R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(10
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+ ,dim=c(10
+ ,142)
+ ,dimnames=list(c('PStress'
+ ,'BelInSprt'
+ ,'KunnenRekRel'
+ ,'ExtraCurAct'
+ ,'Verwouders'
+ ,'Populariteit'
+ ,'Depressie'
+ ,'Slaapgebrek'
+ ,'ToekZorgen'
+ ,'MateGeorgZijn
')
+ ,1:142))
> y <- array(NA,dim=c(10,142),dimnames=list(c('PStress','BelInSprt','KunnenRekRel','ExtraCurAct','Verwouders','Populariteit','Depressie','Slaapgebrek','ToekZorgen','MateGeorgZijn
'),1:142))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '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
PStress BelInSprt KunnenRekRel ExtraCurAct Verwouders Populariteit
1 10 53 7 6 15 11
2 6 86 4 6 15 12
3 13 66 6 5 14 15
4 12 67 5 4 10 10
5 8 76 4 4 10 12
6 6 78 3 6 12 11
7 10 53 5 7 18 5
8 10 80 6 5 12 16
9 9 74 5 4 14 11
10 9 76 6 6 18 15
11 7 79 7 1 9 12
12 5 54 6 4 11 9
13 14 67 7 6 11 11
14 6 87 6 6 17 15
15 10 58 4 5 8 12
16 10 75 6 3 16 16
17 7 88 4 7 21 14
18 10 64 5 2 24 11
19 8 57 3 5 21 10
20 6 66 3 5 14 7
21 10 54 4 3 7 11
22 12 56 5 5 18 10
23 7 86 3 5 18 11
24 15 80 7 6 13 16
25 8 76 7 4 11 14
26 10 69 4 4 13 12
27 13 67 4 4 13 12
28 8 80 5 2 18 11
29 11 54 6 3 14 6
30 7 71 5 6 12 14
31 9 84 4 6 9 9
32 10 74 6 5 12 15
33 8 71 5 3 8 12
34 15 63 5 3 5 12
35 9 71 6 4 10 9
36 7 76 2 4 11 13
37 11 69 6 5 11 15
38 9 74 7 3 12 11
39 8 75 5 5 12 10
40 8 54 5 4 15 13
41 12 69 5 3 16 16
42 13 68 6 3 14 13
43 9 75 4 4 17 14
44 11 75 6 6 10 16
45 8 72 5 5 17 9
46 10 67 5 3 12 8
47 13 63 3 4 13 8
48 12 62 4 2 13 12
49 12 63 4 3 11 10
50 9 76 2 5 13 16
51 8 74 3 5 12 13
52 9 67 6 5 12 11
53 12 73 5 4 12 14
54 12 70 6 5 9 15
55 16 53 2 3 7 8
56 11 77 3 6 17 9
57 13 77 6 3 12 17
58 10 52 3 2 12 9
59 9 54 6 3 9 13
60 14 80 6 4 9 6
61 13 66 4 3 13 13
62 12 73 7 4 10 8
63 9 63 6 4 11 12
64 9 69 3 7 12 13
65 10 67 7 2 10 14
66 8 54 2 2 13 11
67 9 81 4 5 6 15
68 9 69 6 3 7 7
69 11 84 4 6 13 16
70 7 70 1 6 11 16
71 11 69 4 4 18 14
72 9 77 7 6 9 11
73 11 54 4 6 9 13
74 9 79 4 4 11 13
75 8 30 4 2 11 7
76 9 71 6 6 15 15
77 8 73 2 3 8 11
78 9 72 3 5 11 15
79 10 77 4 3 14 13
80 9 75 4 4 14 11
81 17 70 4 6 12 12
82 7 73 6 2 12 10
83 11 54 2 7 8 12
84 9 77 4 2 11 12
85 10 82 3 3 10 12
86 11 80 7 6 17 14
87 8 80 4 4 16 6
88 12 69 5 4 13 14
89 10 78 6 3 15 15
90 7 81 5 5 11 8
91 9 76 4 4 12 12
92 7 76 5 5 16 10
93 12 73 4 5 20 15
94 8 85 5 7 16 11
95 13 66 7 4 11 9
96 9 79 7 6 15 14
97 15 68 4 3 15 10
98 8 76 6 6 12 16
99 14 54 4 3 9 5
100 14 46 1 2 24 8
101 9 82 3 4 15 13
102 13 74 6 3 18 16
103 11 88 7 3 17 16
104 10 38 6 4 12 14
105 6 76 6 4 15 14
106 8 86 6 5 11 10
107 10 54 4 5 11 9
108 10 69 1 7 12 8
109 10 90 3 7 14 8
110 12 54 7 1 11 16
111 10 76 2 4 20 12
112 9 89 7 6 11 9
113 9 76 4 5 12 15
114 11 79 5 4 12 12
115 7 90 6 5 11 14
116 7 74 6 5 10 12
117 5 81 5 6 11 16
118 9 72 5 5 12 12
119 11 71 4 3 9 14
120 15 66 2 4 8 8
121 9 77 2 4 6 15
122 9 74 4 5 12 16
123 8 82 4 6 15 12
124 13 54 6 2 13 4
125 10 63 5 4 17 8
126 13 54 5 5 14 11
127 9 64 6 6 16 4
128 11 69 5 6 15 14
129 8 84 7 5 11 14
130 10 86 5 4 11 13
131 9 77 3 5 16 14
132 8 89 5 6 15 7
133 8 76 1 6 14 19
134 13 60 5 5 9 12
135 11 79 7 6 13 10
136 8 76 7 4 11 14
137 12 72 6 5 14 16
138 15 69 4 5 11 11
139 11 54 2 7 8 12
140 10 69 6 5 7 12
141 5 81 5 6 11 16
142 11 84 1 6 13 12
Depressie Slaapgebrek ToekZorgen MateGeorgZijn\r
1 12 2 4 25
2 11 4 3 24
3 14 7 5 21
4 12 3 3 23
5 21 7 6 17
6 12 2 5 19
7 22 7 6 18
8 11 2 6 27
9 10 1 5 23
10 13 2 5 23
11 10 6 3 29
12 8 1 5 21
13 15 1 7 26
14 10 1 5 25
15 14 2 5 25
16 14 2 3 23
17 11 2 5 26
18 10 1 6 20
19 13 7 5 29
20 7 1 2 24
21 12 2 5 23
22 14 4 4 24
23 11 2 6 30
24 9 1 3 22
25 11 1 5 22
26 15 5 4 13
27 13 2 5 24
28 9 1 2 17
29 15 3 2 24
30 10 1 5 21
31 11 2 2 23
32 13 5 2 24
33 8 2 2 24
34 20 6 5 24
35 12 4 5 23
36 10 1 1 26
37 10 3 5 24
38 9 6 2 21
39 14 7 6 23
40 8 4 1 28
41 11 5 3 22
42 13 3 2 24
43 11 2 5 21
44 11 2 3 23
45 10 2 4 20
46 14 2 3 23
47 18 1 6 21
48 14 2 4 27
49 11 1 5 12
50 12 2 2 15
51 13 2 5 22
52 9 5 5 21
53 10 5 3 21
54 15 2 5 20
55 20 1 7 24
56 12 1 4 24
57 12 2 2 29
58 14 3 3 25
59 13 7 6 14
60 11 4 7 30
61 17 4 4 19
62 12 1 4 29
63 13 2 4 25
64 14 2 5 25
65 13 2 2 25
66 15 5 3 16
67 13 1 3 25
68 10 6 4 28
69 11 2 3 24
70 13 2 4 25
71 17 4 6 21
72 13 6 2 22
73 9 2 4 20
74 11 2 5 25
75 10 2 2 27
76 9 1 1 21
77 12 1 2 13
78 12 2 5 26
79 13 2 4 26
80 13 3 4 25
81 22 3 6 22
82 13 5 1 19
83 15 2 4 23
84 13 5 5 25
85 15 3 2 15
86 10 1 3 21
87 11 2 3 23
88 16 2 6 25
89 11 1 5 24
90 11 2 4 24
91 10 2 4 21
92 10 5 5 24
93 16 5 5 22
94 12 2 6 24
95 11 3 6 28
96 16 5 5 21
97 19 5 7 17
98 11 6 5 28
99 15 2 5 24
100 24 7 7 10
101 14 1 5 20
102 15 1 6 22
103 11 6 6 19
104 15 6 4 22
105 12 2 5 22
106 10 1 1 26
107 14 2 6 24
108 9 1 5 20
109 15 2 2 20
110 15 1 1 15
111 14 3 5 20
112 11 3 6 20
113 8 6 5 24
114 11 4 5 29
115 8 1 4 23
116 10 2 2 24
117 11 5 3 22
118 13 6 3 16
119 11 3 5 23
120 20 5 3 27
121 10 3 2 16
122 12 2 2 21
123 14 3 3 26
124 23 2 6 22
125 14 5 5 23
126 16 5 6 19
127 11 7 2 18
128 12 4 5 24
129 14 5 5 29
130 12 1 1 22
131 12 4 4 24
132 11 1 2 22
133 12 4 2 12
134 13 6 7 26
135 17 7 6 18
136 11 1 5 22
137 12 3 5 24
138 19 5 5 21
139 15 2 4 23
140 14 4 3 22
141 11 5 3 22
142 9 1 3 24
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) BelInSprt KunnenRekRel ExtraCurAct
5.77337 -0.03420 0.16213 -0.14475
Verwouders Populariteit Depressie Slaapgebrek
-0.05281 0.06199 0.40504 -0.18835
ToekZorgen `MateGeorgZijn\r`
0.20200 0.04321
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.5929 -1.2893 -0.0675 1.4426 6.3778
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.77337 2.09734 2.753 0.00674 **
BelInSprt -0.03420 0.01730 -1.978 0.05005 .
KunnenRekRel 0.16213 0.11053 1.467 0.14479
ExtraCurAct -0.14475 0.12382 -1.169 0.24447
Verwouders -0.05281 0.04868 -1.085 0.28000
Populariteit 0.06199 0.05793 1.070 0.28658
Depressie 0.40504 0.06363 6.366 2.96e-09 ***
Slaapgebrek -0.18835 0.09340 -2.017 0.04577 *
ToekZorgen 0.20200 0.11624 1.738 0.08457 .
`MateGeorgZijn\r` 0.04321 0.04614 0.936 0.35078
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.927 on 132 degrees of freedom
Multiple R-squared: 0.3894, Adjusted R-squared: 0.3477
F-statistic: 9.352 on 9 and 132 DF, p-value: 6.574e-11
> 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,] 0.9878016 0.02439687 0.01219844
[2,] 0.9751571 0.04968589 0.02484294
[3,] 0.9803656 0.03926878 0.01963439
[4,] 0.9631678 0.07366440 0.03683220
[5,] 0.9412574 0.11748518 0.05874259
[6,] 0.9472922 0.10541553 0.05270776
[7,] 0.9325690 0.13486209 0.06743105
[8,] 0.9080687 0.18386267 0.09193134
[9,] 0.8676619 0.26467616 0.13233808
[10,] 0.8518145 0.29637101 0.14818551
[11,] 0.8104631 0.37907378 0.18953689
[12,] 0.9810920 0.03781602 0.01890801
[13,] 0.9808375 0.03832497 0.01916248
[14,] 0.9737554 0.05248925 0.02624463
[15,] 0.9854724 0.02905518 0.01452759
[16,] 0.9783270 0.04334599 0.02167299
[17,] 0.9679581 0.06408386 0.03204193
[18,] 0.9705919 0.05881622 0.02940811
[19,] 0.9671851 0.06562977 0.03281488
[20,] 0.9573815 0.08523703 0.04261852
[21,] 0.9419417 0.11611656 0.05805828
[22,] 0.9436076 0.11278473 0.05639237
[23,] 0.9300085 0.13998302 0.06999151
[24,] 0.9227212 0.15455765 0.07727883
[25,] 0.9098147 0.18037055 0.09018527
[26,] 0.8898370 0.22032604 0.11016302
[27,] 0.8728343 0.25433144 0.12716572
[28,] 0.8583766 0.28324681 0.14162341
[29,] 0.8791193 0.24176134 0.12088067
[30,] 0.8892664 0.22146719 0.11073359
[31,] 0.8617585 0.27648291 0.13824146
[32,] 0.8406279 0.31874424 0.15937212
[33,] 0.8092206 0.38155888 0.19077944
[34,] 0.7704698 0.45906042 0.22953021
[35,] 0.7689369 0.46212615 0.23106308
[36,] 0.7293531 0.54129381 0.27064691
[37,] 0.7669740 0.46605196 0.23302598
[38,] 0.7278777 0.54424460 0.27212230
[39,] 0.7195050 0.56098997 0.28049498
[40,] 0.6861488 0.62770242 0.31385121
[41,] 0.7903539 0.41929217 0.20964608
[42,] 0.7528885 0.49422307 0.24711153
[43,] 0.7797373 0.44052545 0.22026272
[44,] 0.8210973 0.35780548 0.17890274
[45,] 0.8548299 0.29034014 0.14517007
[46,] 0.8327770 0.33444598 0.16722299
[47,] 0.8248478 0.35030447 0.17515223
[48,] 0.9493548 0.10129050 0.05064525
[49,] 0.9432436 0.11351271 0.05675635
[50,] 0.9366274 0.12674529 0.06337265
[51,] 0.9397826 0.12043481 0.06021740
[52,] 0.9333508 0.13329846 0.06664923
[53,] 0.9316441 0.13671189 0.06835594
[54,] 0.9363418 0.12731640 0.06365820
[55,] 0.9264459 0.14710829 0.07355414
[56,] 0.9072277 0.18554470 0.09277235
[57,] 0.9199617 0.16007669 0.08003835
[58,] 0.9399522 0.12009559 0.06004780
[59,] 0.9261724 0.14765514 0.07382757
[60,] 0.9085111 0.18297778 0.09148889
[61,] 0.9111841 0.17763180 0.08881590
[62,] 0.8904200 0.21915996 0.10957998
[63,] 0.8932879 0.21342428 0.10671214
[64,] 0.8821992 0.23560152 0.11780076
[65,] 0.8680496 0.26390086 0.13195043
[66,] 0.8511854 0.29762918 0.14881459
[67,] 0.8192425 0.36151506 0.18075753
[68,] 0.7908411 0.41831781 0.20915891
[69,] 0.8559899 0.28802011 0.14401006
[70,] 0.8679309 0.26413823 0.13206911
[71,] 0.8399462 0.32010762 0.16005381
[72,] 0.8327450 0.33451007 0.16725503
[73,] 0.7994206 0.40115882 0.20057941
[74,] 0.8788921 0.24221581 0.12110791
[75,] 0.8596787 0.28064268 0.14032134
[76,] 0.8279445 0.34411098 0.17205549
[77,] 0.7932709 0.41345812 0.20672906
[78,] 0.8144673 0.37106537 0.18553269
[79,] 0.7799284 0.44014320 0.22007160
[80,] 0.7827251 0.43454974 0.21727487
[81,] 0.7848976 0.43020474 0.21510237
[82,] 0.7508476 0.49830484 0.24915242
[83,] 0.7795908 0.44081830 0.22040915
[84,] 0.7572590 0.48548190 0.24274095
[85,] 0.7686632 0.46267352 0.23133676
[86,] 0.7315253 0.53694932 0.26847466
[87,] 0.7133511 0.57329784 0.28664892
[88,] 0.7140734 0.57185330 0.28592665
[89,] 0.7191668 0.56166638 0.28083319
[90,] 0.7320123 0.53597540 0.26798770
[91,] 0.7547727 0.49045452 0.24522726
[92,] 0.7343965 0.53120702 0.26560351
[93,] 0.8471472 0.30570566 0.15285283
[94,] 0.8094266 0.38114670 0.19057335
[95,] 0.8334770 0.33304597 0.16652299
[96,] 0.8205247 0.35895054 0.17947527
[97,] 0.7911350 0.41773003 0.20886502
[98,] 0.8140361 0.37192779 0.18596389
[99,] 0.7936912 0.41261765 0.20630883
[100,] 0.7392693 0.52146136 0.26073068
[101,] 0.6879226 0.62415476 0.31207738
[102,] 0.6383140 0.72337208 0.36168604
[103,] 0.5731752 0.85364955 0.42682478
[104,] 0.5128649 0.97427021 0.48713510
[105,] 0.6101242 0.77975153 0.38987576
[106,] 0.5308185 0.93836310 0.46918155
[107,] 0.4515767 0.90315340 0.54842330
[108,] 0.4435698 0.88713967 0.55643016
[109,] 0.3693940 0.73878791 0.63060604
[110,] 0.2928699 0.58573985 0.70713008
[111,] 0.2403059 0.48061188 0.75969406
[112,] 0.3186795 0.63735907 0.68132047
[113,] 0.4554186 0.91083723 0.54458139
[114,] 0.3937086 0.78741710 0.60629145
[115,] 0.2892728 0.57854568 0.71072716
[116,] 0.2235768 0.44715358 0.77642321
[117,] 0.1620474 0.32409481 0.83795260
> postscript(file="/var/www/html/freestat/rcomp/tmp/1rk441291409073.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2jblp1291409073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3jblp1291409073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4jblp1291409073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5u2ls1291409073.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
-0.48854716 -1.90843681 2.77522852 2.29978320 -4.59294701 -3.08610033
7 8 9 10 11 12
-2.69940403 -0.10137003 -0.28216445 -1.14986102 -2.10916176 -4.26635107
13 14 15 16 17 18
1.72641126 -2.88605649 -1.41960794 -0.78696681 -1.36951557 0.54196624
19 20 21 22 23 24
-1.30724136 -1.06092365 -0.94026751 1.62380806 -1.91258883 6.37782161
25 26 27 28 29 30
-2.24423117 -0.04359755 2.45576347 0.11506109 -0.04891015 -2.30043492
31 32 33 34 35 36
0.76077459 0.44798143 -0.34713490 1.50772391 -0.87919361 -1.33136926
37 38 39 40 41 42
1.45656269 1.18241370 -1.83889165 -0.07031522 2.99330909 2.80613088
43 44 45 46 47 48
-0.24364647 1.54557064 -0.40345680 -0.61381277 0.44308493 0.66257725
49 50 51 52 53 54
2.33273771 0.38456516 -2.02630230 0.60026260 3.63587107 0.34442406
55 56 57 58 59 60
1.65987060 2.06533142 2.76107716 -0.90742689 -1.55932928 4.26040328
61 62 63 64 65 66
1.38938670 1.46679316 -1.95213375 -1.44247269 -1.03973009 -2.38754652
67 68 69 70 71 72
-1.30377817 0.04594971 2.29289125 -2.86048859 -0.65160725 -0.10249474
73 74 75 76 77 78
2.02237335 -0.53450545 -2.20349783 0.84691776 -1.06360267 -1.03927331
79 80 81 82 83 84
-0.24052986 -0.80865648 3.22248098 -2.29252642 -0.05928686 -1.07546554
85 86 87 88 89 90
0.20091120 2.35119010 -0.31194326 -0.22225570 -0.09561297 -1.92833116
91 92 93 94 95 96
0.25753575 -1.19120228 1.42578165 -1.23296867 2.63912201 -1.86946974
97 98 99 100 101 102
2.60804713 -1.18123683 2.27894305 0.45035336 -1.24597537 1.12825300
103 104 105 106 107 108
2.08370969 -1.80548365 -4.08756083 -0.30712547 -1.37084180 2.24459334
109 110 111 112 113 114
1.10836832 -0.06469082 0.41364997 0.06098310 1.44819426 1.62203939
115 116 117 118 119 120
-1.08456322 -1.82160574 -3.42600672 0.05965051 1.15426024 2.73388901
121 122 123 124 125 126
0.92157470 -0.32014195 -1.53513583 -2.27276351 -0.18079282 1.47247470
127 128 129 130 131 132
1.58978051 1.41493436 -2.58999084 0.88703091 0.12288760 0.06534184
133 134 135 136 137 138
0.06461289 2.25071216 0.17213637 -2.24423117 1.84553367 2.88972754
139 140 141 142
-0.05928686 -0.51010201 -3.42600672 3.64895605
> postscript(file="/var/www/html/freestat/rcomp/tmp/6u2ls1291409073.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 -0.48854716 NA
1 -1.90843681 -0.48854716
2 2.77522852 -1.90843681
3 2.29978320 2.77522852
4 -4.59294701 2.29978320
5 -3.08610033 -4.59294701
6 -2.69940403 -3.08610033
7 -0.10137003 -2.69940403
8 -0.28216445 -0.10137003
9 -1.14986102 -0.28216445
10 -2.10916176 -1.14986102
11 -4.26635107 -2.10916176
12 1.72641126 -4.26635107
13 -2.88605649 1.72641126
14 -1.41960794 -2.88605649
15 -0.78696681 -1.41960794
16 -1.36951557 -0.78696681
17 0.54196624 -1.36951557
18 -1.30724136 0.54196624
19 -1.06092365 -1.30724136
20 -0.94026751 -1.06092365
21 1.62380806 -0.94026751
22 -1.91258883 1.62380806
23 6.37782161 -1.91258883
24 -2.24423117 6.37782161
25 -0.04359755 -2.24423117
26 2.45576347 -0.04359755
27 0.11506109 2.45576347
28 -0.04891015 0.11506109
29 -2.30043492 -0.04891015
30 0.76077459 -2.30043492
31 0.44798143 0.76077459
32 -0.34713490 0.44798143
33 1.50772391 -0.34713490
34 -0.87919361 1.50772391
35 -1.33136926 -0.87919361
36 1.45656269 -1.33136926
37 1.18241370 1.45656269
38 -1.83889165 1.18241370
39 -0.07031522 -1.83889165
40 2.99330909 -0.07031522
41 2.80613088 2.99330909
42 -0.24364647 2.80613088
43 1.54557064 -0.24364647
44 -0.40345680 1.54557064
45 -0.61381277 -0.40345680
46 0.44308493 -0.61381277
47 0.66257725 0.44308493
48 2.33273771 0.66257725
49 0.38456516 2.33273771
50 -2.02630230 0.38456516
51 0.60026260 -2.02630230
52 3.63587107 0.60026260
53 0.34442406 3.63587107
54 1.65987060 0.34442406
55 2.06533142 1.65987060
56 2.76107716 2.06533142
57 -0.90742689 2.76107716
58 -1.55932928 -0.90742689
59 4.26040328 -1.55932928
60 1.38938670 4.26040328
61 1.46679316 1.38938670
62 -1.95213375 1.46679316
63 -1.44247269 -1.95213375
64 -1.03973009 -1.44247269
65 -2.38754652 -1.03973009
66 -1.30377817 -2.38754652
67 0.04594971 -1.30377817
68 2.29289125 0.04594971
69 -2.86048859 2.29289125
70 -0.65160725 -2.86048859
71 -0.10249474 -0.65160725
72 2.02237335 -0.10249474
73 -0.53450545 2.02237335
74 -2.20349783 -0.53450545
75 0.84691776 -2.20349783
76 -1.06360267 0.84691776
77 -1.03927331 -1.06360267
78 -0.24052986 -1.03927331
79 -0.80865648 -0.24052986
80 3.22248098 -0.80865648
81 -2.29252642 3.22248098
82 -0.05928686 -2.29252642
83 -1.07546554 -0.05928686
84 0.20091120 -1.07546554
85 2.35119010 0.20091120
86 -0.31194326 2.35119010
87 -0.22225570 -0.31194326
88 -0.09561297 -0.22225570
89 -1.92833116 -0.09561297
90 0.25753575 -1.92833116
91 -1.19120228 0.25753575
92 1.42578165 -1.19120228
93 -1.23296867 1.42578165
94 2.63912201 -1.23296867
95 -1.86946974 2.63912201
96 2.60804713 -1.86946974
97 -1.18123683 2.60804713
98 2.27894305 -1.18123683
99 0.45035336 2.27894305
100 -1.24597537 0.45035336
101 1.12825300 -1.24597537
102 2.08370969 1.12825300
103 -1.80548365 2.08370969
104 -4.08756083 -1.80548365
105 -0.30712547 -4.08756083
106 -1.37084180 -0.30712547
107 2.24459334 -1.37084180
108 1.10836832 2.24459334
109 -0.06469082 1.10836832
110 0.41364997 -0.06469082
111 0.06098310 0.41364997
112 1.44819426 0.06098310
113 1.62203939 1.44819426
114 -1.08456322 1.62203939
115 -1.82160574 -1.08456322
116 -3.42600672 -1.82160574
117 0.05965051 -3.42600672
118 1.15426024 0.05965051
119 2.73388901 1.15426024
120 0.92157470 2.73388901
121 -0.32014195 0.92157470
122 -1.53513583 -0.32014195
123 -2.27276351 -1.53513583
124 -0.18079282 -2.27276351
125 1.47247470 -0.18079282
126 1.58978051 1.47247470
127 1.41493436 1.58978051
128 -2.58999084 1.41493436
129 0.88703091 -2.58999084
130 0.12288760 0.88703091
131 0.06534184 0.12288760
132 0.06461289 0.06534184
133 2.25071216 0.06461289
134 0.17213637 2.25071216
135 -2.24423117 0.17213637
136 1.84553367 -2.24423117
137 2.88972754 1.84553367
138 -0.05928686 2.88972754
139 -0.51010201 -0.05928686
140 -3.42600672 -0.51010201
141 3.64895605 -3.42600672
142 NA 3.64895605
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.90843681 -0.48854716
[2,] 2.77522852 -1.90843681
[3,] 2.29978320 2.77522852
[4,] -4.59294701 2.29978320
[5,] -3.08610033 -4.59294701
[6,] -2.69940403 -3.08610033
[7,] -0.10137003 -2.69940403
[8,] -0.28216445 -0.10137003
[9,] -1.14986102 -0.28216445
[10,] -2.10916176 -1.14986102
[11,] -4.26635107 -2.10916176
[12,] 1.72641126 -4.26635107
[13,] -2.88605649 1.72641126
[14,] -1.41960794 -2.88605649
[15,] -0.78696681 -1.41960794
[16,] -1.36951557 -0.78696681
[17,] 0.54196624 -1.36951557
[18,] -1.30724136 0.54196624
[19,] -1.06092365 -1.30724136
[20,] -0.94026751 -1.06092365
[21,] 1.62380806 -0.94026751
[22,] -1.91258883 1.62380806
[23,] 6.37782161 -1.91258883
[24,] -2.24423117 6.37782161
[25,] -0.04359755 -2.24423117
[26,] 2.45576347 -0.04359755
[27,] 0.11506109 2.45576347
[28,] -0.04891015 0.11506109
[29,] -2.30043492 -0.04891015
[30,] 0.76077459 -2.30043492
[31,] 0.44798143 0.76077459
[32,] -0.34713490 0.44798143
[33,] 1.50772391 -0.34713490
[34,] -0.87919361 1.50772391
[35,] -1.33136926 -0.87919361
[36,] 1.45656269 -1.33136926
[37,] 1.18241370 1.45656269
[38,] -1.83889165 1.18241370
[39,] -0.07031522 -1.83889165
[40,] 2.99330909 -0.07031522
[41,] 2.80613088 2.99330909
[42,] -0.24364647 2.80613088
[43,] 1.54557064 -0.24364647
[44,] -0.40345680 1.54557064
[45,] -0.61381277 -0.40345680
[46,] 0.44308493 -0.61381277
[47,] 0.66257725 0.44308493
[48,] 2.33273771 0.66257725
[49,] 0.38456516 2.33273771
[50,] -2.02630230 0.38456516
[51,] 0.60026260 -2.02630230
[52,] 3.63587107 0.60026260
[53,] 0.34442406 3.63587107
[54,] 1.65987060 0.34442406
[55,] 2.06533142 1.65987060
[56,] 2.76107716 2.06533142
[57,] -0.90742689 2.76107716
[58,] -1.55932928 -0.90742689
[59,] 4.26040328 -1.55932928
[60,] 1.38938670 4.26040328
[61,] 1.46679316 1.38938670
[62,] -1.95213375 1.46679316
[63,] -1.44247269 -1.95213375
[64,] -1.03973009 -1.44247269
[65,] -2.38754652 -1.03973009
[66,] -1.30377817 -2.38754652
[67,] 0.04594971 -1.30377817
[68,] 2.29289125 0.04594971
[69,] -2.86048859 2.29289125
[70,] -0.65160725 -2.86048859
[71,] -0.10249474 -0.65160725
[72,] 2.02237335 -0.10249474
[73,] -0.53450545 2.02237335
[74,] -2.20349783 -0.53450545
[75,] 0.84691776 -2.20349783
[76,] -1.06360267 0.84691776
[77,] -1.03927331 -1.06360267
[78,] -0.24052986 -1.03927331
[79,] -0.80865648 -0.24052986
[80,] 3.22248098 -0.80865648
[81,] -2.29252642 3.22248098
[82,] -0.05928686 -2.29252642
[83,] -1.07546554 -0.05928686
[84,] 0.20091120 -1.07546554
[85,] 2.35119010 0.20091120
[86,] -0.31194326 2.35119010
[87,] -0.22225570 -0.31194326
[88,] -0.09561297 -0.22225570
[89,] -1.92833116 -0.09561297
[90,] 0.25753575 -1.92833116
[91,] -1.19120228 0.25753575
[92,] 1.42578165 -1.19120228
[93,] -1.23296867 1.42578165
[94,] 2.63912201 -1.23296867
[95,] -1.86946974 2.63912201
[96,] 2.60804713 -1.86946974
[97,] -1.18123683 2.60804713
[98,] 2.27894305 -1.18123683
[99,] 0.45035336 2.27894305
[100,] -1.24597537 0.45035336
[101,] 1.12825300 -1.24597537
[102,] 2.08370969 1.12825300
[103,] -1.80548365 2.08370969
[104,] -4.08756083 -1.80548365
[105,] -0.30712547 -4.08756083
[106,] -1.37084180 -0.30712547
[107,] 2.24459334 -1.37084180
[108,] 1.10836832 2.24459334
[109,] -0.06469082 1.10836832
[110,] 0.41364997 -0.06469082
[111,] 0.06098310 0.41364997
[112,] 1.44819426 0.06098310
[113,] 1.62203939 1.44819426
[114,] -1.08456322 1.62203939
[115,] -1.82160574 -1.08456322
[116,] -3.42600672 -1.82160574
[117,] 0.05965051 -3.42600672
[118,] 1.15426024 0.05965051
[119,] 2.73388901 1.15426024
[120,] 0.92157470 2.73388901
[121,] -0.32014195 0.92157470
[122,] -1.53513583 -0.32014195
[123,] -2.27276351 -1.53513583
[124,] -0.18079282 -2.27276351
[125,] 1.47247470 -0.18079282
[126,] 1.58978051 1.47247470
[127,] 1.41493436 1.58978051
[128,] -2.58999084 1.41493436
[129,] 0.88703091 -2.58999084
[130,] 0.12288760 0.88703091
[131,] 0.06534184 0.12288760
[132,] 0.06461289 0.06534184
[133,] 2.25071216 0.06461289
[134,] 0.17213637 2.25071216
[135,] -2.24423117 0.17213637
[136,] 1.84553367 -2.24423117
[137,] 2.88972754 1.84553367
[138,] -0.05928686 2.88972754
[139,] -0.51010201 -0.05928686
[140,] -3.42600672 -0.51010201
[141,] 3.64895605 -3.42600672
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.90843681 -0.48854716
2 2.77522852 -1.90843681
3 2.29978320 2.77522852
4 -4.59294701 2.29978320
5 -3.08610033 -4.59294701
6 -2.69940403 -3.08610033
7 -0.10137003 -2.69940403
8 -0.28216445 -0.10137003
9 -1.14986102 -0.28216445
10 -2.10916176 -1.14986102
11 -4.26635107 -2.10916176
12 1.72641126 -4.26635107
13 -2.88605649 1.72641126
14 -1.41960794 -2.88605649
15 -0.78696681 -1.41960794
16 -1.36951557 -0.78696681
17 0.54196624 -1.36951557
18 -1.30724136 0.54196624
19 -1.06092365 -1.30724136
20 -0.94026751 -1.06092365
21 1.62380806 -0.94026751
22 -1.91258883 1.62380806
23 6.37782161 -1.91258883
24 -2.24423117 6.37782161
25 -0.04359755 -2.24423117
26 2.45576347 -0.04359755
27 0.11506109 2.45576347
28 -0.04891015 0.11506109
29 -2.30043492 -0.04891015
30 0.76077459 -2.30043492
31 0.44798143 0.76077459
32 -0.34713490 0.44798143
33 1.50772391 -0.34713490
34 -0.87919361 1.50772391
35 -1.33136926 -0.87919361
36 1.45656269 -1.33136926
37 1.18241370 1.45656269
38 -1.83889165 1.18241370
39 -0.07031522 -1.83889165
40 2.99330909 -0.07031522
41 2.80613088 2.99330909
42 -0.24364647 2.80613088
43 1.54557064 -0.24364647
44 -0.40345680 1.54557064
45 -0.61381277 -0.40345680
46 0.44308493 -0.61381277
47 0.66257725 0.44308493
48 2.33273771 0.66257725
49 0.38456516 2.33273771
50 -2.02630230 0.38456516
51 0.60026260 -2.02630230
52 3.63587107 0.60026260
53 0.34442406 3.63587107
54 1.65987060 0.34442406
55 2.06533142 1.65987060
56 2.76107716 2.06533142
57 -0.90742689 2.76107716
58 -1.55932928 -0.90742689
59 4.26040328 -1.55932928
60 1.38938670 4.26040328
61 1.46679316 1.38938670
62 -1.95213375 1.46679316
63 -1.44247269 -1.95213375
64 -1.03973009 -1.44247269
65 -2.38754652 -1.03973009
66 -1.30377817 -2.38754652
67 0.04594971 -1.30377817
68 2.29289125 0.04594971
69 -2.86048859 2.29289125
70 -0.65160725 -2.86048859
71 -0.10249474 -0.65160725
72 2.02237335 -0.10249474
73 -0.53450545 2.02237335
74 -2.20349783 -0.53450545
75 0.84691776 -2.20349783
76 -1.06360267 0.84691776
77 -1.03927331 -1.06360267
78 -0.24052986 -1.03927331
79 -0.80865648 -0.24052986
80 3.22248098 -0.80865648
81 -2.29252642 3.22248098
82 -0.05928686 -2.29252642
83 -1.07546554 -0.05928686
84 0.20091120 -1.07546554
85 2.35119010 0.20091120
86 -0.31194326 2.35119010
87 -0.22225570 -0.31194326
88 -0.09561297 -0.22225570
89 -1.92833116 -0.09561297
90 0.25753575 -1.92833116
91 -1.19120228 0.25753575
92 1.42578165 -1.19120228
93 -1.23296867 1.42578165
94 2.63912201 -1.23296867
95 -1.86946974 2.63912201
96 2.60804713 -1.86946974
97 -1.18123683 2.60804713
98 2.27894305 -1.18123683
99 0.45035336 2.27894305
100 -1.24597537 0.45035336
101 1.12825300 -1.24597537
102 2.08370969 1.12825300
103 -1.80548365 2.08370969
104 -4.08756083 -1.80548365
105 -0.30712547 -4.08756083
106 -1.37084180 -0.30712547
107 2.24459334 -1.37084180
108 1.10836832 2.24459334
109 -0.06469082 1.10836832
110 0.41364997 -0.06469082
111 0.06098310 0.41364997
112 1.44819426 0.06098310
113 1.62203939 1.44819426
114 -1.08456322 1.62203939
115 -1.82160574 -1.08456322
116 -3.42600672 -1.82160574
117 0.05965051 -3.42600672
118 1.15426024 0.05965051
119 2.73388901 1.15426024
120 0.92157470 2.73388901
121 -0.32014195 0.92157470
122 -1.53513583 -0.32014195
123 -2.27276351 -1.53513583
124 -0.18079282 -2.27276351
125 1.47247470 -0.18079282
126 1.58978051 1.47247470
127 1.41493436 1.58978051
128 -2.58999084 1.41493436
129 0.88703091 -2.58999084
130 0.12288760 0.88703091
131 0.06534184 0.12288760
132 0.06461289 0.06534184
133 2.25071216 0.06461289
134 0.17213637 2.25071216
135 -2.24423117 0.17213637
136 1.84553367 -2.24423117
137 2.88972754 1.84553367
138 -0.05928686 2.88972754
139 -0.51010201 -0.05928686
140 -3.42600672 -0.51010201
141 3.64895605 -3.42600672
> 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/freestat/rcomp/tmp/75ckv1291409073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/85ckv1291409073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9g31y1291409073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10g31y1291409073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11cdh71291409073.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/freestat/rcomp/tmp/124mgr1291409073.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/freestat/rcomp/tmp/13b5d31291409073.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/freestat/rcomp/tmp/14wnc91291409073.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/freestat/rcomp/tmp/15pftc1291409073.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/freestat/rcomp/tmp/16lp931291409073.tab")
+ }
>
> try(system("convert tmp/1rk441291409073.ps tmp/1rk441291409073.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jblp1291409073.ps tmp/2jblp1291409073.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jblp1291409073.ps tmp/3jblp1291409073.png",intern=TRUE))
character(0)
> try(system("convert tmp/4jblp1291409073.ps tmp/4jblp1291409073.png",intern=TRUE))
character(0)
> try(system("convert tmp/5u2ls1291409073.ps tmp/5u2ls1291409073.png",intern=TRUE))
character(0)
> try(system("convert tmp/6u2ls1291409073.ps tmp/6u2ls1291409073.png",intern=TRUE))
character(0)
> try(system("convert tmp/75ckv1291409073.ps tmp/75ckv1291409073.png",intern=TRUE))
character(0)
> try(system("convert tmp/85ckv1291409073.ps tmp/85ckv1291409073.png",intern=TRUE))
character(0)
> try(system("convert tmp/9g31y1291409073.ps tmp/9g31y1291409073.png",intern=TRUE))
character(0)
> try(system("convert tmp/10g31y1291409073.ps tmp/10g31y1291409073.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.826 2.713 6.141