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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+ ,dim=c(10
+ ,145)
+ ,dimnames=list(c('Depression'
+ ,'CriticParents'
+ ,'ExpecParents'
+ ,'FutureWorrying'
+ ,'SleepDepri'
+ ,'ChangesLastYear'
+ ,'FreqSmoking'
+ ,'FreqHighAlc'
+ ,'FreqBeerOrWine'
+ ,'Month
')
+ ,1:145))
> y <- array(NA,dim=c(10,145),dimnames=list(c('Depression','CriticParents','ExpecParents','FutureWorrying','SleepDepri','ChangesLastYear','FreqSmoking','FreqHighAlc','FreqBeerOrWine','Month
'),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 Month\r
1 2 2 2 2 9
2 4 1 2 2 9
3 7 4 3 4 9
4 3 1 2 3 9
5 7 5 4 4 9
6 2 1 2 3 9
7 7 1 2 3 9
8 2 1 3 4 9
9 1 1 2 3 9
10 2 1 2 4 9
11 6 2 3 3 9
12 1 1 2 2 9
13 1 3 3 3 9
14 1 1 1 3 9
15 2 1 3 3 9
16 2 1 1 2 9
17 2 1 3 3 9
18 1 1 2 2 9
19 7 2 3 4 9
20 1 4 4 5 9
21 2 1 3 3 9
22 4 2 3 3 9
23 2 1 1 1 9
24 1 2 2 4 9
25 1 3 1 3 9
26 5 1 3 4 9
27 2 1 3 3 9
28 1 1 2 3 9
29 3 1 2 1 9
30 1 1 3 4 9
31 2 2 2 4 9
32 5 1 2 2 9
33 2 1 2 2 9
34 6 1 1 1 9
35 4 1 2 3 9
36 1 1 3 4 9
37 3 1 1 1 9
38 6 1 2 3 9
39 7 2 3 3 9
40 4 1 2 2 9
41 1 2 1 4 9
42 5 1 1 3 9
43 3 1 3 3 9
44 2 2 3 2 9
45 2 1 3 3 9
46 2 1 3 2 9
47 2 1 2 1 9
48 1 1 3 3 9
49 2 1 2 3 9
50 1 4 3 5 9
51 2 2 4 1 9
52 2 1 3 3 9
53 5 1 3 4 9
54 5 4 3 3 9
55 2 2 3 4 9
56 1 1 2 2 9
57 1 1 3 3 9
58 2 1 3 4 9
59 3 1 1 1 9
60 7 1 1 1 9
61 4 1 1 1 10
62 4 2 4 4 10
63 1 1 3 2 10
64 2 1 2 3 10
65 2 2 3 4 10
66 2 1 1 2 10
67 5 2 4 5 10
68 1 2 3 3 10
69 6 4 2 3 10
70 2 1 3 3 10
71 2 1 3 4 10
72 4 3 3 4 10
73 6 1 2 3 10
74 2 1 1 1 10
75 2 1 1 3 10
76 2 1 1 1 10
77 1 1 3 3 10
78 1 1 4 5 10
79 2 1 2 3 10
80 2 1 2 3 10
81 3 4 2 4 10
82 3 1 2 5 10
83 5 1 3 4 10
84 2 2 4 4 10
85 5 1 2 4 10
86 3 1 3 4 10
87 1 1 3 4 10
88 2 1 2 3 10
89 2 1 2 4 10
90 1 1 3 3 10
91 2 1 3 3 10
92 2 1 3 3 10
93 5 1 3 4 10
94 5 1 3 3 10
95 2 1 3 4 10
96 3 1 2 2 10
97 5 5 3 5 10
98 5 1 3 3 10
99 6 1 2 4 10
100 2 1 1 2 10
101 7 3 3 4 10
102 1 1 2 3 10
103 1 1 2 4 10
104 6 1 3 3 10
105 6 1 1 1 10
106 2 1 3 4 10
107 1 1 2 4 10
108 2 1 2 2 10
109 1 4 2 5 10
110 2 4 2 4 10
111 1 1 2 4 10
112 3 1 3 3 10
113 3 1 3 4 10
114 6 4 3 4 10
115 4 2 3 4 10
116 1 1 3 3 10
117 2 1 1 5 10
118 5 1 3 3 10
119 6 1 4 4 10
120 3 1 2 4 10
121 5 1 2 4 10
122 3 2 4 4 10
123 2 4 3 4 10
124 3 4 2 5 10
125 2 1 3 3 10
126 5 1 1 1 10
127 5 1 2 4 10
128 7 2 4 4 10
129 4 1 3 3 10
130 4 1 3 4 10
131 5 1 3 4 10
132 1 3 2 4 10
133 4 2 4 4 10
134 1 2 1 4 10
135 4 1 3 4 10
136 6 1 1 3 10
137 7 2 2 5 10
138 1 3 1 3 9
139 3 1 2 4 10
140 5 1 4 4 9
141 2 2 4 4 10
142 4 2 3 4 10
143 5 1 3 3 10
144 1 1 1 4 10
145 2 1 4 4 10
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CriticParents ExpecParents FutureWorrying
3.57528 0.04877 -0.06532 0.58391
SleepDepri ChangesLastYear FreqSmoking FreqHighAlc
0.20915 0.34483 -0.09573 0.27876
FreqBeerOrWine `Month\r`
0.21968 0.41894
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.368 -1.974 -0.355 1.408 9.056
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.57528 5.10419 0.700 0.484846
CriticParents 0.04877 0.11632 0.419 0.675678
ExpecParents -0.06532 0.08672 -0.753 0.452630
FutureWorrying 0.58391 0.16852 3.465 0.000711 ***
SleepDepri 0.20915 0.14166 1.476 0.142170
ChangesLastYear 0.34483 0.13586 2.538 0.012283 *
FreqSmoking -0.09573 0.27571 -0.347 0.728973
FreqHighAlc 0.27876 0.31551 0.884 0.378523
FreqBeerOrWine 0.21968 0.29341 0.749 0.455319
`Month\r` 0.41894 0.52355 0.800 0.425007
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.904 on 135 degrees of freedom
Multiple R-squared: 0.2094, Adjusted R-squared: 0.1567
F-statistic: 3.972 on 9 and 135 DF, p-value: 0.0001621
> 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,] 0.46372017 0.92744034 0.5362798
[2,] 0.59524449 0.80951102 0.4047555
[3,] 0.52650285 0.94699430 0.4734972
[4,] 0.66027178 0.67945644 0.3397282
[5,] 0.57266392 0.85467216 0.4273361
[6,] 0.47944735 0.95889471 0.5205526
[7,] 0.57840681 0.84318639 0.4215932
[8,] 0.50182862 0.99634277 0.4981714
[9,] 0.42794197 0.85588394 0.5720580
[10,] 0.47076025 0.94152049 0.5292398
[11,] 0.51286476 0.97427047 0.4871352
[12,] 0.43576048 0.87152097 0.5642395
[13,] 0.37355132 0.74710264 0.6264487
[14,] 0.32770194 0.65540389 0.6722981
[15,] 0.27136291 0.54272581 0.7286371
[16,] 0.23030034 0.46060069 0.7696997
[17,] 0.29264871 0.58529742 0.7073513
[18,] 0.24667619 0.49335239 0.7533238
[19,] 0.20458391 0.40916782 0.7954161
[20,] 0.16254154 0.32508309 0.8374585
[21,] 0.15564837 0.31129674 0.8443516
[22,] 0.17508252 0.35016505 0.8249175
[23,] 0.17637913 0.35275827 0.8236209
[24,] 0.18037383 0.36074766 0.8196262
[25,] 0.22249854 0.44499708 0.7775015
[26,] 0.27636677 0.55273354 0.7236332
[27,] 0.29986516 0.59973033 0.7001348
[28,] 0.29206353 0.58412707 0.7079365
[29,] 0.30293504 0.60587008 0.6970650
[30,] 0.27031347 0.54062695 0.7296865
[31,] 0.26822901 0.53645802 0.7317710
[32,] 0.23008933 0.46017867 0.7699107
[33,] 0.19313122 0.38626244 0.8068688
[34,] 0.17151878 0.34303757 0.8284812
[35,] 0.17604693 0.35209387 0.8239531
[36,] 0.27906158 0.55812316 0.7209384
[37,] 0.26156577 0.52313154 0.7384342
[38,] 0.23327065 0.46654131 0.7667293
[39,] 0.19804535 0.39609070 0.8019547
[40,] 0.16287034 0.32574068 0.8371297
[41,] 0.24080384 0.48160767 0.7591962
[42,] 0.23956605 0.47913210 0.7604339
[43,] 0.22005169 0.44010339 0.7799483
[44,] 0.29942225 0.59884449 0.7005778
[45,] 0.26107955 0.52215909 0.7389205
[46,] 0.23343018 0.46686036 0.7665698
[47,] 0.20864040 0.41728080 0.7913596
[48,] 0.22503589 0.45007178 0.7749641
[49,] 0.19037036 0.38074073 0.8096296
[50,] 0.31376575 0.62753150 0.6862343
[51,] 0.26937264 0.53874528 0.7306274
[52,] 0.23056321 0.46112643 0.7694368
[53,] 0.19526060 0.39052121 0.8047394
[54,] 0.18046866 0.36093732 0.8195313
[55,] 0.15265943 0.30531887 0.8473406
[56,] 0.12759944 0.25519888 0.8724006
[57,] 0.14021317 0.28042635 0.8597868
[58,] 0.11399387 0.22798774 0.8860061
[59,] 0.09146604 0.18293208 0.9085340
[60,] 0.09524700 0.19049400 0.9047530
[61,] 0.07571599 0.15143198 0.9242840
[62,] 0.07685647 0.15371294 0.9231435
[63,] 0.06340368 0.12680735 0.9365963
[64,] 0.05059604 0.10119209 0.9494040
[65,] 0.04399039 0.08798077 0.9560096
[66,] 0.03387350 0.06774701 0.9661265
[67,] 0.02567776 0.05135553 0.9743222
[68,] 0.01943350 0.03886700 0.9805665
[69,] 0.01492667 0.02985333 0.9850733
[70,] 0.07444448 0.14888897 0.9255555
[71,] 0.05870097 0.11740195 0.9412990
[72,] 0.04621591 0.09243183 0.9537841
[73,] 0.03732530 0.07465060 0.9626747
[74,] 0.03693791 0.07387583 0.9630621
[75,] 0.03162401 0.06324803 0.9683760
[76,] 0.02348056 0.04696111 0.9765194
[77,] 0.02009464 0.04018927 0.9799054
[78,] 0.01584519 0.03169038 0.9841548
[79,] 0.01304104 0.02608208 0.9869590
[80,] 0.01285850 0.02571699 0.9871415
[81,] 0.01915087 0.03830174 0.9808491
[82,] 0.01507946 0.03015892 0.9849205
[83,] 0.01179797 0.02359595 0.9882020
[84,] 0.01218915 0.02437829 0.9878109
[85,] 0.01070919 0.02141837 0.9892908
[86,] 0.01667735 0.03335469 0.9833227
[87,] 0.01903697 0.03807394 0.9809630
[88,] 0.01560051 0.03120103 0.9843995
[89,] 0.07606437 0.15212874 0.9239356
[90,] 0.06810506 0.13621012 0.9318949
[91,] 0.05552747 0.11105494 0.9444725
[92,] 0.05747744 0.11495488 0.9425226
[93,] 0.04645804 0.09291607 0.9535420
[94,] 0.03547682 0.07095365 0.9645232
[95,] 0.03003944 0.06007888 0.9699606
[96,] 0.02170561 0.04341122 0.9782944
[97,] 0.01989002 0.03978004 0.9801100
[98,] 0.02865837 0.05731674 0.9713416
[99,] 0.03683109 0.07366219 0.9631689
[100,] 0.02711483 0.05422967 0.9728852
[101,] 0.02602313 0.05204627 0.9739769
[102,] 0.04800504 0.09601009 0.9519950
[103,] 0.04842185 0.09684371 0.9515781
[104,] 0.07648242 0.15296485 0.9235176
[105,] 0.05806998 0.11613997 0.9419300
[106,] 0.04770626 0.09541252 0.9522937
[107,] 0.03392635 0.06785270 0.9660737
[108,] 0.04021469 0.08042938 0.9597853
[109,] 0.17455205 0.34910410 0.8254480
[110,] 0.19609984 0.39219969 0.8039002
[111,] 0.14739510 0.29479020 0.8526049
[112,] 0.12502983 0.25005966 0.8749702
[113,] 0.53848783 0.92302434 0.4615122
[114,] 0.89214278 0.21571444 0.1078572
[115,] 0.89561963 0.20876074 0.1043804
[116,] 0.84659408 0.30681184 0.1534059
[117,] 0.83895125 0.32209751 0.1610488
[118,] 0.79359862 0.41280275 0.2064014
[119,] 0.74140023 0.51719953 0.2585998
[120,] 0.59484314 0.81031373 0.4051569
> postscript(file="/var/www/html/rcomp/tmp/1njj21290539827.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ys041290539827.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ys041290539827.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ys041290539827.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5rjz81290539827.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
0.046676885 -0.736507906 -1.194781884 0.382800523 5.069636204 -1.194920444
7 8 9 10 11 12
7.084018633 -1.973596282 -2.091997665 0.298068957 -3.527496983 -4.165816765
13 14 15 16 17 18
0.683159409 -1.986807330 1.408339323 3.778371862 -1.822974275 -2.966976730
19 20 21 22 23 24
-1.218072810 -3.582897728 -1.751506408 1.194255475 -1.299254270 -2.176287727
25 26 27 28 29 30
-0.978110649 1.757969786 0.009938878 -0.549088980 4.890565125 -2.463158376
31 32 33 34 35 36
0.380191243 1.627367251 -2.439036568 6.348809563 -0.920696057 -0.192846269
37 38 39 40 41 42
-2.210717931 -3.467055760 -0.591490987 -2.889449154 2.195323575 -1.347120811
43 44 45 46 47 48
1.851679411 -0.995067684 -0.760280253 -2.022737042 3.506788785 4.401333396
49 50 51 52 53 54
1.614685230 -1.879270105 0.760075714 0.042164207 -4.633334261 -2.119020685
55 56 57 58 59 60
1.624708925 6.070271731 0.506651213 0.155902603 2.862038636 -1.771665111
61 62 63 64 65 66
-3.544923788 3.549607919 -0.407758860 -0.046494749 -0.016569871 2.070293119
67 68 69 70 71 72
0.941548107 0.888463707 -3.786515041 -0.467411134 0.068451008 2.678641287
73 74 75 76 77 78
-0.081948918 -3.187020029 -1.515037717 -0.421491689 -1.437516026 0.514147656
79 80 81 82 83 84
-0.584648540 0.518992543 0.450820911 7.324733222 0.913832716 1.138905178
85 86 87 88 89 90
-1.257128747 3.258478840 -1.757210460 0.056080828 1.745416970 -1.096922692
91 92 93 94 95 96
-1.488653131 -2.744090766 -4.627595354 1.755820867 -1.437949595 -2.921156960
97 98 99 100 101 102
1.533159295 3.986405592 -3.954942718 2.253250209 7.434523308 2.328155936
103 104 105 106 107 108
1.382134464 -3.901802704 1.503943622 -0.919361067 -0.814155246 0.102917748
109 110 111 112 113 114
-3.856579640 4.332997602 5.231598823 0.926619667 -3.172211750 -6.367831459
115 116 117 118 119 120
-3.239159263 -3.788353651 -1.282062072 -2.615465068 -1.086727129 -2.760937883
121 122 123 124 125 126
7.279347596 -2.199890687 1.069914293 1.462062919 9.055868186 1.072599545
127 128 129 130 131 132
0.790302769 -2.427808867 -2.177171786 -3.641249746 0.203168210 2.179199027
133 134 135 136 137 138
-1.623963384 0.830448389 -0.354983289 -2.722726579 0.702216647 -0.978110649
139 140 141 142 143 144
-1.629430547 4.346359999 1.138905178 0.587998306 -2.615465068 -1.526816596
145
6.245168055
> postscript(file="/var/www/html/rcomp/tmp/6rjz81290539827.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 0.046676885 NA
1 -0.736507906 0.046676885
2 -1.194781884 -0.736507906
3 0.382800523 -1.194781884
4 5.069636204 0.382800523
5 -1.194920444 5.069636204
6 7.084018633 -1.194920444
7 -1.973596282 7.084018633
8 -2.091997665 -1.973596282
9 0.298068957 -2.091997665
10 -3.527496983 0.298068957
11 -4.165816765 -3.527496983
12 0.683159409 -4.165816765
13 -1.986807330 0.683159409
14 1.408339323 -1.986807330
15 3.778371862 1.408339323
16 -1.822974275 3.778371862
17 -2.966976730 -1.822974275
18 -1.218072810 -2.966976730
19 -3.582897728 -1.218072810
20 -1.751506408 -3.582897728
21 1.194255475 -1.751506408
22 -1.299254270 1.194255475
23 -2.176287727 -1.299254270
24 -0.978110649 -2.176287727
25 1.757969786 -0.978110649
26 0.009938878 1.757969786
27 -0.549088980 0.009938878
28 4.890565125 -0.549088980
29 -2.463158376 4.890565125
30 0.380191243 -2.463158376
31 1.627367251 0.380191243
32 -2.439036568 1.627367251
33 6.348809563 -2.439036568
34 -0.920696057 6.348809563
35 -0.192846269 -0.920696057
36 -2.210717931 -0.192846269
37 -3.467055760 -2.210717931
38 -0.591490987 -3.467055760
39 -2.889449154 -0.591490987
40 2.195323575 -2.889449154
41 -1.347120811 2.195323575
42 1.851679411 -1.347120811
43 -0.995067684 1.851679411
44 -0.760280253 -0.995067684
45 -2.022737042 -0.760280253
46 3.506788785 -2.022737042
47 4.401333396 3.506788785
48 1.614685230 4.401333396
49 -1.879270105 1.614685230
50 0.760075714 -1.879270105
51 0.042164207 0.760075714
52 -4.633334261 0.042164207
53 -2.119020685 -4.633334261
54 1.624708925 -2.119020685
55 6.070271731 1.624708925
56 0.506651213 6.070271731
57 0.155902603 0.506651213
58 2.862038636 0.155902603
59 -1.771665111 2.862038636
60 -3.544923788 -1.771665111
61 3.549607919 -3.544923788
62 -0.407758860 3.549607919
63 -0.046494749 -0.407758860
64 -0.016569871 -0.046494749
65 2.070293119 -0.016569871
66 0.941548107 2.070293119
67 0.888463707 0.941548107
68 -3.786515041 0.888463707
69 -0.467411134 -3.786515041
70 0.068451008 -0.467411134
71 2.678641287 0.068451008
72 -0.081948918 2.678641287
73 -3.187020029 -0.081948918
74 -1.515037717 -3.187020029
75 -0.421491689 -1.515037717
76 -1.437516026 -0.421491689
77 0.514147656 -1.437516026
78 -0.584648540 0.514147656
79 0.518992543 -0.584648540
80 0.450820911 0.518992543
81 7.324733222 0.450820911
82 0.913832716 7.324733222
83 1.138905178 0.913832716
84 -1.257128747 1.138905178
85 3.258478840 -1.257128747
86 -1.757210460 3.258478840
87 0.056080828 -1.757210460
88 1.745416970 0.056080828
89 -1.096922692 1.745416970
90 -1.488653131 -1.096922692
91 -2.744090766 -1.488653131
92 -4.627595354 -2.744090766
93 1.755820867 -4.627595354
94 -1.437949595 1.755820867
95 -2.921156960 -1.437949595
96 1.533159295 -2.921156960
97 3.986405592 1.533159295
98 -3.954942718 3.986405592
99 2.253250209 -3.954942718
100 7.434523308 2.253250209
101 2.328155936 7.434523308
102 1.382134464 2.328155936
103 -3.901802704 1.382134464
104 1.503943622 -3.901802704
105 -0.919361067 1.503943622
106 -0.814155246 -0.919361067
107 0.102917748 -0.814155246
108 -3.856579640 0.102917748
109 4.332997602 -3.856579640
110 5.231598823 4.332997602
111 0.926619667 5.231598823
112 -3.172211750 0.926619667
113 -6.367831459 -3.172211750
114 -3.239159263 -6.367831459
115 -3.788353651 -3.239159263
116 -1.282062072 -3.788353651
117 -2.615465068 -1.282062072
118 -1.086727129 -2.615465068
119 -2.760937883 -1.086727129
120 7.279347596 -2.760937883
121 -2.199890687 7.279347596
122 1.069914293 -2.199890687
123 1.462062919 1.069914293
124 9.055868186 1.462062919
125 1.072599545 9.055868186
126 0.790302769 1.072599545
127 -2.427808867 0.790302769
128 -2.177171786 -2.427808867
129 -3.641249746 -2.177171786
130 0.203168210 -3.641249746
131 2.179199027 0.203168210
132 -1.623963384 2.179199027
133 0.830448389 -1.623963384
134 -0.354983289 0.830448389
135 -2.722726579 -0.354983289
136 0.702216647 -2.722726579
137 -0.978110649 0.702216647
138 -1.629430547 -0.978110649
139 4.346359999 -1.629430547
140 1.138905178 4.346359999
141 0.587998306 1.138905178
142 -2.615465068 0.587998306
143 -1.526816596 -2.615465068
144 6.245168055 -1.526816596
145 NA 6.245168055
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.736507906 0.046676885
[2,] -1.194781884 -0.736507906
[3,] 0.382800523 -1.194781884
[4,] 5.069636204 0.382800523
[5,] -1.194920444 5.069636204
[6,] 7.084018633 -1.194920444
[7,] -1.973596282 7.084018633
[8,] -2.091997665 -1.973596282
[9,] 0.298068957 -2.091997665
[10,] -3.527496983 0.298068957
[11,] -4.165816765 -3.527496983
[12,] 0.683159409 -4.165816765
[13,] -1.986807330 0.683159409
[14,] 1.408339323 -1.986807330
[15,] 3.778371862 1.408339323
[16,] -1.822974275 3.778371862
[17,] -2.966976730 -1.822974275
[18,] -1.218072810 -2.966976730
[19,] -3.582897728 -1.218072810
[20,] -1.751506408 -3.582897728
[21,] 1.194255475 -1.751506408
[22,] -1.299254270 1.194255475
[23,] -2.176287727 -1.299254270
[24,] -0.978110649 -2.176287727
[25,] 1.757969786 -0.978110649
[26,] 0.009938878 1.757969786
[27,] -0.549088980 0.009938878
[28,] 4.890565125 -0.549088980
[29,] -2.463158376 4.890565125
[30,] 0.380191243 -2.463158376
[31,] 1.627367251 0.380191243
[32,] -2.439036568 1.627367251
[33,] 6.348809563 -2.439036568
[34,] -0.920696057 6.348809563
[35,] -0.192846269 -0.920696057
[36,] -2.210717931 -0.192846269
[37,] -3.467055760 -2.210717931
[38,] -0.591490987 -3.467055760
[39,] -2.889449154 -0.591490987
[40,] 2.195323575 -2.889449154
[41,] -1.347120811 2.195323575
[42,] 1.851679411 -1.347120811
[43,] -0.995067684 1.851679411
[44,] -0.760280253 -0.995067684
[45,] -2.022737042 -0.760280253
[46,] 3.506788785 -2.022737042
[47,] 4.401333396 3.506788785
[48,] 1.614685230 4.401333396
[49,] -1.879270105 1.614685230
[50,] 0.760075714 -1.879270105
[51,] 0.042164207 0.760075714
[52,] -4.633334261 0.042164207
[53,] -2.119020685 -4.633334261
[54,] 1.624708925 -2.119020685
[55,] 6.070271731 1.624708925
[56,] 0.506651213 6.070271731
[57,] 0.155902603 0.506651213
[58,] 2.862038636 0.155902603
[59,] -1.771665111 2.862038636
[60,] -3.544923788 -1.771665111
[61,] 3.549607919 -3.544923788
[62,] -0.407758860 3.549607919
[63,] -0.046494749 -0.407758860
[64,] -0.016569871 -0.046494749
[65,] 2.070293119 -0.016569871
[66,] 0.941548107 2.070293119
[67,] 0.888463707 0.941548107
[68,] -3.786515041 0.888463707
[69,] -0.467411134 -3.786515041
[70,] 0.068451008 -0.467411134
[71,] 2.678641287 0.068451008
[72,] -0.081948918 2.678641287
[73,] -3.187020029 -0.081948918
[74,] -1.515037717 -3.187020029
[75,] -0.421491689 -1.515037717
[76,] -1.437516026 -0.421491689
[77,] 0.514147656 -1.437516026
[78,] -0.584648540 0.514147656
[79,] 0.518992543 -0.584648540
[80,] 0.450820911 0.518992543
[81,] 7.324733222 0.450820911
[82,] 0.913832716 7.324733222
[83,] 1.138905178 0.913832716
[84,] -1.257128747 1.138905178
[85,] 3.258478840 -1.257128747
[86,] -1.757210460 3.258478840
[87,] 0.056080828 -1.757210460
[88,] 1.745416970 0.056080828
[89,] -1.096922692 1.745416970
[90,] -1.488653131 -1.096922692
[91,] -2.744090766 -1.488653131
[92,] -4.627595354 -2.744090766
[93,] 1.755820867 -4.627595354
[94,] -1.437949595 1.755820867
[95,] -2.921156960 -1.437949595
[96,] 1.533159295 -2.921156960
[97,] 3.986405592 1.533159295
[98,] -3.954942718 3.986405592
[99,] 2.253250209 -3.954942718
[100,] 7.434523308 2.253250209
[101,] 2.328155936 7.434523308
[102,] 1.382134464 2.328155936
[103,] -3.901802704 1.382134464
[104,] 1.503943622 -3.901802704
[105,] -0.919361067 1.503943622
[106,] -0.814155246 -0.919361067
[107,] 0.102917748 -0.814155246
[108,] -3.856579640 0.102917748
[109,] 4.332997602 -3.856579640
[110,] 5.231598823 4.332997602
[111,] 0.926619667 5.231598823
[112,] -3.172211750 0.926619667
[113,] -6.367831459 -3.172211750
[114,] -3.239159263 -6.367831459
[115,] -3.788353651 -3.239159263
[116,] -1.282062072 -3.788353651
[117,] -2.615465068 -1.282062072
[118,] -1.086727129 -2.615465068
[119,] -2.760937883 -1.086727129
[120,] 7.279347596 -2.760937883
[121,] -2.199890687 7.279347596
[122,] 1.069914293 -2.199890687
[123,] 1.462062919 1.069914293
[124,] 9.055868186 1.462062919
[125,] 1.072599545 9.055868186
[126,] 0.790302769 1.072599545
[127,] -2.427808867 0.790302769
[128,] -2.177171786 -2.427808867
[129,] -3.641249746 -2.177171786
[130,] 0.203168210 -3.641249746
[131,] 2.179199027 0.203168210
[132,] -1.623963384 2.179199027
[133,] 0.830448389 -1.623963384
[134,] -0.354983289 0.830448389
[135,] -2.722726579 -0.354983289
[136,] 0.702216647 -2.722726579
[137,] -0.978110649 0.702216647
[138,] -1.629430547 -0.978110649
[139,] 4.346359999 -1.629430547
[140,] 1.138905178 4.346359999
[141,] 0.587998306 1.138905178
[142,] -2.615465068 0.587998306
[143,] -1.526816596 -2.615465068
[144,] 6.245168055 -1.526816596
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.736507906 0.046676885
2 -1.194781884 -0.736507906
3 0.382800523 -1.194781884
4 5.069636204 0.382800523
5 -1.194920444 5.069636204
6 7.084018633 -1.194920444
7 -1.973596282 7.084018633
8 -2.091997665 -1.973596282
9 0.298068957 -2.091997665
10 -3.527496983 0.298068957
11 -4.165816765 -3.527496983
12 0.683159409 -4.165816765
13 -1.986807330 0.683159409
14 1.408339323 -1.986807330
15 3.778371862 1.408339323
16 -1.822974275 3.778371862
17 -2.966976730 -1.822974275
18 -1.218072810 -2.966976730
19 -3.582897728 -1.218072810
20 -1.751506408 -3.582897728
21 1.194255475 -1.751506408
22 -1.299254270 1.194255475
23 -2.176287727 -1.299254270
24 -0.978110649 -2.176287727
25 1.757969786 -0.978110649
26 0.009938878 1.757969786
27 -0.549088980 0.009938878
28 4.890565125 -0.549088980
29 -2.463158376 4.890565125
30 0.380191243 -2.463158376
31 1.627367251 0.380191243
32 -2.439036568 1.627367251
33 6.348809563 -2.439036568
34 -0.920696057 6.348809563
35 -0.192846269 -0.920696057
36 -2.210717931 -0.192846269
37 -3.467055760 -2.210717931
38 -0.591490987 -3.467055760
39 -2.889449154 -0.591490987
40 2.195323575 -2.889449154
41 -1.347120811 2.195323575
42 1.851679411 -1.347120811
43 -0.995067684 1.851679411
44 -0.760280253 -0.995067684
45 -2.022737042 -0.760280253
46 3.506788785 -2.022737042
47 4.401333396 3.506788785
48 1.614685230 4.401333396
49 -1.879270105 1.614685230
50 0.760075714 -1.879270105
51 0.042164207 0.760075714
52 -4.633334261 0.042164207
53 -2.119020685 -4.633334261
54 1.624708925 -2.119020685
55 6.070271731 1.624708925
56 0.506651213 6.070271731
57 0.155902603 0.506651213
58 2.862038636 0.155902603
59 -1.771665111 2.862038636
60 -3.544923788 -1.771665111
61 3.549607919 -3.544923788
62 -0.407758860 3.549607919
63 -0.046494749 -0.407758860
64 -0.016569871 -0.046494749
65 2.070293119 -0.016569871
66 0.941548107 2.070293119
67 0.888463707 0.941548107
68 -3.786515041 0.888463707
69 -0.467411134 -3.786515041
70 0.068451008 -0.467411134
71 2.678641287 0.068451008
72 -0.081948918 2.678641287
73 -3.187020029 -0.081948918
74 -1.515037717 -3.187020029
75 -0.421491689 -1.515037717
76 -1.437516026 -0.421491689
77 0.514147656 -1.437516026
78 -0.584648540 0.514147656
79 0.518992543 -0.584648540
80 0.450820911 0.518992543
81 7.324733222 0.450820911
82 0.913832716 7.324733222
83 1.138905178 0.913832716
84 -1.257128747 1.138905178
85 3.258478840 -1.257128747
86 -1.757210460 3.258478840
87 0.056080828 -1.757210460
88 1.745416970 0.056080828
89 -1.096922692 1.745416970
90 -1.488653131 -1.096922692
91 -2.744090766 -1.488653131
92 -4.627595354 -2.744090766
93 1.755820867 -4.627595354
94 -1.437949595 1.755820867
95 -2.921156960 -1.437949595
96 1.533159295 -2.921156960
97 3.986405592 1.533159295
98 -3.954942718 3.986405592
99 2.253250209 -3.954942718
100 7.434523308 2.253250209
101 2.328155936 7.434523308
102 1.382134464 2.328155936
103 -3.901802704 1.382134464
104 1.503943622 -3.901802704
105 -0.919361067 1.503943622
106 -0.814155246 -0.919361067
107 0.102917748 -0.814155246
108 -3.856579640 0.102917748
109 4.332997602 -3.856579640
110 5.231598823 4.332997602
111 0.926619667 5.231598823
112 -3.172211750 0.926619667
113 -6.367831459 -3.172211750
114 -3.239159263 -6.367831459
115 -3.788353651 -3.239159263
116 -1.282062072 -3.788353651
117 -2.615465068 -1.282062072
118 -1.086727129 -2.615465068
119 -2.760937883 -1.086727129
120 7.279347596 -2.760937883
121 -2.199890687 7.279347596
122 1.069914293 -2.199890687
123 1.462062919 1.069914293
124 9.055868186 1.462062919
125 1.072599545 9.055868186
126 0.790302769 1.072599545
127 -2.427808867 0.790302769
128 -2.177171786 -2.427808867
129 -3.641249746 -2.177171786
130 0.203168210 -3.641249746
131 2.179199027 0.203168210
132 -1.623963384 2.179199027
133 0.830448389 -1.623963384
134 -0.354983289 0.830448389
135 -2.722726579 -0.354983289
136 0.702216647 -2.722726579
137 -0.978110649 0.702216647
138 -1.629430547 -0.978110649
139 4.346359999 -1.629430547
140 1.138905178 4.346359999
141 0.587998306 1.138905178
142 -2.615465068 0.587998306
143 -1.526816596 -2.615465068
144 6.245168055 -1.526816596
> 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/71aga1290539827.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81aga1290539827.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9u2yd1290539827.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10u2yd1290539827.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xkwj1290539827.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/121lvp1290539827.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/13q4s11290539827.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/140v941290539827.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/154e7s1290539827.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/16ion11290539827.tab")
+ }
>
> try(system("convert tmp/1njj21290539827.ps tmp/1njj21290539827.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ys041290539827.ps tmp/2ys041290539827.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ys041290539827.ps tmp/3ys041290539827.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ys041290539827.ps tmp/4ys041290539827.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rjz81290539827.ps tmp/5rjz81290539827.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rjz81290539827.ps tmp/6rjz81290539827.png",intern=TRUE))
character(0)
> try(system("convert tmp/71aga1290539827.ps tmp/71aga1290539827.png",intern=TRUE))
character(0)
> try(system("convert tmp/81aga1290539827.ps tmp/81aga1290539827.png",intern=TRUE))
character(0)
> try(system("convert tmp/9u2yd1290539827.ps tmp/9u2yd1290539827.png",intern=TRUE))
character(0)
> try(system("convert tmp/10u2yd1290539827.ps tmp/10u2yd1290539827.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.025 1.661 10.072