R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(24,25,30,19,22,22,25,23,17,21,19,19,15,16,23,27,22,14,22,23,23,21,19,18,20,23,25,19,24,22,25,26,29,32,25,29,28,17,28,29,26,25,14,25,26,20,18,32,25,25,23,21,20,15,30,24,26,24,22,14,24,24,24,24,19,31,22,27,19,25,20,21,27,23,25,20,21,22,23,25,25,17,19,25,19,20,26,23,27,17,17,19,17,22,21,32,21,21,18,18,23,19,20,21,20,17,18,19,22,15,14,18,24,35,29,21,25,20,22,13,26,17,25,20,19,21,22,24,21,26,24,16,23,18,16,26,19,21,21,22,23,29,21,21,23,27,25,21,10,20,26,24,29,19,24,19,24,22,17),dim=c(1,159),dimnames=list(c('PS'),1:159))
> y <- array(NA,dim=c(1,159),dimnames=list(c('PS'),1:159))
> 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 = 'Include Monthly 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.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
PS M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 24 1 0 0 0 0 0 0 0 0 0 0
2 25 0 1 0 0 0 0 0 0 0 0 0
3 30 0 0 1 0 0 0 0 0 0 0 0
4 19 0 0 0 1 0 0 0 0 0 0 0
5 22 0 0 0 0 1 0 0 0 0 0 0
6 22 0 0 0 0 0 1 0 0 0 0 0
7 25 0 0 0 0 0 0 1 0 0 0 0
8 23 0 0 0 0 0 0 0 1 0 0 0
9 17 0 0 0 0 0 0 0 0 1 0 0
10 21 0 0 0 0 0 0 0 0 0 1 0
11 19 0 0 0 0 0 0 0 0 0 0 1
12 19 0 0 0 0 0 0 0 0 0 0 0
13 15 1 0 0 0 0 0 0 0 0 0 0
14 16 0 1 0 0 0 0 0 0 0 0 0
15 23 0 0 1 0 0 0 0 0 0 0 0
16 27 0 0 0 1 0 0 0 0 0 0 0
17 22 0 0 0 0 1 0 0 0 0 0 0
18 14 0 0 0 0 0 1 0 0 0 0 0
19 22 0 0 0 0 0 0 1 0 0 0 0
20 23 0 0 0 0 0 0 0 1 0 0 0
21 23 0 0 0 0 0 0 0 0 1 0 0
22 21 0 0 0 0 0 0 0 0 0 1 0
23 19 0 0 0 0 0 0 0 0 0 0 1
24 18 0 0 0 0 0 0 0 0 0 0 0
25 20 1 0 0 0 0 0 0 0 0 0 0
26 23 0 1 0 0 0 0 0 0 0 0 0
27 25 0 0 1 0 0 0 0 0 0 0 0
28 19 0 0 0 1 0 0 0 0 0 0 0
29 24 0 0 0 0 1 0 0 0 0 0 0
30 22 0 0 0 0 0 1 0 0 0 0 0
31 25 0 0 0 0 0 0 1 0 0 0 0
32 26 0 0 0 0 0 0 0 1 0 0 0
33 29 0 0 0 0 0 0 0 0 1 0 0
34 32 0 0 0 0 0 0 0 0 0 1 0
35 25 0 0 0 0 0 0 0 0 0 0 1
36 29 0 0 0 0 0 0 0 0 0 0 0
37 28 1 0 0 0 0 0 0 0 0 0 0
38 17 0 1 0 0 0 0 0 0 0 0 0
39 28 0 0 1 0 0 0 0 0 0 0 0
40 29 0 0 0 1 0 0 0 0 0 0 0
41 26 0 0 0 0 1 0 0 0 0 0 0
42 25 0 0 0 0 0 1 0 0 0 0 0
43 14 0 0 0 0 0 0 1 0 0 0 0
44 25 0 0 0 0 0 0 0 1 0 0 0
45 26 0 0 0 0 0 0 0 0 1 0 0
46 20 0 0 0 0 0 0 0 0 0 1 0
47 18 0 0 0 0 0 0 0 0 0 0 1
48 32 0 0 0 0 0 0 0 0 0 0 0
49 25 1 0 0 0 0 0 0 0 0 0 0
50 25 0 1 0 0 0 0 0 0 0 0 0
51 23 0 0 1 0 0 0 0 0 0 0 0
52 21 0 0 0 1 0 0 0 0 0 0 0
53 20 0 0 0 0 1 0 0 0 0 0 0
54 15 0 0 0 0 0 1 0 0 0 0 0
55 30 0 0 0 0 0 0 1 0 0 0 0
56 24 0 0 0 0 0 0 0 1 0 0 0
57 26 0 0 0 0 0 0 0 0 1 0 0
58 24 0 0 0 0 0 0 0 0 0 1 0
59 22 0 0 0 0 0 0 0 0 0 0 1
60 14 0 0 0 0 0 0 0 0 0 0 0
61 24 1 0 0 0 0 0 0 0 0 0 0
62 24 0 1 0 0 0 0 0 0 0 0 0
63 24 0 0 1 0 0 0 0 0 0 0 0
64 24 0 0 0 1 0 0 0 0 0 0 0
65 19 0 0 0 0 1 0 0 0 0 0 0
66 31 0 0 0 0 0 1 0 0 0 0 0
67 22 0 0 0 0 0 0 1 0 0 0 0
68 27 0 0 0 0 0 0 0 1 0 0 0
69 19 0 0 0 0 0 0 0 0 1 0 0
70 25 0 0 0 0 0 0 0 0 0 1 0
71 20 0 0 0 0 0 0 0 0 0 0 1
72 21 0 0 0 0 0 0 0 0 0 0 0
73 27 1 0 0 0 0 0 0 0 0 0 0
74 23 0 1 0 0 0 0 0 0 0 0 0
75 25 0 0 1 0 0 0 0 0 0 0 0
76 20 0 0 0 1 0 0 0 0 0 0 0
77 21 0 0 0 0 1 0 0 0 0 0 0
78 22 0 0 0 0 0 1 0 0 0 0 0
79 23 0 0 0 0 0 0 1 0 0 0 0
80 25 0 0 0 0 0 0 0 1 0 0 0
81 25 0 0 0 0 0 0 0 0 1 0 0
82 17 0 0 0 0 0 0 0 0 0 1 0
83 19 0 0 0 0 0 0 0 0 0 0 1
84 25 0 0 0 0 0 0 0 0 0 0 0
85 19 1 0 0 0 0 0 0 0 0 0 0
86 20 0 1 0 0 0 0 0 0 0 0 0
87 26 0 0 1 0 0 0 0 0 0 0 0
88 23 0 0 0 1 0 0 0 0 0 0 0
89 27 0 0 0 0 1 0 0 0 0 0 0
90 17 0 0 0 0 0 1 0 0 0 0 0
91 17 0 0 0 0 0 0 1 0 0 0 0
92 19 0 0 0 0 0 0 0 1 0 0 0
93 17 0 0 0 0 0 0 0 0 1 0 0
94 22 0 0 0 0 0 0 0 0 0 1 0
95 21 0 0 0 0 0 0 0 0 0 0 1
96 32 0 0 0 0 0 0 0 0 0 0 0
97 21 1 0 0 0 0 0 0 0 0 0 0
98 21 0 1 0 0 0 0 0 0 0 0 0
99 18 0 0 1 0 0 0 0 0 0 0 0
100 18 0 0 0 1 0 0 0 0 0 0 0
101 23 0 0 0 0 1 0 0 0 0 0 0
102 19 0 0 0 0 0 1 0 0 0 0 0
103 20 0 0 0 0 0 0 1 0 0 0 0
104 21 0 0 0 0 0 0 0 1 0 0 0
105 20 0 0 0 0 0 0 0 0 1 0 0
106 17 0 0 0 0 0 0 0 0 0 1 0
107 18 0 0 0 0 0 0 0 0 0 0 1
108 19 0 0 0 0 0 0 0 0 0 0 0
109 22 1 0 0 0 0 0 0 0 0 0 0
110 15 0 1 0 0 0 0 0 0 0 0 0
111 14 0 0 1 0 0 0 0 0 0 0 0
112 18 0 0 0 1 0 0 0 0 0 0 0
113 24 0 0 0 0 1 0 0 0 0 0 0
114 35 0 0 0 0 0 1 0 0 0 0 0
115 29 0 0 0 0 0 0 1 0 0 0 0
116 21 0 0 0 0 0 0 0 1 0 0 0
117 25 0 0 0 0 0 0 0 0 1 0 0
118 20 0 0 0 0 0 0 0 0 0 1 0
119 22 0 0 0 0 0 0 0 0 0 0 1
120 13 0 0 0 0 0 0 0 0 0 0 0
121 26 1 0 0 0 0 0 0 0 0 0 0
122 17 0 1 0 0 0 0 0 0 0 0 0
123 25 0 0 1 0 0 0 0 0 0 0 0
124 20 0 0 0 1 0 0 0 0 0 0 0
125 19 0 0 0 0 1 0 0 0 0 0 0
126 21 0 0 0 0 0 1 0 0 0 0 0
127 22 0 0 0 0 0 0 1 0 0 0 0
128 24 0 0 0 0 0 0 0 1 0 0 0
129 21 0 0 0 0 0 0 0 0 1 0 0
130 26 0 0 0 0 0 0 0 0 0 1 0
131 24 0 0 0 0 0 0 0 0 0 0 1
132 16 0 0 0 0 0 0 0 0 0 0 0
133 23 1 0 0 0 0 0 0 0 0 0 0
134 18 0 1 0 0 0 0 0 0 0 0 0
135 16 0 0 1 0 0 0 0 0 0 0 0
136 26 0 0 0 1 0 0 0 0 0 0 0
137 19 0 0 0 0 1 0 0 0 0 0 0
138 21 0 0 0 0 0 1 0 0 0 0 0
139 21 0 0 0 0 0 0 1 0 0 0 0
140 22 0 0 0 0 0 0 0 1 0 0 0
141 23 0 0 0 0 0 0 0 0 1 0 0
142 29 0 0 0 0 0 0 0 0 0 1 0
143 21 0 0 0 0 0 0 0 0 0 0 1
144 21 0 0 0 0 0 0 0 0 0 0 0
145 23 1 0 0 0 0 0 0 0 0 0 0
146 27 0 1 0 0 0 0 0 0 0 0 0
147 25 0 0 1 0 0 0 0 0 0 0 0
148 21 0 0 0 1 0 0 0 0 0 0 0
149 10 0 0 0 0 1 0 0 0 0 0 0
150 20 0 0 0 0 0 1 0 0 0 0 0
151 26 0 0 0 0 0 0 1 0 0 0 0
152 24 0 0 0 0 0 0 0 1 0 0 0
153 29 0 0 0 0 0 0 0 0 1 0 0
154 19 0 0 0 0 0 0 0 0 0 1 0
155 24 0 0 0 0 0 0 0 0 0 0 1
156 19 0 0 0 0 0 0 0 0 0 0 0
157 24 1 0 0 0 0 0 0 0 0 0 0
158 22 0 1 0 0 0 0 0 0 0 0 0
159 17 0 0 1 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
21.3846 1.5440 -0.4560 1.4011 0.5385 -0.1538
M6 M7 M8 M9 M10 M11
0.4615 1.3846 2.0000 1.6923 1.1538 -0.4615
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.23077 -2.46154 0.07143 2.34615 13.15385
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.3846 1.1879 18.002 <2e-16 ***
M1 1.5440 1.6497 0.936 0.351
M2 -0.4560 1.6497 -0.276 0.783
M3 1.4011 1.6497 0.849 0.397
M4 0.5385 1.6800 0.321 0.749
M5 -0.1538 1.6800 -0.092 0.927
M6 0.4615 1.6800 0.275 0.784
M7 1.3846 1.6800 0.824 0.411
M8 2.0000 1.6800 1.191 0.236
M9 1.6923 1.6800 1.007 0.315
M10 1.1538 1.6800 0.687 0.493
M11 -0.4615 1.6800 -0.275 0.784
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.283 on 147 degrees of freedom
Multiple R-squared: 0.04022, Adjusted R-squared: -0.0316
F-statistic: 0.56 on 11 and 147 DF, p-value: 0.8585
> 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,] 0.86475180 0.2704964 0.13524820
[2,] 0.87643122 0.2471376 0.12356878
[3,] 0.79506819 0.4098636 0.20493181
[4,] 0.82913678 0.3417264 0.17086322
[5,] 0.76452351 0.4709530 0.23547649
[6,] 0.67516369 0.6496726 0.32483631
[7,] 0.65667359 0.6866528 0.34332641
[8,] 0.56490285 0.8701943 0.43509715
[9,] 0.47331626 0.9466325 0.52668374
[10,] 0.39139463 0.7827893 0.60860537
[11,] 0.31485359 0.6297072 0.68514641
[12,] 0.25961764 0.5192353 0.74038236
[13,] 0.20381603 0.4076321 0.79618397
[14,] 0.18384654 0.3676931 0.81615346
[15,] 0.14455632 0.2891126 0.85544368
[16,] 0.12832784 0.2566557 0.87167216
[17,] 0.09649454 0.1929891 0.90350546
[18,] 0.07861171 0.1572234 0.92138829
[19,] 0.15732312 0.3146462 0.84267688
[20,] 0.36041181 0.7208236 0.63958819
[21,] 0.37599696 0.7519939 0.62400304
[22,] 0.55218741 0.8956252 0.44781259
[23,] 0.63385406 0.7322919 0.36614594
[24,] 0.61762281 0.7647544 0.38237719
[25,] 0.59085260 0.8182948 0.40914740
[26,] 0.66071503 0.6785699 0.33928497
[27,] 0.64311768 0.7137646 0.35688232
[28,] 0.64453353 0.7109329 0.35546647
[29,] 0.78308481 0.4338304 0.21691519
[30,] 0.74237097 0.5152581 0.25762903
[31,] 0.71314934 0.5737013 0.28685066
[32,] 0.69669246 0.6066151 0.30330754
[33,] 0.66597463 0.6680507 0.33402537
[34,] 0.82520341 0.3495932 0.17479659
[35,] 0.80123850 0.3975230 0.19876150
[36,] 0.79732302 0.4053540 0.20267698
[37,] 0.77365768 0.4526846 0.22634232
[38,] 0.73921695 0.5215661 0.26078305
[39,] 0.71006312 0.5798738 0.28993688
[40,] 0.75288985 0.4942203 0.24711015
[41,] 0.82330666 0.3533867 0.17669334
[42,] 0.78856478 0.4228704 0.21143522
[43,] 0.76395300 0.4720940 0.23604700
[44,] 0.72626131 0.5474774 0.27373869
[45,] 0.68636441 0.6272712 0.31363559
[46,] 0.79505876 0.4098825 0.20494124
[47,] 0.76040288 0.4791942 0.23959712
[48,] 0.73829346 0.5234131 0.26170654
[49,] 0.70816713 0.5836657 0.29183287
[50,] 0.67335814 0.6532837 0.32664186
[51,] 0.64640829 0.7071834 0.35359171
[52,] 0.80080460 0.3983908 0.19919540
[53,] 0.76589427 0.4682115 0.23410573
[54,] 0.75214103 0.4957179 0.24785897
[55,] 0.75004938 0.4999012 0.24995062
[56,] 0.72246303 0.5550739 0.27753697
[57,] 0.68163360 0.6367328 0.31836640
[58,] 0.63870170 0.7225966 0.36129830
[59,] 0.63276742 0.7344652 0.36723258
[60,] 0.59701233 0.8059753 0.40298767
[61,] 0.57435194 0.8512961 0.42564806
[62,] 0.53851253 0.9229749 0.46148747
[63,] 0.49171778 0.9834356 0.50828222
[64,] 0.44334163 0.8866833 0.55665837
[65,] 0.39575918 0.7915184 0.60424082
[66,] 0.35959082 0.7191816 0.64040918
[67,] 0.32380201 0.6476040 0.67619799
[68,] 0.35434186 0.7086837 0.64565814
[69,] 0.31910642 0.6382128 0.68089358
[70,] 0.30834531 0.6166906 0.69165469
[71,] 0.30076765 0.6015353 0.69923235
[72,] 0.26261674 0.5252335 0.73738326
[73,] 0.26694498 0.5338900 0.73305502
[74,] 0.23319397 0.4663879 0.76680603
[75,] 0.27603863 0.5520773 0.72396137
[76,] 0.29305349 0.5861070 0.70694651
[77,] 0.33133399 0.6626680 0.66866601
[78,] 0.33041004 0.6608201 0.66958996
[79,] 0.37724561 0.7544912 0.62275439
[80,] 0.33154895 0.6630979 0.66845105
[81,] 0.28862352 0.5772470 0.71137648
[82,] 0.60689096 0.7862181 0.39310904
[83,] 0.57055855 0.8588829 0.42944145
[84,] 0.52271915 0.9545617 0.47728085
[85,] 0.52602891 0.9479422 0.47397109
[86,] 0.51063369 0.9787326 0.48936631
[87,] 0.49721390 0.9944278 0.50278610
[88,] 0.49169145 0.9833829 0.50830855
[89,] 0.47608134 0.9521627 0.52391866
[90,] 0.43667630 0.8733526 0.56332370
[91,] 0.42332174 0.8466435 0.57667826
[92,] 0.46726857 0.9345371 0.53273143
[93,] 0.45685752 0.9137150 0.54314248
[94,] 0.42701095 0.8540219 0.57298905
[95,] 0.38149563 0.7629913 0.61850437
[96,] 0.42256171 0.8451234 0.57743829
[97,] 0.53431913 0.9313617 0.46568087
[98,] 0.52379376 0.9524125 0.47620624
[99,] 0.58308123 0.8338375 0.41691877
[100,] 0.92810048 0.1437990 0.07189952
[101,] 0.94512804 0.1097439 0.05487196
[102,] 0.93201546 0.1359691 0.06798454
[103,] 0.91138555 0.1772289 0.08861445
[104,] 0.90931157 0.1813769 0.09068843
[105,] 0.88310832 0.2337834 0.11689168
[106,] 0.91516990 0.1696602 0.08483010
[107,] 0.89851638 0.2029672 0.10148362
[108,] 0.90640067 0.1871987 0.09359933
[109,] 0.91634062 0.1673188 0.08365938
[110,] 0.90205025 0.1958995 0.09794975
[111,] 0.89806554 0.2038689 0.10193446
[112,] 0.86357333 0.2728533 0.13642667
[113,] 0.82436733 0.3512653 0.17563267
[114,] 0.77527713 0.4494457 0.22472287
[115,] 0.77116973 0.4576605 0.22883027
[116,] 0.72484608 0.5503078 0.27515392
[117,] 0.66721885 0.6655623 0.33278115
[118,] 0.64887030 0.7022594 0.35112970
[119,] 0.57122230 0.8575554 0.42877770
[120,] 0.60997591 0.7800482 0.39002409
[121,] 0.61985759 0.7602848 0.38014241
[122,] 0.59557334 0.8088533 0.40442666
[123,] 0.70725416 0.5854917 0.29274584
[124,] 0.61729606 0.7654079 0.38270394
[125,] 0.58527228 0.8294554 0.41472772
[126,] 0.48605509 0.9721102 0.51394491
[127,] 0.47702112 0.9540422 0.52297888
[128,] 0.73068450 0.5386310 0.26931550
[129,] 0.63250472 0.7349906 0.36749528
[130,] 0.48097131 0.9619426 0.51902869
> postscript(file="/var/www/html/rcomp/tmp/1szdw1291026316.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2szdw1291026316.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3szdw1291026316.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/438uz1291026316.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/538uz1291026316.ps",horizontal=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 = 159
Frequency = 1
1 2 3 4 5 6
1.07142857 4.07142857 7.21428571 -2.92307692 0.76923077 0.15384615
7 8 9 10 11 12
2.23076923 -0.38461538 -6.07692308 -1.53846154 -1.92307692 -2.38461538
13 14 15 16 17 18
-7.92857143 -4.92857143 0.21428571 5.07692308 0.76923077 -7.84615385
19 20 21 22 23 24
-0.76923077 -0.38461538 -0.07692308 -1.53846154 -1.92307692 -3.38461538
25 26 27 28 29 30
-2.92857143 2.07142857 2.21428571 -2.92307692 2.76923077 0.15384615
31 32 33 34 35 36
2.23076923 2.61538462 5.92307692 9.46153846 4.07692308 7.61538462
37 38 39 40 41 42
5.07142857 -3.92857143 5.21428571 7.07692308 4.76923077 3.15384615
43 44 45 46 47 48
-8.76923077 1.61538462 2.92307692 -2.53846154 -2.92307692 10.61538462
49 50 51 52 53 54
2.07142857 4.07142857 0.21428571 -0.92307692 -1.23076923 -6.84615385
55 56 57 58 59 60
7.23076923 0.61538462 2.92307692 1.46153846 1.07692308 -7.38461538
61 62 63 64 65 66
1.07142857 3.07142857 1.21428571 2.07692308 -2.23076923 9.15384615
67 68 69 70 71 72
-0.76923077 3.61538462 -4.07692308 2.46153846 -0.92307692 -0.38461538
73 74 75 76 77 78
4.07142857 2.07142857 2.21428571 -1.92307692 -0.23076923 0.15384615
79 80 81 82 83 84
0.23076923 1.61538462 1.92307692 -5.53846154 -1.92307692 3.61538462
85 86 87 88 89 90
-3.92857143 -0.92857143 3.21428571 1.07692308 5.76923077 -4.84615385
91 92 93 94 95 96
-5.76923077 -4.38461538 -6.07692308 -0.53846154 0.07692308 10.61538462
97 98 99 100 101 102
-1.92857143 0.07142857 -4.78571429 -3.92307692 1.76923077 -2.84615385
103 104 105 106 107 108
-2.76923077 -2.38461538 -3.07692308 -5.53846154 -2.92307692 -2.38461538
109 110 111 112 113 114
-0.92857143 -5.92857143 -8.78571429 -3.92307692 2.76923077 13.15384615
115 116 117 118 119 120
6.23076923 -2.38461538 1.92307692 -2.53846154 1.07692308 -8.38461538
121 122 123 124 125 126
3.07142857 -3.92857143 2.21428571 -1.92307692 -2.23076923 -0.84615385
127 128 129 130 131 132
-0.76923077 0.61538462 -2.07692308 3.46153846 3.07692308 -5.38461538
133 134 135 136 137 138
0.07142857 -2.92857143 -6.78571429 4.07692308 -2.23076923 -0.84615385
139 140 141 142 143 144
-1.76923077 -1.38461538 -0.07692308 6.46153846 0.07692308 -0.38461538
145 146 147 148 149 150
0.07142857 6.07142857 2.21428571 -0.92307692 -11.23076923 -1.84615385
151 152 153 154 155 156
3.23076923 0.61538462 5.92307692 -3.53846154 3.07692308 -2.38461538
157 158 159
1.07142857 1.07142857 -5.78571429
> postscript(file="/var/www/html/rcomp/tmp/638uz1291026316.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 1.07142857 NA
1 4.07142857 1.07142857
2 7.21428571 4.07142857
3 -2.92307692 7.21428571
4 0.76923077 -2.92307692
5 0.15384615 0.76923077
6 2.23076923 0.15384615
7 -0.38461538 2.23076923
8 -6.07692308 -0.38461538
9 -1.53846154 -6.07692308
10 -1.92307692 -1.53846154
11 -2.38461538 -1.92307692
12 -7.92857143 -2.38461538
13 -4.92857143 -7.92857143
14 0.21428571 -4.92857143
15 5.07692308 0.21428571
16 0.76923077 5.07692308
17 -7.84615385 0.76923077
18 -0.76923077 -7.84615385
19 -0.38461538 -0.76923077
20 -0.07692308 -0.38461538
21 -1.53846154 -0.07692308
22 -1.92307692 -1.53846154
23 -3.38461538 -1.92307692
24 -2.92857143 -3.38461538
25 2.07142857 -2.92857143
26 2.21428571 2.07142857
27 -2.92307692 2.21428571
28 2.76923077 -2.92307692
29 0.15384615 2.76923077
30 2.23076923 0.15384615
31 2.61538462 2.23076923
32 5.92307692 2.61538462
33 9.46153846 5.92307692
34 4.07692308 9.46153846
35 7.61538462 4.07692308
36 5.07142857 7.61538462
37 -3.92857143 5.07142857
38 5.21428571 -3.92857143
39 7.07692308 5.21428571
40 4.76923077 7.07692308
41 3.15384615 4.76923077
42 -8.76923077 3.15384615
43 1.61538462 -8.76923077
44 2.92307692 1.61538462
45 -2.53846154 2.92307692
46 -2.92307692 -2.53846154
47 10.61538462 -2.92307692
48 2.07142857 10.61538462
49 4.07142857 2.07142857
50 0.21428571 4.07142857
51 -0.92307692 0.21428571
52 -1.23076923 -0.92307692
53 -6.84615385 -1.23076923
54 7.23076923 -6.84615385
55 0.61538462 7.23076923
56 2.92307692 0.61538462
57 1.46153846 2.92307692
58 1.07692308 1.46153846
59 -7.38461538 1.07692308
60 1.07142857 -7.38461538
61 3.07142857 1.07142857
62 1.21428571 3.07142857
63 2.07692308 1.21428571
64 -2.23076923 2.07692308
65 9.15384615 -2.23076923
66 -0.76923077 9.15384615
67 3.61538462 -0.76923077
68 -4.07692308 3.61538462
69 2.46153846 -4.07692308
70 -0.92307692 2.46153846
71 -0.38461538 -0.92307692
72 4.07142857 -0.38461538
73 2.07142857 4.07142857
74 2.21428571 2.07142857
75 -1.92307692 2.21428571
76 -0.23076923 -1.92307692
77 0.15384615 -0.23076923
78 0.23076923 0.15384615
79 1.61538462 0.23076923
80 1.92307692 1.61538462
81 -5.53846154 1.92307692
82 -1.92307692 -5.53846154
83 3.61538462 -1.92307692
84 -3.92857143 3.61538462
85 -0.92857143 -3.92857143
86 3.21428571 -0.92857143
87 1.07692308 3.21428571
88 5.76923077 1.07692308
89 -4.84615385 5.76923077
90 -5.76923077 -4.84615385
91 -4.38461538 -5.76923077
92 -6.07692308 -4.38461538
93 -0.53846154 -6.07692308
94 0.07692308 -0.53846154
95 10.61538462 0.07692308
96 -1.92857143 10.61538462
97 0.07142857 -1.92857143
98 -4.78571429 0.07142857
99 -3.92307692 -4.78571429
100 1.76923077 -3.92307692
101 -2.84615385 1.76923077
102 -2.76923077 -2.84615385
103 -2.38461538 -2.76923077
104 -3.07692308 -2.38461538
105 -5.53846154 -3.07692308
106 -2.92307692 -5.53846154
107 -2.38461538 -2.92307692
108 -0.92857143 -2.38461538
109 -5.92857143 -0.92857143
110 -8.78571429 -5.92857143
111 -3.92307692 -8.78571429
112 2.76923077 -3.92307692
113 13.15384615 2.76923077
114 6.23076923 13.15384615
115 -2.38461538 6.23076923
116 1.92307692 -2.38461538
117 -2.53846154 1.92307692
118 1.07692308 -2.53846154
119 -8.38461538 1.07692308
120 3.07142857 -8.38461538
121 -3.92857143 3.07142857
122 2.21428571 -3.92857143
123 -1.92307692 2.21428571
124 -2.23076923 -1.92307692
125 -0.84615385 -2.23076923
126 -0.76923077 -0.84615385
127 0.61538462 -0.76923077
128 -2.07692308 0.61538462
129 3.46153846 -2.07692308
130 3.07692308 3.46153846
131 -5.38461538 3.07692308
132 0.07142857 -5.38461538
133 -2.92857143 0.07142857
134 -6.78571429 -2.92857143
135 4.07692308 -6.78571429
136 -2.23076923 4.07692308
137 -0.84615385 -2.23076923
138 -1.76923077 -0.84615385
139 -1.38461538 -1.76923077
140 -0.07692308 -1.38461538
141 6.46153846 -0.07692308
142 0.07692308 6.46153846
143 -0.38461538 0.07692308
144 0.07142857 -0.38461538
145 6.07142857 0.07142857
146 2.21428571 6.07142857
147 -0.92307692 2.21428571
148 -11.23076923 -0.92307692
149 -1.84615385 -11.23076923
150 3.23076923 -1.84615385
151 0.61538462 3.23076923
152 5.92307692 0.61538462
153 -3.53846154 5.92307692
154 3.07692308 -3.53846154
155 -2.38461538 3.07692308
156 1.07142857 -2.38461538
157 1.07142857 1.07142857
158 -5.78571429 1.07142857
159 NA -5.78571429
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.07142857 1.07142857
[2,] 7.21428571 4.07142857
[3,] -2.92307692 7.21428571
[4,] 0.76923077 -2.92307692
[5,] 0.15384615 0.76923077
[6,] 2.23076923 0.15384615
[7,] -0.38461538 2.23076923
[8,] -6.07692308 -0.38461538
[9,] -1.53846154 -6.07692308
[10,] -1.92307692 -1.53846154
[11,] -2.38461538 -1.92307692
[12,] -7.92857143 -2.38461538
[13,] -4.92857143 -7.92857143
[14,] 0.21428571 -4.92857143
[15,] 5.07692308 0.21428571
[16,] 0.76923077 5.07692308
[17,] -7.84615385 0.76923077
[18,] -0.76923077 -7.84615385
[19,] -0.38461538 -0.76923077
[20,] -0.07692308 -0.38461538
[21,] -1.53846154 -0.07692308
[22,] -1.92307692 -1.53846154
[23,] -3.38461538 -1.92307692
[24,] -2.92857143 -3.38461538
[25,] 2.07142857 -2.92857143
[26,] 2.21428571 2.07142857
[27,] -2.92307692 2.21428571
[28,] 2.76923077 -2.92307692
[29,] 0.15384615 2.76923077
[30,] 2.23076923 0.15384615
[31,] 2.61538462 2.23076923
[32,] 5.92307692 2.61538462
[33,] 9.46153846 5.92307692
[34,] 4.07692308 9.46153846
[35,] 7.61538462 4.07692308
[36,] 5.07142857 7.61538462
[37,] -3.92857143 5.07142857
[38,] 5.21428571 -3.92857143
[39,] 7.07692308 5.21428571
[40,] 4.76923077 7.07692308
[41,] 3.15384615 4.76923077
[42,] -8.76923077 3.15384615
[43,] 1.61538462 -8.76923077
[44,] 2.92307692 1.61538462
[45,] -2.53846154 2.92307692
[46,] -2.92307692 -2.53846154
[47,] 10.61538462 -2.92307692
[48,] 2.07142857 10.61538462
[49,] 4.07142857 2.07142857
[50,] 0.21428571 4.07142857
[51,] -0.92307692 0.21428571
[52,] -1.23076923 -0.92307692
[53,] -6.84615385 -1.23076923
[54,] 7.23076923 -6.84615385
[55,] 0.61538462 7.23076923
[56,] 2.92307692 0.61538462
[57,] 1.46153846 2.92307692
[58,] 1.07692308 1.46153846
[59,] -7.38461538 1.07692308
[60,] 1.07142857 -7.38461538
[61,] 3.07142857 1.07142857
[62,] 1.21428571 3.07142857
[63,] 2.07692308 1.21428571
[64,] -2.23076923 2.07692308
[65,] 9.15384615 -2.23076923
[66,] -0.76923077 9.15384615
[67,] 3.61538462 -0.76923077
[68,] -4.07692308 3.61538462
[69,] 2.46153846 -4.07692308
[70,] -0.92307692 2.46153846
[71,] -0.38461538 -0.92307692
[72,] 4.07142857 -0.38461538
[73,] 2.07142857 4.07142857
[74,] 2.21428571 2.07142857
[75,] -1.92307692 2.21428571
[76,] -0.23076923 -1.92307692
[77,] 0.15384615 -0.23076923
[78,] 0.23076923 0.15384615
[79,] 1.61538462 0.23076923
[80,] 1.92307692 1.61538462
[81,] -5.53846154 1.92307692
[82,] -1.92307692 -5.53846154
[83,] 3.61538462 -1.92307692
[84,] -3.92857143 3.61538462
[85,] -0.92857143 -3.92857143
[86,] 3.21428571 -0.92857143
[87,] 1.07692308 3.21428571
[88,] 5.76923077 1.07692308
[89,] -4.84615385 5.76923077
[90,] -5.76923077 -4.84615385
[91,] -4.38461538 -5.76923077
[92,] -6.07692308 -4.38461538
[93,] -0.53846154 -6.07692308
[94,] 0.07692308 -0.53846154
[95,] 10.61538462 0.07692308
[96,] -1.92857143 10.61538462
[97,] 0.07142857 -1.92857143
[98,] -4.78571429 0.07142857
[99,] -3.92307692 -4.78571429
[100,] 1.76923077 -3.92307692
[101,] -2.84615385 1.76923077
[102,] -2.76923077 -2.84615385
[103,] -2.38461538 -2.76923077
[104,] -3.07692308 -2.38461538
[105,] -5.53846154 -3.07692308
[106,] -2.92307692 -5.53846154
[107,] -2.38461538 -2.92307692
[108,] -0.92857143 -2.38461538
[109,] -5.92857143 -0.92857143
[110,] -8.78571429 -5.92857143
[111,] -3.92307692 -8.78571429
[112,] 2.76923077 -3.92307692
[113,] 13.15384615 2.76923077
[114,] 6.23076923 13.15384615
[115,] -2.38461538 6.23076923
[116,] 1.92307692 -2.38461538
[117,] -2.53846154 1.92307692
[118,] 1.07692308 -2.53846154
[119,] -8.38461538 1.07692308
[120,] 3.07142857 -8.38461538
[121,] -3.92857143 3.07142857
[122,] 2.21428571 -3.92857143
[123,] -1.92307692 2.21428571
[124,] -2.23076923 -1.92307692
[125,] -0.84615385 -2.23076923
[126,] -0.76923077 -0.84615385
[127,] 0.61538462 -0.76923077
[128,] -2.07692308 0.61538462
[129,] 3.46153846 -2.07692308
[130,] 3.07692308 3.46153846
[131,] -5.38461538 3.07692308
[132,] 0.07142857 -5.38461538
[133,] -2.92857143 0.07142857
[134,] -6.78571429 -2.92857143
[135,] 4.07692308 -6.78571429
[136,] -2.23076923 4.07692308
[137,] -0.84615385 -2.23076923
[138,] -1.76923077 -0.84615385
[139,] -1.38461538 -1.76923077
[140,] -0.07692308 -1.38461538
[141,] 6.46153846 -0.07692308
[142,] 0.07692308 6.46153846
[143,] -0.38461538 0.07692308
[144,] 0.07142857 -0.38461538
[145,] 6.07142857 0.07142857
[146,] 2.21428571 6.07142857
[147,] -0.92307692 2.21428571
[148,] -11.23076923 -0.92307692
[149,] -1.84615385 -11.23076923
[150,] 3.23076923 -1.84615385
[151,] 0.61538462 3.23076923
[152,] 5.92307692 0.61538462
[153,] -3.53846154 5.92307692
[154,] 3.07692308 -3.53846154
[155,] -2.38461538 3.07692308
[156,] 1.07142857 -2.38461538
[157,] 1.07142857 1.07142857
[158,] -5.78571429 1.07142857
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.07142857 1.07142857
2 7.21428571 4.07142857
3 -2.92307692 7.21428571
4 0.76923077 -2.92307692
5 0.15384615 0.76923077
6 2.23076923 0.15384615
7 -0.38461538 2.23076923
8 -6.07692308 -0.38461538
9 -1.53846154 -6.07692308
10 -1.92307692 -1.53846154
11 -2.38461538 -1.92307692
12 -7.92857143 -2.38461538
13 -4.92857143 -7.92857143
14 0.21428571 -4.92857143
15 5.07692308 0.21428571
16 0.76923077 5.07692308
17 -7.84615385 0.76923077
18 -0.76923077 -7.84615385
19 -0.38461538 -0.76923077
20 -0.07692308 -0.38461538
21 -1.53846154 -0.07692308
22 -1.92307692 -1.53846154
23 -3.38461538 -1.92307692
24 -2.92857143 -3.38461538
25 2.07142857 -2.92857143
26 2.21428571 2.07142857
27 -2.92307692 2.21428571
28 2.76923077 -2.92307692
29 0.15384615 2.76923077
30 2.23076923 0.15384615
31 2.61538462 2.23076923
32 5.92307692 2.61538462
33 9.46153846 5.92307692
34 4.07692308 9.46153846
35 7.61538462 4.07692308
36 5.07142857 7.61538462
37 -3.92857143 5.07142857
38 5.21428571 -3.92857143
39 7.07692308 5.21428571
40 4.76923077 7.07692308
41 3.15384615 4.76923077
42 -8.76923077 3.15384615
43 1.61538462 -8.76923077
44 2.92307692 1.61538462
45 -2.53846154 2.92307692
46 -2.92307692 -2.53846154
47 10.61538462 -2.92307692
48 2.07142857 10.61538462
49 4.07142857 2.07142857
50 0.21428571 4.07142857
51 -0.92307692 0.21428571
52 -1.23076923 -0.92307692
53 -6.84615385 -1.23076923
54 7.23076923 -6.84615385
55 0.61538462 7.23076923
56 2.92307692 0.61538462
57 1.46153846 2.92307692
58 1.07692308 1.46153846
59 -7.38461538 1.07692308
60 1.07142857 -7.38461538
61 3.07142857 1.07142857
62 1.21428571 3.07142857
63 2.07692308 1.21428571
64 -2.23076923 2.07692308
65 9.15384615 -2.23076923
66 -0.76923077 9.15384615
67 3.61538462 -0.76923077
68 -4.07692308 3.61538462
69 2.46153846 -4.07692308
70 -0.92307692 2.46153846
71 -0.38461538 -0.92307692
72 4.07142857 -0.38461538
73 2.07142857 4.07142857
74 2.21428571 2.07142857
75 -1.92307692 2.21428571
76 -0.23076923 -1.92307692
77 0.15384615 -0.23076923
78 0.23076923 0.15384615
79 1.61538462 0.23076923
80 1.92307692 1.61538462
81 -5.53846154 1.92307692
82 -1.92307692 -5.53846154
83 3.61538462 -1.92307692
84 -3.92857143 3.61538462
85 -0.92857143 -3.92857143
86 3.21428571 -0.92857143
87 1.07692308 3.21428571
88 5.76923077 1.07692308
89 -4.84615385 5.76923077
90 -5.76923077 -4.84615385
91 -4.38461538 -5.76923077
92 -6.07692308 -4.38461538
93 -0.53846154 -6.07692308
94 0.07692308 -0.53846154
95 10.61538462 0.07692308
96 -1.92857143 10.61538462
97 0.07142857 -1.92857143
98 -4.78571429 0.07142857
99 -3.92307692 -4.78571429
100 1.76923077 -3.92307692
101 -2.84615385 1.76923077
102 -2.76923077 -2.84615385
103 -2.38461538 -2.76923077
104 -3.07692308 -2.38461538
105 -5.53846154 -3.07692308
106 -2.92307692 -5.53846154
107 -2.38461538 -2.92307692
108 -0.92857143 -2.38461538
109 -5.92857143 -0.92857143
110 -8.78571429 -5.92857143
111 -3.92307692 -8.78571429
112 2.76923077 -3.92307692
113 13.15384615 2.76923077
114 6.23076923 13.15384615
115 -2.38461538 6.23076923
116 1.92307692 -2.38461538
117 -2.53846154 1.92307692
118 1.07692308 -2.53846154
119 -8.38461538 1.07692308
120 3.07142857 -8.38461538
121 -3.92857143 3.07142857
122 2.21428571 -3.92857143
123 -1.92307692 2.21428571
124 -2.23076923 -1.92307692
125 -0.84615385 -2.23076923
126 -0.76923077 -0.84615385
127 0.61538462 -0.76923077
128 -2.07692308 0.61538462
129 3.46153846 -2.07692308
130 3.07692308 3.46153846
131 -5.38461538 3.07692308
132 0.07142857 -5.38461538
133 -2.92857143 0.07142857
134 -6.78571429 -2.92857143
135 4.07692308 -6.78571429
136 -2.23076923 4.07692308
137 -0.84615385 -2.23076923
138 -1.76923077 -0.84615385
139 -1.38461538 -1.76923077
140 -0.07692308 -1.38461538
141 6.46153846 -0.07692308
142 0.07692308 6.46153846
143 -0.38461538 0.07692308
144 0.07142857 -0.38461538
145 6.07142857 0.07142857
146 2.21428571 6.07142857
147 -0.92307692 2.21428571
148 -11.23076923 -0.92307692
149 -1.84615385 -11.23076923
150 3.23076923 -1.84615385
151 0.61538462 3.23076923
152 5.92307692 0.61538462
153 -3.53846154 5.92307692
154 3.07692308 -3.53846154
155 -2.38461538 3.07692308
156 1.07142857 -2.38461538
157 1.07142857 1.07142857
158 -5.78571429 1.07142857
> 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/www/html/rcomp/tmp/7ehb21291026316.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/879tn1291026316.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/979tn1291026316.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ziaq1291026316.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/html/rcomp/tmp/11308e1291026316.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/www/html/rcomp/tmp/12ojpk1291026316.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/www/html/rcomp/tmp/132b5a1291026316.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/www/html/rcomp/tmp/146t3g1291026316.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/www/html/rcomp/tmp/159ck41291026316.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/www/html/rcomp/tmp/16uu0s1291026316.tab")
+ }
> try(system("convert tmp/1szdw1291026316.ps tmp/1szdw1291026316.png",intern=TRUE))
character(0)
> try(system("convert tmp/2szdw1291026316.ps tmp/2szdw1291026316.png",intern=TRUE))
character(0)
> try(system("convert tmp/3szdw1291026316.ps tmp/3szdw1291026316.png",intern=TRUE))
character(0)
> try(system("convert tmp/438uz1291026316.ps tmp/438uz1291026316.png",intern=TRUE))
character(0)
> try(system("convert tmp/538uz1291026316.ps tmp/538uz1291026316.png",intern=TRUE))
character(0)
> try(system("convert tmp/638uz1291026316.ps tmp/638uz1291026316.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ehb21291026316.ps tmp/7ehb21291026316.png",intern=TRUE))
character(0)
> try(system("convert tmp/879tn1291026316.ps tmp/879tn1291026316.png",intern=TRUE))
character(0)
> try(system("convert tmp/979tn1291026316.ps tmp/979tn1291026316.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ziaq1291026316.ps tmp/10ziaq1291026316.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.927 1.727 9.773