R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(4
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+ ,dim=c(8
+ ,152)
+ ,dimnames=list(c('A'
+ ,'B'
+ ,'C'
+ ,'D'
+ ,'E'
+ ,'F'
+ ,'G'
+ ,'H')
+ ,1:152))
> y <- array(NA,dim=c(8,152),dimnames=list(c('A','B','C','D','E','F','G','H'),1:152))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
B A C D E F G H
1 4 4 4 4 4 4 4 4
2 3 4 4 4 5 4 4 4
3 5 5 5 4 5 4 4 4
4 4 3 4 3 3 2 3 4
5 2 2 4 3 4 2 3 4
6 4 5 5 4 5 4 3 4
7 3 4 4 4 4 3 3 4
8 2 2 4 2 4 2 4 4
9 4 4 5 3 4 2 4 4
10 2 4 2 3 4 4 3 2
11 4 4 4 4 4 3 4 4
12 2 2 3 2 4 3 4 3
13 5 5 5 4 5 4 5 5
14 3 3 4 4 4 3 3 4
15 4 4 4 4 4 4 4 3
16 4 4 5 4 4 4 4 4
17 3 3 3 3 3 3 3 4
18 4 4 4 4 5 3 4 4
19 2 2 4 2 3 2 2 3
20 3 4 4 4 4 2 4 4
21 4 3 4 3 4 3 4 4
22 2 3 3 4 4 4 4 4
23 4 4 4 4 4 4 4 4
24 4 4 4 4 4 4 4 4
25 4 4 4 4 4 4 4 4
26 3 4 4 3 5 3 4 4
27 4 5 4 4 5 4 4 4
28 4 2 4 3 4 3 4 4
29 4 4 4 4 4 4 4 4
30 4 4 3 4 4 4 3 4
31 4 4 4 5 2 4 4 4
32 4 2 4 4 3 3 4 4
33 4 4 3 4 2 4 4 4
34 5 4 4 4 4 4 4 4
35 4 4 4 4 4 4 4 4
36 4 4 4 4 3 4 4 5
37 5 3 4 3 3 3 4 4
38 4 4 4 4 4 4 4 4
39 4 3 4 4 4 3 4 4
40 4 3 4 4 3 4 2 3
41 3 3 3 4 4 4 4 4
42 4 3 4 4 4 4 4 2
43 3 2 2 4 2 3 2 2
44 5 2 2 4 2 2 3 4
45 5 4 4 4 2 4 4 4
46 4 2 4 4 4 4 2 5
47 5 4 5 4 4 4 5 4
48 4 4 4 4 3 4 4 4
49 4 3 3 5 3 4 4 4
50 4 4 4 4 4 4 4 5
51 4 4 4 5 4 4 4 3
52 4 3 3 4 4 3 4 2
53 4 1 4 4 4 2 4 4
54 4 4 4 4 3 4 4 4
55 4 4 3 3 4 4 3 2
56 4 2 3 4 4 4 4 2
57 4 2 2 3 3 2 3 4
58 4 4 4 4 3 4 4 4
59 4 4 4 4 2 4 4 4
60 4 3 4 3 2 4 4 3
61 4 3 4 4 3 4 4 2
62 4 1 3 3 3 3 4 4
63 4 4 4 4 4 4 4 5
64 4 2 4 5 2 4 4 4
65 4 4 4 5 3 4 3 5
66 5 5 5 5 4 5 5 4
67 4 3 4 4 2 4 4 3
68 4 3 3 3 3 3 4 3
69 3 2 2 4 3 3 4 4
70 4 4 4 5 4 4 2 3
71 4 2 2 4 3 3 4 2
72 2 2 2 4 2 3 2 4
73 4 2 2 4 3 2 2 3
74 5 4 4 5 4 4 4 5
75 4 5 4 4 3 4 4 5
76 5 4 5 5 4 4 5 4
77 3 2 2 4 4 2 4 5
78 4 5 5 4 5 4 5 4
79 4 2 4 5 3 4 4 3
80 3 3 3 3 2 3 4 3
81 4 4 3 3 3 3 4 4
82 4 3 4 4 4 3 4 4
83 4 2 4 5 4 4 4 3
84 4 3 3 4 2 3 4 4
85 4 4 4 4 3 3 3 3
86 4 1 2 4 2 2 4 3
87 5 2 4 4 3 4 4 3
88 4 2 3 4 2 4 5 2
89 4 3 2 4 3 2 4 5
90 4 3 4 4 4 4 4 3
91 4 2 4 4 3 3 3 4
92 5 2 4 4 3 2 4 4
93 4 4 3 4 3 4 4 3
94 4 3 3 4 3 3 3 3
95 3 3 2 3 3 3 3 4
96 4 4 3 3 4 3 3 4
97 4 4 3 4 4 5 4 3
98 4 2 2 3 3 4 4 3
99 5 2 4 4 3 2 5 4
100 5 4 4 4 4 4 4 2
101 4 3 3 4 3 3 4 4
102 4 2 2 4 2 2 4 4
103 5 4 4 4 3 4 4 4
104 5 3 4 5 4 4 4 5
105 4 3 5 5 5 3 4 5
106 5 4 5 4 4 4 5 5
107 4 3 4 4 3 4 4 4
108 4 4 3 4 4 4 4 4
109 4 3 4 4 3 4 4 3
110 4 3 4 4 3 4 4 2
111 2 3 3 4 3 4 3 2
112 4 2 2 4 2 2 4 4
113 5 2 5 5 4 4 4 2
114 4 1 3 4 3 3 4 3
115 4 2 4 4 3 3 4 4
116 4 3 3 3 3 4 3 4
117 4 3 3 4 3 3 4 4
118 4 3 5 4 4 4 4 4
119 3 3 4 4 4 3 2 3
120 4 2 3 4 4 3 4 4
121 4 4 4 5 5 4 3 3
122 3 2 4 4 3 3 4 4
123 4 3 4 4 3 4 3 4
124 5 3 4 4 2 3 4 4
125 5 4 4 4 4 4 4 2
126 3 3 4 4 3 2 4 5
127 5 3 4 4 3 3 4 5
128 4 3 4 4 3 4 4 3
129 4 4 2 3 2 3 4 3
130 3 4 5 4 4 4 4 3
131 3 3 4 4 4 3 3 4
132 4 4 4 4 4 4 3 3
133 5 5 4 5 5 1 4 2
134 2 2 1 4 2 2 4 5
135 4 4 4 4 3 4 4 4
136 4 4 4 4 4 4 4 2
137 4 3 4 3 4 3 4 4
138 5 3 4 4 4 4 4 3
139 4 4 2 4 4 4 2 2
140 4 3 4 4 4 2 4 3
141 4 2 4 4 4 4 4 3
142 4 4 3 4 3 4 3 4
143 4 4 4 4 4 4 4 4
144 4 4 4 4 3 4 4 4
145 5 3 4 4 4 4 4 2
146 3 4 3 3 2 2 4 5
147 4 4 4 4 4 4 4 4
148 4 4 2 5 4 3 3 4
149 4 4 4 4 4 4 4 3
150 4 3 4 4 3 3 4 3
151 4 3 4 4 3 4 2 5
152 5 5 5 5 5 5 5 4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) A C D E F
1.028308 0.084248 0.207214 0.386522 -0.134026 0.004101
G H
0.279830 -0.068410
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.77476 -0.25282 -0.02765 0.39878 1.53668
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.028308 0.478598 2.149 0.033340 *
A 0.084248 0.064963 1.297 0.196758
C 0.207214 0.074490 2.782 0.006131 **
D 0.386522 0.090406 4.275 3.45e-05 ***
E -0.134026 0.071517 -1.874 0.062949 .
F 0.004101 0.078835 0.052 0.958585
G 0.279830 0.079493 3.520 0.000578 ***
H -0.068410 0.062201 -1.100 0.273251
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6049 on 144 degrees of freedom
Multiple R-squared: 0.3262, Adjusted R-squared: 0.2934
F-statistic: 9.958 on 7 and 144 DF, p-value: 4.146e-10
> 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,] 0.4438859 0.88777170 0.55611415
[2,] 0.2945417 0.58908338 0.70545831
[3,] 0.2236507 0.44730141 0.77634929
[4,] 0.1359278 0.27185560 0.86407220
[5,] 0.1016495 0.20329909 0.89835046
[6,] 0.2061075 0.41221510 0.79389245
[7,] 0.1371488 0.27429759 0.86285120
[8,] 0.1820075 0.36401499 0.81799251
[9,] 0.1431359 0.28627172 0.85686414
[10,] 0.2806062 0.56121244 0.71939378
[11,] 0.3552123 0.71042466 0.64478767
[12,] 0.5134873 0.97302544 0.48651272
[13,] 0.4365143 0.87302857 0.56348571
[14,] 0.3628665 0.72573308 0.63713346
[15,] 0.2949515 0.58990297 0.70504851
[16,] 0.2705565 0.54111295 0.72944353
[17,] 0.2144614 0.42892288 0.78553856
[18,] 0.4790368 0.95807365 0.52096317
[19,] 0.4118443 0.82368859 0.58815570
[20,] 0.5163266 0.96734673 0.48367336
[21,] 0.5505074 0.89898518 0.44949259
[22,] 0.5860768 0.82784645 0.41392323
[23,] 0.5244051 0.95118986 0.47559493
[24,] 0.6464588 0.70708250 0.35354125
[25,] 0.5890093 0.82198137 0.41099068
[26,] 0.5732827 0.85343453 0.42671727
[27,] 0.7519538 0.49609247 0.24804623
[28,] 0.7044260 0.59114801 0.29557401
[29,] 0.6900988 0.61980248 0.30990124
[30,] 0.6857286 0.62854287 0.31427143
[31,] 0.6702079 0.65958421 0.32979211
[32,] 0.6323805 0.73523902 0.36761951
[33,] 0.6587227 0.68255452 0.34127726
[34,] 0.9611307 0.07773860 0.03886930
[35,] 0.9596918 0.08061634 0.04030817
[36,] 0.9594748 0.08105033 0.04052517
[37,] 0.9513620 0.09727596 0.04863798
[38,] 0.9416177 0.11676460 0.05838230
[39,] 0.9267045 0.14659092 0.07329546
[40,] 0.9080407 0.18391869 0.09195934
[41,] 0.8922948 0.21541042 0.10770521
[42,] 0.8999873 0.20002547 0.10001274
[43,] 0.8818580 0.23628400 0.11814200
[44,] 0.8605314 0.27893720 0.13946860
[45,] 0.8955531 0.20889374 0.10444687
[46,] 0.8841944 0.23161124 0.11580562
[47,] 0.9317582 0.13648351 0.06824175
[48,] 0.9167125 0.16657501 0.08328751
[49,] 0.9052351 0.18952972 0.09476486
[50,] 0.8826816 0.23463679 0.11731840
[51,] 0.8593821 0.28123581 0.14061791
[52,] 0.8548086 0.29038276 0.14519138
[53,] 0.8252048 0.34959030 0.17479515
[54,] 0.8198648 0.36027040 0.18013520
[55,] 0.7923833 0.41523345 0.20761672
[56,] 0.7557648 0.48847040 0.24423520
[57,] 0.7226123 0.55477534 0.27738767
[58,] 0.6985503 0.60289933 0.30144966
[59,] 0.6988284 0.60234313 0.30117157
[60,] 0.6578746 0.68425082 0.34212541
[61,] 0.6292056 0.74158887 0.37079443
[62,] 0.7380545 0.52389103 0.26194551
[63,] 0.7818471 0.43630587 0.21815294
[64,] 0.7849720 0.43005591 0.21502796
[65,] 0.7519652 0.49606968 0.24803484
[66,] 0.7130044 0.57399121 0.28699561
[67,] 0.7014702 0.59705957 0.29852978
[68,] 0.7023858 0.59522849 0.29761425
[69,] 0.6759794 0.64804124 0.32402062
[70,] 0.6956363 0.60872745 0.30436372
[71,] 0.6649781 0.67004381 0.33502191
[72,] 0.6213663 0.75726748 0.37863374
[73,] 0.5887261 0.82254776 0.41127388
[74,] 0.5405781 0.91884371 0.45942186
[75,] 0.4923585 0.98471698 0.50764151
[76,] 0.4550755 0.91015109 0.54492446
[77,] 0.5156865 0.96862698 0.48431349
[78,] 0.4807633 0.96152658 0.51923671
[79,] 0.4448941 0.88978827 0.55510586
[80,] 0.4021237 0.80424746 0.59787627
[81,] 0.3643029 0.72860582 0.63569709
[82,] 0.4400528 0.88010562 0.55994719
[83,] 0.3947441 0.78948813 0.60525593
[84,] 0.3628607 0.72572131 0.63713935
[85,] 0.3209934 0.64198684 0.67900658
[86,] 0.3357344 0.67146884 0.66426558
[87,] 0.3052794 0.61055877 0.69472061
[88,] 0.2990063 0.59801264 0.70099368
[89,] 0.3191537 0.63830745 0.68084628
[90,] 0.3378752 0.67575033 0.66212483
[91,] 0.2925503 0.58510059 0.70744970
[92,] 0.2661720 0.53234400 0.73382800
[93,] 0.2924897 0.58497949 0.70751025
[94,] 0.3051690 0.61033805 0.69483098
[95,] 0.2732462 0.54649234 0.72675383
[96,] 0.2538782 0.50775634 0.74612183
[97,] 0.2133496 0.42669916 0.78665042
[98,] 0.1772101 0.35442024 0.82278988
[99,] 0.1456832 0.29136637 0.85431681
[100,] 0.1205839 0.24116785 0.87941607
[101,] 0.5282290 0.94354193 0.47177096
[102,] 0.4938392 0.98767841 0.50616079
[103,] 0.4525185 0.90503695 0.54748153
[104,] 0.4014295 0.80285890 0.59857055
[105,] 0.3529744 0.70594882 0.64702559
[106,] 0.3534854 0.70697077 0.64651462
[107,] 0.3054714 0.61094271 0.69452865
[108,] 0.2560082 0.51201641 0.74399179
[109,] 0.2585055 0.51701099 0.74149450
[110,] 0.2451950 0.49039000 0.75480500
[111,] 0.2154226 0.43084511 0.78457745
[112,] 0.2580644 0.51612888 0.74193556
[113,] 0.2086843 0.41736869 0.79131566
[114,] 0.2914104 0.58282074 0.70858963
[115,] 0.2844669 0.56893374 0.71553313
[116,] 0.2953656 0.59073111 0.70463444
[117,] 0.5842299 0.83154029 0.41577015
[118,] 0.5093767 0.98124654 0.49062327
[119,] 0.5021888 0.99562238 0.49781119
[120,] 0.8905896 0.21882072 0.10941036
[121,] 0.9748859 0.05022811 0.02511405
[122,] 0.9760961 0.04780782 0.02390391
[123,] 0.9611109 0.07777829 0.03888915
[124,] 0.9742327 0.05153464 0.02576732
[125,] 0.9520227 0.09595467 0.04797733
[126,] 0.9455541 0.10889187 0.05444594
[127,] 0.9209129 0.15817420 0.07908710
[128,] 0.9730349 0.05393022 0.02696511
[129,] 0.9512107 0.09757853 0.04878926
[130,] 0.8884867 0.22302650 0.11151325
[131,] 0.8573048 0.28539043 0.14269522
> postscript(file="/var/www/rcomp/tmp/1h7841290269273.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2h7841290269273.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3agpp1290269273.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4agpp1290269273.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5agpp1290269273.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 152
Frequency = 1
1 2 3 4 5 6
-0.066220959 -0.932194653 0.776344169 0.558554218 -1.223171974 0.056174016
7 8 9 10 11 12
-0.782290101 -1.116479711 0.121289498 -1.122260925 -0.062119948 -0.981776684
13 14 15 16 17 18
0.564923958 -0.698042597 -0.134630596 -0.273434634 -0.238333118 0.071906358
19 20 21 22 23 24
-0.759255959 -1.058018937 0.408649665 -1.774759780 -0.066220959 -0.066220959
25 26 27 28 29 30
-0.066220959 -0.541571532 -0.016442156 0.492897168 -0.066220959 0.420822564
31 32 33 34 35 36
-0.720795680 -0.027651247 -0.127059895 0.933779041 -0.066220959 -0.131837627
37 38 39 40 41 42
1.274623359 -0.066220959 0.022127555 0.375250296 -0.774759780 -0.118792729
43 44 45 46 47 48
-0.324409782 1.536680656 0.665726430 0.730343379 0.446735519 -0.200247264
49 50 51 52 53 54
-0.295308196 0.002188678 -0.521152705 0.092521957 0.194723573 -0.200247264
55 56 57 58 59 60
0.670525400 0.172668449 1.057229071 -0.200247264 -0.334273570 0.068086406
61 62 63 64 65 66
-0.252819035 0.650332041 0.002188678 -0.552300673 -0.238529890 -0.028135105
67 68 69 70 71 72
-0.318435704 0.413427398 -0.613223897 0.038506989 0.249956830 -1.187590508
73 74 75 76 77 78
0.882127172 0.615666569 -0.216085131 0.060213409 -0.406686943 -0.503485679
79 80 81 82 83 84
-0.486684004 -0.720598908 0.397589531 0.022127555 -0.352657699 -0.038711381
85 86 87 88 89 90
0.015273957 0.272688675 0.899838105 -0.375214009 0.375039248 -0.050383092
91 92 93 94 95 96
0.252178600 0.976449764 -0.061443226 0.306735135 -0.031119443 0.811445684
97 98 99 100 101 102
0.068482069 0.700787565 0.696619917 0.796959767 0.095314925 0.256850809
103 104 105 106 107 108
0.799752736 0.699914072 -0.369172287 0.515145156 -0.115999761 0.140992717
109 110 111 112 113 114
-0.184409398 -0.252819035 -1.765775512 0.256850809 0.371718989 0.195400294
115 116 117 118 119 120
-0.027651247 0.757565871 0.095314925 -0.189187131 -0.486622387 0.313588734
121 122 123 124 125 126
-0.107296552 -1.027651247 0.163830086 0.754074944 0.796959767 -1.039388102
127 128 129 130 131 132
0.956510887 -0.184409398 0.402367264 -1.341844271 -0.698042597 0.145199252
133 134 135 136 137 138
0.472519493 -1.467525879 -0.200247264 -0.203040233 0.408649665 0.949616908
139 140 141 142 143 144
0.771046812 -0.042181071 0.033864411 0.286796258 -0.066220959 -0.200247264
145 146 147 148 149 150
0.881207271 -0.663926126 -0.066220959 0.245615140 -0.134630596 -0.180308387
151 152
0.512069570 0.105891201
> postscript(file="/var/www/rcomp/tmp/6l7pa1290269273.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 152
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.066220959 NA
1 -0.932194653 -0.066220959
2 0.776344169 -0.932194653
3 0.558554218 0.776344169
4 -1.223171974 0.558554218
5 0.056174016 -1.223171974
6 -0.782290101 0.056174016
7 -1.116479711 -0.782290101
8 0.121289498 -1.116479711
9 -1.122260925 0.121289498
10 -0.062119948 -1.122260925
11 -0.981776684 -0.062119948
12 0.564923958 -0.981776684
13 -0.698042597 0.564923958
14 -0.134630596 -0.698042597
15 -0.273434634 -0.134630596
16 -0.238333118 -0.273434634
17 0.071906358 -0.238333118
18 -0.759255959 0.071906358
19 -1.058018937 -0.759255959
20 0.408649665 -1.058018937
21 -1.774759780 0.408649665
22 -0.066220959 -1.774759780
23 -0.066220959 -0.066220959
24 -0.066220959 -0.066220959
25 -0.541571532 -0.066220959
26 -0.016442156 -0.541571532
27 0.492897168 -0.016442156
28 -0.066220959 0.492897168
29 0.420822564 -0.066220959
30 -0.720795680 0.420822564
31 -0.027651247 -0.720795680
32 -0.127059895 -0.027651247
33 0.933779041 -0.127059895
34 -0.066220959 0.933779041
35 -0.131837627 -0.066220959
36 1.274623359 -0.131837627
37 -0.066220959 1.274623359
38 0.022127555 -0.066220959
39 0.375250296 0.022127555
40 -0.774759780 0.375250296
41 -0.118792729 -0.774759780
42 -0.324409782 -0.118792729
43 1.536680656 -0.324409782
44 0.665726430 1.536680656
45 0.730343379 0.665726430
46 0.446735519 0.730343379
47 -0.200247264 0.446735519
48 -0.295308196 -0.200247264
49 0.002188678 -0.295308196
50 -0.521152705 0.002188678
51 0.092521957 -0.521152705
52 0.194723573 0.092521957
53 -0.200247264 0.194723573
54 0.670525400 -0.200247264
55 0.172668449 0.670525400
56 1.057229071 0.172668449
57 -0.200247264 1.057229071
58 -0.334273570 -0.200247264
59 0.068086406 -0.334273570
60 -0.252819035 0.068086406
61 0.650332041 -0.252819035
62 0.002188678 0.650332041
63 -0.552300673 0.002188678
64 -0.238529890 -0.552300673
65 -0.028135105 -0.238529890
66 -0.318435704 -0.028135105
67 0.413427398 -0.318435704
68 -0.613223897 0.413427398
69 0.038506989 -0.613223897
70 0.249956830 0.038506989
71 -1.187590508 0.249956830
72 0.882127172 -1.187590508
73 0.615666569 0.882127172
74 -0.216085131 0.615666569
75 0.060213409 -0.216085131
76 -0.406686943 0.060213409
77 -0.503485679 -0.406686943
78 -0.486684004 -0.503485679
79 -0.720598908 -0.486684004
80 0.397589531 -0.720598908
81 0.022127555 0.397589531
82 -0.352657699 0.022127555
83 -0.038711381 -0.352657699
84 0.015273957 -0.038711381
85 0.272688675 0.015273957
86 0.899838105 0.272688675
87 -0.375214009 0.899838105
88 0.375039248 -0.375214009
89 -0.050383092 0.375039248
90 0.252178600 -0.050383092
91 0.976449764 0.252178600
92 -0.061443226 0.976449764
93 0.306735135 -0.061443226
94 -0.031119443 0.306735135
95 0.811445684 -0.031119443
96 0.068482069 0.811445684
97 0.700787565 0.068482069
98 0.696619917 0.700787565
99 0.796959767 0.696619917
100 0.095314925 0.796959767
101 0.256850809 0.095314925
102 0.799752736 0.256850809
103 0.699914072 0.799752736
104 -0.369172287 0.699914072
105 0.515145156 -0.369172287
106 -0.115999761 0.515145156
107 0.140992717 -0.115999761
108 -0.184409398 0.140992717
109 -0.252819035 -0.184409398
110 -1.765775512 -0.252819035
111 0.256850809 -1.765775512
112 0.371718989 0.256850809
113 0.195400294 0.371718989
114 -0.027651247 0.195400294
115 0.757565871 -0.027651247
116 0.095314925 0.757565871
117 -0.189187131 0.095314925
118 -0.486622387 -0.189187131
119 0.313588734 -0.486622387
120 -0.107296552 0.313588734
121 -1.027651247 -0.107296552
122 0.163830086 -1.027651247
123 0.754074944 0.163830086
124 0.796959767 0.754074944
125 -1.039388102 0.796959767
126 0.956510887 -1.039388102
127 -0.184409398 0.956510887
128 0.402367264 -0.184409398
129 -1.341844271 0.402367264
130 -0.698042597 -1.341844271
131 0.145199252 -0.698042597
132 0.472519493 0.145199252
133 -1.467525879 0.472519493
134 -0.200247264 -1.467525879
135 -0.203040233 -0.200247264
136 0.408649665 -0.203040233
137 0.949616908 0.408649665
138 0.771046812 0.949616908
139 -0.042181071 0.771046812
140 0.033864411 -0.042181071
141 0.286796258 0.033864411
142 -0.066220959 0.286796258
143 -0.200247264 -0.066220959
144 0.881207271 -0.200247264
145 -0.663926126 0.881207271
146 -0.066220959 -0.663926126
147 0.245615140 -0.066220959
148 -0.134630596 0.245615140
149 -0.180308387 -0.134630596
150 0.512069570 -0.180308387
151 0.105891201 0.512069570
152 NA 0.105891201
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.932194653 -0.066220959
[2,] 0.776344169 -0.932194653
[3,] 0.558554218 0.776344169
[4,] -1.223171974 0.558554218
[5,] 0.056174016 -1.223171974
[6,] -0.782290101 0.056174016
[7,] -1.116479711 -0.782290101
[8,] 0.121289498 -1.116479711
[9,] -1.122260925 0.121289498
[10,] -0.062119948 -1.122260925
[11,] -0.981776684 -0.062119948
[12,] 0.564923958 -0.981776684
[13,] -0.698042597 0.564923958
[14,] -0.134630596 -0.698042597
[15,] -0.273434634 -0.134630596
[16,] -0.238333118 -0.273434634
[17,] 0.071906358 -0.238333118
[18,] -0.759255959 0.071906358
[19,] -1.058018937 -0.759255959
[20,] 0.408649665 -1.058018937
[21,] -1.774759780 0.408649665
[22,] -0.066220959 -1.774759780
[23,] -0.066220959 -0.066220959
[24,] -0.066220959 -0.066220959
[25,] -0.541571532 -0.066220959
[26,] -0.016442156 -0.541571532
[27,] 0.492897168 -0.016442156
[28,] -0.066220959 0.492897168
[29,] 0.420822564 -0.066220959
[30,] -0.720795680 0.420822564
[31,] -0.027651247 -0.720795680
[32,] -0.127059895 -0.027651247
[33,] 0.933779041 -0.127059895
[34,] -0.066220959 0.933779041
[35,] -0.131837627 -0.066220959
[36,] 1.274623359 -0.131837627
[37,] -0.066220959 1.274623359
[38,] 0.022127555 -0.066220959
[39,] 0.375250296 0.022127555
[40,] -0.774759780 0.375250296
[41,] -0.118792729 -0.774759780
[42,] -0.324409782 -0.118792729
[43,] 1.536680656 -0.324409782
[44,] 0.665726430 1.536680656
[45,] 0.730343379 0.665726430
[46,] 0.446735519 0.730343379
[47,] -0.200247264 0.446735519
[48,] -0.295308196 -0.200247264
[49,] 0.002188678 -0.295308196
[50,] -0.521152705 0.002188678
[51,] 0.092521957 -0.521152705
[52,] 0.194723573 0.092521957
[53,] -0.200247264 0.194723573
[54,] 0.670525400 -0.200247264
[55,] 0.172668449 0.670525400
[56,] 1.057229071 0.172668449
[57,] -0.200247264 1.057229071
[58,] -0.334273570 -0.200247264
[59,] 0.068086406 -0.334273570
[60,] -0.252819035 0.068086406
[61,] 0.650332041 -0.252819035
[62,] 0.002188678 0.650332041
[63,] -0.552300673 0.002188678
[64,] -0.238529890 -0.552300673
[65,] -0.028135105 -0.238529890
[66,] -0.318435704 -0.028135105
[67,] 0.413427398 -0.318435704
[68,] -0.613223897 0.413427398
[69,] 0.038506989 -0.613223897
[70,] 0.249956830 0.038506989
[71,] -1.187590508 0.249956830
[72,] 0.882127172 -1.187590508
[73,] 0.615666569 0.882127172
[74,] -0.216085131 0.615666569
[75,] 0.060213409 -0.216085131
[76,] -0.406686943 0.060213409
[77,] -0.503485679 -0.406686943
[78,] -0.486684004 -0.503485679
[79,] -0.720598908 -0.486684004
[80,] 0.397589531 -0.720598908
[81,] 0.022127555 0.397589531
[82,] -0.352657699 0.022127555
[83,] -0.038711381 -0.352657699
[84,] 0.015273957 -0.038711381
[85,] 0.272688675 0.015273957
[86,] 0.899838105 0.272688675
[87,] -0.375214009 0.899838105
[88,] 0.375039248 -0.375214009
[89,] -0.050383092 0.375039248
[90,] 0.252178600 -0.050383092
[91,] 0.976449764 0.252178600
[92,] -0.061443226 0.976449764
[93,] 0.306735135 -0.061443226
[94,] -0.031119443 0.306735135
[95,] 0.811445684 -0.031119443
[96,] 0.068482069 0.811445684
[97,] 0.700787565 0.068482069
[98,] 0.696619917 0.700787565
[99,] 0.796959767 0.696619917
[100,] 0.095314925 0.796959767
[101,] 0.256850809 0.095314925
[102,] 0.799752736 0.256850809
[103,] 0.699914072 0.799752736
[104,] -0.369172287 0.699914072
[105,] 0.515145156 -0.369172287
[106,] -0.115999761 0.515145156
[107,] 0.140992717 -0.115999761
[108,] -0.184409398 0.140992717
[109,] -0.252819035 -0.184409398
[110,] -1.765775512 -0.252819035
[111,] 0.256850809 -1.765775512
[112,] 0.371718989 0.256850809
[113,] 0.195400294 0.371718989
[114,] -0.027651247 0.195400294
[115,] 0.757565871 -0.027651247
[116,] 0.095314925 0.757565871
[117,] -0.189187131 0.095314925
[118,] -0.486622387 -0.189187131
[119,] 0.313588734 -0.486622387
[120,] -0.107296552 0.313588734
[121,] -1.027651247 -0.107296552
[122,] 0.163830086 -1.027651247
[123,] 0.754074944 0.163830086
[124,] 0.796959767 0.754074944
[125,] -1.039388102 0.796959767
[126,] 0.956510887 -1.039388102
[127,] -0.184409398 0.956510887
[128,] 0.402367264 -0.184409398
[129,] -1.341844271 0.402367264
[130,] -0.698042597 -1.341844271
[131,] 0.145199252 -0.698042597
[132,] 0.472519493 0.145199252
[133,] -1.467525879 0.472519493
[134,] -0.200247264 -1.467525879
[135,] -0.203040233 -0.200247264
[136,] 0.408649665 -0.203040233
[137,] 0.949616908 0.408649665
[138,] 0.771046812 0.949616908
[139,] -0.042181071 0.771046812
[140,] 0.033864411 -0.042181071
[141,] 0.286796258 0.033864411
[142,] -0.066220959 0.286796258
[143,] -0.200247264 -0.066220959
[144,] 0.881207271 -0.200247264
[145,] -0.663926126 0.881207271
[146,] -0.066220959 -0.663926126
[147,] 0.245615140 -0.066220959
[148,] -0.134630596 0.245615140
[149,] -0.180308387 -0.134630596
[150,] 0.512069570 -0.180308387
[151,] 0.105891201 0.512069570
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.932194653 -0.066220959
2 0.776344169 -0.932194653
3 0.558554218 0.776344169
4 -1.223171974 0.558554218
5 0.056174016 -1.223171974
6 -0.782290101 0.056174016
7 -1.116479711 -0.782290101
8 0.121289498 -1.116479711
9 -1.122260925 0.121289498
10 -0.062119948 -1.122260925
11 -0.981776684 -0.062119948
12 0.564923958 -0.981776684
13 -0.698042597 0.564923958
14 -0.134630596 -0.698042597
15 -0.273434634 -0.134630596
16 -0.238333118 -0.273434634
17 0.071906358 -0.238333118
18 -0.759255959 0.071906358
19 -1.058018937 -0.759255959
20 0.408649665 -1.058018937
21 -1.774759780 0.408649665
22 -0.066220959 -1.774759780
23 -0.066220959 -0.066220959
24 -0.066220959 -0.066220959
25 -0.541571532 -0.066220959
26 -0.016442156 -0.541571532
27 0.492897168 -0.016442156
28 -0.066220959 0.492897168
29 0.420822564 -0.066220959
30 -0.720795680 0.420822564
31 -0.027651247 -0.720795680
32 -0.127059895 -0.027651247
33 0.933779041 -0.127059895
34 -0.066220959 0.933779041
35 -0.131837627 -0.066220959
36 1.274623359 -0.131837627
37 -0.066220959 1.274623359
38 0.022127555 -0.066220959
39 0.375250296 0.022127555
40 -0.774759780 0.375250296
41 -0.118792729 -0.774759780
42 -0.324409782 -0.118792729
43 1.536680656 -0.324409782
44 0.665726430 1.536680656
45 0.730343379 0.665726430
46 0.446735519 0.730343379
47 -0.200247264 0.446735519
48 -0.295308196 -0.200247264
49 0.002188678 -0.295308196
50 -0.521152705 0.002188678
51 0.092521957 -0.521152705
52 0.194723573 0.092521957
53 -0.200247264 0.194723573
54 0.670525400 -0.200247264
55 0.172668449 0.670525400
56 1.057229071 0.172668449
57 -0.200247264 1.057229071
58 -0.334273570 -0.200247264
59 0.068086406 -0.334273570
60 -0.252819035 0.068086406
61 0.650332041 -0.252819035
62 0.002188678 0.650332041
63 -0.552300673 0.002188678
64 -0.238529890 -0.552300673
65 -0.028135105 -0.238529890
66 -0.318435704 -0.028135105
67 0.413427398 -0.318435704
68 -0.613223897 0.413427398
69 0.038506989 -0.613223897
70 0.249956830 0.038506989
71 -1.187590508 0.249956830
72 0.882127172 -1.187590508
73 0.615666569 0.882127172
74 -0.216085131 0.615666569
75 0.060213409 -0.216085131
76 -0.406686943 0.060213409
77 -0.503485679 -0.406686943
78 -0.486684004 -0.503485679
79 -0.720598908 -0.486684004
80 0.397589531 -0.720598908
81 0.022127555 0.397589531
82 -0.352657699 0.022127555
83 -0.038711381 -0.352657699
84 0.015273957 -0.038711381
85 0.272688675 0.015273957
86 0.899838105 0.272688675
87 -0.375214009 0.899838105
88 0.375039248 -0.375214009
89 -0.050383092 0.375039248
90 0.252178600 -0.050383092
91 0.976449764 0.252178600
92 -0.061443226 0.976449764
93 0.306735135 -0.061443226
94 -0.031119443 0.306735135
95 0.811445684 -0.031119443
96 0.068482069 0.811445684
97 0.700787565 0.068482069
98 0.696619917 0.700787565
99 0.796959767 0.696619917
100 0.095314925 0.796959767
101 0.256850809 0.095314925
102 0.799752736 0.256850809
103 0.699914072 0.799752736
104 -0.369172287 0.699914072
105 0.515145156 -0.369172287
106 -0.115999761 0.515145156
107 0.140992717 -0.115999761
108 -0.184409398 0.140992717
109 -0.252819035 -0.184409398
110 -1.765775512 -0.252819035
111 0.256850809 -1.765775512
112 0.371718989 0.256850809
113 0.195400294 0.371718989
114 -0.027651247 0.195400294
115 0.757565871 -0.027651247
116 0.095314925 0.757565871
117 -0.189187131 0.095314925
118 -0.486622387 -0.189187131
119 0.313588734 -0.486622387
120 -0.107296552 0.313588734
121 -1.027651247 -0.107296552
122 0.163830086 -1.027651247
123 0.754074944 0.163830086
124 0.796959767 0.754074944
125 -1.039388102 0.796959767
126 0.956510887 -1.039388102
127 -0.184409398 0.956510887
128 0.402367264 -0.184409398
129 -1.341844271 0.402367264
130 -0.698042597 -1.341844271
131 0.145199252 -0.698042597
132 0.472519493 0.145199252
133 -1.467525879 0.472519493
134 -0.200247264 -1.467525879
135 -0.203040233 -0.200247264
136 0.408649665 -0.203040233
137 0.949616908 0.408649665
138 0.771046812 0.949616908
139 -0.042181071 0.771046812
140 0.033864411 -0.042181071
141 0.286796258 0.033864411
142 -0.066220959 0.286796258
143 -0.200247264 -0.066220959
144 0.881207271 -0.200247264
145 -0.663926126 0.881207271
146 -0.066220959 -0.663926126
147 0.245615140 -0.066220959
148 -0.134630596 0.245615140
149 -0.180308387 -0.134630596
150 0.512069570 -0.180308387
151 0.105891201 0.512069570
> 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/7l7pa1290269273.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8vhod1290269273.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9vhod1290269273.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10o8ng1290269273.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11r8441290269273.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/12dr2a1290269273.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/1391001290269273.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/14u1zo1290269273.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/15ykxc1290269273.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/1612wi1290269273.tab")
+ }
>
> try(system("convert tmp/1h7841290269273.ps tmp/1h7841290269273.png",intern=TRUE))
character(0)
> try(system("convert tmp/2h7841290269273.ps tmp/2h7841290269273.png",intern=TRUE))
character(0)
> try(system("convert tmp/3agpp1290269273.ps tmp/3agpp1290269273.png",intern=TRUE))
character(0)
> try(system("convert tmp/4agpp1290269273.ps tmp/4agpp1290269273.png",intern=TRUE))
character(0)
> try(system("convert tmp/5agpp1290269273.ps tmp/5agpp1290269273.png",intern=TRUE))
character(0)
> try(system("convert tmp/6l7pa1290269273.ps tmp/6l7pa1290269273.png",intern=TRUE))
character(0)
> try(system("convert tmp/7l7pa1290269273.ps tmp/7l7pa1290269273.png",intern=TRUE))
character(0)
> try(system("convert tmp/8vhod1290269273.ps tmp/8vhod1290269273.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vhod1290269273.ps tmp/9vhod1290269273.png",intern=TRUE))
character(0)
> try(system("convert tmp/10o8ng1290269273.ps tmp/10o8ng1290269273.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.610 2.150 7.734