R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(7
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+ ,5)
+ ,dim=c(5
+ ,164)
+ ,dimnames=list(c('Q1'
+ ,'Q2'
+ ,'Q3'
+ ,'Q4'
+ ,'Q5')
+ ,1:164))
> y <- array(NA,dim=c(5,164),dimnames=list(c('Q1','Q2','Q3','Q4','Q5'),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
> 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 Q2 Q3 Q4 Q5
1 7 7 7 7 7
2 5 5 5 5 5
3 6 5 6 4 5
4 5 5 6 5 6
5 6 7 5 6 7
6 6 5 6 5 7
7 6 3 7 7 7
8 6 6 6 5 6
9 4 5 6 4 5
10 6 3 6 6 6
11 6 7 7 7 7
12 3 7 7 4 7
13 5 6 7 6 6
14 5 7 7 5 7
15 2 4 5 2 6
16 3 7 7 5 7
17 6 7 6 6 5
18 6 7 6 6 5
19 5 3 6 5 7
20 7 5 6 5 6
21 5 5 5 6 6
22 5 5 3 5 1
23 5 7 7 5 7
24 5 7 6 5 6
25 5 6 7 5 7
26 6 6 7 7 6
27 5 7 6 5 6
28 5 6 6 3 6
29 6 5 6 5 6
30 4 5 6 4 5
31 4 3 5 6 5
32 6 7 7 5 7
33 3 6 4 4 3
34 6 5 5 5 6
35 5 5 6 5 5
36 6 7 7 6 6
37 7 6 7 5 7
38 4 6 6 5 6
39 5 7 6 5 5
40 4 5 4 4 5
41 5 6 7 5 6
42 3 5 7 5 7
43 5 5 7 5 7
44 6 6 5 6 5
45 6 7 7 6 7
46 4 6 5 4 5
47 4 5 5 4 5
48 6 6 6 5 5
49 6 6 6 6 6
50 5 7 6 6 6
51 6 7 7 6 7
52 4 5 5 4 7
53 4 3 7 6 7
54 5 6 6 5 7
55 3 6 5 4 2
56 6 6 7 6 6
57 6 6 7 6 6
58 4 6 6 4 6
59 5 7 7 5 7
60 5 6 5 5 5
61 4 6 6 6 7
62 6 5 6 6 6
63 5 6 6 6 6
64 4 6 5 5 5
65 6 6 7 5 6
66 5 4 7 7 7
67 6 6 6 6 6
68 5 7 7 7 7
69 6 7 7 6 7
70 5 5 4 5 5
71 4 5 5 4 6
72 6 7 7 6 7
73 5 7 7 3 7
74 5 5 6 5 7
75 3 5 7 5 7
76 5 3 0 5 7
77 4 6 6 5 6
78 5 5 6 5 5
79 5 4 3 3 5
80 7 7 7 7 7
81 7 7 7 6 6
82 5 2 6 4 6
83 4 6 6 4 6
84 6 4 6 6 6
85 5 7 7 5 7
86 5 6 7 6 6
87 4 2 6 5 7
88 5 7 7 5 5
89 2 7 7 2 5
90 7 5 7 6 7
91 4 6 6 5 5
92 5 5 7 5 7
93 5 6 7 6 7
94 7 7 5 7 5
95 2 6 6 6 6
96 4 7 7 4 7
97 6 6 7 6 6
98 5 5 6 6 5
99 5 5 6 5 5
100 4 4 5 5 7
101 4 4 6 5 7
102 4 5 6 5 6
103 7 7 7 7 6
104 5 7 7 4 7
105 5 6 7 6 7
106 5 5 6 6 6
107 7 7 7 6 7
108 3 7 7 6 7
109 3 5 5 4 4
110 6 7 6 6 7
111 5 7 6 5 6
112 6 7 6 6 6
113 4 4 3 4 5
114 4 5 5 6 7
115 6 6 6 5 5
116 5 5 7 5 5
117 7 7 7 7 7
118 6 7 6 7 5
119 7 6 5 6 6
120 5 4 6 4 5
121 5 7 7 6 7
122 2 6 7 4 7
123 6 6 7 6 6
124 1 7 7 6 6
125 5 7 7 6 7
126 6 7 6 5 4
127 6 7 6 5 6
128 6 6 6 6 6
129 5 5 7 6 7
130 6 6 7 6 7
131 5 6 6 6 6
132 6 7 7 5 6
133 7 7 7 6 7
134 4 6 2 3 3
135 5 7 6 7 4
136 3 6 5 5 6
137 7 7 6 6 6
138 7 5 6 7 5
139 6 6 6 6 5
140 6 6 5 4 6
141 6 7 6 7 6
142 5 5 6 5 4
143 5 6 5 5 5
144 4 5 5 5 5
145 4 3 7 4 7
146 6 7 5 5 5
147 5 6 6 6 7
148 4 5 5 4 6
149 6 6 6 6 6
150 4 6 7 6 6
151 4 2 6 2 5
152 4 6 7 5 6
153 6 7 6 5 7
154 3 7 7 4 7
155 6 6 7 6 6
156 5 5 6 6 6
157 4 5 7 6 7
158 7 6 6 7 5
159 6 6 5 5 6
160 5 6 4 5 5
161 6 7 7 7 7
162 6 6 6 6 6
163 5 6 5 5 6
164 5 5 5 4 5
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Q2 Q3 Q4 Q5
1.72352 0.10579 -0.06691 0.60265 -0.00752
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.5665 -0.4677 0.0436 0.5952 2.1808
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.72352 0.63031 2.734 0.00696 **
Q2 0.10579 0.07292 1.451 0.14878
Q3 -0.06691 0.09365 -0.714 0.47602
Q4 0.60265 0.08274 7.284 1.42e-11 ***
Q5 -0.00752 0.09155 -0.082 0.93464
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.011 on 159 degrees of freedom
Multiple R-squared: 0.2943, Adjusted R-squared: 0.2765
F-statistic: 16.57 on 4 and 159 DF, p-value: 2.264e-11
> 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,] 0.133820690 0.26764138 0.86617931
[2,] 0.319656374 0.63931275 0.68034363
[3,] 0.249974413 0.49994883 0.75002559
[4,] 0.199084072 0.39816814 0.80091593
[5,] 0.422044794 0.84408959 0.57795521
[6,] 0.357698680 0.71539736 0.64230132
[7,] 0.270453675 0.54090735 0.72954633
[8,] 0.272403104 0.54480621 0.72759690
[9,] 0.395946491 0.79189298 0.60405351
[10,] 0.321856206 0.64371241 0.67814379
[11,] 0.251292974 0.50258595 0.74870703
[12,] 0.189096683 0.37819337 0.81090332
[13,] 0.390156215 0.78031243 0.60984378
[14,] 0.443871250 0.88774250 0.55612875
[15,] 0.463417240 0.92683448 0.53658276
[16,] 0.394979963 0.78995993 0.60502004
[17,] 0.327603406 0.65520681 0.67239659
[18,] 0.267252540 0.53450508 0.73274746
[19,] 0.224683436 0.44936687 0.77531656
[20,] 0.176596848 0.35319370 0.82340315
[21,] 0.229363881 0.45872776 0.77063612
[22,] 0.224500113 0.44900023 0.77549989
[23,] 0.188737672 0.37747534 0.81126233
[24,] 0.296199542 0.59239908 0.70380046
[25,] 0.291098195 0.58219639 0.70890180
[26,] 0.343394657 0.68678931 0.65660534
[27,] 0.343120605 0.68624121 0.65687939
[28,] 0.290716983 0.58143397 0.70928302
[29,] 0.247553376 0.49510675 0.75244662
[30,] 0.382374125 0.76474825 0.61762587
[31,] 0.389333051 0.77866610 0.61066695
[32,] 0.336623559 0.67324712 0.66337644
[33,] 0.291355834 0.58271167 0.70864417
[34,] 0.246856308 0.49371262 0.75314369
[35,] 0.394632110 0.78926422 0.60536789
[36,] 0.345358045 0.69071609 0.65464195
[37,] 0.302867947 0.60573589 0.69713205
[38,] 0.261941513 0.52388303 0.73805849
[39,] 0.225862219 0.45172444 0.77413778
[40,] 0.190971063 0.38194213 0.80902894
[41,] 0.192839397 0.38567879 0.80716060
[42,] 0.163207334 0.32641467 0.83679267
[43,] 0.149473121 0.29894624 0.85052688
[44,] 0.124198271 0.24839654 0.87580173
[45,] 0.101733441 0.20346688 0.89826656
[46,] 0.124682869 0.24936574 0.87531713
[47,] 0.100809781 0.20161956 0.89919022
[48,] 0.118362165 0.23672433 0.88163783
[49,] 0.099787991 0.19957598 0.90021201
[50,] 0.083389098 0.16677820 0.91661090
[51,] 0.067795874 0.13559175 0.93220413
[52,] 0.053506272 0.10701254 0.94649373
[53,] 0.041439841 0.08287968 0.95856016
[54,] 0.065030526 0.13006105 0.93496947
[55,] 0.054511700 0.10902340 0.94548830
[56,] 0.046595221 0.09319044 0.95340478
[57,] 0.045977343 0.09195469 0.95402266
[58,] 0.047414510 0.09482902 0.95258549
[59,] 0.047792673 0.09558535 0.95220733
[60,] 0.039085131 0.07817026 0.96091487
[61,] 0.045409984 0.09081997 0.95459002
[62,] 0.036720283 0.07344057 0.96327972
[63,] 0.028594312 0.05718862 0.97140569
[64,] 0.022090366 0.04418073 0.97790963
[65,] 0.017419751 0.03483950 0.98258025
[66,] 0.019549912 0.03909982 0.98045009
[67,] 0.014841318 0.02968264 0.98515868
[68,] 0.028814914 0.05762983 0.97118509
[69,] 0.022758252 0.04551650 0.97724175
[70,] 0.022042725 0.04408545 0.97795727
[71,] 0.016790078 0.03358016 0.98320992
[72,] 0.020404888 0.04080978 0.97959511
[73,] 0.018707262 0.03741452 0.98129274
[74,] 0.024566270 0.04913254 0.97543373
[75,] 0.025689583 0.05137917 0.97431042
[76,] 0.020282509 0.04056502 0.97971749
[77,] 0.017353272 0.03470654 0.98264673
[78,] 0.013141383 0.02628277 0.98685862
[79,] 0.010447181 0.02089436 0.98955282
[80,] 0.008366266 0.01673253 0.99163373
[81,] 0.006088348 0.01217670 0.99391165
[82,] 0.006842508 0.01368502 0.99315749
[83,] 0.011923189 0.02384638 0.98807681
[84,] 0.011600161 0.02320032 0.98839984
[85,] 0.008923267 0.01784653 0.99107673
[86,] 0.007002928 0.01400586 0.99299707
[87,] 0.005802426 0.01160485 0.99419757
[88,] 0.100852093 0.20170419 0.89914791
[89,] 0.084078590 0.16815718 0.91592141
[90,] 0.072188049 0.14437610 0.92781195
[91,] 0.059871247 0.11974249 0.94012875
[92,] 0.047571099 0.09514220 0.95242890
[93,] 0.041580993 0.08316199 0.95841901
[94,] 0.035535641 0.07107128 0.96446436
[95,] 0.031952850 0.06390570 0.96804715
[96,] 0.029273581 0.05854716 0.97072642
[97,] 0.025312503 0.05062501 0.97468750
[98,] 0.020037047 0.04007409 0.97996295
[99,] 0.015686175 0.03137235 0.98431383
[100,] 0.022109335 0.04421867 0.97789066
[101,] 0.071356752 0.14271350 0.92864325
[102,] 0.087650095 0.17530019 0.91234991
[103,] 0.073661953 0.14732391 0.92633805
[104,] 0.058199438 0.11639888 0.94180056
[105,] 0.047057243 0.09411449 0.95294276
[106,] 0.039369983 0.07873997 0.96063002
[107,] 0.053766734 0.10753347 0.94623327
[108,] 0.053919892 0.10783978 0.94608011
[109,] 0.042485683 0.08497137 0.95751432
[110,] 0.041455324 0.08291065 0.95854468
[111,] 0.031908050 0.06381610 0.96809195
[112,] 0.036674953 0.07334991 0.96332505
[113,] 0.032181638 0.06436328 0.96781836
[114,] 0.025137447 0.05027489 0.97486255
[115,] 0.059058796 0.11811759 0.94094120
[116,] 0.049870639 0.09974128 0.95012936
[117,] 0.798164563 0.40367087 0.20183544
[118,] 0.773743665 0.45251267 0.22625634
[119,] 0.747199804 0.50560039 0.25280020
[120,] 0.730149467 0.53970107 0.26985053
[121,] 0.691104677 0.61779065 0.30889532
[122,] 0.642741997 0.71451601 0.35725800
[123,] 0.603827844 0.79234431 0.39617216
[124,] 0.561449629 0.87710074 0.43855037
[125,] 0.554689465 0.89062107 0.44531054
[126,] 0.643717125 0.71256575 0.35628288
[127,] 0.624610079 0.75077984 0.37538992
[128,] 0.692262105 0.61547579 0.30773790
[129,] 0.885490221 0.22901956 0.11450978
[130,] 0.919519597 0.16096081 0.08048040
[131,] 0.916746461 0.16650708 0.08325354
[132,] 0.892246140 0.21550772 0.10775386
[133,] 0.923869251 0.15226150 0.07613075
[134,] 0.893045845 0.21390831 0.10695415
[135,] 0.855378471 0.28924306 0.14462153
[136,] 0.814780132 0.37043974 0.18521987
[137,] 0.882569448 0.23486110 0.11743055
[138,] 0.850551211 0.29889758 0.14944879
[139,] 0.801709132 0.39658174 0.19829087
[140,] 0.737193495 0.52561301 0.26280651
[141,] 0.697204941 0.60559012 0.30279506
[142,] 0.627999950 0.74400010 0.37200005
[143,] 0.688288114 0.62342377 0.31171189
[144,] 0.797517656 0.40496469 0.20248234
[145,] 0.741701913 0.51659617 0.25829809
[146,] 0.839607133 0.32078573 0.16039287
[147,] 0.920367120 0.15926576 0.07963288
[148,] 0.843262474 0.31347505 0.15673753
[149,] 0.719764385 0.56047123 0.28023562
> postscript(file="/var/www/rcomp/tmp/1xkdr1322059309.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2hsz51322059309.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3vfsg1322059309.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4cg9r1322059309.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5o6ny1322059309.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 = 164
Frequency = 1
1 2 3 4 5
0.8383424266 0.1063820621 1.7759422899 0.1808091516 0.3071803180
6 7 8 9 10
1.1883287958 0.2615189265 1.0750150267 -0.2240577101 0.7897446192
11 12 13 14 15
-0.1616575734 -1.3536992264 -0.4607303102 0.0436479913 -0.9723458218
16 17 18 19 20
-1.9563520087 0.3590484753 0.3590484753 0.3999170457 2.1808091516
21 22 23 24 25
-0.4887510761 -0.0575114053 0.0436479913 -0.0307790983 0.1494421163
26 27 28 29 30
-0.0633830925 -0.0307790983 1.2803205914 1.1808091516 -0.2240577101
31 32 33 34 35
-1.2846824703 1.0436479913 -1.4787060142 1.1139017062 0.1732895075
36 37 38 39 40
0.4334755648 2.1494421163 -0.9249849733 -0.0382987424 -0.3578726010
41 42 43 44 45
0.1419224721 -1.7447637588 0.2552362412 0.3979351548 0.4409952090
46 47 48 49 50
-0.3967592805 -0.2909651556 1.0674953826 0.4723622443 -0.6334318806
51 52 53 54 55
0.4409952090 -0.2759258674 -1.1358282912 0.0825346708 -1.4193182129
56 57 58 59 60
0.5392696898 0.5392696898 -0.3223321910 0.0436479913 0.0005879371
61 62 63 64 65
-1.5201181115 0.5781563693 -0.5276377557 -0.9994120629 1.1419224721
66 67 68 69 70
-0.8442751985 0.4723622443 -1.1616575734 0.4409952090 0.0394746166
71 72 73 74 75
-0.2834455115 0.4409952090 1.2489535560 0.1883287958 -1.7447637588
76 77 78 79 80
-0.0015276270 -0.9249849733 0.1732895075 1.2836668608 0.8383424266
81 82 83 84 85
1.4334755648 1.1008443089 -0.3223321910 0.6839504943 0.0436479913
86 87 88 89 90
-0.4607303102 -0.4942888294 0.0286087031 -1.1634329499 1.6525834589
91 92 93 94 95
-0.9325046174 0.2552362412 -0.4532106661 0.6894882475 -3.5276377557
96 97 98 99 100
-0.3536992264 0.5392696898 -0.4293632748 0.1732895075 -0.7727845247
101 102 103 104 105
-0.7058770793 -0.8191908484 0.8308227825 0.6463007736 -0.4532106661
106 107 108 109 110
-0.4218436307 1.4409952090 -2.5590047910 -1.2984847997 0.3740877635
111 112 113 114 115
-0.0307790983 0.3665681194 -0.3189859215 -1.4812314320 1.0674953826
116 117 118 119 120
0.2401969530 0.8383424266 -0.2436043071 1.4054547989 0.8817364148
121 122 123 124 125
-0.5590047910 -2.2479051014 0.5392696898 -4.5665244352 -0.5590047910
126 127 128 129 130
0.9541816135 0.9692209017 0.4723622443 -0.3474165411 0.5467893339
131 132 133 134 135
-0.5276377557 1.0361283472 1.4409952090 -0.0098681228 -1.2511239512
136 137 138 139 140
-1.9918924188 1.3665681194 0.9679839429 0.4648426002 1.6107603636
141 142 143 144 145
-0.2360846629 0.1657698634 0.0005879371 -0.8936179379 0.0694772735
146 147 148 149 150
0.8947938122 -0.5201181115 -0.2834455115 0.4723622443 -1.4607303102
151 152 153 154 155
1.2986302294 -0.8580775279 0.9767405458 -1.3536992264 0.5392696898
156 157 158 159 160
-0.4218436307 -1.3474165411 0.8621898179 1.0081075812 -0.0663195083
161 162 163 164
-0.1616575734 0.4723622443 0.0081075812 0.7090348444
> postscript(file="/var/www/rcomp/tmp/64b7z1322059309.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 = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 0.8383424266 NA
1 0.1063820621 0.8383424266
2 1.7759422899 0.1063820621
3 0.1808091516 1.7759422899
4 0.3071803180 0.1808091516
5 1.1883287958 0.3071803180
6 0.2615189265 1.1883287958
7 1.0750150267 0.2615189265
8 -0.2240577101 1.0750150267
9 0.7897446192 -0.2240577101
10 -0.1616575734 0.7897446192
11 -1.3536992264 -0.1616575734
12 -0.4607303102 -1.3536992264
13 0.0436479913 -0.4607303102
14 -0.9723458218 0.0436479913
15 -1.9563520087 -0.9723458218
16 0.3590484753 -1.9563520087
17 0.3590484753 0.3590484753
18 0.3999170457 0.3590484753
19 2.1808091516 0.3999170457
20 -0.4887510761 2.1808091516
21 -0.0575114053 -0.4887510761
22 0.0436479913 -0.0575114053
23 -0.0307790983 0.0436479913
24 0.1494421163 -0.0307790983
25 -0.0633830925 0.1494421163
26 -0.0307790983 -0.0633830925
27 1.2803205914 -0.0307790983
28 1.1808091516 1.2803205914
29 -0.2240577101 1.1808091516
30 -1.2846824703 -0.2240577101
31 1.0436479913 -1.2846824703
32 -1.4787060142 1.0436479913
33 1.1139017062 -1.4787060142
34 0.1732895075 1.1139017062
35 0.4334755648 0.1732895075
36 2.1494421163 0.4334755648
37 -0.9249849733 2.1494421163
38 -0.0382987424 -0.9249849733
39 -0.3578726010 -0.0382987424
40 0.1419224721 -0.3578726010
41 -1.7447637588 0.1419224721
42 0.2552362412 -1.7447637588
43 0.3979351548 0.2552362412
44 0.4409952090 0.3979351548
45 -0.3967592805 0.4409952090
46 -0.2909651556 -0.3967592805
47 1.0674953826 -0.2909651556
48 0.4723622443 1.0674953826
49 -0.6334318806 0.4723622443
50 0.4409952090 -0.6334318806
51 -0.2759258674 0.4409952090
52 -1.1358282912 -0.2759258674
53 0.0825346708 -1.1358282912
54 -1.4193182129 0.0825346708
55 0.5392696898 -1.4193182129
56 0.5392696898 0.5392696898
57 -0.3223321910 0.5392696898
58 0.0436479913 -0.3223321910
59 0.0005879371 0.0436479913
60 -1.5201181115 0.0005879371
61 0.5781563693 -1.5201181115
62 -0.5276377557 0.5781563693
63 -0.9994120629 -0.5276377557
64 1.1419224721 -0.9994120629
65 -0.8442751985 1.1419224721
66 0.4723622443 -0.8442751985
67 -1.1616575734 0.4723622443
68 0.4409952090 -1.1616575734
69 0.0394746166 0.4409952090
70 -0.2834455115 0.0394746166
71 0.4409952090 -0.2834455115
72 1.2489535560 0.4409952090
73 0.1883287958 1.2489535560
74 -1.7447637588 0.1883287958
75 -0.0015276270 -1.7447637588
76 -0.9249849733 -0.0015276270
77 0.1732895075 -0.9249849733
78 1.2836668608 0.1732895075
79 0.8383424266 1.2836668608
80 1.4334755648 0.8383424266
81 1.1008443089 1.4334755648
82 -0.3223321910 1.1008443089
83 0.6839504943 -0.3223321910
84 0.0436479913 0.6839504943
85 -0.4607303102 0.0436479913
86 -0.4942888294 -0.4607303102
87 0.0286087031 -0.4942888294
88 -1.1634329499 0.0286087031
89 1.6525834589 -1.1634329499
90 -0.9325046174 1.6525834589
91 0.2552362412 -0.9325046174
92 -0.4532106661 0.2552362412
93 0.6894882475 -0.4532106661
94 -3.5276377557 0.6894882475
95 -0.3536992264 -3.5276377557
96 0.5392696898 -0.3536992264
97 -0.4293632748 0.5392696898
98 0.1732895075 -0.4293632748
99 -0.7727845247 0.1732895075
100 -0.7058770793 -0.7727845247
101 -0.8191908484 -0.7058770793
102 0.8308227825 -0.8191908484
103 0.6463007736 0.8308227825
104 -0.4532106661 0.6463007736
105 -0.4218436307 -0.4532106661
106 1.4409952090 -0.4218436307
107 -2.5590047910 1.4409952090
108 -1.2984847997 -2.5590047910
109 0.3740877635 -1.2984847997
110 -0.0307790983 0.3740877635
111 0.3665681194 -0.0307790983
112 -0.3189859215 0.3665681194
113 -1.4812314320 -0.3189859215
114 1.0674953826 -1.4812314320
115 0.2401969530 1.0674953826
116 0.8383424266 0.2401969530
117 -0.2436043071 0.8383424266
118 1.4054547989 -0.2436043071
119 0.8817364148 1.4054547989
120 -0.5590047910 0.8817364148
121 -2.2479051014 -0.5590047910
122 0.5392696898 -2.2479051014
123 -4.5665244352 0.5392696898
124 -0.5590047910 -4.5665244352
125 0.9541816135 -0.5590047910
126 0.9692209017 0.9541816135
127 0.4723622443 0.9692209017
128 -0.3474165411 0.4723622443
129 0.5467893339 -0.3474165411
130 -0.5276377557 0.5467893339
131 1.0361283472 -0.5276377557
132 1.4409952090 1.0361283472
133 -0.0098681228 1.4409952090
134 -1.2511239512 -0.0098681228
135 -1.9918924188 -1.2511239512
136 1.3665681194 -1.9918924188
137 0.9679839429 1.3665681194
138 0.4648426002 0.9679839429
139 1.6107603636 0.4648426002
140 -0.2360846629 1.6107603636
141 0.1657698634 -0.2360846629
142 0.0005879371 0.1657698634
143 -0.8936179379 0.0005879371
144 0.0694772735 -0.8936179379
145 0.8947938122 0.0694772735
146 -0.5201181115 0.8947938122
147 -0.2834455115 -0.5201181115
148 0.4723622443 -0.2834455115
149 -1.4607303102 0.4723622443
150 1.2986302294 -1.4607303102
151 -0.8580775279 1.2986302294
152 0.9767405458 -0.8580775279
153 -1.3536992264 0.9767405458
154 0.5392696898 -1.3536992264
155 -0.4218436307 0.5392696898
156 -1.3474165411 -0.4218436307
157 0.8621898179 -1.3474165411
158 1.0081075812 0.8621898179
159 -0.0663195083 1.0081075812
160 -0.1616575734 -0.0663195083
161 0.4723622443 -0.1616575734
162 0.0081075812 0.4723622443
163 0.7090348444 0.0081075812
164 NA 0.7090348444
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.1063820621 0.8383424266
[2,] 1.7759422899 0.1063820621
[3,] 0.1808091516 1.7759422899
[4,] 0.3071803180 0.1808091516
[5,] 1.1883287958 0.3071803180
[6,] 0.2615189265 1.1883287958
[7,] 1.0750150267 0.2615189265
[8,] -0.2240577101 1.0750150267
[9,] 0.7897446192 -0.2240577101
[10,] -0.1616575734 0.7897446192
[11,] -1.3536992264 -0.1616575734
[12,] -0.4607303102 -1.3536992264
[13,] 0.0436479913 -0.4607303102
[14,] -0.9723458218 0.0436479913
[15,] -1.9563520087 -0.9723458218
[16,] 0.3590484753 -1.9563520087
[17,] 0.3590484753 0.3590484753
[18,] 0.3999170457 0.3590484753
[19,] 2.1808091516 0.3999170457
[20,] -0.4887510761 2.1808091516
[21,] -0.0575114053 -0.4887510761
[22,] 0.0436479913 -0.0575114053
[23,] -0.0307790983 0.0436479913
[24,] 0.1494421163 -0.0307790983
[25,] -0.0633830925 0.1494421163
[26,] -0.0307790983 -0.0633830925
[27,] 1.2803205914 -0.0307790983
[28,] 1.1808091516 1.2803205914
[29,] -0.2240577101 1.1808091516
[30,] -1.2846824703 -0.2240577101
[31,] 1.0436479913 -1.2846824703
[32,] -1.4787060142 1.0436479913
[33,] 1.1139017062 -1.4787060142
[34,] 0.1732895075 1.1139017062
[35,] 0.4334755648 0.1732895075
[36,] 2.1494421163 0.4334755648
[37,] -0.9249849733 2.1494421163
[38,] -0.0382987424 -0.9249849733
[39,] -0.3578726010 -0.0382987424
[40,] 0.1419224721 -0.3578726010
[41,] -1.7447637588 0.1419224721
[42,] 0.2552362412 -1.7447637588
[43,] 0.3979351548 0.2552362412
[44,] 0.4409952090 0.3979351548
[45,] -0.3967592805 0.4409952090
[46,] -0.2909651556 -0.3967592805
[47,] 1.0674953826 -0.2909651556
[48,] 0.4723622443 1.0674953826
[49,] -0.6334318806 0.4723622443
[50,] 0.4409952090 -0.6334318806
[51,] -0.2759258674 0.4409952090
[52,] -1.1358282912 -0.2759258674
[53,] 0.0825346708 -1.1358282912
[54,] -1.4193182129 0.0825346708
[55,] 0.5392696898 -1.4193182129
[56,] 0.5392696898 0.5392696898
[57,] -0.3223321910 0.5392696898
[58,] 0.0436479913 -0.3223321910
[59,] 0.0005879371 0.0436479913
[60,] -1.5201181115 0.0005879371
[61,] 0.5781563693 -1.5201181115
[62,] -0.5276377557 0.5781563693
[63,] -0.9994120629 -0.5276377557
[64,] 1.1419224721 -0.9994120629
[65,] -0.8442751985 1.1419224721
[66,] 0.4723622443 -0.8442751985
[67,] -1.1616575734 0.4723622443
[68,] 0.4409952090 -1.1616575734
[69,] 0.0394746166 0.4409952090
[70,] -0.2834455115 0.0394746166
[71,] 0.4409952090 -0.2834455115
[72,] 1.2489535560 0.4409952090
[73,] 0.1883287958 1.2489535560
[74,] -1.7447637588 0.1883287958
[75,] -0.0015276270 -1.7447637588
[76,] -0.9249849733 -0.0015276270
[77,] 0.1732895075 -0.9249849733
[78,] 1.2836668608 0.1732895075
[79,] 0.8383424266 1.2836668608
[80,] 1.4334755648 0.8383424266
[81,] 1.1008443089 1.4334755648
[82,] -0.3223321910 1.1008443089
[83,] 0.6839504943 -0.3223321910
[84,] 0.0436479913 0.6839504943
[85,] -0.4607303102 0.0436479913
[86,] -0.4942888294 -0.4607303102
[87,] 0.0286087031 -0.4942888294
[88,] -1.1634329499 0.0286087031
[89,] 1.6525834589 -1.1634329499
[90,] -0.9325046174 1.6525834589
[91,] 0.2552362412 -0.9325046174
[92,] -0.4532106661 0.2552362412
[93,] 0.6894882475 -0.4532106661
[94,] -3.5276377557 0.6894882475
[95,] -0.3536992264 -3.5276377557
[96,] 0.5392696898 -0.3536992264
[97,] -0.4293632748 0.5392696898
[98,] 0.1732895075 -0.4293632748
[99,] -0.7727845247 0.1732895075
[100,] -0.7058770793 -0.7727845247
[101,] -0.8191908484 -0.7058770793
[102,] 0.8308227825 -0.8191908484
[103,] 0.6463007736 0.8308227825
[104,] -0.4532106661 0.6463007736
[105,] -0.4218436307 -0.4532106661
[106,] 1.4409952090 -0.4218436307
[107,] -2.5590047910 1.4409952090
[108,] -1.2984847997 -2.5590047910
[109,] 0.3740877635 -1.2984847997
[110,] -0.0307790983 0.3740877635
[111,] 0.3665681194 -0.0307790983
[112,] -0.3189859215 0.3665681194
[113,] -1.4812314320 -0.3189859215
[114,] 1.0674953826 -1.4812314320
[115,] 0.2401969530 1.0674953826
[116,] 0.8383424266 0.2401969530
[117,] -0.2436043071 0.8383424266
[118,] 1.4054547989 -0.2436043071
[119,] 0.8817364148 1.4054547989
[120,] -0.5590047910 0.8817364148
[121,] -2.2479051014 -0.5590047910
[122,] 0.5392696898 -2.2479051014
[123,] -4.5665244352 0.5392696898
[124,] -0.5590047910 -4.5665244352
[125,] 0.9541816135 -0.5590047910
[126,] 0.9692209017 0.9541816135
[127,] 0.4723622443 0.9692209017
[128,] -0.3474165411 0.4723622443
[129,] 0.5467893339 -0.3474165411
[130,] -0.5276377557 0.5467893339
[131,] 1.0361283472 -0.5276377557
[132,] 1.4409952090 1.0361283472
[133,] -0.0098681228 1.4409952090
[134,] -1.2511239512 -0.0098681228
[135,] -1.9918924188 -1.2511239512
[136,] 1.3665681194 -1.9918924188
[137,] 0.9679839429 1.3665681194
[138,] 0.4648426002 0.9679839429
[139,] 1.6107603636 0.4648426002
[140,] -0.2360846629 1.6107603636
[141,] 0.1657698634 -0.2360846629
[142,] 0.0005879371 0.1657698634
[143,] -0.8936179379 0.0005879371
[144,] 0.0694772735 -0.8936179379
[145,] 0.8947938122 0.0694772735
[146,] -0.5201181115 0.8947938122
[147,] -0.2834455115 -0.5201181115
[148,] 0.4723622443 -0.2834455115
[149,] -1.4607303102 0.4723622443
[150,] 1.2986302294 -1.4607303102
[151,] -0.8580775279 1.2986302294
[152,] 0.9767405458 -0.8580775279
[153,] -1.3536992264 0.9767405458
[154,] 0.5392696898 -1.3536992264
[155,] -0.4218436307 0.5392696898
[156,] -1.3474165411 -0.4218436307
[157,] 0.8621898179 -1.3474165411
[158,] 1.0081075812 0.8621898179
[159,] -0.0663195083 1.0081075812
[160,] -0.1616575734 -0.0663195083
[161,] 0.4723622443 -0.1616575734
[162,] 0.0081075812 0.4723622443
[163,] 0.7090348444 0.0081075812
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.1063820621 0.8383424266
2 1.7759422899 0.1063820621
3 0.1808091516 1.7759422899
4 0.3071803180 0.1808091516
5 1.1883287958 0.3071803180
6 0.2615189265 1.1883287958
7 1.0750150267 0.2615189265
8 -0.2240577101 1.0750150267
9 0.7897446192 -0.2240577101
10 -0.1616575734 0.7897446192
11 -1.3536992264 -0.1616575734
12 -0.4607303102 -1.3536992264
13 0.0436479913 -0.4607303102
14 -0.9723458218 0.0436479913
15 -1.9563520087 -0.9723458218
16 0.3590484753 -1.9563520087
17 0.3590484753 0.3590484753
18 0.3999170457 0.3590484753
19 2.1808091516 0.3999170457
20 -0.4887510761 2.1808091516
21 -0.0575114053 -0.4887510761
22 0.0436479913 -0.0575114053
23 -0.0307790983 0.0436479913
24 0.1494421163 -0.0307790983
25 -0.0633830925 0.1494421163
26 -0.0307790983 -0.0633830925
27 1.2803205914 -0.0307790983
28 1.1808091516 1.2803205914
29 -0.2240577101 1.1808091516
30 -1.2846824703 -0.2240577101
31 1.0436479913 -1.2846824703
32 -1.4787060142 1.0436479913
33 1.1139017062 -1.4787060142
34 0.1732895075 1.1139017062
35 0.4334755648 0.1732895075
36 2.1494421163 0.4334755648
37 -0.9249849733 2.1494421163
38 -0.0382987424 -0.9249849733
39 -0.3578726010 -0.0382987424
40 0.1419224721 -0.3578726010
41 -1.7447637588 0.1419224721
42 0.2552362412 -1.7447637588
43 0.3979351548 0.2552362412
44 0.4409952090 0.3979351548
45 -0.3967592805 0.4409952090
46 -0.2909651556 -0.3967592805
47 1.0674953826 -0.2909651556
48 0.4723622443 1.0674953826
49 -0.6334318806 0.4723622443
50 0.4409952090 -0.6334318806
51 -0.2759258674 0.4409952090
52 -1.1358282912 -0.2759258674
53 0.0825346708 -1.1358282912
54 -1.4193182129 0.0825346708
55 0.5392696898 -1.4193182129
56 0.5392696898 0.5392696898
57 -0.3223321910 0.5392696898
58 0.0436479913 -0.3223321910
59 0.0005879371 0.0436479913
60 -1.5201181115 0.0005879371
61 0.5781563693 -1.5201181115
62 -0.5276377557 0.5781563693
63 -0.9994120629 -0.5276377557
64 1.1419224721 -0.9994120629
65 -0.8442751985 1.1419224721
66 0.4723622443 -0.8442751985
67 -1.1616575734 0.4723622443
68 0.4409952090 -1.1616575734
69 0.0394746166 0.4409952090
70 -0.2834455115 0.0394746166
71 0.4409952090 -0.2834455115
72 1.2489535560 0.4409952090
73 0.1883287958 1.2489535560
74 -1.7447637588 0.1883287958
75 -0.0015276270 -1.7447637588
76 -0.9249849733 -0.0015276270
77 0.1732895075 -0.9249849733
78 1.2836668608 0.1732895075
79 0.8383424266 1.2836668608
80 1.4334755648 0.8383424266
81 1.1008443089 1.4334755648
82 -0.3223321910 1.1008443089
83 0.6839504943 -0.3223321910
84 0.0436479913 0.6839504943
85 -0.4607303102 0.0436479913
86 -0.4942888294 -0.4607303102
87 0.0286087031 -0.4942888294
88 -1.1634329499 0.0286087031
89 1.6525834589 -1.1634329499
90 -0.9325046174 1.6525834589
91 0.2552362412 -0.9325046174
92 -0.4532106661 0.2552362412
93 0.6894882475 -0.4532106661
94 -3.5276377557 0.6894882475
95 -0.3536992264 -3.5276377557
96 0.5392696898 -0.3536992264
97 -0.4293632748 0.5392696898
98 0.1732895075 -0.4293632748
99 -0.7727845247 0.1732895075
100 -0.7058770793 -0.7727845247
101 -0.8191908484 -0.7058770793
102 0.8308227825 -0.8191908484
103 0.6463007736 0.8308227825
104 -0.4532106661 0.6463007736
105 -0.4218436307 -0.4532106661
106 1.4409952090 -0.4218436307
107 -2.5590047910 1.4409952090
108 -1.2984847997 -2.5590047910
109 0.3740877635 -1.2984847997
110 -0.0307790983 0.3740877635
111 0.3665681194 -0.0307790983
112 -0.3189859215 0.3665681194
113 -1.4812314320 -0.3189859215
114 1.0674953826 -1.4812314320
115 0.2401969530 1.0674953826
116 0.8383424266 0.2401969530
117 -0.2436043071 0.8383424266
118 1.4054547989 -0.2436043071
119 0.8817364148 1.4054547989
120 -0.5590047910 0.8817364148
121 -2.2479051014 -0.5590047910
122 0.5392696898 -2.2479051014
123 -4.5665244352 0.5392696898
124 -0.5590047910 -4.5665244352
125 0.9541816135 -0.5590047910
126 0.9692209017 0.9541816135
127 0.4723622443 0.9692209017
128 -0.3474165411 0.4723622443
129 0.5467893339 -0.3474165411
130 -0.5276377557 0.5467893339
131 1.0361283472 -0.5276377557
132 1.4409952090 1.0361283472
133 -0.0098681228 1.4409952090
134 -1.2511239512 -0.0098681228
135 -1.9918924188 -1.2511239512
136 1.3665681194 -1.9918924188
137 0.9679839429 1.3665681194
138 0.4648426002 0.9679839429
139 1.6107603636 0.4648426002
140 -0.2360846629 1.6107603636
141 0.1657698634 -0.2360846629
142 0.0005879371 0.1657698634
143 -0.8936179379 0.0005879371
144 0.0694772735 -0.8936179379
145 0.8947938122 0.0694772735
146 -0.5201181115 0.8947938122
147 -0.2834455115 -0.5201181115
148 0.4723622443 -0.2834455115
149 -1.4607303102 0.4723622443
150 1.2986302294 -1.4607303102
151 -0.8580775279 1.2986302294
152 0.9767405458 -0.8580775279
153 -1.3536992264 0.9767405458
154 0.5392696898 -1.3536992264
155 -0.4218436307 0.5392696898
156 -1.3474165411 -0.4218436307
157 0.8621898179 -1.3474165411
158 1.0081075812 0.8621898179
159 -0.0663195083 1.0081075812
160 -0.1616575734 -0.0663195083
161 0.4723622443 -0.1616575734
162 0.0081075812 0.4723622443
163 0.7090348444 0.0081075812
> 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/rcomp/tmp/72jkr1322059309.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8mbik1322059309.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9vwbd1322059309.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10obik1322059309.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11qb131322059309.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/rcomp/tmp/12raug1322059309.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/rcomp/tmp/13hhem1322059309.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/rcomp/tmp/14tmlv1322059309.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/rcomp/tmp/1556ns1322059309.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/rcomp/tmp/16h5cd1322059309.tab")
+ }
>
> try(system("convert tmp/1xkdr1322059309.ps tmp/1xkdr1322059309.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hsz51322059309.ps tmp/2hsz51322059309.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vfsg1322059309.ps tmp/3vfsg1322059309.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cg9r1322059309.ps tmp/4cg9r1322059309.png",intern=TRUE))
character(0)
> try(system("convert tmp/5o6ny1322059309.ps tmp/5o6ny1322059309.png",intern=TRUE))
character(0)
> try(system("convert tmp/64b7z1322059309.ps tmp/64b7z1322059309.png",intern=TRUE))
character(0)
> try(system("convert tmp/72jkr1322059309.ps tmp/72jkr1322059309.png",intern=TRUE))
character(0)
> try(system("convert tmp/8mbik1322059309.ps tmp/8mbik1322059309.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vwbd1322059309.ps tmp/9vwbd1322059309.png",intern=TRUE))
character(0)
> try(system("convert tmp/10obik1322059309.ps tmp/10obik1322059309.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.460 0.350 5.787