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(14
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+ ,4)
+ ,dim=c(9
+ ,144)
+ ,dimnames=list(c('Happiness'
+ ,'Age'
+ ,'Concern_over_mistakes'
+ ,'Doubts_about_actions'
+ ,'Parental_expectations'
+ ,'Parental_criticism'
+ ,'Popularity'
+ ,'Perceived_learning_competence'
+ ,'Amotivation')
+ ,1:144))
> y <- array(NA,dim=c(9,144),dimnames=list(c('Happiness','Age','Concern_over_mistakes','Doubts_about_actions','Parental_expectations','Parental_criticism','Popularity','Perceived_learning_competence','Amotivation'),1:144))
> 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
Happiness Age Concern_over_mistakes Doubts_about_actions
1 14 23 26 9
2 18 21 20 9
3 11 21 21 9
4 12 21 31 14
5 16 24 21 8
6 18 22 18 8
7 14 21 26 11
8 14 22 22 10
9 15 21 22 9
10 15 20 29 15
11 17 22 15 14
12 19 21 16 11
13 10 21 24 14
14 18 23 17 6
15 14 22 19 20
16 14 23 22 9
17 17 22 31 10
18 14 24 28 8
19 16 23 38 11
20 18 21 26 14
21 14 23 25 11
22 12 23 25 16
23 17 21 29 14
24 9 20 28 11
25 16 32 15 11
26 14 22 18 12
27 11 21 21 9
28 16 21 25 7
29 13 21 23 13
30 17 22 23 10
31 15 21 19 9
32 14 21 18 9
33 16 21 18 13
34 9 22 26 16
35 15 21 18 12
36 17 21 18 6
37 13 21 28 14
38 15 21 17 14
39 16 23 29 10
40 16 21 12 4
41 12 23 28 12
42 11 23 20 14
43 15 21 17 9
44 17 20 17 9
45 13 21 20 10
46 16 20 31 14
47 14 21 21 10
48 11 21 19 9
49 12 22 23 14
50 12 21 15 8
51 15 21 24 9
52 16 22 28 8
53 15 20 16 9
54 12 22 19 9
55 12 22 21 9
56 8 21 21 15
57 13 23 20 8
58 11 22 16 10
59 14 24 25 8
60 15 23 30 14
61 10 21 29 11
62 11 22 22 10
63 12 22 19 12
64 15 21 33 14
65 15 21 17 9
66 14 21 9 13
67 16 21 14 15
68 15 20 15 8
69 15 22 12 7
70 13 22 21 10
71 17 22 20 10
72 13 23 29 13
73 15 21 33 11
74 13 23 21 8
75 15 22 15 12
76 16 21 19 9
77 15 21 23 10
78 16 20 20 11
79 15 24 20 11
80 14 24 18 10
81 15 21 31 16
82 7 20 18 16
83 17 21 13 8
84 13 21 9 6
85 15 21 20 11
86 14 21 18 12
87 13 22 23 14
88 16 22 17 9
89 12 21 17 11
90 14 22 16 8
91 17 21 31 8
92 15 23 15 7
93 17 21 28 16
94 12 22 26 13
95 16 22 20 8
96 11 22 19 11
97 15 20 25 14
98 9 21 18 10
99 16 21 20 10
100 10 22 33 14
101 10 25 24 14
102 15 22 22 10
103 11 22 32 12
104 13 21 31 9
105 14 22 13 16
106 18 21 18 8
107 16 24 17 9
108 14 23 29 16
109 14 0 22 13
110 14 23 18 13
111 14 22 22 8
112 12 22 25 14
113 14 25 20 11
114 15 23 20 9
115 15 22 17 8
116 13 21 26 13
117 17 21 10 10
118 17 22 15 8
119 19 22 20 7
120 15 21 14 11
121 13 0 16 11
122 9 21 23 14
123 15 22 11 6
124 15 21 19 10
125 16 24 30 9
126 11 21 21 12
127 14 23 20 11
128 11 23 22 14
129 15 22 30 12
130 13 21 25 14
131 16 21 23 14
132 14 21 23 8
133 15 21 21 11
134 16 22 30 12
135 16 20 22 9
136 11 21 32 16
137 13 23 22 11
138 16 32 15 11
139 12 22 21 12
140 9 24 27 15
141 13 20 22 13
142 13 21 9 6
143 19 22 20 7
144 13 23 16 8
Parental_expectations Parental_criticism Popularity
1 15 6 11
2 15 6 12
3 14 13 15
4 10 8 10
5 10 7 12
6 12 9 11
7 18 5 5
8 12 8 16
9 14 9 11
10 18 11 15
11 9 8 12
12 11 11 9
13 11 12 11
14 17 8 15
15 8 7 12
16 16 9 16
17 21 12 14
18 24 20 11
19 21 7 10
20 14 8 7
21 7 8 11
22 18 16 10
23 18 10 11
24 13 6 16
25 11 8 14
26 13 9 12
27 13 9 12
28 18 11 11
29 14 12 6
30 12 8 14
31 9 7 9
32 12 8 15
33 8 9 12
34 5 4 12
35 10 8 9
36 11 8 13
37 11 8 15
38 12 6 11
39 12 8 10
40 15 4 13
41 16 14 16
42 14 10 13
43 17 9 14
44 13 6 14
45 10 8 16
46 17 11 9
47 12 8 8
48 13 8 8
49 13 10 12
50 11 8 10
51 13 10 16
52 12 7 13
53 12 8 11
54 12 7 14
55 9 9 15
56 7 5 8
57 17 7 9
58 12 7 17
59 12 7 9
60 9 9 13
61 9 5 6
62 13 8 13
63 10 8 8
64 11 8 12
65 12 9 13
66 10 6 14
67 13 8 11
68 6 6 15
69 7 4 7
70 13 6 16
71 11 4 16
72 18 12 14
73 9 6 11
74 9 11 13
75 11 8 13
76 11 10 7
77 15 10 15
78 8 4 11
79 11 8 15
80 14 9 13
81 14 9 11
82 12 7 12
83 12 7 10
84 8 11 12
85 11 8 12
86 10 8 12
87 17 7 14
88 16 5 6
89 13 7 14
90 15 9 15
91 11 8 8
92 12 6 12
93 16 8 10
94 20 10 15
95 16 10 11
96 11 8 9
97 15 11 14
98 15 8 10
99 12 8 16
100 9 6 5
101 24 20 8
102 15 6 13
103 18 12 16
104 17 9 16
105 12 5 14
106 15 10 14
107 11 5 10
108 11 6 9
109 15 10 14
110 12 6 8
111 14 10 8
112 11 5 16
113 20 13 12
114 11 7 9
115 12 9 15
116 12 8 12
117 11 5 14
118 10 4 12
119 11 9 16
120 12 7 12
121 9 5 14
122 8 5 8
123 6 4 15
124 12 7 16
125 15 9 12
126 13 8 4
127 17 8 8
128 14 11 11
129 16 10 4
130 15 9 14
131 11 10 14
132 11 10 13
133 16 7 14
134 15 10 7
135 14 6 19
136 9 6 12
137 13 11 10
138 11 8 14
139 14 9 16
140 11 9 11
141 12 13 16
142 8 11 12
143 11 9 16
144 13 5 12
Perceived_learning_competence Amotivation
1 13 4
2 16 4
3 19 6
4 15 8
5 14 8
6 13 4
7 19 4
8 15 5
9 14 5
10 15 8
11 16 4
12 16 4
13 16 4
14 17 4
15 15 4
16 15 8
17 20 4
18 18 4
19 16 4
20 16 4
21 19 8
22 16 3
23 17 4
24 17 4
25 16 4
26 15 10
27 14 5
28 15 4
29 12 4
30 14 4
31 16 4
32 14 4
33 7 10
34 10 4
35 14 8
36 16 4
37 16 4
38 16 4
39 14 7
40 20 4
41 14 4
42 11 4
43 15 4
44 16 6
45 14 5
46 16 16
47 14 5
48 12 12
49 16 6
50 9 9
51 14 9
52 16 4
53 16 4
54 15 4
55 16 5
56 12 4
57 16 5
58 16 4
59 14 6
60 16 4
61 17 4
62 18 18
63 18 4
64 12 4
65 16 6
66 10 4
67 14 5
68 18 4
69 18 4
70 16 5
71 16 5
72 16 8
73 13 5
74 16 4
75 16 4
76 20 4
77 16 5
78 15 4
79 15 4
80 16 4
81 14 8
82 15 14
83 12 4
84 17 8
85 16 8
86 15 4
87 13 6
88 16 4
89 16 7
90 16 3
91 16 4
92 14 4
93 16 4
94 16 7
95 20 4
96 15 4
97 16 6
98 13 8
99 17 4
100 16 4
101 12 4
102 16 5
103 16 6
104 17 4
105 13 5
106 12 7
107 18 4
108 14 8
109 14 6
110 13 8
111 16 8
112 13 4
113 16 5
114 13 6
115 16 5
116 16 5
117 15 4
118 17 4
119 15 6
120 12 7
121 16 4
122 10 10
123 16 8
124 14 5
125 15 11
126 13 7
127 15 4
128 11 8
129 12 6
130 8 4
131 15 8
132 17 5
133 16 4
134 10 8
135 18 4
136 13 6
137 15 4
138 16 4
139 16 6
140 14 15
141 10 16
142 17 8
143 15 6
144 16 4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Age
16.06060 0.01914
Concern_over_mistakes Doubts_about_actions
-0.01156 -0.25044
Parental_expectations Parental_criticism
0.08793 -0.09094
Popularity Perceived_learning_competence
0.03518 0.04208
Amotivation
-0.14389
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.6649 -1.5497 0.1070 1.5975 5.0961
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.06060 2.47541 6.488 1.52e-09 ***
Age 0.01914 0.06212 0.308 0.75845
Concern_over_mistakes -0.01156 0.03842 -0.301 0.76390
Doubts_about_actions -0.25044 0.07797 -3.212 0.00165 **
Parental_expectations 0.08793 0.06926 1.270 0.20644
Parental_criticism -0.09094 0.08611 -1.056 0.29280
Popularity 0.03518 0.06357 0.553 0.58092
Perceived_learning_competence 0.04208 0.08930 0.471 0.63825
Amotivation -0.14389 0.07338 -1.961 0.05195 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.227 on 135 degrees of freedom
Multiple R-squared: 0.176, Adjusted R-squared: 0.1272
F-statistic: 3.605 on 8 and 135 DF, p-value: 0.0007935
> 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,] 0.2907796 0.58155926 0.70922037
[2,] 0.1878836 0.37576717 0.81211642
[3,] 0.1783081 0.35661628 0.82169186
[4,] 0.1379980 0.27599600 0.86200200
[5,] 0.1726730 0.34534600 0.82732700
[6,] 0.8339217 0.33215667 0.16607833
[7,] 0.7872093 0.42558131 0.21279065
[8,] 0.7677474 0.46450514 0.23225257
[9,] 0.8123351 0.37532990 0.18766495
[10,] 0.7936184 0.41276319 0.20638159
[11,] 0.7760222 0.44795568 0.22397784
[12,] 0.7850598 0.42988033 0.21494016
[13,] 0.8991596 0.20168071 0.10084035
[14,] 0.8714424 0.25711518 0.12855759
[15,] 0.8676719 0.26465621 0.13232810
[16,] 0.9260446 0.14791085 0.07395543
[17,] 0.9008558 0.19828831 0.09914415
[18,] 0.8893849 0.22123020 0.11061510
[19,] 0.9034646 0.19307072 0.09653536
[20,] 0.8736822 0.25263562 0.12631781
[21,] 0.8441663 0.31166737 0.15583368
[22,] 0.8538966 0.29220685 0.14610342
[23,] 0.8824304 0.23513912 0.11756956
[24,] 0.8613738 0.27725231 0.13862616
[25,] 0.8463421 0.30731581 0.15365790
[26,] 0.8182104 0.36357916 0.18178958
[27,] 0.7932361 0.41352784 0.20676392
[28,] 0.7888802 0.42223958 0.21111979
[29,] 0.7862336 0.42753281 0.21376640
[30,] 0.7553805 0.48923895 0.24461948
[31,] 0.7952599 0.40948010 0.20474005
[32,] 0.7566416 0.48671678 0.24335839
[33,] 0.7370283 0.52594347 0.26297174
[34,] 0.6977908 0.60441850 0.30220925
[35,] 0.7346859 0.53062828 0.26531414
[36,] 0.7039269 0.59214611 0.29607306
[37,] 0.8318325 0.33633492 0.16816746
[38,] 0.8154405 0.36911893 0.18455947
[39,] 0.8248270 0.35034609 0.17517305
[40,] 0.8011026 0.39779482 0.19889741
[41,] 0.7772981 0.44540387 0.22270194
[42,] 0.7369693 0.52606147 0.26303074
[43,] 0.7677249 0.46455026 0.23227513
[44,] 0.7680542 0.46389168 0.23194584
[45,] 0.8944474 0.21110526 0.10555263
[46,] 0.9075167 0.18496665 0.09248332
[47,] 0.9430996 0.11380077 0.05690039
[48,] 0.9293616 0.14127678 0.07063839
[49,] 0.9257214 0.14855724 0.07427862
[50,] 0.9571112 0.08577758 0.04288879
[51,] 0.9615726 0.07685470 0.03842735
[52,] 0.9579809 0.08403823 0.04201911
[53,] 0.9543502 0.09129964 0.04564982
[54,] 0.9417953 0.11640941 0.05820471
[55,] 0.9256071 0.14878571 0.07439285
[56,] 0.9362314 0.12753726 0.06376863
[57,] 0.9221366 0.15572685 0.07786342
[58,] 0.9022558 0.19548844 0.09774422
[59,] 0.8972166 0.20556677 0.10278338
[60,] 0.8946087 0.21078267 0.10539133
[61,] 0.8721132 0.25577357 0.12788678
[62,] 0.8551080 0.28978396 0.14489198
[63,] 0.8484124 0.30317515 0.15158757
[64,] 0.8229791 0.35404170 0.17702085
[65,] 0.8113577 0.37728456 0.18864228
[66,] 0.7762858 0.44742832 0.22371416
[67,] 0.7584891 0.48302187 0.24151094
[68,] 0.7198067 0.56038660 0.28019330
[69,] 0.6805953 0.63880934 0.31940467
[70,] 0.7094140 0.58117198 0.29058599
[71,] 0.8147262 0.37054750 0.18527375
[72,] 0.8015750 0.39684993 0.19842496
[73,] 0.7813138 0.43737248 0.21868624
[74,] 0.7575458 0.48490831 0.24245415
[75,] 0.7153649 0.56927029 0.28463514
[76,] 0.6748936 0.65021286 0.32510643
[77,] 0.6411713 0.71765732 0.35882866
[78,] 0.6325197 0.73496058 0.36748029
[79,] 0.6103655 0.77926907 0.38963454
[80,] 0.6099565 0.78008703 0.39004351
[81,] 0.5648827 0.87023459 0.43511729
[82,] 0.7296258 0.54074835 0.27037418
[83,] 0.7090792 0.58184152 0.29092076
[84,] 0.6758014 0.64839725 0.32419862
[85,] 0.7075268 0.58494640 0.29247320
[86,] 0.7172316 0.56553673 0.28276836
[87,] 0.9049240 0.19015192 0.09507596
[88,] 0.8902139 0.21957218 0.10978609
[89,] 0.8871173 0.22576549 0.11288275
[90,] 0.8970564 0.20588719 0.10294360
[91,] 0.8679701 0.26405987 0.13202993
[92,] 0.8917256 0.21654877 0.10827439
[93,] 0.9201785 0.15964300 0.07982150
[94,] 0.9136968 0.17260643 0.08630321
[95,] 0.9144646 0.17107084 0.08553542
[96,] 0.8977322 0.20453570 0.10226785
[97,] 0.9239861 0.15202774 0.07601387
[98,] 0.9058522 0.18829565 0.09414783
[99,] 0.9077742 0.18445166 0.09222583
[100,] 0.8810308 0.23793834 0.11896917
[101,] 0.8685233 0.26295338 0.13147669
[102,] 0.8336667 0.33266662 0.16633331
[103,] 0.7872344 0.42553117 0.21276559
[104,] 0.7495093 0.50098134 0.25049067
[105,] 0.6891842 0.62163168 0.31081584
[106,] 0.7428070 0.51438602 0.25719301
[107,] 0.7633510 0.47329795 0.23664897
[108,] 0.7699987 0.46000254 0.23000127
[109,] 0.7768859 0.44622816 0.22311408
[110,] 0.7910617 0.41787654 0.20893827
[111,] 0.7486247 0.50275064 0.25137532
[112,] 0.6938400 0.61232005 0.30616003
[113,] 0.6159224 0.76815518 0.38407759
[114,] 0.5434027 0.91319461 0.45659730
[115,] 0.4585018 0.91700354 0.54149823
[116,] 0.3743000 0.74859990 0.62570005
[117,] 0.3163680 0.63273605 0.68363198
[118,] 0.2480292 0.49605836 0.75197082
[119,] 0.2857235 0.57144691 0.71427654
[120,] 0.7598254 0.48034927 0.24017464
[121,] 0.6956168 0.60876637 0.30438318
> postscript(file="/var/www/html/rcomp/tmp/1v17z1290542775.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2v17z1290542775.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3oao21290542775.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4oao21290542775.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5oao21290542775.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 = 144
Frequency = 1
1 2 3 4 5
-1.0779522882 2.7295473387 -3.4783540692 -0.5815235675 1.6235506988
6 7 8 9 10
3.1348597075 -0.9349117583 -0.5250987612 0.3766512200 2.0584457491
11 12 13 14 15
3.6142641269 5.0961392739 -3.0394480670 1.7636615179 2.2022369214
16 17 18 19 20
-0.6237786151 1.8674409339 -1.0529811588 1.0339928954 4.4968354660
21 22 23 24 25
0.6197845173 -0.9257332183 3.1789019138 -5.6648587410 1.4252989822
26 27 28 29 30
0.7927262837 -3.5821600313 0.5546491240 -0.2210378229 2.4550041049
31 32 33 34 35
0.4420305978 -0.8692849828 3.8385800955 -3.5172485436 1.8445383375
36 37 38 39 40
1.4535128515 -0.4976758394 1.2460399574 2.0776233013 -1.0005388761
41 42 43 44 45
-1.8818619789 -2.4296137885 -0.2364487998 2.1072905455 -1.3111454373
46 47 48 49 50
4.2391849181 -0.1940192508 -2.4641094884 -1.1752845452 -1.9607429900
51 52 53 54 55
0.9783282320 0.8720081527 0.1832872006 -2.9747072137 -2.4392776828
56 57 58 59 60
-4.8347214829 -2.3946704296 -3.9065615455 -0.6883104130 1.8243174553
61 62 63 64 65
-4.0598900072 -1.7631232914 -1.8717481205 1.8339805719 0.4840786506
66 67 68 69 70
0.2258932343 2.7838003003 0.0421031090 -0.2697013987 -1.8485504803
71 72 73 74 75
2.1338615451 -0.4158267460 1.2136173839 -1.6005177995 0.9023431430
76 77 78 79 80
1.4410402651 0.4168024549 1.7604485529 0.6431488783 -0.7749855796
81 82 83 84 85
2.6654907544 -4.6855997391 1.9915644116 -1.5452660369 1.3396015651
86 87 88 89 90
0.1213562160 -0.7439434804 0.7079144971 -2.1761312750 -1.5628880164
91 92 93 94 95
2.2805949760 -0.5194923230 3.7394561288 -1.9681810482 0.6026638091
96 97 98 99 100
-3.1190627660 1.7308535234 -5.0890899484 1.2428701343 -3.1132570564
101 102 103 104 105
-3.2577107599 0.0926875327 -2.9705819710 -2.2290907699 0.7551885332
106 107 108 109 110
3.3493815171 0.8844053827 1.6653983279 0.8217857119 0.7762128071
111 112 113 114 115
-0.3489381412 -1.7332718180 -0.5052954316 0.6534711967 0.0002451294
116 117 118 119 120
-0.6097461097 2.0968648114 1.6178326658 4.0232101792 1.1157790140
121 122 123 124 125
-1.0475514263 -3.2024559720 -0.0654718211 0.4104939899 2.1098933379
126 127 128 129 130
-2.3104871668 -0.6190363894 -1.6696218414 1.5907124316 -0.4213314061
131 132 133 134 135
3.2792232562 -0.7040927994 0.1746534523 2.9450499758 0.5293425457
136 137 138 139 140
-1.4370137923 -1.0417290804 1.4252989822 -2.0188756070 -2.4175931514
141 142 143 144
1.5124339208 -1.5452660369 4.0232101792 -2.5205134150
> postscript(file="/var/www/html/rcomp/tmp/6z15n1290542775.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 = 144
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.0779522882 NA
1 2.7295473387 -1.0779522882
2 -3.4783540692 2.7295473387
3 -0.5815235675 -3.4783540692
4 1.6235506988 -0.5815235675
5 3.1348597075 1.6235506988
6 -0.9349117583 3.1348597075
7 -0.5250987612 -0.9349117583
8 0.3766512200 -0.5250987612
9 2.0584457491 0.3766512200
10 3.6142641269 2.0584457491
11 5.0961392739 3.6142641269
12 -3.0394480670 5.0961392739
13 1.7636615179 -3.0394480670
14 2.2022369214 1.7636615179
15 -0.6237786151 2.2022369214
16 1.8674409339 -0.6237786151
17 -1.0529811588 1.8674409339
18 1.0339928954 -1.0529811588
19 4.4968354660 1.0339928954
20 0.6197845173 4.4968354660
21 -0.9257332183 0.6197845173
22 3.1789019138 -0.9257332183
23 -5.6648587410 3.1789019138
24 1.4252989822 -5.6648587410
25 0.7927262837 1.4252989822
26 -3.5821600313 0.7927262837
27 0.5546491240 -3.5821600313
28 -0.2210378229 0.5546491240
29 2.4550041049 -0.2210378229
30 0.4420305978 2.4550041049
31 -0.8692849828 0.4420305978
32 3.8385800955 -0.8692849828
33 -3.5172485436 3.8385800955
34 1.8445383375 -3.5172485436
35 1.4535128515 1.8445383375
36 -0.4976758394 1.4535128515
37 1.2460399574 -0.4976758394
38 2.0776233013 1.2460399574
39 -1.0005388761 2.0776233013
40 -1.8818619789 -1.0005388761
41 -2.4296137885 -1.8818619789
42 -0.2364487998 -2.4296137885
43 2.1072905455 -0.2364487998
44 -1.3111454373 2.1072905455
45 4.2391849181 -1.3111454373
46 -0.1940192508 4.2391849181
47 -2.4641094884 -0.1940192508
48 -1.1752845452 -2.4641094884
49 -1.9607429900 -1.1752845452
50 0.9783282320 -1.9607429900
51 0.8720081527 0.9783282320
52 0.1832872006 0.8720081527
53 -2.9747072137 0.1832872006
54 -2.4392776828 -2.9747072137
55 -4.8347214829 -2.4392776828
56 -2.3946704296 -4.8347214829
57 -3.9065615455 -2.3946704296
58 -0.6883104130 -3.9065615455
59 1.8243174553 -0.6883104130
60 -4.0598900072 1.8243174553
61 -1.7631232914 -4.0598900072
62 -1.8717481205 -1.7631232914
63 1.8339805719 -1.8717481205
64 0.4840786506 1.8339805719
65 0.2258932343 0.4840786506
66 2.7838003003 0.2258932343
67 0.0421031090 2.7838003003
68 -0.2697013987 0.0421031090
69 -1.8485504803 -0.2697013987
70 2.1338615451 -1.8485504803
71 -0.4158267460 2.1338615451
72 1.2136173839 -0.4158267460
73 -1.6005177995 1.2136173839
74 0.9023431430 -1.6005177995
75 1.4410402651 0.9023431430
76 0.4168024549 1.4410402651
77 1.7604485529 0.4168024549
78 0.6431488783 1.7604485529
79 -0.7749855796 0.6431488783
80 2.6654907544 -0.7749855796
81 -4.6855997391 2.6654907544
82 1.9915644116 -4.6855997391
83 -1.5452660369 1.9915644116
84 1.3396015651 -1.5452660369
85 0.1213562160 1.3396015651
86 -0.7439434804 0.1213562160
87 0.7079144971 -0.7439434804
88 -2.1761312750 0.7079144971
89 -1.5628880164 -2.1761312750
90 2.2805949760 -1.5628880164
91 -0.5194923230 2.2805949760
92 3.7394561288 -0.5194923230
93 -1.9681810482 3.7394561288
94 0.6026638091 -1.9681810482
95 -3.1190627660 0.6026638091
96 1.7308535234 -3.1190627660
97 -5.0890899484 1.7308535234
98 1.2428701343 -5.0890899484
99 -3.1132570564 1.2428701343
100 -3.2577107599 -3.1132570564
101 0.0926875327 -3.2577107599
102 -2.9705819710 0.0926875327
103 -2.2290907699 -2.9705819710
104 0.7551885332 -2.2290907699
105 3.3493815171 0.7551885332
106 0.8844053827 3.3493815171
107 1.6653983279 0.8844053827
108 0.8217857119 1.6653983279
109 0.7762128071 0.8217857119
110 -0.3489381412 0.7762128071
111 -1.7332718180 -0.3489381412
112 -0.5052954316 -1.7332718180
113 0.6534711967 -0.5052954316
114 0.0002451294 0.6534711967
115 -0.6097461097 0.0002451294
116 2.0968648114 -0.6097461097
117 1.6178326658 2.0968648114
118 4.0232101792 1.6178326658
119 1.1157790140 4.0232101792
120 -1.0475514263 1.1157790140
121 -3.2024559720 -1.0475514263
122 -0.0654718211 -3.2024559720
123 0.4104939899 -0.0654718211
124 2.1098933379 0.4104939899
125 -2.3104871668 2.1098933379
126 -0.6190363894 -2.3104871668
127 -1.6696218414 -0.6190363894
128 1.5907124316 -1.6696218414
129 -0.4213314061 1.5907124316
130 3.2792232562 -0.4213314061
131 -0.7040927994 3.2792232562
132 0.1746534523 -0.7040927994
133 2.9450499758 0.1746534523
134 0.5293425457 2.9450499758
135 -1.4370137923 0.5293425457
136 -1.0417290804 -1.4370137923
137 1.4252989822 -1.0417290804
138 -2.0188756070 1.4252989822
139 -2.4175931514 -2.0188756070
140 1.5124339208 -2.4175931514
141 -1.5452660369 1.5124339208
142 4.0232101792 -1.5452660369
143 -2.5205134150 4.0232101792
144 NA -2.5205134150
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.7295473387 -1.0779522882
[2,] -3.4783540692 2.7295473387
[3,] -0.5815235675 -3.4783540692
[4,] 1.6235506988 -0.5815235675
[5,] 3.1348597075 1.6235506988
[6,] -0.9349117583 3.1348597075
[7,] -0.5250987612 -0.9349117583
[8,] 0.3766512200 -0.5250987612
[9,] 2.0584457491 0.3766512200
[10,] 3.6142641269 2.0584457491
[11,] 5.0961392739 3.6142641269
[12,] -3.0394480670 5.0961392739
[13,] 1.7636615179 -3.0394480670
[14,] 2.2022369214 1.7636615179
[15,] -0.6237786151 2.2022369214
[16,] 1.8674409339 -0.6237786151
[17,] -1.0529811588 1.8674409339
[18,] 1.0339928954 -1.0529811588
[19,] 4.4968354660 1.0339928954
[20,] 0.6197845173 4.4968354660
[21,] -0.9257332183 0.6197845173
[22,] 3.1789019138 -0.9257332183
[23,] -5.6648587410 3.1789019138
[24,] 1.4252989822 -5.6648587410
[25,] 0.7927262837 1.4252989822
[26,] -3.5821600313 0.7927262837
[27,] 0.5546491240 -3.5821600313
[28,] -0.2210378229 0.5546491240
[29,] 2.4550041049 -0.2210378229
[30,] 0.4420305978 2.4550041049
[31,] -0.8692849828 0.4420305978
[32,] 3.8385800955 -0.8692849828
[33,] -3.5172485436 3.8385800955
[34,] 1.8445383375 -3.5172485436
[35,] 1.4535128515 1.8445383375
[36,] -0.4976758394 1.4535128515
[37,] 1.2460399574 -0.4976758394
[38,] 2.0776233013 1.2460399574
[39,] -1.0005388761 2.0776233013
[40,] -1.8818619789 -1.0005388761
[41,] -2.4296137885 -1.8818619789
[42,] -0.2364487998 -2.4296137885
[43,] 2.1072905455 -0.2364487998
[44,] -1.3111454373 2.1072905455
[45,] 4.2391849181 -1.3111454373
[46,] -0.1940192508 4.2391849181
[47,] -2.4641094884 -0.1940192508
[48,] -1.1752845452 -2.4641094884
[49,] -1.9607429900 -1.1752845452
[50,] 0.9783282320 -1.9607429900
[51,] 0.8720081527 0.9783282320
[52,] 0.1832872006 0.8720081527
[53,] -2.9747072137 0.1832872006
[54,] -2.4392776828 -2.9747072137
[55,] -4.8347214829 -2.4392776828
[56,] -2.3946704296 -4.8347214829
[57,] -3.9065615455 -2.3946704296
[58,] -0.6883104130 -3.9065615455
[59,] 1.8243174553 -0.6883104130
[60,] -4.0598900072 1.8243174553
[61,] -1.7631232914 -4.0598900072
[62,] -1.8717481205 -1.7631232914
[63,] 1.8339805719 -1.8717481205
[64,] 0.4840786506 1.8339805719
[65,] 0.2258932343 0.4840786506
[66,] 2.7838003003 0.2258932343
[67,] 0.0421031090 2.7838003003
[68,] -0.2697013987 0.0421031090
[69,] -1.8485504803 -0.2697013987
[70,] 2.1338615451 -1.8485504803
[71,] -0.4158267460 2.1338615451
[72,] 1.2136173839 -0.4158267460
[73,] -1.6005177995 1.2136173839
[74,] 0.9023431430 -1.6005177995
[75,] 1.4410402651 0.9023431430
[76,] 0.4168024549 1.4410402651
[77,] 1.7604485529 0.4168024549
[78,] 0.6431488783 1.7604485529
[79,] -0.7749855796 0.6431488783
[80,] 2.6654907544 -0.7749855796
[81,] -4.6855997391 2.6654907544
[82,] 1.9915644116 -4.6855997391
[83,] -1.5452660369 1.9915644116
[84,] 1.3396015651 -1.5452660369
[85,] 0.1213562160 1.3396015651
[86,] -0.7439434804 0.1213562160
[87,] 0.7079144971 -0.7439434804
[88,] -2.1761312750 0.7079144971
[89,] -1.5628880164 -2.1761312750
[90,] 2.2805949760 -1.5628880164
[91,] -0.5194923230 2.2805949760
[92,] 3.7394561288 -0.5194923230
[93,] -1.9681810482 3.7394561288
[94,] 0.6026638091 -1.9681810482
[95,] -3.1190627660 0.6026638091
[96,] 1.7308535234 -3.1190627660
[97,] -5.0890899484 1.7308535234
[98,] 1.2428701343 -5.0890899484
[99,] -3.1132570564 1.2428701343
[100,] -3.2577107599 -3.1132570564
[101,] 0.0926875327 -3.2577107599
[102,] -2.9705819710 0.0926875327
[103,] -2.2290907699 -2.9705819710
[104,] 0.7551885332 -2.2290907699
[105,] 3.3493815171 0.7551885332
[106,] 0.8844053827 3.3493815171
[107,] 1.6653983279 0.8844053827
[108,] 0.8217857119 1.6653983279
[109,] 0.7762128071 0.8217857119
[110,] -0.3489381412 0.7762128071
[111,] -1.7332718180 -0.3489381412
[112,] -0.5052954316 -1.7332718180
[113,] 0.6534711967 -0.5052954316
[114,] 0.0002451294 0.6534711967
[115,] -0.6097461097 0.0002451294
[116,] 2.0968648114 -0.6097461097
[117,] 1.6178326658 2.0968648114
[118,] 4.0232101792 1.6178326658
[119,] 1.1157790140 4.0232101792
[120,] -1.0475514263 1.1157790140
[121,] -3.2024559720 -1.0475514263
[122,] -0.0654718211 -3.2024559720
[123,] 0.4104939899 -0.0654718211
[124,] 2.1098933379 0.4104939899
[125,] -2.3104871668 2.1098933379
[126,] -0.6190363894 -2.3104871668
[127,] -1.6696218414 -0.6190363894
[128,] 1.5907124316 -1.6696218414
[129,] -0.4213314061 1.5907124316
[130,] 3.2792232562 -0.4213314061
[131,] -0.7040927994 3.2792232562
[132,] 0.1746534523 -0.7040927994
[133,] 2.9450499758 0.1746534523
[134,] 0.5293425457 2.9450499758
[135,] -1.4370137923 0.5293425457
[136,] -1.0417290804 -1.4370137923
[137,] 1.4252989822 -1.0417290804
[138,] -2.0188756070 1.4252989822
[139,] -2.4175931514 -2.0188756070
[140,] 1.5124339208 -2.4175931514
[141,] -1.5452660369 1.5124339208
[142,] 4.0232101792 -1.5452660369
[143,] -2.5205134150 4.0232101792
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.7295473387 -1.0779522882
2 -3.4783540692 2.7295473387
3 -0.5815235675 -3.4783540692
4 1.6235506988 -0.5815235675
5 3.1348597075 1.6235506988
6 -0.9349117583 3.1348597075
7 -0.5250987612 -0.9349117583
8 0.3766512200 -0.5250987612
9 2.0584457491 0.3766512200
10 3.6142641269 2.0584457491
11 5.0961392739 3.6142641269
12 -3.0394480670 5.0961392739
13 1.7636615179 -3.0394480670
14 2.2022369214 1.7636615179
15 -0.6237786151 2.2022369214
16 1.8674409339 -0.6237786151
17 -1.0529811588 1.8674409339
18 1.0339928954 -1.0529811588
19 4.4968354660 1.0339928954
20 0.6197845173 4.4968354660
21 -0.9257332183 0.6197845173
22 3.1789019138 -0.9257332183
23 -5.6648587410 3.1789019138
24 1.4252989822 -5.6648587410
25 0.7927262837 1.4252989822
26 -3.5821600313 0.7927262837
27 0.5546491240 -3.5821600313
28 -0.2210378229 0.5546491240
29 2.4550041049 -0.2210378229
30 0.4420305978 2.4550041049
31 -0.8692849828 0.4420305978
32 3.8385800955 -0.8692849828
33 -3.5172485436 3.8385800955
34 1.8445383375 -3.5172485436
35 1.4535128515 1.8445383375
36 -0.4976758394 1.4535128515
37 1.2460399574 -0.4976758394
38 2.0776233013 1.2460399574
39 -1.0005388761 2.0776233013
40 -1.8818619789 -1.0005388761
41 -2.4296137885 -1.8818619789
42 -0.2364487998 -2.4296137885
43 2.1072905455 -0.2364487998
44 -1.3111454373 2.1072905455
45 4.2391849181 -1.3111454373
46 -0.1940192508 4.2391849181
47 -2.4641094884 -0.1940192508
48 -1.1752845452 -2.4641094884
49 -1.9607429900 -1.1752845452
50 0.9783282320 -1.9607429900
51 0.8720081527 0.9783282320
52 0.1832872006 0.8720081527
53 -2.9747072137 0.1832872006
54 -2.4392776828 -2.9747072137
55 -4.8347214829 -2.4392776828
56 -2.3946704296 -4.8347214829
57 -3.9065615455 -2.3946704296
58 -0.6883104130 -3.9065615455
59 1.8243174553 -0.6883104130
60 -4.0598900072 1.8243174553
61 -1.7631232914 -4.0598900072
62 -1.8717481205 -1.7631232914
63 1.8339805719 -1.8717481205
64 0.4840786506 1.8339805719
65 0.2258932343 0.4840786506
66 2.7838003003 0.2258932343
67 0.0421031090 2.7838003003
68 -0.2697013987 0.0421031090
69 -1.8485504803 -0.2697013987
70 2.1338615451 -1.8485504803
71 -0.4158267460 2.1338615451
72 1.2136173839 -0.4158267460
73 -1.6005177995 1.2136173839
74 0.9023431430 -1.6005177995
75 1.4410402651 0.9023431430
76 0.4168024549 1.4410402651
77 1.7604485529 0.4168024549
78 0.6431488783 1.7604485529
79 -0.7749855796 0.6431488783
80 2.6654907544 -0.7749855796
81 -4.6855997391 2.6654907544
82 1.9915644116 -4.6855997391
83 -1.5452660369 1.9915644116
84 1.3396015651 -1.5452660369
85 0.1213562160 1.3396015651
86 -0.7439434804 0.1213562160
87 0.7079144971 -0.7439434804
88 -2.1761312750 0.7079144971
89 -1.5628880164 -2.1761312750
90 2.2805949760 -1.5628880164
91 -0.5194923230 2.2805949760
92 3.7394561288 -0.5194923230
93 -1.9681810482 3.7394561288
94 0.6026638091 -1.9681810482
95 -3.1190627660 0.6026638091
96 1.7308535234 -3.1190627660
97 -5.0890899484 1.7308535234
98 1.2428701343 -5.0890899484
99 -3.1132570564 1.2428701343
100 -3.2577107599 -3.1132570564
101 0.0926875327 -3.2577107599
102 -2.9705819710 0.0926875327
103 -2.2290907699 -2.9705819710
104 0.7551885332 -2.2290907699
105 3.3493815171 0.7551885332
106 0.8844053827 3.3493815171
107 1.6653983279 0.8844053827
108 0.8217857119 1.6653983279
109 0.7762128071 0.8217857119
110 -0.3489381412 0.7762128071
111 -1.7332718180 -0.3489381412
112 -0.5052954316 -1.7332718180
113 0.6534711967 -0.5052954316
114 0.0002451294 0.6534711967
115 -0.6097461097 0.0002451294
116 2.0968648114 -0.6097461097
117 1.6178326658 2.0968648114
118 4.0232101792 1.6178326658
119 1.1157790140 4.0232101792
120 -1.0475514263 1.1157790140
121 -3.2024559720 -1.0475514263
122 -0.0654718211 -3.2024559720
123 0.4104939899 -0.0654718211
124 2.1098933379 0.4104939899
125 -2.3104871668 2.1098933379
126 -0.6190363894 -2.3104871668
127 -1.6696218414 -0.6190363894
128 1.5907124316 -1.6696218414
129 -0.4213314061 1.5907124316
130 3.2792232562 -0.4213314061
131 -0.7040927994 3.2792232562
132 0.1746534523 -0.7040927994
133 2.9450499758 0.1746534523
134 0.5293425457 2.9450499758
135 -1.4370137923 0.5293425457
136 -1.0417290804 -1.4370137923
137 1.4252989822 -1.0417290804
138 -2.0188756070 1.4252989822
139 -2.4175931514 -2.0188756070
140 1.5124339208 -2.4175931514
141 -1.5452660369 1.5124339208
142 4.0232101792 -1.5452660369
143 -2.5205134150 4.0232101792
> 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/7rs5q1290542775.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8rs5q1290542775.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rs5q1290542775.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10kk4b1290542775.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1152kh1290542775.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/12eoqt1290542775.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/13ndhd1290542775.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/14qdfj1290542775.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/15ceep1290542775.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/16xwcv1290542775.tab")
+ }
>
> try(system("convert tmp/1v17z1290542775.ps tmp/1v17z1290542775.png",intern=TRUE))
character(0)
> try(system("convert tmp/2v17z1290542775.ps tmp/2v17z1290542775.png",intern=TRUE))
character(0)
> try(system("convert tmp/3oao21290542775.ps tmp/3oao21290542775.png",intern=TRUE))
character(0)
> try(system("convert tmp/4oao21290542775.ps tmp/4oao21290542775.png",intern=TRUE))
character(0)
> try(system("convert tmp/5oao21290542775.ps tmp/5oao21290542775.png",intern=TRUE))
character(0)
> try(system("convert tmp/6z15n1290542775.ps tmp/6z15n1290542775.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rs5q1290542775.ps tmp/7rs5q1290542775.png",intern=TRUE))
character(0)
> try(system("convert tmp/8rs5q1290542775.ps tmp/8rs5q1290542775.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rs5q1290542775.ps tmp/9rs5q1290542775.png",intern=TRUE))
character(0)
> try(system("convert tmp/10kk4b1290542775.ps tmp/10kk4b1290542775.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.059 1.732 9.576