R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(14
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+ ,dim=c(8
+ ,156)
+ ,dimnames=list(c('Schoolprestaties'
+ ,'Sport'
+ ,'GoingOut'
+ ,'Relation'
+ ,'Family'
+ ,'Friends'
+ ,'Coach'
+ ,'Job')
+ ,1:156))
> y <- array(NA,dim=c(8,156),dimnames=list(c('Schoolprestaties','Sport','GoingOut','Relation','Family','Friends','Coach','Job'),1:156))
> 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
Schoolprestaties Sport GoingOut Relation Family Friends Coach Job
1 14 3 2 3 3 3 7 6
2 8 5 6 0 7 7 2 7
3 12 6 6 0 6 8 3 8
4 7 6 6 6 6 9 8 8
5 10 7 8 5 5 5 7 9
6 9 3 1 0 7 7 7 8
7 16 8 9 8 8 8 9 8
8 7 4 4 0 2 3 2 7
9 14 7 7 0 4 8 4 7
10 6 4 4 9 9 4 4 4
11 16 6 6 6 6 6 6 6
12 11 6 5 6 6 4 4 7
13 17 7 7 5 5 8 9 5
14 12 4 5 4 4 8 8 8
15 7 6 6 0 2 2 7 5
16 13 5 5 0 4 9 4 4
17 9 0 2 2 2 2 2 9
18 15 9 9 6 6 8 8 8
19 7 4 4 0 4 8 4 4
20 9 4 4 4 4 4 4 6
21 7 2 5 5 5 5 2 6
22 14 7 7 7 7 7 9 7
23 15 5 5 5 5 3 3 3
24 7 9 9 4 4 4 4 4
25 13 6 6 6 6 6 6 6
26 17 6 6 6 6 6 6 6
27 15 7 3 0 7 9 7 7
28 14 3 3 1 2 2 2 5
29 14 6 5 0 6 6 6 8
30 8 6 5 4 4 4 4 6
31 8 4 4 4 4 8 2 4
32 12 7 7 7 7 3 9 9
33 14 7 6 7 7 7 7 7
34 8 7 7 0 4 4 4 4
35 11 4 4 4 4 4 4 6
36 16 5 5 5 5 8 7 8
37 11 6 6 0 6 6 6 6
38 8 5 5 5 5 5 5 5
39 14 6 0 1 6 6 6 6
40 16 6 6 2 2 9 2 6
41 14 6 5 0 6 4 2 4
42 5 3 3 9 9 7 7 7
43 8 3 3 3 3 3 3 9
44 10 3 3 0 4 4 4 8
45 8 6 7 6 6 6 6 6
46 13 7 7 1 5 8 5 6
47 15 5 1 5 5 5 7 5
48 6 5 5 0 4 4 4 7
49 12 5 5 0 2 2 2 5
50 14 6 6 0 6 9 6 8
51 5 6 2 6 6 6 9 6
52 15 6 6 7 7 8 8 8
53 11 5 5 0 5 5 5 5
54 8 4 2 4 4 4 4 4
55 13 7 7 5 5 5 2 5
56 14 5 5 1 5 9 9 6
57 12 3 3 4 4 4 4 4
58 16 6 6 9 9 8 6 6
59 10 2 2 2 2 2 2 9
60 15 8 8 8 8 8 8 7
61 8 3 5 3 3 3 3 3
62 16 0 2 1 6 3 3 6
63 19 6 6 0 6 6 7 6
64 14 8 2 6 6 6 2 6
65 7 4 1 0 5 5 9 5
66 13 5 5 0 5 5 5 5
67 15 6 6 6 6 4 4 5
68 7 5 2 2 2 9 2 9
69 13 6 6 1 6 6 6 8
70 4 2 2 5 5 5 5 5
71 14 6 6 5 5 5 5 6
72 13 5 5 5 5 3 9 7
73 11 5 0 5 5 8 2 5
74 14 6 2 6 6 9 6 6
75 12 4 4 6 6 6 6 6
76 15 6 1 0 9 6 6 6
77 14 5 5 0 5 5 5 6
78 13 5 5 1 5 3 3 9
79 7 4 2 7 7 4 2 7
80 5 2 2 2 2 9 2 9
81 7 7 7 4 4 4 4 4
82 13 5 5 0 6 8 8 8
83 13 6 2 5 5 5 5 5
84 11 5 5 5 5 5 9 8
85 6 3 3 3 3 8 2 9
86 12 6 6 0 6 6 6 6
87 8 4 1 4 4 9 4 4
88 11 5 5 9 9 5 5 7
89 12 7 7 0 8 8 8 8
90 9 4 2 4 4 3 3 9
91 12 6 6 2 2 2 2 9
92 13 8 8 7 7 7 7 7
93 16 7 7 7 7 7 7 8
94 16 6 6 6 6 4 9 4
95 11 7 7 0 5 5 5 6
96 8 4 4 5 5 9 5 7
97 4 0 5 6 6 6 2 6
98 7 3 2 0 3 3 3 7
99 14 5 5 5 5 5 5 5
100 11 6 2 9 9 2 2 9
101 17 5 5 0 7 7 7 7
102 15 7 7 7 7 7 7 7
103 14 6 5 1 6 6 6 6
104 5 8 8 3 3 8 3 6
105 4 7 2 7 7 9 3 9
106 19 8 8 8 8 8 2 9
107 11 3 3 0 3 3 3 8
108 15 8 2 5 5 5 5 8
109 10 3 3 3 3 3 3 3
110 9 4 5 0 4 4 4 6
111 12 2 2 5 5 5 5 5
112 15 7 2 7 7 9 7 7
113 7 6 6 0 6 6 6 6
114 13 2 2 0 7 7 7 7
115 14 7 7 0 9 7 2 7
116 14 6 6 6 6 6 6 6
117 14 6 2 0 6 3 9 8
118 8 6 2 6 6 9 4 9
119 15 6 5 6 6 6 6 6
120 15 6 6 2 2 2 2 9
121 9 4 4 5 5 5 2 5
122 16 5 5 0 5 5 5 6
123 9 7 7 4 4 9 4 4
124 15 6 6 0 7 7 7 7
125 15 6 6 6 6 6 6 6
126 6 5 5 5 5 8 7 8
127 8 8 2 8 8 8 8 8
128 15 6 6 6 6 6 6 9
129 10 0 3 5 5 3 3 8
130 9 4 2 0 4 4 4 4
131 14 8 8 8 8 9 8 6
132 12 6 6 0 6 6 9 6
133 8 4 4 9 9 4 2 7
134 11 6 6 5 5 5 5 9
135 13 2 5 0 6 6 6 8
136 9 4 4 0 4 4 4 4
137 15 6 2 0 6 6 6 6
138 13 3 3 3 3 3 3 9
139 15 6 6 6 6 6 6 6
140 14 5 5 0 5 5 5 5
141 16 4 4 4 4 9 8 8
142 12 6 6 6 6 6 6 6
143 14 1 1 0 5 9 5 6
144 10 4 5 4 4 3 3 6
145 10 4 2 7 7 7 2 7
146 4 6 6 0 6 6 6 7
147 8 5 5 5 5 5 5 9
148 17 9 2 6 6 6 6 6
149 16 6 6 6 6 9 6 6
150 12 8 8 8 8 8 9 6
151 12 7 7 2 2 4 4 4
152 15 7 7 7 7 7 7 7
153 9 0 9 0 4 4 4 8
154 13 6 2 0 6 8 7 7
155 14 6 6 5 5 5 5 9
156 11 5 5 0 2 9 2 6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Sport GoingOut Relation Family Friends
6.43367 0.45018 0.08537 -0.13816 0.29524 -0.07492
Coach Job
0.31831 0.02064
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.023 -2.094 0.645 2.147 6.908
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.43367 1.45681 4.416 1.93e-05 ***
Sport 0.45018 0.17659 2.549 0.0118 *
GoingOut 0.08537 0.14353 0.595 0.5529
Relation -0.13816 0.10057 -1.374 0.1716
Family 0.29524 0.18935 1.559 0.1211
Friends -0.07492 0.13832 -0.542 0.5889
Coach 0.31831 0.13907 2.289 0.0235 *
Job 0.02064 0.16715 0.123 0.9019
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.225 on 148 degrees of freedom
Multiple R-squared: 0.1862, Adjusted R-squared: 0.1478
F-statistic: 4.839 on 7 and 148 DF, p-value: 6.203e-05
> 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,] 0.5871268 0.8257464 0.41287318
[2,] 0.4294500 0.8589001 0.57054996
[3,] 0.5054570 0.9890861 0.49454304
[4,] 0.6006882 0.7986235 0.39931176
[5,] 0.8541247 0.2917507 0.14587534
[6,] 0.7903948 0.4192104 0.20960522
[7,] 0.7912955 0.4174090 0.20870451
[8,] 0.7197822 0.5604357 0.28021784
[9,] 0.7462969 0.5074061 0.25370306
[10,] 0.6792196 0.6415609 0.32078045
[11,] 0.6055781 0.7888438 0.39442192
[12,] 0.5293331 0.9413338 0.47066692
[13,] 0.6247425 0.7505150 0.37525751
[14,] 0.7387537 0.5224927 0.26124634
[15,] 0.6811209 0.6377581 0.31887906
[16,] 0.7426142 0.5147716 0.25738580
[17,] 0.6900665 0.6198670 0.30993350
[18,] 0.7552498 0.4895005 0.24475024
[19,] 0.7454912 0.5090176 0.25450882
[20,] 0.7611837 0.4776325 0.23881627
[21,] 0.7400414 0.5199172 0.25995860
[22,] 0.6936022 0.6127957 0.30639783
[23,] 0.6393092 0.7213816 0.36069080
[24,] 0.6252391 0.7495217 0.37476085
[25,] 0.5677274 0.8645451 0.43227256
[26,] 0.5648893 0.8702214 0.43511068
[27,] 0.5133945 0.9732110 0.48660550
[28,] 0.5105423 0.9789154 0.48945769
[29,] 0.4558791 0.9117583 0.54412086
[30,] 0.4928585 0.9857171 0.50714147
[31,] 0.5465602 0.9068796 0.45343981
[32,] 0.6662544 0.6674913 0.33374563
[33,] 0.6314703 0.7370594 0.36852970
[34,] 0.5801549 0.8396902 0.41984510
[35,] 0.5912396 0.8175208 0.40876042
[36,] 0.5390583 0.9218833 0.46094167
[37,] 0.5107189 0.9785623 0.48928114
[38,] 0.5679918 0.8640164 0.43200821
[39,] 0.5364593 0.9270814 0.46354069
[40,] 0.5001277 0.9997446 0.49987231
[41,] 0.7830937 0.4338127 0.21690635
[42,] 0.7645962 0.4708077 0.23540385
[43,] 0.7270483 0.5459034 0.27295169
[44,] 0.7048232 0.5903536 0.29517682
[45,] 0.6699082 0.6601836 0.33009181
[46,] 0.6324526 0.7350948 0.36754741
[47,] 0.6134102 0.7731797 0.38658984
[48,] 0.6342608 0.7314785 0.36573925
[49,] 0.5937083 0.8125833 0.40629166
[50,] 0.5513471 0.8973057 0.44865286
[51,] 0.5086737 0.9826526 0.49132628
[52,] 0.7306207 0.5387586 0.26937932
[53,] 0.8190564 0.3618872 0.18094361
[54,] 0.8031957 0.3936087 0.19680433
[55,] 0.8497940 0.3004121 0.15020604
[56,] 0.8255591 0.3488819 0.17444095
[57,] 0.8296858 0.3406283 0.17031417
[58,] 0.8292681 0.3414639 0.17073195
[59,] 0.7972828 0.4054344 0.20271722
[60,] 0.8611666 0.2776668 0.13883339
[61,] 0.8459830 0.3080341 0.15401704
[62,] 0.8219472 0.3561057 0.17805283
[63,] 0.7964561 0.4070878 0.20354389
[64,] 0.7791096 0.4417808 0.22089038
[65,] 0.7439368 0.5121263 0.25606317
[66,] 0.7159580 0.5680841 0.28404203
[67,] 0.6925420 0.6149160 0.30745802
[68,] 0.6605086 0.6789829 0.33949143
[69,] 0.6548858 0.6902283 0.34511416
[70,] 0.6479157 0.7041685 0.35208426
[71,] 0.7017594 0.5964811 0.29824057
[72,] 0.6588365 0.6823271 0.34116353
[73,] 0.6227883 0.7544235 0.37721173
[74,] 0.5989441 0.8021119 0.40105595
[75,] 0.5809746 0.8380509 0.41902544
[76,] 0.5370984 0.9258033 0.46290163
[77,] 0.5001607 0.9996786 0.49983929
[78,] 0.4573861 0.9147722 0.54261391
[79,] 0.4375202 0.8750404 0.56247982
[80,] 0.3974564 0.7949127 0.60254363
[81,] 0.3579731 0.7159462 0.64202689
[82,] 0.3166060 0.6332120 0.68339400
[83,] 0.3010546 0.6021092 0.69894542
[84,] 0.2809062 0.5618124 0.71909381
[85,] 0.2542398 0.5084797 0.74576017
[86,] 0.2361247 0.4722494 0.76387528
[87,] 0.2649724 0.5299448 0.73502758
[88,] 0.2583089 0.5166178 0.74169109
[89,] 0.2413570 0.4827141 0.75864295
[90,] 0.2061792 0.4123585 0.79382075
[91,] 0.2250434 0.4500867 0.77495664
[92,] 0.1989594 0.3979188 0.80104060
[93,] 0.1709730 0.3419460 0.82902701
[94,] 0.2843770 0.5687540 0.71562299
[95,] 0.5146535 0.9706930 0.48534652
[96,] 0.6834959 0.6330083 0.31650413
[97,] 0.6386179 0.7227641 0.36138206
[98,] 0.6106179 0.7787642 0.38938211
[99,] 0.5627258 0.8745484 0.43727421
[100,] 0.5362015 0.9275970 0.46379850
[101,] 0.4946037 0.9892074 0.50539628
[102,] 0.4647666 0.9295332 0.53523339
[103,] 0.5832033 0.8335935 0.41679675
[104,] 0.5370060 0.9259880 0.46299398
[105,] 0.5337676 0.9324649 0.46623244
[106,] 0.4905100 0.9810201 0.50948995
[107,] 0.4346074 0.8692147 0.56539264
[108,] 0.4351878 0.8703757 0.56481215
[109,] 0.4163873 0.8327746 0.58361271
[110,] 0.4383254 0.8766507 0.56167464
[111,] 0.3875953 0.7751907 0.61240467
[112,] 0.4348858 0.8697716 0.56511420
[113,] 0.4366313 0.8732625 0.56336875
[114,] 0.4435803 0.8871606 0.55641969
[115,] 0.4198508 0.8397015 0.58014925
[116,] 0.7108991 0.5782019 0.28910093
[117,] 0.9110509 0.1778982 0.08894909
[118,] 0.9020916 0.1958167 0.09790835
[119,] 0.8726271 0.2547459 0.12737293
[120,] 0.8784577 0.2430846 0.12154232
[121,] 0.8370579 0.3258842 0.16294210
[122,] 0.7918734 0.4162531 0.20812656
[123,] 0.7367648 0.5264703 0.26323515
[124,] 0.6660016 0.6679968 0.33399840
[125,] 0.6488863 0.7022275 0.35111374
[126,] 0.6524735 0.6950530 0.34752651
[127,] 0.6348562 0.7302877 0.36514383
[128,] 0.5680783 0.8638435 0.43192174
[129,] 0.4991125 0.9982249 0.50088753
[130,] 0.5580027 0.8839946 0.44199731
[131,] 0.5656790 0.8686421 0.43432105
[132,] 0.4599300 0.9198601 0.54006996
[133,] 0.3671412 0.7342824 0.63285881
[134,] 0.2594991 0.5189981 0.74050094
[135,] 0.1946397 0.3892794 0.80536030
> postscript(file="/var/www/rcomp/tmp/1c4ak1321958307.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2cf8e1321958307.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mai31321958307.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/41cp81321958307.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5hjr81321958307.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 = 156
Frequency = 1
1 2 3 4 5 6
3.44662454 -3.52006182 0.06097375 -5.62666238 -3.09253195 -2.80501932
7 8 9 10 11 12
1.50948632 -2.72264783 1.81822054 -5.04561183 3.82645763 -0.62203278
13 14 15 16 17 18
3.66353204 0.59828965 -5.41894723 2.02615240 1.40894919 0.69175968
19 20 21 22 23 24
-3.51321847 -1.30152289 -1.93206900 0.23319518 4.31114525 -5.93801126
25 26 27 28 29 30
0.82645763 4.82645763 1.39399938 4.91741591 1.04157488 -3.28725379
31 32 33 34 35 36
-1.32394439 -2.10776584 0.95518528 -4.41956434 0.69847711 4.30934736
37 38 39 40 41 42
-2.00252729 -3.21689740 1.64787263 5.95274803 2.24750546 -6.30212404
43 44 45 46 47 48
-1.42741883 -0.35989819 -4.25891500 0.36347515 3.48797564 -5.41036649
49 50 51 52 53 54
1.70814822 1.18097018 -7.78697807 2.14134310 -0.90771816 -2.08950717
55 56 57 58 59 60
1.66692528 1.23626638 2.27529934 3.50508727 1.50859091 0.93380291
61 62 63 64 65 66
-1.47435271 6.90836050 5.67916397 2.54082481 -5.38928346 1.09228184
67 68 69 70 71 72
3.33386505 -2.31748801 0.09436641 -5.61024210 2.22691561 0.31875191
73 74 75 76 77 78
1.38965987 2.39271606 0.89756116 1.53862775 2.07164661 1.63467726
79 80 81 82 83 84
-2.98601103 -2.96695059 -4.86690773 0.00498183 1.58904134 -1.55203803
85 86 87 88 89 90
-2.73449688 -1.00252729 -1.62952134 -0.88645538 -2.65659377 -0.94929722
91 92 93 94 95 96
1.36638385 -0.66573911 2.84917743 2.76295659 -1.99945693 -2.42292552
97 98 99 100 101 102
-4.11385996 -2.71526820 2.78310260 -0.39162881 3.97376712 1.86981266
103 104 105 106 107 108
1.22100949 -6.66865877 -7.32151443 6.80238488 1.17872394 2.62677738
109 110 111 112 113 114
0.69639254 -1.93955212 2.38975790 2.44652108 -6.00252729 1.58042242
115 116 117 118 119 120
0.90373520 1.82645763 0.11799863 -3.03257215 2.91183026 4.36638385
121 122 123 124 125 126
-0.72641942 4.07164661 -2.49229452 1.43821535 2.82645763 -5.69065264
127 128 129 130 131 132
-5.57459655 2.76455194 1.62961006 -1.64216378 0.02936078 -1.95745350
133 134 135 136 137 138
-2.47090005 -0.83499008 1.84229144 -1.81290904 2.33896322 3.57258117
139 140 141 142 143 144
2.82645763 2.09228184 4.75858492 -0.17354237 4.51354424 -0.14350942
145 146 147 148 149 150
0.23875690 -9.02316252 -3.29943832 3.81741072 4.05122555 -2.36387059
151 152 153 154 155 156
0.44723603 1.86981266 -0.52159653 0.14986454 2.16500992 1.21197149
> postscript(file="/var/www/rcomp/tmp/6npf31321958307.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 3.44662454 NA
1 -3.52006182 3.44662454
2 0.06097375 -3.52006182
3 -5.62666238 0.06097375
4 -3.09253195 -5.62666238
5 -2.80501932 -3.09253195
6 1.50948632 -2.80501932
7 -2.72264783 1.50948632
8 1.81822054 -2.72264783
9 -5.04561183 1.81822054
10 3.82645763 -5.04561183
11 -0.62203278 3.82645763
12 3.66353204 -0.62203278
13 0.59828965 3.66353204
14 -5.41894723 0.59828965
15 2.02615240 -5.41894723
16 1.40894919 2.02615240
17 0.69175968 1.40894919
18 -3.51321847 0.69175968
19 -1.30152289 -3.51321847
20 -1.93206900 -1.30152289
21 0.23319518 -1.93206900
22 4.31114525 0.23319518
23 -5.93801126 4.31114525
24 0.82645763 -5.93801126
25 4.82645763 0.82645763
26 1.39399938 4.82645763
27 4.91741591 1.39399938
28 1.04157488 4.91741591
29 -3.28725379 1.04157488
30 -1.32394439 -3.28725379
31 -2.10776584 -1.32394439
32 0.95518528 -2.10776584
33 -4.41956434 0.95518528
34 0.69847711 -4.41956434
35 4.30934736 0.69847711
36 -2.00252729 4.30934736
37 -3.21689740 -2.00252729
38 1.64787263 -3.21689740
39 5.95274803 1.64787263
40 2.24750546 5.95274803
41 -6.30212404 2.24750546
42 -1.42741883 -6.30212404
43 -0.35989819 -1.42741883
44 -4.25891500 -0.35989819
45 0.36347515 -4.25891500
46 3.48797564 0.36347515
47 -5.41036649 3.48797564
48 1.70814822 -5.41036649
49 1.18097018 1.70814822
50 -7.78697807 1.18097018
51 2.14134310 -7.78697807
52 -0.90771816 2.14134310
53 -2.08950717 -0.90771816
54 1.66692528 -2.08950717
55 1.23626638 1.66692528
56 2.27529934 1.23626638
57 3.50508727 2.27529934
58 1.50859091 3.50508727
59 0.93380291 1.50859091
60 -1.47435271 0.93380291
61 6.90836050 -1.47435271
62 5.67916397 6.90836050
63 2.54082481 5.67916397
64 -5.38928346 2.54082481
65 1.09228184 -5.38928346
66 3.33386505 1.09228184
67 -2.31748801 3.33386505
68 0.09436641 -2.31748801
69 -5.61024210 0.09436641
70 2.22691561 -5.61024210
71 0.31875191 2.22691561
72 1.38965987 0.31875191
73 2.39271606 1.38965987
74 0.89756116 2.39271606
75 1.53862775 0.89756116
76 2.07164661 1.53862775
77 1.63467726 2.07164661
78 -2.98601103 1.63467726
79 -2.96695059 -2.98601103
80 -4.86690773 -2.96695059
81 0.00498183 -4.86690773
82 1.58904134 0.00498183
83 -1.55203803 1.58904134
84 -2.73449688 -1.55203803
85 -1.00252729 -2.73449688
86 -1.62952134 -1.00252729
87 -0.88645538 -1.62952134
88 -2.65659377 -0.88645538
89 -0.94929722 -2.65659377
90 1.36638385 -0.94929722
91 -0.66573911 1.36638385
92 2.84917743 -0.66573911
93 2.76295659 2.84917743
94 -1.99945693 2.76295659
95 -2.42292552 -1.99945693
96 -4.11385996 -2.42292552
97 -2.71526820 -4.11385996
98 2.78310260 -2.71526820
99 -0.39162881 2.78310260
100 3.97376712 -0.39162881
101 1.86981266 3.97376712
102 1.22100949 1.86981266
103 -6.66865877 1.22100949
104 -7.32151443 -6.66865877
105 6.80238488 -7.32151443
106 1.17872394 6.80238488
107 2.62677738 1.17872394
108 0.69639254 2.62677738
109 -1.93955212 0.69639254
110 2.38975790 -1.93955212
111 2.44652108 2.38975790
112 -6.00252729 2.44652108
113 1.58042242 -6.00252729
114 0.90373520 1.58042242
115 1.82645763 0.90373520
116 0.11799863 1.82645763
117 -3.03257215 0.11799863
118 2.91183026 -3.03257215
119 4.36638385 2.91183026
120 -0.72641942 4.36638385
121 4.07164661 -0.72641942
122 -2.49229452 4.07164661
123 1.43821535 -2.49229452
124 2.82645763 1.43821535
125 -5.69065264 2.82645763
126 -5.57459655 -5.69065264
127 2.76455194 -5.57459655
128 1.62961006 2.76455194
129 -1.64216378 1.62961006
130 0.02936078 -1.64216378
131 -1.95745350 0.02936078
132 -2.47090005 -1.95745350
133 -0.83499008 -2.47090005
134 1.84229144 -0.83499008
135 -1.81290904 1.84229144
136 2.33896322 -1.81290904
137 3.57258117 2.33896322
138 2.82645763 3.57258117
139 2.09228184 2.82645763
140 4.75858492 2.09228184
141 -0.17354237 4.75858492
142 4.51354424 -0.17354237
143 -0.14350942 4.51354424
144 0.23875690 -0.14350942
145 -9.02316252 0.23875690
146 -3.29943832 -9.02316252
147 3.81741072 -3.29943832
148 4.05122555 3.81741072
149 -2.36387059 4.05122555
150 0.44723603 -2.36387059
151 1.86981266 0.44723603
152 -0.52159653 1.86981266
153 0.14986454 -0.52159653
154 2.16500992 0.14986454
155 1.21197149 2.16500992
156 NA 1.21197149
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.52006182 3.44662454
[2,] 0.06097375 -3.52006182
[3,] -5.62666238 0.06097375
[4,] -3.09253195 -5.62666238
[5,] -2.80501932 -3.09253195
[6,] 1.50948632 -2.80501932
[7,] -2.72264783 1.50948632
[8,] 1.81822054 -2.72264783
[9,] -5.04561183 1.81822054
[10,] 3.82645763 -5.04561183
[11,] -0.62203278 3.82645763
[12,] 3.66353204 -0.62203278
[13,] 0.59828965 3.66353204
[14,] -5.41894723 0.59828965
[15,] 2.02615240 -5.41894723
[16,] 1.40894919 2.02615240
[17,] 0.69175968 1.40894919
[18,] -3.51321847 0.69175968
[19,] -1.30152289 -3.51321847
[20,] -1.93206900 -1.30152289
[21,] 0.23319518 -1.93206900
[22,] 4.31114525 0.23319518
[23,] -5.93801126 4.31114525
[24,] 0.82645763 -5.93801126
[25,] 4.82645763 0.82645763
[26,] 1.39399938 4.82645763
[27,] 4.91741591 1.39399938
[28,] 1.04157488 4.91741591
[29,] -3.28725379 1.04157488
[30,] -1.32394439 -3.28725379
[31,] -2.10776584 -1.32394439
[32,] 0.95518528 -2.10776584
[33,] -4.41956434 0.95518528
[34,] 0.69847711 -4.41956434
[35,] 4.30934736 0.69847711
[36,] -2.00252729 4.30934736
[37,] -3.21689740 -2.00252729
[38,] 1.64787263 -3.21689740
[39,] 5.95274803 1.64787263
[40,] 2.24750546 5.95274803
[41,] -6.30212404 2.24750546
[42,] -1.42741883 -6.30212404
[43,] -0.35989819 -1.42741883
[44,] -4.25891500 -0.35989819
[45,] 0.36347515 -4.25891500
[46,] 3.48797564 0.36347515
[47,] -5.41036649 3.48797564
[48,] 1.70814822 -5.41036649
[49,] 1.18097018 1.70814822
[50,] -7.78697807 1.18097018
[51,] 2.14134310 -7.78697807
[52,] -0.90771816 2.14134310
[53,] -2.08950717 -0.90771816
[54,] 1.66692528 -2.08950717
[55,] 1.23626638 1.66692528
[56,] 2.27529934 1.23626638
[57,] 3.50508727 2.27529934
[58,] 1.50859091 3.50508727
[59,] 0.93380291 1.50859091
[60,] -1.47435271 0.93380291
[61,] 6.90836050 -1.47435271
[62,] 5.67916397 6.90836050
[63,] 2.54082481 5.67916397
[64,] -5.38928346 2.54082481
[65,] 1.09228184 -5.38928346
[66,] 3.33386505 1.09228184
[67,] -2.31748801 3.33386505
[68,] 0.09436641 -2.31748801
[69,] -5.61024210 0.09436641
[70,] 2.22691561 -5.61024210
[71,] 0.31875191 2.22691561
[72,] 1.38965987 0.31875191
[73,] 2.39271606 1.38965987
[74,] 0.89756116 2.39271606
[75,] 1.53862775 0.89756116
[76,] 2.07164661 1.53862775
[77,] 1.63467726 2.07164661
[78,] -2.98601103 1.63467726
[79,] -2.96695059 -2.98601103
[80,] -4.86690773 -2.96695059
[81,] 0.00498183 -4.86690773
[82,] 1.58904134 0.00498183
[83,] -1.55203803 1.58904134
[84,] -2.73449688 -1.55203803
[85,] -1.00252729 -2.73449688
[86,] -1.62952134 -1.00252729
[87,] -0.88645538 -1.62952134
[88,] -2.65659377 -0.88645538
[89,] -0.94929722 -2.65659377
[90,] 1.36638385 -0.94929722
[91,] -0.66573911 1.36638385
[92,] 2.84917743 -0.66573911
[93,] 2.76295659 2.84917743
[94,] -1.99945693 2.76295659
[95,] -2.42292552 -1.99945693
[96,] -4.11385996 -2.42292552
[97,] -2.71526820 -4.11385996
[98,] 2.78310260 -2.71526820
[99,] -0.39162881 2.78310260
[100,] 3.97376712 -0.39162881
[101,] 1.86981266 3.97376712
[102,] 1.22100949 1.86981266
[103,] -6.66865877 1.22100949
[104,] -7.32151443 -6.66865877
[105,] 6.80238488 -7.32151443
[106,] 1.17872394 6.80238488
[107,] 2.62677738 1.17872394
[108,] 0.69639254 2.62677738
[109,] -1.93955212 0.69639254
[110,] 2.38975790 -1.93955212
[111,] 2.44652108 2.38975790
[112,] -6.00252729 2.44652108
[113,] 1.58042242 -6.00252729
[114,] 0.90373520 1.58042242
[115,] 1.82645763 0.90373520
[116,] 0.11799863 1.82645763
[117,] -3.03257215 0.11799863
[118,] 2.91183026 -3.03257215
[119,] 4.36638385 2.91183026
[120,] -0.72641942 4.36638385
[121,] 4.07164661 -0.72641942
[122,] -2.49229452 4.07164661
[123,] 1.43821535 -2.49229452
[124,] 2.82645763 1.43821535
[125,] -5.69065264 2.82645763
[126,] -5.57459655 -5.69065264
[127,] 2.76455194 -5.57459655
[128,] 1.62961006 2.76455194
[129,] -1.64216378 1.62961006
[130,] 0.02936078 -1.64216378
[131,] -1.95745350 0.02936078
[132,] -2.47090005 -1.95745350
[133,] -0.83499008 -2.47090005
[134,] 1.84229144 -0.83499008
[135,] -1.81290904 1.84229144
[136,] 2.33896322 -1.81290904
[137,] 3.57258117 2.33896322
[138,] 2.82645763 3.57258117
[139,] 2.09228184 2.82645763
[140,] 4.75858492 2.09228184
[141,] -0.17354237 4.75858492
[142,] 4.51354424 -0.17354237
[143,] -0.14350942 4.51354424
[144,] 0.23875690 -0.14350942
[145,] -9.02316252 0.23875690
[146,] -3.29943832 -9.02316252
[147,] 3.81741072 -3.29943832
[148,] 4.05122555 3.81741072
[149,] -2.36387059 4.05122555
[150,] 0.44723603 -2.36387059
[151,] 1.86981266 0.44723603
[152,] -0.52159653 1.86981266
[153,] 0.14986454 -0.52159653
[154,] 2.16500992 0.14986454
[155,] 1.21197149 2.16500992
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.52006182 3.44662454
2 0.06097375 -3.52006182
3 -5.62666238 0.06097375
4 -3.09253195 -5.62666238
5 -2.80501932 -3.09253195
6 1.50948632 -2.80501932
7 -2.72264783 1.50948632
8 1.81822054 -2.72264783
9 -5.04561183 1.81822054
10 3.82645763 -5.04561183
11 -0.62203278 3.82645763
12 3.66353204 -0.62203278
13 0.59828965 3.66353204
14 -5.41894723 0.59828965
15 2.02615240 -5.41894723
16 1.40894919 2.02615240
17 0.69175968 1.40894919
18 -3.51321847 0.69175968
19 -1.30152289 -3.51321847
20 -1.93206900 -1.30152289
21 0.23319518 -1.93206900
22 4.31114525 0.23319518
23 -5.93801126 4.31114525
24 0.82645763 -5.93801126
25 4.82645763 0.82645763
26 1.39399938 4.82645763
27 4.91741591 1.39399938
28 1.04157488 4.91741591
29 -3.28725379 1.04157488
30 -1.32394439 -3.28725379
31 -2.10776584 -1.32394439
32 0.95518528 -2.10776584
33 -4.41956434 0.95518528
34 0.69847711 -4.41956434
35 4.30934736 0.69847711
36 -2.00252729 4.30934736
37 -3.21689740 -2.00252729
38 1.64787263 -3.21689740
39 5.95274803 1.64787263
40 2.24750546 5.95274803
41 -6.30212404 2.24750546
42 -1.42741883 -6.30212404
43 -0.35989819 -1.42741883
44 -4.25891500 -0.35989819
45 0.36347515 -4.25891500
46 3.48797564 0.36347515
47 -5.41036649 3.48797564
48 1.70814822 -5.41036649
49 1.18097018 1.70814822
50 -7.78697807 1.18097018
51 2.14134310 -7.78697807
52 -0.90771816 2.14134310
53 -2.08950717 -0.90771816
54 1.66692528 -2.08950717
55 1.23626638 1.66692528
56 2.27529934 1.23626638
57 3.50508727 2.27529934
58 1.50859091 3.50508727
59 0.93380291 1.50859091
60 -1.47435271 0.93380291
61 6.90836050 -1.47435271
62 5.67916397 6.90836050
63 2.54082481 5.67916397
64 -5.38928346 2.54082481
65 1.09228184 -5.38928346
66 3.33386505 1.09228184
67 -2.31748801 3.33386505
68 0.09436641 -2.31748801
69 -5.61024210 0.09436641
70 2.22691561 -5.61024210
71 0.31875191 2.22691561
72 1.38965987 0.31875191
73 2.39271606 1.38965987
74 0.89756116 2.39271606
75 1.53862775 0.89756116
76 2.07164661 1.53862775
77 1.63467726 2.07164661
78 -2.98601103 1.63467726
79 -2.96695059 -2.98601103
80 -4.86690773 -2.96695059
81 0.00498183 -4.86690773
82 1.58904134 0.00498183
83 -1.55203803 1.58904134
84 -2.73449688 -1.55203803
85 -1.00252729 -2.73449688
86 -1.62952134 -1.00252729
87 -0.88645538 -1.62952134
88 -2.65659377 -0.88645538
89 -0.94929722 -2.65659377
90 1.36638385 -0.94929722
91 -0.66573911 1.36638385
92 2.84917743 -0.66573911
93 2.76295659 2.84917743
94 -1.99945693 2.76295659
95 -2.42292552 -1.99945693
96 -4.11385996 -2.42292552
97 -2.71526820 -4.11385996
98 2.78310260 -2.71526820
99 -0.39162881 2.78310260
100 3.97376712 -0.39162881
101 1.86981266 3.97376712
102 1.22100949 1.86981266
103 -6.66865877 1.22100949
104 -7.32151443 -6.66865877
105 6.80238488 -7.32151443
106 1.17872394 6.80238488
107 2.62677738 1.17872394
108 0.69639254 2.62677738
109 -1.93955212 0.69639254
110 2.38975790 -1.93955212
111 2.44652108 2.38975790
112 -6.00252729 2.44652108
113 1.58042242 -6.00252729
114 0.90373520 1.58042242
115 1.82645763 0.90373520
116 0.11799863 1.82645763
117 -3.03257215 0.11799863
118 2.91183026 -3.03257215
119 4.36638385 2.91183026
120 -0.72641942 4.36638385
121 4.07164661 -0.72641942
122 -2.49229452 4.07164661
123 1.43821535 -2.49229452
124 2.82645763 1.43821535
125 -5.69065264 2.82645763
126 -5.57459655 -5.69065264
127 2.76455194 -5.57459655
128 1.62961006 2.76455194
129 -1.64216378 1.62961006
130 0.02936078 -1.64216378
131 -1.95745350 0.02936078
132 -2.47090005 -1.95745350
133 -0.83499008 -2.47090005
134 1.84229144 -0.83499008
135 -1.81290904 1.84229144
136 2.33896322 -1.81290904
137 3.57258117 2.33896322
138 2.82645763 3.57258117
139 2.09228184 2.82645763
140 4.75858492 2.09228184
141 -0.17354237 4.75858492
142 4.51354424 -0.17354237
143 -0.14350942 4.51354424
144 0.23875690 -0.14350942
145 -9.02316252 0.23875690
146 -3.29943832 -9.02316252
147 3.81741072 -3.29943832
148 4.05122555 3.81741072
149 -2.36387059 4.05122555
150 0.44723603 -2.36387059
151 1.86981266 0.44723603
152 -0.52159653 1.86981266
153 0.14986454 -0.52159653
154 2.16500992 0.14986454
155 1.21197149 2.16500992
> 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/7xvrn1321958307.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/88bck1321958307.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9gm5s1321958307.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/102d7o1321958307.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11opsp1321958307.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/122hyj1321958307.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/131psp1321958307.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/14e7tc1321958307.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/152k0b1321958307.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/16wcwc1321958307.tab")
+ }
>
> try(system("convert tmp/1c4ak1321958307.ps tmp/1c4ak1321958307.png",intern=TRUE))
character(0)
> try(system("convert tmp/2cf8e1321958307.ps tmp/2cf8e1321958307.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mai31321958307.ps tmp/3mai31321958307.png",intern=TRUE))
character(0)
> try(system("convert tmp/41cp81321958307.ps tmp/41cp81321958307.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hjr81321958307.ps tmp/5hjr81321958307.png",intern=TRUE))
character(0)
> try(system("convert tmp/6npf31321958307.ps tmp/6npf31321958307.png",intern=TRUE))
character(0)
> try(system("convert tmp/7xvrn1321958307.ps tmp/7xvrn1321958307.png",intern=TRUE))
character(0)
> try(system("convert tmp/88bck1321958307.ps tmp/88bck1321958307.png",intern=TRUE))
character(0)
> try(system("convert tmp/9gm5s1321958307.ps tmp/9gm5s1321958307.png",intern=TRUE))
character(0)
> try(system("convert tmp/102d7o1321958307.ps tmp/102d7o1321958307.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.632 0.584 12.769