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.
> x <- array(list(1
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+ ,dim=c(6
+ ,154)
+ ,dimnames=list(c('UseLimit'
+ ,'T20'
+ ,'Used'
+ ,'CorrectAnalysis'
+ ,'Useful'
+ ,'Outcome')
+ ,1:154))
> y <- array(NA,dim=c(6,154),dimnames=list(c('UseLimit','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'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '6'
> #'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, 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
Outcome UseLimit T20 Used CorrectAnalysis Useful
1 1 1 1 0 0 0
2 0 0 0 0 0 0
3 0 0 0 0 0 0
4 0 0 0 0 0 0
5 0 0 0 0 0 0
6 1 1 0 0 0 1
7 0 0 0 0 0 0
8 0 0 1 0 0 0
9 1 0 0 0 0 0
10 0 1 0 0 0 0
11 0 1 1 0 0 0
12 0 0 0 0 0 0
13 0 0 0 1 0 1
14 0 1 1 0 0 0
15 1 0 0 1 0 1
16 1 0 1 1 0 1
17 0 1 1 1 1 1
18 0 1 1 0 0 0
19 1 0 0 0 0 0
20 1 0 1 1 1 1
21 0 1 0 0 0 1
22 1 1 0 1 0 1
23 1 0 0 0 0 1
24 1 1 0 0 0 1
25 1 0 1 1 0 0
26 0 0 0 1 0 1
27 1 1 0 0 0 0
28 0 0 0 1 0 0
29 1 0 0 0 0 0
30 0 0 0 0 0 1
31 0 0 0 0 0 0
32 0 1 0 0 0 0
33 0 1 0 0 0 1
34 1 0 1 0 0 0
35 0 0 0 0 0 0
36 0 0 0 0 0 0
37 0 1 1 1 0 1
38 1 0 0 1 0 0
39 1 0 0 0 0 1
40 0 0 1 0 0 1
41 1 0 0 1 1 1
42 1 0 0 1 0 0
43 1 1 0 0 0 1
44 0 1 1 0 0 0
45 0 0 0 0 0 1
46 1 0 0 0 0 1
47 0 0 0 0 0 0
48 1 0 0 0 0 0
49 1 0 0 0 0 1
50 0 0 0 0 0 0
51 0 0 1 1 0 0
52 0 1 1 1 1 1
53 1 0 0 0 0 0
54 0 0 0 1 1 0
55 0 0 0 0 0 0
56 1 0 1 1 0 0
57 1 0 0 1 0 1
58 1 0 0 0 0 0
59 1 0 0 0 0 0
60 1 1 1 1 1 1
61 1 1 1 0 0 0
62 0 0 0 1 0 1
63 0 0 0 0 0 0
64 1 1 1 0 0 0
65 0 0 0 0 0 0
66 0 0 0 0 0 0
67 0 0 1 1 1 1
68 0 1 0 0 0 0
69 1 0 0 0 0 0
70 0 0 0 1 0 0
71 0 0 0 0 0 0
72 1 0 0 0 0 0
73 1 0 0 1 0 0
74 0 1 0 1 0 0
75 1 0 0 0 0 0
76 1 0 1 0 0 1
77 1 0 0 0 0 0
78 1 0 0 1 0 1
79 1 0 1 1 1 0
80 0 0 1 0 0 1
81 0 0 0 0 0 0
82 1 1 0 1 0 0
83 0 0 0 0 0 0
84 0 0 0 1 1 0
85 1 0 0 0 0 1
86 0 1 0 0 0 0
87 1 1 0 0 0 0
88 1 1 1 1 0 0
89 0 0 0 0 0 0
90 1 0 0 0 0 0
91 0 0 0 0 0 1
92 0 1 1 0 0 0
93 0 1 0 0 0 1
94 0 0 0 0 0 0
95 0 0 1 0 0 0
96 1 0 0 0 0 0
97 0 1 1 0 0 0
98 0 0 0 0 0 0
99 0 1 0 0 0 0
100 1 0 0 0 0 0
101 1 1 0 0 0 0
102 0 0 0 0 0 0
103 0 0 0 0 0 0
104 0 0 0 0 0 0
105 0 0 1 1 0 0
106 0 0 0 0 0 0
107 0 0 0 0 0 0
108 0 1 1 1 0 0
109 0 0 0 0 0 0
110 0 1 0 0 0 0
111 0 1 1 1 0 1
112 0 0 1 0 0 0
113 0 0 0 1 0 0
114 0 1 1 1 0 0
115 0 1 0 0 0 0
116 0 0 0 0 0 0
117 1 1 0 0 0 0
118 0 1 0 0 0 0
119 0 0 0 0 0 0
120 1 0 0 0 0 0
121 0 1 0 0 0 0
122 0 0 0 0 0 0
123 0 1 1 1 0 0
124 1 0 0 1 0 1
125 1 0 0 0 0 0
126 0 0 1 0 0 0
127 0 0 0 0 0 1
128 1 0 0 0 0 0
129 0 0 0 0 0 0
130 1 0 0 0 0 0
131 0 1 0 0 0 0
132 1 1 0 0 0 0
133 0 1 0 1 0 0
134 0 0 0 0 0 0
135 0 0 0 0 0 0
136 0 0 0 0 0 0
137 1 1 0 1 0 1
138 1 1 1 1 0 1
139 0 0 1 0 0 0
140 0 0 0 0 0 0
141 1 0 0 1 1 0
142 1 0 1 1 0 0
143 0 1 0 0 0 0
144 1 0 0 0 0 1
145 0 0 0 0 0 1
146 1 0 1 0 0 0
147 0 0 1 1 0 0
148 0 0 1 0 0 0
149 0 1 0 0 0 0
150 1 0 0 0 0 1
151 1 0 0 0 0 0
152 0 1 0 1 1 0
153 0 1 0 1 1 1
154 0 1 0 1 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) UseLimit T20 Used
0.36528 -0.09254 -0.03629 0.09879
CorrectAnalysis Useful
-0.09825 0.18437
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6484 -0.3653 -0.2727 0.5429 0.7635
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.36528 0.05727 6.378 2.16e-09 ***
UseLimit -0.09254 0.08566 -1.080 0.2817
T20 -0.03629 0.09460 -0.384 0.7018
Used 0.09879 0.10140 0.974 0.3315
CorrectAnalysis -0.09825 0.16550 -0.594 0.5537
Useful 0.18437 0.09267 1.990 0.0485 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4877 on 148 degrees of freedom
Multiple R-squared: 0.04456, Adjusted R-squared: 0.01228
F-statistic: 1.381 on 5 and 148 DF, p-value: 0.2348
> 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,] 0.6756187 0.6487626 0.3243813
[2,] 0.7346575 0.5306850 0.2653425
[3,] 0.7094785 0.5810430 0.2905215
[4,] 0.6014926 0.7970149 0.3985074
[5,] 0.4931366 0.9862732 0.5068634
[6,] 0.4287278 0.8574556 0.5712722
[7,] 0.5328572 0.9342857 0.4671428
[8,] 0.4704728 0.9409456 0.5295272
[9,] 0.3816274 0.7632547 0.6183726
[10,] 0.3227888 0.6455776 0.6772112
[11,] 0.4865121 0.9730242 0.5134879
[12,] 0.5073032 0.9853936 0.4926968
[13,] 0.5767240 0.8465520 0.4232760
[14,] 0.5759742 0.8480516 0.4240258
[15,] 0.5306555 0.9386890 0.4693445
[16,] 0.4992590 0.9985180 0.5007410
[17,] 0.5310391 0.9379218 0.4689609
[18,] 0.6169504 0.7660992 0.3830496
[19,] 0.7071787 0.5856427 0.2928213
[20,] 0.6744884 0.6510231 0.3255116
[21,] 0.7215655 0.5568690 0.2784345
[22,] 0.7425623 0.5148753 0.2574377
[23,] 0.7091950 0.5816100 0.2908050
[24,] 0.6706121 0.6587757 0.3293879
[25,] 0.6674430 0.6651140 0.3325570
[26,] 0.6863065 0.6273871 0.3136935
[27,] 0.6518455 0.6963089 0.3481545
[28,] 0.6151411 0.7697177 0.3848589
[29,] 0.6455894 0.7088213 0.3544106
[30,] 0.6684345 0.6631311 0.3315655
[31,] 0.6620278 0.6759443 0.3379722
[32,] 0.6721031 0.6557939 0.3278969
[33,] 0.6535777 0.6928445 0.3464223
[34,] 0.6508931 0.6982138 0.3491069
[35,] 0.6652870 0.6694260 0.3347130
[36,] 0.6266145 0.7467711 0.3733855
[37,] 0.6258237 0.7483526 0.3741763
[38,] 0.6255594 0.7488813 0.3744406
[39,] 0.6004521 0.7990957 0.3995479
[40,] 0.6313302 0.7373395 0.3686698
[41,] 0.6249577 0.7500845 0.3750423
[42,] 0.6024611 0.7950778 0.3975389
[43,] 0.5882045 0.8235909 0.4117955
[44,] 0.5815797 0.8368406 0.4184203
[45,] 0.6085622 0.7828755 0.3914378
[46,] 0.5880881 0.8238239 0.4119119
[47,] 0.5654909 0.8690182 0.4345091
[48,] 0.5805215 0.8389571 0.4194785
[49,] 0.5533043 0.8933914 0.4466957
[50,] 0.5820263 0.8359474 0.4179737
[51,] 0.6080833 0.7838334 0.3919167
[52,] 0.6267679 0.7464642 0.3732321
[53,] 0.6827570 0.6344859 0.3172430
[54,] 0.7125491 0.5749018 0.2874509
[55,] 0.6950048 0.6099903 0.3049952
[56,] 0.7471064 0.5057872 0.2528936
[57,] 0.7305438 0.5389123 0.2694562
[58,] 0.7129768 0.5740463 0.2870232
[59,] 0.7108600 0.5782800 0.2891400
[60,] 0.6837479 0.6325042 0.3162521
[61,] 0.7088534 0.5822932 0.2911466
[62,] 0.7082367 0.5835265 0.2917633
[63,] 0.6898331 0.6203339 0.3101669
[64,] 0.7145846 0.5708308 0.2854154
[65,] 0.7160474 0.5679052 0.2839526
[66,] 0.7006198 0.5987604 0.2993802
[67,] 0.7252511 0.5494978 0.2747489
[68,] 0.7280369 0.5439262 0.2719631
[69,] 0.7527225 0.4945550 0.2472775
[70,] 0.7329259 0.5341481 0.2670741
[71,] 0.7770975 0.4458051 0.2229025
[72,] 0.7748781 0.4502438 0.2251219
[73,] 0.7590545 0.4818910 0.2409455
[74,] 0.7755468 0.4489064 0.2244532
[75,] 0.7590903 0.4818194 0.2409097
[76,] 0.7386623 0.5226754 0.2613377
[77,] 0.7371055 0.5257890 0.2628945
[78,] 0.7095482 0.5809035 0.2904518
[79,] 0.7594561 0.4810878 0.2405439
[80,] 0.8000379 0.3999243 0.1999621
[81,] 0.7843247 0.4313506 0.2156753
[82,] 0.8111581 0.3776839 0.1888419
[83,] 0.8178592 0.3642816 0.1821408
[84,] 0.7923355 0.4153289 0.2076645
[85,] 0.7876616 0.4246767 0.2123384
[86,] 0.7703871 0.4592258 0.2296129
[87,] 0.7453682 0.5092636 0.2546318
[88,] 0.7769274 0.4461452 0.2230726
[89,] 0.7454562 0.5090877 0.2545438
[90,] 0.7247744 0.5504512 0.2752256
[91,] 0.6928509 0.6142983 0.3071491
[92,] 0.7301546 0.5396908 0.2698454
[93,] 0.7947283 0.4105433 0.2052717
[94,] 0.7745139 0.4509721 0.2254861
[95,] 0.7535276 0.4929448 0.2464724
[96,] 0.7320233 0.5359534 0.2679767
[97,] 0.7149330 0.5701341 0.2850670
[98,] 0.6926431 0.6147138 0.3073569
[99,] 0.6706632 0.6586736 0.3293368
[100,] 0.6358586 0.7282828 0.3641414
[101,] 0.6133552 0.7732897 0.3866448
[102,] 0.5710436 0.8579127 0.4289564
[103,] 0.5672644 0.8654712 0.4327356
[104,] 0.5330803 0.9338394 0.4669197
[105,] 0.5340470 0.9319061 0.4659530
[106,] 0.4964478 0.9928956 0.5035522
[107,] 0.4503331 0.9006662 0.5496669
[108,] 0.4281214 0.8562427 0.5718786
[109,] 0.5301848 0.9396304 0.4698152
[110,] 0.4786108 0.9572216 0.5213892
[111,] 0.4576321 0.9152643 0.5423679
[112,] 0.4860080 0.9720160 0.5139920
[113,] 0.4322626 0.8645251 0.5677374
[114,] 0.4088284 0.8176569 0.5911716
[115,] 0.3683500 0.7367000 0.6316500
[116,] 0.3194982 0.6389965 0.6805018
[117,] 0.3490794 0.6981587 0.6509206
[118,] 0.3183460 0.6366919 0.6816540
[119,] 0.3540093 0.7080187 0.6459907
[120,] 0.3935575 0.7871150 0.6064425
[121,] 0.3581275 0.7162549 0.6418725
[122,] 0.4099635 0.8199269 0.5900365
[123,] 0.3472165 0.6944330 0.6527835
[124,] 0.5493919 0.9012162 0.4506081
[125,] 0.4892082 0.9784164 0.5107918
[126,] 0.4347591 0.8695181 0.5652409
[127,] 0.3860991 0.7721982 0.6139009
[128,] 0.3476352 0.6952704 0.6523648
[129,] 0.3171841 0.6343683 0.6828159
[130,] 0.4327079 0.8654159 0.5672921
[131,] 0.3976553 0.7953106 0.6023447
[132,] 0.5245788 0.9508424 0.4754212
[133,] 0.4219688 0.8439376 0.5780312
[134,] 0.5022388 0.9955224 0.4977612
[135,] 0.3820298 0.7640595 0.6179702
[136,] 0.3144847 0.6289694 0.6855153
[137,] 0.4970175 0.9940351 0.5029825
> postscript(file="/var/fisher/rcomp/tmp/1b4sr1356123864.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2suto1356123864.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3seyb1356123864.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/48sp71356123864.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5trav1356123864.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 7
0.7635512 -0.3652782 -0.3652782 -0.3652782 -0.3652782 0.5428922 -0.3652782
8 9 10 11 12 13 14
-0.3289867 0.6347218 -0.2727404 -0.2364488 -0.3652782 -0.6484370 -0.2364488
15 16 17 18 19 20 21
0.3515630 0.3878546 -0.4213602 -0.2364488 0.6347218 0.4861020 -0.4571078
22 23 24 25 26 27 28
0.4441008 0.4503544 0.5428922 0.5722219 -0.6484370 0.7272596 -0.4640696
29 30 31 32 33 34 35
0.6347218 -0.5496456 -0.3652782 -0.2727404 -0.4571078 0.6710133 -0.3652782
36 37 38 39 40 41 42
-0.3652782 -0.5196076 0.5359304 0.4503544 -0.5133540 0.4498104 0.5359304
43 44 45 46 47 48 49
0.5428922 -0.2364488 -0.5496456 0.4503544 -0.3652782 0.6347218 0.4503544
50 51 52 53 54 55 56
-0.3652782 -0.4277781 -0.4213602 0.6347218 -0.3658222 -0.3652782 0.5722219
57 58 59 60 61 62 63
0.3515630 0.6347218 0.6347218 0.5786398 0.7635512 -0.6484370 -0.3652782
64 65 66 67 68 69 70
0.7635512 -0.3652782 -0.3652782 -0.5138980 -0.2727404 0.6347218 -0.4640696
71 72 73 74 75 76 77
-0.3652782 0.6347218 0.5359304 -0.3715318 0.6347218 0.4866460 0.6347218
78 79 80 81 82 83 84
0.3515630 0.6704693 -0.5133540 -0.3652782 0.6284682 -0.3652782 -0.3658222
85 86 87 88 89 90 91
0.4503544 -0.2727404 0.7272596 0.6647598 -0.3652782 0.6347218 -0.5496456
92 93 94 95 96 97 98
-0.2364488 -0.4571078 -0.3652782 -0.3289867 0.6347218 -0.2364488 -0.3652782
99 100 101 102 103 104 105
-0.2727404 0.6347218 0.7272596 -0.3652782 -0.3652782 -0.3652782 -0.4277781
106 107 108 109 110 111 112
-0.3652782 -0.3652782 -0.3352402 -0.3652782 -0.2727404 -0.5196076 -0.3289867
113 114 115 116 117 118 119
-0.4640696 -0.3352402 -0.2727404 -0.3652782 0.7272596 -0.2727404 -0.3652782
120 121 122 123 124 125 126
0.6347218 -0.2727404 -0.3652782 -0.3352402 0.3515630 0.6347218 -0.3289867
127 128 129 130 131 132 133
-0.5496456 0.6347218 -0.3652782 0.6347218 -0.2727404 0.7272596 -0.3715318
134 135 136 137 138 139 140
-0.3652782 -0.3652782 -0.3652782 0.4441008 0.4803924 -0.3289867 -0.3652782
141 142 143 144 145 146 147
0.6341778 0.5722219 -0.2727404 0.4503544 -0.5496456 0.6710133 -0.4277781
148 149 150 151 152 153 154
-0.3289867 -0.2727404 0.4503544 0.6347218 -0.2732844 -0.4576518 -0.3715318
> postscript(file="/var/fisher/rcomp/tmp/635yb1356123864.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 0.7635512 NA
1 -0.3652782 0.7635512
2 -0.3652782 -0.3652782
3 -0.3652782 -0.3652782
4 -0.3652782 -0.3652782
5 0.5428922 -0.3652782
6 -0.3652782 0.5428922
7 -0.3289867 -0.3652782
8 0.6347218 -0.3289867
9 -0.2727404 0.6347218
10 -0.2364488 -0.2727404
11 -0.3652782 -0.2364488
12 -0.6484370 -0.3652782
13 -0.2364488 -0.6484370
14 0.3515630 -0.2364488
15 0.3878546 0.3515630
16 -0.4213602 0.3878546
17 -0.2364488 -0.4213602
18 0.6347218 -0.2364488
19 0.4861020 0.6347218
20 -0.4571078 0.4861020
21 0.4441008 -0.4571078
22 0.4503544 0.4441008
23 0.5428922 0.4503544
24 0.5722219 0.5428922
25 -0.6484370 0.5722219
26 0.7272596 -0.6484370
27 -0.4640696 0.7272596
28 0.6347218 -0.4640696
29 -0.5496456 0.6347218
30 -0.3652782 -0.5496456
31 -0.2727404 -0.3652782
32 -0.4571078 -0.2727404
33 0.6710133 -0.4571078
34 -0.3652782 0.6710133
35 -0.3652782 -0.3652782
36 -0.5196076 -0.3652782
37 0.5359304 -0.5196076
38 0.4503544 0.5359304
39 -0.5133540 0.4503544
40 0.4498104 -0.5133540
41 0.5359304 0.4498104
42 0.5428922 0.5359304
43 -0.2364488 0.5428922
44 -0.5496456 -0.2364488
45 0.4503544 -0.5496456
46 -0.3652782 0.4503544
47 0.6347218 -0.3652782
48 0.4503544 0.6347218
49 -0.3652782 0.4503544
50 -0.4277781 -0.3652782
51 -0.4213602 -0.4277781
52 0.6347218 -0.4213602
53 -0.3658222 0.6347218
54 -0.3652782 -0.3658222
55 0.5722219 -0.3652782
56 0.3515630 0.5722219
57 0.6347218 0.3515630
58 0.6347218 0.6347218
59 0.5786398 0.6347218
60 0.7635512 0.5786398
61 -0.6484370 0.7635512
62 -0.3652782 -0.6484370
63 0.7635512 -0.3652782
64 -0.3652782 0.7635512
65 -0.3652782 -0.3652782
66 -0.5138980 -0.3652782
67 -0.2727404 -0.5138980
68 0.6347218 -0.2727404
69 -0.4640696 0.6347218
70 -0.3652782 -0.4640696
71 0.6347218 -0.3652782
72 0.5359304 0.6347218
73 -0.3715318 0.5359304
74 0.6347218 -0.3715318
75 0.4866460 0.6347218
76 0.6347218 0.4866460
77 0.3515630 0.6347218
78 0.6704693 0.3515630
79 -0.5133540 0.6704693
80 -0.3652782 -0.5133540
81 0.6284682 -0.3652782
82 -0.3652782 0.6284682
83 -0.3658222 -0.3652782
84 0.4503544 -0.3658222
85 -0.2727404 0.4503544
86 0.7272596 -0.2727404
87 0.6647598 0.7272596
88 -0.3652782 0.6647598
89 0.6347218 -0.3652782
90 -0.5496456 0.6347218
91 -0.2364488 -0.5496456
92 -0.4571078 -0.2364488
93 -0.3652782 -0.4571078
94 -0.3289867 -0.3652782
95 0.6347218 -0.3289867
96 -0.2364488 0.6347218
97 -0.3652782 -0.2364488
98 -0.2727404 -0.3652782
99 0.6347218 -0.2727404
100 0.7272596 0.6347218
101 -0.3652782 0.7272596
102 -0.3652782 -0.3652782
103 -0.3652782 -0.3652782
104 -0.4277781 -0.3652782
105 -0.3652782 -0.4277781
106 -0.3652782 -0.3652782
107 -0.3352402 -0.3652782
108 -0.3652782 -0.3352402
109 -0.2727404 -0.3652782
110 -0.5196076 -0.2727404
111 -0.3289867 -0.5196076
112 -0.4640696 -0.3289867
113 -0.3352402 -0.4640696
114 -0.2727404 -0.3352402
115 -0.3652782 -0.2727404
116 0.7272596 -0.3652782
117 -0.2727404 0.7272596
118 -0.3652782 -0.2727404
119 0.6347218 -0.3652782
120 -0.2727404 0.6347218
121 -0.3652782 -0.2727404
122 -0.3352402 -0.3652782
123 0.3515630 -0.3352402
124 0.6347218 0.3515630
125 -0.3289867 0.6347218
126 -0.5496456 -0.3289867
127 0.6347218 -0.5496456
128 -0.3652782 0.6347218
129 0.6347218 -0.3652782
130 -0.2727404 0.6347218
131 0.7272596 -0.2727404
132 -0.3715318 0.7272596
133 -0.3652782 -0.3715318
134 -0.3652782 -0.3652782
135 -0.3652782 -0.3652782
136 0.4441008 -0.3652782
137 0.4803924 0.4441008
138 -0.3289867 0.4803924
139 -0.3652782 -0.3289867
140 0.6341778 -0.3652782
141 0.5722219 0.6341778
142 -0.2727404 0.5722219
143 0.4503544 -0.2727404
144 -0.5496456 0.4503544
145 0.6710133 -0.5496456
146 -0.4277781 0.6710133
147 -0.3289867 -0.4277781
148 -0.2727404 -0.3289867
149 0.4503544 -0.2727404
150 0.6347218 0.4503544
151 -0.2732844 0.6347218
152 -0.4576518 -0.2732844
153 -0.3715318 -0.4576518
154 NA -0.3715318
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.3652782 0.7635512
[2,] -0.3652782 -0.3652782
[3,] -0.3652782 -0.3652782
[4,] -0.3652782 -0.3652782
[5,] 0.5428922 -0.3652782
[6,] -0.3652782 0.5428922
[7,] -0.3289867 -0.3652782
[8,] 0.6347218 -0.3289867
[9,] -0.2727404 0.6347218
[10,] -0.2364488 -0.2727404
[11,] -0.3652782 -0.2364488
[12,] -0.6484370 -0.3652782
[13,] -0.2364488 -0.6484370
[14,] 0.3515630 -0.2364488
[15,] 0.3878546 0.3515630
[16,] -0.4213602 0.3878546
[17,] -0.2364488 -0.4213602
[18,] 0.6347218 -0.2364488
[19,] 0.4861020 0.6347218
[20,] -0.4571078 0.4861020
[21,] 0.4441008 -0.4571078
[22,] 0.4503544 0.4441008
[23,] 0.5428922 0.4503544
[24,] 0.5722219 0.5428922
[25,] -0.6484370 0.5722219
[26,] 0.7272596 -0.6484370
[27,] -0.4640696 0.7272596
[28,] 0.6347218 -0.4640696
[29,] -0.5496456 0.6347218
[30,] -0.3652782 -0.5496456
[31,] -0.2727404 -0.3652782
[32,] -0.4571078 -0.2727404
[33,] 0.6710133 -0.4571078
[34,] -0.3652782 0.6710133
[35,] -0.3652782 -0.3652782
[36,] -0.5196076 -0.3652782
[37,] 0.5359304 -0.5196076
[38,] 0.4503544 0.5359304
[39,] -0.5133540 0.4503544
[40,] 0.4498104 -0.5133540
[41,] 0.5359304 0.4498104
[42,] 0.5428922 0.5359304
[43,] -0.2364488 0.5428922
[44,] -0.5496456 -0.2364488
[45,] 0.4503544 -0.5496456
[46,] -0.3652782 0.4503544
[47,] 0.6347218 -0.3652782
[48,] 0.4503544 0.6347218
[49,] -0.3652782 0.4503544
[50,] -0.4277781 -0.3652782
[51,] -0.4213602 -0.4277781
[52,] 0.6347218 -0.4213602
[53,] -0.3658222 0.6347218
[54,] -0.3652782 -0.3658222
[55,] 0.5722219 -0.3652782
[56,] 0.3515630 0.5722219
[57,] 0.6347218 0.3515630
[58,] 0.6347218 0.6347218
[59,] 0.5786398 0.6347218
[60,] 0.7635512 0.5786398
[61,] -0.6484370 0.7635512
[62,] -0.3652782 -0.6484370
[63,] 0.7635512 -0.3652782
[64,] -0.3652782 0.7635512
[65,] -0.3652782 -0.3652782
[66,] -0.5138980 -0.3652782
[67,] -0.2727404 -0.5138980
[68,] 0.6347218 -0.2727404
[69,] -0.4640696 0.6347218
[70,] -0.3652782 -0.4640696
[71,] 0.6347218 -0.3652782
[72,] 0.5359304 0.6347218
[73,] -0.3715318 0.5359304
[74,] 0.6347218 -0.3715318
[75,] 0.4866460 0.6347218
[76,] 0.6347218 0.4866460
[77,] 0.3515630 0.6347218
[78,] 0.6704693 0.3515630
[79,] -0.5133540 0.6704693
[80,] -0.3652782 -0.5133540
[81,] 0.6284682 -0.3652782
[82,] -0.3652782 0.6284682
[83,] -0.3658222 -0.3652782
[84,] 0.4503544 -0.3658222
[85,] -0.2727404 0.4503544
[86,] 0.7272596 -0.2727404
[87,] 0.6647598 0.7272596
[88,] -0.3652782 0.6647598
[89,] 0.6347218 -0.3652782
[90,] -0.5496456 0.6347218
[91,] -0.2364488 -0.5496456
[92,] -0.4571078 -0.2364488
[93,] -0.3652782 -0.4571078
[94,] -0.3289867 -0.3652782
[95,] 0.6347218 -0.3289867
[96,] -0.2364488 0.6347218
[97,] -0.3652782 -0.2364488
[98,] -0.2727404 -0.3652782
[99,] 0.6347218 -0.2727404
[100,] 0.7272596 0.6347218
[101,] -0.3652782 0.7272596
[102,] -0.3652782 -0.3652782
[103,] -0.3652782 -0.3652782
[104,] -0.4277781 -0.3652782
[105,] -0.3652782 -0.4277781
[106,] -0.3652782 -0.3652782
[107,] -0.3352402 -0.3652782
[108,] -0.3652782 -0.3352402
[109,] -0.2727404 -0.3652782
[110,] -0.5196076 -0.2727404
[111,] -0.3289867 -0.5196076
[112,] -0.4640696 -0.3289867
[113,] -0.3352402 -0.4640696
[114,] -0.2727404 -0.3352402
[115,] -0.3652782 -0.2727404
[116,] 0.7272596 -0.3652782
[117,] -0.2727404 0.7272596
[118,] -0.3652782 -0.2727404
[119,] 0.6347218 -0.3652782
[120,] -0.2727404 0.6347218
[121,] -0.3652782 -0.2727404
[122,] -0.3352402 -0.3652782
[123,] 0.3515630 -0.3352402
[124,] 0.6347218 0.3515630
[125,] -0.3289867 0.6347218
[126,] -0.5496456 -0.3289867
[127,] 0.6347218 -0.5496456
[128,] -0.3652782 0.6347218
[129,] 0.6347218 -0.3652782
[130,] -0.2727404 0.6347218
[131,] 0.7272596 -0.2727404
[132,] -0.3715318 0.7272596
[133,] -0.3652782 -0.3715318
[134,] -0.3652782 -0.3652782
[135,] -0.3652782 -0.3652782
[136,] 0.4441008 -0.3652782
[137,] 0.4803924 0.4441008
[138,] -0.3289867 0.4803924
[139,] -0.3652782 -0.3289867
[140,] 0.6341778 -0.3652782
[141,] 0.5722219 0.6341778
[142,] -0.2727404 0.5722219
[143,] 0.4503544 -0.2727404
[144,] -0.5496456 0.4503544
[145,] 0.6710133 -0.5496456
[146,] -0.4277781 0.6710133
[147,] -0.3289867 -0.4277781
[148,] -0.2727404 -0.3289867
[149,] 0.4503544 -0.2727404
[150,] 0.6347218 0.4503544
[151,] -0.2732844 0.6347218
[152,] -0.4576518 -0.2732844
[153,] -0.3715318 -0.4576518
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.3652782 0.7635512
2 -0.3652782 -0.3652782
3 -0.3652782 -0.3652782
4 -0.3652782 -0.3652782
5 0.5428922 -0.3652782
6 -0.3652782 0.5428922
7 -0.3289867 -0.3652782
8 0.6347218 -0.3289867
9 -0.2727404 0.6347218
10 -0.2364488 -0.2727404
11 -0.3652782 -0.2364488
12 -0.6484370 -0.3652782
13 -0.2364488 -0.6484370
14 0.3515630 -0.2364488
15 0.3878546 0.3515630
16 -0.4213602 0.3878546
17 -0.2364488 -0.4213602
18 0.6347218 -0.2364488
19 0.4861020 0.6347218
20 -0.4571078 0.4861020
21 0.4441008 -0.4571078
22 0.4503544 0.4441008
23 0.5428922 0.4503544
24 0.5722219 0.5428922
25 -0.6484370 0.5722219
26 0.7272596 -0.6484370
27 -0.4640696 0.7272596
28 0.6347218 -0.4640696
29 -0.5496456 0.6347218
30 -0.3652782 -0.5496456
31 -0.2727404 -0.3652782
32 -0.4571078 -0.2727404
33 0.6710133 -0.4571078
34 -0.3652782 0.6710133
35 -0.3652782 -0.3652782
36 -0.5196076 -0.3652782
37 0.5359304 -0.5196076
38 0.4503544 0.5359304
39 -0.5133540 0.4503544
40 0.4498104 -0.5133540
41 0.5359304 0.4498104
42 0.5428922 0.5359304
43 -0.2364488 0.5428922
44 -0.5496456 -0.2364488
45 0.4503544 -0.5496456
46 -0.3652782 0.4503544
47 0.6347218 -0.3652782
48 0.4503544 0.6347218
49 -0.3652782 0.4503544
50 -0.4277781 -0.3652782
51 -0.4213602 -0.4277781
52 0.6347218 -0.4213602
53 -0.3658222 0.6347218
54 -0.3652782 -0.3658222
55 0.5722219 -0.3652782
56 0.3515630 0.5722219
57 0.6347218 0.3515630
58 0.6347218 0.6347218
59 0.5786398 0.6347218
60 0.7635512 0.5786398
61 -0.6484370 0.7635512
62 -0.3652782 -0.6484370
63 0.7635512 -0.3652782
64 -0.3652782 0.7635512
65 -0.3652782 -0.3652782
66 -0.5138980 -0.3652782
67 -0.2727404 -0.5138980
68 0.6347218 -0.2727404
69 -0.4640696 0.6347218
70 -0.3652782 -0.4640696
71 0.6347218 -0.3652782
72 0.5359304 0.6347218
73 -0.3715318 0.5359304
74 0.6347218 -0.3715318
75 0.4866460 0.6347218
76 0.6347218 0.4866460
77 0.3515630 0.6347218
78 0.6704693 0.3515630
79 -0.5133540 0.6704693
80 -0.3652782 -0.5133540
81 0.6284682 -0.3652782
82 -0.3652782 0.6284682
83 -0.3658222 -0.3652782
84 0.4503544 -0.3658222
85 -0.2727404 0.4503544
86 0.7272596 -0.2727404
87 0.6647598 0.7272596
88 -0.3652782 0.6647598
89 0.6347218 -0.3652782
90 -0.5496456 0.6347218
91 -0.2364488 -0.5496456
92 -0.4571078 -0.2364488
93 -0.3652782 -0.4571078
94 -0.3289867 -0.3652782
95 0.6347218 -0.3289867
96 -0.2364488 0.6347218
97 -0.3652782 -0.2364488
98 -0.2727404 -0.3652782
99 0.6347218 -0.2727404
100 0.7272596 0.6347218
101 -0.3652782 0.7272596
102 -0.3652782 -0.3652782
103 -0.3652782 -0.3652782
104 -0.4277781 -0.3652782
105 -0.3652782 -0.4277781
106 -0.3652782 -0.3652782
107 -0.3352402 -0.3652782
108 -0.3652782 -0.3352402
109 -0.2727404 -0.3652782
110 -0.5196076 -0.2727404
111 -0.3289867 -0.5196076
112 -0.4640696 -0.3289867
113 -0.3352402 -0.4640696
114 -0.2727404 -0.3352402
115 -0.3652782 -0.2727404
116 0.7272596 -0.3652782
117 -0.2727404 0.7272596
118 -0.3652782 -0.2727404
119 0.6347218 -0.3652782
120 -0.2727404 0.6347218
121 -0.3652782 -0.2727404
122 -0.3352402 -0.3652782
123 0.3515630 -0.3352402
124 0.6347218 0.3515630
125 -0.3289867 0.6347218
126 -0.5496456 -0.3289867
127 0.6347218 -0.5496456
128 -0.3652782 0.6347218
129 0.6347218 -0.3652782
130 -0.2727404 0.6347218
131 0.7272596 -0.2727404
132 -0.3715318 0.7272596
133 -0.3652782 -0.3715318
134 -0.3652782 -0.3652782
135 -0.3652782 -0.3652782
136 0.4441008 -0.3652782
137 0.4803924 0.4441008
138 -0.3289867 0.4803924
139 -0.3652782 -0.3289867
140 0.6341778 -0.3652782
141 0.5722219 0.6341778
142 -0.2727404 0.5722219
143 0.4503544 -0.2727404
144 -0.5496456 0.4503544
145 0.6710133 -0.5496456
146 -0.4277781 0.6710133
147 -0.3289867 -0.4277781
148 -0.2727404 -0.3289867
149 0.4503544 -0.2727404
150 0.6347218 0.4503544
151 -0.2732844 0.6347218
152 -0.4576518 -0.2732844
153 -0.3715318 -0.4576518
> 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/7v1n51356123864.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/83s2i1356123864.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/93olx1356123864.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10vylg1356123864.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11z06v1356123864.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/124uq41356123865.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/13lr4p1356123865.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/14r9ep1356123865.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/1579ia1356123865.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/16lvgb1356123865.tab")
+ }
>
> try(system("convert tmp/1b4sr1356123864.ps tmp/1b4sr1356123864.png",intern=TRUE))
character(0)
> try(system("convert tmp/2suto1356123864.ps tmp/2suto1356123864.png",intern=TRUE))
character(0)
> try(system("convert tmp/3seyb1356123864.ps tmp/3seyb1356123864.png",intern=TRUE))
character(0)
> try(system("convert tmp/48sp71356123864.ps tmp/48sp71356123864.png",intern=TRUE))
character(0)
> try(system("convert tmp/5trav1356123864.ps tmp/5trav1356123864.png",intern=TRUE))
character(0)
> try(system("convert tmp/635yb1356123864.ps tmp/635yb1356123864.png",intern=TRUE))
character(0)
> try(system("convert tmp/7v1n51356123864.ps tmp/7v1n51356123864.png",intern=TRUE))
character(0)
> try(system("convert tmp/83s2i1356123864.ps tmp/83s2i1356123864.png",intern=TRUE))
character(0)
> try(system("convert tmp/93olx1356123864.ps tmp/93olx1356123864.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vylg1356123864.ps tmp/10vylg1356123864.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.847 1.801 9.655