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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+ ,6
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+ ,69
+ ,4
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+ ,5
+ ,5
+ ,1
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+ ,11
+ ,54
+ ,2
+ ,7
+ ,6
+ ,8
+ ,14
+ ,15
+ ,2
+ ,4
+ ,2
+ ,12
+ ,22
+ ,10
+ ,69
+ ,6
+ ,5
+ ,6
+ ,7
+ ,14
+ ,14
+ ,4
+ ,3
+ ,2
+ ,8
+ ,22
+ ,5
+ ,81
+ ,5
+ ,6
+ ,6
+ ,11
+ ,14
+ ,11
+ ,5
+ ,3
+ ,1
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+ ,1
+ ,6
+ ,1
+ ,13
+ ,11
+ ,9
+ ,1
+ ,3
+ ,1
+ ,7)
+ ,dim=c(13
+ ,142)
+ ,dimnames=list(c('AGE'
+ ,'PStress'
+ ,'BelInSprt'
+ ,'KunnenRekRel'
+ ,'ExtraCurAct'
+ ,'VerandVorigJr'
+ ,'VerwOuders'
+ ,'KenStudenten'
+ ,'Depressie'
+ ,'Slaapgebrek'
+ ,'Toekomstzorgen'
+ ,'Rookgedrag'
+ ,'MateAlcCon')
+ ,1:142))
> y <- array(NA,dim=c(13,142),dimnames=list(c('AGE','PStress','BelInSprt','KunnenRekRel','ExtraCurAct','VerandVorigJr','VerwOuders','KenStudenten','Depressie','Slaapgebrek','Toekomstzorgen','Rookgedrag','MateAlcCon'),1:142))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> ylab = ''
> xlab = ''
> main = ''
> #'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 BelInSprt KunnenRekRel ExtraCurAct VerandVorigJr VerwOuders
1 10 23 53 7 6 7 15
2 6 21 86 4 6 5 15
3 13 21 66 6 5 7 14
4 12 21 67 5 4 3 10
5 8 24 76 4 4 7 10
6 6 22 78 3 6 7 12
7 10 21 53 5 7 7 18
8 10 22 80 6 5 1 12
9 9 21 74 5 4 4 14
10 9 20 76 6 6 5 18
11 7 22 79 7 1 6 9
12 5 21 54 6 4 4 11
13 14 21 67 7 6 7 11
14 6 23 87 6 6 6 17
15 10 22 58 4 5 2 8
16 10 23 75 6 3 2 16
17 7 22 88 4 7 6 21
18 10 24 64 5 2 7 24
19 8 23 57 3 5 5 21
20 6 21 66 3 5 2 14
21 10 23 54 4 3 7 7
22 12 23 56 5 5 4 18
23 7 21 86 3 5 5 18
24 15 20 80 7 6 5 13
25 8 32 76 7 4 5 11
26 10 22 69 4 4 3 13
27 13 21 67 4 4 5 13
28 8 21 80 5 2 1 18
29 11 21 54 6 3 1 14
30 7 22 71 5 6 3 12
31 9 21 84 4 6 2 9
32 10 21 74 6 5 3 12
33 8 21 71 5 3 2 8
34 15 22 63 5 3 5 5
35 9 21 71 6 4 2 10
36 7 21 76 2 4 3 11
37 11 21 69 6 5 4 11
38 9 21 74 7 3 6 12
39 8 23 75 5 5 2 12
40 8 21 54 5 4 7 15
41 12 23 69 5 3 5 16
42 13 23 68 6 3 3 14
43 9 21 75 4 4 3 17
44 11 21 75 6 6 4 10
45 8 20 72 5 5 5 17
46 10 21 67 5 3 2 12
47 13 21 63 3 4 7 13
48 12 22 62 4 2 6 13
49 12 21 63 4 3 5 11
50 9 21 76 2 5 6 13
51 8 22 74 3 5 5 12
52 9 20 67 6 5 2 12
53 12 22 73 5 4 3 12
54 12 22 70 6 5 5 9
55 16 21 53 2 3 7 7
56 11 23 77 3 6 4 17
57 13 22 77 6 3 7 12
58 10 24 52 3 2 5 12
59 9 23 54 6 3 6 9
60 14 21 80 6 4 6 9
61 13 22 66 4 3 3 13
62 12 22 73 7 4 5 10
63 9 21 63 6 4 7 11
64 9 21 69 3 7 7 12
65 10 21 67 7 2 5 10
66 8 21 54 2 2 6 13
67 9 20 81 4 5 5 6
68 9 22 69 6 3 5 7
69 11 22 84 4 6 2 13
70 7 22 70 1 6 5 11
71 11 23 69 4 4 4 18
72 9 21 77 7 6 6 9
73 11 23 54 4 6 5 9
74 9 22 79 4 4 3 11
75 8 21 30 4 2 3 11
76 9 21 71 6 6 4 15
77 8 20 73 2 3 2 8
78 9 24 72 3 5 2 11
79 10 24 77 4 3 5 14
80 9 21 75 4 4 4 14
81 17 20 70 4 6 6 12
82 7 21 73 6 2 4 12
83 11 21 54 2 7 6 8
84 9 21 77 4 2 4 11
85 10 21 82 3 3 2 10
86 11 22 80 7 6 5 17
87 8 22 80 4 4 2 16
88 12 21 69 5 4 7 13
89 10 22 78 6 3 1 15
90 7 21 81 5 5 3 11
91 9 23 76 4 4 5 12
92 7 21 76 5 5 6 16
93 12 22 73 4 5 6 20
94 8 22 85 5 7 2 16
95 13 22 66 7 4 5 11
96 9 20 79 7 6 5 15
97 15 21 68 4 3 3 15
98 8 21 76 6 6 6 12
99 14 22 54 4 3 5 9
100 14 25 46 1 2 7 24
101 9 22 82 3 4 1 15
102 13 22 74 6 3 6 18
103 11 21 88 7 3 4 17
104 10 22 38 6 4 7 12
105 6 21 76 6 4 2 15
106 8 24 86 6 5 6 11
107 10 23 54 4 5 7 11
108 10 23 69 1 7 5 12
109 10 22 90 3 7 2 14
110 12 22 54 7 1 1 11
111 10 25 76 2 4 3 20
112 9 23 89 7 6 3 11
113 9 22 76 4 5 3 12
114 11 21 79 5 4 5 12
115 7 21 90 6 5 2 11
116 7 22 74 6 5 4 10
117 5 22 81 5 6 6 11
118 9 21 72 5 5 5 12
119 11 0 71 4 3 5 9
120 15 21 66 2 4 2 8
121 9 22 77 2 4 3 6
122 9 21 74 4 5 2 12
123 8 24 82 4 6 6 15
124 13 21 54 6 2 5 13
125 10 23 63 5 4 4 17
126 13 23 54 5 5 6 14
127 9 22 64 6 6 4 16
128 11 21 69 5 6 6 15
129 8 21 84 7 5 0 11
130 10 21 86 5 4 1 11
131 9 21 77 3 5 5 16
132 8 22 89 5 6 2 15
133 8 20 76 1 6 5 14
134 13 21 60 5 5 6 9
135 11 23 79 7 6 7 13
136 8 32 76 7 4 5 11
137 12 22 72 6 5 5 14
138 15 24 69 4 5 5 11
139 11 21 54 2 7 6 8
140 10 22 69 6 5 6 7
141 5 22 81 5 6 6 11
142 11 23 84 1 6 1 13
KenStudenten Depressie Slaapgebrek Toekomstzorgen Rookgedrag MateAlcCon
1 11 12 2 4 2 6
2 8 11 4 3 1 6
3 12 14 7 5 4 11
4 10 12 3 3 1 7
5 7 21 7 6 5 12
6 6 12 2 5 1 8
7 8 22 7 6 1 7
8 16 11 2 6 1 11
9 8 10 1 5 1 8
10 16 13 2 5 1 9
11 7 10 6 3 2 9
12 11 8 1 5 1 6
13 16 15 1 7 3 9
14 16 10 1 5 1 5
15 12 14 2 5 1 9
16 13 14 2 3 1 4
17 19 11 2 5 1 9
18 7 10 1 6 1 6
19 8 13 7 5 2 8
20 12 7 1 2 4 12
21 13 12 2 5 1 7
22 11 14 4 4 2 8
23 8 11 2 6 1 3
24 16 9 1 3 2 9
25 15 11 1 5 3 7
26 11 15 5 4 1 9
27 12 13 2 5 1 9
28 7 9 1 2 1 7
29 9 15 3 2 1 5
30 15 10 1 5 1 8
31 6 11 2 2 2 7
32 14 13 5 2 1 6
33 14 8 2 2 1 6
34 7 20 6 5 1 4
35 15 12 4 5 1 8
36 14 10 1 1 1 8
37 17 10 3 5 1 3
38 14 9 6 2 1 8
39 5 14 7 6 2 9
40 14 8 4 1 1 6
41 8 11 5 3 1 5
42 8 13 3 2 1 8
43 13 11 2 5 2 6
44 16 11 2 3 1 9
45 11 10 2 4 1 8
46 10 14 2 3 1 5
47 10 18 1 6 1 9
48 10 14 2 4 1 8
49 8 11 1 5 4 11
50 14 12 2 2 2 7
51 14 13 2 5 1 9
52 12 9 5 5 1 11
53 13 10 5 3 4 9
54 5 15 2 5 2 10
55 10 20 1 7 1 6
56 6 12 1 4 1 9
57 15 12 2 2 1 9
58 12 14 3 3 1 3
59 16 13 7 6 1 3
60 15 11 4 7 1 3
61 12 17 4 4 2 12
62 8 12 1 4 1 8
63 14 13 2 4 1 9
64 14 14 2 5 2 10
65 13 13 2 2 1 4
66 12 15 5 3 2 14
67 15 13 1 3 2 8
68 8 10 6 4 4 6
69 16 11 2 3 1 9
70 14 13 2 4 1 10
71 13 17 4 6 3 10
72 15 13 6 2 1 7
73 7 9 2 4 1 3
74 5 11 2 5 1 6
75 7 10 2 2 1 4
76 13 9 1 1 1 9
77 14 12 1 2 1 11
78 14 12 2 5 1 6
79 13 13 2 4 1 7
80 11 13 3 4 4 8
81 15 22 3 6 1 11
82 13 13 5 1 1 9
83 14 15 2 4 2 12
84 13 13 5 5 1 7
85 9 15 3 2 1 9
86 8 10 1 3 1 10
87 6 11 2 3 1 8
88 13 16 2 6 1 9
89 16 11 1 5 1 9
90 7 11 2 4 1 9
91 11 10 2 4 1 9
92 8 10 5 5 1 9
93 13 16 5 5 1 7
94 5 12 2 6 1 11
95 8 11 3 6 1 6
96 10 16 5 5 5 11
97 9 19 5 7 1 9
98 16 11 6 5 1 7
99 4 15 2 5 1 5
100 4 24 7 7 3 9
101 11 14 1 5 1 7
102 14 15 1 6 1 9
103 15 11 6 6 1 9
104 17 15 6 4 1 3
105 10 12 2 5 1 11
106 15 10 1 1 1 7
107 11 14 2 6 1 6
108 10 9 1 5 4 10
109 9 15 2 2 4 8
110 14 15 1 1 1 9
111 15 14 3 5 1 8
112 9 11 3 6 1 10
113 12 8 6 5 4 10
114 10 11 4 5 2 9
115 16 8 1 4 1 9
116 15 10 2 2 1 7
117 14 11 5 3 1 9
118 12 13 6 3 1 12
119 15 11 3 5 1 10
120 9 20 5 3 1 9
121 12 10 3 2 2 12
122 15 12 2 2 4 10
123 6 14 3 3 4 10
124 4 23 2 6 1 9
125 8 14 5 5 1 3
126 10 16 5 6 1 7
127 6 11 7 2 2 10
128 12 12 4 5 1 9
129 14 14 5 5 1 11
130 11 12 1 1 3 10
131 15 12 4 4 2 11
132 13 11 1 2 2 7
133 15 12 4 2 1 10
134 16 13 6 7 1 5
135 4 17 7 6 2 8
136 15 11 1 5 3 7
137 12 12 3 5 1 10
138 15 19 5 5 1 11
139 14 15 2 4 2 12
140 14 14 4 3 2 8
141 14 11 5 3 1 9
142 11 9 1 3 1 7
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) AGE BelInSprt KunnenRekRel ExtraCurAct
9.03106 -0.10259 -0.02945 0.19127 -0.12938
VerandVorigJr VerwOuders KenStudenten Depressie Slaapgebrek
-0.01352 -0.04541 0.02739 0.40382 -0.20575
Toekomstzorgen Rookgedrag MateAlcCon
0.22071 0.07959 -0.04172
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.6218 -1.2891 -0.1693 1.4887 6.1594
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.03106 2.35863 3.829 0.0002 ***
AGE -0.10259 0.07102 -1.444 0.1510
BelInSprt -0.02945 0.01822 -1.616 0.1085
KunnenRekRel 0.19127 0.11191 1.709 0.0898 .
ExtraCurAct -0.12938 0.13024 -0.993 0.3223
VerandVorigJr -0.01352 0.10239 -0.132 0.8952
VerwOuders -0.04541 0.04965 -0.915 0.3621
KenStudenten 0.02739 0.05037 0.544 0.5875
Depressie 0.40382 0.06496 6.217 6.51e-09 ***
Slaapgebrek -0.20575 0.09597 -2.144 0.0339 *
Toekomstzorgen 0.22071 0.11863 1.861 0.0651 .
Rookgedrag 0.07959 0.18540 0.429 0.6684
MateAlcCon -0.04172 0.08360 -0.499 0.6186
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.945 on 129 degrees of freedom
Multiple R-squared: 0.3925, Adjusted R-squared: 0.336
F-statistic: 6.946 on 12 and 129 DF, p-value: 1.340e-09
> 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,] 0.95089576 0.09820847 0.049104236
[2,] 0.91408957 0.17182086 0.085910430
[3,] 0.97010816 0.05978369 0.029891843
[4,] 0.94720474 0.10559051 0.052795255
[5,] 0.96946108 0.06107784 0.030538922
[6,] 0.95136485 0.09727031 0.048635154
[7,] 0.94423044 0.11153912 0.055769558
[8,] 0.93113997 0.13772007 0.068860033
[9,] 0.99306371 0.01387257 0.006936286
[10,] 0.99007844 0.01984312 0.009921561
[11,] 0.98456619 0.03086762 0.015433809
[12,] 0.98886939 0.02226123 0.011130613
[13,] 0.98316275 0.03367450 0.016837248
[14,] 0.97511651 0.04976699 0.024883494
[15,] 0.97194976 0.05610049 0.028050245
[16,] 0.97242173 0.05515654 0.027578269
[17,] 0.96002107 0.07995786 0.039978932
[18,] 0.94470295 0.11059411 0.055297054
[19,] 0.96534779 0.06930442 0.034652210
[20,] 0.95452282 0.09095436 0.045477178
[21,] 0.94376379 0.11247242 0.056236209
[22,] 0.93316571 0.13366858 0.066834291
[23,] 0.91325978 0.17348044 0.086740218
[24,] 0.89892465 0.20215070 0.101075348
[25,] 0.87126702 0.25746596 0.128732978
[26,] 0.93302807 0.13394386 0.066971929
[27,] 0.95192428 0.09615144 0.048075720
[28,] 0.93654498 0.12691004 0.063455019
[29,] 0.92681579 0.14636841 0.073184207
[30,] 0.90988954 0.18022093 0.090110465
[31,] 0.89282519 0.21434961 0.107174807
[32,] 0.87163302 0.25673397 0.128366984
[33,] 0.84718117 0.30563766 0.152818828
[34,] 0.84157427 0.31685146 0.158425732
[35,] 0.80671747 0.38656507 0.193282534
[36,] 0.79300537 0.41398926 0.206994630
[37,] 0.75530093 0.48939814 0.244699071
[38,] 0.83902393 0.32195215 0.160976073
[39,] 0.80904018 0.38191964 0.190959818
[40,] 0.80964055 0.38071889 0.190359446
[41,] 0.85829898 0.28340205 0.141701023
[42,] 0.89798920 0.20402160 0.102010802
[43,] 0.87366183 0.25267634 0.126338170
[44,] 0.86456480 0.27087040 0.135435198
[45,] 0.93632397 0.12735205 0.063676026
[46,] 0.92933948 0.14132103 0.070660517
[47,] 0.92407753 0.15184494 0.075922469
[48,] 0.93153274 0.13693451 0.068467257
[49,] 0.92241290 0.15517420 0.077587101
[50,] 0.92406530 0.15186941 0.075934704
[51,] 0.92691415 0.14617170 0.073085852
[52,] 0.91886161 0.16227678 0.081138389
[53,] 0.90024315 0.19951369 0.099756847
[54,] 0.91579895 0.16840210 0.084201049
[55,] 0.93117218 0.13765563 0.068827816
[56,] 0.91442039 0.17115922 0.085579611
[57,] 0.89449031 0.21101938 0.105509691
[58,] 0.90880957 0.18238087 0.091190434
[59,] 0.88579839 0.22840321 0.114201606
[60,] 0.88630655 0.22738690 0.113693451
[61,] 0.87042963 0.25914075 0.129570374
[62,] 0.86107729 0.27784541 0.138922706
[63,] 0.84458401 0.31083199 0.155415993
[64,] 0.81133021 0.37733958 0.188669791
[65,] 0.78383200 0.43233601 0.216168004
[66,] 0.83712765 0.32574470 0.162872348
[67,] 0.85364111 0.29271778 0.146358892
[68,] 0.82342833 0.35314334 0.176571672
[69,] 0.80833680 0.38332640 0.191663198
[70,] 0.77095580 0.45808840 0.229044200
[71,] 0.85788032 0.28423937 0.142119684
[72,] 0.82882301 0.34235399 0.171176994
[73,] 0.79215765 0.41568469 0.207842347
[74,] 0.75033916 0.49932169 0.249660843
[75,] 0.76606183 0.46787633 0.233938167
[76,] 0.72392369 0.55215262 0.276076312
[77,] 0.70366132 0.59267736 0.296338681
[78,] 0.70086676 0.59826649 0.299133244
[79,] 0.66606137 0.66787726 0.333938630
[80,] 0.70554064 0.58891873 0.294459364
[81,] 0.69182039 0.61635922 0.308179609
[82,] 0.68468784 0.63062432 0.315312159
[83,] 0.63763026 0.72473949 0.362369744
[84,] 0.62545381 0.74909238 0.374546191
[85,] 0.62133284 0.75733431 0.378667157
[86,] 0.63472234 0.73055532 0.365277660
[87,] 0.64414474 0.71171051 0.355855255
[88,] 0.67057812 0.65884377 0.329421883
[89,] 0.66150637 0.67698727 0.338493634
[90,] 0.81571570 0.36856860 0.184284302
[91,] 0.83237262 0.33525476 0.167627380
[92,] 0.83245680 0.33508640 0.167543199
[93,] 0.81094562 0.37810875 0.189054376
[94,] 0.76329876 0.47340249 0.236701244
[95,] 0.72891894 0.54216212 0.271081061
[96,] 0.68827396 0.62345208 0.311726038
[97,] 0.61862684 0.76274632 0.381373161
[98,] 0.57700273 0.84599454 0.422997268
[99,] 0.54864418 0.90271165 0.451355823
[100,] 0.47506880 0.95013761 0.524931197
[101,] 0.40226570 0.80453139 0.597734303
[102,] 0.44635401 0.89270802 0.553645991
[103,] 0.36042795 0.72085589 0.639572053
[104,] 0.28612121 0.57224242 0.713878789
[105,] 0.25668704 0.51337409 0.743312956
[106,] 0.18630630 0.37261260 0.813693700
[107,] 0.12556677 0.25113353 0.874433234
[108,] 0.09027535 0.18055070 0.909724649
[109,] 0.14595601 0.29191202 0.854043992
[110,] 0.29670723 0.59341445 0.703292774
[111,] 0.91834248 0.16331505 0.081657523
> postscript(file="/var/www/html/rcomp/tmp/1e19r1292929276.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2psqt1292929276.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3psqt1292929276.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4psqt1292929276.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5psqt1292929276.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 6
-0.424765875 -1.913508156 2.791838528 2.200201446 -4.482693176 -3.082947192
7 8 9 10 11 12
-3.229134303 0.226024846 -0.347283471 -1.311433868 -1.608429741 -4.621772512
13 14 15 16 17 18
1.430039792 -2.872792698 -1.172083194 -0.641473291 -1.365742699 0.625050671
19 20 21 22 23 24
-0.856037791 -1.405901703 -0.727091302 1.699712412 -1.914203668 6.159408460
25 26 27 28 29 30
-1.461612574 -0.275426570 2.532397819 -0.236668214 -0.395513005 -2.370350430
31 32 33 34 35 36
0.540722410 0.512391813 -0.436779146 1.757929531 -1.217680744 -1.145837719
37 38 39 40 41 42
1.263726729 1.007366832 -1.658281848 0.209576977 3.421018488 3.209200191
43 44 45 46 47 48
-0.501931859 1.633988260 -0.885371242 -0.948715406 0.242143438 1.072434510
49 50 51 52 53 54
1.750506262 0.171004800 -1.938397459 0.406662537 3.551922043 0.634809490
55 56 57 58 59 60
1.583576050 2.233345599 3.382973472 -0.721492856 -1.877165113 3.809827903
61 62 63 64 65 66
1.511636125 1.588295979 -1.865787743 -1.344123570 -1.010263453 -2.333128896
67 68 69 70 71 72
-1.314159858 -0.089690203 2.493346451 -2.327260471 -0.559341533 -0.336719470
73 74 75 76 77 78
2.154453043 -0.255244383 -2.131981658 0.868684845 -1.519068529 -0.599433393
79 80 81 82 83 84
0.160563367 -1.026764167 3.097017339 -2.518427226 0.110577105 -1.029776690
85 86 87 88 89 90
0.001692216 2.482843733 -0.514813464 -0.032476638 -0.023916164 -2.069719639
91 92 93 94 95 96
0.416772093 -1.176426658 1.567400936 -1.084052070 2.718000286 -2.215341868
97 98 99 100 101 102
2.331358868 -0.920574238 2.185732059 0.371118645 -1.360863920 1.280899286
103 104 105 106 107 108
1.943538583 -1.771204406 -3.992772954 -0.207803489 -1.301977780 1.980251603
109 110 111 112 113 114
0.552789447 -0.040037634 0.559180023 -0.130810090 1.602003216 1.843457222
115 116 117 118 119 120
-1.250275861 -1.853788521 -3.150977355 -0.038048578 -0.842228361 2.932033510
121 122 123 124 125 126
1.092044645 -0.431317800 -1.144337149 -2.531489486 -0.330721534 1.508191154
127 128 129 130 131 132
1.170556782 1.578447967 -2.327650985 0.746639437 0.241471999 -0.525760213
133 134 135 136 137 138
0.009826057 2.201384243 0.093592152 -1.461612574 2.225787700 3.133266477
139 140 141 142
0.110577105 -0.545173497 -3.150977355 3.811607344
> postscript(file="/var/www/html/rcomp/tmp/601pe1292929276.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 -0.424765875 NA
1 -1.913508156 -0.424765875
2 2.791838528 -1.913508156
3 2.200201446 2.791838528
4 -4.482693176 2.200201446
5 -3.082947192 -4.482693176
6 -3.229134303 -3.082947192
7 0.226024846 -3.229134303
8 -0.347283471 0.226024846
9 -1.311433868 -0.347283471
10 -1.608429741 -1.311433868
11 -4.621772512 -1.608429741
12 1.430039792 -4.621772512
13 -2.872792698 1.430039792
14 -1.172083194 -2.872792698
15 -0.641473291 -1.172083194
16 -1.365742699 -0.641473291
17 0.625050671 -1.365742699
18 -0.856037791 0.625050671
19 -1.405901703 -0.856037791
20 -0.727091302 -1.405901703
21 1.699712412 -0.727091302
22 -1.914203668 1.699712412
23 6.159408460 -1.914203668
24 -1.461612574 6.159408460
25 -0.275426570 -1.461612574
26 2.532397819 -0.275426570
27 -0.236668214 2.532397819
28 -0.395513005 -0.236668214
29 -2.370350430 -0.395513005
30 0.540722410 -2.370350430
31 0.512391813 0.540722410
32 -0.436779146 0.512391813
33 1.757929531 -0.436779146
34 -1.217680744 1.757929531
35 -1.145837719 -1.217680744
36 1.263726729 -1.145837719
37 1.007366832 1.263726729
38 -1.658281848 1.007366832
39 0.209576977 -1.658281848
40 3.421018488 0.209576977
41 3.209200191 3.421018488
42 -0.501931859 3.209200191
43 1.633988260 -0.501931859
44 -0.885371242 1.633988260
45 -0.948715406 -0.885371242
46 0.242143438 -0.948715406
47 1.072434510 0.242143438
48 1.750506262 1.072434510
49 0.171004800 1.750506262
50 -1.938397459 0.171004800
51 0.406662537 -1.938397459
52 3.551922043 0.406662537
53 0.634809490 3.551922043
54 1.583576050 0.634809490
55 2.233345599 1.583576050
56 3.382973472 2.233345599
57 -0.721492856 3.382973472
58 -1.877165113 -0.721492856
59 3.809827903 -1.877165113
60 1.511636125 3.809827903
61 1.588295979 1.511636125
62 -1.865787743 1.588295979
63 -1.344123570 -1.865787743
64 -1.010263453 -1.344123570
65 -2.333128896 -1.010263453
66 -1.314159858 -2.333128896
67 -0.089690203 -1.314159858
68 2.493346451 -0.089690203
69 -2.327260471 2.493346451
70 -0.559341533 -2.327260471
71 -0.336719470 -0.559341533
72 2.154453043 -0.336719470
73 -0.255244383 2.154453043
74 -2.131981658 -0.255244383
75 0.868684845 -2.131981658
76 -1.519068529 0.868684845
77 -0.599433393 -1.519068529
78 0.160563367 -0.599433393
79 -1.026764167 0.160563367
80 3.097017339 -1.026764167
81 -2.518427226 3.097017339
82 0.110577105 -2.518427226
83 -1.029776690 0.110577105
84 0.001692216 -1.029776690
85 2.482843733 0.001692216
86 -0.514813464 2.482843733
87 -0.032476638 -0.514813464
88 -0.023916164 -0.032476638
89 -2.069719639 -0.023916164
90 0.416772093 -2.069719639
91 -1.176426658 0.416772093
92 1.567400936 -1.176426658
93 -1.084052070 1.567400936
94 2.718000286 -1.084052070
95 -2.215341868 2.718000286
96 2.331358868 -2.215341868
97 -0.920574238 2.331358868
98 2.185732059 -0.920574238
99 0.371118645 2.185732059
100 -1.360863920 0.371118645
101 1.280899286 -1.360863920
102 1.943538583 1.280899286
103 -1.771204406 1.943538583
104 -3.992772954 -1.771204406
105 -0.207803489 -3.992772954
106 -1.301977780 -0.207803489
107 1.980251603 -1.301977780
108 0.552789447 1.980251603
109 -0.040037634 0.552789447
110 0.559180023 -0.040037634
111 -0.130810090 0.559180023
112 1.602003216 -0.130810090
113 1.843457222 1.602003216
114 -1.250275861 1.843457222
115 -1.853788521 -1.250275861
116 -3.150977355 -1.853788521
117 -0.038048578 -3.150977355
118 -0.842228361 -0.038048578
119 2.932033510 -0.842228361
120 1.092044645 2.932033510
121 -0.431317800 1.092044645
122 -1.144337149 -0.431317800
123 -2.531489486 -1.144337149
124 -0.330721534 -2.531489486
125 1.508191154 -0.330721534
126 1.170556782 1.508191154
127 1.578447967 1.170556782
128 -2.327650985 1.578447967
129 0.746639437 -2.327650985
130 0.241471999 0.746639437
131 -0.525760213 0.241471999
132 0.009826057 -0.525760213
133 2.201384243 0.009826057
134 0.093592152 2.201384243
135 -1.461612574 0.093592152
136 2.225787700 -1.461612574
137 3.133266477 2.225787700
138 0.110577105 3.133266477
139 -0.545173497 0.110577105
140 -3.150977355 -0.545173497
141 3.811607344 -3.150977355
142 NA 3.811607344
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.913508156 -0.424765875
[2,] 2.791838528 -1.913508156
[3,] 2.200201446 2.791838528
[4,] -4.482693176 2.200201446
[5,] -3.082947192 -4.482693176
[6,] -3.229134303 -3.082947192
[7,] 0.226024846 -3.229134303
[8,] -0.347283471 0.226024846
[9,] -1.311433868 -0.347283471
[10,] -1.608429741 -1.311433868
[11,] -4.621772512 -1.608429741
[12,] 1.430039792 -4.621772512
[13,] -2.872792698 1.430039792
[14,] -1.172083194 -2.872792698
[15,] -0.641473291 -1.172083194
[16,] -1.365742699 -0.641473291
[17,] 0.625050671 -1.365742699
[18,] -0.856037791 0.625050671
[19,] -1.405901703 -0.856037791
[20,] -0.727091302 -1.405901703
[21,] 1.699712412 -0.727091302
[22,] -1.914203668 1.699712412
[23,] 6.159408460 -1.914203668
[24,] -1.461612574 6.159408460
[25,] -0.275426570 -1.461612574
[26,] 2.532397819 -0.275426570
[27,] -0.236668214 2.532397819
[28,] -0.395513005 -0.236668214
[29,] -2.370350430 -0.395513005
[30,] 0.540722410 -2.370350430
[31,] 0.512391813 0.540722410
[32,] -0.436779146 0.512391813
[33,] 1.757929531 -0.436779146
[34,] -1.217680744 1.757929531
[35,] -1.145837719 -1.217680744
[36,] 1.263726729 -1.145837719
[37,] 1.007366832 1.263726729
[38,] -1.658281848 1.007366832
[39,] 0.209576977 -1.658281848
[40,] 3.421018488 0.209576977
[41,] 3.209200191 3.421018488
[42,] -0.501931859 3.209200191
[43,] 1.633988260 -0.501931859
[44,] -0.885371242 1.633988260
[45,] -0.948715406 -0.885371242
[46,] 0.242143438 -0.948715406
[47,] 1.072434510 0.242143438
[48,] 1.750506262 1.072434510
[49,] 0.171004800 1.750506262
[50,] -1.938397459 0.171004800
[51,] 0.406662537 -1.938397459
[52,] 3.551922043 0.406662537
[53,] 0.634809490 3.551922043
[54,] 1.583576050 0.634809490
[55,] 2.233345599 1.583576050
[56,] 3.382973472 2.233345599
[57,] -0.721492856 3.382973472
[58,] -1.877165113 -0.721492856
[59,] 3.809827903 -1.877165113
[60,] 1.511636125 3.809827903
[61,] 1.588295979 1.511636125
[62,] -1.865787743 1.588295979
[63,] -1.344123570 -1.865787743
[64,] -1.010263453 -1.344123570
[65,] -2.333128896 -1.010263453
[66,] -1.314159858 -2.333128896
[67,] -0.089690203 -1.314159858
[68,] 2.493346451 -0.089690203
[69,] -2.327260471 2.493346451
[70,] -0.559341533 -2.327260471
[71,] -0.336719470 -0.559341533
[72,] 2.154453043 -0.336719470
[73,] -0.255244383 2.154453043
[74,] -2.131981658 -0.255244383
[75,] 0.868684845 -2.131981658
[76,] -1.519068529 0.868684845
[77,] -0.599433393 -1.519068529
[78,] 0.160563367 -0.599433393
[79,] -1.026764167 0.160563367
[80,] 3.097017339 -1.026764167
[81,] -2.518427226 3.097017339
[82,] 0.110577105 -2.518427226
[83,] -1.029776690 0.110577105
[84,] 0.001692216 -1.029776690
[85,] 2.482843733 0.001692216
[86,] -0.514813464 2.482843733
[87,] -0.032476638 -0.514813464
[88,] -0.023916164 -0.032476638
[89,] -2.069719639 -0.023916164
[90,] 0.416772093 -2.069719639
[91,] -1.176426658 0.416772093
[92,] 1.567400936 -1.176426658
[93,] -1.084052070 1.567400936
[94,] 2.718000286 -1.084052070
[95,] -2.215341868 2.718000286
[96,] 2.331358868 -2.215341868
[97,] -0.920574238 2.331358868
[98,] 2.185732059 -0.920574238
[99,] 0.371118645 2.185732059
[100,] -1.360863920 0.371118645
[101,] 1.280899286 -1.360863920
[102,] 1.943538583 1.280899286
[103,] -1.771204406 1.943538583
[104,] -3.992772954 -1.771204406
[105,] -0.207803489 -3.992772954
[106,] -1.301977780 -0.207803489
[107,] 1.980251603 -1.301977780
[108,] 0.552789447 1.980251603
[109,] -0.040037634 0.552789447
[110,] 0.559180023 -0.040037634
[111,] -0.130810090 0.559180023
[112,] 1.602003216 -0.130810090
[113,] 1.843457222 1.602003216
[114,] -1.250275861 1.843457222
[115,] -1.853788521 -1.250275861
[116,] -3.150977355 -1.853788521
[117,] -0.038048578 -3.150977355
[118,] -0.842228361 -0.038048578
[119,] 2.932033510 -0.842228361
[120,] 1.092044645 2.932033510
[121,] -0.431317800 1.092044645
[122,] -1.144337149 -0.431317800
[123,] -2.531489486 -1.144337149
[124,] -0.330721534 -2.531489486
[125,] 1.508191154 -0.330721534
[126,] 1.170556782 1.508191154
[127,] 1.578447967 1.170556782
[128,] -2.327650985 1.578447967
[129,] 0.746639437 -2.327650985
[130,] 0.241471999 0.746639437
[131,] -0.525760213 0.241471999
[132,] 0.009826057 -0.525760213
[133,] 2.201384243 0.009826057
[134,] 0.093592152 2.201384243
[135,] -1.461612574 0.093592152
[136,] 2.225787700 -1.461612574
[137,] 3.133266477 2.225787700
[138,] 0.110577105 3.133266477
[139,] -0.545173497 0.110577105
[140,] -3.150977355 -0.545173497
[141,] 3.811607344 -3.150977355
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.913508156 -0.424765875
2 2.791838528 -1.913508156
3 2.200201446 2.791838528
4 -4.482693176 2.200201446
5 -3.082947192 -4.482693176
6 -3.229134303 -3.082947192
7 0.226024846 -3.229134303
8 -0.347283471 0.226024846
9 -1.311433868 -0.347283471
10 -1.608429741 -1.311433868
11 -4.621772512 -1.608429741
12 1.430039792 -4.621772512
13 -2.872792698 1.430039792
14 -1.172083194 -2.872792698
15 -0.641473291 -1.172083194
16 -1.365742699 -0.641473291
17 0.625050671 -1.365742699
18 -0.856037791 0.625050671
19 -1.405901703 -0.856037791
20 -0.727091302 -1.405901703
21 1.699712412 -0.727091302
22 -1.914203668 1.699712412
23 6.159408460 -1.914203668
24 -1.461612574 6.159408460
25 -0.275426570 -1.461612574
26 2.532397819 -0.275426570
27 -0.236668214 2.532397819
28 -0.395513005 -0.236668214
29 -2.370350430 -0.395513005
30 0.540722410 -2.370350430
31 0.512391813 0.540722410
32 -0.436779146 0.512391813
33 1.757929531 -0.436779146
34 -1.217680744 1.757929531
35 -1.145837719 -1.217680744
36 1.263726729 -1.145837719
37 1.007366832 1.263726729
38 -1.658281848 1.007366832
39 0.209576977 -1.658281848
40 3.421018488 0.209576977
41 3.209200191 3.421018488
42 -0.501931859 3.209200191
43 1.633988260 -0.501931859
44 -0.885371242 1.633988260
45 -0.948715406 -0.885371242
46 0.242143438 -0.948715406
47 1.072434510 0.242143438
48 1.750506262 1.072434510
49 0.171004800 1.750506262
50 -1.938397459 0.171004800
51 0.406662537 -1.938397459
52 3.551922043 0.406662537
53 0.634809490 3.551922043
54 1.583576050 0.634809490
55 2.233345599 1.583576050
56 3.382973472 2.233345599
57 -0.721492856 3.382973472
58 -1.877165113 -0.721492856
59 3.809827903 -1.877165113
60 1.511636125 3.809827903
61 1.588295979 1.511636125
62 -1.865787743 1.588295979
63 -1.344123570 -1.865787743
64 -1.010263453 -1.344123570
65 -2.333128896 -1.010263453
66 -1.314159858 -2.333128896
67 -0.089690203 -1.314159858
68 2.493346451 -0.089690203
69 -2.327260471 2.493346451
70 -0.559341533 -2.327260471
71 -0.336719470 -0.559341533
72 2.154453043 -0.336719470
73 -0.255244383 2.154453043
74 -2.131981658 -0.255244383
75 0.868684845 -2.131981658
76 -1.519068529 0.868684845
77 -0.599433393 -1.519068529
78 0.160563367 -0.599433393
79 -1.026764167 0.160563367
80 3.097017339 -1.026764167
81 -2.518427226 3.097017339
82 0.110577105 -2.518427226
83 -1.029776690 0.110577105
84 0.001692216 -1.029776690
85 2.482843733 0.001692216
86 -0.514813464 2.482843733
87 -0.032476638 -0.514813464
88 -0.023916164 -0.032476638
89 -2.069719639 -0.023916164
90 0.416772093 -2.069719639
91 -1.176426658 0.416772093
92 1.567400936 -1.176426658
93 -1.084052070 1.567400936
94 2.718000286 -1.084052070
95 -2.215341868 2.718000286
96 2.331358868 -2.215341868
97 -0.920574238 2.331358868
98 2.185732059 -0.920574238
99 0.371118645 2.185732059
100 -1.360863920 0.371118645
101 1.280899286 -1.360863920
102 1.943538583 1.280899286
103 -1.771204406 1.943538583
104 -3.992772954 -1.771204406
105 -0.207803489 -3.992772954
106 -1.301977780 -0.207803489
107 1.980251603 -1.301977780
108 0.552789447 1.980251603
109 -0.040037634 0.552789447
110 0.559180023 -0.040037634
111 -0.130810090 0.559180023
112 1.602003216 -0.130810090
113 1.843457222 1.602003216
114 -1.250275861 1.843457222
115 -1.853788521 -1.250275861
116 -3.150977355 -1.853788521
117 -0.038048578 -3.150977355
118 -0.842228361 -0.038048578
119 2.932033510 -0.842228361
120 1.092044645 2.932033510
121 -0.431317800 1.092044645
122 -1.144337149 -0.431317800
123 -2.531489486 -1.144337149
124 -0.330721534 -2.531489486
125 1.508191154 -0.330721534
126 1.170556782 1.508191154
127 1.578447967 1.170556782
128 -2.327650985 1.578447967
129 0.746639437 -2.327650985
130 0.241471999 0.746639437
131 -0.525760213 0.241471999
132 0.009826057 -0.525760213
133 2.201384243 0.009826057
134 0.093592152 2.201384243
135 -1.461612574 0.093592152
136 2.225787700 -1.461612574
137 3.133266477 2.225787700
138 0.110577105 3.133266477
139 -0.545173497 0.110577105
140 -3.150977355 -0.545173497
141 3.811607344 -3.150977355
> 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/7atph1292929276.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8atph1292929276.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9atph1292929276.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10lk6k1292929276.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11okmq1292929276.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/12kv5r1292929277.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/13g53i1292929277.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/14knk51292929277.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/15no0t1292929277.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/1686zh1292929277.tab")
+ }
>
> try(system("convert tmp/1e19r1292929276.ps tmp/1e19r1292929276.png",intern=TRUE))
character(0)
> try(system("convert tmp/2psqt1292929276.ps tmp/2psqt1292929276.png",intern=TRUE))
character(0)
> try(system("convert tmp/3psqt1292929276.ps tmp/3psqt1292929276.png",intern=TRUE))
character(0)
> try(system("convert tmp/4psqt1292929276.ps tmp/4psqt1292929276.png",intern=TRUE))
character(0)
> try(system("convert tmp/5psqt1292929276.ps tmp/5psqt1292929276.png",intern=TRUE))
character(0)
> try(system("convert tmp/601pe1292929276.ps tmp/601pe1292929276.png",intern=TRUE))
character(0)
> try(system("convert tmp/7atph1292929276.ps tmp/7atph1292929276.png",intern=TRUE))
character(0)
> try(system("convert tmp/8atph1292929276.ps tmp/8atph1292929276.png",intern=TRUE))
character(0)
> try(system("convert tmp/9atph1292929276.ps tmp/9atph1292929276.png",intern=TRUE))
character(0)
> try(system("convert tmp/10lk6k1292929276.ps tmp/10lk6k1292929276.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.379 1.850 11.552