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(1,4,1,3,1,3,1,3,1,3,1,3,1,3,1,4,1,3,1,3,1,4,1,3,1,3,1,4,1,3,1,4,2,4,1,4,1,3,2,4,1,3,1,3,1,3,1,3,1,4,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,4,1,3,1,3,1,4,1,3,1,3,1,4,2,3,1,3,1,3,1,4,1,3,1,3,1,3,1,3,1,3,1,3,1,4,2,4,1,3,2,3,1,3,1,4,1,3,1,3,1,3,2,4,1,4,1,3,1,3,1,4,1,3,1,3,2,4,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,4,1,3,1,3,2,4,1,4,1,3,1,3,1,3,2,3,1,3,1,3,1,3,1,4,1,3,1,3,1,3,1,4,1,3,1,3,1,4,1,3,1,4,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,4,1,3,1,3,1,4,1,3,1,3,1,4,1,4,1,3,1,4,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,4,1,3,1,3,1,4,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,4,1,4,1,3,2,3,1,4,1,3,1,3,1,3,1,4,1,4,1,4,1,3,1,3,1,3,2,3,2,3,1,3),dim=c(2,154),dimnames=list(c('CorrectAnalysis','T40T20'),1:154))
> y <- array(NA,dim=c(2,154),dimnames=list(c('CorrectAnalysis','T40T20'),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 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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 T40T20
1 1 4
2 1 3
3 1 3
4 1 3
5 1 3
6 1 3
7 1 3
8 1 4
9 1 3
10 1 3
11 1 4
12 1 3
13 1 3
14 1 4
15 1 3
16 1 4
17 2 4
18 1 4
19 1 3
20 2 4
21 1 3
22 1 3
23 1 3
24 1 3
25 1 4
26 1 3
27 1 3
28 1 3
29 1 3
30 1 3
31 1 3
32 1 3
33 1 3
34 1 4
35 1 3
36 1 3
37 1 4
38 1 3
39 1 3
40 1 4
41 2 3
42 1 3
43 1 3
44 1 4
45 1 3
46 1 3
47 1 3
48 1 3
49 1 3
50 1 3
51 1 4
52 2 4
53 1 3
54 2 3
55 1 3
56 1 4
57 1 3
58 1 3
59 1 3
60 2 4
61 1 4
62 1 3
63 1 3
64 1 4
65 1 3
66 1 3
67 2 4
68 1 3
69 1 3
70 1 3
71 1 3
72 1 3
73 1 3
74 1 3
75 1 3
76 1 4
77 1 3
78 1 3
79 2 4
80 1 4
81 1 3
82 1 3
83 1 3
84 2 3
85 1 3
86 1 3
87 1 3
88 1 4
89 1 3
90 1 3
91 1 3
92 1 4
93 1 3
94 1 3
95 1 4
96 1 3
97 1 4
98 1 3
99 1 3
100 1 3
101 1 3
102 1 3
103 1 3
104 1 3
105 1 4
106 1 3
107 1 3
108 1 4
109 1 3
110 1 3
111 1 4
112 1 4
113 1 3
114 1 4
115 1 3
116 1 3
117 1 3
118 1 3
119 1 3
120 1 3
121 1 3
122 1 3
123 1 4
124 1 3
125 1 3
126 1 4
127 1 3
128 1 3
129 1 3
130 1 3
131 1 3
132 1 3
133 1 3
134 1 3
135 1 3
136 1 3
137 1 3
138 1 4
139 1 4
140 1 3
141 2 3
142 1 4
143 1 3
144 1 3
145 1 3
146 1 4
147 1 4
148 1 4
149 1 3
150 1 3
151 1 3
152 2 3
153 2 3
154 1 3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) T40T20
0.76053 0.09737
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.15000 -0.05263 -0.05263 -0.05263 0.94737
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.76053 0.16100 4.724 5.23e-06 ***
T40T20 0.09737 0.04895 1.989 0.0485 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2664 on 152 degrees of freedom
Multiple R-squared: 0.02537, Adjusted R-squared: 0.01896
F-statistic: 3.957 on 1 and 152 DF, p-value: 0.04848
> 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,] 0.000000e+00 0.000000e+00 1.0000000000
[2,] 7.208139e-128 1.441628e-127 1.0000000000
[3,] 7.580038e-162 1.516008e-161 1.0000000000
[4,] 2.199728e-92 4.399455e-92 1.0000000000
[5,] 2.199893e-110 4.399787e-110 1.0000000000
[6,] 4.479759e-126 8.959518e-126 1.0000000000
[7,] 7.192991e-148 1.438598e-147 1.0000000000
[8,] 1.658395e-153 3.316790e-153 1.0000000000
[9,] 1.351964e-191 2.703929e-191 1.0000000000
[10,] 1.890554e-182 3.781107e-182 1.0000000000
[11,] 8.045688e-198 1.609138e-197 1.0000000000
[12,] 0.000000e+00 0.000000e+00 1.0000000000
[13,] 2.769984e-01 5.539969e-01 0.7230015604
[14,] 2.360283e-01 4.720566e-01 0.7639716952
[15,] 1.801316e-01 3.602632e-01 0.8198684083
[16,] 7.319990e-01 5.360019e-01 0.2680009595
[17,] 6.718749e-01 6.562501e-01 0.3281250679
[18,] 6.079769e-01 7.840463e-01 0.3920231284
[19,] 5.420291e-01 9.159417e-01 0.4579708676
[20,] 4.758485e-01 9.516970e-01 0.5241515200
[21,] 4.564318e-01 9.128636e-01 0.5435681971
[22,] 3.933802e-01 7.867605e-01 0.6066197597
[23,] 3.337253e-01 6.674506e-01 0.6662747108
[24,] 2.786274e-01 5.572547e-01 0.7213726324
[25,] 2.289102e-01 4.578204e-01 0.7710898036
[26,] 1.850493e-01 3.700986e-01 0.8149507114
[27,] 1.471926e-01 2.943852e-01 0.8528073902
[28,] 1.152056e-01 2.304112e-01 0.8847943847
[29,] 8.873137e-02 1.774627e-01 0.9112686320
[30,] 8.059814e-02 1.611963e-01 0.9194018550
[31,] 6.090107e-02 1.218021e-01 0.9390989350
[32,] 4.530429e-02 9.060858e-02 0.9546957078
[33,] 3.946083e-02 7.892166e-02 0.9605391697
[34,] 2.876507e-02 5.753013e-02 0.9712349330
[35,] 2.065296e-02 4.130592e-02 0.9793470413
[36,] 1.727318e-02 3.454637e-02 0.9827268168
[37,] 3.946317e-01 7.892634e-01 0.6053682813
[38,] 3.456169e-01 6.912338e-01 0.6543831101
[39,] 2.993054e-01 5.986108e-01 0.7006945896
[40,] 2.703577e-01 5.407154e-01 0.7296423086
[41,] 2.298143e-01 4.596286e-01 0.7701856936
[42,] 1.930921e-01 3.861842e-01 0.8069079113
[43,] 1.603434e-01 3.206867e-01 0.8396566260
[44,] 1.315820e-01 2.631641e-01 0.8684179699
[45,] 1.067013e-01 2.134027e-01 0.8932986741
[46,] 8.549625e-02 1.709925e-01 0.9145037535
[47,] 7.299560e-02 1.459912e-01 0.9270043950
[48,] 3.817635e-01 7.635271e-01 0.6182364638
[49,] 3.367784e-01 6.735569e-01 0.6632215538
[50,] 8.377203e-01 3.245593e-01 0.1622796610
[51,] 8.075549e-01 3.848901e-01 0.1924450683
[52,] 7.866907e-01 4.266185e-01 0.2133092685
[53,] 7.515559e-01 4.968882e-01 0.2484441089
[54,] 7.136358e-01 5.727284e-01 0.2863642083
[55,] 6.732546e-01 6.534909e-01 0.3267454290
[56,] 9.287495e-01 1.425010e-01 0.0712504960
[57,] 9.191824e-01 1.616352e-01 0.0808175965
[58,] 9.008129e-01 1.983742e-01 0.0991871137
[59,] 8.795982e-01 2.408036e-01 0.1204018146
[60,] 8.648030e-01 2.703941e-01 0.1351970261
[61,] 8.387385e-01 3.225229e-01 0.1612614641
[62,] 8.096414e-01 3.807172e-01 0.1903585763
[63,] 9.748591e-01 5.028185e-02 0.0251409246
[64,] 9.675371e-01 6.492577e-02 0.0324628868
[65,] 9.585499e-01 8.290012e-02 0.0414500611
[66,] 9.476575e-01 1.046851e-01 0.0523425491
[67,] 9.346210e-01 1.307579e-01 0.0653789640
[68,] 9.192135e-01 1.615731e-01 0.0807865449
[69,] 9.012300e-01 1.975400e-01 0.0987700020
[70,] 8.805004e-01 2.389992e-01 0.1194995968
[71,] 8.569009e-01 2.861982e-01 0.1430991105
[72,] 8.407765e-01 3.184471e-01 0.1592235289
[73,] 8.123883e-01 3.752235e-01 0.1876117419
[74,] 7.810962e-01 4.378077e-01 0.2189038461
[75,] 9.784853e-01 4.302934e-02 0.0215146724
[76,] 9.746516e-01 5.069682e-02 0.0253484088
[77,] 9.673669e-01 6.526614e-02 0.0326330699
[78,] 9.584423e-01 8.311543e-02 0.0415577146
[79,] 9.476425e-01 1.047149e-01 0.0523574725
[80,] 9.992597e-01 1.480669e-03 0.0007403344
[81,] 9.989117e-01 2.176605e-03 0.0010883026
[82,] 9.984188e-01 3.162386e-03 0.0015811928
[83,] 9.977294e-01 4.541208e-03 0.0022706038
[84,] 9.970534e-01 5.893215e-03 0.0029466075
[85,] 9.958504e-01 8.299163e-03 0.0041495817
[86,] 9.942240e-01 1.155208e-02 0.0057760409
[87,] 9.920529e-01 1.589430e-02 0.0079471479
[88,] 9.898970e-01 2.020605e-02 0.0101030229
[89,] 9.863714e-01 2.725716e-02 0.0136285819
[90,] 9.818265e-01 3.634692e-02 0.0181734607
[91,] 9.772613e-01 4.547741e-02 0.0227387035
[92,] 9.702775e-01 5.944493e-02 0.0297224632
[93,] 9.631742e-01 7.365170e-02 0.0368258499
[94,] 9.528236e-01 9.435285e-02 0.0471764246
[95,] 9.402459e-01 1.195082e-01 0.0597540797
[96,] 9.251640e-01 1.496721e-01 0.0748360444
[97,] 9.073192e-01 1.853616e-01 0.0926807830
[98,] 8.864874e-01 2.270252e-01 0.1135125943
[99,] 8.624947e-01 2.750105e-01 0.1375052667
[100,] 8.352342e-01 3.295317e-01 0.1647658381
[101,] 8.085509e-01 3.828982e-01 0.1914490788
[102,] 7.749771e-01 4.500459e-01 0.2250229346
[103,] 7.382770e-01 5.234460e-01 0.2617230092
[104,] 7.025465e-01 5.949069e-01 0.2974534687
[105,] 6.604507e-01 6.790986e-01 0.3395492858
[106,] 6.162337e-01 7.675325e-01 0.3837662645
[107,] 5.735678e-01 8.528644e-01 0.4264321919
[108,] 5.291306e-01 9.417389e-01 0.4708694397
[109,] 4.814017e-01 9.628035e-01 0.5185982617
[110,] 4.356582e-01 8.713163e-01 0.5643418263
[111,] 3.887244e-01 7.774489e-01 0.6112755547
[112,] 3.433455e-01 6.866910e-01 0.6566544759
[113,] 3.001208e-01 6.002416e-01 0.6998791885
[114,] 2.595613e-01 5.191227e-01 0.7404386696
[115,] 2.220714e-01 4.441428e-01 0.7779285974
[116,] 1.879375e-01 3.758750e-01 0.8120625041
[117,] 1.573247e-01 3.146494e-01 0.8426753123
[118,] 1.302805e-01 2.605610e-01 0.8697195064
[119,] 1.054156e-01 2.108312e-01 0.8945844078
[120,] 8.509034e-02 1.701807e-01 0.9149096617
[121,] 6.794630e-02 1.358926e-01 0.9320537043
[122,] 5.231617e-02 1.046323e-01 0.9476838302
[123,] 4.065459e-02 8.130917e-02 0.9593454142
[124,] 3.127500e-02 6.255000e-02 0.9687250004
[125,] 2.384245e-02 4.768491e-02 0.9761575459
[126,] 1.803805e-02 3.607610e-02 0.9819619518
[127,] 1.356918e-02 2.713835e-02 0.9864308225
[128,] 1.017607e-02 2.035215e-02 0.9898239256
[129,] 7.635046e-03 1.527009e-02 0.9923649545
[130,] 5.759036e-03 1.151807e-02 0.9942409641
[131,] 4.396310e-03 8.792619e-03 0.9956036904
[132,] 3.428174e-03 6.856348e-03 0.9965718261
[133,] 2.766940e-03 5.533880e-03 0.9972330598
[134,] 1.656775e-03 3.313549e-03 0.9983432254
[135,] 9.534765e-04 1.906953e-03 0.9990465235
[136,] 7.728636e-04 1.545727e-03 0.9992271364
[137,] 1.642012e-02 3.284023e-02 0.9835798826
[138,] 9.881408e-03 1.976282e-02 0.9901185916
[139,] 7.284576e-03 1.456915e-02 0.9927154244
[140,] 5.654119e-03 1.130824e-02 0.9943458807
[141,] 4.859629e-03 9.719257e-03 0.9951403714
[142,] 2.381662e-03 4.763324e-03 0.9976183382
[143,] 1.057286e-03 2.114573e-03 0.9989427137
[144,] 4.147927e-04 8.295855e-04 0.9995852073
[145,] 3.384572e-04 6.769145e-04 0.9996615428
> postscript(file="/var/fisher/rcomp/tmp/1k7hl1356081648.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2mafn1356081648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/347hi1356081648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4x3bi1356081648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5jr7i1356081648.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
-0.15000000 -0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158
7 8 9 10 11 12
-0.05263158 -0.15000000 -0.05263158 -0.05263158 -0.15000000 -0.05263158
13 14 15 16 17 18
-0.05263158 -0.15000000 -0.05263158 -0.15000000 0.85000000 -0.15000000
19 20 21 22 23 24
-0.05263158 0.85000000 -0.05263158 -0.05263158 -0.05263158 -0.05263158
25 26 27 28 29 30
-0.15000000 -0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158
31 32 33 34 35 36
-0.05263158 -0.05263158 -0.05263158 -0.15000000 -0.05263158 -0.05263158
37 38 39 40 41 42
-0.15000000 -0.05263158 -0.05263158 -0.15000000 0.94736842 -0.05263158
43 44 45 46 47 48
-0.05263158 -0.15000000 -0.05263158 -0.05263158 -0.05263158 -0.05263158
49 50 51 52 53 54
-0.05263158 -0.05263158 -0.15000000 0.85000000 -0.05263158 0.94736842
55 56 57 58 59 60
-0.05263158 -0.15000000 -0.05263158 -0.05263158 -0.05263158 0.85000000
61 62 63 64 65 66
-0.15000000 -0.05263158 -0.05263158 -0.15000000 -0.05263158 -0.05263158
67 68 69 70 71 72
0.85000000 -0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158
73 74 75 76 77 78
-0.05263158 -0.05263158 -0.05263158 -0.15000000 -0.05263158 -0.05263158
79 80 81 82 83 84
0.85000000 -0.15000000 -0.05263158 -0.05263158 -0.05263158 0.94736842
85 86 87 88 89 90
-0.05263158 -0.05263158 -0.05263158 -0.15000000 -0.05263158 -0.05263158
91 92 93 94 95 96
-0.05263158 -0.15000000 -0.05263158 -0.05263158 -0.15000000 -0.05263158
97 98 99 100 101 102
-0.15000000 -0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158
103 104 105 106 107 108
-0.05263158 -0.05263158 -0.15000000 -0.05263158 -0.05263158 -0.15000000
109 110 111 112 113 114
-0.05263158 -0.05263158 -0.15000000 -0.15000000 -0.05263158 -0.15000000
115 116 117 118 119 120
-0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158
121 122 123 124 125 126
-0.05263158 -0.05263158 -0.15000000 -0.05263158 -0.05263158 -0.15000000
127 128 129 130 131 132
-0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158
133 134 135 136 137 138
-0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.15000000
139 140 141 142 143 144
-0.15000000 -0.05263158 0.94736842 -0.15000000 -0.05263158 -0.05263158
145 146 147 148 149 150
-0.05263158 -0.15000000 -0.15000000 -0.15000000 -0.05263158 -0.05263158
151 152 153 154
-0.05263158 0.94736842 0.94736842 -0.05263158
> postscript(file="/var/fisher/rcomp/tmp/6q31u1356081648.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 -0.15000000 NA
1 -0.05263158 -0.15000000
2 -0.05263158 -0.05263158
3 -0.05263158 -0.05263158
4 -0.05263158 -0.05263158
5 -0.05263158 -0.05263158
6 -0.05263158 -0.05263158
7 -0.15000000 -0.05263158
8 -0.05263158 -0.15000000
9 -0.05263158 -0.05263158
10 -0.15000000 -0.05263158
11 -0.05263158 -0.15000000
12 -0.05263158 -0.05263158
13 -0.15000000 -0.05263158
14 -0.05263158 -0.15000000
15 -0.15000000 -0.05263158
16 0.85000000 -0.15000000
17 -0.15000000 0.85000000
18 -0.05263158 -0.15000000
19 0.85000000 -0.05263158
20 -0.05263158 0.85000000
21 -0.05263158 -0.05263158
22 -0.05263158 -0.05263158
23 -0.05263158 -0.05263158
24 -0.15000000 -0.05263158
25 -0.05263158 -0.15000000
26 -0.05263158 -0.05263158
27 -0.05263158 -0.05263158
28 -0.05263158 -0.05263158
29 -0.05263158 -0.05263158
30 -0.05263158 -0.05263158
31 -0.05263158 -0.05263158
32 -0.05263158 -0.05263158
33 -0.15000000 -0.05263158
34 -0.05263158 -0.15000000
35 -0.05263158 -0.05263158
36 -0.15000000 -0.05263158
37 -0.05263158 -0.15000000
38 -0.05263158 -0.05263158
39 -0.15000000 -0.05263158
40 0.94736842 -0.15000000
41 -0.05263158 0.94736842
42 -0.05263158 -0.05263158
43 -0.15000000 -0.05263158
44 -0.05263158 -0.15000000
45 -0.05263158 -0.05263158
46 -0.05263158 -0.05263158
47 -0.05263158 -0.05263158
48 -0.05263158 -0.05263158
49 -0.05263158 -0.05263158
50 -0.15000000 -0.05263158
51 0.85000000 -0.15000000
52 -0.05263158 0.85000000
53 0.94736842 -0.05263158
54 -0.05263158 0.94736842
55 -0.15000000 -0.05263158
56 -0.05263158 -0.15000000
57 -0.05263158 -0.05263158
58 -0.05263158 -0.05263158
59 0.85000000 -0.05263158
60 -0.15000000 0.85000000
61 -0.05263158 -0.15000000
62 -0.05263158 -0.05263158
63 -0.15000000 -0.05263158
64 -0.05263158 -0.15000000
65 -0.05263158 -0.05263158
66 0.85000000 -0.05263158
67 -0.05263158 0.85000000
68 -0.05263158 -0.05263158
69 -0.05263158 -0.05263158
70 -0.05263158 -0.05263158
71 -0.05263158 -0.05263158
72 -0.05263158 -0.05263158
73 -0.05263158 -0.05263158
74 -0.05263158 -0.05263158
75 -0.15000000 -0.05263158
76 -0.05263158 -0.15000000
77 -0.05263158 -0.05263158
78 0.85000000 -0.05263158
79 -0.15000000 0.85000000
80 -0.05263158 -0.15000000
81 -0.05263158 -0.05263158
82 -0.05263158 -0.05263158
83 0.94736842 -0.05263158
84 -0.05263158 0.94736842
85 -0.05263158 -0.05263158
86 -0.05263158 -0.05263158
87 -0.15000000 -0.05263158
88 -0.05263158 -0.15000000
89 -0.05263158 -0.05263158
90 -0.05263158 -0.05263158
91 -0.15000000 -0.05263158
92 -0.05263158 -0.15000000
93 -0.05263158 -0.05263158
94 -0.15000000 -0.05263158
95 -0.05263158 -0.15000000
96 -0.15000000 -0.05263158
97 -0.05263158 -0.15000000
98 -0.05263158 -0.05263158
99 -0.05263158 -0.05263158
100 -0.05263158 -0.05263158
101 -0.05263158 -0.05263158
102 -0.05263158 -0.05263158
103 -0.05263158 -0.05263158
104 -0.15000000 -0.05263158
105 -0.05263158 -0.15000000
106 -0.05263158 -0.05263158
107 -0.15000000 -0.05263158
108 -0.05263158 -0.15000000
109 -0.05263158 -0.05263158
110 -0.15000000 -0.05263158
111 -0.15000000 -0.15000000
112 -0.05263158 -0.15000000
113 -0.15000000 -0.05263158
114 -0.05263158 -0.15000000
115 -0.05263158 -0.05263158
116 -0.05263158 -0.05263158
117 -0.05263158 -0.05263158
118 -0.05263158 -0.05263158
119 -0.05263158 -0.05263158
120 -0.05263158 -0.05263158
121 -0.05263158 -0.05263158
122 -0.15000000 -0.05263158
123 -0.05263158 -0.15000000
124 -0.05263158 -0.05263158
125 -0.15000000 -0.05263158
126 -0.05263158 -0.15000000
127 -0.05263158 -0.05263158
128 -0.05263158 -0.05263158
129 -0.05263158 -0.05263158
130 -0.05263158 -0.05263158
131 -0.05263158 -0.05263158
132 -0.05263158 -0.05263158
133 -0.05263158 -0.05263158
134 -0.05263158 -0.05263158
135 -0.05263158 -0.05263158
136 -0.05263158 -0.05263158
137 -0.15000000 -0.05263158
138 -0.15000000 -0.15000000
139 -0.05263158 -0.15000000
140 0.94736842 -0.05263158
141 -0.15000000 0.94736842
142 -0.05263158 -0.15000000
143 -0.05263158 -0.05263158
144 -0.05263158 -0.05263158
145 -0.15000000 -0.05263158
146 -0.15000000 -0.15000000
147 -0.15000000 -0.15000000
148 -0.05263158 -0.15000000
149 -0.05263158 -0.05263158
150 -0.05263158 -0.05263158
151 0.94736842 -0.05263158
152 0.94736842 0.94736842
153 -0.05263158 0.94736842
154 NA -0.05263158
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.05263158 -0.15000000
[2,] -0.05263158 -0.05263158
[3,] -0.05263158 -0.05263158
[4,] -0.05263158 -0.05263158
[5,] -0.05263158 -0.05263158
[6,] -0.05263158 -0.05263158
[7,] -0.15000000 -0.05263158
[8,] -0.05263158 -0.15000000
[9,] -0.05263158 -0.05263158
[10,] -0.15000000 -0.05263158
[11,] -0.05263158 -0.15000000
[12,] -0.05263158 -0.05263158
[13,] -0.15000000 -0.05263158
[14,] -0.05263158 -0.15000000
[15,] -0.15000000 -0.05263158
[16,] 0.85000000 -0.15000000
[17,] -0.15000000 0.85000000
[18,] -0.05263158 -0.15000000
[19,] 0.85000000 -0.05263158
[20,] -0.05263158 0.85000000
[21,] -0.05263158 -0.05263158
[22,] -0.05263158 -0.05263158
[23,] -0.05263158 -0.05263158
[24,] -0.15000000 -0.05263158
[25,] -0.05263158 -0.15000000
[26,] -0.05263158 -0.05263158
[27,] -0.05263158 -0.05263158
[28,] -0.05263158 -0.05263158
[29,] -0.05263158 -0.05263158
[30,] -0.05263158 -0.05263158
[31,] -0.05263158 -0.05263158
[32,] -0.05263158 -0.05263158
[33,] -0.15000000 -0.05263158
[34,] -0.05263158 -0.15000000
[35,] -0.05263158 -0.05263158
[36,] -0.15000000 -0.05263158
[37,] -0.05263158 -0.15000000
[38,] -0.05263158 -0.05263158
[39,] -0.15000000 -0.05263158
[40,] 0.94736842 -0.15000000
[41,] -0.05263158 0.94736842
[42,] -0.05263158 -0.05263158
[43,] -0.15000000 -0.05263158
[44,] -0.05263158 -0.15000000
[45,] -0.05263158 -0.05263158
[46,] -0.05263158 -0.05263158
[47,] -0.05263158 -0.05263158
[48,] -0.05263158 -0.05263158
[49,] -0.05263158 -0.05263158
[50,] -0.15000000 -0.05263158
[51,] 0.85000000 -0.15000000
[52,] -0.05263158 0.85000000
[53,] 0.94736842 -0.05263158
[54,] -0.05263158 0.94736842
[55,] -0.15000000 -0.05263158
[56,] -0.05263158 -0.15000000
[57,] -0.05263158 -0.05263158
[58,] -0.05263158 -0.05263158
[59,] 0.85000000 -0.05263158
[60,] -0.15000000 0.85000000
[61,] -0.05263158 -0.15000000
[62,] -0.05263158 -0.05263158
[63,] -0.15000000 -0.05263158
[64,] -0.05263158 -0.15000000
[65,] -0.05263158 -0.05263158
[66,] 0.85000000 -0.05263158
[67,] -0.05263158 0.85000000
[68,] -0.05263158 -0.05263158
[69,] -0.05263158 -0.05263158
[70,] -0.05263158 -0.05263158
[71,] -0.05263158 -0.05263158
[72,] -0.05263158 -0.05263158
[73,] -0.05263158 -0.05263158
[74,] -0.05263158 -0.05263158
[75,] -0.15000000 -0.05263158
[76,] -0.05263158 -0.15000000
[77,] -0.05263158 -0.05263158
[78,] 0.85000000 -0.05263158
[79,] -0.15000000 0.85000000
[80,] -0.05263158 -0.15000000
[81,] -0.05263158 -0.05263158
[82,] -0.05263158 -0.05263158
[83,] 0.94736842 -0.05263158
[84,] -0.05263158 0.94736842
[85,] -0.05263158 -0.05263158
[86,] -0.05263158 -0.05263158
[87,] -0.15000000 -0.05263158
[88,] -0.05263158 -0.15000000
[89,] -0.05263158 -0.05263158
[90,] -0.05263158 -0.05263158
[91,] -0.15000000 -0.05263158
[92,] -0.05263158 -0.15000000
[93,] -0.05263158 -0.05263158
[94,] -0.15000000 -0.05263158
[95,] -0.05263158 -0.15000000
[96,] -0.15000000 -0.05263158
[97,] -0.05263158 -0.15000000
[98,] -0.05263158 -0.05263158
[99,] -0.05263158 -0.05263158
[100,] -0.05263158 -0.05263158
[101,] -0.05263158 -0.05263158
[102,] -0.05263158 -0.05263158
[103,] -0.05263158 -0.05263158
[104,] -0.15000000 -0.05263158
[105,] -0.05263158 -0.15000000
[106,] -0.05263158 -0.05263158
[107,] -0.15000000 -0.05263158
[108,] -0.05263158 -0.15000000
[109,] -0.05263158 -0.05263158
[110,] -0.15000000 -0.05263158
[111,] -0.15000000 -0.15000000
[112,] -0.05263158 -0.15000000
[113,] -0.15000000 -0.05263158
[114,] -0.05263158 -0.15000000
[115,] -0.05263158 -0.05263158
[116,] -0.05263158 -0.05263158
[117,] -0.05263158 -0.05263158
[118,] -0.05263158 -0.05263158
[119,] -0.05263158 -0.05263158
[120,] -0.05263158 -0.05263158
[121,] -0.05263158 -0.05263158
[122,] -0.15000000 -0.05263158
[123,] -0.05263158 -0.15000000
[124,] -0.05263158 -0.05263158
[125,] -0.15000000 -0.05263158
[126,] -0.05263158 -0.15000000
[127,] -0.05263158 -0.05263158
[128,] -0.05263158 -0.05263158
[129,] -0.05263158 -0.05263158
[130,] -0.05263158 -0.05263158
[131,] -0.05263158 -0.05263158
[132,] -0.05263158 -0.05263158
[133,] -0.05263158 -0.05263158
[134,] -0.05263158 -0.05263158
[135,] -0.05263158 -0.05263158
[136,] -0.05263158 -0.05263158
[137,] -0.15000000 -0.05263158
[138,] -0.15000000 -0.15000000
[139,] -0.05263158 -0.15000000
[140,] 0.94736842 -0.05263158
[141,] -0.15000000 0.94736842
[142,] -0.05263158 -0.15000000
[143,] -0.05263158 -0.05263158
[144,] -0.05263158 -0.05263158
[145,] -0.15000000 -0.05263158
[146,] -0.15000000 -0.15000000
[147,] -0.15000000 -0.15000000
[148,] -0.05263158 -0.15000000
[149,] -0.05263158 -0.05263158
[150,] -0.05263158 -0.05263158
[151,] 0.94736842 -0.05263158
[152,] 0.94736842 0.94736842
[153,] -0.05263158 0.94736842
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.05263158 -0.15000000
2 -0.05263158 -0.05263158
3 -0.05263158 -0.05263158
4 -0.05263158 -0.05263158
5 -0.05263158 -0.05263158
6 -0.05263158 -0.05263158
7 -0.15000000 -0.05263158
8 -0.05263158 -0.15000000
9 -0.05263158 -0.05263158
10 -0.15000000 -0.05263158
11 -0.05263158 -0.15000000
12 -0.05263158 -0.05263158
13 -0.15000000 -0.05263158
14 -0.05263158 -0.15000000
15 -0.15000000 -0.05263158
16 0.85000000 -0.15000000
17 -0.15000000 0.85000000
18 -0.05263158 -0.15000000
19 0.85000000 -0.05263158
20 -0.05263158 0.85000000
21 -0.05263158 -0.05263158
22 -0.05263158 -0.05263158
23 -0.05263158 -0.05263158
24 -0.15000000 -0.05263158
25 -0.05263158 -0.15000000
26 -0.05263158 -0.05263158
27 -0.05263158 -0.05263158
28 -0.05263158 -0.05263158
29 -0.05263158 -0.05263158
30 -0.05263158 -0.05263158
31 -0.05263158 -0.05263158
32 -0.05263158 -0.05263158
33 -0.15000000 -0.05263158
34 -0.05263158 -0.15000000
35 -0.05263158 -0.05263158
36 -0.15000000 -0.05263158
37 -0.05263158 -0.15000000
38 -0.05263158 -0.05263158
39 -0.15000000 -0.05263158
40 0.94736842 -0.15000000
41 -0.05263158 0.94736842
42 -0.05263158 -0.05263158
43 -0.15000000 -0.05263158
44 -0.05263158 -0.15000000
45 -0.05263158 -0.05263158
46 -0.05263158 -0.05263158
47 -0.05263158 -0.05263158
48 -0.05263158 -0.05263158
49 -0.05263158 -0.05263158
50 -0.15000000 -0.05263158
51 0.85000000 -0.15000000
52 -0.05263158 0.85000000
53 0.94736842 -0.05263158
54 -0.05263158 0.94736842
55 -0.15000000 -0.05263158
56 -0.05263158 -0.15000000
57 -0.05263158 -0.05263158
58 -0.05263158 -0.05263158
59 0.85000000 -0.05263158
60 -0.15000000 0.85000000
61 -0.05263158 -0.15000000
62 -0.05263158 -0.05263158
63 -0.15000000 -0.05263158
64 -0.05263158 -0.15000000
65 -0.05263158 -0.05263158
66 0.85000000 -0.05263158
67 -0.05263158 0.85000000
68 -0.05263158 -0.05263158
69 -0.05263158 -0.05263158
70 -0.05263158 -0.05263158
71 -0.05263158 -0.05263158
72 -0.05263158 -0.05263158
73 -0.05263158 -0.05263158
74 -0.05263158 -0.05263158
75 -0.15000000 -0.05263158
76 -0.05263158 -0.15000000
77 -0.05263158 -0.05263158
78 0.85000000 -0.05263158
79 -0.15000000 0.85000000
80 -0.05263158 -0.15000000
81 -0.05263158 -0.05263158
82 -0.05263158 -0.05263158
83 0.94736842 -0.05263158
84 -0.05263158 0.94736842
85 -0.05263158 -0.05263158
86 -0.05263158 -0.05263158
87 -0.15000000 -0.05263158
88 -0.05263158 -0.15000000
89 -0.05263158 -0.05263158
90 -0.05263158 -0.05263158
91 -0.15000000 -0.05263158
92 -0.05263158 -0.15000000
93 -0.05263158 -0.05263158
94 -0.15000000 -0.05263158
95 -0.05263158 -0.15000000
96 -0.15000000 -0.05263158
97 -0.05263158 -0.15000000
98 -0.05263158 -0.05263158
99 -0.05263158 -0.05263158
100 -0.05263158 -0.05263158
101 -0.05263158 -0.05263158
102 -0.05263158 -0.05263158
103 -0.05263158 -0.05263158
104 -0.15000000 -0.05263158
105 -0.05263158 -0.15000000
106 -0.05263158 -0.05263158
107 -0.15000000 -0.05263158
108 -0.05263158 -0.15000000
109 -0.05263158 -0.05263158
110 -0.15000000 -0.05263158
111 -0.15000000 -0.15000000
112 -0.05263158 -0.15000000
113 -0.15000000 -0.05263158
114 -0.05263158 -0.15000000
115 -0.05263158 -0.05263158
116 -0.05263158 -0.05263158
117 -0.05263158 -0.05263158
118 -0.05263158 -0.05263158
119 -0.05263158 -0.05263158
120 -0.05263158 -0.05263158
121 -0.05263158 -0.05263158
122 -0.15000000 -0.05263158
123 -0.05263158 -0.15000000
124 -0.05263158 -0.05263158
125 -0.15000000 -0.05263158
126 -0.05263158 -0.15000000
127 -0.05263158 -0.05263158
128 -0.05263158 -0.05263158
129 -0.05263158 -0.05263158
130 -0.05263158 -0.05263158
131 -0.05263158 -0.05263158
132 -0.05263158 -0.05263158
133 -0.05263158 -0.05263158
134 -0.05263158 -0.05263158
135 -0.05263158 -0.05263158
136 -0.05263158 -0.05263158
137 -0.15000000 -0.05263158
138 -0.15000000 -0.15000000
139 -0.05263158 -0.15000000
140 0.94736842 -0.05263158
141 -0.15000000 0.94736842
142 -0.05263158 -0.15000000
143 -0.05263158 -0.05263158
144 -0.05263158 -0.05263158
145 -0.15000000 -0.05263158
146 -0.15000000 -0.15000000
147 -0.15000000 -0.15000000
148 -0.05263158 -0.15000000
149 -0.05263158 -0.05263158
150 -0.05263158 -0.05263158
151 0.94736842 -0.05263158
152 0.94736842 0.94736842
153 -0.05263158 0.94736842
> 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/7wvlb1356081648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8exgz1356081648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9kmlx1356081648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10iamf1356081648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11c8dt1356081648.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/12hr1d1356081648.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/13oehp1356081648.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/14fkd21356081648.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/154o801356081648.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/167h1q1356081648.tab")
+ }
>
> try(system("convert tmp/1k7hl1356081648.ps tmp/1k7hl1356081648.png",intern=TRUE))
character(0)
> try(system("convert tmp/2mafn1356081648.ps tmp/2mafn1356081648.png",intern=TRUE))
character(0)
> try(system("convert tmp/347hi1356081648.ps tmp/347hi1356081648.png",intern=TRUE))
character(0)
> try(system("convert tmp/4x3bi1356081648.ps tmp/4x3bi1356081648.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jr7i1356081648.ps tmp/5jr7i1356081648.png",intern=TRUE))
character(0)
> try(system("convert tmp/6q31u1356081648.ps tmp/6q31u1356081648.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wvlb1356081648.ps tmp/7wvlb1356081648.png",intern=TRUE))
character(0)
> try(system("convert tmp/8exgz1356081648.ps tmp/8exgz1356081648.png",intern=TRUE))
character(0)
> try(system("convert tmp/9kmlx1356081648.ps tmp/9kmlx1356081648.png",intern=TRUE))
character(0)
> try(system("convert tmp/10iamf1356081648.ps tmp/10iamf1356081648.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.712 1.820 9.555