R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
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+ ,0)
+ ,dim=c(8
+ ,154)
+ ,dimnames=list(c('Weeks'
+ ,'UseLimit'
+ ,'T40'
+ ,'T20'
+ ,'Used'
+ ,'CorrectAnalysis'
+ ,'Useful'
+ ,'Outcome')
+ ,1:154))
> y <- array(NA,dim=c(8,154),dimnames=list(c('Weeks','UseLimit','T40','T20','Used','CorrectAnalysis','Useful','Outcome'),1:154))
> 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 = '6'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
CorrectAnalysis Weeks UseLimit T40 T20 Used Useful Outcome
1 0 4 1 1 0 0 0 1
2 0 4 0 2 0 0 0 0
3 0 4 0 2 0 0 0 0
4 0 4 0 2 0 0 0 0
5 0 4 0 2 0 0 0 0
6 0 4 1 2 0 0 1 1
7 0 4 0 2 0 0 0 0
8 0 4 0 1 0 0 0 0
9 0 4 0 2 0 0 0 1
10 0 4 1 2 0 0 0 0
11 0 4 1 1 0 0 0 0
12 0 4 0 2 0 0 0 0
13 0 4 0 2 0 1 1 0
14 0 4 1 1 0 0 0 0
15 0 4 0 2 0 1 1 1
16 0 4 0 1 0 1 1 1
17 1 4 1 1 0 1 1 0
18 0 4 1 1 0 0 0 0
19 0 4 0 2 0 0 0 1
20 1 4 0 1 0 1 1 1
21 0 4 1 2 0 0 1 0
22 0 4 1 2 0 1 1 1
23 0 4 0 2 0 0 1 1
24 0 4 1 2 0 0 1 1
25 0 4 0 1 0 1 0 1
26 0 4 0 2 0 1 1 0
27 0 4 1 2 0 0 0 1
28 0 4 0 2 0 1 0 0
29 0 4 0 2 0 0 0 1
30 0 4 0 2 0 0 1 0
31 0 4 0 2 0 0 0 0
32 0 4 1 2 0 0 0 0
33 0 4 1 2 0 0 1 0
34 0 4 0 1 0 0 0 1
35 0 4 0 2 0 0 0 0
36 0 4 0 2 0 0 0 0
37 0 4 1 1 0 1 1 0
38 0 4 0 2 0 1 0 1
39 0 4 0 2 0 0 1 1
40 0 4 0 1 0 0 1 0
41 1 4 0 2 0 1 1 1
42 0 4 0 2 0 1 0 1
43 0 4 1 2 0 0 1 1
44 0 4 1 1 0 0 0 0
45 0 4 0 2 0 0 1 0
46 0 4 0 2 0 0 1 1
47 0 4 0 2 0 0 0 0
48 0 4 0 2 0 0 0 1
49 0 4 0 2 0 0 1 1
50 0 4 0 2 0 0 0 0
51 0 4 0 1 0 1 0 0
52 1 4 1 1 0 1 1 0
53 0 4 0 2 0 0 0 1
54 1 4 0 2 0 1 0 0
55 0 4 0 2 0 0 0 0
56 0 4 0 1 0 1 0 1
57 0 4 0 2 0 1 1 1
58 0 4 0 2 0 0 0 1
59 0 4 0 2 0 0 0 1
60 1 4 1 1 0 1 1 1
61 0 4 1 1 0 0 0 1
62 0 4 0 2 0 1 1 0
63 0 4 0 2 0 0 0 0
64 0 4 1 1 0 0 0 1
65 0 4 0 2 0 0 0 0
66 0 4 0 2 0 0 0 0
67 1 4 0 1 0 1 1 0
68 0 4 1 2 0 0 0 0
69 0 4 0 2 0 0 0 1
70 0 4 0 2 0 1 0 0
71 0 4 0 2 0 0 0 0
72 0 4 0 2 0 0 0 1
73 0 4 0 2 0 1 0 1
74 0 4 1 2 0 1 0 0
75 0 4 0 2 0 0 0 1
76 0 4 0 1 0 0 1 1
77 0 4 0 2 0 0 0 1
78 0 4 0 2 0 1 1 1
79 1 4 0 1 0 1 0 1
80 0 4 0 1 0 0 1 0
81 0 4 0 2 0 0 0 0
82 0 4 1 2 0 1 0 1
83 0 4 0 2 0 0 0 0
84 1 4 0 2 0 1 0 0
85 0 4 0 2 0 0 1 1
86 0 4 1 2 0 0 0 0
87 0 2 1 0 2 0 0 1
88 0 2 1 0 1 1 0 1
89 0 2 0 0 2 0 0 0
90 0 2 0 0 2 0 0 1
91 0 2 0 0 2 0 1 0
92 0 2 1 0 1 0 0 0
93 0 2 1 0 2 0 1 0
94 0 2 0 0 2 0 0 0
95 0 2 0 0 1 0 0 0
96 0 2 0 0 2 0 0 1
97 0 2 1 0 1 0 0 0
98 0 2 0 0 2 0 0 0
99 0 2 1 0 2 0 0 0
100 0 2 0 0 2 0 0 1
101 0 2 1 0 2 0 0 1
102 0 2 0 0 2 0 0 0
103 0 2 0 0 2 0 0 0
104 0 2 0 0 2 0 0 0
105 0 2 0 0 1 1 0 0
106 0 2 0 0 2 0 0 0
107 0 2 0 0 2 0 0 0
108 0 2 1 0 1 1 0 0
109 0 2 0 0 2 0 0 0
110 0 2 1 0 2 0 0 0
111 0 2 1 0 1 1 1 0
112 0 2 0 0 1 0 0 0
113 0 2 0 0 2 1 0 0
114 0 2 1 0 1 1 0 0
115 0 2 1 0 2 0 0 0
116 0 2 0 0 2 0 0 0
117 0 2 1 0 2 0 0 1
118 0 2 1 0 2 0 0 0
119 0 2 0 0 2 0 0 0
120 0 2 0 0 2 0 0 1
121 0 2 1 0 2 0 0 0
122 0 2 0 0 2 0 0 0
123 0 2 1 0 1 1 0 0
124 0 2 0 0 2 1 1 1
125 0 2 0 0 2 0 0 1
126 0 2 0 0 1 0 0 0
127 0 2 0 0 2 0 1 0
128 0 2 0 0 2 0 0 1
129 0 2 0 0 2 0 0 0
130 0 2 0 0 2 0 0 1
131 0 2 1 0 2 0 0 0
132 0 2 1 0 2 0 0 1
133 0 2 1 0 2 1 0 0
134 0 2 0 0 2 0 0 0
135 0 2 0 0 2 0 0 0
136 0 2 0 0 2 0 0 0
137 0 2 1 0 2 1 1 1
138 0 2 1 0 1 1 1 1
139 0 2 0 0 1 0 0 0
140 0 2 0 0 2 0 0 0
141 1 2 0 0 2 1 0 1
142 0 2 0 0 1 1 0 1
143 0 2 1 0 2 0 0 0
144 0 2 0 0 2 0 1 1
145 0 2 0 0 2 0 1 0
146 0 2 0 0 1 0 0 1
147 0 2 0 0 1 1 0 0
148 0 2 0 0 1 0 0 0
149 0 2 1 0 2 0 0 0
150 0 2 0 0 2 0 1 1
151 0 2 0 0 2 0 0 1
152 1 2 1 0 2 1 0 0
153 1 2 1 0 2 1 1 0
154 0 2 1 0 2 1 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Weeks UseLimit T40 T20 Used
-0.873143 0.292012 -0.008643 -0.156530 0.157197 0.263764
Useful Outcome
0.040381 -0.035707
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.43388 -0.11807 -0.01663 0.02136 0.75439
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.873143 0.263274 -3.316 0.001150 **
Weeks 0.292012 0.079092 3.692 0.000314 ***
UseLimit -0.008643 0.041496 -0.208 0.835293
T40 -0.156530 0.058477 -2.677 0.008284 **
T20 0.157197 0.067800 2.319 0.021808 *
Used 0.263764 0.044810 5.886 2.6e-08 ***
Useful 0.040381 0.045562 0.886 0.376919
Outcome -0.035707 0.039538 -0.903 0.367957
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2329 on 146 degrees of freedom
Multiple R-squared: 0.284, Adjusted R-squared: 0.2497
F-statistic: 8.274 on 7 and 146 DF, p-value: 1.741e-08
> 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,] 0.0000000000 0.000000000 1.000000000
[2,] 0.0000000000 0.000000000 1.000000000
[3,] 0.0000000000 0.000000000 1.000000000
[4,] 0.0000000000 0.000000000 1.000000000
[5,] 0.0000000000 0.000000000 1.000000000
[6,] 0.0000000000 0.000000000 1.000000000
[7,] 0.5523333815 0.895333237 0.447666618
[8,] 0.4988084336 0.997616867 0.501191566
[9,] 0.4590338641 0.918067728 0.540966136
[10,] 0.8982567134 0.203486573 0.101743287
[11,] 0.8583044430 0.283391114 0.141695557
[12,] 0.8473365983 0.305326803 0.152663402
[13,] 0.7976021755 0.404795649 0.202397825
[14,] 0.7415485238 0.516902952 0.258451476
[15,] 0.7445444510 0.510911098 0.255455549
[16,] 0.7466036189 0.506792762 0.253396381
[17,] 0.7091237549 0.581752490 0.290876245
[18,] 0.6616193109 0.676761378 0.338380689
[19,] 0.6103462328 0.779307534 0.389653767
[20,] 0.5525276735 0.894944653 0.447472327
[21,] 0.4910988760 0.982197752 0.508901124
[22,] 0.4308111729 0.861622346 0.569188827
[23,] 0.3730625175 0.746125035 0.626937483
[24,] 0.3256477874 0.651295575 0.674352213
[25,] 0.2741575243 0.548315049 0.725842476
[26,] 0.2271280343 0.454256069 0.772871966
[27,] 0.3078687087 0.615737417 0.692131291
[28,] 0.2686946015 0.537389203 0.731305399
[29,] 0.2228094778 0.445618956 0.777190522
[30,] 0.2155302899 0.431060580 0.784469710
[31,] 0.7569086795 0.486182641 0.243091321
[32,] 0.7325202231 0.534959554 0.267479777
[33,] 0.6871915204 0.625616959 0.312808480
[34,] 0.6517964632 0.696407074 0.348203537
[35,] 0.6020677141 0.795864572 0.397932286
[36,] 0.5516443232 0.896711354 0.448355677
[37,] 0.5001761344 0.999647731 0.499823866
[38,] 0.4488654745 0.897730949 0.551134526
[39,] 0.3991407817 0.798281563 0.600859218
[40,] 0.3505868151 0.701173630 0.649413185
[41,] 0.4301763542 0.860352708 0.569823646
[42,] 0.7010426663 0.597914667 0.298957334
[43,] 0.6593231633 0.681353673 0.340676837
[44,] 0.9475304379 0.104939124 0.052469562
[45,] 0.9328010927 0.134397815 0.067198907
[46,] 0.9612014508 0.077597098 0.038798549
[47,] 0.9608453898 0.078309220 0.039154610
[48,] 0.9504103154 0.099179369 0.049589685
[49,] 0.9378469327 0.124306135 0.062153067
[50,] 0.9823656071 0.035268786 0.017634393
[51,] 0.9796657861 0.040668428 0.020334214
[52,] 0.9812268563 0.037546287 0.018773144
[53,] 0.9750304891 0.049939022 0.024969511
[54,] 0.9739696868 0.052060626 0.026030313
[55,] 0.9658798832 0.068240234 0.034120117
[56,] 0.9558212912 0.088357418 0.044178709
[57,] 0.9835133464 0.032973307 0.016486654
[58,] 0.9779495510 0.044100898 0.022050449
[59,] 0.9713953240 0.057209352 0.028604676
[60,] 0.9721837280 0.055632544 0.027816272
[61,] 0.9636559022 0.072688196 0.036344098
[62,] 0.9537002210 0.092599558 0.046299779
[63,] 0.9532315650 0.093536870 0.046768435
[64,] 0.9564050656 0.087189869 0.043594934
[65,] 0.9447983962 0.110403208 0.055201604
[66,] 0.9428792058 0.114241588 0.057120794
[67,] 0.9286201347 0.142759731 0.071379865
[68,] 0.9346838434 0.130632313 0.065316157
[69,] 0.9839701953 0.032059609 0.016029805
[70,] 0.9800563359 0.039887328 0.019943664
[71,] 0.9746382968 0.050723406 0.025361703
[72,] 0.9806367598 0.038726480 0.019363240
[73,] 0.9787858770 0.042428246 0.021214123
[74,] 0.9980797548 0.003840490 0.001920245
[75,] 0.9971720817 0.005655837 0.002827918
[76,] 0.9958918117 0.008216377 0.004108188
[77,] 0.9941020867 0.011795827 0.005897913
[78,] 0.9920758431 0.015848314 0.007924157
[79,] 0.9889564548 0.022087090 0.011043545
[80,] 0.9847468332 0.030506334 0.015253167
[81,] 0.9794112454 0.041177509 0.020588755
[82,] 0.9749279702 0.050144060 0.025072030
[83,] 0.9666797868 0.066640426 0.033320213
[84,] 0.9561894822 0.087621036 0.043810518
[85,] 0.9468635576 0.106272885 0.053136442
[86,] 0.9317005482 0.136598904 0.068299452
[87,] 0.9197238524 0.160552295 0.080276148
[88,] 0.8989070582 0.202185884 0.101092942
[89,] 0.8741146388 0.251770722 0.125885361
[90,] 0.8455326680 0.308934664 0.154467332
[91,] 0.8127446868 0.374510626 0.187255313
[92,] 0.7758857491 0.448228502 0.224114251
[93,] 0.7350833720 0.529833256 0.264916628
[94,] 0.6906624786 0.618675043 0.309337521
[95,] 0.6604520531 0.679095894 0.339547947
[96,] 0.6111542501 0.777691500 0.388845750
[97,] 0.5598492398 0.880301520 0.440150760
[98,] 0.5206753369 0.958649326 0.479324663
[99,] 0.4675849246 0.935169849 0.532415075
[100,] 0.4138453111 0.827690622 0.586154689
[101,] 0.3761257724 0.752251545 0.623874228
[102,] 0.3398021267 0.679604253 0.660197873
[103,] 0.3871091110 0.774218222 0.612890889
[104,] 0.3512368859 0.702473772 0.648763114
[105,] 0.2999936728 0.599987346 0.700006327
[106,] 0.2541314528 0.508262906 0.745868547
[107,] 0.2106758722 0.421351744 0.789324128
[108,] 0.1710330705 0.342066141 0.828966930
[109,] 0.1379845483 0.275969097 0.862015452
[110,] 0.1079358619 0.215871724 0.892064138
[111,] 0.0826669413 0.165333883 0.917333059
[112,] 0.0631268739 0.126253748 0.936873126
[113,] 0.0527508146 0.105501629 0.947249185
[114,] 0.0674250306 0.134850061 0.932574969
[115,] 0.0492769259 0.098553852 0.950723074
[116,] 0.0392307560 0.078461512 0.960769244
[117,] 0.0278069383 0.055613877 0.972193062
[118,] 0.0189054148 0.037810830 0.981094585
[119,] 0.0128448348 0.025689670 0.987155165
[120,] 0.0082640315 0.016528063 0.991735969
[121,] 0.0050626498 0.010125300 0.994937350
[122,] 0.0030672851 0.006134570 0.996932715
[123,] 0.0060803120 0.012160624 0.993919688
[124,] 0.0038472606 0.007694521 0.996152739
[125,] 0.0024307008 0.004861402 0.997569299
[126,] 0.0015759700 0.003151940 0.998424030
[127,] 0.0028786984 0.005757397 0.997121302
[128,] 0.0023137579 0.004627516 0.997686242
[129,] 0.0018032681 0.003606536 0.998196732
[130,] 0.0008144158 0.001628832 0.999185584
[131,] 0.0126092641 0.025218528 0.987390736
[132,] 0.0093783337 0.018756667 0.990621666
[133,] 0.0043546948 0.008709390 0.995645305
> postscript(file="/var/fisher/rcomp/tmp/1xjq91356021387.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/fisher/rcomp/tmp/2mne41356021387.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3rx6n1356021387.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4tnmz1356021387.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/59hmc1356021387.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 154
Frequency = 1
1 2 3 4 5 6
-0.09402503 0.01815461 0.01815461 0.01815461 0.01815461 0.02212414
7 8 9 10 11 12
0.01815461 -0.13837554 0.05386193 0.02679780 -0.12973235 0.01815461
13 14 15 16 17 18
-0.28599077 -0.12973235 -0.25028345 -0.40681360 0.56612227 -0.12973235
19 20 21 22 23 24
0.05386193 0.59318640 -0.01358318 -0.24164026 0.01348096 0.02212414
25 26 27 28 29 30
-0.36643262 -0.28599077 0.06250512 -0.24560979 0.05386193 -0.02222636
31 32 33 34 35 36
0.01815461 0.02679780 -0.01358318 -0.10266822 0.01815461 0.01815461
37 38 39 40 41 42
-0.43387773 -0.20990247 0.01348096 -0.17875652 0.74971655 -0.20990247
43 44 45 46 47 48
0.02212414 -0.12973235 -0.02222636 0.01348096 0.01815461 0.05386193
49 50 51 52 53 54
0.01348096 0.01815461 -0.40213994 0.56612227 0.05386193 0.75439021
55 56 57 58 59 60
0.01815461 -0.36643262 -0.25028345 0.05386193 0.05386193 0.60182959
61 62 63 64 65 66
-0.09402503 -0.28599077 0.01815461 -0.09402503 0.01815461 0.01815461
67 68 69 70 71 72
0.55747908 0.02679780 0.05386193 -0.24560979 0.01815461 0.05386193
73 74 75 76 77 78
-0.20990247 -0.23696660 0.05386193 -0.14304920 0.05386193 -0.25028345
79 80 81 82 83 84
0.63356738 -0.17875652 0.01815461 -0.20125928 0.01815461 0.75439021
85 86 87 88 89 90
0.01348096 0.02679780 0.01907486 -0.08749232 -0.02527564 0.01043168
91 92 93 94 95 96
-0.06565662 0.14056476 -0.05701343 -0.02527564 0.13192158 0.01043168
97 98 99 100 101 102
0.14056476 -0.02527564 -0.01663246 0.01043168 0.01907486 -0.02527564
103 104 105 106 107 108
-0.02527564 -0.02527564 -0.13184283 -0.02527564 -0.02527564 -0.12319964
109 110 111 112 113 114
-0.02527564 -0.01663246 -0.16358062 0.13192158 -0.28904005 -0.12319964
115 116 117 118 119 120
-0.01663246 -0.02527564 0.01907486 -0.01663246 -0.02527564 0.01043168
121 122 123 124 125 126
-0.01663246 -0.02527564 -0.12319964 -0.29371370 0.01043168 0.13192158
127 128 129 130 131 132
-0.06565662 0.01043168 -0.02527564 0.01043168 -0.01663246 0.01907486
133 134 135 136 137 138
-0.28039686 -0.02527564 -0.02527564 -0.02527564 -0.28507052 -0.12787330
139 140 141 142 143 144
0.13192158 -0.02527564 0.74666727 -0.09613551 -0.01663246 -0.02994930
145 146 147 148 149 150
-0.06565662 0.16762890 -0.13184283 0.13192158 -0.01663246 -0.02994930
151 152 153 154
0.01043168 0.71960314 0.67922217 -0.28039686
> postscript(file="/var/fisher/rcomp/tmp/6imo11356021387.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 = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.09402503 NA
1 0.01815461 -0.09402503
2 0.01815461 0.01815461
3 0.01815461 0.01815461
4 0.01815461 0.01815461
5 0.02212414 0.01815461
6 0.01815461 0.02212414
7 -0.13837554 0.01815461
8 0.05386193 -0.13837554
9 0.02679780 0.05386193
10 -0.12973235 0.02679780
11 0.01815461 -0.12973235
12 -0.28599077 0.01815461
13 -0.12973235 -0.28599077
14 -0.25028345 -0.12973235
15 -0.40681360 -0.25028345
16 0.56612227 -0.40681360
17 -0.12973235 0.56612227
18 0.05386193 -0.12973235
19 0.59318640 0.05386193
20 -0.01358318 0.59318640
21 -0.24164026 -0.01358318
22 0.01348096 -0.24164026
23 0.02212414 0.01348096
24 -0.36643262 0.02212414
25 -0.28599077 -0.36643262
26 0.06250512 -0.28599077
27 -0.24560979 0.06250512
28 0.05386193 -0.24560979
29 -0.02222636 0.05386193
30 0.01815461 -0.02222636
31 0.02679780 0.01815461
32 -0.01358318 0.02679780
33 -0.10266822 -0.01358318
34 0.01815461 -0.10266822
35 0.01815461 0.01815461
36 -0.43387773 0.01815461
37 -0.20990247 -0.43387773
38 0.01348096 -0.20990247
39 -0.17875652 0.01348096
40 0.74971655 -0.17875652
41 -0.20990247 0.74971655
42 0.02212414 -0.20990247
43 -0.12973235 0.02212414
44 -0.02222636 -0.12973235
45 0.01348096 -0.02222636
46 0.01815461 0.01348096
47 0.05386193 0.01815461
48 0.01348096 0.05386193
49 0.01815461 0.01348096
50 -0.40213994 0.01815461
51 0.56612227 -0.40213994
52 0.05386193 0.56612227
53 0.75439021 0.05386193
54 0.01815461 0.75439021
55 -0.36643262 0.01815461
56 -0.25028345 -0.36643262
57 0.05386193 -0.25028345
58 0.05386193 0.05386193
59 0.60182959 0.05386193
60 -0.09402503 0.60182959
61 -0.28599077 -0.09402503
62 0.01815461 -0.28599077
63 -0.09402503 0.01815461
64 0.01815461 -0.09402503
65 0.01815461 0.01815461
66 0.55747908 0.01815461
67 0.02679780 0.55747908
68 0.05386193 0.02679780
69 -0.24560979 0.05386193
70 0.01815461 -0.24560979
71 0.05386193 0.01815461
72 -0.20990247 0.05386193
73 -0.23696660 -0.20990247
74 0.05386193 -0.23696660
75 -0.14304920 0.05386193
76 0.05386193 -0.14304920
77 -0.25028345 0.05386193
78 0.63356738 -0.25028345
79 -0.17875652 0.63356738
80 0.01815461 -0.17875652
81 -0.20125928 0.01815461
82 0.01815461 -0.20125928
83 0.75439021 0.01815461
84 0.01348096 0.75439021
85 0.02679780 0.01348096
86 0.01907486 0.02679780
87 -0.08749232 0.01907486
88 -0.02527564 -0.08749232
89 0.01043168 -0.02527564
90 -0.06565662 0.01043168
91 0.14056476 -0.06565662
92 -0.05701343 0.14056476
93 -0.02527564 -0.05701343
94 0.13192158 -0.02527564
95 0.01043168 0.13192158
96 0.14056476 0.01043168
97 -0.02527564 0.14056476
98 -0.01663246 -0.02527564
99 0.01043168 -0.01663246
100 0.01907486 0.01043168
101 -0.02527564 0.01907486
102 -0.02527564 -0.02527564
103 -0.02527564 -0.02527564
104 -0.13184283 -0.02527564
105 -0.02527564 -0.13184283
106 -0.02527564 -0.02527564
107 -0.12319964 -0.02527564
108 -0.02527564 -0.12319964
109 -0.01663246 -0.02527564
110 -0.16358062 -0.01663246
111 0.13192158 -0.16358062
112 -0.28904005 0.13192158
113 -0.12319964 -0.28904005
114 -0.01663246 -0.12319964
115 -0.02527564 -0.01663246
116 0.01907486 -0.02527564
117 -0.01663246 0.01907486
118 -0.02527564 -0.01663246
119 0.01043168 -0.02527564
120 -0.01663246 0.01043168
121 -0.02527564 -0.01663246
122 -0.12319964 -0.02527564
123 -0.29371370 -0.12319964
124 0.01043168 -0.29371370
125 0.13192158 0.01043168
126 -0.06565662 0.13192158
127 0.01043168 -0.06565662
128 -0.02527564 0.01043168
129 0.01043168 -0.02527564
130 -0.01663246 0.01043168
131 0.01907486 -0.01663246
132 -0.28039686 0.01907486
133 -0.02527564 -0.28039686
134 -0.02527564 -0.02527564
135 -0.02527564 -0.02527564
136 -0.28507052 -0.02527564
137 -0.12787330 -0.28507052
138 0.13192158 -0.12787330
139 -0.02527564 0.13192158
140 0.74666727 -0.02527564
141 -0.09613551 0.74666727
142 -0.01663246 -0.09613551
143 -0.02994930 -0.01663246
144 -0.06565662 -0.02994930
145 0.16762890 -0.06565662
146 -0.13184283 0.16762890
147 0.13192158 -0.13184283
148 -0.01663246 0.13192158
149 -0.02994930 -0.01663246
150 0.01043168 -0.02994930
151 0.71960314 0.01043168
152 0.67922217 0.71960314
153 -0.28039686 0.67922217
154 NA -0.28039686
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.01815461 -0.09402503
[2,] 0.01815461 0.01815461
[3,] 0.01815461 0.01815461
[4,] 0.01815461 0.01815461
[5,] 0.02212414 0.01815461
[6,] 0.01815461 0.02212414
[7,] -0.13837554 0.01815461
[8,] 0.05386193 -0.13837554
[9,] 0.02679780 0.05386193
[10,] -0.12973235 0.02679780
[11,] 0.01815461 -0.12973235
[12,] -0.28599077 0.01815461
[13,] -0.12973235 -0.28599077
[14,] -0.25028345 -0.12973235
[15,] -0.40681360 -0.25028345
[16,] 0.56612227 -0.40681360
[17,] -0.12973235 0.56612227
[18,] 0.05386193 -0.12973235
[19,] 0.59318640 0.05386193
[20,] -0.01358318 0.59318640
[21,] -0.24164026 -0.01358318
[22,] 0.01348096 -0.24164026
[23,] 0.02212414 0.01348096
[24,] -0.36643262 0.02212414
[25,] -0.28599077 -0.36643262
[26,] 0.06250512 -0.28599077
[27,] -0.24560979 0.06250512
[28,] 0.05386193 -0.24560979
[29,] -0.02222636 0.05386193
[30,] 0.01815461 -0.02222636
[31,] 0.02679780 0.01815461
[32,] -0.01358318 0.02679780
[33,] -0.10266822 -0.01358318
[34,] 0.01815461 -0.10266822
[35,] 0.01815461 0.01815461
[36,] -0.43387773 0.01815461
[37,] -0.20990247 -0.43387773
[38,] 0.01348096 -0.20990247
[39,] -0.17875652 0.01348096
[40,] 0.74971655 -0.17875652
[41,] -0.20990247 0.74971655
[42,] 0.02212414 -0.20990247
[43,] -0.12973235 0.02212414
[44,] -0.02222636 -0.12973235
[45,] 0.01348096 -0.02222636
[46,] 0.01815461 0.01348096
[47,] 0.05386193 0.01815461
[48,] 0.01348096 0.05386193
[49,] 0.01815461 0.01348096
[50,] -0.40213994 0.01815461
[51,] 0.56612227 -0.40213994
[52,] 0.05386193 0.56612227
[53,] 0.75439021 0.05386193
[54,] 0.01815461 0.75439021
[55,] -0.36643262 0.01815461
[56,] -0.25028345 -0.36643262
[57,] 0.05386193 -0.25028345
[58,] 0.05386193 0.05386193
[59,] 0.60182959 0.05386193
[60,] -0.09402503 0.60182959
[61,] -0.28599077 -0.09402503
[62,] 0.01815461 -0.28599077
[63,] -0.09402503 0.01815461
[64,] 0.01815461 -0.09402503
[65,] 0.01815461 0.01815461
[66,] 0.55747908 0.01815461
[67,] 0.02679780 0.55747908
[68,] 0.05386193 0.02679780
[69,] -0.24560979 0.05386193
[70,] 0.01815461 -0.24560979
[71,] 0.05386193 0.01815461
[72,] -0.20990247 0.05386193
[73,] -0.23696660 -0.20990247
[74,] 0.05386193 -0.23696660
[75,] -0.14304920 0.05386193
[76,] 0.05386193 -0.14304920
[77,] -0.25028345 0.05386193
[78,] 0.63356738 -0.25028345
[79,] -0.17875652 0.63356738
[80,] 0.01815461 -0.17875652
[81,] -0.20125928 0.01815461
[82,] 0.01815461 -0.20125928
[83,] 0.75439021 0.01815461
[84,] 0.01348096 0.75439021
[85,] 0.02679780 0.01348096
[86,] 0.01907486 0.02679780
[87,] -0.08749232 0.01907486
[88,] -0.02527564 -0.08749232
[89,] 0.01043168 -0.02527564
[90,] -0.06565662 0.01043168
[91,] 0.14056476 -0.06565662
[92,] -0.05701343 0.14056476
[93,] -0.02527564 -0.05701343
[94,] 0.13192158 -0.02527564
[95,] 0.01043168 0.13192158
[96,] 0.14056476 0.01043168
[97,] -0.02527564 0.14056476
[98,] -0.01663246 -0.02527564
[99,] 0.01043168 -0.01663246
[100,] 0.01907486 0.01043168
[101,] -0.02527564 0.01907486
[102,] -0.02527564 -0.02527564
[103,] -0.02527564 -0.02527564
[104,] -0.13184283 -0.02527564
[105,] -0.02527564 -0.13184283
[106,] -0.02527564 -0.02527564
[107,] -0.12319964 -0.02527564
[108,] -0.02527564 -0.12319964
[109,] -0.01663246 -0.02527564
[110,] -0.16358062 -0.01663246
[111,] 0.13192158 -0.16358062
[112,] -0.28904005 0.13192158
[113,] -0.12319964 -0.28904005
[114,] -0.01663246 -0.12319964
[115,] -0.02527564 -0.01663246
[116,] 0.01907486 -0.02527564
[117,] -0.01663246 0.01907486
[118,] -0.02527564 -0.01663246
[119,] 0.01043168 -0.02527564
[120,] -0.01663246 0.01043168
[121,] -0.02527564 -0.01663246
[122,] -0.12319964 -0.02527564
[123,] -0.29371370 -0.12319964
[124,] 0.01043168 -0.29371370
[125,] 0.13192158 0.01043168
[126,] -0.06565662 0.13192158
[127,] 0.01043168 -0.06565662
[128,] -0.02527564 0.01043168
[129,] 0.01043168 -0.02527564
[130,] -0.01663246 0.01043168
[131,] 0.01907486 -0.01663246
[132,] -0.28039686 0.01907486
[133,] -0.02527564 -0.28039686
[134,] -0.02527564 -0.02527564
[135,] -0.02527564 -0.02527564
[136,] -0.28507052 -0.02527564
[137,] -0.12787330 -0.28507052
[138,] 0.13192158 -0.12787330
[139,] -0.02527564 0.13192158
[140,] 0.74666727 -0.02527564
[141,] -0.09613551 0.74666727
[142,] -0.01663246 -0.09613551
[143,] -0.02994930 -0.01663246
[144,] -0.06565662 -0.02994930
[145,] 0.16762890 -0.06565662
[146,] -0.13184283 0.16762890
[147,] 0.13192158 -0.13184283
[148,] -0.01663246 0.13192158
[149,] -0.02994930 -0.01663246
[150,] 0.01043168 -0.02994930
[151,] 0.71960314 0.01043168
[152,] 0.67922217 0.71960314
[153,] -0.28039686 0.67922217
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.01815461 -0.09402503
2 0.01815461 0.01815461
3 0.01815461 0.01815461
4 0.01815461 0.01815461
5 0.02212414 0.01815461
6 0.01815461 0.02212414
7 -0.13837554 0.01815461
8 0.05386193 -0.13837554
9 0.02679780 0.05386193
10 -0.12973235 0.02679780
11 0.01815461 -0.12973235
12 -0.28599077 0.01815461
13 -0.12973235 -0.28599077
14 -0.25028345 -0.12973235
15 -0.40681360 -0.25028345
16 0.56612227 -0.40681360
17 -0.12973235 0.56612227
18 0.05386193 -0.12973235
19 0.59318640 0.05386193
20 -0.01358318 0.59318640
21 -0.24164026 -0.01358318
22 0.01348096 -0.24164026
23 0.02212414 0.01348096
24 -0.36643262 0.02212414
25 -0.28599077 -0.36643262
26 0.06250512 -0.28599077
27 -0.24560979 0.06250512
28 0.05386193 -0.24560979
29 -0.02222636 0.05386193
30 0.01815461 -0.02222636
31 0.02679780 0.01815461
32 -0.01358318 0.02679780
33 -0.10266822 -0.01358318
34 0.01815461 -0.10266822
35 0.01815461 0.01815461
36 -0.43387773 0.01815461
37 -0.20990247 -0.43387773
38 0.01348096 -0.20990247
39 -0.17875652 0.01348096
40 0.74971655 -0.17875652
41 -0.20990247 0.74971655
42 0.02212414 -0.20990247
43 -0.12973235 0.02212414
44 -0.02222636 -0.12973235
45 0.01348096 -0.02222636
46 0.01815461 0.01348096
47 0.05386193 0.01815461
48 0.01348096 0.05386193
49 0.01815461 0.01348096
50 -0.40213994 0.01815461
51 0.56612227 -0.40213994
52 0.05386193 0.56612227
53 0.75439021 0.05386193
54 0.01815461 0.75439021
55 -0.36643262 0.01815461
56 -0.25028345 -0.36643262
57 0.05386193 -0.25028345
58 0.05386193 0.05386193
59 0.60182959 0.05386193
60 -0.09402503 0.60182959
61 -0.28599077 -0.09402503
62 0.01815461 -0.28599077
63 -0.09402503 0.01815461
64 0.01815461 -0.09402503
65 0.01815461 0.01815461
66 0.55747908 0.01815461
67 0.02679780 0.55747908
68 0.05386193 0.02679780
69 -0.24560979 0.05386193
70 0.01815461 -0.24560979
71 0.05386193 0.01815461
72 -0.20990247 0.05386193
73 -0.23696660 -0.20990247
74 0.05386193 -0.23696660
75 -0.14304920 0.05386193
76 0.05386193 -0.14304920
77 -0.25028345 0.05386193
78 0.63356738 -0.25028345
79 -0.17875652 0.63356738
80 0.01815461 -0.17875652
81 -0.20125928 0.01815461
82 0.01815461 -0.20125928
83 0.75439021 0.01815461
84 0.01348096 0.75439021
85 0.02679780 0.01348096
86 0.01907486 0.02679780
87 -0.08749232 0.01907486
88 -0.02527564 -0.08749232
89 0.01043168 -0.02527564
90 -0.06565662 0.01043168
91 0.14056476 -0.06565662
92 -0.05701343 0.14056476
93 -0.02527564 -0.05701343
94 0.13192158 -0.02527564
95 0.01043168 0.13192158
96 0.14056476 0.01043168
97 -0.02527564 0.14056476
98 -0.01663246 -0.02527564
99 0.01043168 -0.01663246
100 0.01907486 0.01043168
101 -0.02527564 0.01907486
102 -0.02527564 -0.02527564
103 -0.02527564 -0.02527564
104 -0.13184283 -0.02527564
105 -0.02527564 -0.13184283
106 -0.02527564 -0.02527564
107 -0.12319964 -0.02527564
108 -0.02527564 -0.12319964
109 -0.01663246 -0.02527564
110 -0.16358062 -0.01663246
111 0.13192158 -0.16358062
112 -0.28904005 0.13192158
113 -0.12319964 -0.28904005
114 -0.01663246 -0.12319964
115 -0.02527564 -0.01663246
116 0.01907486 -0.02527564
117 -0.01663246 0.01907486
118 -0.02527564 -0.01663246
119 0.01043168 -0.02527564
120 -0.01663246 0.01043168
121 -0.02527564 -0.01663246
122 -0.12319964 -0.02527564
123 -0.29371370 -0.12319964
124 0.01043168 -0.29371370
125 0.13192158 0.01043168
126 -0.06565662 0.13192158
127 0.01043168 -0.06565662
128 -0.02527564 0.01043168
129 0.01043168 -0.02527564
130 -0.01663246 0.01043168
131 0.01907486 -0.01663246
132 -0.28039686 0.01907486
133 -0.02527564 -0.28039686
134 -0.02527564 -0.02527564
135 -0.02527564 -0.02527564
136 -0.28507052 -0.02527564
137 -0.12787330 -0.28507052
138 0.13192158 -0.12787330
139 -0.02527564 0.13192158
140 0.74666727 -0.02527564
141 -0.09613551 0.74666727
142 -0.01663246 -0.09613551
143 -0.02994930 -0.01663246
144 -0.06565662 -0.02994930
145 0.16762890 -0.06565662
146 -0.13184283 0.16762890
147 0.13192158 -0.13184283
148 -0.01663246 0.13192158
149 -0.02994930 -0.01663246
150 0.01043168 -0.02994930
151 0.71960314 0.01043168
152 0.67922217 0.71960314
153 -0.28039686 0.67922217
> 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/fisher/rcomp/tmp/7f1bz1356021387.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/fisher/rcomp/tmp/85ttn1356021387.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/fisher/rcomp/tmp/9bd021356021387.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/fisher/rcomp/tmp/10j1nd1356021387.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11zmqu1356021387.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12b0031356021387.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13ozql1356021387.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14ngoy1356021387.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/fisher/rcomp/tmp/151y6x1356021387.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/fisher/rcomp/tmp/16zaeb1356021387.tab")
+ }
>
> try(system("convert tmp/1xjq91356021387.ps tmp/1xjq91356021387.png",intern=TRUE))
character(0)
> try(system("convert tmp/2mne41356021387.ps tmp/2mne41356021387.png",intern=TRUE))
character(0)
> try(system("convert tmp/3rx6n1356021387.ps tmp/3rx6n1356021387.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tnmz1356021387.ps tmp/4tnmz1356021387.png",intern=TRUE))
character(0)
> try(system("convert tmp/59hmc1356021387.ps tmp/59hmc1356021387.png",intern=TRUE))
character(0)
> try(system("convert tmp/6imo11356021387.ps tmp/6imo11356021387.png",intern=TRUE))
character(0)
> try(system("convert tmp/7f1bz1356021387.ps tmp/7f1bz1356021387.png",intern=TRUE))
character(0)
> try(system("convert tmp/85ttn1356021387.ps tmp/85ttn1356021387.png",intern=TRUE))
character(0)
> try(system("convert tmp/9bd021356021387.ps tmp/9bd021356021387.png",intern=TRUE))
character(0)
> try(system("convert tmp/10j1nd1356021387.ps tmp/10j1nd1356021387.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.956 1.728 9.684