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(2,0,1,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,1,0,0,2,0,0,1,0,1,1,0,0,2,0,0,1,0,0,1,0,0,2,0,0,1,0,1,2,0,1,2,0,0,2,0,0,1,0,1,2,0,1,1,0,0,1,0,1,1,0,1,1,0,1,2,0,1,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,1,0,0,1,0,0,1,0,0,2,0,1,1,0,0,1,0,0,2,0,0,1,0,1,1,0,1,2,0,0,1,0,1,1,0,1,1,0,1,2,0,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,1,1,0,0,2,0,0,2,0,0,1,0,1,1,0,0,1,0,0,2,0,1,1,0,1,1,0,1,1,0,1,2,0,1,2,0,1,1,0,0,1,0,0,2,0,1,1,0,0,1,0,0,2,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,1,2,0,1,1,0,1,1,0,1,2,0,1,2,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0),dim=c(3,154),dimnames=list(c('T40','T20','Outcome'),1:154))
> y <- array(NA,dim=c(3,154),dimnames=list(c('T40','T20','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 = '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
T40 T20 Outcome
1 2 0 1
2 1 0 0
3 1 0 0
4 1 0 0
5 1 0 0
6 1 0 1
7 1 0 0
8 2 0 0
9 1 0 1
10 1 0 0
11 2 0 0
12 1 0 0
13 1 0 0
14 2 0 0
15 1 0 1
16 2 0 1
17 2 0 0
18 2 0 0
19 1 0 1
20 2 0 1
21 1 0 0
22 1 0 1
23 1 0 1
24 1 0 1
25 2 0 1
26 1 0 0
27 1 0 1
28 1 0 0
29 1 0 1
30 1 0 0
31 1 0 0
32 1 0 0
33 1 0 0
34 2 0 1
35 1 0 0
36 1 0 0
37 2 0 0
38 1 0 1
39 1 0 1
40 2 0 0
41 1 0 1
42 1 0 1
43 1 0 1
44 2 0 0
45 1 0 0
46 1 0 1
47 1 0 0
48 1 0 1
49 1 0 1
50 1 0 0
51 2 0 0
52 2 0 0
53 1 0 1
54 1 0 0
55 1 0 0
56 2 0 1
57 1 0 1
58 1 0 1
59 1 0 1
60 2 0 1
61 2 0 1
62 1 0 0
63 1 0 0
64 2 0 1
65 1 0 0
66 1 0 0
67 2 0 0
68 1 0 0
69 1 0 1
70 1 0 0
71 1 0 0
72 1 0 1
73 1 0 1
74 1 0 0
75 1 0 1
76 2 0 1
77 1 0 1
78 1 0 1
79 2 0 1
80 2 0 0
81 1 0 0
82 1 0 1
83 1 0 0
84 1 0 0
85 1 0 1
86 1 0 0
87 0 0 1
88 0 1 1
89 0 0 0
90 0 0 1
91 0 0 0
92 0 1 0
93 0 0 0
94 0 0 0
95 0 1 0
96 0 0 1
97 0 1 0
98 0 0 0
99 0 0 0
100 0 0 1
101 0 0 1
102 0 0 0
103 0 0 0
104 0 0 0
105 0 1 0
106 0 0 0
107 0 0 0
108 0 1 0
109 0 0 0
110 0 0 0
111 0 1 0
112 0 1 0
113 0 0 0
114 0 1 0
115 0 0 0
116 0 0 0
117 0 0 1
118 0 0 0
119 0 0 0
120 0 0 1
121 0 0 0
122 0 0 0
123 0 1 0
124 0 0 1
125 0 0 1
126 0 1 0
127 0 0 0
128 0 0 1
129 0 0 0
130 0 0 1
131 0 0 0
132 0 0 1
133 0 0 0
134 0 0 0
135 0 0 0
136 0 0 0
137 0 0 1
138 0 1 1
139 0 1 0
140 0 0 0
141 0 0 1
142 0 1 1
143 0 0 0
144 0 0 1
145 0 0 0
146 0 1 1
147 0 1 0
148 0 1 0
149 0 0 0
150 0 0 1
151 0 0 1
152 0 0 0
153 0 0 0
154 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) T20 Outcome
0.7309 -0.7675 0.1555
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.8864 -0.7309 0.1136 0.2691 1.2691
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.73094 0.07338 9.962 < 2e-16 ***
T20 -0.76752 0.17296 -4.438 1.75e-05 ***
Outcome 0.15545 0.11082 1.403 0.163
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6681 on 151 degrees of freedom
Multiple R-squared: 0.1343, Adjusted R-squared: 0.1228
F-statistic: 11.71 on 2 and 151 DF, p-value: 1.875e-05
> 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,] 0.223214428 4.464289e-01 7.767856e-01
[2,] 0.105529096 2.110582e-01 8.944709e-01
[3,] 0.296438126 5.928763e-01 7.035619e-01
[4,] 0.234082765 4.681655e-01 7.659172e-01
[5,] 0.152154389 3.043088e-01 8.478456e-01
[6,] 0.231255379 4.625108e-01 7.687446e-01
[7,] 0.166474809 3.329496e-01 8.335252e-01
[8,] 0.115469841 2.309397e-01 8.845302e-01
[9,] 0.165053979 3.301080e-01 8.349460e-01
[10,] 0.123575178 2.471504e-01 8.764248e-01
[11,] 0.146676711 2.933534e-01 8.533233e-01
[12,] 0.181122683 3.622454e-01 8.188773e-01
[13,] 0.207989501 4.159790e-01 7.920105e-01
[14,] 0.172757589 3.455152e-01 8.272424e-01
[15,] 0.190162006 3.803240e-01 8.098380e-01
[16,] 0.160696990 3.213940e-01 8.393030e-01
[17,] 0.136466292 2.729326e-01 8.635337e-01
[18,] 0.111973535 2.239471e-01 8.880265e-01
[19,] 0.089260669 1.785213e-01 9.107393e-01
[20,] 0.109236382 2.184728e-01 8.907636e-01
[21,] 0.090306052 1.806121e-01 9.096939e-01
[22,] 0.073581354 1.471627e-01 9.264186e-01
[23,] 0.059395308 1.187906e-01 9.406047e-01
[24,] 0.046979729 9.395946e-02 9.530203e-01
[25,] 0.037034742 7.406948e-02 9.629653e-01
[26,] 0.028728159 5.745632e-02 9.712718e-01
[27,] 0.021947347 4.389469e-02 9.780527e-01
[28,] 0.016525655 3.305131e-02 9.834743e-01
[29,] 0.023181965 4.636393e-02 9.768180e-01
[30,] 0.017585483 3.517097e-02 9.824145e-01
[31,] 0.013175046 2.635009e-02 9.868250e-01
[32,] 0.023712315 4.742463e-02 9.762877e-01
[33,] 0.019017822 3.803564e-02 9.809822e-01
[34,] 0.014953437 2.990687e-02 9.850466e-01
[35,] 0.026184703 5.236941e-02 9.738153e-01
[36,] 0.020726460 4.145292e-02 9.792735e-01
[37,] 0.016153644 3.230729e-02 9.838464e-01
[38,] 0.012407353 2.481471e-02 9.875926e-01
[39,] 0.022262641 4.452528e-02 9.777374e-01
[40,] 0.018599431 3.719886e-02 9.814006e-01
[41,] 0.014417439 2.883488e-02 9.855826e-01
[42,] 0.011912777 2.382555e-02 9.880872e-01
[43,] 0.009083099 1.816620e-02 9.909169e-01
[44,] 0.006845612 1.369122e-02 9.931544e-01
[45,] 0.005575270 1.115054e-02 9.944247e-01
[46,] 0.012089969 2.417994e-02 9.879100e-01
[47,] 0.025330811 5.066162e-02 9.746692e-01
[48,] 0.020074771 4.014954e-02 9.799252e-01
[49,] 0.017926790 3.585358e-02 9.820732e-01
[50,] 0.016001063 3.200213e-02 9.839989e-01
[51,] 0.032144325 6.428865e-02 9.678557e-01
[52,] 0.026431079 5.286216e-02 9.735689e-01
[53,] 0.021599246 4.319849e-02 9.784008e-01
[54,] 0.017554307 3.510861e-02 9.824457e-01
[55,] 0.038528557 7.705711e-02 9.614714e-01
[56,] 0.080277819 1.605556e-01 9.197222e-01
[57,] 0.076484566 1.529691e-01 9.235154e-01
[58,] 0.073191545 1.463831e-01 9.268085e-01
[59,] 0.151300073 3.026001e-01 8.486999e-01
[60,] 0.148776886 2.975538e-01 8.512231e-01
[61,] 0.147303322 2.946066e-01 8.526967e-01
[62,] 0.331736905 6.634738e-01 6.682631e-01
[63,] 0.342638042 6.852761e-01 6.573620e-01
[64,] 0.336191479 6.723830e-01 6.638085e-01
[65,] 0.351524438 7.030489e-01 6.484756e-01
[66,] 0.370989991 7.419800e-01 6.290100e-01
[67,] 0.369150216 7.383004e-01 6.308498e-01
[68,] 0.370088173 7.401763e-01 6.299118e-01
[69,] 0.397922064 7.958441e-01 6.020779e-01
[70,] 0.404359213 8.087184e-01 5.956408e-01
[71,] 0.735799851 5.284003e-01 2.642001e-01
[72,] 0.764688780 4.706224e-01 2.353112e-01
[73,] 0.798605141 4.027897e-01 2.013949e-01
[74,] 0.987405867 2.518827e-02 1.259413e-02
[75,] 0.999991761 1.647810e-05 8.239051e-06
[76,] 0.999999406 1.187193e-06 5.935963e-07
[77,] 0.999999985 3.033203e-08 1.516601e-08
[78,] 1.000000000 1.396847e-10 6.984237e-11
[79,] 1.000000000 1.850268e-14 9.251341e-15
[80,] 1.000000000 3.492072e-22 1.746036e-22
[81,] 1.000000000 0.000000e+00 0.000000e+00
[82,] 1.000000000 0.000000e+00 0.000000e+00
[83,] 1.000000000 0.000000e+00 0.000000e+00
[84,] 1.000000000 0.000000e+00 0.000000e+00
[85,] 1.000000000 0.000000e+00 0.000000e+00
[86,] 1.000000000 0.000000e+00 0.000000e+00
[87,] 1.000000000 0.000000e+00 0.000000e+00
[88,] 1.000000000 0.000000e+00 0.000000e+00
[89,] 1.000000000 0.000000e+00 0.000000e+00
[90,] 1.000000000 0.000000e+00 0.000000e+00
[91,] 1.000000000 0.000000e+00 0.000000e+00
[92,] 1.000000000 0.000000e+00 0.000000e+00
[93,] 1.000000000 0.000000e+00 0.000000e+00
[94,] 1.000000000 0.000000e+00 0.000000e+00
[95,] 1.000000000 0.000000e+00 0.000000e+00
[96,] 1.000000000 0.000000e+00 0.000000e+00
[97,] 1.000000000 0.000000e+00 0.000000e+00
[98,] 1.000000000 0.000000e+00 0.000000e+00
[99,] 1.000000000 0.000000e+00 0.000000e+00
[100,] 1.000000000 0.000000e+00 0.000000e+00
[101,] 1.000000000 0.000000e+00 0.000000e+00
[102,] 1.000000000 0.000000e+00 0.000000e+00
[103,] 1.000000000 0.000000e+00 0.000000e+00
[104,] 1.000000000 0.000000e+00 0.000000e+00
[105,] 1.000000000 0.000000e+00 0.000000e+00
[106,] 1.000000000 0.000000e+00 0.000000e+00
[107,] 1.000000000 0.000000e+00 0.000000e+00
[108,] 1.000000000 0.000000e+00 0.000000e+00
[109,] 1.000000000 0.000000e+00 0.000000e+00
[110,] 1.000000000 0.000000e+00 0.000000e+00
[111,] 1.000000000 0.000000e+00 0.000000e+00
[112,] 1.000000000 0.000000e+00 0.000000e+00
[113,] 1.000000000 0.000000e+00 0.000000e+00
[114,] 1.000000000 0.000000e+00 0.000000e+00
[115,] 1.000000000 0.000000e+00 0.000000e+00
[116,] 1.000000000 0.000000e+00 0.000000e+00
[117,] 1.000000000 0.000000e+00 0.000000e+00
[118,] 1.000000000 0.000000e+00 0.000000e+00
[119,] 1.000000000 0.000000e+00 0.000000e+00
[120,] 1.000000000 0.000000e+00 0.000000e+00
[121,] 1.000000000 0.000000e+00 0.000000e+00
[122,] 1.000000000 0.000000e+00 0.000000e+00
[123,] 1.000000000 0.000000e+00 0.000000e+00
[124,] 1.000000000 0.000000e+00 0.000000e+00
[125,] 1.000000000 0.000000e+00 0.000000e+00
[126,] 1.000000000 0.000000e+00 0.000000e+00
[127,] 1.000000000 0.000000e+00 0.000000e+00
[128,] 1.000000000 0.000000e+00 0.000000e+00
[129,] 1.000000000 0.000000e+00 0.000000e+00
[130,] 1.000000000 0.000000e+00 0.000000e+00
[131,] 1.000000000 0.000000e+00 0.000000e+00
[132,] 1.000000000 0.000000e+00 0.000000e+00
[133,] 1.000000000 0.000000e+00 0.000000e+00
[134,] 1.000000000 0.000000e+00 0.000000e+00
[135,] 1.000000000 0.000000e+00 0.000000e+00
[136,] 1.000000000 0.000000e+00 0.000000e+00
[137,] 1.000000000 0.000000e+00 0.000000e+00
[138,] 1.000000000 0.000000e+00 0.000000e+00
[139,] 1.000000000 0.000000e+00 0.000000e+00
[140,] 1.000000000 0.000000e+00 0.000000e+00
[141,] 1.000000000 0.000000e+00 0.000000e+00
[142,] 1.000000000 0.000000e+00 0.000000e+00
[143,] 1.000000000 0.000000e+00 0.000000e+00
> postscript(file="/var/fisher/rcomp/tmp/1c7r91355956097.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2erzu1355956097.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3t2wk1355956097.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4qigw1355956097.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5desc1355956097.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
1.11360522 0.26905628 0.26905628 0.26905628 0.26905628 0.11360522
7 8 9 10 11 12
0.26905628 1.26905628 0.11360522 0.26905628 1.26905628 0.26905628
13 14 15 16 17 18
0.26905628 1.26905628 0.11360522 1.11360522 1.26905628 1.26905628
19 20 21 22 23 24
0.11360522 1.11360522 0.26905628 0.11360522 0.11360522 0.11360522
25 26 27 28 29 30
1.11360522 0.26905628 0.11360522 0.26905628 0.11360522 0.26905628
31 32 33 34 35 36
0.26905628 0.26905628 0.26905628 1.11360522 0.26905628 0.26905628
37 38 39 40 41 42
1.26905628 0.11360522 0.11360522 1.26905628 0.11360522 0.11360522
43 44 45 46 47 48
0.11360522 1.26905628 0.26905628 0.11360522 0.26905628 0.11360522
49 50 51 52 53 54
0.11360522 0.26905628 1.26905628 1.26905628 0.11360522 0.26905628
55 56 57 58 59 60
0.26905628 1.11360522 0.11360522 0.11360522 0.11360522 1.11360522
61 62 63 64 65 66
1.11360522 0.26905628 0.26905628 1.11360522 0.26905628 0.26905628
67 68 69 70 71 72
1.26905628 0.26905628 0.11360522 0.26905628 0.26905628 0.11360522
73 74 75 76 77 78
0.11360522 0.26905628 0.11360522 1.11360522 0.11360522 0.11360522
79 80 81 82 83 84
1.11360522 1.26905628 0.26905628 0.11360522 0.26905628 0.26905628
85 86 87 88 89 90
0.11360522 0.26905628 -0.88639478 -0.11887434 -0.73094372 -0.88639478
91 92 93 94 95 96
-0.73094372 0.03657672 -0.73094372 -0.73094372 0.03657672 -0.88639478
97 98 99 100 101 102
0.03657672 -0.73094372 -0.73094372 -0.88639478 -0.88639478 -0.73094372
103 104 105 106 107 108
-0.73094372 -0.73094372 0.03657672 -0.73094372 -0.73094372 0.03657672
109 110 111 112 113 114
-0.73094372 -0.73094372 0.03657672 0.03657672 -0.73094372 0.03657672
115 116 117 118 119 120
-0.73094372 -0.73094372 -0.88639478 -0.73094372 -0.73094372 -0.88639478
121 122 123 124 125 126
-0.73094372 -0.73094372 0.03657672 -0.88639478 -0.88639478 0.03657672
127 128 129 130 131 132
-0.73094372 -0.88639478 -0.73094372 -0.88639478 -0.73094372 -0.88639478
133 134 135 136 137 138
-0.73094372 -0.73094372 -0.73094372 -0.73094372 -0.88639478 -0.11887434
139 140 141 142 143 144
0.03657672 -0.73094372 -0.88639478 -0.11887434 -0.73094372 -0.88639478
145 146 147 148 149 150
-0.73094372 -0.11887434 0.03657672 0.03657672 -0.73094372 -0.88639478
151 152 153 154
-0.88639478 -0.73094372 -0.73094372 -0.73094372
> postscript(file="/var/fisher/rcomp/tmp/6af781355956097.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 1.11360522 NA
1 0.26905628 1.11360522
2 0.26905628 0.26905628
3 0.26905628 0.26905628
4 0.26905628 0.26905628
5 0.11360522 0.26905628
6 0.26905628 0.11360522
7 1.26905628 0.26905628
8 0.11360522 1.26905628
9 0.26905628 0.11360522
10 1.26905628 0.26905628
11 0.26905628 1.26905628
12 0.26905628 0.26905628
13 1.26905628 0.26905628
14 0.11360522 1.26905628
15 1.11360522 0.11360522
16 1.26905628 1.11360522
17 1.26905628 1.26905628
18 0.11360522 1.26905628
19 1.11360522 0.11360522
20 0.26905628 1.11360522
21 0.11360522 0.26905628
22 0.11360522 0.11360522
23 0.11360522 0.11360522
24 1.11360522 0.11360522
25 0.26905628 1.11360522
26 0.11360522 0.26905628
27 0.26905628 0.11360522
28 0.11360522 0.26905628
29 0.26905628 0.11360522
30 0.26905628 0.26905628
31 0.26905628 0.26905628
32 0.26905628 0.26905628
33 1.11360522 0.26905628
34 0.26905628 1.11360522
35 0.26905628 0.26905628
36 1.26905628 0.26905628
37 0.11360522 1.26905628
38 0.11360522 0.11360522
39 1.26905628 0.11360522
40 0.11360522 1.26905628
41 0.11360522 0.11360522
42 0.11360522 0.11360522
43 1.26905628 0.11360522
44 0.26905628 1.26905628
45 0.11360522 0.26905628
46 0.26905628 0.11360522
47 0.11360522 0.26905628
48 0.11360522 0.11360522
49 0.26905628 0.11360522
50 1.26905628 0.26905628
51 1.26905628 1.26905628
52 0.11360522 1.26905628
53 0.26905628 0.11360522
54 0.26905628 0.26905628
55 1.11360522 0.26905628
56 0.11360522 1.11360522
57 0.11360522 0.11360522
58 0.11360522 0.11360522
59 1.11360522 0.11360522
60 1.11360522 1.11360522
61 0.26905628 1.11360522
62 0.26905628 0.26905628
63 1.11360522 0.26905628
64 0.26905628 1.11360522
65 0.26905628 0.26905628
66 1.26905628 0.26905628
67 0.26905628 1.26905628
68 0.11360522 0.26905628
69 0.26905628 0.11360522
70 0.26905628 0.26905628
71 0.11360522 0.26905628
72 0.11360522 0.11360522
73 0.26905628 0.11360522
74 0.11360522 0.26905628
75 1.11360522 0.11360522
76 0.11360522 1.11360522
77 0.11360522 0.11360522
78 1.11360522 0.11360522
79 1.26905628 1.11360522
80 0.26905628 1.26905628
81 0.11360522 0.26905628
82 0.26905628 0.11360522
83 0.26905628 0.26905628
84 0.11360522 0.26905628
85 0.26905628 0.11360522
86 -0.88639478 0.26905628
87 -0.11887434 -0.88639478
88 -0.73094372 -0.11887434
89 -0.88639478 -0.73094372
90 -0.73094372 -0.88639478
91 0.03657672 -0.73094372
92 -0.73094372 0.03657672
93 -0.73094372 -0.73094372
94 0.03657672 -0.73094372
95 -0.88639478 0.03657672
96 0.03657672 -0.88639478
97 -0.73094372 0.03657672
98 -0.73094372 -0.73094372
99 -0.88639478 -0.73094372
100 -0.88639478 -0.88639478
101 -0.73094372 -0.88639478
102 -0.73094372 -0.73094372
103 -0.73094372 -0.73094372
104 0.03657672 -0.73094372
105 -0.73094372 0.03657672
106 -0.73094372 -0.73094372
107 0.03657672 -0.73094372
108 -0.73094372 0.03657672
109 -0.73094372 -0.73094372
110 0.03657672 -0.73094372
111 0.03657672 0.03657672
112 -0.73094372 0.03657672
113 0.03657672 -0.73094372
114 -0.73094372 0.03657672
115 -0.73094372 -0.73094372
116 -0.88639478 -0.73094372
117 -0.73094372 -0.88639478
118 -0.73094372 -0.73094372
119 -0.88639478 -0.73094372
120 -0.73094372 -0.88639478
121 -0.73094372 -0.73094372
122 0.03657672 -0.73094372
123 -0.88639478 0.03657672
124 -0.88639478 -0.88639478
125 0.03657672 -0.88639478
126 -0.73094372 0.03657672
127 -0.88639478 -0.73094372
128 -0.73094372 -0.88639478
129 -0.88639478 -0.73094372
130 -0.73094372 -0.88639478
131 -0.88639478 -0.73094372
132 -0.73094372 -0.88639478
133 -0.73094372 -0.73094372
134 -0.73094372 -0.73094372
135 -0.73094372 -0.73094372
136 -0.88639478 -0.73094372
137 -0.11887434 -0.88639478
138 0.03657672 -0.11887434
139 -0.73094372 0.03657672
140 -0.88639478 -0.73094372
141 -0.11887434 -0.88639478
142 -0.73094372 -0.11887434
143 -0.88639478 -0.73094372
144 -0.73094372 -0.88639478
145 -0.11887434 -0.73094372
146 0.03657672 -0.11887434
147 0.03657672 0.03657672
148 -0.73094372 0.03657672
149 -0.88639478 -0.73094372
150 -0.88639478 -0.88639478
151 -0.73094372 -0.88639478
152 -0.73094372 -0.73094372
153 -0.73094372 -0.73094372
154 NA -0.73094372
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.26905628 1.11360522
[2,] 0.26905628 0.26905628
[3,] 0.26905628 0.26905628
[4,] 0.26905628 0.26905628
[5,] 0.11360522 0.26905628
[6,] 0.26905628 0.11360522
[7,] 1.26905628 0.26905628
[8,] 0.11360522 1.26905628
[9,] 0.26905628 0.11360522
[10,] 1.26905628 0.26905628
[11,] 0.26905628 1.26905628
[12,] 0.26905628 0.26905628
[13,] 1.26905628 0.26905628
[14,] 0.11360522 1.26905628
[15,] 1.11360522 0.11360522
[16,] 1.26905628 1.11360522
[17,] 1.26905628 1.26905628
[18,] 0.11360522 1.26905628
[19,] 1.11360522 0.11360522
[20,] 0.26905628 1.11360522
[21,] 0.11360522 0.26905628
[22,] 0.11360522 0.11360522
[23,] 0.11360522 0.11360522
[24,] 1.11360522 0.11360522
[25,] 0.26905628 1.11360522
[26,] 0.11360522 0.26905628
[27,] 0.26905628 0.11360522
[28,] 0.11360522 0.26905628
[29,] 0.26905628 0.11360522
[30,] 0.26905628 0.26905628
[31,] 0.26905628 0.26905628
[32,] 0.26905628 0.26905628
[33,] 1.11360522 0.26905628
[34,] 0.26905628 1.11360522
[35,] 0.26905628 0.26905628
[36,] 1.26905628 0.26905628
[37,] 0.11360522 1.26905628
[38,] 0.11360522 0.11360522
[39,] 1.26905628 0.11360522
[40,] 0.11360522 1.26905628
[41,] 0.11360522 0.11360522
[42,] 0.11360522 0.11360522
[43,] 1.26905628 0.11360522
[44,] 0.26905628 1.26905628
[45,] 0.11360522 0.26905628
[46,] 0.26905628 0.11360522
[47,] 0.11360522 0.26905628
[48,] 0.11360522 0.11360522
[49,] 0.26905628 0.11360522
[50,] 1.26905628 0.26905628
[51,] 1.26905628 1.26905628
[52,] 0.11360522 1.26905628
[53,] 0.26905628 0.11360522
[54,] 0.26905628 0.26905628
[55,] 1.11360522 0.26905628
[56,] 0.11360522 1.11360522
[57,] 0.11360522 0.11360522
[58,] 0.11360522 0.11360522
[59,] 1.11360522 0.11360522
[60,] 1.11360522 1.11360522
[61,] 0.26905628 1.11360522
[62,] 0.26905628 0.26905628
[63,] 1.11360522 0.26905628
[64,] 0.26905628 1.11360522
[65,] 0.26905628 0.26905628
[66,] 1.26905628 0.26905628
[67,] 0.26905628 1.26905628
[68,] 0.11360522 0.26905628
[69,] 0.26905628 0.11360522
[70,] 0.26905628 0.26905628
[71,] 0.11360522 0.26905628
[72,] 0.11360522 0.11360522
[73,] 0.26905628 0.11360522
[74,] 0.11360522 0.26905628
[75,] 1.11360522 0.11360522
[76,] 0.11360522 1.11360522
[77,] 0.11360522 0.11360522
[78,] 1.11360522 0.11360522
[79,] 1.26905628 1.11360522
[80,] 0.26905628 1.26905628
[81,] 0.11360522 0.26905628
[82,] 0.26905628 0.11360522
[83,] 0.26905628 0.26905628
[84,] 0.11360522 0.26905628
[85,] 0.26905628 0.11360522
[86,] -0.88639478 0.26905628
[87,] -0.11887434 -0.88639478
[88,] -0.73094372 -0.11887434
[89,] -0.88639478 -0.73094372
[90,] -0.73094372 -0.88639478
[91,] 0.03657672 -0.73094372
[92,] -0.73094372 0.03657672
[93,] -0.73094372 -0.73094372
[94,] 0.03657672 -0.73094372
[95,] -0.88639478 0.03657672
[96,] 0.03657672 -0.88639478
[97,] -0.73094372 0.03657672
[98,] -0.73094372 -0.73094372
[99,] -0.88639478 -0.73094372
[100,] -0.88639478 -0.88639478
[101,] -0.73094372 -0.88639478
[102,] -0.73094372 -0.73094372
[103,] -0.73094372 -0.73094372
[104,] 0.03657672 -0.73094372
[105,] -0.73094372 0.03657672
[106,] -0.73094372 -0.73094372
[107,] 0.03657672 -0.73094372
[108,] -0.73094372 0.03657672
[109,] -0.73094372 -0.73094372
[110,] 0.03657672 -0.73094372
[111,] 0.03657672 0.03657672
[112,] -0.73094372 0.03657672
[113,] 0.03657672 -0.73094372
[114,] -0.73094372 0.03657672
[115,] -0.73094372 -0.73094372
[116,] -0.88639478 -0.73094372
[117,] -0.73094372 -0.88639478
[118,] -0.73094372 -0.73094372
[119,] -0.88639478 -0.73094372
[120,] -0.73094372 -0.88639478
[121,] -0.73094372 -0.73094372
[122,] 0.03657672 -0.73094372
[123,] -0.88639478 0.03657672
[124,] -0.88639478 -0.88639478
[125,] 0.03657672 -0.88639478
[126,] -0.73094372 0.03657672
[127,] -0.88639478 -0.73094372
[128,] -0.73094372 -0.88639478
[129,] -0.88639478 -0.73094372
[130,] -0.73094372 -0.88639478
[131,] -0.88639478 -0.73094372
[132,] -0.73094372 -0.88639478
[133,] -0.73094372 -0.73094372
[134,] -0.73094372 -0.73094372
[135,] -0.73094372 -0.73094372
[136,] -0.88639478 -0.73094372
[137,] -0.11887434 -0.88639478
[138,] 0.03657672 -0.11887434
[139,] -0.73094372 0.03657672
[140,] -0.88639478 -0.73094372
[141,] -0.11887434 -0.88639478
[142,] -0.73094372 -0.11887434
[143,] -0.88639478 -0.73094372
[144,] -0.73094372 -0.88639478
[145,] -0.11887434 -0.73094372
[146,] 0.03657672 -0.11887434
[147,] 0.03657672 0.03657672
[148,] -0.73094372 0.03657672
[149,] -0.88639478 -0.73094372
[150,] -0.88639478 -0.88639478
[151,] -0.73094372 -0.88639478
[152,] -0.73094372 -0.73094372
[153,] -0.73094372 -0.73094372
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.26905628 1.11360522
2 0.26905628 0.26905628
3 0.26905628 0.26905628
4 0.26905628 0.26905628
5 0.11360522 0.26905628
6 0.26905628 0.11360522
7 1.26905628 0.26905628
8 0.11360522 1.26905628
9 0.26905628 0.11360522
10 1.26905628 0.26905628
11 0.26905628 1.26905628
12 0.26905628 0.26905628
13 1.26905628 0.26905628
14 0.11360522 1.26905628
15 1.11360522 0.11360522
16 1.26905628 1.11360522
17 1.26905628 1.26905628
18 0.11360522 1.26905628
19 1.11360522 0.11360522
20 0.26905628 1.11360522
21 0.11360522 0.26905628
22 0.11360522 0.11360522
23 0.11360522 0.11360522
24 1.11360522 0.11360522
25 0.26905628 1.11360522
26 0.11360522 0.26905628
27 0.26905628 0.11360522
28 0.11360522 0.26905628
29 0.26905628 0.11360522
30 0.26905628 0.26905628
31 0.26905628 0.26905628
32 0.26905628 0.26905628
33 1.11360522 0.26905628
34 0.26905628 1.11360522
35 0.26905628 0.26905628
36 1.26905628 0.26905628
37 0.11360522 1.26905628
38 0.11360522 0.11360522
39 1.26905628 0.11360522
40 0.11360522 1.26905628
41 0.11360522 0.11360522
42 0.11360522 0.11360522
43 1.26905628 0.11360522
44 0.26905628 1.26905628
45 0.11360522 0.26905628
46 0.26905628 0.11360522
47 0.11360522 0.26905628
48 0.11360522 0.11360522
49 0.26905628 0.11360522
50 1.26905628 0.26905628
51 1.26905628 1.26905628
52 0.11360522 1.26905628
53 0.26905628 0.11360522
54 0.26905628 0.26905628
55 1.11360522 0.26905628
56 0.11360522 1.11360522
57 0.11360522 0.11360522
58 0.11360522 0.11360522
59 1.11360522 0.11360522
60 1.11360522 1.11360522
61 0.26905628 1.11360522
62 0.26905628 0.26905628
63 1.11360522 0.26905628
64 0.26905628 1.11360522
65 0.26905628 0.26905628
66 1.26905628 0.26905628
67 0.26905628 1.26905628
68 0.11360522 0.26905628
69 0.26905628 0.11360522
70 0.26905628 0.26905628
71 0.11360522 0.26905628
72 0.11360522 0.11360522
73 0.26905628 0.11360522
74 0.11360522 0.26905628
75 1.11360522 0.11360522
76 0.11360522 1.11360522
77 0.11360522 0.11360522
78 1.11360522 0.11360522
79 1.26905628 1.11360522
80 0.26905628 1.26905628
81 0.11360522 0.26905628
82 0.26905628 0.11360522
83 0.26905628 0.26905628
84 0.11360522 0.26905628
85 0.26905628 0.11360522
86 -0.88639478 0.26905628
87 -0.11887434 -0.88639478
88 -0.73094372 -0.11887434
89 -0.88639478 -0.73094372
90 -0.73094372 -0.88639478
91 0.03657672 -0.73094372
92 -0.73094372 0.03657672
93 -0.73094372 -0.73094372
94 0.03657672 -0.73094372
95 -0.88639478 0.03657672
96 0.03657672 -0.88639478
97 -0.73094372 0.03657672
98 -0.73094372 -0.73094372
99 -0.88639478 -0.73094372
100 -0.88639478 -0.88639478
101 -0.73094372 -0.88639478
102 -0.73094372 -0.73094372
103 -0.73094372 -0.73094372
104 0.03657672 -0.73094372
105 -0.73094372 0.03657672
106 -0.73094372 -0.73094372
107 0.03657672 -0.73094372
108 -0.73094372 0.03657672
109 -0.73094372 -0.73094372
110 0.03657672 -0.73094372
111 0.03657672 0.03657672
112 -0.73094372 0.03657672
113 0.03657672 -0.73094372
114 -0.73094372 0.03657672
115 -0.73094372 -0.73094372
116 -0.88639478 -0.73094372
117 -0.73094372 -0.88639478
118 -0.73094372 -0.73094372
119 -0.88639478 -0.73094372
120 -0.73094372 -0.88639478
121 -0.73094372 -0.73094372
122 0.03657672 -0.73094372
123 -0.88639478 0.03657672
124 -0.88639478 -0.88639478
125 0.03657672 -0.88639478
126 -0.73094372 0.03657672
127 -0.88639478 -0.73094372
128 -0.73094372 -0.88639478
129 -0.88639478 -0.73094372
130 -0.73094372 -0.88639478
131 -0.88639478 -0.73094372
132 -0.73094372 -0.88639478
133 -0.73094372 -0.73094372
134 -0.73094372 -0.73094372
135 -0.73094372 -0.73094372
136 -0.88639478 -0.73094372
137 -0.11887434 -0.88639478
138 0.03657672 -0.11887434
139 -0.73094372 0.03657672
140 -0.88639478 -0.73094372
141 -0.11887434 -0.88639478
142 -0.73094372 -0.11887434
143 -0.88639478 -0.73094372
144 -0.73094372 -0.88639478
145 -0.11887434 -0.73094372
146 0.03657672 -0.11887434
147 0.03657672 0.03657672
148 -0.73094372 0.03657672
149 -0.88639478 -0.73094372
150 -0.88639478 -0.88639478
151 -0.73094372 -0.88639478
152 -0.73094372 -0.73094372
153 -0.73094372 -0.73094372
> 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/7003d1355956097.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8shmy1355956097.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9ywy61355956097.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10jksv1355956097.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wr4a1355956098.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/1269oy1355956098.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/135zc21355956098.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/14n9151355956098.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/154wv01355956098.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/168j1p1355956098.tab")
+ }
>
> try(system("convert tmp/1c7r91355956097.ps tmp/1c7r91355956097.png",intern=TRUE))
character(0)
> try(system("convert tmp/2erzu1355956097.ps tmp/2erzu1355956097.png",intern=TRUE))
character(0)
> try(system("convert tmp/3t2wk1355956097.ps tmp/3t2wk1355956097.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qigw1355956097.ps tmp/4qigw1355956097.png",intern=TRUE))
character(0)
> try(system("convert tmp/5desc1355956097.ps tmp/5desc1355956097.png",intern=TRUE))
character(0)
> try(system("convert tmp/6af781355956097.ps tmp/6af781355956097.png",intern=TRUE))
character(0)
> try(system("convert tmp/7003d1355956097.ps tmp/7003d1355956097.png",intern=TRUE))
character(0)
> try(system("convert tmp/8shmy1355956097.ps tmp/8shmy1355956097.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ywy61355956097.ps tmp/9ywy61355956097.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jksv1355956097.ps tmp/10jksv1355956097.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.318 1.681 9.012