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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1,0,2,0,2,0,2,0,2,0,2,0,2,0,1,0,2,0,2,0,1,0,2,0,2,0,1,0,2,0,1,0,1,1,1,0,2,0,1,1,2,0,2,0,2,0,2,0,1,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,1,0,2,0,2,0,1,0,2,0,2,0,1,0,2,1,2,0,2,0,1,0,2,0,2,0,2,0,2,0,2,0,2,0,1,0,1,1,2,0,2,1,2,0,1,0,2,0,2,0,2,0,1,1,1,0,2,0,2,0,1,0,2,0,2,0,1,1,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,1,0,2,0,2,0,1,1,1,0,2,0,2,0,2,0,2,1,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0),dim=c(2,154),dimnames=list(c('T40','CorrectAnlysis'),1:154))
> y <- array(NA,dim=c(2,154),dimnames=list(c('T40','CorrectAnlysis'),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 = '2'
> 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
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
CorrectAnlysis T40
1 0 1
2 0 2
3 0 2
4 0 2
5 0 2
6 0 2
7 0 2
8 0 1
9 0 2
10 0 2
11 0 1
12 0 2
13 0 2
14 0 1
15 0 2
16 0 1
17 1 1
18 0 1
19 0 2
20 1 1
21 0 2
22 0 2
23 0 2
24 0 2
25 0 1
26 0 2
27 0 2
28 0 2
29 0 2
30 0 2
31 0 2
32 0 2
33 0 2
34 0 1
35 0 2
36 0 2
37 0 1
38 0 2
39 0 2
40 0 1
41 1 2
42 0 2
43 0 2
44 0 1
45 0 2
46 0 2
47 0 2
48 0 2
49 0 2
50 0 2
51 0 1
52 1 1
53 0 2
54 1 2
55 0 2
56 0 1
57 0 2
58 0 2
59 0 2
60 1 1
61 0 1
62 0 2
63 0 2
64 0 1
65 0 2
66 0 2
67 1 1
68 0 2
69 0 2
70 0 2
71 0 2
72 0 2
73 0 2
74 0 2
75 0 2
76 0 1
77 0 2
78 0 2
79 1 1
80 0 1
81 0 2
82 0 2
83 0 2
84 1 2
85 0 2
86 0 2
87 0 0
88 0 0
89 0 0
90 0 0
91 0 0
92 0 0
93 0 0
94 0 0
95 0 0
96 0 0
97 0 0
98 0 0
99 0 0
100 0 0
101 0 0
102 0 0
103 0 0
104 0 0
105 0 0
106 0 0
107 0 0
108 0 0
109 0 0
110 0 0
111 0 0
112 0 0
113 0 0
114 0 0
115 0 0
116 0 0
117 0 0
118 0 0
119 0 0
120 0 0
121 0 0
122 0 0
123 0 0
124 0 0
125 0 0
126 0 0
127 0 0
128 0 0
129 0 0
130 0 0
131 0 0
132 0 0
133 0 0
134 0 0
135 0 0
136 0 0
137 0 0
138 0 0
139 0 0
140 0 0
141 1 0
142 0 0
143 0 0
144 0 0
145 0 0
146 0 0
147 0 0
148 0 0
149 0 0
150 0 0
151 0 0
152 1 0
153 1 0
154 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) T40
0.075041 0.002978
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.08100 -0.08100 -0.07653 -0.07504 0.92496
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.075041 0.031519 2.381 0.0185 *
T40 0.002978 0.023586 0.126 0.8997
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2698 on 152 degrees of freedom
Multiple R-squared: 0.0001049, Adjusted R-squared: -0.006473
F-statistic: 0.01594 on 1 and 152 DF, p-value: 0.8997
> 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 0.0000000000 1.0000000000
[2,] 0.0000000000 0.0000000000 1.0000000000
[3,] 0.0000000000 0.0000000000 1.0000000000
[4,] 0.0000000000 0.0000000000 1.0000000000
[5,] 0.0000000000 0.0000000000 1.0000000000
[6,] 0.0000000000 0.0000000000 1.0000000000
[7,] 0.0000000000 0.0000000000 1.0000000000
[8,] 0.0000000000 0.0000000000 1.0000000000
[9,] 0.0000000000 0.0000000000 1.0000000000
[10,] 0.0000000000 0.0000000000 1.0000000000
[11,] 0.0000000000 0.0000000000 1.0000000000
[12,] 0.0000000000 0.0000000000 1.0000000000
[13,] 0.2621884267 0.5243768534 0.7378115733
[14,] 0.2210408536 0.4420817072 0.7789591464
[15,] 0.1676153454 0.3352306908 0.8323846546
[16,] 0.7221640057 0.5556719885 0.2778359943
[17,] 0.6615181384 0.6769637232 0.3384818616
[18,] 0.5974548737 0.8050902525 0.4025451263
[19,] 0.5317096225 0.9365807550 0.4682903775
[20,] 0.4660758893 0.9321517786 0.5339241107
[21,] 0.4452509764 0.8905019528 0.5547490236
[22,] 0.3831687515 0.7663375030 0.6168312485
[23,] 0.3247066382 0.6494132763 0.6752933618
[24,] 0.2709331770 0.5418663539 0.7290668230
[25,] 0.2225813143 0.4451626287 0.7774186857
[26,] 0.1800458801 0.3600917602 0.8199541199
[27,] 0.1434105505 0.2868211010 0.8565894495
[28,] 0.1124961191 0.2249922382 0.8875038809
[29,] 0.0869209538 0.1738419076 0.9130790462
[30,] 0.0784058401 0.1568116803 0.9215941599
[31,] 0.0594920655 0.1189841309 0.9405079345
[32,] 0.0444959261 0.0889918522 0.9555040739
[33,] 0.0384294179 0.0768588357 0.9615705821
[34,] 0.0282043518 0.0564087036 0.9717956482
[35,] 0.0204209144 0.0408418289 0.9795790856
[36,] 0.0169083050 0.0338166100 0.9830916950
[37,] 0.3742939460 0.7485878921 0.6257060540
[38,] 0.3273787898 0.6547575795 0.6726212102
[39,] 0.2833751612 0.5667503223 0.7166248388
[40,] 0.2531451581 0.5062903162 0.7468548419
[41,] 0.2152402863 0.4304805725 0.7847597137
[42,] 0.1811130735 0.3622261470 0.8188869265
[43,] 0.1508274574 0.3016549147 0.8491725426
[44,] 0.1243273773 0.2486547546 0.8756726227
[45,] 0.1014566023 0.2029132045 0.8985433977
[46,] 0.0819810963 0.1639621927 0.9180189037
[47,] 0.0690619233 0.1381238467 0.9309380767
[48,] 0.3841601820 0.7683203641 0.6158398180
[49,] 0.3412764922 0.6825529844 0.6587235078
[50,] 0.8267721762 0.3464556476 0.1732278238
[51,] 0.7964350563 0.4071298873 0.2035649437
[52,] 0.7731163848 0.4537672303 0.2268836152
[53,] 0.7382227344 0.5235545311 0.2617772656
[54,] 0.7010314011 0.5979371978 0.2989685989
[55,] 0.6619183010 0.6761633980 0.3380816990
[56,] 0.9289095813 0.1421808375 0.0710904187
[57,] 0.9190620117 0.1618759766 0.0809379883
[58,] 0.9013993987 0.1972012025 0.0986006013
[59,] 0.8812299489 0.2375401021 0.1187700511
[60,] 0.8661676112 0.2676647776 0.1338323888
[61,] 0.8417062229 0.3165875542 0.1582937771
[62,] 0.8147781374 0.3704437251 0.1852218626
[63,] 0.9757606814 0.0484786372 0.0242393186
[64,] 0.9689442402 0.0621115196 0.0310557598
[65,] 0.9607021087 0.0785957826 0.0392978913
[66,] 0.9508903737 0.0982192525 0.0491096263
[67,] 0.9394003249 0.1211993503 0.0605996751
[68,] 0.9261801662 0.1476396677 0.0738198338
[69,] 0.9112636392 0.1774727217 0.0887363608
[70,] 0.8948091557 0.2103816887 0.1051908443
[71,] 0.8771567040 0.2456865919 0.1228432960
[72,] 0.8645410834 0.2709178331 0.1354589166
[73,] 0.8456559619 0.3086880762 0.1543440381
[74,] 0.8277256951 0.3445486098 0.1722743049
[75,] 0.9789332137 0.0421335727 0.0210667863
[76,] 0.9758618428 0.0482763144 0.0241381572
[77,] 0.9710754260 0.0578491480 0.0289245740
[78,] 0.9669880811 0.0660238378 0.0330119189
[79,] 0.9655964075 0.0688071850 0.0344035925
[80,] 0.9991110213 0.0017779575 0.0008889787
[81,] 0.9986806804 0.0026386391 0.0013193196
[82,] 0.9980656323 0.0038687353 0.0019343677
[83,] 0.9979804744 0.0040390512 0.0020195256
[84,] 0.9976786059 0.0046427882 0.0023213941
[85,] 0.9971759551 0.0056480898 0.0028240449
[86,] 0.9964447240 0.0071105519 0.0035552760
[87,] 0.9954310348 0.0091379305 0.0045689652
[88,] 0.9940586478 0.0118827044 0.0059413522
[89,] 0.9922284441 0.0155431118 0.0077715559
[90,] 0.9898164850 0.0203670300 0.0101835150
[91,] 0.9866717753 0.0266564494 0.0133282247
[92,] 0.9826143917 0.0347712166 0.0173856083
[93,] 0.9774345205 0.0451309589 0.0225654795
[94,] 0.9708929253 0.0582141495 0.0291070747
[95,] 0.9627233521 0.0745532958 0.0372766479
[96,] 0.9526373392 0.0947253216 0.0473626608
[97,] 0.9403318010 0.1193363981 0.0596681990
[98,] 0.9254995980 0.1490008039 0.0745004020
[99,] 0.9078430778 0.1843138444 0.0921569222
[100,] 0.8870902776 0.2258194449 0.1129097224
[101,] 0.8630131520 0.2739736960 0.1369868480
[102,] 0.8354468400 0.3291063200 0.1645531600
[103,] 0.8043086676 0.3913826648 0.1956913324
[104,] 0.7696153391 0.4607693217 0.2303846609
[105,] 0.7314966476 0.5370067048 0.2685033524
[106,] 0.6902040699 0.6195918602 0.3097959301
[107,] 0.6461128396 0.7077743208 0.3538871604
[108,] 0.5997165021 0.8005669957 0.4002834979
[109,] 0.5516135393 0.8967729214 0.4483864607
[110,] 0.5024863495 0.9950273010 0.4975136505
[111,] 0.4530736133 0.9061472267 0.5469263867
[112,] 0.4041377772 0.8082755544 0.5958622228
[113,] 0.3564299507 0.7128599013 0.6435700493
[114,] 0.3106548680 0.6213097361 0.6893451320
[115,] 0.2674386396 0.5348772791 0.7325613604
[116,] 0.2273018040 0.4546036079 0.7726981960
[117,] 0.1906397008 0.3812794017 0.8093602992
[118,] 0.1577114699 0.3154229398 0.8422885301
[119,] 0.1286381417 0.2572762835 0.8713618583
[120,] 0.1034094185 0.2068188369 0.8965905815
[121,] 0.0818979592 0.1637959183 0.9181020408
[122,] 0.0638793804 0.1277587607 0.9361206196
[123,] 0.0490558153 0.0981116306 0.9509441847
[124,] 0.0370807781 0.0741615562 0.9629192219
[125,] 0.0275832358 0.0551664716 0.9724167642
[126,] 0.0201891536 0.0403783072 0.9798108464
[127,] 0.0145392798 0.0290785597 0.9854607202
[128,] 0.0103024939 0.0206049879 0.9896975061
[129,] 0.0071845753 0.0143691506 0.9928154247
[130,] 0.0049327022 0.0098654045 0.9950672978
[131,] 0.0033363186 0.0066726373 0.9966636814
[132,] 0.0022251930 0.0044503860 0.9977748070
[133,] 0.0014655514 0.0029311028 0.9985344486
[134,] 0.0009551118 0.0019102236 0.9990448882
[135,] 0.0006177202 0.0012354405 0.9993822798
[136,] 0.0003981208 0.0007962417 0.9996018792
[137,] 0.0125746146 0.0251492291 0.9874253854
[138,] 0.0082084035 0.0164168069 0.9917915965
[139,] 0.0052493643 0.0104987285 0.9947506357
[140,] 0.0033024713 0.0066049427 0.9966975287
[141,] 0.0020589281 0.0041178562 0.9979410719
[142,] 0.0012885900 0.0025771801 0.9987114100
[143,] 0.0008284914 0.0016569829 0.9991715086
[144,] 0.0005712646 0.0011425291 0.9994287354
[145,] 0.0004601067 0.0009202133 0.9995398933
> postscript(file="/var/wessaorg/rcomp/tmp/1e2pv1355920295.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/24mky1355920295.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3fxog1355920295.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4uv451355920295.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/52s221355920295.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
-0.07801876 -0.08099658 -0.08099658 -0.08099658 -0.08099658 -0.08099658
7 8 9 10 11 12
-0.08099658 -0.07801876 -0.08099658 -0.08099658 -0.07801876 -0.08099658
13 14 15 16 17 18
-0.08099658 -0.07801876 -0.08099658 -0.07801876 0.92198124 -0.07801876
19 20 21 22 23 24
-0.08099658 0.92198124 -0.08099658 -0.08099658 -0.08099658 -0.08099658
25 26 27 28 29 30
-0.07801876 -0.08099658 -0.08099658 -0.08099658 -0.08099658 -0.08099658
31 32 33 34 35 36
-0.08099658 -0.08099658 -0.08099658 -0.07801876 -0.08099658 -0.08099658
37 38 39 40 41 42
-0.07801876 -0.08099658 -0.08099658 -0.07801876 0.91900342 -0.08099658
43 44 45 46 47 48
-0.08099658 -0.07801876 -0.08099658 -0.08099658 -0.08099658 -0.08099658
49 50 51 52 53 54
-0.08099658 -0.08099658 -0.07801876 0.92198124 -0.08099658 0.91900342
55 56 57 58 59 60
-0.08099658 -0.07801876 -0.08099658 -0.08099658 -0.08099658 0.92198124
61 62 63 64 65 66
-0.07801876 -0.08099658 -0.08099658 -0.07801876 -0.08099658 -0.08099658
67 68 69 70 71 72
0.92198124 -0.08099658 -0.08099658 -0.08099658 -0.08099658 -0.08099658
73 74 75 76 77 78
-0.08099658 -0.08099658 -0.08099658 -0.07801876 -0.08099658 -0.08099658
79 80 81 82 83 84
0.92198124 -0.07801876 -0.08099658 -0.08099658 -0.08099658 0.91900342
85 86 87 88 89 90
-0.08099658 -0.08099658 -0.07504094 -0.07504094 -0.07504094 -0.07504094
91 92 93 94 95 96
-0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094
97 98 99 100 101 102
-0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094
103 104 105 106 107 108
-0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094
109 110 111 112 113 114
-0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094
115 116 117 118 119 120
-0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094
121 122 123 124 125 126
-0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094
127 128 129 130 131 132
-0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094
133 134 135 136 137 138
-0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094
139 140 141 142 143 144
-0.07504094 -0.07504094 0.92495906 -0.07504094 -0.07504094 -0.07504094
145 146 147 148 149 150
-0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094 -0.07504094
151 152 153 154
-0.07504094 0.92495906 0.92495906 -0.07504094
> postscript(file="/var/wessaorg/rcomp/tmp/6reff1355920295.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 -0.07801876 NA
1 -0.08099658 -0.07801876
2 -0.08099658 -0.08099658
3 -0.08099658 -0.08099658
4 -0.08099658 -0.08099658
5 -0.08099658 -0.08099658
6 -0.08099658 -0.08099658
7 -0.07801876 -0.08099658
8 -0.08099658 -0.07801876
9 -0.08099658 -0.08099658
10 -0.07801876 -0.08099658
11 -0.08099658 -0.07801876
12 -0.08099658 -0.08099658
13 -0.07801876 -0.08099658
14 -0.08099658 -0.07801876
15 -0.07801876 -0.08099658
16 0.92198124 -0.07801876
17 -0.07801876 0.92198124
18 -0.08099658 -0.07801876
19 0.92198124 -0.08099658
20 -0.08099658 0.92198124
21 -0.08099658 -0.08099658
22 -0.08099658 -0.08099658
23 -0.08099658 -0.08099658
24 -0.07801876 -0.08099658
25 -0.08099658 -0.07801876
26 -0.08099658 -0.08099658
27 -0.08099658 -0.08099658
28 -0.08099658 -0.08099658
29 -0.08099658 -0.08099658
30 -0.08099658 -0.08099658
31 -0.08099658 -0.08099658
32 -0.08099658 -0.08099658
33 -0.07801876 -0.08099658
34 -0.08099658 -0.07801876
35 -0.08099658 -0.08099658
36 -0.07801876 -0.08099658
37 -0.08099658 -0.07801876
38 -0.08099658 -0.08099658
39 -0.07801876 -0.08099658
40 0.91900342 -0.07801876
41 -0.08099658 0.91900342
42 -0.08099658 -0.08099658
43 -0.07801876 -0.08099658
44 -0.08099658 -0.07801876
45 -0.08099658 -0.08099658
46 -0.08099658 -0.08099658
47 -0.08099658 -0.08099658
48 -0.08099658 -0.08099658
49 -0.08099658 -0.08099658
50 -0.07801876 -0.08099658
51 0.92198124 -0.07801876
52 -0.08099658 0.92198124
53 0.91900342 -0.08099658
54 -0.08099658 0.91900342
55 -0.07801876 -0.08099658
56 -0.08099658 -0.07801876
57 -0.08099658 -0.08099658
58 -0.08099658 -0.08099658
59 0.92198124 -0.08099658
60 -0.07801876 0.92198124
61 -0.08099658 -0.07801876
62 -0.08099658 -0.08099658
63 -0.07801876 -0.08099658
64 -0.08099658 -0.07801876
65 -0.08099658 -0.08099658
66 0.92198124 -0.08099658
67 -0.08099658 0.92198124
68 -0.08099658 -0.08099658
69 -0.08099658 -0.08099658
70 -0.08099658 -0.08099658
71 -0.08099658 -0.08099658
72 -0.08099658 -0.08099658
73 -0.08099658 -0.08099658
74 -0.08099658 -0.08099658
75 -0.07801876 -0.08099658
76 -0.08099658 -0.07801876
77 -0.08099658 -0.08099658
78 0.92198124 -0.08099658
79 -0.07801876 0.92198124
80 -0.08099658 -0.07801876
81 -0.08099658 -0.08099658
82 -0.08099658 -0.08099658
83 0.91900342 -0.08099658
84 -0.08099658 0.91900342
85 -0.08099658 -0.08099658
86 -0.07504094 -0.08099658
87 -0.07504094 -0.07504094
88 -0.07504094 -0.07504094
89 -0.07504094 -0.07504094
90 -0.07504094 -0.07504094
91 -0.07504094 -0.07504094
92 -0.07504094 -0.07504094
93 -0.07504094 -0.07504094
94 -0.07504094 -0.07504094
95 -0.07504094 -0.07504094
96 -0.07504094 -0.07504094
97 -0.07504094 -0.07504094
98 -0.07504094 -0.07504094
99 -0.07504094 -0.07504094
100 -0.07504094 -0.07504094
101 -0.07504094 -0.07504094
102 -0.07504094 -0.07504094
103 -0.07504094 -0.07504094
104 -0.07504094 -0.07504094
105 -0.07504094 -0.07504094
106 -0.07504094 -0.07504094
107 -0.07504094 -0.07504094
108 -0.07504094 -0.07504094
109 -0.07504094 -0.07504094
110 -0.07504094 -0.07504094
111 -0.07504094 -0.07504094
112 -0.07504094 -0.07504094
113 -0.07504094 -0.07504094
114 -0.07504094 -0.07504094
115 -0.07504094 -0.07504094
116 -0.07504094 -0.07504094
117 -0.07504094 -0.07504094
118 -0.07504094 -0.07504094
119 -0.07504094 -0.07504094
120 -0.07504094 -0.07504094
121 -0.07504094 -0.07504094
122 -0.07504094 -0.07504094
123 -0.07504094 -0.07504094
124 -0.07504094 -0.07504094
125 -0.07504094 -0.07504094
126 -0.07504094 -0.07504094
127 -0.07504094 -0.07504094
128 -0.07504094 -0.07504094
129 -0.07504094 -0.07504094
130 -0.07504094 -0.07504094
131 -0.07504094 -0.07504094
132 -0.07504094 -0.07504094
133 -0.07504094 -0.07504094
134 -0.07504094 -0.07504094
135 -0.07504094 -0.07504094
136 -0.07504094 -0.07504094
137 -0.07504094 -0.07504094
138 -0.07504094 -0.07504094
139 -0.07504094 -0.07504094
140 0.92495906 -0.07504094
141 -0.07504094 0.92495906
142 -0.07504094 -0.07504094
143 -0.07504094 -0.07504094
144 -0.07504094 -0.07504094
145 -0.07504094 -0.07504094
146 -0.07504094 -0.07504094
147 -0.07504094 -0.07504094
148 -0.07504094 -0.07504094
149 -0.07504094 -0.07504094
150 -0.07504094 -0.07504094
151 0.92495906 -0.07504094
152 0.92495906 0.92495906
153 -0.07504094 0.92495906
154 NA -0.07504094
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.08099658 -0.07801876
[2,] -0.08099658 -0.08099658
[3,] -0.08099658 -0.08099658
[4,] -0.08099658 -0.08099658
[5,] -0.08099658 -0.08099658
[6,] -0.08099658 -0.08099658
[7,] -0.07801876 -0.08099658
[8,] -0.08099658 -0.07801876
[9,] -0.08099658 -0.08099658
[10,] -0.07801876 -0.08099658
[11,] -0.08099658 -0.07801876
[12,] -0.08099658 -0.08099658
[13,] -0.07801876 -0.08099658
[14,] -0.08099658 -0.07801876
[15,] -0.07801876 -0.08099658
[16,] 0.92198124 -0.07801876
[17,] -0.07801876 0.92198124
[18,] -0.08099658 -0.07801876
[19,] 0.92198124 -0.08099658
[20,] -0.08099658 0.92198124
[21,] -0.08099658 -0.08099658
[22,] -0.08099658 -0.08099658
[23,] -0.08099658 -0.08099658
[24,] -0.07801876 -0.08099658
[25,] -0.08099658 -0.07801876
[26,] -0.08099658 -0.08099658
[27,] -0.08099658 -0.08099658
[28,] -0.08099658 -0.08099658
[29,] -0.08099658 -0.08099658
[30,] -0.08099658 -0.08099658
[31,] -0.08099658 -0.08099658
[32,] -0.08099658 -0.08099658
[33,] -0.07801876 -0.08099658
[34,] -0.08099658 -0.07801876
[35,] -0.08099658 -0.08099658
[36,] -0.07801876 -0.08099658
[37,] -0.08099658 -0.07801876
[38,] -0.08099658 -0.08099658
[39,] -0.07801876 -0.08099658
[40,] 0.91900342 -0.07801876
[41,] -0.08099658 0.91900342
[42,] -0.08099658 -0.08099658
[43,] -0.07801876 -0.08099658
[44,] -0.08099658 -0.07801876
[45,] -0.08099658 -0.08099658
[46,] -0.08099658 -0.08099658
[47,] -0.08099658 -0.08099658
[48,] -0.08099658 -0.08099658
[49,] -0.08099658 -0.08099658
[50,] -0.07801876 -0.08099658
[51,] 0.92198124 -0.07801876
[52,] -0.08099658 0.92198124
[53,] 0.91900342 -0.08099658
[54,] -0.08099658 0.91900342
[55,] -0.07801876 -0.08099658
[56,] -0.08099658 -0.07801876
[57,] -0.08099658 -0.08099658
[58,] -0.08099658 -0.08099658
[59,] 0.92198124 -0.08099658
[60,] -0.07801876 0.92198124
[61,] -0.08099658 -0.07801876
[62,] -0.08099658 -0.08099658
[63,] -0.07801876 -0.08099658
[64,] -0.08099658 -0.07801876
[65,] -0.08099658 -0.08099658
[66,] 0.92198124 -0.08099658
[67,] -0.08099658 0.92198124
[68,] -0.08099658 -0.08099658
[69,] -0.08099658 -0.08099658
[70,] -0.08099658 -0.08099658
[71,] -0.08099658 -0.08099658
[72,] -0.08099658 -0.08099658
[73,] -0.08099658 -0.08099658
[74,] -0.08099658 -0.08099658
[75,] -0.07801876 -0.08099658
[76,] -0.08099658 -0.07801876
[77,] -0.08099658 -0.08099658
[78,] 0.92198124 -0.08099658
[79,] -0.07801876 0.92198124
[80,] -0.08099658 -0.07801876
[81,] -0.08099658 -0.08099658
[82,] -0.08099658 -0.08099658
[83,] 0.91900342 -0.08099658
[84,] -0.08099658 0.91900342
[85,] -0.08099658 -0.08099658
[86,] -0.07504094 -0.08099658
[87,] -0.07504094 -0.07504094
[88,] -0.07504094 -0.07504094
[89,] -0.07504094 -0.07504094
[90,] -0.07504094 -0.07504094
[91,] -0.07504094 -0.07504094
[92,] -0.07504094 -0.07504094
[93,] -0.07504094 -0.07504094
[94,] -0.07504094 -0.07504094
[95,] -0.07504094 -0.07504094
[96,] -0.07504094 -0.07504094
[97,] -0.07504094 -0.07504094
[98,] -0.07504094 -0.07504094
[99,] -0.07504094 -0.07504094
[100,] -0.07504094 -0.07504094
[101,] -0.07504094 -0.07504094
[102,] -0.07504094 -0.07504094
[103,] -0.07504094 -0.07504094
[104,] -0.07504094 -0.07504094
[105,] -0.07504094 -0.07504094
[106,] -0.07504094 -0.07504094
[107,] -0.07504094 -0.07504094
[108,] -0.07504094 -0.07504094
[109,] -0.07504094 -0.07504094
[110,] -0.07504094 -0.07504094
[111,] -0.07504094 -0.07504094
[112,] -0.07504094 -0.07504094
[113,] -0.07504094 -0.07504094
[114,] -0.07504094 -0.07504094
[115,] -0.07504094 -0.07504094
[116,] -0.07504094 -0.07504094
[117,] -0.07504094 -0.07504094
[118,] -0.07504094 -0.07504094
[119,] -0.07504094 -0.07504094
[120,] -0.07504094 -0.07504094
[121,] -0.07504094 -0.07504094
[122,] -0.07504094 -0.07504094
[123,] -0.07504094 -0.07504094
[124,] -0.07504094 -0.07504094
[125,] -0.07504094 -0.07504094
[126,] -0.07504094 -0.07504094
[127,] -0.07504094 -0.07504094
[128,] -0.07504094 -0.07504094
[129,] -0.07504094 -0.07504094
[130,] -0.07504094 -0.07504094
[131,] -0.07504094 -0.07504094
[132,] -0.07504094 -0.07504094
[133,] -0.07504094 -0.07504094
[134,] -0.07504094 -0.07504094
[135,] -0.07504094 -0.07504094
[136,] -0.07504094 -0.07504094
[137,] -0.07504094 -0.07504094
[138,] -0.07504094 -0.07504094
[139,] -0.07504094 -0.07504094
[140,] 0.92495906 -0.07504094
[141,] -0.07504094 0.92495906
[142,] -0.07504094 -0.07504094
[143,] -0.07504094 -0.07504094
[144,] -0.07504094 -0.07504094
[145,] -0.07504094 -0.07504094
[146,] -0.07504094 -0.07504094
[147,] -0.07504094 -0.07504094
[148,] -0.07504094 -0.07504094
[149,] -0.07504094 -0.07504094
[150,] -0.07504094 -0.07504094
[151,] 0.92495906 -0.07504094
[152,] 0.92495906 0.92495906
[153,] -0.07504094 0.92495906
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.08099658 -0.07801876
2 -0.08099658 -0.08099658
3 -0.08099658 -0.08099658
4 -0.08099658 -0.08099658
5 -0.08099658 -0.08099658
6 -0.08099658 -0.08099658
7 -0.07801876 -0.08099658
8 -0.08099658 -0.07801876
9 -0.08099658 -0.08099658
10 -0.07801876 -0.08099658
11 -0.08099658 -0.07801876
12 -0.08099658 -0.08099658
13 -0.07801876 -0.08099658
14 -0.08099658 -0.07801876
15 -0.07801876 -0.08099658
16 0.92198124 -0.07801876
17 -0.07801876 0.92198124
18 -0.08099658 -0.07801876
19 0.92198124 -0.08099658
20 -0.08099658 0.92198124
21 -0.08099658 -0.08099658
22 -0.08099658 -0.08099658
23 -0.08099658 -0.08099658
24 -0.07801876 -0.08099658
25 -0.08099658 -0.07801876
26 -0.08099658 -0.08099658
27 -0.08099658 -0.08099658
28 -0.08099658 -0.08099658
29 -0.08099658 -0.08099658
30 -0.08099658 -0.08099658
31 -0.08099658 -0.08099658
32 -0.08099658 -0.08099658
33 -0.07801876 -0.08099658
34 -0.08099658 -0.07801876
35 -0.08099658 -0.08099658
36 -0.07801876 -0.08099658
37 -0.08099658 -0.07801876
38 -0.08099658 -0.08099658
39 -0.07801876 -0.08099658
40 0.91900342 -0.07801876
41 -0.08099658 0.91900342
42 -0.08099658 -0.08099658
43 -0.07801876 -0.08099658
44 -0.08099658 -0.07801876
45 -0.08099658 -0.08099658
46 -0.08099658 -0.08099658
47 -0.08099658 -0.08099658
48 -0.08099658 -0.08099658
49 -0.08099658 -0.08099658
50 -0.07801876 -0.08099658
51 0.92198124 -0.07801876
52 -0.08099658 0.92198124
53 0.91900342 -0.08099658
54 -0.08099658 0.91900342
55 -0.07801876 -0.08099658
56 -0.08099658 -0.07801876
57 -0.08099658 -0.08099658
58 -0.08099658 -0.08099658
59 0.92198124 -0.08099658
60 -0.07801876 0.92198124
61 -0.08099658 -0.07801876
62 -0.08099658 -0.08099658
63 -0.07801876 -0.08099658
64 -0.08099658 -0.07801876
65 -0.08099658 -0.08099658
66 0.92198124 -0.08099658
67 -0.08099658 0.92198124
68 -0.08099658 -0.08099658
69 -0.08099658 -0.08099658
70 -0.08099658 -0.08099658
71 -0.08099658 -0.08099658
72 -0.08099658 -0.08099658
73 -0.08099658 -0.08099658
74 -0.08099658 -0.08099658
75 -0.07801876 -0.08099658
76 -0.08099658 -0.07801876
77 -0.08099658 -0.08099658
78 0.92198124 -0.08099658
79 -0.07801876 0.92198124
80 -0.08099658 -0.07801876
81 -0.08099658 -0.08099658
82 -0.08099658 -0.08099658
83 0.91900342 -0.08099658
84 -0.08099658 0.91900342
85 -0.08099658 -0.08099658
86 -0.07504094 -0.08099658
87 -0.07504094 -0.07504094
88 -0.07504094 -0.07504094
89 -0.07504094 -0.07504094
90 -0.07504094 -0.07504094
91 -0.07504094 -0.07504094
92 -0.07504094 -0.07504094
93 -0.07504094 -0.07504094
94 -0.07504094 -0.07504094
95 -0.07504094 -0.07504094
96 -0.07504094 -0.07504094
97 -0.07504094 -0.07504094
98 -0.07504094 -0.07504094
99 -0.07504094 -0.07504094
100 -0.07504094 -0.07504094
101 -0.07504094 -0.07504094
102 -0.07504094 -0.07504094
103 -0.07504094 -0.07504094
104 -0.07504094 -0.07504094
105 -0.07504094 -0.07504094
106 -0.07504094 -0.07504094
107 -0.07504094 -0.07504094
108 -0.07504094 -0.07504094
109 -0.07504094 -0.07504094
110 -0.07504094 -0.07504094
111 -0.07504094 -0.07504094
112 -0.07504094 -0.07504094
113 -0.07504094 -0.07504094
114 -0.07504094 -0.07504094
115 -0.07504094 -0.07504094
116 -0.07504094 -0.07504094
117 -0.07504094 -0.07504094
118 -0.07504094 -0.07504094
119 -0.07504094 -0.07504094
120 -0.07504094 -0.07504094
121 -0.07504094 -0.07504094
122 -0.07504094 -0.07504094
123 -0.07504094 -0.07504094
124 -0.07504094 -0.07504094
125 -0.07504094 -0.07504094
126 -0.07504094 -0.07504094
127 -0.07504094 -0.07504094
128 -0.07504094 -0.07504094
129 -0.07504094 -0.07504094
130 -0.07504094 -0.07504094
131 -0.07504094 -0.07504094
132 -0.07504094 -0.07504094
133 -0.07504094 -0.07504094
134 -0.07504094 -0.07504094
135 -0.07504094 -0.07504094
136 -0.07504094 -0.07504094
137 -0.07504094 -0.07504094
138 -0.07504094 -0.07504094
139 -0.07504094 -0.07504094
140 0.92495906 -0.07504094
141 -0.07504094 0.92495906
142 -0.07504094 -0.07504094
143 -0.07504094 -0.07504094
144 -0.07504094 -0.07504094
145 -0.07504094 -0.07504094
146 -0.07504094 -0.07504094
147 -0.07504094 -0.07504094
148 -0.07504094 -0.07504094
149 -0.07504094 -0.07504094
150 -0.07504094 -0.07504094
151 0.92495906 -0.07504094
152 0.92495906 0.92495906
153 -0.07504094 0.92495906
> 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/wessaorg/rcomp/tmp/7fngh1355920295.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8yp1q1355920295.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/99eb61355920295.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10save1355920295.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/1161cm1355920295.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/wessaorg/rcomp/tmp/12z97c1355920295.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/wessaorg/rcomp/tmp/13g80e1355920295.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/wessaorg/rcomp/tmp/14k4r21355920295.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/wessaorg/rcomp/tmp/15326n1355920295.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/wessaorg/rcomp/tmp/16ttcc1355920295.tab")
+ }
>
> try(system("convert tmp/1e2pv1355920295.ps tmp/1e2pv1355920295.png",intern=TRUE))
character(0)
> try(system("convert tmp/24mky1355920295.ps tmp/24mky1355920295.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fxog1355920295.ps tmp/3fxog1355920295.png",intern=TRUE))
character(0)
> try(system("convert tmp/4uv451355920295.ps tmp/4uv451355920295.png",intern=TRUE))
character(0)
> try(system("convert tmp/52s221355920295.ps tmp/52s221355920295.png",intern=TRUE))
character(0)
> try(system("convert tmp/6reff1355920295.ps tmp/6reff1355920295.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fngh1355920295.ps tmp/7fngh1355920295.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yp1q1355920295.ps tmp/8yp1q1355920295.png",intern=TRUE))
character(0)
> try(system("convert tmp/99eb61355920295.ps tmp/99eb61355920295.png",intern=TRUE))
character(0)
> try(system("convert tmp/10save1355920295.ps tmp/10save1355920295.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.315 1.160 8.909