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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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(24
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+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('CM'
+ ,'D'
+ ,'PE'
+ ,'PC'
+ ,'PS'
+ ,'O'
+ ,'Time')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('CM','D','PE','PC','PS','O','Time'),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 = 'Do not include Seasonal Dummies'
> par1 = '5'
> #'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 CM D PE PC O Time
1 24 24 14 11 12 26 237.588
2 25 25 11 7 8 23 164.083
3 30 17 6 17 8 25 278.261
4 19 18 12 10 8 23 220.360
5 22 18 8 12 9 19 253.967
6 22 16 10 12 7 29 422.310
7 25 20 10 11 4 25 136.921
8 23 16 11 11 11 21 143.495
9 17 18 16 12 7 22 189.785
10 21 17 11 13 7 25 219.529
11 19 23 13 14 12 24 217.761
12 19 30 12 16 10 18 221.754
13 15 23 8 11 10 22 159.854
14 16 18 12 10 8 15 209.464
15 23 15 11 11 8 22 174.283
16 27 12 4 15 4 28 154.550
17 22 21 9 9 9 20 153.024
18 14 15 8 11 8 12 162.490
19 22 20 8 17 7 24 154.462
20 23 31 14 17 11 20 249.671
21 23 27 15 11 9 21 259.473
22 21 34 16 18 11 20 155.337
23 19 21 9 14 13 21 151.289
24 18 31 14 10 8 23 276.614
25 20 19 11 11 8 28 188.214
26 23 16 8 15 9 24 181.098
27 25 20 9 15 6 24 240.898
28 19 21 9 13 9 24 244.551
29 24 22 9 16 9 23 250.238
30 22 17 9 13 6 23 183.129
31 25 24 10 9 6 29 310.331
32 26 25 16 18 16 24 281.942
33 29 26 11 18 5 18 230.343
34 32 25 8 12 7 25 161.563
35 25 17 9 17 9 21 392.527
36 29 32 16 9 6 26 1077.414
37 28 33 11 9 6 22 248.275
38 17 13 16 12 5 22 557.386
39 28 32 12 18 12 22 731.874
40 29 25 12 12 7 23 301.429
41 26 29 14 18 10 30 226.360
42 25 22 9 14 9 23 215.018
43 14 18 10 15 8 17 157.672
44 25 17 9 16 5 23 219.118
45 26 20 10 10 8 23 213.019
46 20 15 12 11 8 25 390.642
47 18 20 14 14 10 24 157.124
48 32 33 14 9 6 24 227.652
49 25 29 10 12 8 23 239.266
50 25 23 14 17 7 21 506.343
51 23 26 16 5 4 24 149.219
52 21 18 9 12 8 24 213.351
53 20 20 10 12 8 28 174.517
54 15 11 6 6 4 16 172.531
55 30 28 8 24 20 20 320.656
56 24 26 13 12 8 29 305.011
57 26 22 10 12 8 27 266.495
58 24 17 8 14 6 22 361.511
59 22 12 7 7 4 28 361.019
60 14 14 15 13 8 16 382.187
61 24 17 9 12 9 25 196.763
62 24 21 10 13 6 24 273.212
63 24 19 12 14 7 28 186.397
64 24 18 13 8 9 24 294.205
65 19 10 10 11 5 23 364.685
66 31 29 11 9 5 30 230.501
67 22 31 8 11 8 24 217.510
68 27 19 9 13 8 21 262.297
69 19 9 13 10 6 25 169.246
70 25 20 11 11 8 25 260.428
71 20 28 8 12 7 22 348.187
72 21 19 9 9 7 23 512.937
73 27 30 9 15 9 26 164.496
74 23 29 15 18 11 23 111.187
75 25 26 9 15 6 25 169.999
76 20 23 10 12 8 21 240.187
77 21 13 14 13 6 25 187.158
78 22 21 12 14 9 24 194.096
79 23 19 12 10 8 29 265.846
80 25 28 11 13 6 22 283.319
81 25 23 14 13 10 27 356.938
82 17 18 6 11 8 26 240.802
83 19 21 12 13 8 22 326.662
84 25 20 8 16 10 24 249.266
85 19 23 14 8 5 27 277.368
86 20 21 11 16 7 24 394.618
87 26 21 10 11 5 24 235.686
88 23 15 14 9 8 29 227.641
89 27 28 12 16 14 22 159.593
90 17 19 10 12 7 21 268.866
91 17 26 14 14 8 24 206.466
92 19 10 5 8 6 24 233.064
93 17 16 11 9 5 23 133.824
94 22 22 10 15 6 20 486.783
95 21 19 9 11 10 27 228.859
96 32 31 10 21 12 26 155.238
97 21 31 16 14 9 25 2042.451
98 21 29 13 18 12 21 205.218
99 18 19 9 12 7 21 373.648
100 18 22 10 13 8 19 229.151
101 23 23 10 15 10 21 199.156
102 19 15 7 12 6 21 234.410
103 20 20 9 19 10 16 56.519
104 21 18 8 15 10 22 289.239
105 20 23 14 11 10 29 199.227
106 17 25 14 11 5 15 274.513
107 18 21 8 10 7 17 174.499
108 19 24 9 13 10 15 217.714
109 22 25 14 15 11 21 239.717
110 15 17 14 12 6 21 241.529
111 14 13 8 12 7 19 155.561
112 18 28 8 16 12 24 204.107
113 24 21 8 9 11 20 745.970
114 35 25 7 18 11 17 241.772
115 29 9 6 8 11 23 110.267
116 21 16 8 13 5 24 186.580
117 25 19 6 17 8 14 227.906
118 20 17 11 9 6 19 197.518
119 22 25 14 15 9 24 254.094
120 13 20 11 8 4 13 173.942
121 26 29 11 7 4 22 294.420
122 17 14 11 12 7 16 211.924
123 25 22 14 14 11 19 262.479
124 20 15 8 6 6 25 193.495
125 19 19 20 8 7 25 165.972
126 21 20 11 17 8 23 237.352
127 22 15 8 10 4 24 205.814
128 24 20 11 11 8 26 227.526
129 21 18 10 14 9 26 250.439
130 26 33 14 11 8 25 470.849
131 24 22 11 13 11 18 176.469
132 16 16 9 12 8 21 298.691
133 23 17 9 11 5 26 193.922
134 18 16 8 9 4 23 212.422
135 16 21 10 12 8 23 203.284
136 26 26 13 20 10 22 240.560
137 19 18 13 12 6 20 445.327
138 21 18 12 13 9 13 248.984
139 21 17 8 12 9 24 174.440
140 22 22 13 12 13 15 165.024
141 23 30 14 9 9 14 249.681
142 29 30 12 15 10 22 238.312
143 21 24 14 24 20 10 250.437
144 21 21 15 7 5 24 174.750
145 23 21 13 17 11 22 4941.633
146 27 29 16 11 6 24 138.936
147 25 31 9 17 9 19 203.181
148 21 20 9 11 7 20 187.747
149 10 16 9 12 9 13 270.950
150 20 22 8 14 10 20 307.688
151 26 20 7 11 9 22 184.477
152 24 28 16 16 8 24 230.916
153 29 38 11 21 7 29 187.286
154 19 22 9 14 6 12 169.376
155 24 20 11 20 13 20 182.838
156 19 17 9 13 6 21 176.081
157 24 28 14 11 8 24 248.056
158 22 22 13 15 10 22 235.240
159 17 31 16 19 16 20 76.347
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CM D PE PC O
7.4942167 0.3285661 -0.3679288 0.1839608 0.0231775 0.4002793
Time
0.0002462
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.629103 -2.143165 -0.001636 2.208520 11.436603
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.4942167 2.2563151 3.321 0.001122 **
CM 0.3285661 0.0557123 5.898 2.30e-08 ***
D -0.3679288 0.1083311 -3.396 0.000872 ***
PE 0.1839608 0.1016680 1.809 0.072361 .
PC 0.0231775 0.1289859 0.180 0.857635
O 0.4002793 0.0720258 5.557 1.20e-07 ***
Time 0.0002462 0.0006643 0.371 0.711400
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.419 on 152 degrees of freedom
Multiple R-squared: 0.3676, Adjusted R-squared: 0.3427
F-statistic: 14.73 on 6 and 152 DF, p-value: 3.171e-13
> 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,] 0.11182655 0.22365311 0.88817345
[2,] 0.31202322 0.62404644 0.68797678
[3,] 0.19233940 0.38467880 0.80766060
[4,] 0.90421902 0.19156195 0.09578098
[5,] 0.84964149 0.30071702 0.15035851
[6,] 0.79960463 0.40079074 0.20039537
[7,] 0.75465455 0.49069090 0.24534545
[8,] 0.67900832 0.64198335 0.32099168
[9,] 0.64438095 0.71123809 0.35561905
[10,] 0.58564313 0.82871373 0.41435687
[11,] 0.58175757 0.83648486 0.41824243
[12,] 0.56250166 0.87499667 0.43749834
[13,] 0.49309047 0.98618095 0.50690953
[14,] 0.44462124 0.88924248 0.55537876
[15,] 0.48902915 0.97805831 0.51097085
[16,] 0.50208797 0.99582406 0.49791203
[17,] 0.43124697 0.86249395 0.56875303
[18,] 0.37745706 0.75491413 0.62254294
[19,] 0.39145941 0.78291883 0.60854059
[20,] 0.33441509 0.66883017 0.66558491
[21,] 0.27664870 0.55329741 0.72335130
[22,] 0.22525609 0.45051217 0.77474391
[23,] 0.24521505 0.49043011 0.75478495
[24,] 0.39601587 0.79203174 0.60398413
[25,] 0.63266987 0.73466027 0.36733013
[26,] 0.60669986 0.78660027 0.39330014
[27,] 0.57362352 0.85275296 0.42637648
[28,] 0.58044343 0.83911315 0.41955657
[29,] 0.55080495 0.89839010 0.44919505
[30,] 0.49676448 0.99352896 0.50323552
[31,] 0.58959489 0.82081021 0.41040511
[32,] 0.54343864 0.91312271 0.45656136
[33,] 0.49667880 0.99335761 0.50332120
[34,] 0.58224461 0.83551079 0.41775539
[35,] 0.55333947 0.89332107 0.44666053
[36,] 0.58109711 0.83780578 0.41890289
[37,] 0.53280170 0.93439661 0.46719830
[38,] 0.51084114 0.97831772 0.48915886
[39,] 0.65778996 0.68442008 0.34221004
[40,] 0.61376985 0.77246031 0.38623015
[41,] 0.59346799 0.81306403 0.40653201
[42,] 0.55556058 0.88887885 0.44443942
[43,] 0.51369088 0.97261824 0.48630912
[44,] 0.54120541 0.91758919 0.45879459
[45,] 0.51260919 0.97478162 0.48739081
[46,] 0.54533898 0.90932205 0.45466102
[47,] 0.50991183 0.98017634 0.49008817
[48,] 0.46889342 0.93778683 0.53110658
[49,] 0.43409166 0.86818331 0.56590834
[50,] 0.39060250 0.78120501 0.60939750
[51,] 0.35268129 0.70536258 0.64731871
[52,] 0.32271851 0.64543702 0.67728149
[53,] 0.28535789 0.57071578 0.71464211
[54,] 0.24833959 0.49667919 0.75166041
[55,] 0.27476066 0.54952132 0.72523934
[56,] 0.23742467 0.47484934 0.76257533
[57,] 0.25231215 0.50462431 0.74768785
[58,] 0.31220735 0.62441470 0.68779265
[59,] 0.38194041 0.76388081 0.61805959
[60,] 0.34859503 0.69719006 0.65140497
[61,] 0.33286819 0.66573637 0.66713181
[62,] 0.41477135 0.82954270 0.58522865
[63,] 0.37808918 0.75617837 0.62191082
[64,] 0.33598079 0.67196157 0.66401921
[65,] 0.29697895 0.59395789 0.70302105
[66,] 0.26372172 0.52744344 0.73627828
[67,] 0.24175685 0.48351370 0.75824315
[68,] 0.22245349 0.44490699 0.77754651
[69,] 0.18930027 0.37860055 0.81069973
[70,] 0.16045960 0.32091920 0.83954040
[71,] 0.13696580 0.27393160 0.86303420
[72,] 0.11814408 0.23628817 0.88185592
[73,] 0.20550467 0.41100935 0.79449533
[74,] 0.19102690 0.38205379 0.80897310
[75,] 0.16432802 0.32865603 0.83567198
[76,] 0.16471223 0.32942447 0.83528777
[77,] 0.16598753 0.33197506 0.83401247
[78,] 0.17343064 0.34686128 0.82656936
[79,] 0.16253112 0.32506223 0.83746888
[80,] 0.15426056 0.30852111 0.84573944
[81,] 0.15950902 0.31901804 0.84049098
[82,] 0.22444077 0.44888154 0.77555923
[83,] 0.19328339 0.38656678 0.80671661
[84,] 0.17454898 0.34909797 0.82545102
[85,] 0.14832065 0.29664130 0.85167935
[86,] 0.13519100 0.27038201 0.86480900
[87,] 0.14111218 0.28222437 0.85888782
[88,] 0.16542087 0.33084174 0.83457913
[89,] 0.16077397 0.32154794 0.83922603
[90,] 0.15345502 0.30691004 0.84654498
[91,] 0.14801540 0.29603081 0.85198460
[92,] 0.12182807 0.24365614 0.87817193
[93,] 0.10181718 0.20363435 0.89818282
[94,] 0.08212504 0.16425008 0.91787496
[95,] 0.06661620 0.13323241 0.93338380
[96,] 0.07072440 0.14144880 0.92927560
[97,] 0.05815630 0.11631260 0.94184370
[98,] 0.05126356 0.10252712 0.94873644
[99,] 0.04476470 0.08952940 0.95523530
[100,] 0.03429281 0.06858563 0.96570719
[101,] 0.03355394 0.06710788 0.96644606
[102,] 0.04190476 0.08380953 0.95809524
[103,] 0.16900639 0.33801277 0.83099361
[104,] 0.15300492 0.30600985 0.84699508
[105,] 0.57344985 0.85310029 0.42655015
[106,] 0.86136941 0.27726118 0.13863059
[107,] 0.82952069 0.34095861 0.17047931
[108,] 0.88216468 0.23567064 0.11783532
[109,] 0.85837603 0.28324793 0.14162397
[110,] 0.83037307 0.33925387 0.16962693
[111,] 0.86332870 0.27334260 0.13667130
[112,] 0.83993125 0.32013751 0.16006875
[113,] 0.80097622 0.39804755 0.19902378
[114,] 0.83239561 0.33520879 0.16760439
[115,] 0.79148596 0.41702809 0.20851404
[116,] 0.75151416 0.49697167 0.24848584
[117,] 0.70311831 0.59376338 0.29688169
[118,] 0.66755758 0.66488483 0.33244242
[119,] 0.62707027 0.74585947 0.37292973
[120,] 0.56845264 0.86309472 0.43154736
[121,] 0.50601446 0.98797107 0.49398554
[122,] 0.50462007 0.99075987 0.49537993
[123,] 0.50118923 0.99762154 0.49881077
[124,] 0.45301078 0.90602155 0.54698922
[125,] 0.40326806 0.80653612 0.59673194
[126,] 0.54775707 0.90448585 0.45224293
[127,] 0.51373369 0.97253261 0.48626631
[128,] 0.43879450 0.87758899 0.56120550
[129,] 0.45280698 0.90561395 0.54719302
[130,] 0.37808135 0.75616270 0.62191865
[131,] 0.34197505 0.68395010 0.65802495
[132,] 0.29794936 0.59589871 0.70205064
[133,] 0.34617448 0.69234896 0.65382552
[134,] 0.50555877 0.98888245 0.49444123
[135,] 0.42475135 0.84950270 0.57524865
[136,] 0.36504459 0.73008918 0.63495541
[137,] 0.32391802 0.64783605 0.67608198
[138,] 0.27662103 0.55324207 0.72337897
[139,] 0.17632039 0.35264079 0.82367961
[140,] 0.31242086 0.62484172 0.68757914
> postscript(file="/var/www/html/freestat/rcomp/tmp/1xvuj1290610956.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2xvuj1290610956.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38mbm1290610956.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/48mbm1290610956.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/58mbm1290610956.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
1.003735793 2.618874557 5.739476885 -1.278974249 1.451052720 -1.153848563
7 8 9 10 11 12
2.456773195 3.576221861 -1.744194748 -0.647395718 -3.782068519 -4.370834301
13 14 15 16 17 18
-8.208657394 -1.074057121 3.566459920 2.936706959 1.009743142 -2.531630072
19 20 21 22 23 24
-2.056422015 -0.978114004 1.451506141 -3.388686115 -3.402623019 -5.828328162
25 26 27 28 29 30
-3.152910453 0.572849909 1.681321490 -4.349755167 0.168675199 0.449445988
31 32 33 34 35 36
-0.179743120 2.820228174 6.321352361 6.818523628 3.393065401 4.411248219
37 38 39 40 41 42
3.048326605 -0.145529220 1.831025112 6.056356129 -1.478817384 1.545269555
43 44 45 46 47 48
-5.517523749 2.911879011 4.329843564 -0.319726412 -3.367154722 7.356632925
49 50 51 52 53 54
-0.001636132 3.279641401 2.193965330 -1.149235675 -4.029993364 -1.544298774
55 56 57 58 59 60
3.286208113 -1.330016252 1.690504533 2.253909864 0.461337808 -1.650700097
61 62 63 64 65 66
1.759958349 1.080648648 0.686760960 4.015235398 0.463729331 4.187911271
67 68 69 70 71 72
-4.605587149 5.527022465 1.304432643 2.701578847 -5.012292206 -0.576234260
73 74 75 76 77 78
-0.455616996 -1.303750483 -0.672895813 -2.229907815 1.801803942 -0.417505142
79 80 81 82 83 84
-0.020416595 0.946684493 1.581066735 -6.876379582 -2.442452062 1.034661118
85 86 87 88 89 90
-3.363647839 -3.156378113 3.480988385 2.223074081 2.607777584 -3.899527947
91 92 93 94 95 96
-6.304346484 -1.215078194 -2.714968381 -0.067312900 -2.544852594 3.412728081
97 98 99 100 101 102
-4.086878937 -3.285381886 -3.293258869 -3.282026438 0.181958410 -1.657387889
103 104 105 106 107 108
-0.899595338 -1.333530542 -3.812734574 -1.768608342 -2.300241770 -1.749509226
109 110 111 112 113 114
-0.036623939 -3.740771250 -4.835529679 -8.629102564 2.449449010 11.436602795
115 116 117 118 119 120
9.796046498 -0.967868258 4.497816174 1.518720645 -1.194646895 -3.829180770
121 122 123 124 125 126
2.765504683 0.126649368 4.927988630 -0.776736159 0.939823766 -1.595945067
127 128 129 130 131 132
0.931021489 1.309401558 -1.982097230 0.482190859 3.429621736 -4.312280292
133 134 135 136 137 138
0.637049630 -2.814931749 -6.364246931 1.969767838 -0.087171249 4.141709841
139 140 141 142 143 144
-1.202194297 3.506741173 3.270166617 4.207932146 1.828129189 1.071480968
145 146 147 148 149 150
-0.016314343 4.060679311 -0.359675910 0.008192347 -7.126392700 -2.667818616
151 152 153 154 155 156
3.426226307 0.400436697 -2.612147523 0.029113107 1.950546432 -1.748259965
157 158 159
0.580162362 0.205146249 -6.683385384
> postscript(file="/var/www/html/freestat/rcomp/tmp/61dtp1290610956.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 1.003735793 NA
1 2.618874557 1.003735793
2 5.739476885 2.618874557
3 -1.278974249 5.739476885
4 1.451052720 -1.278974249
5 -1.153848563 1.451052720
6 2.456773195 -1.153848563
7 3.576221861 2.456773195
8 -1.744194748 3.576221861
9 -0.647395718 -1.744194748
10 -3.782068519 -0.647395718
11 -4.370834301 -3.782068519
12 -8.208657394 -4.370834301
13 -1.074057121 -8.208657394
14 3.566459920 -1.074057121
15 2.936706959 3.566459920
16 1.009743142 2.936706959
17 -2.531630072 1.009743142
18 -2.056422015 -2.531630072
19 -0.978114004 -2.056422015
20 1.451506141 -0.978114004
21 -3.388686115 1.451506141
22 -3.402623019 -3.388686115
23 -5.828328162 -3.402623019
24 -3.152910453 -5.828328162
25 0.572849909 -3.152910453
26 1.681321490 0.572849909
27 -4.349755167 1.681321490
28 0.168675199 -4.349755167
29 0.449445988 0.168675199
30 -0.179743120 0.449445988
31 2.820228174 -0.179743120
32 6.321352361 2.820228174
33 6.818523628 6.321352361
34 3.393065401 6.818523628
35 4.411248219 3.393065401
36 3.048326605 4.411248219
37 -0.145529220 3.048326605
38 1.831025112 -0.145529220
39 6.056356129 1.831025112
40 -1.478817384 6.056356129
41 1.545269555 -1.478817384
42 -5.517523749 1.545269555
43 2.911879011 -5.517523749
44 4.329843564 2.911879011
45 -0.319726412 4.329843564
46 -3.367154722 -0.319726412
47 7.356632925 -3.367154722
48 -0.001636132 7.356632925
49 3.279641401 -0.001636132
50 2.193965330 3.279641401
51 -1.149235675 2.193965330
52 -4.029993364 -1.149235675
53 -1.544298774 -4.029993364
54 3.286208113 -1.544298774
55 -1.330016252 3.286208113
56 1.690504533 -1.330016252
57 2.253909864 1.690504533
58 0.461337808 2.253909864
59 -1.650700097 0.461337808
60 1.759958349 -1.650700097
61 1.080648648 1.759958349
62 0.686760960 1.080648648
63 4.015235398 0.686760960
64 0.463729331 4.015235398
65 4.187911271 0.463729331
66 -4.605587149 4.187911271
67 5.527022465 -4.605587149
68 1.304432643 5.527022465
69 2.701578847 1.304432643
70 -5.012292206 2.701578847
71 -0.576234260 -5.012292206
72 -0.455616996 -0.576234260
73 -1.303750483 -0.455616996
74 -0.672895813 -1.303750483
75 -2.229907815 -0.672895813
76 1.801803942 -2.229907815
77 -0.417505142 1.801803942
78 -0.020416595 -0.417505142
79 0.946684493 -0.020416595
80 1.581066735 0.946684493
81 -6.876379582 1.581066735
82 -2.442452062 -6.876379582
83 1.034661118 -2.442452062
84 -3.363647839 1.034661118
85 -3.156378113 -3.363647839
86 3.480988385 -3.156378113
87 2.223074081 3.480988385
88 2.607777584 2.223074081
89 -3.899527947 2.607777584
90 -6.304346484 -3.899527947
91 -1.215078194 -6.304346484
92 -2.714968381 -1.215078194
93 -0.067312900 -2.714968381
94 -2.544852594 -0.067312900
95 3.412728081 -2.544852594
96 -4.086878937 3.412728081
97 -3.285381886 -4.086878937
98 -3.293258869 -3.285381886
99 -3.282026438 -3.293258869
100 0.181958410 -3.282026438
101 -1.657387889 0.181958410
102 -0.899595338 -1.657387889
103 -1.333530542 -0.899595338
104 -3.812734574 -1.333530542
105 -1.768608342 -3.812734574
106 -2.300241770 -1.768608342
107 -1.749509226 -2.300241770
108 -0.036623939 -1.749509226
109 -3.740771250 -0.036623939
110 -4.835529679 -3.740771250
111 -8.629102564 -4.835529679
112 2.449449010 -8.629102564
113 11.436602795 2.449449010
114 9.796046498 11.436602795
115 -0.967868258 9.796046498
116 4.497816174 -0.967868258
117 1.518720645 4.497816174
118 -1.194646895 1.518720645
119 -3.829180770 -1.194646895
120 2.765504683 -3.829180770
121 0.126649368 2.765504683
122 4.927988630 0.126649368
123 -0.776736159 4.927988630
124 0.939823766 -0.776736159
125 -1.595945067 0.939823766
126 0.931021489 -1.595945067
127 1.309401558 0.931021489
128 -1.982097230 1.309401558
129 0.482190859 -1.982097230
130 3.429621736 0.482190859
131 -4.312280292 3.429621736
132 0.637049630 -4.312280292
133 -2.814931749 0.637049630
134 -6.364246931 -2.814931749
135 1.969767838 -6.364246931
136 -0.087171249 1.969767838
137 4.141709841 -0.087171249
138 -1.202194297 4.141709841
139 3.506741173 -1.202194297
140 3.270166617 3.506741173
141 4.207932146 3.270166617
142 1.828129189 4.207932146
143 1.071480968 1.828129189
144 -0.016314343 1.071480968
145 4.060679311 -0.016314343
146 -0.359675910 4.060679311
147 0.008192347 -0.359675910
148 -7.126392700 0.008192347
149 -2.667818616 -7.126392700
150 3.426226307 -2.667818616
151 0.400436697 3.426226307
152 -2.612147523 0.400436697
153 0.029113107 -2.612147523
154 1.950546432 0.029113107
155 -1.748259965 1.950546432
156 0.580162362 -1.748259965
157 0.205146249 0.580162362
158 -6.683385384 0.205146249
159 NA -6.683385384
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.618874557 1.003735793
[2,] 5.739476885 2.618874557
[3,] -1.278974249 5.739476885
[4,] 1.451052720 -1.278974249
[5,] -1.153848563 1.451052720
[6,] 2.456773195 -1.153848563
[7,] 3.576221861 2.456773195
[8,] -1.744194748 3.576221861
[9,] -0.647395718 -1.744194748
[10,] -3.782068519 -0.647395718
[11,] -4.370834301 -3.782068519
[12,] -8.208657394 -4.370834301
[13,] -1.074057121 -8.208657394
[14,] 3.566459920 -1.074057121
[15,] 2.936706959 3.566459920
[16,] 1.009743142 2.936706959
[17,] -2.531630072 1.009743142
[18,] -2.056422015 -2.531630072
[19,] -0.978114004 -2.056422015
[20,] 1.451506141 -0.978114004
[21,] -3.388686115 1.451506141
[22,] -3.402623019 -3.388686115
[23,] -5.828328162 -3.402623019
[24,] -3.152910453 -5.828328162
[25,] 0.572849909 -3.152910453
[26,] 1.681321490 0.572849909
[27,] -4.349755167 1.681321490
[28,] 0.168675199 -4.349755167
[29,] 0.449445988 0.168675199
[30,] -0.179743120 0.449445988
[31,] 2.820228174 -0.179743120
[32,] 6.321352361 2.820228174
[33,] 6.818523628 6.321352361
[34,] 3.393065401 6.818523628
[35,] 4.411248219 3.393065401
[36,] 3.048326605 4.411248219
[37,] -0.145529220 3.048326605
[38,] 1.831025112 -0.145529220
[39,] 6.056356129 1.831025112
[40,] -1.478817384 6.056356129
[41,] 1.545269555 -1.478817384
[42,] -5.517523749 1.545269555
[43,] 2.911879011 -5.517523749
[44,] 4.329843564 2.911879011
[45,] -0.319726412 4.329843564
[46,] -3.367154722 -0.319726412
[47,] 7.356632925 -3.367154722
[48,] -0.001636132 7.356632925
[49,] 3.279641401 -0.001636132
[50,] 2.193965330 3.279641401
[51,] -1.149235675 2.193965330
[52,] -4.029993364 -1.149235675
[53,] -1.544298774 -4.029993364
[54,] 3.286208113 -1.544298774
[55,] -1.330016252 3.286208113
[56,] 1.690504533 -1.330016252
[57,] 2.253909864 1.690504533
[58,] 0.461337808 2.253909864
[59,] -1.650700097 0.461337808
[60,] 1.759958349 -1.650700097
[61,] 1.080648648 1.759958349
[62,] 0.686760960 1.080648648
[63,] 4.015235398 0.686760960
[64,] 0.463729331 4.015235398
[65,] 4.187911271 0.463729331
[66,] -4.605587149 4.187911271
[67,] 5.527022465 -4.605587149
[68,] 1.304432643 5.527022465
[69,] 2.701578847 1.304432643
[70,] -5.012292206 2.701578847
[71,] -0.576234260 -5.012292206
[72,] -0.455616996 -0.576234260
[73,] -1.303750483 -0.455616996
[74,] -0.672895813 -1.303750483
[75,] -2.229907815 -0.672895813
[76,] 1.801803942 -2.229907815
[77,] -0.417505142 1.801803942
[78,] -0.020416595 -0.417505142
[79,] 0.946684493 -0.020416595
[80,] 1.581066735 0.946684493
[81,] -6.876379582 1.581066735
[82,] -2.442452062 -6.876379582
[83,] 1.034661118 -2.442452062
[84,] -3.363647839 1.034661118
[85,] -3.156378113 -3.363647839
[86,] 3.480988385 -3.156378113
[87,] 2.223074081 3.480988385
[88,] 2.607777584 2.223074081
[89,] -3.899527947 2.607777584
[90,] -6.304346484 -3.899527947
[91,] -1.215078194 -6.304346484
[92,] -2.714968381 -1.215078194
[93,] -0.067312900 -2.714968381
[94,] -2.544852594 -0.067312900
[95,] 3.412728081 -2.544852594
[96,] -4.086878937 3.412728081
[97,] -3.285381886 -4.086878937
[98,] -3.293258869 -3.285381886
[99,] -3.282026438 -3.293258869
[100,] 0.181958410 -3.282026438
[101,] -1.657387889 0.181958410
[102,] -0.899595338 -1.657387889
[103,] -1.333530542 -0.899595338
[104,] -3.812734574 -1.333530542
[105,] -1.768608342 -3.812734574
[106,] -2.300241770 -1.768608342
[107,] -1.749509226 -2.300241770
[108,] -0.036623939 -1.749509226
[109,] -3.740771250 -0.036623939
[110,] -4.835529679 -3.740771250
[111,] -8.629102564 -4.835529679
[112,] 2.449449010 -8.629102564
[113,] 11.436602795 2.449449010
[114,] 9.796046498 11.436602795
[115,] -0.967868258 9.796046498
[116,] 4.497816174 -0.967868258
[117,] 1.518720645 4.497816174
[118,] -1.194646895 1.518720645
[119,] -3.829180770 -1.194646895
[120,] 2.765504683 -3.829180770
[121,] 0.126649368 2.765504683
[122,] 4.927988630 0.126649368
[123,] -0.776736159 4.927988630
[124,] 0.939823766 -0.776736159
[125,] -1.595945067 0.939823766
[126,] 0.931021489 -1.595945067
[127,] 1.309401558 0.931021489
[128,] -1.982097230 1.309401558
[129,] 0.482190859 -1.982097230
[130,] 3.429621736 0.482190859
[131,] -4.312280292 3.429621736
[132,] 0.637049630 -4.312280292
[133,] -2.814931749 0.637049630
[134,] -6.364246931 -2.814931749
[135,] 1.969767838 -6.364246931
[136,] -0.087171249 1.969767838
[137,] 4.141709841 -0.087171249
[138,] -1.202194297 4.141709841
[139,] 3.506741173 -1.202194297
[140,] 3.270166617 3.506741173
[141,] 4.207932146 3.270166617
[142,] 1.828129189 4.207932146
[143,] 1.071480968 1.828129189
[144,] -0.016314343 1.071480968
[145,] 4.060679311 -0.016314343
[146,] -0.359675910 4.060679311
[147,] 0.008192347 -0.359675910
[148,] -7.126392700 0.008192347
[149,] -2.667818616 -7.126392700
[150,] 3.426226307 -2.667818616
[151,] 0.400436697 3.426226307
[152,] -2.612147523 0.400436697
[153,] 0.029113107 -2.612147523
[154,] 1.950546432 0.029113107
[155,] -1.748259965 1.950546432
[156,] 0.580162362 -1.748259965
[157,] 0.205146249 0.580162362
[158,] -6.683385384 0.205146249
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.618874557 1.003735793
2 5.739476885 2.618874557
3 -1.278974249 5.739476885
4 1.451052720 -1.278974249
5 -1.153848563 1.451052720
6 2.456773195 -1.153848563
7 3.576221861 2.456773195
8 -1.744194748 3.576221861
9 -0.647395718 -1.744194748
10 -3.782068519 -0.647395718
11 -4.370834301 -3.782068519
12 -8.208657394 -4.370834301
13 -1.074057121 -8.208657394
14 3.566459920 -1.074057121
15 2.936706959 3.566459920
16 1.009743142 2.936706959
17 -2.531630072 1.009743142
18 -2.056422015 -2.531630072
19 -0.978114004 -2.056422015
20 1.451506141 -0.978114004
21 -3.388686115 1.451506141
22 -3.402623019 -3.388686115
23 -5.828328162 -3.402623019
24 -3.152910453 -5.828328162
25 0.572849909 -3.152910453
26 1.681321490 0.572849909
27 -4.349755167 1.681321490
28 0.168675199 -4.349755167
29 0.449445988 0.168675199
30 -0.179743120 0.449445988
31 2.820228174 -0.179743120
32 6.321352361 2.820228174
33 6.818523628 6.321352361
34 3.393065401 6.818523628
35 4.411248219 3.393065401
36 3.048326605 4.411248219
37 -0.145529220 3.048326605
38 1.831025112 -0.145529220
39 6.056356129 1.831025112
40 -1.478817384 6.056356129
41 1.545269555 -1.478817384
42 -5.517523749 1.545269555
43 2.911879011 -5.517523749
44 4.329843564 2.911879011
45 -0.319726412 4.329843564
46 -3.367154722 -0.319726412
47 7.356632925 -3.367154722
48 -0.001636132 7.356632925
49 3.279641401 -0.001636132
50 2.193965330 3.279641401
51 -1.149235675 2.193965330
52 -4.029993364 -1.149235675
53 -1.544298774 -4.029993364
54 3.286208113 -1.544298774
55 -1.330016252 3.286208113
56 1.690504533 -1.330016252
57 2.253909864 1.690504533
58 0.461337808 2.253909864
59 -1.650700097 0.461337808
60 1.759958349 -1.650700097
61 1.080648648 1.759958349
62 0.686760960 1.080648648
63 4.015235398 0.686760960
64 0.463729331 4.015235398
65 4.187911271 0.463729331
66 -4.605587149 4.187911271
67 5.527022465 -4.605587149
68 1.304432643 5.527022465
69 2.701578847 1.304432643
70 -5.012292206 2.701578847
71 -0.576234260 -5.012292206
72 -0.455616996 -0.576234260
73 -1.303750483 -0.455616996
74 -0.672895813 -1.303750483
75 -2.229907815 -0.672895813
76 1.801803942 -2.229907815
77 -0.417505142 1.801803942
78 -0.020416595 -0.417505142
79 0.946684493 -0.020416595
80 1.581066735 0.946684493
81 -6.876379582 1.581066735
82 -2.442452062 -6.876379582
83 1.034661118 -2.442452062
84 -3.363647839 1.034661118
85 -3.156378113 -3.363647839
86 3.480988385 -3.156378113
87 2.223074081 3.480988385
88 2.607777584 2.223074081
89 -3.899527947 2.607777584
90 -6.304346484 -3.899527947
91 -1.215078194 -6.304346484
92 -2.714968381 -1.215078194
93 -0.067312900 -2.714968381
94 -2.544852594 -0.067312900
95 3.412728081 -2.544852594
96 -4.086878937 3.412728081
97 -3.285381886 -4.086878937
98 -3.293258869 -3.285381886
99 -3.282026438 -3.293258869
100 0.181958410 -3.282026438
101 -1.657387889 0.181958410
102 -0.899595338 -1.657387889
103 -1.333530542 -0.899595338
104 -3.812734574 -1.333530542
105 -1.768608342 -3.812734574
106 -2.300241770 -1.768608342
107 -1.749509226 -2.300241770
108 -0.036623939 -1.749509226
109 -3.740771250 -0.036623939
110 -4.835529679 -3.740771250
111 -8.629102564 -4.835529679
112 2.449449010 -8.629102564
113 11.436602795 2.449449010
114 9.796046498 11.436602795
115 -0.967868258 9.796046498
116 4.497816174 -0.967868258
117 1.518720645 4.497816174
118 -1.194646895 1.518720645
119 -3.829180770 -1.194646895
120 2.765504683 -3.829180770
121 0.126649368 2.765504683
122 4.927988630 0.126649368
123 -0.776736159 4.927988630
124 0.939823766 -0.776736159
125 -1.595945067 0.939823766
126 0.931021489 -1.595945067
127 1.309401558 0.931021489
128 -1.982097230 1.309401558
129 0.482190859 -1.982097230
130 3.429621736 0.482190859
131 -4.312280292 3.429621736
132 0.637049630 -4.312280292
133 -2.814931749 0.637049630
134 -6.364246931 -2.814931749
135 1.969767838 -6.364246931
136 -0.087171249 1.969767838
137 4.141709841 -0.087171249
138 -1.202194297 4.141709841
139 3.506741173 -1.202194297
140 3.270166617 3.506741173
141 4.207932146 3.270166617
142 1.828129189 4.207932146
143 1.071480968 1.828129189
144 -0.016314343 1.071480968
145 4.060679311 -0.016314343
146 -0.359675910 4.060679311
147 0.008192347 -0.359675910
148 -7.126392700 0.008192347
149 -2.667818616 -7.126392700
150 3.426226307 -2.667818616
151 0.400436697 3.426226307
152 -2.612147523 0.400436697
153 0.029113107 -2.612147523
154 1.950546432 0.029113107
155 -1.748259965 1.950546432
156 0.580162362 -1.748259965
157 0.205146249 0.580162362
158 -6.683385384 0.205146249
> 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/7b4aa1290610956.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8b4aa1290610956.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9b4aa1290610956.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10mw9d1290610956.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/117w701290610956.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/12tfo61290610956.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/1377mx1290610956.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/14a72l1290610956.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/15eq191290610956.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/16z8zf1290610956.tab")
+ }
>
> try(system("convert tmp/1xvuj1290610956.ps tmp/1xvuj1290610956.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xvuj1290610956.ps tmp/2xvuj1290610956.png",intern=TRUE))
character(0)
> try(system("convert tmp/38mbm1290610956.ps tmp/38mbm1290610956.png",intern=TRUE))
character(0)
> try(system("convert tmp/48mbm1290610956.ps tmp/48mbm1290610956.png",intern=TRUE))
character(0)
> try(system("convert tmp/58mbm1290610956.ps tmp/58mbm1290610956.png",intern=TRUE))
character(0)
> try(system("convert tmp/61dtp1290610956.ps tmp/61dtp1290610956.png",intern=TRUE))
character(0)
> try(system("convert tmp/7b4aa1290610956.ps tmp/7b4aa1290610956.png",intern=TRUE))
character(0)
> try(system("convert tmp/8b4aa1290610956.ps tmp/8b4aa1290610956.png",intern=TRUE))
character(0)
> try(system("convert tmp/9b4aa1290610956.ps tmp/9b4aa1290610956.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mw9d1290610956.ps tmp/10mw9d1290610956.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.841 2.676 6.321