R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(12
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+ ,4)
+ ,dim=c(9
+ ,145)
+ ,dimnames=list(c('Depression'
+ ,'CriticParents'
+ ,'ExpecParents'
+ ,'FutureWorrying'
+ ,'SleepDepri'
+ ,'ChangesLastYear'
+ ,'FreqSmoking'
+ ,'FreqHighAlc'
+ ,'FreqBeerOrWine')
+ ,1:145))
> y <- array(NA,dim=c(9,145),dimnames=list(c('Depression','CriticParents','ExpecParents','FutureWorrying','SleepDepri','ChangesLastYear','FreqSmoking','FreqHighAlc','FreqBeerOrWine'),1:145))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Depression CriticParents ExpecParents FutureWorrying SleepDepri
1 12 6 15 4 7
2 11 6 15 3 5
3 14 13 14 5 7
4 12 8 10 3 3
5 21 7 10 6 7
6 12 9 12 5 7
7 22 5 18 6 7
8 11 8 12 6 1
9 10 9 14 5 4
10 13 11 18 5 5
11 10 8 9 3 6
12 8 11 11 5 4
13 15 12 11 7 7
14 10 8 17 5 6
15 14 7 8 5 2
16 14 9 16 3 2
17 11 12 21 5 6
18 10 20 24 6 7
19 13 7 21 5 5
20 7 8 14 2 2
21 12 8 7 5 7
22 14 16 18 4 4
23 11 10 18 6 5
24 9 6 13 3 5
25 11 8 11 5 5
26 15 9 13 4 3
27 13 9 13 5 5
28 9 11 18 2 1
29 15 12 14 2 1
30 10 8 12 5 3
31 11 7 9 2 2
32 13 8 12 2 3
33 8 9 8 2 2
34 20 4 5 5 5
35 12 8 10 5 2
36 10 8 11 1 3
37 10 8 11 5 4
38 9 6 12 2 6
39 14 8 12 6 2
40 8 4 15 1 7
41 14 7 12 4 6
42 11 14 16 3 5
43 13 10 14 2 3
44 11 9 17 5 3
45 11 8 10 3 4
46 10 11 17 4 5
47 14 8 12 3 2
48 18 8 13 6 7
49 14 10 13 4 6
50 11 8 11 5 5
51 12 10 13 2 6
52 13 7 12 5 5
53 9 8 12 5 2
54 10 7 12 3 3
55 15 9 9 5 5
56 20 5 7 7 7
57 12 7 17 4 4
58 12 7 12 2 7
59 14 7 12 3 5
60 13 9 9 6 6
61 11 5 9 7 6
62 17 8 13 4 3
63 12 8 10 4 5
64 13 8 11 4 7
65 14 9 12 5 7
66 13 6 10 2 5
67 15 8 13 3 6
68 13 6 6 3 5
69 10 4 7 4 5
70 11 6 13 3 2
71 13 4 11 4 5
72 17 12 18 6 4
73 13 6 9 2 6
74 9 11 9 4 5
75 11 8 11 5 3
76 10 10 11 2 3
77 9 10 15 1 4
78 12 4 8 2 2
79 12 8 11 5 2
80 13 9 14 4 5
81 13 9 14 4 4
82 22 7 12 6 6
83 13 7 12 1 4
84 15 11 8 4 6
85 13 8 11 5 4
86 15 8 10 2 2
87 10 7 17 3 5
88 11 5 16 3 2
89 16 7 13 6 7
90 11 9 15 5 1
91 11 8 11 4 3
92 10 6 12 4 5
93 10 8 16 5 6
94 16 10 20 5 6
95 12 10 16 6 2
96 11 8 11 6 5
97 16 11 15 5 5
98 19 8 15 7 3
99 11 8 12 5 6
100 15 6 9 5 5
101 24 20 24 7 7
102 14 6 15 5 1
103 15 12 18 6 6
104 11 9 17 6 4
105 15 5 12 4 7
106 12 10 15 5 2
107 10 5 11 1 6
108 14 6 11 6 7
109 9 6 12 5 5
110 15 10 14 2 2
111 15 5 11 1 1
112 14 13 20 5 3
113 11 7 11 6 3
114 8 9 12 5 3
115 11 8 12 5 5
116 8 5 11 4 2
117 10 4 10 2 4
118 11 9 11 3 6
119 13 7 12 3 5
120 11 5 9 5 5
121 20 5 8 3 2
122 10 4 6 2 3
123 12 7 12 2 2
124 14 9 15 3 6
125 23 8 13 6 5
126 14 8 17 5 4
127 16 11 14 6 6
128 11 10 16 2 4
129 12 9 15 5 6
130 10 12 16 5 2
131 14 10 11 5 0
132 12 10 11 1 1
133 12 7 16 4 5
134 11 10 15 2 2
135 12 6 14 2 5
136 13 6 9 7 6
137 17 11 13 6 7
138 11 8 11 5 5
139 12 9 14 5 5
140 19 9 11 5 5
141 15 11 8 4 6
142 14 4 7 3 6
143 11 9 11 3 6
144 9 5 13 3 1
145 18 4 9 2 3
ChangesLastYear FreqSmoking FreqHighAlc FreqBeerOrWine
1 2 2 2 2
2 4 1 2 2
3 7 4 3 4
4 3 1 2 3
5 7 5 4 4
6 2 1 2 3
7 7 1 2 3
8 2 1 3 4
9 1 1 2 3
10 2 1 2 4
11 6 2 3 3
12 1 1 2 2
13 1 3 3 3
14 1 1 1 3
15 2 1 3 3
16 2 1 1 2
17 2 1 3 3
18 1 1 2 2
19 7 2 3 4
20 1 4 4 5
21 2 1 3 3
22 4 2 3 3
23 2 1 1 1
24 1 2 2 4
25 1 3 1 3
26 5 1 3 4
27 2 1 3 3
28 1 1 2 3
29 3 1 2 1
30 1 1 3 4
31 2 2 2 4
32 5 1 2 2
33 2 1 2 2
34 6 1 1 1
35 4 1 2 3
36 1 1 3 4
37 3 1 1 1
38 6 1 2 3
39 7 2 3 3
40 4 1 2 2
41 1 2 1 4
42 5 1 1 3
43 3 1 3 3
44 2 2 3 2
45 2 1 3 3
46 2 1 3 2
47 2 1 2 1
48 1 1 3 3
49 2 1 2 3
50 1 4 3 5
51 2 2 4 1
52 2 1 3 3
53 5 1 3 4
54 5 4 3 3
55 2 2 3 4
56 1 1 2 2
57 1 1 3 3
58 2 1 3 4
59 3 1 1 1
60 7 1 1 1
61 4 1 1 1
62 4 2 4 4
63 1 1 3 2
64 2 1 2 3
65 2 2 3 4
66 2 1 1 2
67 5 2 4 5
68 1 2 3 3
69 6 4 2 3
70 2 1 3 3
71 2 1 3 4
72 4 3 3 4
73 6 1 2 3
74 2 1 1 1
75 2 1 1 3
76 2 1 1 1
77 1 1 3 3
78 1 1 4 5
79 2 1 2 3
80 2 1 2 3
81 3 4 2 4
82 3 1 2 5
83 5 1 3 4
84 2 2 4 4
85 5 1 2 4
86 3 1 3 4
87 1 1 3 4
88 2 1 2 3
89 2 1 2 4
90 1 1 3 3
91 2 1 3 3
92 2 1 3 3
93 5 1 3 4
94 5 1 3 3
95 2 1 3 4
96 3 1 2 2
97 5 5 3 5
98 5 1 3 3
99 6 1 2 4
100 2 1 1 2
101 7 3 3 4
102 1 1 2 3
103 1 1 2 4
104 6 1 3 3
105 6 1 1 1
106 2 1 3 4
107 1 1 2 4
108 2 1 2 2
109 1 4 2 5
110 2 4 2 4
111 1 1 2 4
112 3 1 3 3
113 3 1 3 4
114 6 4 3 4
115 4 2 3 4
116 1 1 3 3
117 2 1 1 5
118 5 1 3 3
119 6 1 4 4
120 3 1 2 4
121 5 1 2 4
122 3 2 4 4
123 2 4 3 4
124 3 4 2 5
125 2 1 3 3
126 5 1 1 1
127 5 1 2 4
128 7 2 4 4
129 4 1 3 3
130 4 1 3 4
131 5 1 3 4
132 1 3 2 4
133 4 2 4 4
134 1 2 1 4
135 4 1 3 4
136 6 1 1 3
137 7 2 2 5
138 1 3 1 3
139 3 1 2 4
140 5 1 4 4
141 2 2 4 4
142 4 2 3 4
143 5 1 3 3
144 1 1 1 4
145 2 1 4 4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CriticParents ExpecParents FutureWorrying
7.49201 0.04090 -0.06691 0.58676
SleepDepri ChangesLastYear FreqSmoking FreqHighAlc
0.20217 0.35635 -0.12134 0.26471
FreqBeerOrWine
0.29259
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.2147 -2.0897 -0.1981 1.3630 9.2560
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.49201 1.44524 5.184 7.69e-07 ***
CriticParents 0.04090 0.11575 0.353 0.724412
ExpecParents -0.06691 0.08658 -0.773 0.441000
FutureWorrying 0.58676 0.16826 3.487 0.000658 ***
SleepDepri 0.20217 0.14121 1.432 0.154525
ChangesLastYear 0.35635 0.13492 2.641 0.009230 **
FreqSmoking -0.12134 0.27349 -0.444 0.657980
FreqHighAlc 0.26471 0.31460 0.841 0.401593
FreqBeerOrWine 0.29259 0.27853 1.050 0.295364
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.9 on 136 degrees of freedom
Multiple R-squared: 0.2056, Adjusted R-squared: 0.1589
F-statistic: 4.4 on 8 and 136 DF, p-value: 9.39e-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,] 0.19262592 0.38525183 0.8073741
[2,] 0.29917855 0.59835711 0.7008214
[3,] 0.45582602 0.91165203 0.5441740
[4,] 0.40085223 0.80170446 0.5991478
[5,] 0.55342592 0.89314816 0.4465741
[6,] 0.46928856 0.93857711 0.5307114
[7,] 0.38343901 0.76687802 0.6165610
[8,] 0.48998776 0.97997552 0.5100122
[9,] 0.41933597 0.83867194 0.5806640
[10,] 0.35231785 0.70463570 0.6476822
[11,] 0.39822723 0.79645446 0.6017728
[12,] 0.44437930 0.88875860 0.5556207
[13,] 0.37231151 0.74462301 0.6276885
[14,] 0.31564633 0.63129266 0.6843537
[15,] 0.27439594 0.54879188 0.7256041
[16,] 0.22456711 0.44913423 0.7754329
[17,] 0.18936491 0.37872983 0.8106351
[18,] 0.24797096 0.49594193 0.7520290
[19,] 0.20794026 0.41588052 0.7920597
[20,] 0.17097625 0.34195250 0.8290237
[21,] 0.13419174 0.26838349 0.8658083
[22,] 0.12933158 0.25866317 0.8706684
[23,] 0.14663303 0.29326607 0.8533670
[24,] 0.14907559 0.29815118 0.8509244
[25,] 0.15413378 0.30826757 0.8458662
[26,] 0.19441837 0.38883675 0.8055816
[27,] 0.24676815 0.49353630 0.7532319
[28,] 0.27039157 0.54078315 0.7296084
[29,] 0.26399261 0.52798522 0.7360074
[30,] 0.27565756 0.55131512 0.7243424
[31,] 0.24471346 0.48942692 0.7552865
[32,] 0.24397447 0.48794895 0.7560255
[33,] 0.20806844 0.41613688 0.7919316
[34,] 0.17312952 0.34625905 0.8268705
[35,] 0.15164881 0.30329763 0.8483512
[36,] 0.15838464 0.31676928 0.8416154
[37,] 0.25905798 0.51811597 0.7409420
[38,] 0.24297578 0.48595157 0.7570242
[39,] 0.21484090 0.42968180 0.7851591
[40,] 0.18169442 0.36338883 0.8183056
[41,] 0.14829862 0.29659724 0.8517014
[42,] 0.21210297 0.42420593 0.7878970
[43,] 0.20464331 0.40928663 0.7953567
[44,] 0.18720565 0.37441131 0.8127943
[45,] 0.27981092 0.55962185 0.7201891
[46,] 0.24140225 0.48280450 0.7585978
[47,] 0.20923962 0.41847924 0.7907604
[48,] 0.19512712 0.39025424 0.8048729
[49,] 0.20859603 0.41719206 0.7914040
[50,] 0.25958765 0.51917530 0.7404124
[51,] 0.29740819 0.59481637 0.7025918
[52,] 0.25534143 0.51068286 0.7446586
[53,] 0.21681131 0.43362263 0.7831887
[54,] 0.18277090 0.36554179 0.8172291
[55,] 0.17040667 0.34081335 0.8295933
[56,] 0.14623353 0.29246705 0.8537665
[57,] 0.12244049 0.24488099 0.8775595
[58,] 0.13140714 0.26281429 0.8685929
[59,] 0.10650688 0.21301377 0.8934931
[60,] 0.08521512 0.17043023 0.9147849
[61,] 0.09157829 0.18315658 0.9084217
[62,] 0.07266565 0.14533129 0.9273344
[63,] 0.07298038 0.14596076 0.9270196
[64,] 0.06020302 0.12040605 0.9397970
[65,] 0.04826575 0.09653150 0.9517343
[66,] 0.04192889 0.08385779 0.9580711
[67,] 0.03234286 0.06468572 0.9676571
[68,] 0.02456126 0.04912252 0.9754387
[69,] 0.01872881 0.03745762 0.9812712
[70,] 0.01458481 0.02916962 0.9854152
[71,] 0.07723046 0.15446092 0.9227695
[72,] 0.06179379 0.12358757 0.9382062
[73,] 0.04958237 0.09916473 0.9504176
[74,] 0.03951006 0.07902012 0.9604899
[75,] 0.04045340 0.08090680 0.9595466
[76,] 0.03380603 0.06761205 0.9661940
[77,] 0.02529067 0.05058134 0.9747093
[78,] 0.02184436 0.04368873 0.9781556
[79,] 0.01711022 0.03422044 0.9828898
[80,] 0.01400762 0.02801525 0.9859924
[81,] 0.01354034 0.02708068 0.9864597
[82,] 0.01943072 0.03886145 0.9805693
[83,] 0.01564936 0.03129873 0.9843506
[84,] 0.01225480 0.02450960 0.9877452
[85,] 0.01278846 0.02557691 0.9872115
[86,] 0.01131223 0.02262446 0.9886878
[87,] 0.01789570 0.03579140 0.9821043
[88,] 0.02021784 0.04043568 0.9797822
[89,] 0.01662352 0.03324704 0.9833765
[90,] 0.08264664 0.16529327 0.9173534
[91,] 0.07564854 0.15129708 0.9243515
[92,] 0.06203921 0.12407842 0.9379608
[93,] 0.06222811 0.12445623 0.9377719
[94,] 0.05084742 0.10169484 0.9491526
[95,] 0.03914887 0.07829774 0.9608511
[96,] 0.03333963 0.06667926 0.9666604
[97,] 0.02401552 0.04803103 0.9759845
[98,] 0.02232476 0.04464953 0.9776752
[99,] 0.03234147 0.06468294 0.9676585
[100,] 0.04323080 0.08646159 0.9567692
[101,] 0.03233701 0.06467403 0.9676630
[102,] 0.03164265 0.06328529 0.9683574
[103,] 0.05828248 0.11656497 0.9417175
[104,] 0.06003746 0.12007493 0.9399625
[105,] 0.09508092 0.19016183 0.9049191
[106,] 0.07326606 0.14653211 0.9267339
[107,] 0.06089460 0.12178919 0.9391054
[108,] 0.04420526 0.08841052 0.9557947
[109,] 0.05323290 0.10646579 0.9467671
[110,] 0.21778096 0.43556192 0.7822190
[111,] 0.24441920 0.48883840 0.7555808
[112,] 0.18984655 0.37969310 0.8101534
[113,] 0.16603430 0.33206861 0.8339657
[114,] 0.58113190 0.83773620 0.4188681
[115,] 0.86592112 0.26815775 0.1340789
[116,] 0.85803509 0.28392983 0.1419649
[117,] 0.79514555 0.40970890 0.2048544
[118,] 0.73276889 0.53446221 0.2672311
[119,] 0.71097626 0.57804747 0.2890237
[120,] 0.70649892 0.58700216 0.2935011
[121,] 0.61328444 0.77343112 0.3867156
[122,] 0.77936760 0.44126480 0.2206324
> postscript(file="/var/www/html/rcomp/tmp/1302j1290533238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2302j1290533238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3vr2m1290533238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4vr2m1290533238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vr2m1290533238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 145
Frequency = 1
1 2 3 4 5 6
-0.08056284 -0.92350690 -1.40944156 0.12824941 4.83816905 -1.40466172
7 8 9 10 11 12
6.79188265 -2.29482497 -2.30799789 0.02674168 -3.75757170 -4.29792453
13 14 15 16 17 18
0.56654528 -2.20599713 1.15561594 3.60462142 -1.98771818 -2.98943027
19 20 21 22 23 24
-1.53405604 -3.85308541 -1.96302185 1.04771092 -1.37663561 -2.45212379
25 26 27 28 29 30
-1.16260050 1.43132696 -0.19813285 -0.75538644 4.80857086 -2.75605578
31 32 33 34 35 36
0.07625634 1.42871943 -2.60860372 6.16029690 -1.19944263 -0.47593988
37 38 39 40 41 42
-2.33062781 -3.74492628 -0.86479408 -3.07253704 1.91586242 -1.56798641
43 44 45 46 47 48
1.63613531 -1.11223479 -0.98228388 -2.13294379 3.40575983 4.20801971
49 50 51 52 53 54
1.41027407 -2.15586635 0.76088058 -0.18325023 -4.97927535 -2.31042282
55 56 57 58 59 60
1.36298354 5.89980239 0.29656176 -0.11990544 2.74852173 -1.92181631
61 62 63 64 65 66
-3.27595040 3.68519646 -0.12227011 0.15608144 0.15937560 2.30611291
67 68 69 70 71 72
1.01651573 1.10739352 -3.60500172 -0.29543372 0.16669402 2.86653144
73 74 75 76 77 78
0.05434818 -2.84619561 -1.35729457 -0.09363732 -1.19967366 0.54502432
79 80 81 82 83 84
-0.41984096 0.72024490 0.63749648 7.35101340 1.00431115 1.33416012
85 86 87 88 89 90
-1.18580421 3.35986909 -1.61143891 0.21090052 1.86469179 -0.89930228
91 92 93 94 95 96
-1.29996473 -2.55559856 -4.52030825 1.95812471 -1.31114879 -2.67685382
97 98 99 100 101 102
1.68503518 4.13836129 -3.87957576 2.47893609 7.67852192 2.48809755
103 104 105 106 107 108
1.55326985 -3.74047447 1.77018355 -0.79130120 -0.69504027 0.35695041
109 110 111 112 113 114
-3.74245357 4.53079318 5.31579343 1.15463138 -3.08151798 -6.21466277
115 116 117 118 119 120
-3.10808850 -3.61876471 -1.28769932 -2.42964415 -0.99242750 -2.68640812
121 122 123 124 125 126
7.31400263 -2.08972208 1.25494972 1.59423758 9.25600661 1.35813012
127 128 129 130 131 132
0.90114504 -2.29356340 -1.97917572 -3.51887698 0.27635849 2.35399657
133 134 135 136 137 138
-1.47751591 0.97608043 -0.25355252 -2.61471920 0.74812708 -1.16260050
139 140 141 142 143 144
-1.51544782 4.04170722 1.33416012 0.69229666 -2.42964415 -1.45918861
145
6.34600955
> postscript(file="/var/www/html/rcomp/tmp/6oij71290533238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 145
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.08056284 NA
1 -0.92350690 -0.08056284
2 -1.40944156 -0.92350690
3 0.12824941 -1.40944156
4 4.83816905 0.12824941
5 -1.40466172 4.83816905
6 6.79188265 -1.40466172
7 -2.29482497 6.79188265
8 -2.30799789 -2.29482497
9 0.02674168 -2.30799789
10 -3.75757170 0.02674168
11 -4.29792453 -3.75757170
12 0.56654528 -4.29792453
13 -2.20599713 0.56654528
14 1.15561594 -2.20599713
15 3.60462142 1.15561594
16 -1.98771818 3.60462142
17 -2.98943027 -1.98771818
18 -1.53405604 -2.98943027
19 -3.85308541 -1.53405604
20 -1.96302185 -3.85308541
21 1.04771092 -1.96302185
22 -1.37663561 1.04771092
23 -2.45212379 -1.37663561
24 -1.16260050 -2.45212379
25 1.43132696 -1.16260050
26 -0.19813285 1.43132696
27 -0.75538644 -0.19813285
28 4.80857086 -0.75538644
29 -2.75605578 4.80857086
30 0.07625634 -2.75605578
31 1.42871943 0.07625634
32 -2.60860372 1.42871943
33 6.16029690 -2.60860372
34 -1.19944263 6.16029690
35 -0.47593988 -1.19944263
36 -2.33062781 -0.47593988
37 -3.74492628 -2.33062781
38 -0.86479408 -3.74492628
39 -3.07253704 -0.86479408
40 1.91586242 -3.07253704
41 -1.56798641 1.91586242
42 1.63613531 -1.56798641
43 -1.11223479 1.63613531
44 -0.98228388 -1.11223479
45 -2.13294379 -0.98228388
46 3.40575983 -2.13294379
47 4.20801971 3.40575983
48 1.41027407 4.20801971
49 -2.15586635 1.41027407
50 0.76088058 -2.15586635
51 -0.18325023 0.76088058
52 -4.97927535 -0.18325023
53 -2.31042282 -4.97927535
54 1.36298354 -2.31042282
55 5.89980239 1.36298354
56 0.29656176 5.89980239
57 -0.11990544 0.29656176
58 2.74852173 -0.11990544
59 -1.92181631 2.74852173
60 -3.27595040 -1.92181631
61 3.68519646 -3.27595040
62 -0.12227011 3.68519646
63 0.15608144 -0.12227011
64 0.15937560 0.15608144
65 2.30611291 0.15937560
66 1.01651573 2.30611291
67 1.10739352 1.01651573
68 -3.60500172 1.10739352
69 -0.29543372 -3.60500172
70 0.16669402 -0.29543372
71 2.86653144 0.16669402
72 0.05434818 2.86653144
73 -2.84619561 0.05434818
74 -1.35729457 -2.84619561
75 -0.09363732 -1.35729457
76 -1.19967366 -0.09363732
77 0.54502432 -1.19967366
78 -0.41984096 0.54502432
79 0.72024490 -0.41984096
80 0.63749648 0.72024490
81 7.35101340 0.63749648
82 1.00431115 7.35101340
83 1.33416012 1.00431115
84 -1.18580421 1.33416012
85 3.35986909 -1.18580421
86 -1.61143891 3.35986909
87 0.21090052 -1.61143891
88 1.86469179 0.21090052
89 -0.89930228 1.86469179
90 -1.29996473 -0.89930228
91 -2.55559856 -1.29996473
92 -4.52030825 -2.55559856
93 1.95812471 -4.52030825
94 -1.31114879 1.95812471
95 -2.67685382 -1.31114879
96 1.68503518 -2.67685382
97 4.13836129 1.68503518
98 -3.87957576 4.13836129
99 2.47893609 -3.87957576
100 7.67852192 2.47893609
101 2.48809755 7.67852192
102 1.55326985 2.48809755
103 -3.74047447 1.55326985
104 1.77018355 -3.74047447
105 -0.79130120 1.77018355
106 -0.69504027 -0.79130120
107 0.35695041 -0.69504027
108 -3.74245357 0.35695041
109 4.53079318 -3.74245357
110 5.31579343 4.53079318
111 1.15463138 5.31579343
112 -3.08151798 1.15463138
113 -6.21466277 -3.08151798
114 -3.10808850 -6.21466277
115 -3.61876471 -3.10808850
116 -1.28769932 -3.61876471
117 -2.42964415 -1.28769932
118 -0.99242750 -2.42964415
119 -2.68640812 -0.99242750
120 7.31400263 -2.68640812
121 -2.08972208 7.31400263
122 1.25494972 -2.08972208
123 1.59423758 1.25494972
124 9.25600661 1.59423758
125 1.35813012 9.25600661
126 0.90114504 1.35813012
127 -2.29356340 0.90114504
128 -1.97917572 -2.29356340
129 -3.51887698 -1.97917572
130 0.27635849 -3.51887698
131 2.35399657 0.27635849
132 -1.47751591 2.35399657
133 0.97608043 -1.47751591
134 -0.25355252 0.97608043
135 -2.61471920 -0.25355252
136 0.74812708 -2.61471920
137 -1.16260050 0.74812708
138 -1.51544782 -1.16260050
139 4.04170722 -1.51544782
140 1.33416012 4.04170722
141 0.69229666 1.33416012
142 -2.42964415 0.69229666
143 -1.45918861 -2.42964415
144 6.34600955 -1.45918861
145 NA 6.34600955
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.92350690 -0.08056284
[2,] -1.40944156 -0.92350690
[3,] 0.12824941 -1.40944156
[4,] 4.83816905 0.12824941
[5,] -1.40466172 4.83816905
[6,] 6.79188265 -1.40466172
[7,] -2.29482497 6.79188265
[8,] -2.30799789 -2.29482497
[9,] 0.02674168 -2.30799789
[10,] -3.75757170 0.02674168
[11,] -4.29792453 -3.75757170
[12,] 0.56654528 -4.29792453
[13,] -2.20599713 0.56654528
[14,] 1.15561594 -2.20599713
[15,] 3.60462142 1.15561594
[16,] -1.98771818 3.60462142
[17,] -2.98943027 -1.98771818
[18,] -1.53405604 -2.98943027
[19,] -3.85308541 -1.53405604
[20,] -1.96302185 -3.85308541
[21,] 1.04771092 -1.96302185
[22,] -1.37663561 1.04771092
[23,] -2.45212379 -1.37663561
[24,] -1.16260050 -2.45212379
[25,] 1.43132696 -1.16260050
[26,] -0.19813285 1.43132696
[27,] -0.75538644 -0.19813285
[28,] 4.80857086 -0.75538644
[29,] -2.75605578 4.80857086
[30,] 0.07625634 -2.75605578
[31,] 1.42871943 0.07625634
[32,] -2.60860372 1.42871943
[33,] 6.16029690 -2.60860372
[34,] -1.19944263 6.16029690
[35,] -0.47593988 -1.19944263
[36,] -2.33062781 -0.47593988
[37,] -3.74492628 -2.33062781
[38,] -0.86479408 -3.74492628
[39,] -3.07253704 -0.86479408
[40,] 1.91586242 -3.07253704
[41,] -1.56798641 1.91586242
[42,] 1.63613531 -1.56798641
[43,] -1.11223479 1.63613531
[44,] -0.98228388 -1.11223479
[45,] -2.13294379 -0.98228388
[46,] 3.40575983 -2.13294379
[47,] 4.20801971 3.40575983
[48,] 1.41027407 4.20801971
[49,] -2.15586635 1.41027407
[50,] 0.76088058 -2.15586635
[51,] -0.18325023 0.76088058
[52,] -4.97927535 -0.18325023
[53,] -2.31042282 -4.97927535
[54,] 1.36298354 -2.31042282
[55,] 5.89980239 1.36298354
[56,] 0.29656176 5.89980239
[57,] -0.11990544 0.29656176
[58,] 2.74852173 -0.11990544
[59,] -1.92181631 2.74852173
[60,] -3.27595040 -1.92181631
[61,] 3.68519646 -3.27595040
[62,] -0.12227011 3.68519646
[63,] 0.15608144 -0.12227011
[64,] 0.15937560 0.15608144
[65,] 2.30611291 0.15937560
[66,] 1.01651573 2.30611291
[67,] 1.10739352 1.01651573
[68,] -3.60500172 1.10739352
[69,] -0.29543372 -3.60500172
[70,] 0.16669402 -0.29543372
[71,] 2.86653144 0.16669402
[72,] 0.05434818 2.86653144
[73,] -2.84619561 0.05434818
[74,] -1.35729457 -2.84619561
[75,] -0.09363732 -1.35729457
[76,] -1.19967366 -0.09363732
[77,] 0.54502432 -1.19967366
[78,] -0.41984096 0.54502432
[79,] 0.72024490 -0.41984096
[80,] 0.63749648 0.72024490
[81,] 7.35101340 0.63749648
[82,] 1.00431115 7.35101340
[83,] 1.33416012 1.00431115
[84,] -1.18580421 1.33416012
[85,] 3.35986909 -1.18580421
[86,] -1.61143891 3.35986909
[87,] 0.21090052 -1.61143891
[88,] 1.86469179 0.21090052
[89,] -0.89930228 1.86469179
[90,] -1.29996473 -0.89930228
[91,] -2.55559856 -1.29996473
[92,] -4.52030825 -2.55559856
[93,] 1.95812471 -4.52030825
[94,] -1.31114879 1.95812471
[95,] -2.67685382 -1.31114879
[96,] 1.68503518 -2.67685382
[97,] 4.13836129 1.68503518
[98,] -3.87957576 4.13836129
[99,] 2.47893609 -3.87957576
[100,] 7.67852192 2.47893609
[101,] 2.48809755 7.67852192
[102,] 1.55326985 2.48809755
[103,] -3.74047447 1.55326985
[104,] 1.77018355 -3.74047447
[105,] -0.79130120 1.77018355
[106,] -0.69504027 -0.79130120
[107,] 0.35695041 -0.69504027
[108,] -3.74245357 0.35695041
[109,] 4.53079318 -3.74245357
[110,] 5.31579343 4.53079318
[111,] 1.15463138 5.31579343
[112,] -3.08151798 1.15463138
[113,] -6.21466277 -3.08151798
[114,] -3.10808850 -6.21466277
[115,] -3.61876471 -3.10808850
[116,] -1.28769932 -3.61876471
[117,] -2.42964415 -1.28769932
[118,] -0.99242750 -2.42964415
[119,] -2.68640812 -0.99242750
[120,] 7.31400263 -2.68640812
[121,] -2.08972208 7.31400263
[122,] 1.25494972 -2.08972208
[123,] 1.59423758 1.25494972
[124,] 9.25600661 1.59423758
[125,] 1.35813012 9.25600661
[126,] 0.90114504 1.35813012
[127,] -2.29356340 0.90114504
[128,] -1.97917572 -2.29356340
[129,] -3.51887698 -1.97917572
[130,] 0.27635849 -3.51887698
[131,] 2.35399657 0.27635849
[132,] -1.47751591 2.35399657
[133,] 0.97608043 -1.47751591
[134,] -0.25355252 0.97608043
[135,] -2.61471920 -0.25355252
[136,] 0.74812708 -2.61471920
[137,] -1.16260050 0.74812708
[138,] -1.51544782 -1.16260050
[139,] 4.04170722 -1.51544782
[140,] 1.33416012 4.04170722
[141,] 0.69229666 1.33416012
[142,] -2.42964415 0.69229666
[143,] -1.45918861 -2.42964415
[144,] 6.34600955 -1.45918861
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.92350690 -0.08056284
2 -1.40944156 -0.92350690
3 0.12824941 -1.40944156
4 4.83816905 0.12824941
5 -1.40466172 4.83816905
6 6.79188265 -1.40466172
7 -2.29482497 6.79188265
8 -2.30799789 -2.29482497
9 0.02674168 -2.30799789
10 -3.75757170 0.02674168
11 -4.29792453 -3.75757170
12 0.56654528 -4.29792453
13 -2.20599713 0.56654528
14 1.15561594 -2.20599713
15 3.60462142 1.15561594
16 -1.98771818 3.60462142
17 -2.98943027 -1.98771818
18 -1.53405604 -2.98943027
19 -3.85308541 -1.53405604
20 -1.96302185 -3.85308541
21 1.04771092 -1.96302185
22 -1.37663561 1.04771092
23 -2.45212379 -1.37663561
24 -1.16260050 -2.45212379
25 1.43132696 -1.16260050
26 -0.19813285 1.43132696
27 -0.75538644 -0.19813285
28 4.80857086 -0.75538644
29 -2.75605578 4.80857086
30 0.07625634 -2.75605578
31 1.42871943 0.07625634
32 -2.60860372 1.42871943
33 6.16029690 -2.60860372
34 -1.19944263 6.16029690
35 -0.47593988 -1.19944263
36 -2.33062781 -0.47593988
37 -3.74492628 -2.33062781
38 -0.86479408 -3.74492628
39 -3.07253704 -0.86479408
40 1.91586242 -3.07253704
41 -1.56798641 1.91586242
42 1.63613531 -1.56798641
43 -1.11223479 1.63613531
44 -0.98228388 -1.11223479
45 -2.13294379 -0.98228388
46 3.40575983 -2.13294379
47 4.20801971 3.40575983
48 1.41027407 4.20801971
49 -2.15586635 1.41027407
50 0.76088058 -2.15586635
51 -0.18325023 0.76088058
52 -4.97927535 -0.18325023
53 -2.31042282 -4.97927535
54 1.36298354 -2.31042282
55 5.89980239 1.36298354
56 0.29656176 5.89980239
57 -0.11990544 0.29656176
58 2.74852173 -0.11990544
59 -1.92181631 2.74852173
60 -3.27595040 -1.92181631
61 3.68519646 -3.27595040
62 -0.12227011 3.68519646
63 0.15608144 -0.12227011
64 0.15937560 0.15608144
65 2.30611291 0.15937560
66 1.01651573 2.30611291
67 1.10739352 1.01651573
68 -3.60500172 1.10739352
69 -0.29543372 -3.60500172
70 0.16669402 -0.29543372
71 2.86653144 0.16669402
72 0.05434818 2.86653144
73 -2.84619561 0.05434818
74 -1.35729457 -2.84619561
75 -0.09363732 -1.35729457
76 -1.19967366 -0.09363732
77 0.54502432 -1.19967366
78 -0.41984096 0.54502432
79 0.72024490 -0.41984096
80 0.63749648 0.72024490
81 7.35101340 0.63749648
82 1.00431115 7.35101340
83 1.33416012 1.00431115
84 -1.18580421 1.33416012
85 3.35986909 -1.18580421
86 -1.61143891 3.35986909
87 0.21090052 -1.61143891
88 1.86469179 0.21090052
89 -0.89930228 1.86469179
90 -1.29996473 -0.89930228
91 -2.55559856 -1.29996473
92 -4.52030825 -2.55559856
93 1.95812471 -4.52030825
94 -1.31114879 1.95812471
95 -2.67685382 -1.31114879
96 1.68503518 -2.67685382
97 4.13836129 1.68503518
98 -3.87957576 4.13836129
99 2.47893609 -3.87957576
100 7.67852192 2.47893609
101 2.48809755 7.67852192
102 1.55326985 2.48809755
103 -3.74047447 1.55326985
104 1.77018355 -3.74047447
105 -0.79130120 1.77018355
106 -0.69504027 -0.79130120
107 0.35695041 -0.69504027
108 -3.74245357 0.35695041
109 4.53079318 -3.74245357
110 5.31579343 4.53079318
111 1.15463138 5.31579343
112 -3.08151798 1.15463138
113 -6.21466277 -3.08151798
114 -3.10808850 -6.21466277
115 -3.61876471 -3.10808850
116 -1.28769932 -3.61876471
117 -2.42964415 -1.28769932
118 -0.99242750 -2.42964415
119 -2.68640812 -0.99242750
120 7.31400263 -2.68640812
121 -2.08972208 7.31400263
122 1.25494972 -2.08972208
123 1.59423758 1.25494972
124 9.25600661 1.59423758
125 1.35813012 9.25600661
126 0.90114504 1.35813012
127 -2.29356340 0.90114504
128 -1.97917572 -2.29356340
129 -3.51887698 -1.97917572
130 0.27635849 -3.51887698
131 2.35399657 0.27635849
132 -1.47751591 2.35399657
133 0.97608043 -1.47751591
134 -0.25355252 0.97608043
135 -2.61471920 -0.25355252
136 0.74812708 -2.61471920
137 -1.16260050 0.74812708
138 -1.51544782 -1.16260050
139 4.04170722 -1.51544782
140 1.33416012 4.04170722
141 0.69229666 1.33416012
142 -2.42964415 0.69229666
143 -1.45918861 -2.42964415
144 6.34600955 -1.45918861
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7za0a1290533238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8za0a1290533238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9za0a1290533238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/102t281290533239.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/115t0w1290533239.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/129czk1290533239.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13n4xs1290533239.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14xvev1290533239.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/151du11290533239.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16fnaa1290533239.tab")
+ }
>
> try(system("convert tmp/1302j1290533238.ps tmp/1302j1290533238.png",intern=TRUE))
character(0)
> try(system("convert tmp/2302j1290533238.ps tmp/2302j1290533238.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vr2m1290533238.ps tmp/3vr2m1290533238.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vr2m1290533238.ps tmp/4vr2m1290533238.png",intern=TRUE))
character(0)
> try(system("convert tmp/5vr2m1290533238.ps tmp/5vr2m1290533238.png",intern=TRUE))
character(0)
> try(system("convert tmp/6oij71290533238.ps tmp/6oij71290533238.png",intern=TRUE))
character(0)
> try(system("convert tmp/7za0a1290533238.ps tmp/7za0a1290533238.png",intern=TRUE))
character(0)
> try(system("convert tmp/8za0a1290533238.ps tmp/8za0a1290533238.png",intern=TRUE))
character(0)
> try(system("convert tmp/9za0a1290533238.ps tmp/9za0a1290533238.png",intern=TRUE))
character(0)
> try(system("convert tmp/102t281290533239.ps tmp/102t281290533239.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.923 1.673 8.701