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(2,0,1,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,1,0,0,2,0,0,1,0,1,1,0,0,2,0,0,1,0,0,1,0,0,2,0,0,1,0,1,2,0,1,2,0,0,2,0,0,1,0,1,2,0,1,1,0,0,1,0,1,1,0,1,1,0,1,2,0,1,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,1,0,0,1,0,0,1,0,0,2,0,1,1,0,0,1,0,0,2,0,0,1,0,1,1,0,1,2,0,0,1,0,1,1,0,1,1,0,1,2,0,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,1,1,0,0,2,0,0,2,0,0,1,0,1,1,0,0,1,0,0,2,0,1,1,0,1,1,0,1,1,0,1,2,0,1,2,0,1,1,0,0,1,0,0,2,0,1,1,0,0,1,0,0,2,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,1,2,0,1,1,0,1,1,0,1,2,0,1,2,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,1,1,1,2,1,1,1,0,1,1,1,1,1,0,1,2,0,1,1,0,1,1,0,1,2,0,1,1,1,1,2,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,0,1,1,0,1,1,0,1,2,0,1,1,0,1,1,0,1,2,0,1,1,0,1,1,0,1,2,0,1,2,0,1,1,0,1,2,0,1,1,0,1,1,0,1,1,1,1,1,0,1,1,0,1,1,1,1,1,0,1,1,0,1,2,0,1,1,1,1,1,1,1,2,0,1,1,0,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,0,1,1,0,1,1,0,1,1,0,1,1,1,1,2,1,1,2,0,1,1,0,1,1,1,1,2,1,1,1,0,1,1,1,1,1,0,1,2,1,1,2,0,1,2,0,1,1,0,1,1,1,1,1,1,1,1,0,1,1,0,1,1,0),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 1 1 1
88 1 2 1
89 1 1 0
90 1 1 1
91 1 1 0
92 1 2 0
93 1 1 0
94 1 1 0
95 1 2 0
96 1 1 1
97 1 2 0
98 1 1 0
99 1 1 0
100 1 1 1
101 1 1 1
102 1 1 0
103 1 1 0
104 1 1 0
105 1 2 0
106 1 1 0
107 1 1 0
108 1 2 0
109 1 1 0
110 1 1 0
111 1 2 0
112 1 2 0
113 1 1 0
114 1 2 0
115 1 1 0
116 1 1 0
117 1 1 1
118 1 1 0
119 1 1 0
120 1 1 1
121 1 1 0
122 1 1 0
123 1 2 0
124 1 1 1
125 1 1 1
126 1 2 0
127 1 1 0
128 1 1 1
129 1 1 0
130 1 1 1
131 1 1 0
132 1 1 1
133 1 1 0
134 1 1 0
135 1 1 0
136 1 1 0
137 1 1 1
138 1 2 1
139 1 2 0
140 1 1 0
141 1 1 1
142 1 2 1
143 1 1 0
144 1 1 1
145 1 1 0
146 1 2 1
147 1 2 0
148 1 2 0
149 1 1 0
150 1 1 1
151 1 1 1
152 1 1 0
153 1 1 0
154 1 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) T20 Outcome
1.24187 -0.17489 0.01014
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.25200 -0.24187 -0.07711 0.09778 0.75813
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.24187 0.04377 28.374 < 2e-16 ***
T20 -0.17489 0.04047 -4.321 2.8e-05 ***
Outcome 0.01014 0.05662 0.179 0.858
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3387 on 151 degrees of freedom
Multiple R-squared: 0.1145, Adjusted R-squared: 0.1027
F-statistic: 9.759 on 2 and 151 DF, p-value: 0.0001033
> 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,] 0.7745253 4.509493e-01 2.254747e-01
[2,] 0.6403767 7.192466e-01 3.596233e-01
[3,] 0.9606338 7.873238e-02 3.936619e-02
[4,] 0.9582724 8.345530e-02 4.172765e-02
[5,] 0.9345615 1.308769e-01 6.543846e-02
[6,] 0.9857849 2.843020e-02 1.421510e-02
[7,] 0.9793246 4.135079e-02 2.067540e-02
[8,] 0.9702775 5.944501e-02 2.972251e-02
[9,] 0.9921794 1.564113e-02 7.820565e-03
[10,] 0.9897424 2.051511e-02 1.025755e-02
[11,] 0.9963302 7.339589e-03 3.669794e-03
[12,] 0.9989240 2.151969e-03 1.075985e-03
[13,] 0.9996540 6.919146e-04 3.459573e-04
[14,] 0.9995868 8.264033e-04 4.132016e-04
[15,] 0.9998646 2.707030e-04 1.353515e-04
[16,] 0.9998376 3.247094e-04 1.623547e-04
[17,] 0.9998203 3.594063e-04 1.797031e-04
[18,] 0.9997825 4.349368e-04 2.174684e-04
[19,] 0.9997221 5.557928e-04 2.778964e-04
[20,] 0.9999285 1.430828e-04 7.154140e-05
[21,] 0.9999110 1.780435e-04 8.902176e-05
[22,] 0.9998915 2.169768e-04 1.084884e-04
[23,] 0.9998622 2.756814e-04 1.378407e-04
[24,] 0.9998271 3.457368e-04 1.728684e-04
[25,] 0.9997780 4.440312e-04 2.220156e-04
[26,] 0.9997113 5.774410e-04 2.887205e-04
[27,] 0.9996217 7.565649e-04 3.782824e-04
[28,] 0.9995026 9.947737e-04 4.973869e-04
[29,] 0.9998708 2.584802e-04 1.292401e-04
[30,] 0.9998259 3.481068e-04 1.740534e-04
[31,] 0.9997661 4.678954e-04 2.339477e-04
[32,] 0.9999586 8.286846e-05 4.143423e-05
[33,] 0.9999500 9.999759e-05 4.999880e-05
[34,] 0.9999382 1.236583e-04 6.182917e-05
[35,] 0.9999901 1.975409e-05 9.877043e-06
[36,] 0.9999872 2.551681e-05 1.275841e-05
[37,] 0.9999833 3.332182e-05 1.666091e-05
[38,] 0.9999781 4.377320e-05 2.188660e-05
[39,] 0.9999970 6.045617e-06 3.022809e-06
[40,] 0.9999960 7.988253e-06 3.994127e-06
[41,] 0.9999946 1.072564e-05 5.362820e-06
[42,] 0.9999929 1.414534e-05 7.072671e-06
[43,] 0.9999906 1.880679e-05 9.403396e-06
[44,] 0.9999876 2.487823e-05 1.243911e-05
[45,] 0.9999838 3.236573e-05 1.618286e-05
[46,] 0.9999981 3.706983e-06 1.853492e-06
[47,] 0.9999999 2.715283e-07 1.357642e-07
[48,] 0.9999998 3.750815e-07 1.875407e-07
[49,] 0.9999997 5.119408e-07 2.559704e-07
[50,] 0.9999996 7.017709e-07 3.508854e-07
[51,] 1.0000000 4.091088e-08 2.045544e-08
[52,] 1.0000000 5.700992e-08 2.850496e-08
[53,] 1.0000000 7.843674e-08 3.921837e-08
[54,] 0.9999999 1.061826e-07 5.309131e-08
[55,] 1.0000000 4.037836e-09 2.018918e-09
[56,] 1.0000000 6.525622e-11 3.262811e-11
[57,] 1.0000000 1.035938e-10 5.179688e-11
[58,] 1.0000000 1.640147e-10 8.200737e-11
[59,] 1.0000000 7.827694e-13 3.913847e-13
[60,] 1.0000000 1.343032e-12 6.715159e-13
[61,] 1.0000000 2.284847e-12 1.142424e-12
[62,] 1.0000000 1.095190e-15 5.475950e-16
[63,] 1.0000000 2.149528e-15 1.074764e-15
[64,] 1.0000000 3.872055e-15 1.936028e-15
[65,] 1.0000000 7.439873e-15 3.719937e-15
[66,] 1.0000000 1.410789e-14 7.053945e-15
[67,] 1.0000000 2.431466e-14 1.215733e-14
[68,] 1.0000000 4.092333e-14 2.046167e-14
[69,] 1.0000000 7.350211e-14 3.675106e-14
[70,] 1.0000000 1.172439e-13 5.862194e-14
[71,] 1.0000000 5.825233e-18 2.912616e-18
[72,] 1.0000000 1.166570e-17 5.832851e-18
[73,] 1.0000000 2.248929e-17 1.124465e-17
[74,] 1.0000000 9.763918e-26 4.881959e-26
[75,] 1.0000000 0.000000e+00 0.000000e+00
[76,] 1.0000000 0.000000e+00 0.000000e+00
[77,] 1.0000000 0.000000e+00 0.000000e+00
[78,] 1.0000000 0.000000e+00 0.000000e+00
[79,] 1.0000000 0.000000e+00 0.000000e+00
[80,] 1.0000000 0.000000e+00 0.000000e+00
[81,] 1.0000000 0.000000e+00 0.000000e+00
[82,] 1.0000000 0.000000e+00 0.000000e+00
[83,] 1.0000000 0.000000e+00 0.000000e+00
[84,] 1.0000000 0.000000e+00 0.000000e+00
[85,] 1.0000000 0.000000e+00 0.000000e+00
[86,] 1.0000000 0.000000e+00 0.000000e+00
[87,] 1.0000000 0.000000e+00 0.000000e+00
[88,] 1.0000000 0.000000e+00 0.000000e+00
[89,] 1.0000000 0.000000e+00 0.000000e+00
[90,] 1.0000000 0.000000e+00 0.000000e+00
[91,] 1.0000000 0.000000e+00 0.000000e+00
[92,] 1.0000000 0.000000e+00 0.000000e+00
[93,] 1.0000000 0.000000e+00 0.000000e+00
[94,] 1.0000000 0.000000e+00 0.000000e+00
[95,] 1.0000000 0.000000e+00 0.000000e+00
[96,] 1.0000000 0.000000e+00 0.000000e+00
[97,] 1.0000000 0.000000e+00 0.000000e+00
[98,] 1.0000000 0.000000e+00 0.000000e+00
[99,] 1.0000000 0.000000e+00 0.000000e+00
[100,] 1.0000000 0.000000e+00 0.000000e+00
[101,] 1.0000000 0.000000e+00 0.000000e+00
[102,] 1.0000000 0.000000e+00 0.000000e+00
[103,] 1.0000000 0.000000e+00 0.000000e+00
[104,] 1.0000000 0.000000e+00 0.000000e+00
[105,] 1.0000000 0.000000e+00 0.000000e+00
[106,] 1.0000000 0.000000e+00 0.000000e+00
[107,] 1.0000000 0.000000e+00 0.000000e+00
[108,] 1.0000000 0.000000e+00 0.000000e+00
[109,] 1.0000000 0.000000e+00 0.000000e+00
[110,] 1.0000000 0.000000e+00 0.000000e+00
[111,] 1.0000000 0.000000e+00 0.000000e+00
[112,] 1.0000000 0.000000e+00 0.000000e+00
[113,] 1.0000000 0.000000e+00 0.000000e+00
[114,] 1.0000000 0.000000e+00 0.000000e+00
[115,] 1.0000000 0.000000e+00 0.000000e+00
[116,] 1.0000000 0.000000e+00 0.000000e+00
[117,] 1.0000000 0.000000e+00 0.000000e+00
[118,] 1.0000000 0.000000e+00 0.000000e+00
[119,] 1.0000000 0.000000e+00 0.000000e+00
[120,] 1.0000000 0.000000e+00 0.000000e+00
[121,] 1.0000000 0.000000e+00 0.000000e+00
[122,] 1.0000000 0.000000e+00 0.000000e+00
[123,] 1.0000000 0.000000e+00 0.000000e+00
[124,] 1.0000000 0.000000e+00 0.000000e+00
[125,] 1.0000000 0.000000e+00 0.000000e+00
[126,] 1.0000000 1.853278e-305 9.266391e-306
[127,] 1.0000000 1.039865e-294 5.199325e-295
[128,] 1.0000000 1.208423e-311 6.042113e-312
[129,] 1.0000000 1.207330e-272 6.036650e-273
[130,] 1.0000000 7.759508e-245 3.879754e-245
[131,] 1.0000000 3.004620e-231 1.502310e-231
[132,] 1.0000000 2.529940e-230 1.264970e-230
[133,] 1.0000000 0.000000e+00 0.000000e+00
[134,] 1.0000000 4.390965e-183 2.195483e-183
[135,] 1.0000000 6.987169e-168 3.493585e-168
[136,] 1.0000000 7.555676e-175 3.777838e-175
[137,] 1.0000000 2.905993e-138 1.452997e-138
[138,] 1.0000000 8.745867e-132 4.372934e-132
[139,] 1.0000000 5.902230e-110 2.951115e-110
[140,] 1.0000000 4.528978e-95 2.264489e-95
[141,] 1.0000000 3.291792e-77 1.645896e-77
[142,] 1.0000000 1.017656e-129 5.088281e-130
[143,] 1.0000000 7.995009e-47 3.997505e-47
> postscript(file="/var/wessaorg/rcomp/tmp/12og21355994083.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2c4he1355994083.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3up241355994083.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4fq9u1355994083.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5t02h1355994083.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
0.74799553 -0.24186510 -0.24186510 -0.24186510 -0.24186510 -0.25200447
7 8 9 10 11 12
-0.24186510 0.75813490 -0.25200447 -0.24186510 0.75813490 -0.24186510
13 14 15 16 17 18
-0.24186510 0.75813490 -0.25200447 0.74799553 0.75813490 0.75813490
19 20 21 22 23 24
-0.25200447 0.74799553 -0.24186510 -0.25200447 -0.25200447 -0.25200447
25 26 27 28 29 30
0.74799553 -0.24186510 -0.25200447 -0.24186510 -0.25200447 -0.24186510
31 32 33 34 35 36
-0.24186510 -0.24186510 -0.24186510 0.74799553 -0.24186510 -0.24186510
37 38 39 40 41 42
0.75813490 -0.25200447 -0.25200447 0.75813490 -0.25200447 -0.25200447
43 44 45 46 47 48
-0.25200447 0.75813490 -0.24186510 -0.25200447 -0.24186510 -0.25200447
49 50 51 52 53 54
-0.25200447 -0.24186510 0.75813490 0.75813490 -0.25200447 -0.24186510
55 56 57 58 59 60
-0.24186510 0.74799553 -0.25200447 -0.25200447 -0.25200447 0.74799553
61 62 63 64 65 66
0.74799553 -0.24186510 -0.24186510 0.74799553 -0.24186510 -0.24186510
67 68 69 70 71 72
0.75813490 -0.24186510 -0.25200447 -0.24186510 -0.24186510 -0.25200447
73 74 75 76 77 78
-0.25200447 -0.24186510 -0.25200447 0.74799553 -0.25200447 -0.25200447
79 80 81 82 83 84
0.74799553 0.75813490 -0.24186510 -0.25200447 -0.24186510 -0.24186510
85 86 87 88 89 90
-0.25200447 -0.24186510 -0.07711356 0.09777734 -0.06697419 -0.07711356
91 92 93 94 95 96
-0.06697419 0.10791671 -0.06697419 -0.06697419 0.10791671 -0.07711356
97 98 99 100 101 102
0.10791671 -0.06697419 -0.06697419 -0.07711356 -0.07711356 -0.06697419
103 104 105 106 107 108
-0.06697419 -0.06697419 0.10791671 -0.06697419 -0.06697419 0.10791671
109 110 111 112 113 114
-0.06697419 -0.06697419 0.10791671 0.10791671 -0.06697419 0.10791671
115 116 117 118 119 120
-0.06697419 -0.06697419 -0.07711356 -0.06697419 -0.06697419 -0.07711356
121 122 123 124 125 126
-0.06697419 -0.06697419 0.10791671 -0.07711356 -0.07711356 0.10791671
127 128 129 130 131 132
-0.06697419 -0.07711356 -0.06697419 -0.07711356 -0.06697419 -0.07711356
133 134 135 136 137 138
-0.06697419 -0.06697419 -0.06697419 -0.06697419 -0.07711356 0.09777734
139 140 141 142 143 144
0.10791671 -0.06697419 -0.07711356 0.09777734 -0.06697419 -0.07711356
145 146 147 148 149 150
-0.06697419 0.09777734 0.10791671 0.10791671 -0.06697419 -0.07711356
151 152 153 154
-0.07711356 -0.06697419 -0.06697419 -0.06697419
> postscript(file="/var/wessaorg/rcomp/tmp/6va2n1355994083.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 0.74799553 NA
1 -0.24186510 0.74799553
2 -0.24186510 -0.24186510
3 -0.24186510 -0.24186510
4 -0.24186510 -0.24186510
5 -0.25200447 -0.24186510
6 -0.24186510 -0.25200447
7 0.75813490 -0.24186510
8 -0.25200447 0.75813490
9 -0.24186510 -0.25200447
10 0.75813490 -0.24186510
11 -0.24186510 0.75813490
12 -0.24186510 -0.24186510
13 0.75813490 -0.24186510
14 -0.25200447 0.75813490
15 0.74799553 -0.25200447
16 0.75813490 0.74799553
17 0.75813490 0.75813490
18 -0.25200447 0.75813490
19 0.74799553 -0.25200447
20 -0.24186510 0.74799553
21 -0.25200447 -0.24186510
22 -0.25200447 -0.25200447
23 -0.25200447 -0.25200447
24 0.74799553 -0.25200447
25 -0.24186510 0.74799553
26 -0.25200447 -0.24186510
27 -0.24186510 -0.25200447
28 -0.25200447 -0.24186510
29 -0.24186510 -0.25200447
30 -0.24186510 -0.24186510
31 -0.24186510 -0.24186510
32 -0.24186510 -0.24186510
33 0.74799553 -0.24186510
34 -0.24186510 0.74799553
35 -0.24186510 -0.24186510
36 0.75813490 -0.24186510
37 -0.25200447 0.75813490
38 -0.25200447 -0.25200447
39 0.75813490 -0.25200447
40 -0.25200447 0.75813490
41 -0.25200447 -0.25200447
42 -0.25200447 -0.25200447
43 0.75813490 -0.25200447
44 -0.24186510 0.75813490
45 -0.25200447 -0.24186510
46 -0.24186510 -0.25200447
47 -0.25200447 -0.24186510
48 -0.25200447 -0.25200447
49 -0.24186510 -0.25200447
50 0.75813490 -0.24186510
51 0.75813490 0.75813490
52 -0.25200447 0.75813490
53 -0.24186510 -0.25200447
54 -0.24186510 -0.24186510
55 0.74799553 -0.24186510
56 -0.25200447 0.74799553
57 -0.25200447 -0.25200447
58 -0.25200447 -0.25200447
59 0.74799553 -0.25200447
60 0.74799553 0.74799553
61 -0.24186510 0.74799553
62 -0.24186510 -0.24186510
63 0.74799553 -0.24186510
64 -0.24186510 0.74799553
65 -0.24186510 -0.24186510
66 0.75813490 -0.24186510
67 -0.24186510 0.75813490
68 -0.25200447 -0.24186510
69 -0.24186510 -0.25200447
70 -0.24186510 -0.24186510
71 -0.25200447 -0.24186510
72 -0.25200447 -0.25200447
73 -0.24186510 -0.25200447
74 -0.25200447 -0.24186510
75 0.74799553 -0.25200447
76 -0.25200447 0.74799553
77 -0.25200447 -0.25200447
78 0.74799553 -0.25200447
79 0.75813490 0.74799553
80 -0.24186510 0.75813490
81 -0.25200447 -0.24186510
82 -0.24186510 -0.25200447
83 -0.24186510 -0.24186510
84 -0.25200447 -0.24186510
85 -0.24186510 -0.25200447
86 -0.07711356 -0.24186510
87 0.09777734 -0.07711356
88 -0.06697419 0.09777734
89 -0.07711356 -0.06697419
90 -0.06697419 -0.07711356
91 0.10791671 -0.06697419
92 -0.06697419 0.10791671
93 -0.06697419 -0.06697419
94 0.10791671 -0.06697419
95 -0.07711356 0.10791671
96 0.10791671 -0.07711356
97 -0.06697419 0.10791671
98 -0.06697419 -0.06697419
99 -0.07711356 -0.06697419
100 -0.07711356 -0.07711356
101 -0.06697419 -0.07711356
102 -0.06697419 -0.06697419
103 -0.06697419 -0.06697419
104 0.10791671 -0.06697419
105 -0.06697419 0.10791671
106 -0.06697419 -0.06697419
107 0.10791671 -0.06697419
108 -0.06697419 0.10791671
109 -0.06697419 -0.06697419
110 0.10791671 -0.06697419
111 0.10791671 0.10791671
112 -0.06697419 0.10791671
113 0.10791671 -0.06697419
114 -0.06697419 0.10791671
115 -0.06697419 -0.06697419
116 -0.07711356 -0.06697419
117 -0.06697419 -0.07711356
118 -0.06697419 -0.06697419
119 -0.07711356 -0.06697419
120 -0.06697419 -0.07711356
121 -0.06697419 -0.06697419
122 0.10791671 -0.06697419
123 -0.07711356 0.10791671
124 -0.07711356 -0.07711356
125 0.10791671 -0.07711356
126 -0.06697419 0.10791671
127 -0.07711356 -0.06697419
128 -0.06697419 -0.07711356
129 -0.07711356 -0.06697419
130 -0.06697419 -0.07711356
131 -0.07711356 -0.06697419
132 -0.06697419 -0.07711356
133 -0.06697419 -0.06697419
134 -0.06697419 -0.06697419
135 -0.06697419 -0.06697419
136 -0.07711356 -0.06697419
137 0.09777734 -0.07711356
138 0.10791671 0.09777734
139 -0.06697419 0.10791671
140 -0.07711356 -0.06697419
141 0.09777734 -0.07711356
142 -0.06697419 0.09777734
143 -0.07711356 -0.06697419
144 -0.06697419 -0.07711356
145 0.09777734 -0.06697419
146 0.10791671 0.09777734
147 0.10791671 0.10791671
148 -0.06697419 0.10791671
149 -0.07711356 -0.06697419
150 -0.07711356 -0.07711356
151 -0.06697419 -0.07711356
152 -0.06697419 -0.06697419
153 -0.06697419 -0.06697419
154 NA -0.06697419
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.24186510 0.74799553
[2,] -0.24186510 -0.24186510
[3,] -0.24186510 -0.24186510
[4,] -0.24186510 -0.24186510
[5,] -0.25200447 -0.24186510
[6,] -0.24186510 -0.25200447
[7,] 0.75813490 -0.24186510
[8,] -0.25200447 0.75813490
[9,] -0.24186510 -0.25200447
[10,] 0.75813490 -0.24186510
[11,] -0.24186510 0.75813490
[12,] -0.24186510 -0.24186510
[13,] 0.75813490 -0.24186510
[14,] -0.25200447 0.75813490
[15,] 0.74799553 -0.25200447
[16,] 0.75813490 0.74799553
[17,] 0.75813490 0.75813490
[18,] -0.25200447 0.75813490
[19,] 0.74799553 -0.25200447
[20,] -0.24186510 0.74799553
[21,] -0.25200447 -0.24186510
[22,] -0.25200447 -0.25200447
[23,] -0.25200447 -0.25200447
[24,] 0.74799553 -0.25200447
[25,] -0.24186510 0.74799553
[26,] -0.25200447 -0.24186510
[27,] -0.24186510 -0.25200447
[28,] -0.25200447 -0.24186510
[29,] -0.24186510 -0.25200447
[30,] -0.24186510 -0.24186510
[31,] -0.24186510 -0.24186510
[32,] -0.24186510 -0.24186510
[33,] 0.74799553 -0.24186510
[34,] -0.24186510 0.74799553
[35,] -0.24186510 -0.24186510
[36,] 0.75813490 -0.24186510
[37,] -0.25200447 0.75813490
[38,] -0.25200447 -0.25200447
[39,] 0.75813490 -0.25200447
[40,] -0.25200447 0.75813490
[41,] -0.25200447 -0.25200447
[42,] -0.25200447 -0.25200447
[43,] 0.75813490 -0.25200447
[44,] -0.24186510 0.75813490
[45,] -0.25200447 -0.24186510
[46,] -0.24186510 -0.25200447
[47,] -0.25200447 -0.24186510
[48,] -0.25200447 -0.25200447
[49,] -0.24186510 -0.25200447
[50,] 0.75813490 -0.24186510
[51,] 0.75813490 0.75813490
[52,] -0.25200447 0.75813490
[53,] -0.24186510 -0.25200447
[54,] -0.24186510 -0.24186510
[55,] 0.74799553 -0.24186510
[56,] -0.25200447 0.74799553
[57,] -0.25200447 -0.25200447
[58,] -0.25200447 -0.25200447
[59,] 0.74799553 -0.25200447
[60,] 0.74799553 0.74799553
[61,] -0.24186510 0.74799553
[62,] -0.24186510 -0.24186510
[63,] 0.74799553 -0.24186510
[64,] -0.24186510 0.74799553
[65,] -0.24186510 -0.24186510
[66,] 0.75813490 -0.24186510
[67,] -0.24186510 0.75813490
[68,] -0.25200447 -0.24186510
[69,] -0.24186510 -0.25200447
[70,] -0.24186510 -0.24186510
[71,] -0.25200447 -0.24186510
[72,] -0.25200447 -0.25200447
[73,] -0.24186510 -0.25200447
[74,] -0.25200447 -0.24186510
[75,] 0.74799553 -0.25200447
[76,] -0.25200447 0.74799553
[77,] -0.25200447 -0.25200447
[78,] 0.74799553 -0.25200447
[79,] 0.75813490 0.74799553
[80,] -0.24186510 0.75813490
[81,] -0.25200447 -0.24186510
[82,] -0.24186510 -0.25200447
[83,] -0.24186510 -0.24186510
[84,] -0.25200447 -0.24186510
[85,] -0.24186510 -0.25200447
[86,] -0.07711356 -0.24186510
[87,] 0.09777734 -0.07711356
[88,] -0.06697419 0.09777734
[89,] -0.07711356 -0.06697419
[90,] -0.06697419 -0.07711356
[91,] 0.10791671 -0.06697419
[92,] -0.06697419 0.10791671
[93,] -0.06697419 -0.06697419
[94,] 0.10791671 -0.06697419
[95,] -0.07711356 0.10791671
[96,] 0.10791671 -0.07711356
[97,] -0.06697419 0.10791671
[98,] -0.06697419 -0.06697419
[99,] -0.07711356 -0.06697419
[100,] -0.07711356 -0.07711356
[101,] -0.06697419 -0.07711356
[102,] -0.06697419 -0.06697419
[103,] -0.06697419 -0.06697419
[104,] 0.10791671 -0.06697419
[105,] -0.06697419 0.10791671
[106,] -0.06697419 -0.06697419
[107,] 0.10791671 -0.06697419
[108,] -0.06697419 0.10791671
[109,] -0.06697419 -0.06697419
[110,] 0.10791671 -0.06697419
[111,] 0.10791671 0.10791671
[112,] -0.06697419 0.10791671
[113,] 0.10791671 -0.06697419
[114,] -0.06697419 0.10791671
[115,] -0.06697419 -0.06697419
[116,] -0.07711356 -0.06697419
[117,] -0.06697419 -0.07711356
[118,] -0.06697419 -0.06697419
[119,] -0.07711356 -0.06697419
[120,] -0.06697419 -0.07711356
[121,] -0.06697419 -0.06697419
[122,] 0.10791671 -0.06697419
[123,] -0.07711356 0.10791671
[124,] -0.07711356 -0.07711356
[125,] 0.10791671 -0.07711356
[126,] -0.06697419 0.10791671
[127,] -0.07711356 -0.06697419
[128,] -0.06697419 -0.07711356
[129,] -0.07711356 -0.06697419
[130,] -0.06697419 -0.07711356
[131,] -0.07711356 -0.06697419
[132,] -0.06697419 -0.07711356
[133,] -0.06697419 -0.06697419
[134,] -0.06697419 -0.06697419
[135,] -0.06697419 -0.06697419
[136,] -0.07711356 -0.06697419
[137,] 0.09777734 -0.07711356
[138,] 0.10791671 0.09777734
[139,] -0.06697419 0.10791671
[140,] -0.07711356 -0.06697419
[141,] 0.09777734 -0.07711356
[142,] -0.06697419 0.09777734
[143,] -0.07711356 -0.06697419
[144,] -0.06697419 -0.07711356
[145,] 0.09777734 -0.06697419
[146,] 0.10791671 0.09777734
[147,] 0.10791671 0.10791671
[148,] -0.06697419 0.10791671
[149,] -0.07711356 -0.06697419
[150,] -0.07711356 -0.07711356
[151,] -0.06697419 -0.07711356
[152,] -0.06697419 -0.06697419
[153,] -0.06697419 -0.06697419
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.24186510 0.74799553
2 -0.24186510 -0.24186510
3 -0.24186510 -0.24186510
4 -0.24186510 -0.24186510
5 -0.25200447 -0.24186510
6 -0.24186510 -0.25200447
7 0.75813490 -0.24186510
8 -0.25200447 0.75813490
9 -0.24186510 -0.25200447
10 0.75813490 -0.24186510
11 -0.24186510 0.75813490
12 -0.24186510 -0.24186510
13 0.75813490 -0.24186510
14 -0.25200447 0.75813490
15 0.74799553 -0.25200447
16 0.75813490 0.74799553
17 0.75813490 0.75813490
18 -0.25200447 0.75813490
19 0.74799553 -0.25200447
20 -0.24186510 0.74799553
21 -0.25200447 -0.24186510
22 -0.25200447 -0.25200447
23 -0.25200447 -0.25200447
24 0.74799553 -0.25200447
25 -0.24186510 0.74799553
26 -0.25200447 -0.24186510
27 -0.24186510 -0.25200447
28 -0.25200447 -0.24186510
29 -0.24186510 -0.25200447
30 -0.24186510 -0.24186510
31 -0.24186510 -0.24186510
32 -0.24186510 -0.24186510
33 0.74799553 -0.24186510
34 -0.24186510 0.74799553
35 -0.24186510 -0.24186510
36 0.75813490 -0.24186510
37 -0.25200447 0.75813490
38 -0.25200447 -0.25200447
39 0.75813490 -0.25200447
40 -0.25200447 0.75813490
41 -0.25200447 -0.25200447
42 -0.25200447 -0.25200447
43 0.75813490 -0.25200447
44 -0.24186510 0.75813490
45 -0.25200447 -0.24186510
46 -0.24186510 -0.25200447
47 -0.25200447 -0.24186510
48 -0.25200447 -0.25200447
49 -0.24186510 -0.25200447
50 0.75813490 -0.24186510
51 0.75813490 0.75813490
52 -0.25200447 0.75813490
53 -0.24186510 -0.25200447
54 -0.24186510 -0.24186510
55 0.74799553 -0.24186510
56 -0.25200447 0.74799553
57 -0.25200447 -0.25200447
58 -0.25200447 -0.25200447
59 0.74799553 -0.25200447
60 0.74799553 0.74799553
61 -0.24186510 0.74799553
62 -0.24186510 -0.24186510
63 0.74799553 -0.24186510
64 -0.24186510 0.74799553
65 -0.24186510 -0.24186510
66 0.75813490 -0.24186510
67 -0.24186510 0.75813490
68 -0.25200447 -0.24186510
69 -0.24186510 -0.25200447
70 -0.24186510 -0.24186510
71 -0.25200447 -0.24186510
72 -0.25200447 -0.25200447
73 -0.24186510 -0.25200447
74 -0.25200447 -0.24186510
75 0.74799553 -0.25200447
76 -0.25200447 0.74799553
77 -0.25200447 -0.25200447
78 0.74799553 -0.25200447
79 0.75813490 0.74799553
80 -0.24186510 0.75813490
81 -0.25200447 -0.24186510
82 -0.24186510 -0.25200447
83 -0.24186510 -0.24186510
84 -0.25200447 -0.24186510
85 -0.24186510 -0.25200447
86 -0.07711356 -0.24186510
87 0.09777734 -0.07711356
88 -0.06697419 0.09777734
89 -0.07711356 -0.06697419
90 -0.06697419 -0.07711356
91 0.10791671 -0.06697419
92 -0.06697419 0.10791671
93 -0.06697419 -0.06697419
94 0.10791671 -0.06697419
95 -0.07711356 0.10791671
96 0.10791671 -0.07711356
97 -0.06697419 0.10791671
98 -0.06697419 -0.06697419
99 -0.07711356 -0.06697419
100 -0.07711356 -0.07711356
101 -0.06697419 -0.07711356
102 -0.06697419 -0.06697419
103 -0.06697419 -0.06697419
104 0.10791671 -0.06697419
105 -0.06697419 0.10791671
106 -0.06697419 -0.06697419
107 0.10791671 -0.06697419
108 -0.06697419 0.10791671
109 -0.06697419 -0.06697419
110 0.10791671 -0.06697419
111 0.10791671 0.10791671
112 -0.06697419 0.10791671
113 0.10791671 -0.06697419
114 -0.06697419 0.10791671
115 -0.06697419 -0.06697419
116 -0.07711356 -0.06697419
117 -0.06697419 -0.07711356
118 -0.06697419 -0.06697419
119 -0.07711356 -0.06697419
120 -0.06697419 -0.07711356
121 -0.06697419 -0.06697419
122 0.10791671 -0.06697419
123 -0.07711356 0.10791671
124 -0.07711356 -0.07711356
125 0.10791671 -0.07711356
126 -0.06697419 0.10791671
127 -0.07711356 -0.06697419
128 -0.06697419 -0.07711356
129 -0.07711356 -0.06697419
130 -0.06697419 -0.07711356
131 -0.07711356 -0.06697419
132 -0.06697419 -0.07711356
133 -0.06697419 -0.06697419
134 -0.06697419 -0.06697419
135 -0.06697419 -0.06697419
136 -0.07711356 -0.06697419
137 0.09777734 -0.07711356
138 0.10791671 0.09777734
139 -0.06697419 0.10791671
140 -0.07711356 -0.06697419
141 0.09777734 -0.07711356
142 -0.06697419 0.09777734
143 -0.07711356 -0.06697419
144 -0.06697419 -0.07711356
145 0.09777734 -0.06697419
146 0.10791671 0.09777734
147 0.10791671 0.10791671
148 -0.06697419 0.10791671
149 -0.07711356 -0.06697419
150 -0.07711356 -0.07711356
151 -0.06697419 -0.07711356
152 -0.06697419 -0.06697419
153 -0.06697419 -0.06697419
> 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/7glba1355994083.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8yth41355994083.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9e8jn1355994083.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/101fev1355994083.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jpco1355994083.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/12pswk1355994083.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/13kiin1355994083.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/14dl9f1355994083.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/15wirn1355994083.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/16t83m1355994083.tab")
+ }
>
> try(system("convert tmp/12og21355994083.ps tmp/12og21355994083.png",intern=TRUE))
character(0)
> try(system("convert tmp/2c4he1355994083.ps tmp/2c4he1355994083.png",intern=TRUE))
character(0)
> try(system("convert tmp/3up241355994083.ps tmp/3up241355994083.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fq9u1355994083.ps tmp/4fq9u1355994083.png",intern=TRUE))
character(0)
> try(system("convert tmp/5t02h1355994083.ps tmp/5t02h1355994083.png",intern=TRUE))
character(0)
> try(system("convert tmp/6va2n1355994083.ps tmp/6va2n1355994083.png",intern=TRUE))
character(0)
> try(system("convert tmp/7glba1355994083.ps tmp/7glba1355994083.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yth41355994083.ps tmp/8yth41355994083.png",intern=TRUE))
character(0)
> try(system("convert tmp/9e8jn1355994083.ps tmp/9e8jn1355994083.png",intern=TRUE))
character(0)
> try(system("convert tmp/101fev1355994083.ps tmp/101fev1355994083.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.283 0.870 8.171