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(24
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+ ,20)
+ ,dim=c(6
+ ,159)
+ ,dimnames=list(c('Concern_Mistakes'
+ ,'Doubts_actions'
+ ,'Parental_Expectations'
+ ,'Parental_Criticism'
+ ,'Personal_Standards'
+ ,'Organization')
+ ,1:159))
> y <- array(NA,dim=c(6,159),dimnames=list(c('Concern_Mistakes','Doubts_actions','Parental_Expectations','Parental_Criticism','Personal_Standards','Organization'),1:159))
> 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
Concern_Mistakes Doubts_actions Parental_Expectations Parental_Criticism
1 24 14 11 12
2 25 11 7 8
3 17 6 17 8
4 18 12 10 8
5 18 8 12 9
6 16 10 12 7
7 20 10 11 4
8 16 11 11 11
9 18 16 12 7
10 17 11 13 7
11 23 13 14 12
12 30 12 16 10
13 23 8 11 10
14 18 12 10 8
15 15 11 11 8
16 12 4 15 4
17 21 9 9 9
18 15 8 11 8
19 20 8 17 7
20 31 14 17 11
21 27 15 11 9
22 34 16 18 11
23 21 9 14 13
24 31 14 10 8
25 19 11 11 8
26 16 8 15 9
27 20 9 15 6
28 21 9 13 9
29 22 9 16 9
30 17 9 13 6
31 24 10 9 6
32 25 16 18 16
33 26 11 18 5
34 25 8 12 7
35 17 9 17 9
36 32 16 9 6
37 33 11 9 6
38 13 16 12 5
39 32 12 18 12
40 25 12 12 7
41 29 14 18 10
42 22 9 14 9
43 18 10 15 8
44 17 9 16 5
45 20 10 10 8
46 15 12 11 8
47 20 14 14 10
48 33 14 9 6
49 29 10 12 8
50 23 14 17 7
51 26 16 5 4
52 18 9 12 8
53 20 10 12 8
54 11 6 6 4
55 28 8 24 20
56 26 13 12 8
57 22 10 12 8
58 17 8 14 6
59 12 7 7 4
60 14 15 13 8
61 17 9 12 9
62 21 10 13 6
63 19 12 14 7
64 18 13 8 9
65 10 10 11 5
66 29 11 9 5
67 31 8 11 8
68 19 9 13 8
69 9 13 10 6
70 20 11 11 8
71 28 8 12 7
72 19 9 9 7
73 30 9 15 9
74 29 15 18 11
75 26 9 15 6
76 23 10 12 8
77 13 14 13 6
78 21 12 14 9
79 19 12 10 8
80 28 11 13 6
81 23 14 13 10
82 18 6 11 8
83 21 12 13 8
84 20 8 16 10
85 23 14 8 5
86 21 11 16 7
87 21 10 11 5
88 15 14 9 8
89 28 12 16 14
90 19 10 12 7
91 26 14 14 8
92 10 5 8 6
93 16 11 9 5
94 22 10 15 6
95 19 9 11 10
96 31 10 21 12
97 31 16 14 9
98 29 13 18 12
99 19 9 12 7
100 22 10 13 8
101 23 10 15 10
102 15 7 12 6
103 20 9 19 10
104 18 8 15 10
105 23 14 11 10
106 25 14 11 5
107 21 8 10 7
108 24 9 13 10
109 25 14 15 11
110 17 14 12 6
111 13 8 12 7
112 28 8 16 12
113 21 8 9 11
114 25 7 18 11
115 9 6 8 11
116 16 8 13 5
117 19 6 17 8
118 17 11 9 6
119 25 14 15 9
120 20 11 8 4
121 29 11 7 4
122 14 11 12 7
123 22 14 14 11
124 15 8 6 6
125 19 20 8 7
126 20 11 17 8
127 15 8 10 4
128 20 11 11 8
129 18 10 14 9
130 33 14 11 8
131 22 11 13 11
132 16 9 12 8
133 17 9 11 5
134 16 8 9 4
135 21 10 12 8
136 26 13 20 10
137 18 13 12 6
138 18 12 13 9
139 17 8 12 9
140 22 13 12 13
141 30 14 9 9
142 30 12 15 10
143 24 14 24 20
144 21 15 7 5
145 21 13 17 11
146 29 16 11 6
147 31 9 17 9
148 20 9 11 7
149 16 9 12 9
150 22 8 14 10
151 20 7 11 9
152 28 16 16 8
153 38 11 21 7
154 22 9 14 6
155 20 11 20 13
156 17 9 13 6
157 28 14 11 8
158 22 13 15 10
159 31 16 19 16
Personal_Standards Organization
1 24 26
2 25 23
3 30 25
4 19 23
5 22 19
6 22 29
7 25 25
8 23 21
9 17 22
10 21 25
11 19 24
12 19 18
13 15 22
14 16 15
15 23 22
16 27 28
17 22 20
18 14 12
19 22 24
20 23 20
21 23 21
22 21 20
23 19 21
24 18 23
25 20 28
26 23 24
27 25 24
28 19 24
29 24 23
30 22 23
31 25 29
32 26 24
33 29 18
34 32 25
35 25 21
36 29 26
37 28 22
38 17 22
39 28 22
40 29 23
41 26 30
42 25 23
43 14 17
44 25 23
45 26 23
46 20 25
47 18 24
48 32 24
49 25 23
50 25 21
51 23 24
52 21 24
53 20 28
54 15 16
55 30 20
56 24 29
57 26 27
58 24 22
59 22 28
60 14 16
61 24 25
62 24 24
63 24 28
64 24 24
65 19 23
66 31 30
67 22 24
68 27 21
69 19 25
70 25 25
71 20 22
72 21 23
73 27 26
74 23 23
75 25 25
76 20 21
77 21 25
78 22 24
79 23 29
80 25 22
81 25 27
82 17 26
83 19 22
84 25 24
85 19 27
86 20 24
87 26 24
88 23 29
89 27 22
90 17 21
91 17 24
92 19 24
93 17 23
94 22 20
95 21 27
96 32 26
97 21 25
98 21 21
99 18 21
100 18 19
101 23 21
102 19 21
103 20 16
104 21 22
105 20 29
106 17 15
107 18 17
108 19 15
109 22 21
110 15 21
111 14 19
112 18 24
113 24 20
114 35 17
115 29 23
116 21 24
117 25 14
118 20 19
119 22 24
120 13 13
121 26 22
122 17 16
123 25 19
124 20 25
125 19 25
126 21 23
127 22 24
128 24 26
129 21 26
130 26 25
131 24 18
132 16 21
133 23 26
134 18 23
135 16 23
136 26 22
137 19 20
138 21 13
139 21 24
140 22 15
141 23 14
142 29 22
143 21 10
144 21 24
145 23 22
146 27 24
147 25 19
148 21 20
149 10 13
150 20 20
151 26 22
152 24 24
153 29 29
154 19 12
155 24 20
156 19 21
157 24 24
158 22 22
159 17 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Doubts_actions Parental_Expectations
-1.9716 0.8101 0.2513
Parental_Criticism Personal_Standards Organization
0.1885 0.5661 -0.1157
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.7273 -2.4896 -0.3354 2.7482 12.5424
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.97156 3.05291 -0.646 0.5194
Doubts_actions 0.81012 0.13033 6.216 4.63e-09 ***
Parental_Expectations 0.25125 0.13276 1.893 0.0603 .
Parental_Criticism 0.18852 0.16826 1.120 0.2643
Personal_Standards 0.56606 0.09581 5.908 2.17e-08 ***
Organization -0.11572 0.10302 -1.123 0.2631
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.478 on 153 degrees of freedom
Multiple R-squared: 0.4072, Adjusted R-squared: 0.3878
F-statistic: 21.02 on 5 and 153 DF, p-value: 5.863e-16
> 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,] 0.21139602 0.42279205 0.78860398
[2,] 0.10207638 0.20415275 0.89792362
[3,] 0.22723804 0.45447608 0.77276196
[4,] 0.65168824 0.69662353 0.34831176
[5,] 0.66730723 0.66538553 0.33269277
[6,] 0.64201541 0.71596917 0.35798459
[7,] 0.64742133 0.70515734 0.35257867
[8,] 0.58200717 0.83598566 0.41799283
[9,] 0.49744168 0.99488336 0.50255832
[10,] 0.46685372 0.93370744 0.53314628
[11,] 0.40318516 0.80637032 0.59681484
[12,] 0.43760922 0.87521843 0.56239078
[13,] 0.39690153 0.79380305 0.60309847
[14,] 0.40077797 0.80155594 0.59922203
[15,] 0.33876995 0.67753990 0.66123005
[16,] 0.60334157 0.79331686 0.39665843
[17,] 0.53355120 0.93289760 0.46644880
[18,] 0.50354402 0.99291195 0.49645598
[19,] 0.44173100 0.88346200 0.55826900
[20,] 0.39518317 0.79036634 0.60481683
[21,] 0.33497752 0.66995504 0.66502248
[22,] 0.28291897 0.56583794 0.71708103
[23,] 0.34290182 0.68580363 0.65709818
[24,] 0.40423207 0.80846414 0.59576793
[25,] 0.36039888 0.72079776 0.63960112
[26,] 0.37063137 0.74126275 0.62936863
[27,] 0.38530715 0.77061431 0.61469285
[28,] 0.37594970 0.75189940 0.62405030
[29,] 0.55498970 0.89002059 0.44501030
[30,] 0.75846575 0.48306851 0.24153425
[31,] 0.75451794 0.49096412 0.24548206
[32,] 0.71262005 0.57475991 0.28737995
[33,] 0.68761372 0.62477255 0.31238628
[34,] 0.63817358 0.72365284 0.36182642
[35,] 0.58957534 0.82084931 0.41042466
[36,] 0.57618727 0.84762547 0.42381273
[37,] 0.54173755 0.91652491 0.45826245
[38,] 0.56383049 0.87233902 0.43616951
[39,] 0.52281043 0.95437914 0.47718957
[40,] 0.51322634 0.97354733 0.48677366
[41,] 0.57811910 0.84376180 0.42188090
[42,] 0.55720457 0.88559087 0.44279543
[43,] 0.52183261 0.95633478 0.47816739
[44,] 0.47251870 0.94503740 0.52748130
[45,] 0.43438802 0.86877605 0.56561198
[46,] 0.38759744 0.77519488 0.61240256
[47,] 0.34479943 0.68959887 0.65520057
[48,] 0.31767947 0.63535895 0.68232053
[49,] 0.27475216 0.54950432 0.72524784
[50,] 0.25110592 0.50221185 0.74889408
[51,] 0.23143432 0.46286864 0.76856568
[52,] 0.29014408 0.58028816 0.70985592
[53,] 0.27849719 0.55699437 0.72150281
[54,] 0.24124896 0.48249792 0.75875104
[55,] 0.22867512 0.45735025 0.77132488
[56,] 0.26253626 0.52507253 0.73746374
[57,] 0.33883841 0.67767683 0.66116159
[58,] 0.34155928 0.68311856 0.65844072
[59,] 0.67452622 0.65094756 0.32547378
[60,] 0.66729815 0.66540370 0.33270185
[61,] 0.84420684 0.31158632 0.15579316
[62,] 0.82447819 0.35104362 0.17552181
[63,] 0.93052850 0.13894300 0.06947150
[64,] 0.91423753 0.17152494 0.08576247
[65,] 0.93742258 0.12515485 0.06257742
[66,] 0.92557432 0.14885136 0.07442568
[67,] 0.92732204 0.14535591 0.07267796
[68,] 0.92100240 0.15799519 0.07899760
[69,] 0.97009612 0.05980775 0.02990388
[70,] 0.96273712 0.07452575 0.03726288
[71,] 0.95532211 0.08935577 0.04467789
[72,] 0.95800107 0.08399786 0.04199893
[73,] 0.95032724 0.09934552 0.04967276
[74,] 0.95000977 0.09998046 0.04999023
[75,] 0.93718938 0.12562125 0.06281062
[76,] 0.92442952 0.15114097 0.07557048
[77,] 0.91595167 0.16809665 0.08404833
[78,] 0.90065318 0.19869364 0.09934682
[79,] 0.87971004 0.24057992 0.12028996
[80,] 0.92372740 0.15254520 0.07627260
[81,] 0.90779495 0.18441010 0.09220505
[82,] 0.88852103 0.22295794 0.11147897
[83,] 0.89034851 0.21930298 0.10965149
[84,] 0.87972329 0.24055341 0.12027671
[85,] 0.85848639 0.28302722 0.14151361
[86,] 0.83180233 0.33639533 0.16819767
[87,] 0.79989174 0.40021652 0.20010826
[88,] 0.77136359 0.45727282 0.22863641
[89,] 0.79044571 0.41910859 0.20955429
[90,] 0.78525342 0.42949317 0.21474658
[91,] 0.75131570 0.49736860 0.24868430
[92,] 0.72777236 0.54445529 0.27222764
[93,] 0.68641586 0.62716827 0.31358414
[94,] 0.64866958 0.70266083 0.35133042
[95,] 0.61068874 0.77862253 0.38931126
[96,] 0.56891251 0.86217497 0.43108749
[97,] 0.52334826 0.95330348 0.47665174
[98,] 0.50374480 0.99251039 0.49625520
[99,] 0.50060150 0.99879701 0.49939850
[100,] 0.51206689 0.97586622 0.48793311
[101,] 0.46197061 0.92394121 0.53802939
[102,] 0.43887195 0.87774391 0.56112805
[103,] 0.39924511 0.79849022 0.60075489
[104,] 0.62246400 0.75507199 0.37753600
[105,] 0.61435043 0.77129914 0.38564957
[106,] 0.58601223 0.82797553 0.41398777
[107,] 0.78799506 0.42400989 0.21200494
[108,] 0.76600284 0.46799432 0.23399716
[109,] 0.75505328 0.48989343 0.24494672
[110,] 0.72831003 0.54337994 0.27168997
[111,] 0.68052172 0.63895655 0.31947828
[112,] 0.68982169 0.62035663 0.31017831
[113,] 0.74125046 0.51749909 0.25874954
[114,] 0.74631109 0.50737782 0.25368891
[115,] 0.75206646 0.49586709 0.24793354
[116,] 0.70272287 0.59455427 0.29727713
[117,] 0.74667194 0.50665613 0.25332806
[118,] 0.71971018 0.56057965 0.28028982
[119,] 0.70972070 0.58055860 0.29027930
[120,] 0.67602968 0.64794064 0.32397032
[121,] 0.65088096 0.69823809 0.34911904
[122,] 0.72046223 0.55907554 0.27953777
[123,] 0.66698606 0.66602789 0.33301394
[124,] 0.60623021 0.78753957 0.39376979
[125,] 0.60589517 0.78820965 0.39410483
[126,] 0.54696478 0.90607044 0.45303522
[127,] 0.50621248 0.98757504 0.49378752
[128,] 0.47886978 0.95773956 0.52113022
[129,] 0.48514335 0.97028670 0.51485665
[130,] 0.52873349 0.94253303 0.47126651
[131,] 0.46164830 0.92329660 0.53835170
[132,] 0.38750892 0.77501784 0.61249108
[133,] 0.50039899 0.99920202 0.49960101
[134,] 0.44773143 0.89546286 0.55226857
[135,] 0.37798976 0.75597952 0.62201024
[136,] 0.29800985 0.59601969 0.70199015
[137,] 0.35526328 0.71052655 0.64473672
[138,] 0.26295088 0.52590175 0.73704912
[139,] 0.34057334 0.68114667 0.65942666
[140,] 0.23492127 0.46984254 0.76507873
[141,] 0.14607797 0.29215595 0.85392203
[142,] 0.09191941 0.18383883 0.90808059
> postscript(file="/var/www/html/rcomp/tmp/1a5o61290275622.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2lw5r1290275622.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lw5r1290275622.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lw5r1290275622.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5v54c1290275622.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 = 159
Frequency = 1
1 2 3 4 5 6
-0.97306965 3.30317248 -5.75762753 -1.86432396 -1.47592131 -3.56194532
7 8 9 10 11 12
-0.90620471 -6.36670620 -4.40239954 -3.52013359 0.68218064 7.67252311
13 14 15 16 17 18
7.89642341 -1.09187945 -6.68543104 -5.83544542 1.58343372 -0.31766159
19 20 21 22 23 24
0.22344200 4.57967991 1.76983335 6.84030690 1.38700368 10.08149182
25 26 27 28 29 30
-0.29292415 -4.21715322 -1.59385095 2.73948872 0.03968511 -2.50886805
31 32 33 34 35 36
3.68213985 -5.46973622 -0.73791368 0.93478062 -6.00907079 4.20997728
37 38 39 40 41 42
9.36378971 -9.02536193 4.16126995 -0.83896049 1.97593809 -0.02387250
43 44 45 46 47 48
0.63566868 -4.77230448 -2.20652862 -5.45020489 -2.18484145 4.90059439
49 50 51 52 53 54
6.85702899 -3.68265576 2.75698069 -0.95286850 1.26594676 -0.79025918
55 56 57 58 59 60
-0.97747114 2.68703270 -0.24616086 -3.19784550 -3.42546609 -7.02816544
61 62 63 64 65 66
-3.72386300 -0.33540345 -3.93254992 -5.07506540 -7.92977211 4.77986400
67 68 69 70 71 72
12.54244481 -3.94766720 -11.06597338 -2.47040444 10.38040215 0.87369237
73 74 75 76 77 78
6.93990050 1.86545801 4.52186780 3.45591557 -9.76198833 -1.64033287
79 80 81 82 83 84
-2.43427076 5.05696974 -2.54888532 4.22445539 0.26619649 -1.78905525
85 86 87 88 89 90
3.04636559 0.17645168 -0.77650707 -7.80326620 0.85280436 1.34262882
91 92 93 94 95 96
4.75826097 -3.19818879 -1.10525980 0.83134401 0.26850372 2.22637389
97 98 99 100 101 102
5.80095262 4.19788038 1.58668852 3.10535412 0.62692272 -1.17060845
103 104 105 106 107 108
-1.44836644 -1.50497988 1.01538343 4.03610951 4.43644527 4.50950124
109 110 111 112 113 114
-0.23602934 -2.57722082 -1.57036519 9.79636083 0.88439100 -3.14063607
115 116 117 118 119 120
-11.72727421 -1.82844117 -2.20020963 -2.45484801 0.48816450 4.44158444
121 122 123 124 125 126
7.37546414 -5.04608941 -4.91440766 -0.57640121 -6.42285660 -1.94510428
127 128 129 130 131 132
-2.45222637 -1.78862089 -2.22258154 7.53315720 -1.78243444 -0.46970066
133 134 135 136 137 138
-2.03675063 0.94756774 3.95161231 -1.64219453 -3.14707429 -5.09592061
139 140 141 142 143 144
-1.33126279 -2.74349408 5.27243388 2.72600355 -5.90082233 -0.99179118
145 146 147 148 149 150
-4.37875809 1.60816222 7.75949173 1.02402894 1.81241949 3.08090105
151 152 153 154 155 156
-0.33164622 0.67305104 11.40419406 2.66516664 -5.68680977 -1.04211109
157 158 159
3.54956808 -2.12166727 4.91071853
> postscript(file="/var/www/html/rcomp/tmp/6v54c1290275622.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.97306965 NA
1 3.30317248 -0.97306965
2 -5.75762753 3.30317248
3 -1.86432396 -5.75762753
4 -1.47592131 -1.86432396
5 -3.56194532 -1.47592131
6 -0.90620471 -3.56194532
7 -6.36670620 -0.90620471
8 -4.40239954 -6.36670620
9 -3.52013359 -4.40239954
10 0.68218064 -3.52013359
11 7.67252311 0.68218064
12 7.89642341 7.67252311
13 -1.09187945 7.89642341
14 -6.68543104 -1.09187945
15 -5.83544542 -6.68543104
16 1.58343372 -5.83544542
17 -0.31766159 1.58343372
18 0.22344200 -0.31766159
19 4.57967991 0.22344200
20 1.76983335 4.57967991
21 6.84030690 1.76983335
22 1.38700368 6.84030690
23 10.08149182 1.38700368
24 -0.29292415 10.08149182
25 -4.21715322 -0.29292415
26 -1.59385095 -4.21715322
27 2.73948872 -1.59385095
28 0.03968511 2.73948872
29 -2.50886805 0.03968511
30 3.68213985 -2.50886805
31 -5.46973622 3.68213985
32 -0.73791368 -5.46973622
33 0.93478062 -0.73791368
34 -6.00907079 0.93478062
35 4.20997728 -6.00907079
36 9.36378971 4.20997728
37 -9.02536193 9.36378971
38 4.16126995 -9.02536193
39 -0.83896049 4.16126995
40 1.97593809 -0.83896049
41 -0.02387250 1.97593809
42 0.63566868 -0.02387250
43 -4.77230448 0.63566868
44 -2.20652862 -4.77230448
45 -5.45020489 -2.20652862
46 -2.18484145 -5.45020489
47 4.90059439 -2.18484145
48 6.85702899 4.90059439
49 -3.68265576 6.85702899
50 2.75698069 -3.68265576
51 -0.95286850 2.75698069
52 1.26594676 -0.95286850
53 -0.79025918 1.26594676
54 -0.97747114 -0.79025918
55 2.68703270 -0.97747114
56 -0.24616086 2.68703270
57 -3.19784550 -0.24616086
58 -3.42546609 -3.19784550
59 -7.02816544 -3.42546609
60 -3.72386300 -7.02816544
61 -0.33540345 -3.72386300
62 -3.93254992 -0.33540345
63 -5.07506540 -3.93254992
64 -7.92977211 -5.07506540
65 4.77986400 -7.92977211
66 12.54244481 4.77986400
67 -3.94766720 12.54244481
68 -11.06597338 -3.94766720
69 -2.47040444 -11.06597338
70 10.38040215 -2.47040444
71 0.87369237 10.38040215
72 6.93990050 0.87369237
73 1.86545801 6.93990050
74 4.52186780 1.86545801
75 3.45591557 4.52186780
76 -9.76198833 3.45591557
77 -1.64033287 -9.76198833
78 -2.43427076 -1.64033287
79 5.05696974 -2.43427076
80 -2.54888532 5.05696974
81 4.22445539 -2.54888532
82 0.26619649 4.22445539
83 -1.78905525 0.26619649
84 3.04636559 -1.78905525
85 0.17645168 3.04636559
86 -0.77650707 0.17645168
87 -7.80326620 -0.77650707
88 0.85280436 -7.80326620
89 1.34262882 0.85280436
90 4.75826097 1.34262882
91 -3.19818879 4.75826097
92 -1.10525980 -3.19818879
93 0.83134401 -1.10525980
94 0.26850372 0.83134401
95 2.22637389 0.26850372
96 5.80095262 2.22637389
97 4.19788038 5.80095262
98 1.58668852 4.19788038
99 3.10535412 1.58668852
100 0.62692272 3.10535412
101 -1.17060845 0.62692272
102 -1.44836644 -1.17060845
103 -1.50497988 -1.44836644
104 1.01538343 -1.50497988
105 4.03610951 1.01538343
106 4.43644527 4.03610951
107 4.50950124 4.43644527
108 -0.23602934 4.50950124
109 -2.57722082 -0.23602934
110 -1.57036519 -2.57722082
111 9.79636083 -1.57036519
112 0.88439100 9.79636083
113 -3.14063607 0.88439100
114 -11.72727421 -3.14063607
115 -1.82844117 -11.72727421
116 -2.20020963 -1.82844117
117 -2.45484801 -2.20020963
118 0.48816450 -2.45484801
119 4.44158444 0.48816450
120 7.37546414 4.44158444
121 -5.04608941 7.37546414
122 -4.91440766 -5.04608941
123 -0.57640121 -4.91440766
124 -6.42285660 -0.57640121
125 -1.94510428 -6.42285660
126 -2.45222637 -1.94510428
127 -1.78862089 -2.45222637
128 -2.22258154 -1.78862089
129 7.53315720 -2.22258154
130 -1.78243444 7.53315720
131 -0.46970066 -1.78243444
132 -2.03675063 -0.46970066
133 0.94756774 -2.03675063
134 3.95161231 0.94756774
135 -1.64219453 3.95161231
136 -3.14707429 -1.64219453
137 -5.09592061 -3.14707429
138 -1.33126279 -5.09592061
139 -2.74349408 -1.33126279
140 5.27243388 -2.74349408
141 2.72600355 5.27243388
142 -5.90082233 2.72600355
143 -0.99179118 -5.90082233
144 -4.37875809 -0.99179118
145 1.60816222 -4.37875809
146 7.75949173 1.60816222
147 1.02402894 7.75949173
148 1.81241949 1.02402894
149 3.08090105 1.81241949
150 -0.33164622 3.08090105
151 0.67305104 -0.33164622
152 11.40419406 0.67305104
153 2.66516664 11.40419406
154 -5.68680977 2.66516664
155 -1.04211109 -5.68680977
156 3.54956808 -1.04211109
157 -2.12166727 3.54956808
158 4.91071853 -2.12166727
159 NA 4.91071853
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.30317248 -0.97306965
[2,] -5.75762753 3.30317248
[3,] -1.86432396 -5.75762753
[4,] -1.47592131 -1.86432396
[5,] -3.56194532 -1.47592131
[6,] -0.90620471 -3.56194532
[7,] -6.36670620 -0.90620471
[8,] -4.40239954 -6.36670620
[9,] -3.52013359 -4.40239954
[10,] 0.68218064 -3.52013359
[11,] 7.67252311 0.68218064
[12,] 7.89642341 7.67252311
[13,] -1.09187945 7.89642341
[14,] -6.68543104 -1.09187945
[15,] -5.83544542 -6.68543104
[16,] 1.58343372 -5.83544542
[17,] -0.31766159 1.58343372
[18,] 0.22344200 -0.31766159
[19,] 4.57967991 0.22344200
[20,] 1.76983335 4.57967991
[21,] 6.84030690 1.76983335
[22,] 1.38700368 6.84030690
[23,] 10.08149182 1.38700368
[24,] -0.29292415 10.08149182
[25,] -4.21715322 -0.29292415
[26,] -1.59385095 -4.21715322
[27,] 2.73948872 -1.59385095
[28,] 0.03968511 2.73948872
[29,] -2.50886805 0.03968511
[30,] 3.68213985 -2.50886805
[31,] -5.46973622 3.68213985
[32,] -0.73791368 -5.46973622
[33,] 0.93478062 -0.73791368
[34,] -6.00907079 0.93478062
[35,] 4.20997728 -6.00907079
[36,] 9.36378971 4.20997728
[37,] -9.02536193 9.36378971
[38,] 4.16126995 -9.02536193
[39,] -0.83896049 4.16126995
[40,] 1.97593809 -0.83896049
[41,] -0.02387250 1.97593809
[42,] 0.63566868 -0.02387250
[43,] -4.77230448 0.63566868
[44,] -2.20652862 -4.77230448
[45,] -5.45020489 -2.20652862
[46,] -2.18484145 -5.45020489
[47,] 4.90059439 -2.18484145
[48,] 6.85702899 4.90059439
[49,] -3.68265576 6.85702899
[50,] 2.75698069 -3.68265576
[51,] -0.95286850 2.75698069
[52,] 1.26594676 -0.95286850
[53,] -0.79025918 1.26594676
[54,] -0.97747114 -0.79025918
[55,] 2.68703270 -0.97747114
[56,] -0.24616086 2.68703270
[57,] -3.19784550 -0.24616086
[58,] -3.42546609 -3.19784550
[59,] -7.02816544 -3.42546609
[60,] -3.72386300 -7.02816544
[61,] -0.33540345 -3.72386300
[62,] -3.93254992 -0.33540345
[63,] -5.07506540 -3.93254992
[64,] -7.92977211 -5.07506540
[65,] 4.77986400 -7.92977211
[66,] 12.54244481 4.77986400
[67,] -3.94766720 12.54244481
[68,] -11.06597338 -3.94766720
[69,] -2.47040444 -11.06597338
[70,] 10.38040215 -2.47040444
[71,] 0.87369237 10.38040215
[72,] 6.93990050 0.87369237
[73,] 1.86545801 6.93990050
[74,] 4.52186780 1.86545801
[75,] 3.45591557 4.52186780
[76,] -9.76198833 3.45591557
[77,] -1.64033287 -9.76198833
[78,] -2.43427076 -1.64033287
[79,] 5.05696974 -2.43427076
[80,] -2.54888532 5.05696974
[81,] 4.22445539 -2.54888532
[82,] 0.26619649 4.22445539
[83,] -1.78905525 0.26619649
[84,] 3.04636559 -1.78905525
[85,] 0.17645168 3.04636559
[86,] -0.77650707 0.17645168
[87,] -7.80326620 -0.77650707
[88,] 0.85280436 -7.80326620
[89,] 1.34262882 0.85280436
[90,] 4.75826097 1.34262882
[91,] -3.19818879 4.75826097
[92,] -1.10525980 -3.19818879
[93,] 0.83134401 -1.10525980
[94,] 0.26850372 0.83134401
[95,] 2.22637389 0.26850372
[96,] 5.80095262 2.22637389
[97,] 4.19788038 5.80095262
[98,] 1.58668852 4.19788038
[99,] 3.10535412 1.58668852
[100,] 0.62692272 3.10535412
[101,] -1.17060845 0.62692272
[102,] -1.44836644 -1.17060845
[103,] -1.50497988 -1.44836644
[104,] 1.01538343 -1.50497988
[105,] 4.03610951 1.01538343
[106,] 4.43644527 4.03610951
[107,] 4.50950124 4.43644527
[108,] -0.23602934 4.50950124
[109,] -2.57722082 -0.23602934
[110,] -1.57036519 -2.57722082
[111,] 9.79636083 -1.57036519
[112,] 0.88439100 9.79636083
[113,] -3.14063607 0.88439100
[114,] -11.72727421 -3.14063607
[115,] -1.82844117 -11.72727421
[116,] -2.20020963 -1.82844117
[117,] -2.45484801 -2.20020963
[118,] 0.48816450 -2.45484801
[119,] 4.44158444 0.48816450
[120,] 7.37546414 4.44158444
[121,] -5.04608941 7.37546414
[122,] -4.91440766 -5.04608941
[123,] -0.57640121 -4.91440766
[124,] -6.42285660 -0.57640121
[125,] -1.94510428 -6.42285660
[126,] -2.45222637 -1.94510428
[127,] -1.78862089 -2.45222637
[128,] -2.22258154 -1.78862089
[129,] 7.53315720 -2.22258154
[130,] -1.78243444 7.53315720
[131,] -0.46970066 -1.78243444
[132,] -2.03675063 -0.46970066
[133,] 0.94756774 -2.03675063
[134,] 3.95161231 0.94756774
[135,] -1.64219453 3.95161231
[136,] -3.14707429 -1.64219453
[137,] -5.09592061 -3.14707429
[138,] -1.33126279 -5.09592061
[139,] -2.74349408 -1.33126279
[140,] 5.27243388 -2.74349408
[141,] 2.72600355 5.27243388
[142,] -5.90082233 2.72600355
[143,] -0.99179118 -5.90082233
[144,] -4.37875809 -0.99179118
[145,] 1.60816222 -4.37875809
[146,] 7.75949173 1.60816222
[147,] 1.02402894 7.75949173
[148,] 1.81241949 1.02402894
[149,] 3.08090105 1.81241949
[150,] -0.33164622 3.08090105
[151,] 0.67305104 -0.33164622
[152,] 11.40419406 0.67305104
[153,] 2.66516664 11.40419406
[154,] -5.68680977 2.66516664
[155,] -1.04211109 -5.68680977
[156,] 3.54956808 -1.04211109
[157,] -2.12166727 3.54956808
[158,] 4.91071853 -2.12166727
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.30317248 -0.97306965
2 -5.75762753 3.30317248
3 -1.86432396 -5.75762753
4 -1.47592131 -1.86432396
5 -3.56194532 -1.47592131
6 -0.90620471 -3.56194532
7 -6.36670620 -0.90620471
8 -4.40239954 -6.36670620
9 -3.52013359 -4.40239954
10 0.68218064 -3.52013359
11 7.67252311 0.68218064
12 7.89642341 7.67252311
13 -1.09187945 7.89642341
14 -6.68543104 -1.09187945
15 -5.83544542 -6.68543104
16 1.58343372 -5.83544542
17 -0.31766159 1.58343372
18 0.22344200 -0.31766159
19 4.57967991 0.22344200
20 1.76983335 4.57967991
21 6.84030690 1.76983335
22 1.38700368 6.84030690
23 10.08149182 1.38700368
24 -0.29292415 10.08149182
25 -4.21715322 -0.29292415
26 -1.59385095 -4.21715322
27 2.73948872 -1.59385095
28 0.03968511 2.73948872
29 -2.50886805 0.03968511
30 3.68213985 -2.50886805
31 -5.46973622 3.68213985
32 -0.73791368 -5.46973622
33 0.93478062 -0.73791368
34 -6.00907079 0.93478062
35 4.20997728 -6.00907079
36 9.36378971 4.20997728
37 -9.02536193 9.36378971
38 4.16126995 -9.02536193
39 -0.83896049 4.16126995
40 1.97593809 -0.83896049
41 -0.02387250 1.97593809
42 0.63566868 -0.02387250
43 -4.77230448 0.63566868
44 -2.20652862 -4.77230448
45 -5.45020489 -2.20652862
46 -2.18484145 -5.45020489
47 4.90059439 -2.18484145
48 6.85702899 4.90059439
49 -3.68265576 6.85702899
50 2.75698069 -3.68265576
51 -0.95286850 2.75698069
52 1.26594676 -0.95286850
53 -0.79025918 1.26594676
54 -0.97747114 -0.79025918
55 2.68703270 -0.97747114
56 -0.24616086 2.68703270
57 -3.19784550 -0.24616086
58 -3.42546609 -3.19784550
59 -7.02816544 -3.42546609
60 -3.72386300 -7.02816544
61 -0.33540345 -3.72386300
62 -3.93254992 -0.33540345
63 -5.07506540 -3.93254992
64 -7.92977211 -5.07506540
65 4.77986400 -7.92977211
66 12.54244481 4.77986400
67 -3.94766720 12.54244481
68 -11.06597338 -3.94766720
69 -2.47040444 -11.06597338
70 10.38040215 -2.47040444
71 0.87369237 10.38040215
72 6.93990050 0.87369237
73 1.86545801 6.93990050
74 4.52186780 1.86545801
75 3.45591557 4.52186780
76 -9.76198833 3.45591557
77 -1.64033287 -9.76198833
78 -2.43427076 -1.64033287
79 5.05696974 -2.43427076
80 -2.54888532 5.05696974
81 4.22445539 -2.54888532
82 0.26619649 4.22445539
83 -1.78905525 0.26619649
84 3.04636559 -1.78905525
85 0.17645168 3.04636559
86 -0.77650707 0.17645168
87 -7.80326620 -0.77650707
88 0.85280436 -7.80326620
89 1.34262882 0.85280436
90 4.75826097 1.34262882
91 -3.19818879 4.75826097
92 -1.10525980 -3.19818879
93 0.83134401 -1.10525980
94 0.26850372 0.83134401
95 2.22637389 0.26850372
96 5.80095262 2.22637389
97 4.19788038 5.80095262
98 1.58668852 4.19788038
99 3.10535412 1.58668852
100 0.62692272 3.10535412
101 -1.17060845 0.62692272
102 -1.44836644 -1.17060845
103 -1.50497988 -1.44836644
104 1.01538343 -1.50497988
105 4.03610951 1.01538343
106 4.43644527 4.03610951
107 4.50950124 4.43644527
108 -0.23602934 4.50950124
109 -2.57722082 -0.23602934
110 -1.57036519 -2.57722082
111 9.79636083 -1.57036519
112 0.88439100 9.79636083
113 -3.14063607 0.88439100
114 -11.72727421 -3.14063607
115 -1.82844117 -11.72727421
116 -2.20020963 -1.82844117
117 -2.45484801 -2.20020963
118 0.48816450 -2.45484801
119 4.44158444 0.48816450
120 7.37546414 4.44158444
121 -5.04608941 7.37546414
122 -4.91440766 -5.04608941
123 -0.57640121 -4.91440766
124 -6.42285660 -0.57640121
125 -1.94510428 -6.42285660
126 -2.45222637 -1.94510428
127 -1.78862089 -2.45222637
128 -2.22258154 -1.78862089
129 7.53315720 -2.22258154
130 -1.78243444 7.53315720
131 -0.46970066 -1.78243444
132 -2.03675063 -0.46970066
133 0.94756774 -2.03675063
134 3.95161231 0.94756774
135 -1.64219453 3.95161231
136 -3.14707429 -1.64219453
137 -5.09592061 -3.14707429
138 -1.33126279 -5.09592061
139 -2.74349408 -1.33126279
140 5.27243388 -2.74349408
141 2.72600355 5.27243388
142 -5.90082233 2.72600355
143 -0.99179118 -5.90082233
144 -4.37875809 -0.99179118
145 1.60816222 -4.37875809
146 7.75949173 1.60816222
147 1.02402894 7.75949173
148 1.81241949 1.02402894
149 3.08090105 1.81241949
150 -0.33164622 3.08090105
151 0.67305104 -0.33164622
152 11.40419406 0.67305104
153 2.66516664 11.40419406
154 -5.68680977 2.66516664
155 -1.04211109 -5.68680977
156 3.54956808 -1.04211109
157 -2.12166727 3.54956808
158 4.91071853 -2.12166727
> 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/7oelf1290275622.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8oelf1290275622.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9h6301290275622.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10h6301290275622.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11261o1290275622.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/1257iu1290275622.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/13uqxo1290275622.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/14nzw81290275622.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/158hve1290275622.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/1649sn1290275622.tab")
+ }
>
> try(system("convert tmp/1a5o61290275622.ps tmp/1a5o61290275622.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lw5r1290275622.ps tmp/2lw5r1290275622.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lw5r1290275622.ps tmp/3lw5r1290275622.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lw5r1290275622.ps tmp/4lw5r1290275622.png",intern=TRUE))
character(0)
> try(system("convert tmp/5v54c1290275622.ps tmp/5v54c1290275622.png",intern=TRUE))
character(0)
> try(system("convert tmp/6v54c1290275622.ps tmp/6v54c1290275622.png",intern=TRUE))
character(0)
> try(system("convert tmp/7oelf1290275622.ps tmp/7oelf1290275622.png",intern=TRUE))
character(0)
> try(system("convert tmp/8oelf1290275622.ps tmp/8oelf1290275622.png",intern=TRUE))
character(0)
> try(system("convert tmp/9h6301290275622.ps tmp/9h6301290275622.png",intern=TRUE))
character(0)
> try(system("convert tmp/10h6301290275622.ps tmp/10h6301290275622.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.080 1.792 8.858