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(23
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+ ,3)
+ ,dim=c(9
+ ,142)
+ ,dimnames=list(c('AGE'
+ ,'PStress'
+ ,'Pstress_M'
+ ,'Pstress_OKT'
+ ,'BelInSprt'
+ ,'KunnenRekRel'
+ ,'Depressie'
+ ,'Slaapgebrek'
+ ,'ToekZorgen
')
+ ,1:142))
> y <- array(NA,dim=c(9,142),dimnames=list(c('AGE','PStress','Pstress_M','Pstress_OKT','BelInSprt','KunnenRekRel','Depressie','Slaapgebrek','ToekZorgen
'),1:142))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'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
PStress AGE Pstress_M Pstress_OKT BelInSprt KunnenRekRel Depressie
1 10 23 0 0 53 7 12
2 6 21 0 0 86 4 11
3 13 21 0 0 66 6 14
4 12 21 1 0 67 5 12
5 8 24 0 0 76 4 21
6 6 22 0 0 78 3 12
7 10 21 0 0 53 5 22
8 10 22 0 0 80 6 11
9 9 21 0 0 74 5 10
10 9 20 0 0 76 6 13
11 7 22 1 0 79 7 10
12 5 21 0 0 54 6 8
13 14 21 1 0 67 7 15
14 6 23 0 0 87 6 10
15 10 22 1 0 58 4 14
16 10 23 1 0 75 6 14
17 7 22 0 0 88 4 11
18 10 24 1 0 64 5 10
19 8 23 0 0 57 3 13
20 6 21 1 0 66 3 7
21 10 23 0 0 54 4 12
22 12 23 0 0 56 5 14
23 7 21 1 0 86 3 11
24 15 20 0 0 80 7 9
25 8 32 1 0 76 7 11
26 10 22 0 0 69 4 15
27 13 21 1 0 67 4 13
28 8 21 0 0 80 5 9
29 11 21 1 0 54 6 15
30 7 22 0 0 71 5 10
31 9 21 0 0 84 4 11
32 10 21 1 0 74 6 13
33 8 21 1 0 71 5 8
34 15 22 1 0 63 5 20
35 9 21 1 0 71 6 12
36 7 21 0 0 76 2 10
37 11 21 1 0 69 6 10
38 9 21 1 0 74 7 9
39 8 23 0 0 75 5 14
40 8 21 1 0 54 5 8
41 12 23 0 0 69 5 11
42 13 23 0 0 68 6 13
43 9 21 0 0 75 4 11
44 11 21 1 0 75 6 11
45 8 20 0 0 72 5 10
46 10 21 1 0 67 5 14
47 13 21 1 0 63 3 18
48 12 22 0 0 62 4 14
49 12 21 1 0 63 4 11
50 9 21 0 0 76 2 12
51 8 22 0 0 74 3 13
52 9 20 0 0 67 6 9
53 12 22 1 0 73 5 10
54 12 22 0 0 70 6 15
55 16 21 1 0 53 2 20
56 11 23 1 0 77 3 12
57 13 22 0 0 77 6 12
58 10 24 0 0 52 3 14
59 9 23 0 0 54 6 13
60 14 21 1 1 80 6 11
61 13 22 0 1 66 4 17
62 12 22 1 1 73 7 12
63 9 21 0 1 63 6 13
64 9 21 1 1 69 3 14
65 10 21 1 1 67 7 13
66 8 21 0 1 54 2 15
67 9 20 0 1 81 4 13
68 9 22 1 1 69 6 10
69 11 22 1 1 84 4 11
70 7 22 0 1 70 1 13
71 11 23 0 1 69 4 17
72 9 21 1 1 77 7 13
73 11 23 1 1 54 4 9
74 9 22 1 1 79 4 11
75 8 21 1 1 30 4 10
76 9 21 0 1 71 6 9
77 8 20 1 1 73 2 12
78 9 24 0 1 72 3 12
79 10 24 0 1 77 4 13
80 9 21 1 1 75 4 13
81 17 20 0 1 70 4 22
82 7 21 0 1 73 6 13
83 11 21 0 1 54 2 15
84 9 21 0 1 77 4 13
85 10 21 0 1 82 3 15
86 11 22 0 1 80 7 10
87 8 22 0 1 80 4 11
88 12 21 0 1 69 5 16
89 10 22 0 1 78 6 11
90 7 21 1 1 81 5 11
91 9 23 1 1 76 4 10
92 7 21 0 1 76 5 10
93 12 22 1 1 73 4 16
94 8 22 0 1 85 5 12
95 13 22 1 1 66 7 11
96 9 20 0 1 79 7 16
97 15 21 1 1 68 4 19
98 8 21 0 1 76 6 11
99 14 22 1 1 54 4 15
100 14 25 0 1 46 1 24
101 9 22 0 1 82 3 14
102 13 22 0 1 74 6 15
103 11 21 0 1 88 7 11
104 10 22 1 1 38 6 15
105 6 21 0 1 76 6 12
106 8 24 1 1 86 6 10
107 10 23 0 1 54 4 14
108 10 23 0 1 69 1 9
109 10 22 0 1 90 3 15
110 12 22 0 1 54 7 15
111 10 25 0 1 76 2 14
112 9 23 0 1 89 7 11
113 9 22 0 1 76 4 8
114 11 21 0 1 79 5 11
115 7 21 1 1 90 6 8
116 7 22 0 1 74 6 10
117 5 22 0 1 81 5 11
118 9 21 0 1 72 5 13
119 11 0 1 1 71 4 11
120 15 21 1 1 66 2 20
121 9 22 0 1 77 2 10
122 9 21 1 1 74 4 12
123 8 24 0 1 82 4 14
124 13 21 1 1 54 6 23
125 10 23 1 1 63 5 14
126 13 23 0 1 54 5 16
127 9 22 0 1 64 6 11
128 11 21 1 1 69 5 12
129 8 21 1 1 84 7 14
130 10 21 0 1 86 5 12
131 9 21 1 1 77 3 12
132 8 22 0 1 89 5 11
133 8 20 0 1 76 1 12
134 13 21 1 1 60 5 13
135 11 23 0 1 79 7 17
136 8 32 1 0 76 7 11
137 12 22 0 1 72 6 12
138 15 24 0 0 69 4 19
139 11 21 0 1 54 2 15
140 10 22 0 1 69 6 14
141 5 22 0 1 81 5 11
142 11 23 0 1 84 1 9
Slaapgebrek ToekZorgen\r
1 2 4
2 4 3
3 7 5
4 3 3
5 7 6
6 2 5
7 7 6
8 2 6
9 1 5
10 2 5
11 6 3
12 1 5
13 1 7
14 1 5
15 2 5
16 2 3
17 2 5
18 1 6
19 7 5
20 1 2
21 2 5
22 4 4
23 2 6
24 1 3
25 1 5
26 5 4
27 2 5
28 1 2
29 3 2
30 1 5
31 2 2
32 5 2
33 2 2
34 6 5
35 4 5
36 1 1
37 3 5
38 6 2
39 7 6
40 4 1
41 5 3
42 3 2
43 2 5
44 2 3
45 2 4
46 2 3
47 1 6
48 2 4
49 1 5
50 2 2
51 2 5
52 5 5
53 5 3
54 2 5
55 1 7
56 1 4
57 2 2
58 3 3
59 7 6
60 4 7
61 4 4
62 1 4
63 2 4
64 2 5
65 2 2
66 5 3
67 1 3
68 6 4
69 2 3
70 2 4
71 4 6
72 6 2
73 2 4
74 2 5
75 2 2
76 1 1
77 1 2
78 2 5
79 2 4
80 3 4
81 3 6
82 5 1
83 2 4
84 5 5
85 3 2
86 1 3
87 2 3
88 2 6
89 1 5
90 2 4
91 2 4
92 5 5
93 5 5
94 2 6
95 3 6
96 5 5
97 5 7
98 6 5
99 2 5
100 7 7
101 1 5
102 1 6
103 6 6
104 6 4
105 2 5
106 1 1
107 2 6
108 1 5
109 2 2
110 1 1
111 3 5
112 3 6
113 6 5
114 4 5
115 1 4
116 2 2
117 5 3
118 6 3
119 3 5
120 5 3
121 3 2
122 2 2
123 3 3
124 2 6
125 5 5
126 5 6
127 7 2
128 4 5
129 5 5
130 1 1
131 4 4
132 1 2
133 4 2
134 6 7
135 7 6
136 1 5
137 3 5
138 5 5
139 2 4
140 4 3
141 5 3
142 1 3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) AGE Pstress_M Pstress_OKT BelInSprt
7.671179 -0.092895 0.694276 0.001330 -0.031324
KunnenRekRel Depressie Slaapgebrek `ToekZorgen\r`
0.191375 0.407160 -0.194751 0.175770
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.11849 -1.31429 -0.07172 1.22597 6.35605
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.67118 2.14849 3.571 0.000497 ***
AGE -0.09290 0.06686 -1.389 0.167063
Pstress_M 0.69428 0.33826 2.052 0.042083 *
Pstress_OKT 0.00133 0.33323 0.004 0.996823
BelInSprt -0.03132 0.01624 -1.929 0.055853 .
KunnenRekRel 0.19137 0.10775 1.776 0.078011 .
Depressie 0.40716 0.06166 6.604 8.78e-10 ***
Slaapgebrek -0.19475 0.09242 -2.107 0.036966 *
`ToekZorgen\r` 0.17577 0.11079 1.587 0.114993
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.905 on 133 degrees of freedom
Multiple R-squared: 0.3989, Adjusted R-squared: 0.3628
F-statistic: 11.03 on 8 and 133 DF, p-value: 7.222e-12
> 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,] 0.9951051 0.00978982 0.004894910
[2,] 0.9884361 0.02312778 0.011563890
[3,] 0.9822741 0.03545187 0.017725936
[4,] 0.9672013 0.06559744 0.032798720
[5,] 0.9436185 0.11276309 0.056381543
[6,] 0.9159866 0.16802684 0.084013418
[7,] 0.8841644 0.23167117 0.115835583
[8,] 0.8518294 0.29634122 0.148170612
[9,] 0.8133447 0.37331051 0.186655253
[10,] 0.8243243 0.35135136 0.175675682
[11,] 0.8779615 0.24407693 0.122038466
[12,] 0.8576100 0.28478009 0.142390046
[13,] 0.9904452 0.01910956 0.009554778
[14,] 0.9867030 0.02659408 0.013297042
[15,] 0.9825703 0.03485942 0.017429710
[16,] 0.9885109 0.02297819 0.011489096
[17,] 0.9825669 0.03486626 0.017433128
[18,] 0.9792719 0.04145627 0.020728135
[19,] 0.9783907 0.04321862 0.021609312
[20,] 0.9722031 0.05559389 0.027796943
[21,] 0.9615523 0.07689538 0.038447689
[22,] 0.9492374 0.10152519 0.050762595
[23,] 0.9552099 0.08958010 0.044790052
[24,] 0.9505705 0.09885894 0.049429472
[25,] 0.9368167 0.12636654 0.063183271
[26,] 0.9220352 0.15592963 0.077964813
[27,] 0.8992232 0.20155366 0.100776828
[28,] 0.8902191 0.21956172 0.109780862
[29,] 0.8655978 0.26880432 0.134402162
[30,] 0.9351980 0.12960404 0.064802018
[31,] 0.9528847 0.09423063 0.047115317
[32,] 0.9416489 0.11670228 0.058351138
[33,] 0.9260503 0.14789945 0.073949725
[34,] 0.9139746 0.17205073 0.086025366
[35,] 0.9029263 0.19414742 0.097073710
[36,] 0.8935325 0.21293492 0.106467462
[37,] 0.8867743 0.22645131 0.113225653
[38,] 0.8874126 0.22517481 0.112587406
[39,] 0.8661781 0.26764372 0.133821861
[40,] 0.8701576 0.25968481 0.129842407
[41,] 0.8433448 0.31331034 0.156655168
[42,] 0.8910868 0.21782638 0.108913192
[43,] 0.8676428 0.26471441 0.132357204
[44,] 0.8775869 0.24482612 0.122413061
[45,] 0.8662334 0.26753312 0.133766561
[46,] 0.9143624 0.17127513 0.085637565
[47,] 0.8924434 0.21511322 0.107556610
[48,] 0.8752196 0.24956079 0.124780397
[49,] 0.8976842 0.20463163 0.102315815
[50,] 0.8980433 0.20391346 0.101956730
[51,] 0.9071044 0.18579117 0.092895583
[52,] 0.9284346 0.14313070 0.071565351
[53,] 0.9369744 0.12605123 0.063025616
[54,] 0.9369908 0.12601841 0.063009207
[55,] 0.9394416 0.12111689 0.060558445
[56,] 0.9248666 0.15026686 0.075133430
[57,] 0.9060394 0.18792128 0.093960641
[58,] 0.9128571 0.17428575 0.087142876
[59,] 0.9275246 0.14495075 0.072475374
[60,] 0.9128508 0.17429832 0.087149158
[61,] 0.9032555 0.19348894 0.096744470
[62,] 0.9062333 0.18753339 0.093766694
[63,] 0.8840599 0.23188021 0.115940106
[64,] 0.8902319 0.21953619 0.109768096
[65,] 0.8734287 0.25314257 0.126571285
[66,] 0.8641544 0.27169129 0.135845645
[67,] 0.8440323 0.31193541 0.155967706
[68,] 0.8163017 0.36739664 0.183698318
[69,] 0.7931006 0.41379882 0.206899408
[70,] 0.8489363 0.30212746 0.151063730
[71,] 0.8501028 0.29979448 0.149897238
[72,] 0.8266472 0.34670556 0.173352780
[73,] 0.7951545 0.40969101 0.204845507
[74,] 0.7577847 0.48443066 0.242215331
[75,] 0.7916851 0.41662975 0.208314875
[76,] 0.7550949 0.48981018 0.244905091
[77,] 0.7117149 0.57657029 0.288285146
[78,] 0.6712107 0.65757869 0.328789347
[79,] 0.7069381 0.58612374 0.293061868
[80,] 0.6603085 0.67938296 0.339691480
[81,] 0.6429566 0.71408672 0.357043362
[82,] 0.6009484 0.79810314 0.399051568
[83,] 0.5870793 0.82584133 0.412920663
[84,] 0.6522472 0.69550551 0.347752755
[85,] 0.6540052 0.69198969 0.345994847
[86,] 0.6602585 0.67948299 0.339741493
[87,] 0.6166418 0.76671643 0.383358216
[88,] 0.6445335 0.71093306 0.355466532
[89,] 0.6279667 0.74406656 0.372033278
[90,] 0.6142755 0.77144907 0.385724534
[91,] 0.6104823 0.77903543 0.389517715
[92,] 0.6622766 0.67544687 0.337723437
[93,] 0.6578471 0.68430574 0.342152870
[94,] 0.7997500 0.40049994 0.200249972
[95,] 0.7733833 0.45323334 0.226616672
[96,] 0.7977535 0.40449308 0.202246539
[97,] 0.7815692 0.43686159 0.218430796
[98,] 0.7373258 0.52534847 0.262674235
[99,] 0.7304120 0.53917607 0.269588036
[100,] 0.7140155 0.57196891 0.285984456
[101,] 0.6529749 0.69405028 0.347025142
[102,] 0.6064148 0.78717033 0.393585163
[103,] 0.5968378 0.80632438 0.403162188
[104,] 0.5369164 0.92616728 0.463083641
[105,] 0.4737846 0.94756917 0.526215413
[106,] 0.6001755 0.79964903 0.399824516
[107,] 0.5268048 0.94639049 0.473195246
[108,] 0.4886495 0.97729908 0.511350460
[109,] 0.7889663 0.42206739 0.211033696
[110,] 0.7305064 0.53898722 0.269493611
[111,] 0.6765398 0.64692031 0.323460153
[112,] 0.5892287 0.82154259 0.410771293
[113,] 0.5137623 0.97247544 0.486237720
[114,] 0.4297484 0.85949686 0.570251570
[115,] 0.3366317 0.67326344 0.663368278
[116,] 0.4275616 0.85512317 0.572438416
[117,] 0.3285532 0.65710641 0.671446795
[118,] 0.3654628 0.73092559 0.634537205
[119,] 0.3140034 0.62800676 0.685996618
> postscript(file="/var/www/html/rcomp/tmp/1dlf51291471186.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2dlf51291471186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3ovw81291471186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ovw81291471186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ovw81291471186.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 = 142
Frequency = 1
1 2 3 4 5
-0.4135197581 -2.0190471840 2.9829473170 1.8982263109 -4.0682646143
6 7 8 9 10
-3.1335755170 -3.6659470920 0.5863379903 -0.1149493895 -1.3633004692
11 12 13 14 15
-1.6171612565 -4.1184917891 1.2014113327 -2.7133165670 -1.4600353480
16 17 18 19 20
-0.8658346376 -1.6045463958 -0.0195539719 -1.1318960459 -1.9282789956
21 22 23 24 25
0.0161594050 1.6383856502 -2.4387623348 6.3560524814 -1.5146381453
26 27 28 29 30
-0.0683265204 2.1361490442 0.0074683431 -0.7460762041 -2.1160271803
31 32 33 34 35
0.7045717803 0.0842347652 -0.3668160663 1.8412553345 -1.3246406093
36 37 38 39 40
-0.7750939488 1.2322797874 0.7162520208 -1.5337377082 -0.3340577298
41 42 43 44 45
3.6376051842 3.3868529960 -0.1046588815 1.1698553334 -0.8999718689
46 47 48 49 50
-1.1108454986 -0.2040964053 1.5353090389 1.6304206563 0.4295664929
51 52 53 54 55
-1.6660333338 0.5676749334 3.3828911743 0.8202234538 1.6839438487
56 57 58 59 60
1.2147380991 3.7882860741 -0.0302473714 -0.9757647477 4.0115688955
61 62 63 64 65
1.8272989739 1.2297154274 -1.5031813251 -2.0183168889 -0.9119944918
66 67 68 69 70
-2.0738968344 -0.6684684885 0.0838699736 1.9260908293 -2.2341399466
71 72 73 74 75
-0.3373732173 -0.8197453124 1.7178045651 -0.5820718863 -2.2753911153
76 77 78 79 80
0.7086146210 -1.6476593441 -0.1370606179 0.5967964236 -1.2440636456
81 82 83 84 85
3.1847124642 -2.0783723286 0.1660788461 -0.2734061692 0.3980788491
86 87 88 89 90
2.1333534865 -0.5049302992 0.3031185807 0.5033787494 -2.6279231600
91 92 93 94 95
-0.0002190523 -1.2746248677 0.7784344640 -1.4741547660 2.4555668032
96 97 98 99 100
-2.0992586616 1.9558956193 -0.6784088506 2.0061773159 0.2604435116
101 102 103 104 105
-1.0186792767 1.5736698124 2.3303384305 -1.9229874256 -3.8645743548
106 107 108 109 110
-0.6442700249 -0.9752609868 2.0855503467 0.5468180593 0.6346588028
111 112 113 114 115
0.6529382054 -0.0368003475 2.0187173771 2.2174367473 -1.5106492498
116 117 118 119 120
-1.4926961267 -3.0807270138 -0.0751111116 -0.6816142036 2.5719181939
121 122 123 124 125
1.5615285614 -0.7114383502 -1.2832201714 -2.9025205273 -0.8189686267
126 127 128 129 130
1.6032974109 0.7606562734 0.8027561361 -2.7296972595 1.1483748115
131 132 133 134 135
-0.3881282499 -0.4333667550 -0.0837807349 2.1516382320 -0.0140005000
136 137 138 139 140
-1.5146381453 2.2977747763 3.3130528838 0.1660788461 -0.0642268277
141 142
-3.0807270138 3.9069569851
> postscript(file="/var/www/html/rcomp/tmp/6y4vb1291471186.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 = 142
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.4135197581 NA
1 -2.0190471840 -0.4135197581
2 2.9829473170 -2.0190471840
3 1.8982263109 2.9829473170
4 -4.0682646143 1.8982263109
5 -3.1335755170 -4.0682646143
6 -3.6659470920 -3.1335755170
7 0.5863379903 -3.6659470920
8 -0.1149493895 0.5863379903
9 -1.3633004692 -0.1149493895
10 -1.6171612565 -1.3633004692
11 -4.1184917891 -1.6171612565
12 1.2014113327 -4.1184917891
13 -2.7133165670 1.2014113327
14 -1.4600353480 -2.7133165670
15 -0.8658346376 -1.4600353480
16 -1.6045463958 -0.8658346376
17 -0.0195539719 -1.6045463958
18 -1.1318960459 -0.0195539719
19 -1.9282789956 -1.1318960459
20 0.0161594050 -1.9282789956
21 1.6383856502 0.0161594050
22 -2.4387623348 1.6383856502
23 6.3560524814 -2.4387623348
24 -1.5146381453 6.3560524814
25 -0.0683265204 -1.5146381453
26 2.1361490442 -0.0683265204
27 0.0074683431 2.1361490442
28 -0.7460762041 0.0074683431
29 -2.1160271803 -0.7460762041
30 0.7045717803 -2.1160271803
31 0.0842347652 0.7045717803
32 -0.3668160663 0.0842347652
33 1.8412553345 -0.3668160663
34 -1.3246406093 1.8412553345
35 -0.7750939488 -1.3246406093
36 1.2322797874 -0.7750939488
37 0.7162520208 1.2322797874
38 -1.5337377082 0.7162520208
39 -0.3340577298 -1.5337377082
40 3.6376051842 -0.3340577298
41 3.3868529960 3.6376051842
42 -0.1046588815 3.3868529960
43 1.1698553334 -0.1046588815
44 -0.8999718689 1.1698553334
45 -1.1108454986 -0.8999718689
46 -0.2040964053 -1.1108454986
47 1.5353090389 -0.2040964053
48 1.6304206563 1.5353090389
49 0.4295664929 1.6304206563
50 -1.6660333338 0.4295664929
51 0.5676749334 -1.6660333338
52 3.3828911743 0.5676749334
53 0.8202234538 3.3828911743
54 1.6839438487 0.8202234538
55 1.2147380991 1.6839438487
56 3.7882860741 1.2147380991
57 -0.0302473714 3.7882860741
58 -0.9757647477 -0.0302473714
59 4.0115688955 -0.9757647477
60 1.8272989739 4.0115688955
61 1.2297154274 1.8272989739
62 -1.5031813251 1.2297154274
63 -2.0183168889 -1.5031813251
64 -0.9119944918 -2.0183168889
65 -2.0738968344 -0.9119944918
66 -0.6684684885 -2.0738968344
67 0.0838699736 -0.6684684885
68 1.9260908293 0.0838699736
69 -2.2341399466 1.9260908293
70 -0.3373732173 -2.2341399466
71 -0.8197453124 -0.3373732173
72 1.7178045651 -0.8197453124
73 -0.5820718863 1.7178045651
74 -2.2753911153 -0.5820718863
75 0.7086146210 -2.2753911153
76 -1.6476593441 0.7086146210
77 -0.1370606179 -1.6476593441
78 0.5967964236 -0.1370606179
79 -1.2440636456 0.5967964236
80 3.1847124642 -1.2440636456
81 -2.0783723286 3.1847124642
82 0.1660788461 -2.0783723286
83 -0.2734061692 0.1660788461
84 0.3980788491 -0.2734061692
85 2.1333534865 0.3980788491
86 -0.5049302992 2.1333534865
87 0.3031185807 -0.5049302992
88 0.5033787494 0.3031185807
89 -2.6279231600 0.5033787494
90 -0.0002190523 -2.6279231600
91 -1.2746248677 -0.0002190523
92 0.7784344640 -1.2746248677
93 -1.4741547660 0.7784344640
94 2.4555668032 -1.4741547660
95 -2.0992586616 2.4555668032
96 1.9558956193 -2.0992586616
97 -0.6784088506 1.9558956193
98 2.0061773159 -0.6784088506
99 0.2604435116 2.0061773159
100 -1.0186792767 0.2604435116
101 1.5736698124 -1.0186792767
102 2.3303384305 1.5736698124
103 -1.9229874256 2.3303384305
104 -3.8645743548 -1.9229874256
105 -0.6442700249 -3.8645743548
106 -0.9752609868 -0.6442700249
107 2.0855503467 -0.9752609868
108 0.5468180593 2.0855503467
109 0.6346588028 0.5468180593
110 0.6529382054 0.6346588028
111 -0.0368003475 0.6529382054
112 2.0187173771 -0.0368003475
113 2.2174367473 2.0187173771
114 -1.5106492498 2.2174367473
115 -1.4926961267 -1.5106492498
116 -3.0807270138 -1.4926961267
117 -0.0751111116 -3.0807270138
118 -0.6816142036 -0.0751111116
119 2.5719181939 -0.6816142036
120 1.5615285614 2.5719181939
121 -0.7114383502 1.5615285614
122 -1.2832201714 -0.7114383502
123 -2.9025205273 -1.2832201714
124 -0.8189686267 -2.9025205273
125 1.6032974109 -0.8189686267
126 0.7606562734 1.6032974109
127 0.8027561361 0.7606562734
128 -2.7296972595 0.8027561361
129 1.1483748115 -2.7296972595
130 -0.3881282499 1.1483748115
131 -0.4333667550 -0.3881282499
132 -0.0837807349 -0.4333667550
133 2.1516382320 -0.0837807349
134 -0.0140005000 2.1516382320
135 -1.5146381453 -0.0140005000
136 2.2977747763 -1.5146381453
137 3.3130528838 2.2977747763
138 0.1660788461 3.3130528838
139 -0.0642268277 0.1660788461
140 -3.0807270138 -0.0642268277
141 3.9069569851 -3.0807270138
142 NA 3.9069569851
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.0190471840 -0.4135197581
[2,] 2.9829473170 -2.0190471840
[3,] 1.8982263109 2.9829473170
[4,] -4.0682646143 1.8982263109
[5,] -3.1335755170 -4.0682646143
[6,] -3.6659470920 -3.1335755170
[7,] 0.5863379903 -3.6659470920
[8,] -0.1149493895 0.5863379903
[9,] -1.3633004692 -0.1149493895
[10,] -1.6171612565 -1.3633004692
[11,] -4.1184917891 -1.6171612565
[12,] 1.2014113327 -4.1184917891
[13,] -2.7133165670 1.2014113327
[14,] -1.4600353480 -2.7133165670
[15,] -0.8658346376 -1.4600353480
[16,] -1.6045463958 -0.8658346376
[17,] -0.0195539719 -1.6045463958
[18,] -1.1318960459 -0.0195539719
[19,] -1.9282789956 -1.1318960459
[20,] 0.0161594050 -1.9282789956
[21,] 1.6383856502 0.0161594050
[22,] -2.4387623348 1.6383856502
[23,] 6.3560524814 -2.4387623348
[24,] -1.5146381453 6.3560524814
[25,] -0.0683265204 -1.5146381453
[26,] 2.1361490442 -0.0683265204
[27,] 0.0074683431 2.1361490442
[28,] -0.7460762041 0.0074683431
[29,] -2.1160271803 -0.7460762041
[30,] 0.7045717803 -2.1160271803
[31,] 0.0842347652 0.7045717803
[32,] -0.3668160663 0.0842347652
[33,] 1.8412553345 -0.3668160663
[34,] -1.3246406093 1.8412553345
[35,] -0.7750939488 -1.3246406093
[36,] 1.2322797874 -0.7750939488
[37,] 0.7162520208 1.2322797874
[38,] -1.5337377082 0.7162520208
[39,] -0.3340577298 -1.5337377082
[40,] 3.6376051842 -0.3340577298
[41,] 3.3868529960 3.6376051842
[42,] -0.1046588815 3.3868529960
[43,] 1.1698553334 -0.1046588815
[44,] -0.8999718689 1.1698553334
[45,] -1.1108454986 -0.8999718689
[46,] -0.2040964053 -1.1108454986
[47,] 1.5353090389 -0.2040964053
[48,] 1.6304206563 1.5353090389
[49,] 0.4295664929 1.6304206563
[50,] -1.6660333338 0.4295664929
[51,] 0.5676749334 -1.6660333338
[52,] 3.3828911743 0.5676749334
[53,] 0.8202234538 3.3828911743
[54,] 1.6839438487 0.8202234538
[55,] 1.2147380991 1.6839438487
[56,] 3.7882860741 1.2147380991
[57,] -0.0302473714 3.7882860741
[58,] -0.9757647477 -0.0302473714
[59,] 4.0115688955 -0.9757647477
[60,] 1.8272989739 4.0115688955
[61,] 1.2297154274 1.8272989739
[62,] -1.5031813251 1.2297154274
[63,] -2.0183168889 -1.5031813251
[64,] -0.9119944918 -2.0183168889
[65,] -2.0738968344 -0.9119944918
[66,] -0.6684684885 -2.0738968344
[67,] 0.0838699736 -0.6684684885
[68,] 1.9260908293 0.0838699736
[69,] -2.2341399466 1.9260908293
[70,] -0.3373732173 -2.2341399466
[71,] -0.8197453124 -0.3373732173
[72,] 1.7178045651 -0.8197453124
[73,] -0.5820718863 1.7178045651
[74,] -2.2753911153 -0.5820718863
[75,] 0.7086146210 -2.2753911153
[76,] -1.6476593441 0.7086146210
[77,] -0.1370606179 -1.6476593441
[78,] 0.5967964236 -0.1370606179
[79,] -1.2440636456 0.5967964236
[80,] 3.1847124642 -1.2440636456
[81,] -2.0783723286 3.1847124642
[82,] 0.1660788461 -2.0783723286
[83,] -0.2734061692 0.1660788461
[84,] 0.3980788491 -0.2734061692
[85,] 2.1333534865 0.3980788491
[86,] -0.5049302992 2.1333534865
[87,] 0.3031185807 -0.5049302992
[88,] 0.5033787494 0.3031185807
[89,] -2.6279231600 0.5033787494
[90,] -0.0002190523 -2.6279231600
[91,] -1.2746248677 -0.0002190523
[92,] 0.7784344640 -1.2746248677
[93,] -1.4741547660 0.7784344640
[94,] 2.4555668032 -1.4741547660
[95,] -2.0992586616 2.4555668032
[96,] 1.9558956193 -2.0992586616
[97,] -0.6784088506 1.9558956193
[98,] 2.0061773159 -0.6784088506
[99,] 0.2604435116 2.0061773159
[100,] -1.0186792767 0.2604435116
[101,] 1.5736698124 -1.0186792767
[102,] 2.3303384305 1.5736698124
[103,] -1.9229874256 2.3303384305
[104,] -3.8645743548 -1.9229874256
[105,] -0.6442700249 -3.8645743548
[106,] -0.9752609868 -0.6442700249
[107,] 2.0855503467 -0.9752609868
[108,] 0.5468180593 2.0855503467
[109,] 0.6346588028 0.5468180593
[110,] 0.6529382054 0.6346588028
[111,] -0.0368003475 0.6529382054
[112,] 2.0187173771 -0.0368003475
[113,] 2.2174367473 2.0187173771
[114,] -1.5106492498 2.2174367473
[115,] -1.4926961267 -1.5106492498
[116,] -3.0807270138 -1.4926961267
[117,] -0.0751111116 -3.0807270138
[118,] -0.6816142036 -0.0751111116
[119,] 2.5719181939 -0.6816142036
[120,] 1.5615285614 2.5719181939
[121,] -0.7114383502 1.5615285614
[122,] -1.2832201714 -0.7114383502
[123,] -2.9025205273 -1.2832201714
[124,] -0.8189686267 -2.9025205273
[125,] 1.6032974109 -0.8189686267
[126,] 0.7606562734 1.6032974109
[127,] 0.8027561361 0.7606562734
[128,] -2.7296972595 0.8027561361
[129,] 1.1483748115 -2.7296972595
[130,] -0.3881282499 1.1483748115
[131,] -0.4333667550 -0.3881282499
[132,] -0.0837807349 -0.4333667550
[133,] 2.1516382320 -0.0837807349
[134,] -0.0140005000 2.1516382320
[135,] -1.5146381453 -0.0140005000
[136,] 2.2977747763 -1.5146381453
[137,] 3.3130528838 2.2977747763
[138,] 0.1660788461 3.3130528838
[139,] -0.0642268277 0.1660788461
[140,] -3.0807270138 -0.0642268277
[141,] 3.9069569851 -3.0807270138
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.0190471840 -0.4135197581
2 2.9829473170 -2.0190471840
3 1.8982263109 2.9829473170
4 -4.0682646143 1.8982263109
5 -3.1335755170 -4.0682646143
6 -3.6659470920 -3.1335755170
7 0.5863379903 -3.6659470920
8 -0.1149493895 0.5863379903
9 -1.3633004692 -0.1149493895
10 -1.6171612565 -1.3633004692
11 -4.1184917891 -1.6171612565
12 1.2014113327 -4.1184917891
13 -2.7133165670 1.2014113327
14 -1.4600353480 -2.7133165670
15 -0.8658346376 -1.4600353480
16 -1.6045463958 -0.8658346376
17 -0.0195539719 -1.6045463958
18 -1.1318960459 -0.0195539719
19 -1.9282789956 -1.1318960459
20 0.0161594050 -1.9282789956
21 1.6383856502 0.0161594050
22 -2.4387623348 1.6383856502
23 6.3560524814 -2.4387623348
24 -1.5146381453 6.3560524814
25 -0.0683265204 -1.5146381453
26 2.1361490442 -0.0683265204
27 0.0074683431 2.1361490442
28 -0.7460762041 0.0074683431
29 -2.1160271803 -0.7460762041
30 0.7045717803 -2.1160271803
31 0.0842347652 0.7045717803
32 -0.3668160663 0.0842347652
33 1.8412553345 -0.3668160663
34 -1.3246406093 1.8412553345
35 -0.7750939488 -1.3246406093
36 1.2322797874 -0.7750939488
37 0.7162520208 1.2322797874
38 -1.5337377082 0.7162520208
39 -0.3340577298 -1.5337377082
40 3.6376051842 -0.3340577298
41 3.3868529960 3.6376051842
42 -0.1046588815 3.3868529960
43 1.1698553334 -0.1046588815
44 -0.8999718689 1.1698553334
45 -1.1108454986 -0.8999718689
46 -0.2040964053 -1.1108454986
47 1.5353090389 -0.2040964053
48 1.6304206563 1.5353090389
49 0.4295664929 1.6304206563
50 -1.6660333338 0.4295664929
51 0.5676749334 -1.6660333338
52 3.3828911743 0.5676749334
53 0.8202234538 3.3828911743
54 1.6839438487 0.8202234538
55 1.2147380991 1.6839438487
56 3.7882860741 1.2147380991
57 -0.0302473714 3.7882860741
58 -0.9757647477 -0.0302473714
59 4.0115688955 -0.9757647477
60 1.8272989739 4.0115688955
61 1.2297154274 1.8272989739
62 -1.5031813251 1.2297154274
63 -2.0183168889 -1.5031813251
64 -0.9119944918 -2.0183168889
65 -2.0738968344 -0.9119944918
66 -0.6684684885 -2.0738968344
67 0.0838699736 -0.6684684885
68 1.9260908293 0.0838699736
69 -2.2341399466 1.9260908293
70 -0.3373732173 -2.2341399466
71 -0.8197453124 -0.3373732173
72 1.7178045651 -0.8197453124
73 -0.5820718863 1.7178045651
74 -2.2753911153 -0.5820718863
75 0.7086146210 -2.2753911153
76 -1.6476593441 0.7086146210
77 -0.1370606179 -1.6476593441
78 0.5967964236 -0.1370606179
79 -1.2440636456 0.5967964236
80 3.1847124642 -1.2440636456
81 -2.0783723286 3.1847124642
82 0.1660788461 -2.0783723286
83 -0.2734061692 0.1660788461
84 0.3980788491 -0.2734061692
85 2.1333534865 0.3980788491
86 -0.5049302992 2.1333534865
87 0.3031185807 -0.5049302992
88 0.5033787494 0.3031185807
89 -2.6279231600 0.5033787494
90 -0.0002190523 -2.6279231600
91 -1.2746248677 -0.0002190523
92 0.7784344640 -1.2746248677
93 -1.4741547660 0.7784344640
94 2.4555668032 -1.4741547660
95 -2.0992586616 2.4555668032
96 1.9558956193 -2.0992586616
97 -0.6784088506 1.9558956193
98 2.0061773159 -0.6784088506
99 0.2604435116 2.0061773159
100 -1.0186792767 0.2604435116
101 1.5736698124 -1.0186792767
102 2.3303384305 1.5736698124
103 -1.9229874256 2.3303384305
104 -3.8645743548 -1.9229874256
105 -0.6442700249 -3.8645743548
106 -0.9752609868 -0.6442700249
107 2.0855503467 -0.9752609868
108 0.5468180593 2.0855503467
109 0.6346588028 0.5468180593
110 0.6529382054 0.6346588028
111 -0.0368003475 0.6529382054
112 2.0187173771 -0.0368003475
113 2.2174367473 2.0187173771
114 -1.5106492498 2.2174367473
115 -1.4926961267 -1.5106492498
116 -3.0807270138 -1.4926961267
117 -0.0751111116 -3.0807270138
118 -0.6816142036 -0.0751111116
119 2.5719181939 -0.6816142036
120 1.5615285614 2.5719181939
121 -0.7114383502 1.5615285614
122 -1.2832201714 -0.7114383502
123 -2.9025205273 -1.2832201714
124 -0.8189686267 -2.9025205273
125 1.6032974109 -0.8189686267
126 0.7606562734 1.6032974109
127 0.8027561361 0.7606562734
128 -2.7296972595 0.8027561361
129 1.1483748115 -2.7296972595
130 -0.3881282499 1.1483748115
131 -0.4333667550 -0.3881282499
132 -0.0837807349 -0.4333667550
133 2.1516382320 -0.0837807349
134 -0.0140005000 2.1516382320
135 -1.5146381453 -0.0140005000
136 2.2977747763 -1.5146381453
137 3.3130528838 2.2977747763
138 0.1660788461 3.3130528838
139 -0.0642268277 0.1660788461
140 -3.0807270138 -0.0642268277
141 3.9069569851 -3.0807270138
> 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/7rdcw1291471186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8rdcw1291471186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rdcw1291471186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1024uz1291471186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11n5s51291471186.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/12r69b1291471186.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/13nfpj1291471186.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/148gnp1291471186.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/15bg4v1291471186.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/167rne1291471187.tab")
+ }
>
> try(system("convert tmp/1dlf51291471186.ps tmp/1dlf51291471186.png",intern=TRUE))
character(0)
> try(system("convert tmp/2dlf51291471186.ps tmp/2dlf51291471186.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ovw81291471186.ps tmp/3ovw81291471186.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ovw81291471186.ps tmp/4ovw81291471186.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ovw81291471186.ps tmp/5ovw81291471186.png",intern=TRUE))
character(0)
> try(system("convert tmp/6y4vb1291471186.ps tmp/6y4vb1291471186.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rdcw1291471186.ps tmp/7rdcw1291471186.png",intern=TRUE))
character(0)
> try(system("convert tmp/8rdcw1291471186.ps tmp/8rdcw1291471186.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rdcw1291471186.ps tmp/9rdcw1291471186.png",intern=TRUE))
character(0)
> try(system("convert tmp/1024uz1291471186.ps tmp/1024uz1291471186.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.055 1.820 9.352