
146 51 20 7 11 9 26 22
147 56 16 8 13 5 21 24
148 68 16 8 9 4 18 23
149 39 16 9 12 9 10 13
150 44 18 12 10 8 19 23
151 58 16 9 12 9 10 13
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Concern Doubts Pexpectations Pcriticism
58.39989 -0.35927 0.26256 0.88235 -1.23270
Standards Organization
0.05056 -0.16767
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-35.497 -8.591 -1.097 9.145 32.971
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 58.39989 9.18552 6.358 2.54e-09 ***
Concern -0.35927 0.24217 -1.484 0.1401
Doubts 0.26256 0.44463 0.591 0.5558
Pexpectations 0.88235 0.41030 2.150 0.0332 *
Pcriticism -1.23270 0.51281 -2.404 0.0175 *
Standards 0.05056 0.31061 0.163 0.8709
Organization -0.16767 0.31769 -0.528 0.5985
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 13.25 on 144 degrees of freedom
Multiple R-squared: 0.0591, Adjusted R-squared: 0.01989
F-statistic: 1.507 on 6 and 144 DF, p-value: 0.1797
> 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,] 0.9111465 0.17770700 0.08885350
[2,] 0.8372066 0.32558689 0.16279345
[3,] 0.7495751 0.50084986 0.25042493
[4,] 0.9424447 0.11511066 0.05755533
[5,] 0.9068142 0.18637156 0.09318578
[6,] 0.8622925 0.27541509 0.13770755
[7,] 0.8706232 0.25875365 0.12937682
[8,] 0.8223655 0.35526892 0.17763446
[9,] 0.7776795 0.44464100 0.22232050
[10,] 0.8216278 0.35674440 0.17837220
[11,] 0.8132894 0.37342112 0.18671056
[12,] 0.9015697 0.19686058 0.09843029
[13,] 0.8988984 0.20220311 0.10110156
[14,] 0.9756540 0.04869206 0.02434603
[15,] 0.9826111 0.03477783 0.01738892
[16,] 0.9743330 0.05133404 0.02566702
[17,] 0.9638358 0.07232841 0.03616421
[18,] 0.9652698 0.06946044 0.03473022
[19,] 0.9515677 0.09686459 0.04843229
[20,] 0.9344091 0.13118172 0.06559086
[21,] 0.9145668 0.17086643 0.08543322
[22,] 0.8979978 0.20400441 0.10200220
[23,] 0.8766824 0.24663528 0.12331764
[24,] 0.8497684 0.30046325 0.15023163
[25,] 0.8573213 0.28535746 0.14267873
[26,] 0.8270574 0.34588526 0.17294263
[27,] 0.8906135 0.21877308 0.10938654
[28,] 0.8623383 0.27532337 0.13766169
[29,] 0.8814932 0.23701354 0.11850677
[30,] 0.8863734 0.22725324 0.11362662
[31,] 0.8703794 0.25924126 0.12962063
[32,] 0.8462137 0.30757266 0.15378633
[33,] 0.8158847 0.36823054 0.18411527
[34,] 0.8580828 0.28383443 0.14191721
[35,] 0.8291855 0.34162900 0.17081450
[36,] 0.8025301 0.39493981 0.19746990
[37,] 0.8210803 0.35783941 0.17891970
[38,] 0.7857342 0.42853167 0.21426584
[39,] 0.8919374 0.21612519 0.10806260
[40,] 0.8669035 0.26619309 0.13309654
[41,] 0.8704026 0.25919477 0.12959739
[42,] 0.8668853 0.26622949 0.13311475
[43,] 0.8659151 0.26816979 0.13408490
[44,] 0.8878443 0.22431134 0.11215567
[45,] 0.8646200 0.27075990 0.13537995
[46,] 0.8573858 0.28522830 0.14261415
[47,] 0.9544590 0.09108190 0.04554095
[48,] 0.9708263 0.05834746 0.02917373
[49,] 0.9621532 0.07569358 0.03784679
[50,] 0.9526859 0.09462815 0.04731407
[51,] 0.9402419 0.11951630 0.05975815
[52,] 0.9358752 0.12824969 0.06412484
[53,] 0.9241972 0.15160568 0.07580284
[54,] 0.9207235 0.15855304 0.07927652
[55,] 0.9650905 0.06981896 0.03490948
[56,] 0.9554671 0.08906573 0.04453286
[57,] 0.9657553 0.06848944 0.03424472
[58,] 0.9733051 0.05338985 0.02669492
[59,] 0.9652523 0.06949545 0.03474773
[60,] 0.9587241 0.08255173 0.04127587
[61,] 0.9523172 0.09536552 0.04768276
[62,] 0.9475872 0.10482558 0.05241279
[63,] 0.9380519 0.12389626 0.06194813
[64,] 0.9430321 0.11393584 0.05696792
[65,] 0.9309944 0.13801121 0.06900560
[66,] 0.9155755 0.16884900 0.08442450
[67,] 0.9000777 0.19984452 0.09992226
[68,] 0.8951600 0.20968004 0.10484002
[69,] 0.8972775 0.20544508 0.10272254
[70,] 0.8783843 0.24323144 0.12161572
[71,] 0.8525473 0.29490550 0.14745275
[72,] 0.8332709 0.33345824 0.16672912
[73,] 0.8312179 0.33756427 0.16878213
[74,] 0.7995031 0.40099376 0.20049688
[75,] 0.7758496 0.44830075 0.22415038
[76,] 0.7640688 0.47186239 0.23593119
[77,] 0.7346339 0.53073212 0.26536606
[78,] 0.7275822 0.54483560 0.27241780
[79,] 0.7582018 0.48359635 0.24179817
[80,] 0.7956609 0.40867823 0.20433911
[81,] 0.7600437 0.47991266 0.23995633
[82,] 0.7815915 0.43681703 0.21840851
[83,] 0.7487246 0.50255087 0.25127544
[84,] 0.7294498 0.54110047 0.27055023
[85,] 0.6888748 0.62225031 0.31112515
[86,] 0.6849362 0.63012756 0.31506378
[87,] 0.7509230 0.49815397 0.24907699
[88,] 0.8372915 0.32541702 0.16270851
[89,] 0.8041245 0.39175098 0.19587549
[90,] 0.7665721 0.46685585 0.23342792
[91,] 0.8137307 0.37253861 0.18626931
[92,] 0.7794567 0.44108651 0.22054325
[93,] 0.7939419 0.41211616 0.20605808
[94,] 0.8242431 0.35151378 0.17575689
[95,] 0.7942585 0.41148304 0.20574152
[96,] 0.7607009 0.47859813 0.23929906
[97,] 0.7164360 0.56712809 0.28356405
[98,] 0.6852238 0.62955246 0.31477623
[99,] 0.6485752 0.70284958 0.35142479
[100,] 0.6006134 0.79877324 0.39938662
[101,] 0.6458956 0.70820878 0.35410439
[102,] 0.6803432 0.63931362 0.31965681
[103,] 0.6757745 0.64845096 0.32422548
[104,] 0.6534528 0.69309450 0.34654725
[105,] 0.6512924 0.69741520 0.34870760
[106,] 0.7922709 0.41545822 0.20772911
[107,] 0.7768325 0.44633500 0.22316750
[108,] 0.8582758 0.28344841 0.14172420
[109,] 0.8267790 0.34644198 0.17322099
[110,] 0.8007240 0.39855208 0.19927604
[111,] 0.7533311 0.49333774 0.24666887
[112,] 0.7309875 0.53802494 0.26901247
[113,] 0.7050033 0.58999337 0.29499668
[114,] 0.7017914 0.59641718 0.29820859
[115,] 0.6402779 0.71944415 0.35972208
[116,] 0.6301498 0.73970034 0.36985017
[117,] 0.5598122 0.88037568 0.44018784
[118,] 0.6176577 0.76468470 0.38234235
[119,] 0.5897806 0.82043883 0.41021942
[120,] 0.6741694 0.65166118 0.32583059
[121,] 0.7178107 0.56437857 0.28218929
[122,] 0.7279261 0.54414783 0.27207391
[123,] 0.6523561 0.69528789 0.34764394
[124,] 0.7163001 0.56739982 0.28369991
[125,] 0.6593599 0.68128012 0.34064006
[126,] 0.6058679 0.78826424 0.39413212
[127,] 0.6093794 0.78124118 0.39062059
[128,] 0.6208247 0.75835069 0.37917534
[129,] 0.5135488 0.97290239 0.48645119
[130,] 0.3948021 0.78960423 0.60519788
[131,] 0.3605300 0.72106006 0.63946997
[132,] 0.4184861 0.83697214 0.58151393
> postscript(file="/var/www/html/rcomp/tmp/1h5em1292690628.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2h5em1292690628.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3h5em1292690628.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/49wvp1292690628.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59wvp1292690628.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 = 151
Frequency = 1
1 2 3 4 5 6
14.6666146 -3.6566721 -3.9857566 13.0459049 -2.2101152 10.5458572
7 8 9 10 11 12
-15.1141711 -0.6358213 23.4746519 30.4513128 8.9331004 9.6721436
13 14 15 16 17 18
-24.2209184 5.6687585 1.9760437 -6.2394755 8.1162453 -8.1267338
19 20 21 22 23 24
-11.1402406 -4.0615993 32.9710577 14.9311467 -35.4968586 -14.8782766
25 26 27 28 29 30
4.7824358 5.6578288 16.4687824 2.5019630 -1.0471716 8.0208422
31 32 33 34 35 36
-1.4545714 -5.0605126 8.4996267 -10.8798040 -2.1500456 -23.8528868
37 38 39 40 41 42
3.9500256 18.6023912 16.2589708 -11.1138867 -4.1381959 -1.0273979
43 44 45 46 47 48
-20.8023825 4.6959313 -6.9857619 18.7396670 3.8117644 28.8398928
49 50 51 52 53 54
3.6100250 -15.6931819 15.3485488 -5.7296991 20.2772834 -2.0292266
55 56 57 58 59 60
12.7147530 -29.8241912 22.1555469 2.2255943 -5.4865523 4.0956933
61 62 63 64 65 66
-2.9601349 6.8156150 12.5450238 27.0129607 -1.1870874 18.8288899
67 68 69 70 71 72
-17.7761103 -1.9426905 -5.8288554 -8.5899831 -9.7447080 -6.6875872
73 74 75 76 77 78
14.9749376 -2.7042360 -4.8254351 -6.8504964 -11.3124880 9.2916748
79 80 81 82 83 84
5.1988852 -0.5197348 -8.5922666 -13.2642270 -1.3113775 -7.8509514
85 86 87 88 89 90
-11.0193455 -1.3396784 -9.9266650 19.9828044 -16.8922900 2.9680434
91 92 93 94 95 96
-18.2956568 -4.4804676 -7.9868306 2.5818747 14.6543495 21.3772060
97 98 99 100 101 102
-24.3609336 -1.7898215 2.1166930 15.6411478 -2.3607678 -13.7196640
103 104 105 106 107 108
17.1290233 2.3903812 2.4740200 -0.3149173 8.9986620 3.1670732
109 110 111 112 113 114
4.7587837 17.8542993 -18.2802578 -16.9809278 12.4152811 -15.3629911
115 116 117 118 119 120
-24.9245187 10.8169578 -21.5499683 7.9204696 8.9075626 -1.2899550
121 122 123 124 125 126
-12.3944882 9.6793492 -12.1415687 -4.4623814 12.5027548 -1.1316651
127 128 129 130 131 132
17.1130606 9.5535055 9.9565760 -19.5058076 8.6766617 7.3478663
133 134 135 136 137 138
-13.3403341 15.3142063 -9.2193488 2.5415541 -19.7643877 -2.7562221
139 140 141 142 143 144
-11.4904903 9.7824358 -18.3529468 -2.8915391 -7.0697358 -17.8509514
145 146 147 148 149 150
-14.7408972 1.7100450 -1.0969395 13.1837720 -13.8344639 -7.1500456
151
5.1655361
> postscript(file="/var/www/html/rcomp/tmp/69wvp1292690628.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 = 151
Frequency = 1
lag(myerror, k = 1) myerror
0 14.6666146 NA
1 -3.6566721 14.6666146
2 -3.9857566 -3.6566721
3 13.0459049 -3.9857566
4 -2.2101152 13.0459049
5 10.5458572 -2.2101152
6 -15.1141711 10.5458572
7 -0.6358213 -15.1141711
8 23.4746519 -0.6358213
9 30.4513128 23.4746519
10 8.9331004 30.4513128
11 9.6721436 8.9331004
12 -24.2209184 9.6721436
13 5.6687585 -24.2209184
14 1.9760437 5.6687585
15 -6.2394755 1.9760437
16 8.1162453 -6.2394755
17 -8.1267338 8.1162453
18 -11.1402406 -8.1267338
19 -4.0615993 -11.1402406
20 32.9710577 -4.0615993
21 14.9311467 32.9710577
22 -35.4968586 14.9311467
23 -14.8782766 -35.4968586
24 4.7824358 -14.8782766
25 5.6578288 4.7824358
26 16.4687824 5.6578288
27 2.5019630 16.4687824
28 -1.0471716 2.5019630
29 8.0208422 -1.0471716
30 -1.4545714 8.0208422
31 -5.0605126 -1.4545714
32 8.4996267 -5.0605126
33 -10.8798040 8.4996267
34 -2.1500456 -10.8798040
35 -23.8528868 -2.1500456
36 3.9500256 -23.8528868
37 18.6023912 3.9500256
38 16.2589708 18.6023912
39 -11.1138867 16.2589708
40 -4.1381959 -11.1138867
41 -1.0273979 -4.1381959
42 -20.8023825 -1.0273979
43 4.6959313 -20.8023825
44 -6.9857619 4.6959313
45 18.7396670 -6.9857619
46 3.8117644 18.7396670
47 28.8398928 3.8117644
48 3.6100250 28.8398928
49 -15.6931819 3.6100250
50 15.3485488 -15.6931819
51 -5.7296991 15.3485488
52 20.2772834 -5.7296991
53 -2.0292266 20.2772834
54 12.7147530 -2.0292266
55 -29.8241912 12.7147530
56 22.1555469 -29.8241912
57 2.2255943 22.1555469
58 -5.4865523 2.2255943
59 4.0956933 -5.4865523
60 -2.9601349 4.0956933
61 6.8156150 -2.9601349
62 12.5450238 6.8156150
63 27.0129607 12.5450238
64 -1.1870874 27.0129607
65 18.8288899 -1.1870874
66 -17.7761103 18.8288899
67 -1.9426905 -17.7761103
68 -5.8288554 -1.9426905
69 -8.5899831 -5.8288554
70 -9.7447080 -8.5899831
71 -6.6875872 -9.7447080
72 14.9749376 -6.6875872
73 -2.7042360 14.9749376
74 -4.8254351 -2.7042360
75 -6.8504964 -4.8254351
76 -11.3124880 -6.8504964
77 9.2916748 -11.3124880
78 5.1988852 9.2916748
79 -0.5197348 5.1988852
80 -8.5922666 -0.5197348
81 -13.2642270 -8.5922666
82 -1.3113775 -13.2642270
83 -7.8509514 -1.3113775
84 -11.0193455 -7.8509514
85 -1.3396784 -11.0193455
86 -9.9266650 -1.3396784
87 19.9828044 -9.9266650
88 -16.8922900 19.9828044
89 2.9680434 -16.8922900
90 -18.2956568 2.9680434
91 -4.4804676 -18.2956568
92 -7.9868306 -4.4804676
93 2.5818747 -7.9868306
94 14.6543495 2.5818747
95 21.3772060 14.6543495
96 -24.3609336 21.3772060
97 -1.7898215 -24.3609336
98 2.1166930 -1.7898215
99 15.6411478 2.1166930
100 -2.3607678 15.6411478
101 -13.7196640 -2.3607678
102 17.1290233 -13.7196640
103 2.3903812 17.1290233
104 2.4740200 2.3903812
105 -0.3149173 2.4740200
106 8.9986620 -0.3149173
107 3.1670732 8.9986620
108 4.7587837 3.1670732
109 17.8542993 4.7587837
110 -18.2802578 17.8542993
111 -16.9809278 -18.2802578
112 12.4152811 -16.9809278
113 -15.3629911 12.4152811
114 -24.9245187 -15.3629911
115 10.8169578 -24.9245187
116 -21.5499683 10.8169578
117 7.9204696 -21.5499683
118 8.9075626 7.9204696
119 -1.2899550 8.9075626
120 -12.3944882 -1.2899550
121 9.6793492 -12.3944882
122 -12.1415687 9.6793492
123 -4.4623814 -12.1415687
124 12.5027548 -4.4623814
125 -1.1316651 12.5027548
126 17.1130606 -1.1316651
127 9.5535055 17.1130606
128 9.9565760 9.5535055
129 -19.5058076 9.9565760
130 8.6766617 -19.5058076
131 7.3478663 8.6766617
132 -13.3403341 7.3478663
133 15.3142063 -13.3403341
134 -9.2193488 15.3142063
135 2.5415541 -9.2193488
136 -19.7643877 2.5415541
137 -2.7562221 -19.7643877
138 -11.4904903 -2.7562221
139 9.7824358 -11.4904903
140 -18.3529468 9.7824358
141 -2.8915391 -18.3529468
142 -7.0697358 -2.8915391
143 -17.8509514 -7.0697358
144 -14.7408972 -17.8509514
145 1.7100450 -14.7408972
146 -1.0969395 1.7100450
147 13.1837720 -1.0969395
148 -13.8344639 13.1837720
149 -7.1500456 -13.8344639
150 5.1655361 -7.1500456
151 NA 5.1655361
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.6566721 14.6666146
[2,] -3.9857566 -3.6566721
[3,] 13.0459049 -3.9857566
[4,] -2.2101152 13.0459049
[5,] 10.5458572 -2.2101152
[6,] -15.1141711 10.5458572
[7,] -0.6358213 -15.1141711
[8,] 23.4746519 -0.6358213
[9,] 30.4513128 23.4746519
[10,] 8.9331004 30.4513128
[11,] 9.6721436 8.9331004
[12,] -24.2209184 9.6721436
[13,] 5.6687585 -24.2209184
[14,] 1.9760437 5.6687585
[15,] -6.2394755 1.9760437
[16,] 8.1162453 -6.2394755
[17,] -8.1267338 8.1162453
[18,] -11.1402406 -8.1267338
[19,] -4.0615993 -11.1402406
[20,] 32.9710577 -4.0615993
[21,] 14.9311467 32.9710577
[22,] -35.4968586 14.9311467
[23,] -14.8782766 -35.4968586
[24,] 4.7824358 -14.8782766
[25,] 5.6578288 4.7824358
[26,] 16.4687824 5.6578288
[27,] 2.5019630 16.4687824
[28,] -1.0471716 2.5019630
[29,] 8.0208422 -1.0471716
[30,] -1.4545714 8.0208422
[31,] -5.0605126 -1.4545714
[32,] 8.4996267 -5.0605126
[33,] -10.8798040 8.4996267
[34,] -2.1500456 -10.8798040
[35,] -23.8528868 -2.1500456
[36,] 3.9500256 -23.8528868
[37,] 18.6023912 3.9500256
[38,] 16.2589708 18.6023912
[39,] -11.1138867 16.2589708
[40,] -4.1381959 -11.1138867
[41,] -1.0273979 -4.1381959
[42,] -20.8023825 -1.0273979
[43,] 4.6959313 -20.8023825
[44,] -6.9857619 4.6959313
[45,] 18.7396670 -6.9857619
[46,] 3.8117644 18.7396670
[47,] 28.8398928 3.8117644
[48,] 3.6100250 28.8398928
[49,] -15.6931819 3.6100250
[50,] 15.3485488 -15.6931819
[51,] -5.7296991 15.3485488
[52,] 20.2772834 -5.7296991
[53,] -2.0292266 20.2772834
[54,] 12.7147530 -2.0292266
[55,] -29.8241912 12.7147530
[56,] 22.1555469 -29.8241912
[57,] 2.2255943 22.1555469
[58,] -5.4865523 2.2255943
[59,] 4.0956933 -5.4865523
[60,] -2.9601349 4.0956933
[61,] 6.8156150 -2.9601349
[62,] 12.5450238 6.8156150
[63,] 27.0129607 12.5450238
[64,] -1.1870874 27.0129607
[65,] 18.8288899 -1.1870874
[66,] -17.7761103 18.8288899
[67,] -1.9426905 -17.7761103
[68,] -5.8288554 -1.9426905
[69,] -8.5899831 -5.8288554
[70,] -9.7447080 -8.5899831
[71,] -6.6875872 -9.7447080
[72,] 14.9749376 -6.6875872
[73,] -2.7042360 14.9749376
[74,] -4.8254351 -2.7042360
[75,] -6.8504964 -4.8254351
[76,] -11.3124880 -6.8504964
[77,] 9.2916748 -11.3124880
[78,] 5.1988852 9.2916748
[79,] -0.5197348 5.1988852
[80,] -8.5922666 -0.5197348
[81,] -13.2642270 -8.5922666
[82,] -1.3113775 -13.2642270
[83,] -7.8509514 -1.3113775
[84,] -11.0193455 -7.8509514
[85,] -1.3396784 -11.0193455
[86,] -9.9266650 -1.3396784
[87,] 19.9828044 -9.9266650
[88,] -16.8922900 19.9828044
[89,] 2.9680434 -16.8922900
[90,] -18.2956568 2.9680434
[91,] -4.4804676 -18.2956568
[92,] -7.9868306 -4.4804676
[93,] 2.5818747 -7.9868306
[94,] 14.6543495 2.5818747
[95,] 21.3772060 14.6543495
[96,] -24.3609336 21.3772060
[97,] -1.7898215 -24.3609336
[98,] 2.1166930 -1.7898215
[99,] 15.6411478 2.1166930
[100,] -2.3607678 15.6411478
[101,] -13.7196640 -2.3607678
[102,] 17.1290233 -13.7196640
[103,] 2.3903812 17.1290233
[104,] 2.4740200 2.3903812
[105,] -0.3149173 2.4740200
[106,] 8.9986620 -0.3149173
[107,] 3.1670732 8.9986620
[108,] 4.7587837 3.1670732
[109,] 17.8542993 4.7587837
[110,] -18.2802578 17.8542993
[111,] -16.9809278 -18.2802578
[112,] 12.4152811 -16.9809278
[113,] -15.3629911 12.4152811
[114,] -24.9245187 -15.3629911
[115,] 10.8169578 -24.9245187
[116,] -21.5499683 10.8169578
[117,] 7.9204696 -21.5499683
[118,] 8.9075626 7.9204696
[119,] -1.2899550 8.9075626
[120,] -12.3944882 -1.2899550
[121,] 9.6793492 -12.3944882
[122,] -12.1415687 9.6793492
[123,] -4.4623814 -12.1415687
[124,] 12.5027548 -4.4623814
[125,] -1.1316651 12.5027548
[126,] 17.1130606 -1.1316651
[127,] 9.5535055 17.1130606
[128,] 9.9565760 9.5535055
[129,] -19.5058076 9.9565760
[130,] 8.6766617 -19.5058076
[131,] 7.3478663 8.6766617
[132,] -13.3403341 7.3478663
[133,] 15.3142063 -13.3403341
[134,] -9.2193488 15.3142063
[135,] 2.5415541 -9.2193488
[136,] -19.7643877 2.5415541
[137,] -2.7562221 -19.7643877
[138,] -11.4904903 -2.7562221
[139,] 9.7824358 -11.4904903
[140,] -18.3529468 9.7824358
[141,] -2.8915391 -18.3529468
[142,] -7.0697358 -2.8915391
[143,] -17.8509514 -7.0697358
[144,] -14.7408972 -17.8509514
[145,] 1.7100450 -14.7408972
[146,] -1.0969395 1.7100450
[147,] 13.1837720 -1.0969395
[148,] -13.8344639 13.1837720
[149,] -7.1500456 -13.8344639
[150,] 5.1655361 -7.1500456
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.6566721 14.6666146
2 -3.9857566 -3.6566721
3 13.0459049 -3.9857566
4 -2.2101152 13.0459049
5 10.5458572 -2.2101152
6 -15.1141711 10.5458572
7 -0.6358213 -15.1141711
8 23.4746519 -0.6358213
9 30.4513128 23.4746519
10 8.9331004 30.4513128
11 9.6721436 8.9331004
12 -24.2209184 9.6721436
13 5.6687585 -24.2209184
14 1.9760437 5.6687585
15 -6.2394755 1.9760437
16 8.1162453 -6.2394755
17 -8.1267338 8.1162453
18 -11.1402406 -8.1267338
19 -4.0615993 -11.1402406
20 32.9710577 -4.0615993
21 14.9311467 32.9710577
22 -35.4968586 14.9311467
23 -14.8782766 -35.4968586
24 4.7824358 -14.8782766
25 5.6578288 4.7824358
26 16.4687824 5.6578288
27 2.5019630 16.4687824
28 -1.0471716 2.5019630
29 8.0208422 -1.0471716
30 -1.4545714 8.0208422
31 -5.0605126 -1.4545714
32 8.4996267 -5.0605126
33 -10.8798040 8.4996267
34 -2.1500456 -10.8798040
35 -23.8528868 -2.1500456
36 3.9500256 -23.8528868
37 18.6023912 3.9500256
38 16.2589708 18.6023912
39 -11.1138867 16.2589708
40 -4.1381959 -11.1138867
41 -1.0273979 -4.1381959
42 -20.8023825 -1.0273979
43 4.6959313 -20.8023825
44 -6.9857619 4.6959313
45 18.7396670 -6.9857619
46 3.8117644 18.7396670
47 28.8398928 3.8117644
48 3.6100250 28.8398928
49 -15.6931819 3.6100250
50 15.3485488 -15.6931819
51 -5.7296991 15.3485488
52 20.2772834 -5.7296991
53 -2.0292266 20.2772834
54 12.7147530 -2.0292266
55 -29.8241912 12.7147530
56 22.1555469 -29.8241912
57 2.2255943 22.1555469
58 -5.4865523 2.2255943
59 4.0956933 -5.4865523
60 -2.9601349 4.0956933
61 6.8156150 -2.9601349
62 12.5450238 6.8156150
63 27.0129607 12.5450238
64 -1.1870874 27.0129607
65 18.8288899 -1.1870874
66 -17.7761103 18.8288899
67 -1.9426905 -17.7761103
68 -5.8288554 -1.9426905
69 -8.5899831 -5.8288554
70 -9.7447080 -8.5899831
71 -6.6875872 -9.7447080
72 14.9749376 -6.6875872
73 -2.7042360 14.9749376
74 -4.8254351 -2.7042360
75 -6.8504964 -4.8254351
76 -11.3124880 -6.8504964
77 9.2916748 -11.3124880
78 5.1988852 9.2916748
79 -0.5197348 5.1988852
80 -8.5922666 -0.5197348
81 -13.2642270 -8.5922666
82 -1.3113775 -13.2642270
83 -7.8509514 -1.3113775
84 -11.0193455 -7.8509514
85 -1.3396784 -11.0193455
86 -9.9266650 -1.3396784
87 19.9828044 -9.9266650
88 -16.8922900 19.9828044
89 2.9680434 -16.8922900
90 -18.2956568 2.9680434
91 -4.4804676 -18.2956568
92 -7.9868306 -4.4804676
93 2.5818747 -7.9868306
94 14.6543495 2.5818747
95 21.3772060 14.6543495
96 -24.3609336 21.3772060
97 -1.7898215 -24.3609336
98 2.1166930 -1.7898215
99 15.6411478 2.1166930
100 -2.3607678 15.6411478
101 -13.7196640 -2.3607678
102 17.1290233 -13.7196640
103 2.3903812 17.1290233
104 2.4740200 2.3903812
105 -0.3149173 2.4740200
106 8.9986620 -0.3149173
107 3.1670732 8.9986620
108 4.7587837 3.1670732
109 17.8542993 4.7587837
110 -18.2802578 17.8542993
111 -16.9809278 -18.2802578
112 12.4152811 -16.9809278
113 -15.3629911 12.4152811
114 -24.9245187 -15.3629911
115 10.8169578 -24.9245187
116 -21.5499683 10.8169578
117 7.9204696 -21.5499683
118 8.9075626 7.9204696
119 -1.2899550 8.9075626
120 -12.3944882 -1.2899550
121 9.6793492 -12.3944882
122 -12.1415687 9.6793492
123 -4.4623814 -12.1415687
124 12.5027548 -4.4623814
125 -1.1316651 12.5027548
126 17.1130606 -1.1316651
127 9.5535055 17.1130606
128 9.9565760 9.5535055
129 -19.5058076 9.9565760
130 8.6766617 -19.5058076
131 7.3478663 8.6766617
132 -13.3403341 7.3478663
133 15.3142063 -13.3403341
134 -9.2193488 15.3142063
135 2.5415541 -9.2193488
136 -19.7643877 2.5415541
137 -2.7562221 -19.7643877
138 -11.4904903 -2.7562221
139 9.7824358 -11.4904903
140 -18.3529468 9.7824358
141 -2.8915391 -18.3529468
142 -7.0697358 -2.8915391
143 -17.8509514 -7.0697358
144 -14.7408972 -17.8509514
145 1.7100450 -14.7408972
146 -1.0969395 1.7100450
147 13.1837720 -1.0969395
148 -13.8344639 13.1837720
149 -7.1500456 -13.8344639
150 5.1655361 -7.1500456
> 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/725ua1292690628.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8vwud1292690628.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vwud1292690628.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10vwud1292690628.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11gxai1292690628.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/121fqo1292690628.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/13rho01292690628.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/14ch461292690628.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/15xilc1292690628.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/1610ji1292690628.tab")
+ }
>
> try(system("convert tmp/1h5em1292690628.ps tmp/1h5em1292690628.png",intern=TRUE))
character(0)
> try(system("convert tmp/2h5em1292690628.ps tmp/2h5em1292690628.png",intern=TRUE))
character(0)
> try(system("convert tmp/3h5em1292690628.ps tmp/3h5em1292690628.png",intern=TRUE))
character(0)
> try(system("convert tmp/49wvp1292690628.ps tmp/49wvp1292690628.png",intern=TRUE))
character(0)
> try(system("convert tmp/59wvp1292690628.ps tmp/59wvp1292690628.png",intern=TRUE))
character(0)
> try(system("convert tmp/69wvp1292690628.ps tmp/69wvp1292690628.png",intern=TRUE))
character(0)
> try(system("convert tmp/725ua1292690628.ps tmp/725ua1292690628.png",intern=TRUE))
character(0)
> try(system("convert tmp/8vwud1292690628.ps tmp/8vwud1292690628.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vwud1292690628.ps tmp/9vwud1292690628.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vwud1292690628.ps tmp/10vwud1292690628.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.075 1.870 10.304