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(10
+ ,144)
+ ,dimnames=list(c('Happiness'
+ ,'Month'
+ ,'Age'
+ ,'Concern_over_mistakes'
+ ,'Doubts_about_actions'
+ ,'Parental_expectations'
+ ,'Parental_criticism'
+ ,'Popularity'
+ ,'Perceived_learning_competence'
+ ,'Amotivation')
+ ,1:144))
> y <- array(NA,dim=c(10,144),dimnames=list(c('Happiness','Month','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 Month Age Concern_over_mistakes Doubts_about_actions
1 14 9 23 26 9
2 18 9 21 20 9
3 11 9 21 21 9
4 12 9 21 31 14
5 16 9 24 21 8
6 18 9 22 18 8
7 14 9 21 26 11
8 14 9 22 22 10
9 15 9 21 22 9
10 15 9 20 29 15
11 17 9 22 15 14
12 19 9 21 16 11
13 10 9 21 24 14
14 18 9 23 17 6
15 14 9 22 19 20
16 14 9 23 22 9
17 17 9 22 31 10
18 14 9 24 28 8
19 16 9 23 38 11
20 18 9 21 26 14
21 14 9 23 25 11
22 12 9 23 25 16
23 17 9 21 29 14
24 9 9 20 28 11
25 16 9 32 15 11
26 14 9 22 18 12
27 11 9 21 21 9
28 16 9 21 25 7
29 13 9 21 23 13
30 17 9 22 23 10
31 15 9 21 19 9
32 14 9 21 18 9
33 16 9 21 18 13
34 9 9 22 26 16
35 15 9 21 18 12
36 17 9 21 18 6
37 13 9 21 28 14
38 15 9 21 17 14
39 16 9 23 29 10
40 16 9 21 12 4
41 12 9 23 28 12
42 11 9 23 20 14
43 15 9 21 17 9
44 17 9 20 17 9
45 13 9 21 20 10
46 16 9 20 31 14
47 14 9 21 21 10
48 11 9 21 19 9
49 12 9 22 23 14
50 12 9 21 15 8
51 15 9 21 24 9
52 16 9 22 28 8
53 15 9 20 16 9
54 12 9 22 19 9
55 12 9 22 21 9
56 8 9 21 21 15
57 13 9 23 20 8
58 11 9 22 16 10
59 14 9 24 25 8
60 15 9 23 30 14
61 10 10 21 29 11
62 11 10 22 22 10
63 12 10 22 19 12
64 15 10 21 33 14
65 15 10 21 17 9
66 14 10 21 9 13
67 16 10 21 14 15
68 15 10 20 15 8
69 15 10 22 12 7
70 13 10 22 21 10
71 17 10 22 20 10
72 13 10 23 29 13
73 15 10 21 33 11
74 13 10 23 21 8
75 15 10 22 15 12
76 16 10 21 19 9
77 15 10 21 23 10
78 16 10 20 20 11
79 15 10 24 20 11
80 14 10 24 18 10
81 15 10 21 31 16
82 7 10 20 18 16
83 17 10 21 13 8
84 13 10 21 9 6
85 15 10 21 20 11
86 14 10 21 18 12
87 13 10 22 23 14
88 16 10 22 17 9
89 12 10 21 17 11
90 14 10 22 16 8
91 17 10 21 31 8
92 15 10 23 15 7
93 17 10 21 28 16
94 12 10 22 26 13
95 16 10 22 20 8
96 11 10 22 19 11
97 15 10 20 25 14
98 9 10 21 18 10
99 16 10 21 20 10
100 10 10 22 33 14
101 10 10 25 24 14
102 15 10 22 22 10
103 11 10 22 32 12
104 13 10 21 31 9
105 14 10 22 13 16
106 18 10 21 18 8
107 16 10 24 17 9
108 14 10 23 29 16
109 14 10 0 22 13
110 14 10 23 18 13
111 14 10 22 22 8
112 12 10 22 25 14
113 14 10 25 20 11
114 15 10 23 20 9
115 15 10 22 17 8
116 13 10 21 26 13
117 17 10 21 10 10
118 17 10 22 15 8
119 19 10 22 20 7
120 15 10 21 14 11
121 13 10 0 16 11
122 9 10 21 23 14
123 15 10 22 11 6
124 15 10 21 19 10
125 16 10 24 30 9
126 11 10 21 21 12
127 14 10 23 20 11
128 11 10 23 22 14
129 15 10 22 30 12
130 13 10 21 25 14
131 16 10 21 23 14
132 14 10 21 23 8
133 15 10 21 21 11
134 16 10 22 30 12
135 16 10 20 22 9
136 11 10 21 32 16
137 13 10 23 22 11
138 16 9 32 15 11
139 12 10 22 21 12
140 9 9 24 27 15
141 13 10 20 22 13
142 13 10 21 9 6
143 19 10 22 20 7
144 13 10 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) Month
16.85560 -0.07863
Age Concern_over_mistakes
0.01761 -0.01243
Doubts_about_actions Parental_expectations
-0.24974 0.08860
Parental_criticism Popularity
-0.09262 0.03514
Perceived_learning_competence Amotivation
0.04216 -0.14280
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.7087 -1.5182 0.1290 1.5940 5.0530
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.85560 4.61488 3.652 0.000372 ***
Month -0.07863 0.38469 -0.204 0.838341
Age 0.01761 0.06279 0.281 0.779497
Concern_over_mistakes -0.01243 0.03879 -0.320 0.749091
Doubts_about_actions -0.24974 0.07832 -3.189 0.001779 **
Parental_expectations 0.08860 0.06959 1.273 0.205132
Parental_criticism -0.09262 0.08681 -1.067 0.287883
Popularity 0.03514 0.06380 0.551 0.582718
Perceived_learning_competence 0.04216 0.08962 0.470 0.638805
Amotivation -0.14280 0.07384 -1.934 0.055223 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.235 on 134 degrees of freedom
Multiple R-squared: 0.1763, Adjusted R-squared: 0.121
F-statistic: 3.187 on 9 and 134 DF, p-value: 0.001596
> 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,] 0.3322781 0.66455613 0.66772194
[2,] 0.2955051 0.59101019 0.70449491
[3,] 0.2284169 0.45683380 0.77158310
[4,] 0.2611218 0.52224367 0.73887817
[5,] 0.8844311 0.23113788 0.11556894
[6,] 0.8439214 0.31215721 0.15607861
[7,] 0.8247769 0.35044624 0.17522312
[8,] 0.8609605 0.27807901 0.13903951
[9,] 0.8437481 0.31250385 0.15625192
[10,] 0.8265443 0.34691147 0.17345574
[11,] 0.8360774 0.32784512 0.16392256
[12,] 0.9242467 0.15150662 0.07575331
[13,] 0.9017390 0.19652194 0.09826097
[14,] 0.8985352 0.20292953 0.10146477
[15,] 0.9434900 0.11302006 0.05651003
[16,] 0.9225908 0.15481843 0.07740922
[17,] 0.9126349 0.17473016 0.08736508
[18,] 0.9251648 0.14967031 0.07483516
[19,] 0.9006289 0.19874226 0.09937113
[20,] 0.8742569 0.25148624 0.12574312
[21,] 0.8857929 0.22841413 0.11420706
[22,] 0.9063201 0.18735976 0.09367988
[23,] 0.8915064 0.21698720 0.10849360
[24,] 0.8808555 0.23828897 0.11914448
[25,] 0.8558989 0.28820226 0.14410113
[26,] 0.8404263 0.31914750 0.15957375
[27,] 0.8420741 0.31585180 0.15792590
[28,] 0.8374760 0.32504802 0.16252401
[29,] 0.8059204 0.38815916 0.19407958
[30,] 0.8349454 0.33010921 0.16505461
[31,] 0.8011864 0.39762715 0.19881357
[32,] 0.7943538 0.41129241 0.20564621
[33,] 0.7544914 0.49101718 0.24550859
[34,] 0.8190809 0.36183828 0.18091914
[35,] 0.7991736 0.40165287 0.20082644
[36,] 0.8922260 0.21554797 0.10777398
[37,] 0.8797576 0.24048472 0.12024236
[38,] 0.8799030 0.24019400 0.12009700
[39,] 0.8683987 0.26320261 0.13160130
[40,] 0.8576584 0.28468317 0.14234158
[41,] 0.8393275 0.32134499 0.16067250
[42,] 0.8428167 0.31436663 0.15718332
[43,] 0.8260994 0.34780115 0.17390057
[44,] 0.9103817 0.17923652 0.08961826
[45,] 0.9170858 0.16582848 0.08291424
[46,] 0.9449889 0.11002226 0.05501113
[47,] 0.9323818 0.13523631 0.06761815
[48,] 0.9275726 0.14485488 0.07242744
[49,] 0.9377821 0.12443577 0.06221788
[50,] 0.9206515 0.15869707 0.07934853
[51,] 0.9084315 0.18313698 0.09156849
[52,] 0.9408545 0.11829104 0.05914552
[53,] 0.9322296 0.13554070 0.06777035
[54,] 0.9169845 0.16603109 0.08301554
[55,] 0.9325983 0.13480331 0.06740165
[56,] 0.9196988 0.16060234 0.08030117
[57,] 0.8993873 0.20122548 0.10061274
[58,] 0.8918997 0.21620067 0.10810034
[59,] 0.8921270 0.21574606 0.10787303
[60,] 0.8678205 0.26435910 0.13217955
[61,] 0.8523019 0.29539628 0.14769814
[62,] 0.8446433 0.31071337 0.15535669
[63,] 0.8189481 0.36210382 0.18105191
[64,] 0.8071307 0.38573867 0.19286933
[65,] 0.7713593 0.45728146 0.22864073
[66,] 0.7524921 0.49501570 0.24750785
[67,] 0.7124360 0.57512799 0.28756400
[68,] 0.6729591 0.65408178 0.32704089
[69,] 0.7000611 0.59987776 0.29993888
[70,] 0.8075442 0.38491165 0.19245582
[71,] 0.7937127 0.41257455 0.20628728
[72,] 0.7735346 0.45293088 0.22646544
[73,] 0.7476856 0.50462877 0.25231439
[74,] 0.7041961 0.59160773 0.29580387
[75,] 0.6631401 0.67371990 0.33685995
[76,] 0.6288839 0.74223218 0.37111609
[77,] 0.6207657 0.75846868 0.37923434
[78,] 0.5970051 0.80598976 0.40299488
[79,] 0.5978016 0.80439690 0.40219845
[80,] 0.5513216 0.89735688 0.44867844
[81,] 0.7130619 0.57387628 0.28693814
[82,] 0.6927476 0.61450479 0.30725240
[83,] 0.6575477 0.68490460 0.34245230
[84,] 0.6892208 0.62155834 0.31077917
[85,] 0.6966257 0.60674863 0.30337431
[86,] 0.8920049 0.21599018 0.10799509
[87,] 0.8756425 0.24871497 0.12435748
[88,] 0.8707155 0.25856896 0.12928448
[89,] 0.8784209 0.24315815 0.12157907
[90,] 0.8456586 0.30868286 0.15434143
[91,] 0.8702435 0.25951294 0.12975647
[92,] 0.9004827 0.19903454 0.09951727
[93,] 0.8917922 0.21641558 0.10820779
[94,] 0.8948559 0.21028828 0.10514414
[95,] 0.8751359 0.24972813 0.12486407
[96,] 0.9077121 0.18457577 0.09228789
[97,] 0.8843507 0.23129856 0.11564928
[98,] 0.8896439 0.22071230 0.11035615
[99,] 0.8585691 0.28286179 0.14143090
[100,] 0.8408035 0.31839293 0.15919647
[101,] 0.7996217 0.40075654 0.20037827
[102,] 0.7475148 0.50497046 0.25248523
[103,] 0.7052171 0.58956581 0.29478291
[104,] 0.6392145 0.72157090 0.36078545
[105,] 0.7021546 0.59569072 0.29784536
[106,] 0.7317228 0.53655438 0.26827719
[107,] 0.7329519 0.53409627 0.26704813
[108,] 0.7445717 0.51085667 0.25542834
[109,] 0.7450669 0.50986620 0.25493310
[110,] 0.6997308 0.60053848 0.30026924
[111,] 0.6294171 0.74116589 0.37058294
[112,] 0.5447883 0.91042331 0.45521166
[113,] 0.5043980 0.99120405 0.49560202
[114,] 0.4193943 0.83878856 0.58060572
[115,] 0.3276369 0.65527385 0.67236308
[116,] 0.2980516 0.59610325 0.70194838
[117,] 0.2219259 0.44385189 0.77807405
[118,] 0.2292171 0.45843414 0.77078293
[119,] 0.9811690 0.03766203 0.01883101
> postscript(file="/var/www/html/rcomp/tmp/1k91o1290548303.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2k91o1290548303.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3k91o1290548303.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4d0191290548303.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5d0191290548303.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 6
-1.11874351 2.68028224 -3.51660629 -0.62237851 1.58126171 3.09334872
7 8 9 10 11 12
-0.98459280 -0.56726320 0.33387253 2.01351526 3.56607902 5.05297431
13 14 15 16 17 18
-3.07598977 1.71900763 2.15240541 -0.66800011 1.83439346 -1.07269348
19 20 21 22 23 24
0.99960369 4.45312011 0.58059360 -0.95666096 3.13856082 -5.70866877
25 26 27 28 29 30
1.39322697 0.74106242 -3.62509518 0.51756977 -0.25964403 2.41480143
31 32 33 34 35 36
0.39748064 -0.91462055 3.78869968 -3.56082755 1.79382204 1.41069779
37 38 39 40 41 42
-0.53730643 1.19264475 2.04071421 -1.05691496 -1.91009145 -2.47146385
43 44 45 46 47 48
-0.28444918 2.05312762 -1.35515184 4.18827802 -0.23883188 -2.51812772
49 50 51 52 53 54
-1.21802259 -2.01258588 0.93547389 0.83566057 0.13435546 -3.01945261
55 56 57 58 59 60
-2.47803978 -4.88346438 -2.44105976 -3.95457008 -0.72640162 1.79242208
61 62 63 64 65 66
-4.02208432 -1.74191797 -1.83742574 1.87753266 0.51575634 0.24683609
67 68 69 70 71 72
2.80749794 0.07229872 -0.24272985 -1.81706850 2.16245034 -0.37458288
73 74 75 76 77 78
1.25602431 -1.55404213 0.93288979 1.47842129 0.45384368 1.78826418
79 80 81 82 83 84
0.68196040 -0.73770880 2.70099510 -4.67155085 2.01948866 -1.51470664
85 86 87 88 89 90
1.36924725 0.15369402 -0.71545150 0.73359991 -2.15094079 -1.52850064
91 92 93 94 95 96
2.32610766 -0.48783922 3.77349893 -1.93464464 0.63995181 -3.08442548
97 98 99 100 101 102
1.76585757 -5.06300580 1.27700482 -3.07082039 -3.20456459 0.12357160
103 104 105 106 107 108
-2.92528781 -2.18636861 0.77467444 3.38157198 0.91650409 1.69910007
109 110 111 112 113 114
0.82281085 0.80181929 -0.31274382 -1.70010983 -0.46388141 0.68819522
115 116 117 118 119 120
0.03532660 -0.57367180 2.11801006 1.64498992 4.06129840 1.13927702
121 122 123 124 125 126
-1.05270810 -3.17719916 -0.04187901 0.44122784 2.15000935 -2.28100695
127 128 129 130 131 132
-0.58607031 -1.63387570 1.63207158 -0.38532313 3.31291125 -0.66313494
133 134 135 136 137 138
0.20458977 2.98518061 0.55972182 -1.40002254 -0.99920802 1.39322697
139 140 141 142 143 144
-1.98550725 -2.46456414 1.54054180 -1.51470664 4.06129840 -2.49120770
> postscript(file="/var/www/html/rcomp/tmp/6d0191290548303.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 -1.11874351 NA
1 2.68028224 -1.11874351
2 -3.51660629 2.68028224
3 -0.62237851 -3.51660629
4 1.58126171 -0.62237851
5 3.09334872 1.58126171
6 -0.98459280 3.09334872
7 -0.56726320 -0.98459280
8 0.33387253 -0.56726320
9 2.01351526 0.33387253
10 3.56607902 2.01351526
11 5.05297431 3.56607902
12 -3.07598977 5.05297431
13 1.71900763 -3.07598977
14 2.15240541 1.71900763
15 -0.66800011 2.15240541
16 1.83439346 -0.66800011
17 -1.07269348 1.83439346
18 0.99960369 -1.07269348
19 4.45312011 0.99960369
20 0.58059360 4.45312011
21 -0.95666096 0.58059360
22 3.13856082 -0.95666096
23 -5.70866877 3.13856082
24 1.39322697 -5.70866877
25 0.74106242 1.39322697
26 -3.62509518 0.74106242
27 0.51756977 -3.62509518
28 -0.25964403 0.51756977
29 2.41480143 -0.25964403
30 0.39748064 2.41480143
31 -0.91462055 0.39748064
32 3.78869968 -0.91462055
33 -3.56082755 3.78869968
34 1.79382204 -3.56082755
35 1.41069779 1.79382204
36 -0.53730643 1.41069779
37 1.19264475 -0.53730643
38 2.04071421 1.19264475
39 -1.05691496 2.04071421
40 -1.91009145 -1.05691496
41 -2.47146385 -1.91009145
42 -0.28444918 -2.47146385
43 2.05312762 -0.28444918
44 -1.35515184 2.05312762
45 4.18827802 -1.35515184
46 -0.23883188 4.18827802
47 -2.51812772 -0.23883188
48 -1.21802259 -2.51812772
49 -2.01258588 -1.21802259
50 0.93547389 -2.01258588
51 0.83566057 0.93547389
52 0.13435546 0.83566057
53 -3.01945261 0.13435546
54 -2.47803978 -3.01945261
55 -4.88346438 -2.47803978
56 -2.44105976 -4.88346438
57 -3.95457008 -2.44105976
58 -0.72640162 -3.95457008
59 1.79242208 -0.72640162
60 -4.02208432 1.79242208
61 -1.74191797 -4.02208432
62 -1.83742574 -1.74191797
63 1.87753266 -1.83742574
64 0.51575634 1.87753266
65 0.24683609 0.51575634
66 2.80749794 0.24683609
67 0.07229872 2.80749794
68 -0.24272985 0.07229872
69 -1.81706850 -0.24272985
70 2.16245034 -1.81706850
71 -0.37458288 2.16245034
72 1.25602431 -0.37458288
73 -1.55404213 1.25602431
74 0.93288979 -1.55404213
75 1.47842129 0.93288979
76 0.45384368 1.47842129
77 1.78826418 0.45384368
78 0.68196040 1.78826418
79 -0.73770880 0.68196040
80 2.70099510 -0.73770880
81 -4.67155085 2.70099510
82 2.01948866 -4.67155085
83 -1.51470664 2.01948866
84 1.36924725 -1.51470664
85 0.15369402 1.36924725
86 -0.71545150 0.15369402
87 0.73359991 -0.71545150
88 -2.15094079 0.73359991
89 -1.52850064 -2.15094079
90 2.32610766 -1.52850064
91 -0.48783922 2.32610766
92 3.77349893 -0.48783922
93 -1.93464464 3.77349893
94 0.63995181 -1.93464464
95 -3.08442548 0.63995181
96 1.76585757 -3.08442548
97 -5.06300580 1.76585757
98 1.27700482 -5.06300580
99 -3.07082039 1.27700482
100 -3.20456459 -3.07082039
101 0.12357160 -3.20456459
102 -2.92528781 0.12357160
103 -2.18636861 -2.92528781
104 0.77467444 -2.18636861
105 3.38157198 0.77467444
106 0.91650409 3.38157198
107 1.69910007 0.91650409
108 0.82281085 1.69910007
109 0.80181929 0.82281085
110 -0.31274382 0.80181929
111 -1.70010983 -0.31274382
112 -0.46388141 -1.70010983
113 0.68819522 -0.46388141
114 0.03532660 0.68819522
115 -0.57367180 0.03532660
116 2.11801006 -0.57367180
117 1.64498992 2.11801006
118 4.06129840 1.64498992
119 1.13927702 4.06129840
120 -1.05270810 1.13927702
121 -3.17719916 -1.05270810
122 -0.04187901 -3.17719916
123 0.44122784 -0.04187901
124 2.15000935 0.44122784
125 -2.28100695 2.15000935
126 -0.58607031 -2.28100695
127 -1.63387570 -0.58607031
128 1.63207158 -1.63387570
129 -0.38532313 1.63207158
130 3.31291125 -0.38532313
131 -0.66313494 3.31291125
132 0.20458977 -0.66313494
133 2.98518061 0.20458977
134 0.55972182 2.98518061
135 -1.40002254 0.55972182
136 -0.99920802 -1.40002254
137 1.39322697 -0.99920802
138 -1.98550725 1.39322697
139 -2.46456414 -1.98550725
140 1.54054180 -2.46456414
141 -1.51470664 1.54054180
142 4.06129840 -1.51470664
143 -2.49120770 4.06129840
144 NA -2.49120770
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.68028224 -1.11874351
[2,] -3.51660629 2.68028224
[3,] -0.62237851 -3.51660629
[4,] 1.58126171 -0.62237851
[5,] 3.09334872 1.58126171
[6,] -0.98459280 3.09334872
[7,] -0.56726320 -0.98459280
[8,] 0.33387253 -0.56726320
[9,] 2.01351526 0.33387253
[10,] 3.56607902 2.01351526
[11,] 5.05297431 3.56607902
[12,] -3.07598977 5.05297431
[13,] 1.71900763 -3.07598977
[14,] 2.15240541 1.71900763
[15,] -0.66800011 2.15240541
[16,] 1.83439346 -0.66800011
[17,] -1.07269348 1.83439346
[18,] 0.99960369 -1.07269348
[19,] 4.45312011 0.99960369
[20,] 0.58059360 4.45312011
[21,] -0.95666096 0.58059360
[22,] 3.13856082 -0.95666096
[23,] -5.70866877 3.13856082
[24,] 1.39322697 -5.70866877
[25,] 0.74106242 1.39322697
[26,] -3.62509518 0.74106242
[27,] 0.51756977 -3.62509518
[28,] -0.25964403 0.51756977
[29,] 2.41480143 -0.25964403
[30,] 0.39748064 2.41480143
[31,] -0.91462055 0.39748064
[32,] 3.78869968 -0.91462055
[33,] -3.56082755 3.78869968
[34,] 1.79382204 -3.56082755
[35,] 1.41069779 1.79382204
[36,] -0.53730643 1.41069779
[37,] 1.19264475 -0.53730643
[38,] 2.04071421 1.19264475
[39,] -1.05691496 2.04071421
[40,] -1.91009145 -1.05691496
[41,] -2.47146385 -1.91009145
[42,] -0.28444918 -2.47146385
[43,] 2.05312762 -0.28444918
[44,] -1.35515184 2.05312762
[45,] 4.18827802 -1.35515184
[46,] -0.23883188 4.18827802
[47,] -2.51812772 -0.23883188
[48,] -1.21802259 -2.51812772
[49,] -2.01258588 -1.21802259
[50,] 0.93547389 -2.01258588
[51,] 0.83566057 0.93547389
[52,] 0.13435546 0.83566057
[53,] -3.01945261 0.13435546
[54,] -2.47803978 -3.01945261
[55,] -4.88346438 -2.47803978
[56,] -2.44105976 -4.88346438
[57,] -3.95457008 -2.44105976
[58,] -0.72640162 -3.95457008
[59,] 1.79242208 -0.72640162
[60,] -4.02208432 1.79242208
[61,] -1.74191797 -4.02208432
[62,] -1.83742574 -1.74191797
[63,] 1.87753266 -1.83742574
[64,] 0.51575634 1.87753266
[65,] 0.24683609 0.51575634
[66,] 2.80749794 0.24683609
[67,] 0.07229872 2.80749794
[68,] -0.24272985 0.07229872
[69,] -1.81706850 -0.24272985
[70,] 2.16245034 -1.81706850
[71,] -0.37458288 2.16245034
[72,] 1.25602431 -0.37458288
[73,] -1.55404213 1.25602431
[74,] 0.93288979 -1.55404213
[75,] 1.47842129 0.93288979
[76,] 0.45384368 1.47842129
[77,] 1.78826418 0.45384368
[78,] 0.68196040 1.78826418
[79,] -0.73770880 0.68196040
[80,] 2.70099510 -0.73770880
[81,] -4.67155085 2.70099510
[82,] 2.01948866 -4.67155085
[83,] -1.51470664 2.01948866
[84,] 1.36924725 -1.51470664
[85,] 0.15369402 1.36924725
[86,] -0.71545150 0.15369402
[87,] 0.73359991 -0.71545150
[88,] -2.15094079 0.73359991
[89,] -1.52850064 -2.15094079
[90,] 2.32610766 -1.52850064
[91,] -0.48783922 2.32610766
[92,] 3.77349893 -0.48783922
[93,] -1.93464464 3.77349893
[94,] 0.63995181 -1.93464464
[95,] -3.08442548 0.63995181
[96,] 1.76585757 -3.08442548
[97,] -5.06300580 1.76585757
[98,] 1.27700482 -5.06300580
[99,] -3.07082039 1.27700482
[100,] -3.20456459 -3.07082039
[101,] 0.12357160 -3.20456459
[102,] -2.92528781 0.12357160
[103,] -2.18636861 -2.92528781
[104,] 0.77467444 -2.18636861
[105,] 3.38157198 0.77467444
[106,] 0.91650409 3.38157198
[107,] 1.69910007 0.91650409
[108,] 0.82281085 1.69910007
[109,] 0.80181929 0.82281085
[110,] -0.31274382 0.80181929
[111,] -1.70010983 -0.31274382
[112,] -0.46388141 -1.70010983
[113,] 0.68819522 -0.46388141
[114,] 0.03532660 0.68819522
[115,] -0.57367180 0.03532660
[116,] 2.11801006 -0.57367180
[117,] 1.64498992 2.11801006
[118,] 4.06129840 1.64498992
[119,] 1.13927702 4.06129840
[120,] -1.05270810 1.13927702
[121,] -3.17719916 -1.05270810
[122,] -0.04187901 -3.17719916
[123,] 0.44122784 -0.04187901
[124,] 2.15000935 0.44122784
[125,] -2.28100695 2.15000935
[126,] -0.58607031 -2.28100695
[127,] -1.63387570 -0.58607031
[128,] 1.63207158 -1.63387570
[129,] -0.38532313 1.63207158
[130,] 3.31291125 -0.38532313
[131,] -0.66313494 3.31291125
[132,] 0.20458977 -0.66313494
[133,] 2.98518061 0.20458977
[134,] 0.55972182 2.98518061
[135,] -1.40002254 0.55972182
[136,] -0.99920802 -1.40002254
[137,] 1.39322697 -0.99920802
[138,] -1.98550725 1.39322697
[139,] -2.46456414 -1.98550725
[140,] 1.54054180 -2.46456414
[141,] -1.51470664 1.54054180
[142,] 4.06129840 -1.51470664
[143,] -2.49120770 4.06129840
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.68028224 -1.11874351
2 -3.51660629 2.68028224
3 -0.62237851 -3.51660629
4 1.58126171 -0.62237851
5 3.09334872 1.58126171
6 -0.98459280 3.09334872
7 -0.56726320 -0.98459280
8 0.33387253 -0.56726320
9 2.01351526 0.33387253
10 3.56607902 2.01351526
11 5.05297431 3.56607902
12 -3.07598977 5.05297431
13 1.71900763 -3.07598977
14 2.15240541 1.71900763
15 -0.66800011 2.15240541
16 1.83439346 -0.66800011
17 -1.07269348 1.83439346
18 0.99960369 -1.07269348
19 4.45312011 0.99960369
20 0.58059360 4.45312011
21 -0.95666096 0.58059360
22 3.13856082 -0.95666096
23 -5.70866877 3.13856082
24 1.39322697 -5.70866877
25 0.74106242 1.39322697
26 -3.62509518 0.74106242
27 0.51756977 -3.62509518
28 -0.25964403 0.51756977
29 2.41480143 -0.25964403
30 0.39748064 2.41480143
31 -0.91462055 0.39748064
32 3.78869968 -0.91462055
33 -3.56082755 3.78869968
34 1.79382204 -3.56082755
35 1.41069779 1.79382204
36 -0.53730643 1.41069779
37 1.19264475 -0.53730643
38 2.04071421 1.19264475
39 -1.05691496 2.04071421
40 -1.91009145 -1.05691496
41 -2.47146385 -1.91009145
42 -0.28444918 -2.47146385
43 2.05312762 -0.28444918
44 -1.35515184 2.05312762
45 4.18827802 -1.35515184
46 -0.23883188 4.18827802
47 -2.51812772 -0.23883188
48 -1.21802259 -2.51812772
49 -2.01258588 -1.21802259
50 0.93547389 -2.01258588
51 0.83566057 0.93547389
52 0.13435546 0.83566057
53 -3.01945261 0.13435546
54 -2.47803978 -3.01945261
55 -4.88346438 -2.47803978
56 -2.44105976 -4.88346438
57 -3.95457008 -2.44105976
58 -0.72640162 -3.95457008
59 1.79242208 -0.72640162
60 -4.02208432 1.79242208
61 -1.74191797 -4.02208432
62 -1.83742574 -1.74191797
63 1.87753266 -1.83742574
64 0.51575634 1.87753266
65 0.24683609 0.51575634
66 2.80749794 0.24683609
67 0.07229872 2.80749794
68 -0.24272985 0.07229872
69 -1.81706850 -0.24272985
70 2.16245034 -1.81706850
71 -0.37458288 2.16245034
72 1.25602431 -0.37458288
73 -1.55404213 1.25602431
74 0.93288979 -1.55404213
75 1.47842129 0.93288979
76 0.45384368 1.47842129
77 1.78826418 0.45384368
78 0.68196040 1.78826418
79 -0.73770880 0.68196040
80 2.70099510 -0.73770880
81 -4.67155085 2.70099510
82 2.01948866 -4.67155085
83 -1.51470664 2.01948866
84 1.36924725 -1.51470664
85 0.15369402 1.36924725
86 -0.71545150 0.15369402
87 0.73359991 -0.71545150
88 -2.15094079 0.73359991
89 -1.52850064 -2.15094079
90 2.32610766 -1.52850064
91 -0.48783922 2.32610766
92 3.77349893 -0.48783922
93 -1.93464464 3.77349893
94 0.63995181 -1.93464464
95 -3.08442548 0.63995181
96 1.76585757 -3.08442548
97 -5.06300580 1.76585757
98 1.27700482 -5.06300580
99 -3.07082039 1.27700482
100 -3.20456459 -3.07082039
101 0.12357160 -3.20456459
102 -2.92528781 0.12357160
103 -2.18636861 -2.92528781
104 0.77467444 -2.18636861
105 3.38157198 0.77467444
106 0.91650409 3.38157198
107 1.69910007 0.91650409
108 0.82281085 1.69910007
109 0.80181929 0.82281085
110 -0.31274382 0.80181929
111 -1.70010983 -0.31274382
112 -0.46388141 -1.70010983
113 0.68819522 -0.46388141
114 0.03532660 0.68819522
115 -0.57367180 0.03532660
116 2.11801006 -0.57367180
117 1.64498992 2.11801006
118 4.06129840 1.64498992
119 1.13927702 4.06129840
120 -1.05270810 1.13927702
121 -3.17719916 -1.05270810
122 -0.04187901 -3.17719916
123 0.44122784 -0.04187901
124 2.15000935 0.44122784
125 -2.28100695 2.15000935
126 -0.58607031 -2.28100695
127 -1.63387570 -0.58607031
128 1.63207158 -1.63387570
129 -0.38532313 1.63207158
130 3.31291125 -0.38532313
131 -0.66313494 3.31291125
132 0.20458977 -0.66313494
133 2.98518061 0.20458977
134 0.55972182 2.98518061
135 -1.40002254 0.55972182
136 -0.99920802 -1.40002254
137 1.39322697 -0.99920802
138 -1.98550725 1.39322697
139 -2.46456414 -1.98550725
140 1.54054180 -2.46456414
141 -1.51470664 1.54054180
142 4.06129840 -1.51470664
143 -2.49120770 4.06129840
> 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/7590u1290548303.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8g0hf1290548303.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9g0hf1290548303.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109ay01290548303.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11usfn1290548303.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/12fsvt1290548303.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/13b2bk1290548303.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/14xls81290548303.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/150mqe1290548303.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/16m4pk1290548303.tab")
+ }
>
> try(system("convert tmp/1k91o1290548303.ps tmp/1k91o1290548303.png",intern=TRUE))
character(0)
> try(system("convert tmp/2k91o1290548303.ps tmp/2k91o1290548303.png",intern=TRUE))
character(0)
> try(system("convert tmp/3k91o1290548303.ps tmp/3k91o1290548303.png",intern=TRUE))
character(0)
> try(system("convert tmp/4d0191290548303.ps tmp/4d0191290548303.png",intern=TRUE))
character(0)
> try(system("convert tmp/5d0191290548303.ps tmp/5d0191290548303.png",intern=TRUE))
character(0)
> try(system("convert tmp/6d0191290548303.ps tmp/6d0191290548303.png",intern=TRUE))
character(0)
> try(system("convert tmp/7590u1290548303.ps tmp/7590u1290548303.png",intern=TRUE))
character(0)
> try(system("convert tmp/8g0hf1290548303.ps tmp/8g0hf1290548303.png",intern=TRUE))
character(0)
> try(system("convert tmp/9g0hf1290548303.ps tmp/9g0hf1290548303.png",intern=TRUE))
character(0)
> try(system("convert tmp/109ay01290548303.ps tmp/109ay01290548303.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.102 1.775 10.500