R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(7
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+ ,dim=c(8
+ ,164)
+ ,dimnames=list(c('Q1_2'
+ ,'Q1_3'
+ ,'Q1_5'
+ ,'Q1_7'
+ ,'Q1_8'
+ ,'Q1_12'
+ ,'Q1_16'
+ ,'Q1_22')
+ ,1:164))
> y <- array(NA,dim=c(8,164),dimnames=list(c('Q1_2','Q1_3','Q1_5','Q1_7','Q1_8','Q1_12','Q1_16','Q1_22'),1:164))
> 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'
> #'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
Q1_2 Q1_3 Q1_5 Q1_7 Q1_8 Q1_12 Q1_16 Q1_22
1 7 7 1 7 7 1 7 7
2 5 6 1 5 5 1 5 5
3 6 6 2 5 6 1 4 5
4 4 5 2 5 6 2 5 6
5 5 6 2 5 6 2 5 6
6 6 7 1 7 5 1 6 7
7 7 7 1 7 7 1 7 6
8 6 7 1 5 6 1 5 7
9 6 7 1 3 7 2 7 7
10 6 6 1 6 6 1 5 6
11 5 4 1 7 7 1 4 7
12 5 6 1 6 7 1 6 7
13 4 6 1 5 6 1 4 5
14 6 7 1 3 6 1 6 6
15 6 6 1 7 7 1 7 7
16 5 6 2 5 6 3 6 6
17 3 4 1 7 7 1 4 7
18 7 7 1 7 7 1 6 7
19 3 7 1 7 7 1 6 7
20 5 6 2 6 7 2 6 6
21 3 3 1 5 5 1 4 4
22 5 7 1 7 7 NA 5 7
23 2 5 1 4 5 1 2 6
24 6 7 1 7 6 1 6 7
25 3 6 1 7 7 2 5 7
26 6 5 1 7 6 1 6 5
27 6 5 1 7 6 1 6 5
28 5 6 1 3 6 1 5 7
29 5 5 1 7 6 1 5 6
30 7 6 1 5 6 1 5 6
31 6 6 1 7 6 1 6 6
32 5 5 1 5 5 1 6 6
33 5 4 4 5 3 6 5 1
34 4 5 3 4 3 3 4 5
35 4 4 1 5 5 1 6 7
36 6 6 2 6 6 2 5 5
37 5 6 1 7 7 1 5 7
38 5 7 1 5 7 1 5 5
39 7 7 1 7 7 1 7 7
40 5 7 1 7 6 1 5 6
41 5 7 1 6 7 1 5 7
42 6 5 1 6 7 1 7 6
43 5 6 2 7 6 2 5 6
44 6 6 1 7 6 2 7 5
45 7 3 1 6 5 1 6 6
46 5 6 4 6 6 4 3 6
47 5 5 1 4 6 2 4 5
48 5 4 3 7 7 3 6 7
49 6 6 2 5 6 2 5 6
50 2 6 3 6 7 2 4 7
51 4 6 2 5 6 2 4 5
52 4 5 1 3 5 1 6 5
53 6 6 2 7 7 1 5 7
54 3 5 1 6 4 1 4 3
55 6 7 1 6 7 1 6 6
56 6 6 1 5 5 2 5 6
57 5 6 1 5 6 1 5 5
58 6 7 1 7 7 1 6 6
59 1 4 1 7 7 1 6 6
60 5 3 2 7 7 1 6 7
61 7 4 1 6 7 1 5 7
62 4 4 3 6 6 1 5 6
63 5 5 1 7 6 1 5 5
64 6 4 1 7 6 1 5 4
65 4 6 4 5 4 4 4 5
66 6 7 1 7 6 1 5 6
67 6 6 1 6 6 2 6 6
68 5 6 1 5 7 1 6 7
69 5 6 1 6 7 1 5 6
70 3 6 1 5 7 2 5 7
71 5 7 1 5 7 1 5 7
72 6 6 1 6 7 1 6 7
73 5 6 1 6 6 2 6 6
74 6 6 1 6 5 3 6 5
75 6 7 1 7 7 2 6 7
76 4 5 2 6 5 2 4 5
77 4 4 2 5 5 2 4 5
78 6 7 1 7 7 2 5 6
79 7 7 1 7 7 1 6 7
80 4 6 1 6 2 1 3 3
81 5 7 1 7 6 1 7 4
82 6 6 1 6 6 1 5 5
83 6 5 1 6 6 1 6 6
84 5 7 1 7 6 1 6 6
85 3 6 2 6 5 2 5 6
86 7 5 1 7 6 1 6 6
87 6 6 1 7 7 2 6 7
88 4 5 4 5 5 3 4 7
89 4 7 3 3 7 2 6 7
90 5 6 2 6 6 2 5 7
91 3 2 1 6 5 1 4 2
92 7 5 1 5 6 1 7 5
93 6 7 1 6 7 3 6 6
94 6 7 1 6 7 1 6 6
95 4 7 2 6 6 1 4 6
96 5 7 1 7 7 1 5 7
97 6 6 1 6 6 1 6 5
98 5 5 2 6 5 1 5 5
99 6 6 1 6 5 1 4 6
100 6 6 3 7 6 2 7 6
101 4 5 1 6 6 1 6 7
102 5 7 1 5 6 1 5 4
103 6 5 2 5 6 2 6 6
104 5 6 1 6 6 1 6 6
105 5 5 1 6 5 1 5 5
106 4 5 2 6 5 3 5 5
107 4 5 2 5 5 2 5 5
108 6 5 1 6 7 2 5 6
109 5 7 1 4 7 1 7 7
110 6 6 1 6 6 1 6 6
111 5 7 1 7 7 1 7 7
112 6 6 1 7 7 2 6 7
113 5 5 1 5 4 1 5 5
114 4 5 2 5 5 2 4 6
115 6 7 1 7 7 1 6 7
116 4 6 1 3 7 2 4 7
117 5 5 2 7 7 2 3 7
118 5 7 2 5 6 4 5 7
119 6 4 1 7 5 2 5 5
120 3 3 2 5 7 1 5 7
121 5 7 2 3 NA NA 5 7
122 4 5 2 6 6 2 5 6
123 5 6 2 5 6 1 5 5
124 5 4 4 4 3 3 3 5
125 7 7 1 7 7 1 7 7
126 5 7 2 6 6 1 6 7
127 7 5 1 7 7 1 6 6
128 5 7 1 2 6 2 4 6
129 4 3 1 5 5 1 4 6
130 6 6 1 6 6 1 6 6
131 4 5 3 6 6 2 4 6
132 4 5 2 6 7 2 6 6
133 4 6 1 2 6 7 2 5
134 4 5 1 6 7 1 5 6
135 6 6 1 7 6 2 5 7
136 6 6 2 4 6 3 6 6
137 5 7 5 7 7 3 5 7
138 3 5 1 7 7 4 4 7
139 6 7 1 6 7 1 6 6
140 5 6 2 6 7 2 6 6
141 4 6 1 2 6 2 5 7
142 5 7 2 7 7 2 5 5
143 2 7 1 7 7 2 2 5
144 5 5 1 5 6 1 6 6
145 7 7 1 5 7 5 6 7
146 4 5 1 6 6 1 5 5
147 4 6 2 5 7 3 6 7
148 7 7 1 6 6 2 7 5
149 6 6 1 6 5 1 5 6
150 5 5 2 6 4 3 5 5
151 5 6 1 5 7 2 5 7
152 5 7 1 6 7 2 6 7
153 7 6 1 7 5 1 7 5
154 6 7 1 7 7 1 7 7
155 6 7 1 6 6 1 6 6
156 5 6 2 6 5 1 5 6
157 2 6 2 6 6 2 6 6
158 4 4 4 7 7 4 4 7
159 6 7 1 6 7 3 6 6
160 5 6 1 5 6 1 6 5
161 5 4 1 5 5 1 4 5
162 5 5 1 5 6 1 5 5
163 4 6 1 4 5 1 5 7
164 4 5 5 4 6 4 5 7
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Q1_3 Q1_5 Q1_7 Q1_8 Q1_12
1.4095903 0.2140527 -0.2213095 0.1393213 -0.1700633 0.0973404
Q1_16 Q1_22
0.5330604 0.0002881
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.12673 -0.41867 0.06596 0.58122 2.54536
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4095903 0.7439982 1.895 0.06002 .
Q1_3 0.2140527 0.0801167 2.672 0.00836 **
Q1_5 -0.2213095 0.1164168 -1.901 0.05917 .
Q1_7 0.1393213 0.0734517 1.897 0.05973 .
Q1_8 -0.1700633 0.1142606 -1.488 0.13870
Q1_12 0.0973404 0.0979464 0.994 0.32187
Q1_16 0.5330604 0.0861509 6.188 5.28e-09 ***
Q1_22 0.0002881 0.0998063 0.003 0.99770
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9876 on 154 degrees of freedom
(2 observations deleted due to missingness)
Multiple R-squared: 0.3477, Adjusted R-squared: 0.3181
F-statistic: 11.73 on 7 and 154 DF, p-value: 6.461e-12
> 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,] 0.03964788 0.07929576 0.960352122
[2,] 0.09629101 0.19258202 0.903708989
[3,] 0.31146578 0.62293157 0.688534215
[4,] 0.20061532 0.40123063 0.799384685
[5,] 0.13202710 0.26405419 0.867972905
[6,] 0.08967750 0.17935499 0.910322504
[7,] 0.12080028 0.24160057 0.879199717
[8,] 0.08135412 0.16270824 0.918645882
[9,] 0.82715555 0.34568890 0.172844452
[10,] 0.77610147 0.44779705 0.223898525
[11,] 0.73046902 0.53906196 0.269530980
[12,] 0.72072208 0.55855584 0.279277919
[13,] 0.65173357 0.69653286 0.348266430
[14,] 0.63872561 0.72254878 0.361274390
[15,] 0.59586414 0.80827171 0.404135857
[16,] 0.54058550 0.91882900 0.459414501
[17,] 0.47087683 0.94175367 0.529123166
[18,] 0.40713143 0.81426287 0.592868567
[19,] 0.60308850 0.79382299 0.396911497
[20,] 0.53974507 0.92050985 0.460254926
[21,] 0.50423077 0.99153847 0.495769233
[22,] 0.51230678 0.97538643 0.487693216
[23,] 0.46502189 0.93004378 0.534978108
[24,] 0.43896541 0.87793082 0.561034590
[25,] 0.40531789 0.81063579 0.594682106
[26,] 0.34937710 0.69875420 0.650622902
[27,] 0.33379180 0.66758360 0.666208198
[28,] 0.29360968 0.58721937 0.706390316
[29,] 0.25875266 0.51750531 0.741247345
[30,] 0.21423093 0.42846186 0.785769070
[31,] 0.17542976 0.35085953 0.824570237
[32,] 0.14175767 0.28351535 0.858242325
[33,] 0.11244343 0.22488686 0.887556569
[34,] 0.23269191 0.46538381 0.767308093
[35,] 0.25147083 0.50294166 0.748529169
[36,] 0.30938367 0.61876734 0.690616329
[37,] 0.26751046 0.53502092 0.732489538
[38,] 0.26488682 0.52977363 0.735113183
[39,] 0.54273880 0.91452241 0.457261203
[40,] 0.50196740 0.99606521 0.498032603
[41,] 0.56334031 0.87331938 0.436659688
[42,] 0.55745909 0.88508181 0.442540907
[43,] 0.64926206 0.70147587 0.350737937
[44,] 0.60544164 0.78911671 0.394558357
[45,] 0.61421719 0.77156562 0.385782808
[46,] 0.56612414 0.86775171 0.433875857
[47,] 0.51858087 0.96283827 0.481419133
[48,] 0.96918396 0.06163208 0.030816041
[49,] 0.96159454 0.07681092 0.038405462
[50,] 0.99303555 0.01392890 0.006964451
[51,] 0.99093524 0.01812951 0.009064757
[52,] 0.98767285 0.02465431 0.012327155
[53,] 0.98987385 0.02025230 0.010126152
[54,] 0.98685980 0.02628040 0.013140199
[55,] 0.98403875 0.03192251 0.015961254
[56,] 0.97932380 0.04135240 0.020676198
[57,] 0.97345524 0.05308953 0.026544764
[58,] 0.96548133 0.06903734 0.034518669
[59,] 0.98036650 0.03926700 0.019633498
[60,] 0.97406839 0.05186323 0.025931614
[61,] 0.96883665 0.06232669 0.031163345
[62,] 0.96335926 0.07328148 0.036640742
[63,] 0.95369670 0.09260660 0.046303298
[64,] 0.94165129 0.11669742 0.058348710
[65,] 0.92843266 0.14313469 0.071567344
[66,] 0.91103263 0.17793474 0.088967370
[67,] 0.90216301 0.19567397 0.097836987
[68,] 0.91242474 0.17515052 0.087575258
[69,] 0.90205904 0.19588192 0.097940959
[70,] 0.92761997 0.14476006 0.072380032
[71,] 0.92628141 0.14743719 0.073718594
[72,] 0.91589443 0.16821115 0.084105573
[73,] 0.91380853 0.17238293 0.086191466
[74,] 0.96458745 0.07082510 0.035412550
[75,] 0.97508351 0.04983297 0.024916487
[76,] 0.96889872 0.06220255 0.031101276
[77,] 0.95960358 0.08079283 0.040396415
[78,] 0.95713003 0.08573993 0.042869967
[79,] 0.94516117 0.10967766 0.054838828
[80,] 0.94548793 0.10902414 0.054512072
[81,] 0.95195545 0.09608911 0.048044554
[82,] 0.93943141 0.12113717 0.060568587
[83,] 0.92793598 0.14412804 0.072064019
[84,] 0.91522816 0.16954369 0.084771844
[85,] 0.89549075 0.20901849 0.104509247
[86,] 0.87646465 0.24707070 0.123535351
[87,] 0.85044111 0.29911778 0.149558890
[88,] 0.86901574 0.26196851 0.130984255
[89,] 0.84148856 0.31702288 0.158511438
[90,] 0.86187557 0.27624887 0.138124434
[91,] 0.83337543 0.33324914 0.166624572
[92,] 0.82909233 0.34181533 0.170907666
[93,] 0.80586272 0.38827455 0.194137276
[94,] 0.77004085 0.45991831 0.229959154
[95,] 0.78270947 0.43458105 0.217290527
[96,] 0.77604370 0.44791260 0.223956301
[97,] 0.80609018 0.38781965 0.193909823
[98,] 0.78758440 0.42483120 0.212415601
[99,] 0.75697817 0.48604365 0.243021825
[100,] 0.77204717 0.45590565 0.227952827
[101,] 0.74008215 0.51983569 0.259917846
[102,] 0.70225409 0.59549182 0.297745912
[103,] 0.66210487 0.67579025 0.337895126
[104,] 0.62177749 0.75644501 0.378222507
[105,] 0.57471247 0.85057505 0.425287527
[106,] 0.68098070 0.63803861 0.319019304
[107,] 0.63465092 0.73069817 0.365349084
[108,] 0.61576373 0.76847254 0.384236270
[109,] 0.57821640 0.84356720 0.421783600
[110,] 0.54889762 0.90220475 0.451102375
[111,] 0.49789847 0.99579694 0.502101532
[112,] 0.53959678 0.92080643 0.460403216
[113,] 0.52392675 0.95214651 0.476073253
[114,] 0.47670476 0.95340952 0.523295238
[115,] 0.65181574 0.69636851 0.348184256
[116,] 0.62695161 0.74609677 0.373048387
[117,] 0.57847379 0.84305241 0.421526206
[118,] 0.53927976 0.92144048 0.460720242
[119,] 0.48555669 0.97111338 0.514443310
[120,] 0.46321872 0.92643745 0.536781276
[121,] 0.40943900 0.81887799 0.590561004
[122,] 0.35370844 0.70741688 0.646291559
[123,] 0.36057051 0.72114102 0.639429490
[124,] 0.31857856 0.63715712 0.681421441
[125,] 0.29825870 0.59651740 0.701741300
[126,] 0.35782482 0.71564964 0.642175182
[127,] 0.34480270 0.68960540 0.655197298
[128,] 0.28140209 0.56280418 0.718597910
[129,] 0.22546594 0.45093188 0.774534058
[130,] 0.19987267 0.39974533 0.800127335
[131,] 0.23311754 0.46623508 0.766882461
[132,] 0.18394030 0.36788060 0.816059698
[133,] 0.17214915 0.34429831 0.827850846
[134,] 0.17870747 0.35741494 0.821292532
[135,] 0.13789332 0.27578663 0.862106684
[136,] 0.11984087 0.23968175 0.880159127
[137,] 0.09220403 0.18440805 0.907795975
[138,] 0.05626606 0.11253211 0.943733944
[139,] 0.03358637 0.06717275 0.966413625
[140,] 0.01683668 0.03367336 0.983163322
[141,] 0.12047480 0.24094961 0.879525196
[142,] 0.55855132 0.88289737 0.441448684
[143,] 0.43364263 0.86728525 0.566357374
> postscript(file="/var/www/html/freestat/rcomp/tmp/1w4hg1290538063.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$resid)
Warning message:
In x[, 1] - mysum$resid :
longer object length is not a multiple of shorter object length
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2ovyj1290538063.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3ovyj1290538063.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4ovyj1290538063.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5hmfm1290538063.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 162
Frequency = 1
1 2 3 4 5 6
0.697764315 -0.082969990 1.841463302 -0.575172905 0.210774368 -0.109301909
7 8 9 10 11 12
0.698052405 0.872464438 0.157709056 0.947483969 0.939103823 -0.415801230
13 14 15 16 17 18
-0.379846214 0.618334656 -0.088182958 -0.419626476 -1.060896177 1.230824758
19 20 21 23 24 25
-2.769175242 -0.291544026 -0.907463278 -1.130702739 0.060761424 -2.119402474
26 27 28 29 30 31
0.489443056 0.489443056 0.365159735 0.022215410 2.086805254 0.275102240
32 33 34 35 36 37
-0.402265796 0.183387694 -0.288723974 -1.188501159 1.071741172 -0.022062073
38 39 40 41 42 43
0.043103951 0.697764315 -0.405890043 -0.096793514 0.265479143 -0.067868203
44 45 46 47 48 49
-0.355010514 1.886518372 1.385512197 0.876187397 0.120921166 1.210774368
50 51 52 53 54 55
-2.004401715 -0.255877099 -1.123335135 1.199247443 -1.644665261 0.370434133
56 57 58 59 60 61
0.819401519 0.087093344 0.231112847 -4.126728973 0.308345180 2.545364666
62 63 64 65 66 67
-0.181791547 0.022503499 1.236844315 -0.348065538 0.594109957 0.317083124
68 69 70 71 72 73
-0.276479945 0.117547302 -1.840759903 0.042527772 0.584198770 -0.682916876
74 75 76 77 78 79
0.049967479 0.133484357 -0.351208992 0.002165020 0.666832889 1.230824758
80 81 82 83 84 85
-0.665784212 -1.471434750 0.947772058 0.628476252 -0.938950486 -2.098610251
86 87 88 89 90 91
1.489154967 0.347537083 0.132814744 -0.866611470 0.071164993 -0.832155658
92 93 94 95 96 97
1.235025185 0.175753330 0.370434133 -0.512198800 -0.236114799 0.414711615
98 99 100 101 102 103
0.213070967 1.310481078 0.087320428 -1.371811837 -0.126671293 0.891766652
104 105 106 107 108 109
-0.585576474 -0.008238549 -0.981609836 -0.744948149 1.234259627 -0.884271828
110 111 112 113 114 115
0.414423526 -1.302235685 0.347537083 -0.038980597 -0.212175796 0.230824758
116 117 118 119 120 122
-0.029056890 1.382080653 -0.198247250 0.969152491 -0.879951807 -0.714494191
123 124 125 126 127 128
0.308402859 1.679698711 0.697764315 -0.578607775 1.659218300 0.726436425
129 130 131 132 133 134
0.091960543 0.414423526 0.039875768 -1.077491300 0.520196120 -0.668399971
135 136 137 138 139 140
0.710534192 0.719694810 0.454442460 -1.566970108 0.370434133 -0.291544026
141 142 143 144 145 146
-0.592859380 -0.111569506 -1.733697693 -0.232202462 1.120105723 -0.838175215
147 148 149 150 151 152
-1.249851232 0.570258045 0.777420635 -0.151673170 0.159240097 -0.727194358
153 154 155 156 157 158
0.572266554 -0.302235685 0.200370799 -0.001269849 -3.461607360 0.311011166
159 160 161 162 163 164
0.175753330 -0.445967099 0.878195906 0.301146070 -0.944224884 0.033108035
> postscript(file="/var/www/html/freestat/rcomp/tmp/6hmfm1290538063.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 0.697764315 NA
1 -0.082969990 0.697764315
2 1.841463302 -0.082969990
3 -0.575172905 1.841463302
4 0.210774368 -0.575172905
5 -0.109301909 0.210774368
6 0.698052405 -0.109301909
7 0.872464438 0.698052405
8 0.157709056 0.872464438
9 0.947483969 0.157709056
10 0.939103823 0.947483969
11 -0.415801230 0.939103823
12 -0.379846214 -0.415801230
13 0.618334656 -0.379846214
14 -0.088182958 0.618334656
15 -0.419626476 -0.088182958
16 -1.060896177 -0.419626476
17 1.230824758 -1.060896177
18 -2.769175242 1.230824758
19 -0.291544026 -2.769175242
20 -0.907463278 -0.291544026
21 -1.130702739 -0.907463278
22 0.060761424 -1.130702739
23 -2.119402474 0.060761424
24 0.489443056 -2.119402474
25 0.489443056 0.489443056
26 0.365159735 0.489443056
27 0.022215410 0.365159735
28 2.086805254 0.022215410
29 0.275102240 2.086805254
30 -0.402265796 0.275102240
31 0.183387694 -0.402265796
32 -0.288723974 0.183387694
33 -1.188501159 -0.288723974
34 1.071741172 -1.188501159
35 -0.022062073 1.071741172
36 0.043103951 -0.022062073
37 0.697764315 0.043103951
38 -0.405890043 0.697764315
39 -0.096793514 -0.405890043
40 0.265479143 -0.096793514
41 -0.067868203 0.265479143
42 -0.355010514 -0.067868203
43 1.886518372 -0.355010514
44 1.385512197 1.886518372
45 0.876187397 1.385512197
46 0.120921166 0.876187397
47 1.210774368 0.120921166
48 -2.004401715 1.210774368
49 -0.255877099 -2.004401715
50 -1.123335135 -0.255877099
51 1.199247443 -1.123335135
52 -1.644665261 1.199247443
53 0.370434133 -1.644665261
54 0.819401519 0.370434133
55 0.087093344 0.819401519
56 0.231112847 0.087093344
57 -4.126728973 0.231112847
58 0.308345180 -4.126728973
59 2.545364666 0.308345180
60 -0.181791547 2.545364666
61 0.022503499 -0.181791547
62 1.236844315 0.022503499
63 -0.348065538 1.236844315
64 0.594109957 -0.348065538
65 0.317083124 0.594109957
66 -0.276479945 0.317083124
67 0.117547302 -0.276479945
68 -1.840759903 0.117547302
69 0.042527772 -1.840759903
70 0.584198770 0.042527772
71 -0.682916876 0.584198770
72 0.049967479 -0.682916876
73 0.133484357 0.049967479
74 -0.351208992 0.133484357
75 0.002165020 -0.351208992
76 0.666832889 0.002165020
77 1.230824758 0.666832889
78 -0.665784212 1.230824758
79 -1.471434750 -0.665784212
80 0.947772058 -1.471434750
81 0.628476252 0.947772058
82 -0.938950486 0.628476252
83 -2.098610251 -0.938950486
84 1.489154967 -2.098610251
85 0.347537083 1.489154967
86 0.132814744 0.347537083
87 -0.866611470 0.132814744
88 0.071164993 -0.866611470
89 -0.832155658 0.071164993
90 1.235025185 -0.832155658
91 0.175753330 1.235025185
92 0.370434133 0.175753330
93 -0.512198800 0.370434133
94 -0.236114799 -0.512198800
95 0.414711615 -0.236114799
96 0.213070967 0.414711615
97 1.310481078 0.213070967
98 0.087320428 1.310481078
99 -1.371811837 0.087320428
100 -0.126671293 -1.371811837
101 0.891766652 -0.126671293
102 -0.585576474 0.891766652
103 -0.008238549 -0.585576474
104 -0.981609836 -0.008238549
105 -0.744948149 -0.981609836
106 1.234259627 -0.744948149
107 -0.884271828 1.234259627
108 0.414423526 -0.884271828
109 -1.302235685 0.414423526
110 0.347537083 -1.302235685
111 -0.038980597 0.347537083
112 -0.212175796 -0.038980597
113 0.230824758 -0.212175796
114 -0.029056890 0.230824758
115 1.382080653 -0.029056890
116 -0.198247250 1.382080653
117 0.969152491 -0.198247250
118 -0.879951807 0.969152491
119 -0.714494191 -0.879951807
120 0.308402859 -0.714494191
121 1.679698711 0.308402859
122 0.697764315 1.679698711
123 -0.578607775 0.697764315
124 1.659218300 -0.578607775
125 0.726436425 1.659218300
126 0.091960543 0.726436425
127 0.414423526 0.091960543
128 0.039875768 0.414423526
129 -1.077491300 0.039875768
130 0.520196120 -1.077491300
131 -0.668399971 0.520196120
132 0.710534192 -0.668399971
133 0.719694810 0.710534192
134 0.454442460 0.719694810
135 -1.566970108 0.454442460
136 0.370434133 -1.566970108
137 -0.291544026 0.370434133
138 -0.592859380 -0.291544026
139 -0.111569506 -0.592859380
140 -1.733697693 -0.111569506
141 -0.232202462 -1.733697693
142 1.120105723 -0.232202462
143 -0.838175215 1.120105723
144 -1.249851232 -0.838175215
145 0.570258045 -1.249851232
146 0.777420635 0.570258045
147 -0.151673170 0.777420635
148 0.159240097 -0.151673170
149 -0.727194358 0.159240097
150 0.572266554 -0.727194358
151 -0.302235685 0.572266554
152 0.200370799 -0.302235685
153 -0.001269849 0.200370799
154 -3.461607360 -0.001269849
155 0.311011166 -3.461607360
156 0.175753330 0.311011166
157 -0.445967099 0.175753330
158 0.878195906 -0.445967099
159 0.301146070 0.878195906
160 -0.944224884 0.301146070
161 0.033108035 -0.944224884
162 NA 0.033108035
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.082969990 0.697764315
[2,] 1.841463302 -0.082969990
[3,] -0.575172905 1.841463302
[4,] 0.210774368 -0.575172905
[5,] -0.109301909 0.210774368
[6,] 0.698052405 -0.109301909
[7,] 0.872464438 0.698052405
[8,] 0.157709056 0.872464438
[9,] 0.947483969 0.157709056
[10,] 0.939103823 0.947483969
[11,] -0.415801230 0.939103823
[12,] -0.379846214 -0.415801230
[13,] 0.618334656 -0.379846214
[14,] -0.088182958 0.618334656
[15,] -0.419626476 -0.088182958
[16,] -1.060896177 -0.419626476
[17,] 1.230824758 -1.060896177
[18,] -2.769175242 1.230824758
[19,] -0.291544026 -2.769175242
[20,] -0.907463278 -0.291544026
[21,] -1.130702739 -0.907463278
[22,] 0.060761424 -1.130702739
[23,] -2.119402474 0.060761424
[24,] 0.489443056 -2.119402474
[25,] 0.489443056 0.489443056
[26,] 0.365159735 0.489443056
[27,] 0.022215410 0.365159735
[28,] 2.086805254 0.022215410
[29,] 0.275102240 2.086805254
[30,] -0.402265796 0.275102240
[31,] 0.183387694 -0.402265796
[32,] -0.288723974 0.183387694
[33,] -1.188501159 -0.288723974
[34,] 1.071741172 -1.188501159
[35,] -0.022062073 1.071741172
[36,] 0.043103951 -0.022062073
[37,] 0.697764315 0.043103951
[38,] -0.405890043 0.697764315
[39,] -0.096793514 -0.405890043
[40,] 0.265479143 -0.096793514
[41,] -0.067868203 0.265479143
[42,] -0.355010514 -0.067868203
[43,] 1.886518372 -0.355010514
[44,] 1.385512197 1.886518372
[45,] 0.876187397 1.385512197
[46,] 0.120921166 0.876187397
[47,] 1.210774368 0.120921166
[48,] -2.004401715 1.210774368
[49,] -0.255877099 -2.004401715
[50,] -1.123335135 -0.255877099
[51,] 1.199247443 -1.123335135
[52,] -1.644665261 1.199247443
[53,] 0.370434133 -1.644665261
[54,] 0.819401519 0.370434133
[55,] 0.087093344 0.819401519
[56,] 0.231112847 0.087093344
[57,] -4.126728973 0.231112847
[58,] 0.308345180 -4.126728973
[59,] 2.545364666 0.308345180
[60,] -0.181791547 2.545364666
[61,] 0.022503499 -0.181791547
[62,] 1.236844315 0.022503499
[63,] -0.348065538 1.236844315
[64,] 0.594109957 -0.348065538
[65,] 0.317083124 0.594109957
[66,] -0.276479945 0.317083124
[67,] 0.117547302 -0.276479945
[68,] -1.840759903 0.117547302
[69,] 0.042527772 -1.840759903
[70,] 0.584198770 0.042527772
[71,] -0.682916876 0.584198770
[72,] 0.049967479 -0.682916876
[73,] 0.133484357 0.049967479
[74,] -0.351208992 0.133484357
[75,] 0.002165020 -0.351208992
[76,] 0.666832889 0.002165020
[77,] 1.230824758 0.666832889
[78,] -0.665784212 1.230824758
[79,] -1.471434750 -0.665784212
[80,] 0.947772058 -1.471434750
[81,] 0.628476252 0.947772058
[82,] -0.938950486 0.628476252
[83,] -2.098610251 -0.938950486
[84,] 1.489154967 -2.098610251
[85,] 0.347537083 1.489154967
[86,] 0.132814744 0.347537083
[87,] -0.866611470 0.132814744
[88,] 0.071164993 -0.866611470
[89,] -0.832155658 0.071164993
[90,] 1.235025185 -0.832155658
[91,] 0.175753330 1.235025185
[92,] 0.370434133 0.175753330
[93,] -0.512198800 0.370434133
[94,] -0.236114799 -0.512198800
[95,] 0.414711615 -0.236114799
[96,] 0.213070967 0.414711615
[97,] 1.310481078 0.213070967
[98,] 0.087320428 1.310481078
[99,] -1.371811837 0.087320428
[100,] -0.126671293 -1.371811837
[101,] 0.891766652 -0.126671293
[102,] -0.585576474 0.891766652
[103,] -0.008238549 -0.585576474
[104,] -0.981609836 -0.008238549
[105,] -0.744948149 -0.981609836
[106,] 1.234259627 -0.744948149
[107,] -0.884271828 1.234259627
[108,] 0.414423526 -0.884271828
[109,] -1.302235685 0.414423526
[110,] 0.347537083 -1.302235685
[111,] -0.038980597 0.347537083
[112,] -0.212175796 -0.038980597
[113,] 0.230824758 -0.212175796
[114,] -0.029056890 0.230824758
[115,] 1.382080653 -0.029056890
[116,] -0.198247250 1.382080653
[117,] 0.969152491 -0.198247250
[118,] -0.879951807 0.969152491
[119,] -0.714494191 -0.879951807
[120,] 0.308402859 -0.714494191
[121,] 1.679698711 0.308402859
[122,] 0.697764315 1.679698711
[123,] -0.578607775 0.697764315
[124,] 1.659218300 -0.578607775
[125,] 0.726436425 1.659218300
[126,] 0.091960543 0.726436425
[127,] 0.414423526 0.091960543
[128,] 0.039875768 0.414423526
[129,] -1.077491300 0.039875768
[130,] 0.520196120 -1.077491300
[131,] -0.668399971 0.520196120
[132,] 0.710534192 -0.668399971
[133,] 0.719694810 0.710534192
[134,] 0.454442460 0.719694810
[135,] -1.566970108 0.454442460
[136,] 0.370434133 -1.566970108
[137,] -0.291544026 0.370434133
[138,] -0.592859380 -0.291544026
[139,] -0.111569506 -0.592859380
[140,] -1.733697693 -0.111569506
[141,] -0.232202462 -1.733697693
[142,] 1.120105723 -0.232202462
[143,] -0.838175215 1.120105723
[144,] -1.249851232 -0.838175215
[145,] 0.570258045 -1.249851232
[146,] 0.777420635 0.570258045
[147,] -0.151673170 0.777420635
[148,] 0.159240097 -0.151673170
[149,] -0.727194358 0.159240097
[150,] 0.572266554 -0.727194358
[151,] -0.302235685 0.572266554
[152,] 0.200370799 -0.302235685
[153,] -0.001269849 0.200370799
[154,] -3.461607360 -0.001269849
[155,] 0.311011166 -3.461607360
[156,] 0.175753330 0.311011166
[157,] -0.445967099 0.175753330
[158,] 0.878195906 -0.445967099
[159,] 0.301146070 0.878195906
[160,] -0.944224884 0.301146070
[161,] 0.033108035 -0.944224884
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.082969990 0.697764315
2 1.841463302 -0.082969990
3 -0.575172905 1.841463302
4 0.210774368 -0.575172905
5 -0.109301909 0.210774368
6 0.698052405 -0.109301909
7 0.872464438 0.698052405
8 0.157709056 0.872464438
9 0.947483969 0.157709056
10 0.939103823 0.947483969
11 -0.415801230 0.939103823
12 -0.379846214 -0.415801230
13 0.618334656 -0.379846214
14 -0.088182958 0.618334656
15 -0.419626476 -0.088182958
16 -1.060896177 -0.419626476
17 1.230824758 -1.060896177
18 -2.769175242 1.230824758
19 -0.291544026 -2.769175242
20 -0.907463278 -0.291544026
21 -1.130702739 -0.907463278
22 0.060761424 -1.130702739
23 -2.119402474 0.060761424
24 0.489443056 -2.119402474
25 0.489443056 0.489443056
26 0.365159735 0.489443056
27 0.022215410 0.365159735
28 2.086805254 0.022215410
29 0.275102240 2.086805254
30 -0.402265796 0.275102240
31 0.183387694 -0.402265796
32 -0.288723974 0.183387694
33 -1.188501159 -0.288723974
34 1.071741172 -1.188501159
35 -0.022062073 1.071741172
36 0.043103951 -0.022062073
37 0.697764315 0.043103951
38 -0.405890043 0.697764315
39 -0.096793514 -0.405890043
40 0.265479143 -0.096793514
41 -0.067868203 0.265479143
42 -0.355010514 -0.067868203
43 1.886518372 -0.355010514
44 1.385512197 1.886518372
45 0.876187397 1.385512197
46 0.120921166 0.876187397
47 1.210774368 0.120921166
48 -2.004401715 1.210774368
49 -0.255877099 -2.004401715
50 -1.123335135 -0.255877099
51 1.199247443 -1.123335135
52 -1.644665261 1.199247443
53 0.370434133 -1.644665261
54 0.819401519 0.370434133
55 0.087093344 0.819401519
56 0.231112847 0.087093344
57 -4.126728973 0.231112847
58 0.308345180 -4.126728973
59 2.545364666 0.308345180
60 -0.181791547 2.545364666
61 0.022503499 -0.181791547
62 1.236844315 0.022503499
63 -0.348065538 1.236844315
64 0.594109957 -0.348065538
65 0.317083124 0.594109957
66 -0.276479945 0.317083124
67 0.117547302 -0.276479945
68 -1.840759903 0.117547302
69 0.042527772 -1.840759903
70 0.584198770 0.042527772
71 -0.682916876 0.584198770
72 0.049967479 -0.682916876
73 0.133484357 0.049967479
74 -0.351208992 0.133484357
75 0.002165020 -0.351208992
76 0.666832889 0.002165020
77 1.230824758 0.666832889
78 -0.665784212 1.230824758
79 -1.471434750 -0.665784212
80 0.947772058 -1.471434750
81 0.628476252 0.947772058
82 -0.938950486 0.628476252
83 -2.098610251 -0.938950486
84 1.489154967 -2.098610251
85 0.347537083 1.489154967
86 0.132814744 0.347537083
87 -0.866611470 0.132814744
88 0.071164993 -0.866611470
89 -0.832155658 0.071164993
90 1.235025185 -0.832155658
91 0.175753330 1.235025185
92 0.370434133 0.175753330
93 -0.512198800 0.370434133
94 -0.236114799 -0.512198800
95 0.414711615 -0.236114799
96 0.213070967 0.414711615
97 1.310481078 0.213070967
98 0.087320428 1.310481078
99 -1.371811837 0.087320428
100 -0.126671293 -1.371811837
101 0.891766652 -0.126671293
102 -0.585576474 0.891766652
103 -0.008238549 -0.585576474
104 -0.981609836 -0.008238549
105 -0.744948149 -0.981609836
106 1.234259627 -0.744948149
107 -0.884271828 1.234259627
108 0.414423526 -0.884271828
109 -1.302235685 0.414423526
110 0.347537083 -1.302235685
111 -0.038980597 0.347537083
112 -0.212175796 -0.038980597
113 0.230824758 -0.212175796
114 -0.029056890 0.230824758
115 1.382080653 -0.029056890
116 -0.198247250 1.382080653
117 0.969152491 -0.198247250
118 -0.879951807 0.969152491
119 -0.714494191 -0.879951807
120 0.308402859 -0.714494191
121 1.679698711 0.308402859
122 0.697764315 1.679698711
123 -0.578607775 0.697764315
124 1.659218300 -0.578607775
125 0.726436425 1.659218300
126 0.091960543 0.726436425
127 0.414423526 0.091960543
128 0.039875768 0.414423526
129 -1.077491300 0.039875768
130 0.520196120 -1.077491300
131 -0.668399971 0.520196120
132 0.710534192 -0.668399971
133 0.719694810 0.710534192
134 0.454442460 0.719694810
135 -1.566970108 0.454442460
136 0.370434133 -1.566970108
137 -0.291544026 0.370434133
138 -0.592859380 -0.291544026
139 -0.111569506 -0.592859380
140 -1.733697693 -0.111569506
141 -0.232202462 -1.733697693
142 1.120105723 -0.232202462
143 -0.838175215 1.120105723
144 -1.249851232 -0.838175215
145 0.570258045 -1.249851232
146 0.777420635 0.570258045
147 -0.151673170 0.777420635
148 0.159240097 -0.151673170
149 -0.727194358 0.159240097
150 0.572266554 -0.727194358
151 -0.302235685 0.572266554
152 0.200370799 -0.302235685
153 -0.001269849 0.200370799
154 -3.461607360 -0.001269849
155 0.311011166 -3.461607360
156 0.175753330 0.311011166
157 -0.445967099 0.175753330
158 0.878195906 -0.445967099
159 0.301146070 0.878195906
160 -0.944224884 0.301146070
161 0.033108035 -0.944224884
> 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/freestat/rcomp/tmp/7sde71290538063.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/freestat/rcomp/tmp/8sde71290538063.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/freestat/rcomp/tmp/92nea1290538063.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/freestat/rcomp/tmp/102nea1290538063.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/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/11gwt01290538063.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/129ob31290538063.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/13g7qx1290538063.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/freestat/rcomp/tmp/14j86l1290538063.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/freestat/rcomp/tmp/155qn91290538063.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/freestat/rcomp/tmp/168qlf1290538063.tab")
+ }
>
> try(system("convert tmp/1w4hg1290538063.ps tmp/1w4hg1290538063.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ovyj1290538063.ps tmp/2ovyj1290538063.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ovyj1290538063.ps tmp/3ovyj1290538063.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ovyj1290538063.ps tmp/4ovyj1290538063.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hmfm1290538063.ps tmp/5hmfm1290538063.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hmfm1290538063.ps tmp/6hmfm1290538063.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sde71290538063.ps tmp/7sde71290538063.png",intern=TRUE))
character(0)
> try(system("convert tmp/8sde71290538063.ps tmp/8sde71290538063.png",intern=TRUE))
character(0)
> try(system("convert tmp/92nea1290538063.ps tmp/92nea1290538063.png",intern=TRUE))
character(0)
> try(system("convert tmp/102nea1290538063.ps tmp/102nea1290538063.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.264 2.797 14.091