R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(9
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+ ,4)
+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('month'
+ ,'I1'
+ ,'I2'
+ ,'I3'
+ ,'E1'
+ ,'E2'
+ ,'E3'
+ ,'A')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('month','I1','I2','I3','E1','E2','E3','A'),1:162))
> 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 = '2'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
I1 month I2 I3 E1 E2 E3 A
1 26 9 21 21 23 17 23 4
2 20 9 16 15 24 17 20 4
3 19 9 19 18 22 18 20 6
4 19 9 18 11 20 21 21 8
5 20 9 16 8 24 20 24 8
6 25 9 23 19 27 28 22 4
7 25 9 17 4 28 19 23 4
8 22 9 12 20 27 22 20 8
9 26 9 19 16 24 16 25 5
10 22 9 16 14 23 18 23 4
11 17 9 19 10 24 25 27 4
12 22 9 20 13 27 17 27 4
13 19 9 13 14 27 14 22 4
14 24 9 20 8 28 11 24 4
15 26 9 27 23 27 27 25 4
16 21 9 17 11 23 20 22 8
17 13 9 8 9 24 22 28 4
18 26 9 25 24 28 22 28 4
19 20 9 26 5 27 21 27 4
20 22 9 13 15 25 23 25 8
21 14 9 19 5 19 17 16 4
22 21 9 15 19 24 24 28 7
23 7 9 5 6 20 14 21 4
24 23 9 16 13 28 17 24 4
25 17 9 14 11 26 23 27 5
26 25 9 24 17 23 24 14 4
27 25 9 24 17 23 24 14 4
28 19 9 9 5 20 8 27 4
29 20 9 19 9 11 22 20 4
30 23 9 19 15 24 23 21 4
31 22 9 25 17 25 25 22 4
32 22 9 19 17 23 21 21 4
33 21 9 18 20 18 24 12 15
34 15 9 15 12 20 15 20 10
35 20 9 12 7 20 22 24 4
36 22 9 21 16 24 21 19 8
37 18 9 12 7 23 25 28 4
38 20 9 15 14 25 16 23 4
39 28 9 28 24 28 28 27 4
40 22 9 25 15 26 23 22 4
41 18 9 19 15 26 21 27 7
42 23 9 20 10 23 21 26 4
43 20 9 24 14 22 26 22 6
44 25 9 26 18 24 22 21 5
45 26 9 25 12 21 21 19 4
46 15 9 12 9 20 18 24 16
47 17 9 12 9 22 12 19 5
48 23 9 15 8 20 25 26 12
49 21 9 17 18 25 17 22 6
50 13 9 14 10 20 24 28 9
51 18 9 16 17 22 15 21 9
52 19 9 11 14 23 13 23 4
53 22 9 20 16 25 26 28 5
54 16 9 11 10 23 16 10 4
55 24 10 22 19 23 24 24 4
56 18 10 20 10 22 21 21 5
57 20 10 19 14 24 20 21 4
58 24 10 17 10 25 14 24 4
59 14 10 21 4 21 25 24 4
60 22 10 23 19 12 25 25 5
61 24 10 18 9 17 20 25 4
62 18 10 17 12 20 22 23 6
63 21 10 27 16 23 20 21 4
64 23 10 25 11 23 26 16 4
65 17 10 19 18 20 18 17 18
66 22 10 22 11 28 22 25 4
67 24 10 24 24 24 24 24 6
68 21 10 20 17 24 17 23 4
69 22 10 19 18 24 24 25 4
70 16 10 11 9 24 20 23 5
71 21 10 22 19 28 19 28 4
72 23 10 22 18 25 20 26 4
73 22 10 16 12 21 15 22 5
74 24 10 20 23 25 23 19 10
75 24 10 24 22 25 26 26 5
76 16 10 16 14 18 22 18 8
77 16 10 16 14 17 20 18 8
78 21 10 22 16 26 24 25 5
79 26 10 24 23 28 26 27 4
80 15 10 16 7 21 21 12 4
81 25 10 27 10 27 25 15 4
82 18 10 11 12 22 13 21 5
83 23 10 21 12 21 20 23 4
84 20 10 20 12 25 22 22 4
85 17 10 20 17 22 23 21 8
86 25 10 27 21 23 28 24 4
87 24 10 20 16 26 22 27 5
88 17 10 12 11 19 20 22 14
89 19 10 8 14 25 6 28 8
90 20 10 21 13 21 21 26 8
91 15 10 18 9 13 20 10 4
92 27 10 24 19 24 18 19 4
93 22 10 16 13 25 23 22 6
94 23 10 18 19 26 20 21 4
95 16 10 20 13 25 24 24 7
96 19 10 20 13 25 22 25 7
97 25 10 19 13 22 21 21 4
98 19 10 17 14 21 18 20 6
99 19 10 16 12 23 21 21 4
100 26 10 26 22 25 23 24 7
101 21 10 15 11 24 23 23 4
102 20 10 22 5 21 15 18 4
103 24 10 17 18 21 21 24 8
104 22 10 23 19 25 24 24 4
105 20 10 21 14 22 23 19 4
106 18 10 19 15 20 21 20 10
107 18 10 14 12 20 21 18 8
108 24 10 17 19 23 20 20 6
109 24 11 12 15 28 11 27 4
110 22 11 24 17 23 22 23 4
111 23 11 18 8 28 27 26 4
112 22 11 20 10 24 25 23 5
113 20 11 16 12 18 18 17 4
114 18 11 20 12 20 20 21 6
115 25 11 22 20 28 24 25 4
116 18 11 12 12 21 10 23 5
117 16 11 16 12 21 27 27 7
118 20 11 17 14 25 21 24 8
119 19 11 22 6 19 21 20 5
120 15 11 12 10 18 18 27 8
121 19 11 14 18 21 15 21 10
122 19 11 23 18 22 24 24 8
123 16 11 15 7 24 22 21 5
124 17 11 17 18 15 14 15 12
125 28 11 28 9 28 28 25 4
126 23 11 20 17 26 18 25 5
127 25 11 23 22 23 26 22 4
128 20 11 13 11 26 17 24 6
129 17 11 18 15 20 19 21 4
130 23 11 23 17 22 22 22 4
131 16 11 19 15 20 18 23 7
132 23 11 23 22 23 24 22 7
133 11 11 12 9 22 15 20 10
134 18 11 16 13 24 18 23 4
135 24 11 23 20 23 26 25 5
136 23 11 13 14 22 11 23 8
137 21 11 22 14 26 26 22 11
138 16 11 18 12 23 21 25 7
139 24 11 23 20 27 23 26 4
140 23 11 20 20 23 23 22 8
141 18 11 10 8 21 15 24 6
142 20 11 17 17 26 22 24 7
143 9 11 18 9 23 26 25 5
144 24 11 15 18 21 16 20 4
145 25 11 23 22 27 20 26 8
146 20 11 17 10 19 18 21 4
147 21 11 17 13 23 22 26 8
148 25 11 22 15 25 16 21 6
149 22 11 20 18 23 19 22 4
150 21 11 20 18 22 20 16 9
151 21 11 19 12 22 19 26 5
152 22 11 18 12 25 23 28 6
153 27 11 22 20 25 24 18 4
154 24 11 20 12 28 25 25 4
155 24 11 22 16 28 21 23 4
156 21 11 18 16 20 21 21 5
157 18 11 16 18 25 23 20 6
158 16 11 16 16 19 27 25 16
159 22 11 16 13 25 23 22 6
160 20 11 16 17 22 18 21 6
161 18 11 17 13 18 16 16 4
162 20 11 18 17 20 16 18 4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month I2 I3 E1 E2
9.07917 -0.20072 0.36214 0.25506 0.25766 -0.11655
E3 A
0.04076 -0.20983
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.5510 -1.4638 0.0039 1.7360 7.4735
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.07917 3.13588 2.895 0.004340 **
month -0.20072 0.24383 -0.823 0.411666
I2 0.36214 0.06303 5.746 4.75e-08 ***
I3 0.25506 0.05055 5.046 1.26e-06 ***
E1 0.25766 0.07522 3.425 0.000788 ***
E2 -0.11655 0.05886 -1.980 0.049484 *
E3 0.04076 0.06130 0.665 0.507092
A -0.20983 0.08415 -2.493 0.013711 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.503 on 154 degrees of freedom
Multiple R-squared: 0.5521, Adjusted R-squared: 0.5317
F-statistic: 27.11 on 7 and 154 DF, p-value: < 2.2e-16
> 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,] 0.74870598 0.50258804 0.25129402
[2,] 0.87617009 0.24765983 0.12382991
[3,] 0.89428978 0.21142045 0.10571022
[4,] 0.86213026 0.27573948 0.13786974
[5,] 0.80162751 0.39674497 0.19837249
[6,] 0.72654416 0.54691167 0.27345584
[7,] 0.65450274 0.69099451 0.34549726
[8,] 0.56955889 0.86088222 0.43044111
[9,] 0.65922280 0.68155440 0.34077720
[10,] 0.64803456 0.70393088 0.35196544
[11,] 0.65479800 0.69040401 0.34520200
[12,] 0.57729075 0.84541849 0.42270925
[13,] 0.70968454 0.58063092 0.29031546
[14,] 0.64708339 0.70583321 0.35291661
[15,] 0.60608279 0.78783441 0.39391721
[16,] 0.58859721 0.82280558 0.41140279
[17,] 0.54255085 0.91489830 0.45744915
[18,] 0.77345399 0.45309203 0.22654601
[19,] 0.89739792 0.20520415 0.10260208
[20,] 0.88260137 0.23479727 0.11739863
[21,] 0.87506465 0.24987070 0.12493535
[22,] 0.84045475 0.31909050 0.15954525
[23,] 0.83707777 0.32584445 0.16292223
[24,] 0.89003076 0.21993847 0.10996924
[25,] 0.93141966 0.13716067 0.06858034
[26,] 0.91184901 0.17630198 0.08815099
[27,] 0.89490766 0.21018468 0.10509234
[28,] 0.86866568 0.26266864 0.13133432
[29,] 0.83660537 0.32678927 0.16339463
[30,] 0.82826331 0.34347338 0.17173669
[31,] 0.87095332 0.25809336 0.12904668
[32,] 0.86261649 0.27476702 0.13738351
[33,] 0.85215753 0.29568495 0.14784247
[34,] 0.81911759 0.36176482 0.18088241
[35,] 0.84553068 0.30893864 0.15446932
[36,] 0.81400266 0.37199467 0.18599734
[37,] 0.78079012 0.43841976 0.21920988
[38,] 0.94013149 0.11973703 0.05986851
[39,] 0.92487845 0.15024309 0.07512155
[40,] 0.94576188 0.10847624 0.05423812
[41,] 0.94155863 0.11688273 0.05844137
[42,] 0.92670745 0.14658511 0.07329255
[43,] 0.90768866 0.18462268 0.09231134
[44,] 0.89195232 0.21609536 0.10804768
[45,] 0.86814077 0.26371846 0.13185923
[46,] 0.85765556 0.28468887 0.14234444
[47,] 0.83307058 0.33385885 0.16692942
[48,] 0.86159067 0.27681867 0.13840933
[49,] 0.90459764 0.19080472 0.09540236
[50,] 0.89315696 0.21368607 0.10684304
[51,] 0.96386609 0.07226782 0.03613391
[52,] 0.95447834 0.09104332 0.04552166
[53,] 0.96308006 0.07383989 0.03691994
[54,] 0.95626476 0.08747048 0.04373524
[55,] 0.95015577 0.09968846 0.04984423
[56,] 0.93768527 0.12462946 0.06231473
[57,] 0.92291274 0.15417452 0.07708726
[58,] 0.91339254 0.17321492 0.08660746
[59,] 0.89351911 0.21296178 0.10648089
[60,] 0.87370626 0.25258748 0.12629374
[61,] 0.90778650 0.18442700 0.09221350
[62,] 0.89072318 0.21855365 0.10927682
[63,] 0.89575523 0.20848955 0.10424477
[64,] 0.88228975 0.23542049 0.11771025
[65,] 0.85929394 0.28141213 0.14070606
[66,] 0.84054032 0.31891935 0.15945968
[67,] 0.81896771 0.36206458 0.18103229
[68,] 0.80622009 0.38755981 0.19377991
[69,] 0.77812451 0.44375098 0.22187549
[70,] 0.76909040 0.46181920 0.23090960
[71,] 0.75239348 0.49521304 0.24760652
[72,] 0.71964931 0.56070138 0.28035069
[73,] 0.71173850 0.57652301 0.28826150
[74,] 0.68535235 0.62929531 0.31464765
[75,] 0.73489978 0.53020043 0.26510022
[76,] 0.69499022 0.61001957 0.30500978
[77,] 0.66891317 0.66217366 0.33108683
[78,] 0.67682822 0.64634356 0.32317178
[79,] 0.64240873 0.71518253 0.35759127
[80,] 0.59900376 0.80199247 0.40099624
[81,] 0.56253426 0.87493149 0.43746574
[82,] 0.55256084 0.89487832 0.44743916
[83,] 0.54696151 0.90607699 0.45303849
[84,] 0.50898782 0.98202436 0.49101218
[85,] 0.63938481 0.72123038 0.36061519
[86,] 0.63854333 0.72291334 0.36145667
[87,] 0.71987656 0.56024689 0.28012344
[88,] 0.68392817 0.63214365 0.31607183
[89,] 0.64848555 0.70302890 0.35151445
[90,] 0.60473655 0.79052691 0.39526345
[91,] 0.57985261 0.84029479 0.42014739
[92,] 0.53253750 0.93492499 0.46746250
[93,] 0.60899950 0.78200100 0.39100050
[94,] 0.60473569 0.79052862 0.39526431
[95,] 0.58306503 0.83386993 0.41693497
[96,] 0.56020894 0.87958213 0.43979106
[97,] 0.51452769 0.97094463 0.48547231
[98,] 0.48709577 0.97419154 0.51290423
[99,] 0.47322846 0.94645692 0.52677154
[100,] 0.44152326 0.88304651 0.55847674
[101,] 0.46650170 0.93300339 0.53349830
[102,] 0.45664762 0.91329524 0.54335238
[103,] 0.44968237 0.89936474 0.55031763
[104,] 0.41408726 0.82817451 0.58591274
[105,] 0.36571614 0.73143228 0.63428386
[106,] 0.32028034 0.64056069 0.67971966
[107,] 0.28398161 0.56796322 0.71601839
[108,] 0.24025449 0.48050898 0.75974551
[109,] 0.20915315 0.41830631 0.79084685
[110,] 0.17886308 0.35772615 0.82113692
[111,] 0.14528051 0.29056103 0.85471949
[112,] 0.15022671 0.30045343 0.84977329
[113,] 0.12366109 0.24732217 0.87633891
[114,] 0.09820082 0.19640165 0.90179918
[115,] 0.18705416 0.37410832 0.81294584
[116,] 0.15653954 0.31307908 0.84346046
[117,] 0.12915637 0.25831273 0.87084363
[118,] 0.10252484 0.20504968 0.89747516
[119,] 0.10719447 0.21438894 0.89280553
[120,] 0.08291305 0.16582610 0.91708695
[121,] 0.12159758 0.24319516 0.87840242
[122,] 0.09768900 0.19537799 0.90231100
[123,] 0.21502204 0.43004407 0.78497796
[124,] 0.21906932 0.43813864 0.78093068
[125,] 0.20142195 0.40284390 0.79857805
[126,] 0.17931607 0.35863214 0.82068393
[127,] 0.14018940 0.28037879 0.85981060
[128,] 0.16364088 0.32728176 0.83635912
[129,] 0.12779435 0.25558870 0.87220565
[130,] 0.09872327 0.19744653 0.90127673
[131,] 0.07219846 0.14439691 0.92780154
[132,] 0.06002001 0.12004002 0.93997999
[133,] 0.85877659 0.28244682 0.14122341
[134,] 0.97753554 0.04492891 0.02246446
[135,] 0.95756841 0.08486317 0.04243159
[136,] 0.92489955 0.15020090 0.07510045
[137,] 0.89344255 0.21311490 0.10655745
[138,] 0.87618920 0.24762161 0.12381080
[139,] 0.79418535 0.41162929 0.20581465
[140,] 0.68061731 0.63876538 0.31938269
[141,] 0.51559487 0.96881026 0.48440513
> postscript(file="/var/wessaorg/rcomp/tmp/1tney1353260895.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2zvxz1353260895.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/303911353260895.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4oq401353260895.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5za0x1353260895.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 162
Frequency = 1
1 2 3 4 5 6
1.723203780 -1.071134895 -2.871210808 0.520201090 1.740205222 0.801189290
7 8 9 10 11 12
5.452579116 1.751180502 3.476886765 1.435858759 -3.235171577 -1.067838901
13 14 15 16 17 18
-1.933792092 1.372794224 -0.906419344 1.952059871 -3.386998044 -1.399868813
19 20 21 22 23 24
-2.733994286 3.092421301 -4.155625570 0.390012553 -7.151847206 1.245323980
25 26 27 28 29 30
-2.218128203 1.839675074 1.839675074 2.710839194 3.305104712 1.500976944
31 32 33 34 35 36
-2.247289313 0.015421052 2.925218449 -2.887317405 3.868234297 0.209369722
37 38 39 40 41 42
1.281871573 -0.950413948 0.253757403 -2.227920855 -3.862495983 2.234918985
43 44 45 46 47 48
-1.810796475 0.294114691 3.714728439 0.409847644 -0.909058305 7.473502286
49 50 51 52 53 54
-1.117971841 -3.502022216 -2.290655985 -0.336188661 -0.099711772 -1.436463924
55 56 57 58 59 60
0.846976586 -1.893091127 -1.392884450 3.272420114 -4.333120141 1.604695646
61 62 63 64 65 66
5.885132338 -0.556623753 -3.542445620 1.360197113 -1.514982738 -0.674683803
67 68 69 70 71 72
-0.990603714 -1.951357312 -0.109966483 -1.092172178 -4.187077371 -0.960981411
73 74 75 76 77 78
2.562957857 1.381893908 -0.796388961 -1.565850906 -1.541286441 -1.991747853
79 80 81 82 83 84
-0.075007636 -2.264714978 1.784560144 -0.076349982 2.084421328 -1.310223121
85 86 87 88 89 90
-3.815938374 -0.007643209 1.417918803 2.253059043 0.255319196 -0.337056511
91 92 93 94 95 96
-1.472877109 2.369548446 2.419466579 0.178635668 -4.784223675 -2.058074580
97 98 99 100 101 102
4.494038951 -0.668318161 -0.422146930 0.630863615 2.088971781 0.128756851
103 104 105 106 107 108
3.917706471 -2.030477153 -1.170687883 -1.201047284 1.036680976 2.774158300
109 110 111 112 113 114
2.763638410 -1.358795332 3.281742336 2.176984569 1.880251162 -1.593896214
115 116 117 118 119 120
0.463587311 -0.411265123 -1.621893258 0.108033459 0.417325575 -0.729343821
121 122 123 124 125 126
0.047481534 -2.962416380 -1.515274223 -0.945331702 4.562612461 -0.021095430
127 128 129 130 131 132
1.234985772 1.178264352 -3.171002381 0.301757413 -4.101720706 -0.368627660
133 134 135 136 137 138
-5.149603105 -2.165301239 0.832661326 4.204846107 0.333417358 -3.479251145
139 140 141 142 143 144
-0.798195928 1.321184723 2.085054205 -1.008085162 -9.550989840 3.583687638
145 146 147 148 149 150
0.181350186 1.607547209 1.913442483 2.162170158 -0.474189400 0.193692578
151 152 153 154 155 156
0.722765235 1.906427882 3.521862175 2.344890345 0.215701826 1.016857919
157 158 159 160 161 162
-2.573600933 -0.156862377 2.620185582 -0.169057689 -0.929282920 -0.908491748
> postscript(file="/var/wessaorg/rcomp/tmp/65h6a1353260895.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 1.723203780 NA
1 -1.071134895 1.723203780
2 -2.871210808 -1.071134895
3 0.520201090 -2.871210808
4 1.740205222 0.520201090
5 0.801189290 1.740205222
6 5.452579116 0.801189290
7 1.751180502 5.452579116
8 3.476886765 1.751180502
9 1.435858759 3.476886765
10 -3.235171577 1.435858759
11 -1.067838901 -3.235171577
12 -1.933792092 -1.067838901
13 1.372794224 -1.933792092
14 -0.906419344 1.372794224
15 1.952059871 -0.906419344
16 -3.386998044 1.952059871
17 -1.399868813 -3.386998044
18 -2.733994286 -1.399868813
19 3.092421301 -2.733994286
20 -4.155625570 3.092421301
21 0.390012553 -4.155625570
22 -7.151847206 0.390012553
23 1.245323980 -7.151847206
24 -2.218128203 1.245323980
25 1.839675074 -2.218128203
26 1.839675074 1.839675074
27 2.710839194 1.839675074
28 3.305104712 2.710839194
29 1.500976944 3.305104712
30 -2.247289313 1.500976944
31 0.015421052 -2.247289313
32 2.925218449 0.015421052
33 -2.887317405 2.925218449
34 3.868234297 -2.887317405
35 0.209369722 3.868234297
36 1.281871573 0.209369722
37 -0.950413948 1.281871573
38 0.253757403 -0.950413948
39 -2.227920855 0.253757403
40 -3.862495983 -2.227920855
41 2.234918985 -3.862495983
42 -1.810796475 2.234918985
43 0.294114691 -1.810796475
44 3.714728439 0.294114691
45 0.409847644 3.714728439
46 -0.909058305 0.409847644
47 7.473502286 -0.909058305
48 -1.117971841 7.473502286
49 -3.502022216 -1.117971841
50 -2.290655985 -3.502022216
51 -0.336188661 -2.290655985
52 -0.099711772 -0.336188661
53 -1.436463924 -0.099711772
54 0.846976586 -1.436463924
55 -1.893091127 0.846976586
56 -1.392884450 -1.893091127
57 3.272420114 -1.392884450
58 -4.333120141 3.272420114
59 1.604695646 -4.333120141
60 5.885132338 1.604695646
61 -0.556623753 5.885132338
62 -3.542445620 -0.556623753
63 1.360197113 -3.542445620
64 -1.514982738 1.360197113
65 -0.674683803 -1.514982738
66 -0.990603714 -0.674683803
67 -1.951357312 -0.990603714
68 -0.109966483 -1.951357312
69 -1.092172178 -0.109966483
70 -4.187077371 -1.092172178
71 -0.960981411 -4.187077371
72 2.562957857 -0.960981411
73 1.381893908 2.562957857
74 -0.796388961 1.381893908
75 -1.565850906 -0.796388961
76 -1.541286441 -1.565850906
77 -1.991747853 -1.541286441
78 -0.075007636 -1.991747853
79 -2.264714978 -0.075007636
80 1.784560144 -2.264714978
81 -0.076349982 1.784560144
82 2.084421328 -0.076349982
83 -1.310223121 2.084421328
84 -3.815938374 -1.310223121
85 -0.007643209 -3.815938374
86 1.417918803 -0.007643209
87 2.253059043 1.417918803
88 0.255319196 2.253059043
89 -0.337056511 0.255319196
90 -1.472877109 -0.337056511
91 2.369548446 -1.472877109
92 2.419466579 2.369548446
93 0.178635668 2.419466579
94 -4.784223675 0.178635668
95 -2.058074580 -4.784223675
96 4.494038951 -2.058074580
97 -0.668318161 4.494038951
98 -0.422146930 -0.668318161
99 0.630863615 -0.422146930
100 2.088971781 0.630863615
101 0.128756851 2.088971781
102 3.917706471 0.128756851
103 -2.030477153 3.917706471
104 -1.170687883 -2.030477153
105 -1.201047284 -1.170687883
106 1.036680976 -1.201047284
107 2.774158300 1.036680976
108 2.763638410 2.774158300
109 -1.358795332 2.763638410
110 3.281742336 -1.358795332
111 2.176984569 3.281742336
112 1.880251162 2.176984569
113 -1.593896214 1.880251162
114 0.463587311 -1.593896214
115 -0.411265123 0.463587311
116 -1.621893258 -0.411265123
117 0.108033459 -1.621893258
118 0.417325575 0.108033459
119 -0.729343821 0.417325575
120 0.047481534 -0.729343821
121 -2.962416380 0.047481534
122 -1.515274223 -2.962416380
123 -0.945331702 -1.515274223
124 4.562612461 -0.945331702
125 -0.021095430 4.562612461
126 1.234985772 -0.021095430
127 1.178264352 1.234985772
128 -3.171002381 1.178264352
129 0.301757413 -3.171002381
130 -4.101720706 0.301757413
131 -0.368627660 -4.101720706
132 -5.149603105 -0.368627660
133 -2.165301239 -5.149603105
134 0.832661326 -2.165301239
135 4.204846107 0.832661326
136 0.333417358 4.204846107
137 -3.479251145 0.333417358
138 -0.798195928 -3.479251145
139 1.321184723 -0.798195928
140 2.085054205 1.321184723
141 -1.008085162 2.085054205
142 -9.550989840 -1.008085162
143 3.583687638 -9.550989840
144 0.181350186 3.583687638
145 1.607547209 0.181350186
146 1.913442483 1.607547209
147 2.162170158 1.913442483
148 -0.474189400 2.162170158
149 0.193692578 -0.474189400
150 0.722765235 0.193692578
151 1.906427882 0.722765235
152 3.521862175 1.906427882
153 2.344890345 3.521862175
154 0.215701826 2.344890345
155 1.016857919 0.215701826
156 -2.573600933 1.016857919
157 -0.156862377 -2.573600933
158 2.620185582 -0.156862377
159 -0.169057689 2.620185582
160 -0.929282920 -0.169057689
161 -0.908491748 -0.929282920
162 NA -0.908491748
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.071134895 1.723203780
[2,] -2.871210808 -1.071134895
[3,] 0.520201090 -2.871210808
[4,] 1.740205222 0.520201090
[5,] 0.801189290 1.740205222
[6,] 5.452579116 0.801189290
[7,] 1.751180502 5.452579116
[8,] 3.476886765 1.751180502
[9,] 1.435858759 3.476886765
[10,] -3.235171577 1.435858759
[11,] -1.067838901 -3.235171577
[12,] -1.933792092 -1.067838901
[13,] 1.372794224 -1.933792092
[14,] -0.906419344 1.372794224
[15,] 1.952059871 -0.906419344
[16,] -3.386998044 1.952059871
[17,] -1.399868813 -3.386998044
[18,] -2.733994286 -1.399868813
[19,] 3.092421301 -2.733994286
[20,] -4.155625570 3.092421301
[21,] 0.390012553 -4.155625570
[22,] -7.151847206 0.390012553
[23,] 1.245323980 -7.151847206
[24,] -2.218128203 1.245323980
[25,] 1.839675074 -2.218128203
[26,] 1.839675074 1.839675074
[27,] 2.710839194 1.839675074
[28,] 3.305104712 2.710839194
[29,] 1.500976944 3.305104712
[30,] -2.247289313 1.500976944
[31,] 0.015421052 -2.247289313
[32,] 2.925218449 0.015421052
[33,] -2.887317405 2.925218449
[34,] 3.868234297 -2.887317405
[35,] 0.209369722 3.868234297
[36,] 1.281871573 0.209369722
[37,] -0.950413948 1.281871573
[38,] 0.253757403 -0.950413948
[39,] -2.227920855 0.253757403
[40,] -3.862495983 -2.227920855
[41,] 2.234918985 -3.862495983
[42,] -1.810796475 2.234918985
[43,] 0.294114691 -1.810796475
[44,] 3.714728439 0.294114691
[45,] 0.409847644 3.714728439
[46,] -0.909058305 0.409847644
[47,] 7.473502286 -0.909058305
[48,] -1.117971841 7.473502286
[49,] -3.502022216 -1.117971841
[50,] -2.290655985 -3.502022216
[51,] -0.336188661 -2.290655985
[52,] -0.099711772 -0.336188661
[53,] -1.436463924 -0.099711772
[54,] 0.846976586 -1.436463924
[55,] -1.893091127 0.846976586
[56,] -1.392884450 -1.893091127
[57,] 3.272420114 -1.392884450
[58,] -4.333120141 3.272420114
[59,] 1.604695646 -4.333120141
[60,] 5.885132338 1.604695646
[61,] -0.556623753 5.885132338
[62,] -3.542445620 -0.556623753
[63,] 1.360197113 -3.542445620
[64,] -1.514982738 1.360197113
[65,] -0.674683803 -1.514982738
[66,] -0.990603714 -0.674683803
[67,] -1.951357312 -0.990603714
[68,] -0.109966483 -1.951357312
[69,] -1.092172178 -0.109966483
[70,] -4.187077371 -1.092172178
[71,] -0.960981411 -4.187077371
[72,] 2.562957857 -0.960981411
[73,] 1.381893908 2.562957857
[74,] -0.796388961 1.381893908
[75,] -1.565850906 -0.796388961
[76,] -1.541286441 -1.565850906
[77,] -1.991747853 -1.541286441
[78,] -0.075007636 -1.991747853
[79,] -2.264714978 -0.075007636
[80,] 1.784560144 -2.264714978
[81,] -0.076349982 1.784560144
[82,] 2.084421328 -0.076349982
[83,] -1.310223121 2.084421328
[84,] -3.815938374 -1.310223121
[85,] -0.007643209 -3.815938374
[86,] 1.417918803 -0.007643209
[87,] 2.253059043 1.417918803
[88,] 0.255319196 2.253059043
[89,] -0.337056511 0.255319196
[90,] -1.472877109 -0.337056511
[91,] 2.369548446 -1.472877109
[92,] 2.419466579 2.369548446
[93,] 0.178635668 2.419466579
[94,] -4.784223675 0.178635668
[95,] -2.058074580 -4.784223675
[96,] 4.494038951 -2.058074580
[97,] -0.668318161 4.494038951
[98,] -0.422146930 -0.668318161
[99,] 0.630863615 -0.422146930
[100,] 2.088971781 0.630863615
[101,] 0.128756851 2.088971781
[102,] 3.917706471 0.128756851
[103,] -2.030477153 3.917706471
[104,] -1.170687883 -2.030477153
[105,] -1.201047284 -1.170687883
[106,] 1.036680976 -1.201047284
[107,] 2.774158300 1.036680976
[108,] 2.763638410 2.774158300
[109,] -1.358795332 2.763638410
[110,] 3.281742336 -1.358795332
[111,] 2.176984569 3.281742336
[112,] 1.880251162 2.176984569
[113,] -1.593896214 1.880251162
[114,] 0.463587311 -1.593896214
[115,] -0.411265123 0.463587311
[116,] -1.621893258 -0.411265123
[117,] 0.108033459 -1.621893258
[118,] 0.417325575 0.108033459
[119,] -0.729343821 0.417325575
[120,] 0.047481534 -0.729343821
[121,] -2.962416380 0.047481534
[122,] -1.515274223 -2.962416380
[123,] -0.945331702 -1.515274223
[124,] 4.562612461 -0.945331702
[125,] -0.021095430 4.562612461
[126,] 1.234985772 -0.021095430
[127,] 1.178264352 1.234985772
[128,] -3.171002381 1.178264352
[129,] 0.301757413 -3.171002381
[130,] -4.101720706 0.301757413
[131,] -0.368627660 -4.101720706
[132,] -5.149603105 -0.368627660
[133,] -2.165301239 -5.149603105
[134,] 0.832661326 -2.165301239
[135,] 4.204846107 0.832661326
[136,] 0.333417358 4.204846107
[137,] -3.479251145 0.333417358
[138,] -0.798195928 -3.479251145
[139,] 1.321184723 -0.798195928
[140,] 2.085054205 1.321184723
[141,] -1.008085162 2.085054205
[142,] -9.550989840 -1.008085162
[143,] 3.583687638 -9.550989840
[144,] 0.181350186 3.583687638
[145,] 1.607547209 0.181350186
[146,] 1.913442483 1.607547209
[147,] 2.162170158 1.913442483
[148,] -0.474189400 2.162170158
[149,] 0.193692578 -0.474189400
[150,] 0.722765235 0.193692578
[151,] 1.906427882 0.722765235
[152,] 3.521862175 1.906427882
[153,] 2.344890345 3.521862175
[154,] 0.215701826 2.344890345
[155,] 1.016857919 0.215701826
[156,] -2.573600933 1.016857919
[157,] -0.156862377 -2.573600933
[158,] 2.620185582 -0.156862377
[159,] -0.169057689 2.620185582
[160,] -0.929282920 -0.169057689
[161,] -0.908491748 -0.929282920
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.071134895 1.723203780
2 -2.871210808 -1.071134895
3 0.520201090 -2.871210808
4 1.740205222 0.520201090
5 0.801189290 1.740205222
6 5.452579116 0.801189290
7 1.751180502 5.452579116
8 3.476886765 1.751180502
9 1.435858759 3.476886765
10 -3.235171577 1.435858759
11 -1.067838901 -3.235171577
12 -1.933792092 -1.067838901
13 1.372794224 -1.933792092
14 -0.906419344 1.372794224
15 1.952059871 -0.906419344
16 -3.386998044 1.952059871
17 -1.399868813 -3.386998044
18 -2.733994286 -1.399868813
19 3.092421301 -2.733994286
20 -4.155625570 3.092421301
21 0.390012553 -4.155625570
22 -7.151847206 0.390012553
23 1.245323980 -7.151847206
24 -2.218128203 1.245323980
25 1.839675074 -2.218128203
26 1.839675074 1.839675074
27 2.710839194 1.839675074
28 3.305104712 2.710839194
29 1.500976944 3.305104712
30 -2.247289313 1.500976944
31 0.015421052 -2.247289313
32 2.925218449 0.015421052
33 -2.887317405 2.925218449
34 3.868234297 -2.887317405
35 0.209369722 3.868234297
36 1.281871573 0.209369722
37 -0.950413948 1.281871573
38 0.253757403 -0.950413948
39 -2.227920855 0.253757403
40 -3.862495983 -2.227920855
41 2.234918985 -3.862495983
42 -1.810796475 2.234918985
43 0.294114691 -1.810796475
44 3.714728439 0.294114691
45 0.409847644 3.714728439
46 -0.909058305 0.409847644
47 7.473502286 -0.909058305
48 -1.117971841 7.473502286
49 -3.502022216 -1.117971841
50 -2.290655985 -3.502022216
51 -0.336188661 -2.290655985
52 -0.099711772 -0.336188661
53 -1.436463924 -0.099711772
54 0.846976586 -1.436463924
55 -1.893091127 0.846976586
56 -1.392884450 -1.893091127
57 3.272420114 -1.392884450
58 -4.333120141 3.272420114
59 1.604695646 -4.333120141
60 5.885132338 1.604695646
61 -0.556623753 5.885132338
62 -3.542445620 -0.556623753
63 1.360197113 -3.542445620
64 -1.514982738 1.360197113
65 -0.674683803 -1.514982738
66 -0.990603714 -0.674683803
67 -1.951357312 -0.990603714
68 -0.109966483 -1.951357312
69 -1.092172178 -0.109966483
70 -4.187077371 -1.092172178
71 -0.960981411 -4.187077371
72 2.562957857 -0.960981411
73 1.381893908 2.562957857
74 -0.796388961 1.381893908
75 -1.565850906 -0.796388961
76 -1.541286441 -1.565850906
77 -1.991747853 -1.541286441
78 -0.075007636 -1.991747853
79 -2.264714978 -0.075007636
80 1.784560144 -2.264714978
81 -0.076349982 1.784560144
82 2.084421328 -0.076349982
83 -1.310223121 2.084421328
84 -3.815938374 -1.310223121
85 -0.007643209 -3.815938374
86 1.417918803 -0.007643209
87 2.253059043 1.417918803
88 0.255319196 2.253059043
89 -0.337056511 0.255319196
90 -1.472877109 -0.337056511
91 2.369548446 -1.472877109
92 2.419466579 2.369548446
93 0.178635668 2.419466579
94 -4.784223675 0.178635668
95 -2.058074580 -4.784223675
96 4.494038951 -2.058074580
97 -0.668318161 4.494038951
98 -0.422146930 -0.668318161
99 0.630863615 -0.422146930
100 2.088971781 0.630863615
101 0.128756851 2.088971781
102 3.917706471 0.128756851
103 -2.030477153 3.917706471
104 -1.170687883 -2.030477153
105 -1.201047284 -1.170687883
106 1.036680976 -1.201047284
107 2.774158300 1.036680976
108 2.763638410 2.774158300
109 -1.358795332 2.763638410
110 3.281742336 -1.358795332
111 2.176984569 3.281742336
112 1.880251162 2.176984569
113 -1.593896214 1.880251162
114 0.463587311 -1.593896214
115 -0.411265123 0.463587311
116 -1.621893258 -0.411265123
117 0.108033459 -1.621893258
118 0.417325575 0.108033459
119 -0.729343821 0.417325575
120 0.047481534 -0.729343821
121 -2.962416380 0.047481534
122 -1.515274223 -2.962416380
123 -0.945331702 -1.515274223
124 4.562612461 -0.945331702
125 -0.021095430 4.562612461
126 1.234985772 -0.021095430
127 1.178264352 1.234985772
128 -3.171002381 1.178264352
129 0.301757413 -3.171002381
130 -4.101720706 0.301757413
131 -0.368627660 -4.101720706
132 -5.149603105 -0.368627660
133 -2.165301239 -5.149603105
134 0.832661326 -2.165301239
135 4.204846107 0.832661326
136 0.333417358 4.204846107
137 -3.479251145 0.333417358
138 -0.798195928 -3.479251145
139 1.321184723 -0.798195928
140 2.085054205 1.321184723
141 -1.008085162 2.085054205
142 -9.550989840 -1.008085162
143 3.583687638 -9.550989840
144 0.181350186 3.583687638
145 1.607547209 0.181350186
146 1.913442483 1.607547209
147 2.162170158 1.913442483
148 -0.474189400 2.162170158
149 0.193692578 -0.474189400
150 0.722765235 0.193692578
151 1.906427882 0.722765235
152 3.521862175 1.906427882
153 2.344890345 3.521862175
154 0.215701826 2.344890345
155 1.016857919 0.215701826
156 -2.573600933 1.016857919
157 -0.156862377 -2.573600933
158 2.620185582 -0.156862377
159 -0.169057689 2.620185582
160 -0.929282920 -0.169057689
161 -0.908491748 -0.929282920
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7wmnd1353260895.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8pdw91353260895.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/90bb21353260895.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10wnpa1353260895.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11kael1353260895.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/128ql41353260895.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/133pcc1353260895.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/149oi31353260895.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/1565hn1353260895.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16p8h31353260896.tab")
+ }
>
> try(system("convert tmp/1tney1353260895.ps tmp/1tney1353260895.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zvxz1353260895.ps tmp/2zvxz1353260895.png",intern=TRUE))
character(0)
> try(system("convert tmp/303911353260895.ps tmp/303911353260895.png",intern=TRUE))
character(0)
> try(system("convert tmp/4oq401353260895.ps tmp/4oq401353260895.png",intern=TRUE))
character(0)
> try(system("convert tmp/5za0x1353260895.ps tmp/5za0x1353260895.png",intern=TRUE))
character(0)
> try(system("convert tmp/65h6a1353260895.ps tmp/65h6a1353260895.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wmnd1353260895.ps tmp/7wmnd1353260895.png",intern=TRUE))
character(0)
> try(system("convert tmp/8pdw91353260895.ps tmp/8pdw91353260895.png",intern=TRUE))
character(0)
> try(system("convert tmp/90bb21353260895.ps tmp/90bb21353260895.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wnpa1353260895.ps tmp/10wnpa1353260895.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.665 1.235 9.908