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(1
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+ ,17)
+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('Pop'
+ ,'Organization'
+ ,'concernmistakes'
+ ,'doubtactions'
+ ,'parentalexp'
+ ,'parentalcrit'
+ ,'personalstandards')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('Pop','Organization','concernmistakes','doubtactions','parentalexp','parentalcrit','personalstandards'),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 = '2'
> #'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
Organization Pop concernmistakes doubtactions parentalexp parentalcrit
1 26 1 24 14 11 12
2 23 1 25 11 7 8
3 25 0 17 6 17 8
4 23 1 18 12 10 8
5 19 1 18 8 12 9
6 29 0 16 10 12 7
7 25 1 20 10 11 4
8 21 1 16 11 11 11
9 22 1 18 16 12 7
10 25 1 17 11 13 7
11 24 1 23 13 14 12
12 18 1 30 12 16 10
13 22 1 23 8 11 10
14 15 1 18 12 10 8
15 22 1 15 11 11 8
16 28 1 12 4 15 4
17 20 1 21 9 9 9
18 12 1 15 8 11 8
19 24 1 20 8 17 7
20 20 1 31 14 17 11
21 21 1 27 15 11 9
22 20 1 34 16 18 11
23 21 1 21 9 14 13
24 23 1 31 14 10 8
25 28 1 19 11 11 8
26 24 1 16 8 15 9
27 24 1 20 9 15 6
28 24 1 21 9 13 9
29 23 1 22 9 16 9
30 23 1 17 9 13 6
31 29 1 24 10 9 6
32 24 1 25 16 18 16
33 18 1 26 11 18 5
34 25 1 25 8 12 7
35 21 1 17 9 17 9
36 26 1 32 16 9 6
37 22 1 33 11 9 6
38 22 1 13 16 12 5
39 22 0 32 12 18 12
40 23 1 25 12 12 7
41 30 1 29 14 18 10
42 23 1 22 9 14 9
43 17 1 18 10 15 8
44 23 1 17 9 16 5
45 23 1 20 10 10 8
46 25 1 15 12 11 8
47 24 1 20 14 14 10
48 24 1 33 14 9 6
49 23 1 29 10 12 8
50 21 1 23 14 17 7
51 24 1 26 16 5 4
52 24 1 18 9 12 8
53 28 1 20 10 12 8
54 16 1 11 6 6 4
55 20 1 28 8 24 20
56 29 1 26 13 12 8
57 27 1 22 10 12 8
58 22 1 17 8 14 6
59 28 1 12 7 7 4
60 16 1 14 15 13 8
61 25 1 17 9 12 9
62 24 1 21 10 13 6
63 28 0 19 12 14 7
64 24 1 18 13 8 9
65 23 1 10 10 11 5
66 30 1 29 11 9 5
67 24 1 31 8 11 8
68 21 1 19 9 13 8
69 25 1 9 13 10 6
70 25 0 20 11 11 8
71 22 1 28 8 12 7
72 23 1 19 9 9 7
73 26 1 30 9 15 9
74 23 1 29 15 18 11
75 25 1 26 9 15 6
76 21 1 23 10 12 8
77 25 1 13 14 13 6
78 24 1 21 12 14 9
79 29 1 19 12 10 8
80 22 1 28 11 13 6
81 27 1 23 14 13 10
82 26 0 18 6 11 8
83 22 1 21 12 13 8
84 24 1 20 8 16 10
85 27 0 23 14 8 5
86 24 1 21 11 16 7
87 24 1 21 10 11 5
88 29 1 15 14 9 8
89 22 1 28 12 16 14
90 21 0 19 10 12 7
91 24 1 26 14 14 8
92 24 1 10 5 8 6
93 23 0 16 11 9 5
94 20 1 22 10 15 6
95 27 1 19 9 11 10
96 26 1 31 10 21 12
97 25 1 31 16 14 9
98 21 1 29 13 18 12
99 21 1 19 9 12 7
100 19 1 22 10 13 8
101 21 1 23 10 15 10
102 21 1 15 7 12 6
103 16 1 20 9 19 10
104 22 1 18 8 15 10
105 29 1 23 14 11 10
106 15 0 25 14 11 5
107 17 1 21 8 10 7
108 15 1 24 9 13 10
109 21 1 25 14 15 11
110 21 0 17 14 12 6
111 19 1 13 8 12 7
112 24 1 28 8 16 12
113 20 1 21 8 9 11
114 17 0 25 7 18 11
115 23 1 9 6 8 11
116 24 1 16 8 13 5
117 14 1 19 6 17 8
118 19 1 17 11 9 6
119 24 1 25 14 15 9
120 13 1 20 11 8 4
121 22 1 29 11 7 4
122 16 1 14 11 12 7
123 19 0 22 14 14 11
124 25 1 15 8 6 6
125 25 1 19 20 8 7
126 23 1 20 11 17 8
127 24 0 15 8 10 4
128 26 1 20 11 11 8
129 26 1 18 10 14 9
130 25 1 33 14 11 8
131 18 1 22 11 13 11
132 21 1 16 9 12 8
133 26 1 17 9 11 5
134 23 1 16 8 9 4
135 23 1 21 10 12 8
136 22 1 26 13 20 10
137 20 1 18 13 12 6
138 13 1 18 12 13 9
139 24 1 17 8 12 9
140 15 1 22 13 12 13
141 14 1 30 14 9 9
142 22 0 30 12 15 10
143 10 1 24 14 24 20
144 24 1 21 15 7 5
145 22 1 21 13 17 11
146 24 1 29 16 11 6
147 19 1 31 9 17 9
148 20 0 20 9 11 7
149 13 1 16 9 12 9
150 20 1 22 8 14 10
151 22 1 20 7 11 9
152 24 1 28 16 16 8
153 29 1 38 11 21 7
154 12 1 22 9 14 6
155 20 1 20 11 20 13
156 21 1 17 9 13 6
157 24 1 28 14 11 8
158 22 1 22 13 15 10
159 20 1 31 16 19 16
personalstandards
1 24
2 25
3 30
4 19
5 22
6 22
7 25
8 23
9 17
10 21
11 19
12 19
13 15
14 16
15 23
16 27
17 22
18 14
19 22
20 23
21 23
22 21
23 19
24 18
25 20
26 23
27 25
28 19
29 24
30 22
31 25
32 26
33 29
34 32
35 25
36 29
37 28
38 17
39 28
40 29
41 26
42 25
43 14
44 25
45 26
46 20
47 18
48 32
49 25
50 25
51 23
52 21
53 20
54 15
55 30
56 24
57 26
58 24
59 22
60 14
61 24
62 24
63 24
64 24
65 19
66 31
67 22
68 27
69 19
70 25
71 20
72 21
73 27
74 23
75 25
76 20
77 21
78 22
79 23
80 25
81 25
82 17
83 19
84 25
85 19
86 20
87 26
88 23
89 27
90 17
91 17
92 19
93 17
94 22
95 21
96 32
97 21
98 21
99 18
100 18
101 23
102 19
103 20
104 21
105 20
106 17
107 18
108 19
109 22
110 15
111 14
112 18
113 24
114 35
115 29
116 21
117 25
118 20
119 22
120 13
121 26
122 17
123 25
124 20
125 19
126 21
127 22
128 24
129 21
130 26
131 24
132 16
133 23
134 18
135 16
136 26
137 19
138 21
139 21
140 22
141 23
142 29
143 21
144 21
145 23
146 27
147 25
148 21
149 10
150 20
151 26
152 24
153 29
154 19
155 24
156 19
157 24
158 22
159 17
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pop concernmistakes doubtactions
16.135493 -0.001220 -0.070672 0.218173
parentalexp parentalcrit personalstandards
-0.148958 -0.255148 0.422751
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.1549 -1.7376 0.2699 2.2317 7.1718
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.13549 2.18040 7.400 8.62e-12 ***
Pop -0.00122 0.93151 -0.001 0.9990
concernmistakes -0.07067 0.06319 -1.118 0.2652
doubtactions 0.21817 0.11299 1.931 0.0554 .
parentalexp -0.14896 0.10468 -1.423 0.1568
parentalcrit -0.25515 0.13127 -1.944 0.0538 .
personalstandards 0.42275 0.07601 5.562 1.17e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.511 on 152 degrees of freedom
Multiple R-squared: 0.2224, Adjusted R-squared: 0.1917
F-statistic: 7.244 on 6 and 152 DF, p-value: 8.124e-07
> 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,] 0.434874568 0.869749136 0.5651254
[2,] 0.290017249 0.580034499 0.7099828
[3,] 0.297358081 0.594716162 0.7026419
[4,] 0.267087433 0.534174866 0.7329126
[5,] 0.619368477 0.761263045 0.3806315
[6,] 0.514624950 0.970750101 0.4853751
[7,] 0.592058838 0.815882324 0.4079412
[8,] 0.522320474 0.955359053 0.4776795
[9,] 0.658322042 0.683355915 0.3416780
[10,] 0.589093051 0.821813899 0.4109069
[11,] 0.582919154 0.834161691 0.4170808
[12,] 0.535596172 0.928807655 0.4644038
[13,] 0.464533480 0.929066961 0.5354665
[14,] 0.408743227 0.817486453 0.5912568
[15,] 0.389990794 0.779981588 0.6100092
[16,] 0.544321200 0.911357600 0.4556788
[17,] 0.486015658 0.972031316 0.5139843
[18,] 0.417595348 0.835190696 0.5824047
[19,] 0.412286314 0.824572627 0.5877137
[20,] 0.348736844 0.697473688 0.6512632
[21,] 0.289438780 0.578877559 0.7105612
[22,] 0.317295683 0.634591366 0.6827043
[23,] 0.269967533 0.539935065 0.7300325
[24,] 0.470437124 0.940874247 0.5295629
[25,] 0.417389781 0.834779562 0.5826102
[26,] 0.373384633 0.746769267 0.6266154
[27,] 0.318385613 0.636771227 0.6816144
[28,] 0.297798863 0.595597727 0.7022011
[29,] 0.249668736 0.499337471 0.7503313
[30,] 0.229007452 0.458014904 0.7709925
[31,] 0.202569559 0.405139118 0.7974304
[32,] 0.368166824 0.736333648 0.6318332
[33,] 0.316677643 0.633355286 0.6833224
[34,] 0.284539352 0.569078703 0.7154606
[35,] 0.240898601 0.481797202 0.7591014
[36,] 0.206456408 0.412912815 0.7935436
[37,] 0.184935172 0.369870343 0.8150648
[38,] 0.172106877 0.344213753 0.8278931
[39,] 0.158517498 0.317034996 0.8414825
[40,] 0.129248430 0.258496860 0.8707516
[41,] 0.118395377 0.236790754 0.8816046
[42,] 0.097651860 0.195303719 0.9023481
[43,] 0.082764126 0.165528252 0.9172359
[44,] 0.139165646 0.278331292 0.8608344
[45,] 0.173931039 0.347862077 0.8260690
[46,] 0.159575878 0.319151756 0.8404241
[47,] 0.215218306 0.430436611 0.7847817
[48,] 0.207489330 0.414978661 0.7925107
[49,] 0.176953057 0.353906113 0.8230469
[50,] 0.187517198 0.375034396 0.8124828
[51,] 0.217191090 0.434382180 0.7828089
[52,] 0.191206172 0.382412344 0.8087938
[53,] 0.160350647 0.320701295 0.8396494
[54,] 0.159547828 0.319095655 0.8404522
[55,] 0.133194770 0.266389541 0.8668052
[56,] 0.108806347 0.217612694 0.8911937
[57,] 0.106178531 0.212357062 0.8938215
[58,] 0.096049100 0.192098201 0.9039509
[59,] 0.094458022 0.188916045 0.9055420
[60,] 0.079644935 0.159289871 0.9203551
[61,] 0.067251752 0.134503505 0.9327482
[62,] 0.054110546 0.108221092 0.9458895
[63,] 0.042328188 0.084656376 0.9576718
[64,] 0.039977718 0.079955436 0.9600223
[65,] 0.031933152 0.063866304 0.9680668
[66,] 0.026499133 0.052998266 0.9735009
[67,] 0.020188292 0.040376585 0.9798117
[68,] 0.015826354 0.031652708 0.9841736
[69,] 0.012608075 0.025216151 0.9873919
[70,] 0.019181905 0.038363811 0.9808181
[71,] 0.015205379 0.030410758 0.9847946
[72,] 0.014939138 0.029878276 0.9850609
[73,] 0.024487956 0.048975913 0.9755120
[74,] 0.018614963 0.037229925 0.9813850
[75,] 0.015441169 0.030882338 0.9845588
[76,] 0.016135649 0.032271298 0.9838644
[77,] 0.014236082 0.028472164 0.9857639
[78,] 0.010646889 0.021293778 0.9893531
[79,] 0.013913176 0.027826352 0.9860868
[80,] 0.010729730 0.021459461 0.9892703
[81,] 0.009518010 0.019036019 0.9904820
[82,] 0.009680030 0.019360059 0.9903200
[83,] 0.008363634 0.016727269 0.9916364
[84,] 0.007764271 0.015528542 0.9922357
[85,] 0.006467760 0.012935520 0.9935322
[86,] 0.012282607 0.024565214 0.9877174
[87,] 0.010785868 0.021571736 0.9892141
[88,] 0.010208221 0.020416443 0.9897918
[89,] 0.007776152 0.015552304 0.9922238
[90,] 0.005685009 0.011370019 0.9943150
[91,] 0.004311732 0.008623464 0.9956883
[92,] 0.003144738 0.006289476 0.9968553
[93,] 0.002202517 0.004405035 0.9977975
[94,] 0.002388844 0.004777687 0.9976112
[95,] 0.001842557 0.003685114 0.9981574
[96,] 0.008203177 0.016406355 0.9917968
[97,] 0.018290833 0.036581667 0.9817092
[98,] 0.017998585 0.035997170 0.9820014
[99,] 0.022594411 0.045188823 0.9774056
[100,] 0.017332738 0.034665476 0.9826673
[101,] 0.013760777 0.027521554 0.9862392
[102,] 0.009983635 0.019967270 0.9900164
[103,] 0.025341947 0.050683895 0.9746581
[104,] 0.022761174 0.045522349 0.9772388
[105,] 0.059881269 0.119762539 0.9401187
[106,] 0.050535421 0.101070843 0.9494646
[107,] 0.040834857 0.081669714 0.9591651
[108,] 0.125670111 0.251340222 0.8743299
[109,] 0.122007793 0.244015587 0.8779922
[110,] 0.108078686 0.216157372 0.8919213
[111,] 0.181914326 0.363828653 0.8180857
[112,] 0.177191990 0.354383980 0.8228080
[113,] 0.211836402 0.423672803 0.7881636
[114,] 0.198754973 0.397509946 0.8012450
[115,] 0.193871684 0.387743368 0.8061283
[116,] 0.177529374 0.355058747 0.8224706
[117,] 0.144708963 0.289417925 0.8552910
[118,] 0.114727282 0.229454564 0.8852727
[119,] 0.116280047 0.232560095 0.8837200
[120,] 0.164553153 0.329106307 0.8354468
[121,] 0.140635517 0.281271034 0.8593645
[122,] 0.123611186 0.247222371 0.8763888
[123,] 0.107136604 0.214273208 0.8928634
[124,] 0.097475528 0.194951056 0.9025245
[125,] 0.079373843 0.158747686 0.9206262
[126,] 0.107972369 0.215944739 0.8920276
[127,] 0.080178720 0.160357440 0.9198213
[128,] 0.058512842 0.117025684 0.9414872
[129,] 0.149755405 0.299510810 0.8502446
[130,] 0.209641176 0.419282353 0.7903588
[131,] 0.190479830 0.380959661 0.8095202
[132,] 0.424807529 0.849615058 0.5751925
[133,] 0.359293512 0.718587025 0.6407065
[134,] 0.671530929 0.656938142 0.3284691
[135,] 0.615294099 0.769411802 0.3847059
[136,] 0.504849191 0.990301617 0.4951508
[137,] 0.429177174 0.858354349 0.5708228
[138,] 0.437382042 0.874764084 0.5626180
[139,] 0.305353606 0.610707211 0.6946464
[140,] 0.212991088 0.425982176 0.7870089
> postscript(file="/var/www/html/rcomp/tmp/1y55s1290178602.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2y55s1290178602.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3re4d1290178602.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4re4d1290178602.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5re4d1290178602.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
3.061726966 -1.252258162 0.647840048 1.018246606 -2.824250751 6.086544668
7 8 9 10 11 12
0.187798246 -1.681526217 0.033826565 2.511973231 3.769857056 -1.729646326
13 14 15 16 17 18
3.594553343 -5.713500914 -1.517641052 3.681791738 -2.277283113 -7.058365567
19 20 21 22 23 24
2.551589271 -1.382217163 -2.287122536 -0.612087125 1.756351870 1.923385431
25 26 27 28 29 30
6.033298616 2.058529900 0.512099716 3.586802795 0.990595532 0.270420089
31 32 33 34 35 36
4.682864079 1.913850964 -6.999490779 -1.067351794 -1.636555923 -0.751800943
37 38 39 40 41 42
-3.167514908 -0.829827779 -0.586068361 -2.671790044 7.101997441 0.269927991
43 44 45 46 47 48
-1.686862111 -0.806104998 -1.363320221 2.532438745 3.252124450 -3.513036261
49 50 51 52 53 54
-0.006606505 -2.813683910 -1.745455555 2.125179714 6.471101453 -5.092840721
55 56 57 58 59 60
0.094585368 5.549610883 3.075940088 -1.207950524 3.949360966 -4.358329425
61 62 63 64 65 66
2.041403117 0.489432943 4.314629757 -0.356449244 0.272732913 3.026397744
67 68 69 70 71 72
2.690376576 -3.191695090 1.653732391 0.988996017 1.217673516 0.493828760
73 74 75 76 77 78
3.138759072 1.407224918 1.936130499 -0.316883156 1.319620316 1.812990626
79 80 81 82 83 84
5.397915097 -1.656787986 3.355925699 6.320522446 0.677137069 1.899821472
85 86 87 88 89 90
3.870680211 2.664286318 -0.909133104 4.529924187 -0.232405815 0.412314192
91 92 93 94 95 96
3.588610701 2.171868931 1.024955688 -2.296476893 5.557188513 2.536697236
97 98 99 100 101 102
3.069768695 0.944219615 0.208956312 -1.393094943 -0.627965205 -0.315284018
103 104 105 106 107 108
-3.757722006 1.300522827 7.171763117 -6.695599470 -3.729444126 -4.946034134
109 110 111 112 113 114
-0.681413834 -0.011366157 -0.305898483 5.934746994 -2.394316725 -8.204310994
115 116 117 118 119 120
-2.068745417 1.585524122 -8.095841961 -3.916257051 1.808290808 -7.404239590
121 122 123 124 125 126
-3.412912517 -5.157997212 -4.311860326 2.150042330 0.790472551 1.574969730
127 128 129 130 131 132
0.388858483 2.412967107 4.460071426 0.831680771 -4.382329546 1.097590252
133 134 135 136 137 138
2.294604869 1.002795493 3.232776556 -0.593928553 -2.412304720 -9.125232296
139 140 141 142 143 144
2.527828279 -6.611836256 -9.154851176 -2.107333212 -7.692380476 -0.482075812
145 146 147 148 149 150
0.129237548 -1.820397967 -2.647150763 -2.138802992 -4.110757109 -0.142997515
151 152 153 154 155 156
-1.304696137 0.632269860 5.805741217 -8.959010088 -0.970669281 -0.461327431
157 158 159
0.323823438 0.069595778 2.291597543
> postscript(file="/var/www/html/rcomp/tmp/61nmg1290178602.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 3.061726966 NA
1 -1.252258162 3.061726966
2 0.647840048 -1.252258162
3 1.018246606 0.647840048
4 -2.824250751 1.018246606
5 6.086544668 -2.824250751
6 0.187798246 6.086544668
7 -1.681526217 0.187798246
8 0.033826565 -1.681526217
9 2.511973231 0.033826565
10 3.769857056 2.511973231
11 -1.729646326 3.769857056
12 3.594553343 -1.729646326
13 -5.713500914 3.594553343
14 -1.517641052 -5.713500914
15 3.681791738 -1.517641052
16 -2.277283113 3.681791738
17 -7.058365567 -2.277283113
18 2.551589271 -7.058365567
19 -1.382217163 2.551589271
20 -2.287122536 -1.382217163
21 -0.612087125 -2.287122536
22 1.756351870 -0.612087125
23 1.923385431 1.756351870
24 6.033298616 1.923385431
25 2.058529900 6.033298616
26 0.512099716 2.058529900
27 3.586802795 0.512099716
28 0.990595532 3.586802795
29 0.270420089 0.990595532
30 4.682864079 0.270420089
31 1.913850964 4.682864079
32 -6.999490779 1.913850964
33 -1.067351794 -6.999490779
34 -1.636555923 -1.067351794
35 -0.751800943 -1.636555923
36 -3.167514908 -0.751800943
37 -0.829827779 -3.167514908
38 -0.586068361 -0.829827779
39 -2.671790044 -0.586068361
40 7.101997441 -2.671790044
41 0.269927991 7.101997441
42 -1.686862111 0.269927991
43 -0.806104998 -1.686862111
44 -1.363320221 -0.806104998
45 2.532438745 -1.363320221
46 3.252124450 2.532438745
47 -3.513036261 3.252124450
48 -0.006606505 -3.513036261
49 -2.813683910 -0.006606505
50 -1.745455555 -2.813683910
51 2.125179714 -1.745455555
52 6.471101453 2.125179714
53 -5.092840721 6.471101453
54 0.094585368 -5.092840721
55 5.549610883 0.094585368
56 3.075940088 5.549610883
57 -1.207950524 3.075940088
58 3.949360966 -1.207950524
59 -4.358329425 3.949360966
60 2.041403117 -4.358329425
61 0.489432943 2.041403117
62 4.314629757 0.489432943
63 -0.356449244 4.314629757
64 0.272732913 -0.356449244
65 3.026397744 0.272732913
66 2.690376576 3.026397744
67 -3.191695090 2.690376576
68 1.653732391 -3.191695090
69 0.988996017 1.653732391
70 1.217673516 0.988996017
71 0.493828760 1.217673516
72 3.138759072 0.493828760
73 1.407224918 3.138759072
74 1.936130499 1.407224918
75 -0.316883156 1.936130499
76 1.319620316 -0.316883156
77 1.812990626 1.319620316
78 5.397915097 1.812990626
79 -1.656787986 5.397915097
80 3.355925699 -1.656787986
81 6.320522446 3.355925699
82 0.677137069 6.320522446
83 1.899821472 0.677137069
84 3.870680211 1.899821472
85 2.664286318 3.870680211
86 -0.909133104 2.664286318
87 4.529924187 -0.909133104
88 -0.232405815 4.529924187
89 0.412314192 -0.232405815
90 3.588610701 0.412314192
91 2.171868931 3.588610701
92 1.024955688 2.171868931
93 -2.296476893 1.024955688
94 5.557188513 -2.296476893
95 2.536697236 5.557188513
96 3.069768695 2.536697236
97 0.944219615 3.069768695
98 0.208956312 0.944219615
99 -1.393094943 0.208956312
100 -0.627965205 -1.393094943
101 -0.315284018 -0.627965205
102 -3.757722006 -0.315284018
103 1.300522827 -3.757722006
104 7.171763117 1.300522827
105 -6.695599470 7.171763117
106 -3.729444126 -6.695599470
107 -4.946034134 -3.729444126
108 -0.681413834 -4.946034134
109 -0.011366157 -0.681413834
110 -0.305898483 -0.011366157
111 5.934746994 -0.305898483
112 -2.394316725 5.934746994
113 -8.204310994 -2.394316725
114 -2.068745417 -8.204310994
115 1.585524122 -2.068745417
116 -8.095841961 1.585524122
117 -3.916257051 -8.095841961
118 1.808290808 -3.916257051
119 -7.404239590 1.808290808
120 -3.412912517 -7.404239590
121 -5.157997212 -3.412912517
122 -4.311860326 -5.157997212
123 2.150042330 -4.311860326
124 0.790472551 2.150042330
125 1.574969730 0.790472551
126 0.388858483 1.574969730
127 2.412967107 0.388858483
128 4.460071426 2.412967107
129 0.831680771 4.460071426
130 -4.382329546 0.831680771
131 1.097590252 -4.382329546
132 2.294604869 1.097590252
133 1.002795493 2.294604869
134 3.232776556 1.002795493
135 -0.593928553 3.232776556
136 -2.412304720 -0.593928553
137 -9.125232296 -2.412304720
138 2.527828279 -9.125232296
139 -6.611836256 2.527828279
140 -9.154851176 -6.611836256
141 -2.107333212 -9.154851176
142 -7.692380476 -2.107333212
143 -0.482075812 -7.692380476
144 0.129237548 -0.482075812
145 -1.820397967 0.129237548
146 -2.647150763 -1.820397967
147 -2.138802992 -2.647150763
148 -4.110757109 -2.138802992
149 -0.142997515 -4.110757109
150 -1.304696137 -0.142997515
151 0.632269860 -1.304696137
152 5.805741217 0.632269860
153 -8.959010088 5.805741217
154 -0.970669281 -8.959010088
155 -0.461327431 -0.970669281
156 0.323823438 -0.461327431
157 0.069595778 0.323823438
158 2.291597543 0.069595778
159 NA 2.291597543
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.252258162 3.061726966
[2,] 0.647840048 -1.252258162
[3,] 1.018246606 0.647840048
[4,] -2.824250751 1.018246606
[5,] 6.086544668 -2.824250751
[6,] 0.187798246 6.086544668
[7,] -1.681526217 0.187798246
[8,] 0.033826565 -1.681526217
[9,] 2.511973231 0.033826565
[10,] 3.769857056 2.511973231
[11,] -1.729646326 3.769857056
[12,] 3.594553343 -1.729646326
[13,] -5.713500914 3.594553343
[14,] -1.517641052 -5.713500914
[15,] 3.681791738 -1.517641052
[16,] -2.277283113 3.681791738
[17,] -7.058365567 -2.277283113
[18,] 2.551589271 -7.058365567
[19,] -1.382217163 2.551589271
[20,] -2.287122536 -1.382217163
[21,] -0.612087125 -2.287122536
[22,] 1.756351870 -0.612087125
[23,] 1.923385431 1.756351870
[24,] 6.033298616 1.923385431
[25,] 2.058529900 6.033298616
[26,] 0.512099716 2.058529900
[27,] 3.586802795 0.512099716
[28,] 0.990595532 3.586802795
[29,] 0.270420089 0.990595532
[30,] 4.682864079 0.270420089
[31,] 1.913850964 4.682864079
[32,] -6.999490779 1.913850964
[33,] -1.067351794 -6.999490779
[34,] -1.636555923 -1.067351794
[35,] -0.751800943 -1.636555923
[36,] -3.167514908 -0.751800943
[37,] -0.829827779 -3.167514908
[38,] -0.586068361 -0.829827779
[39,] -2.671790044 -0.586068361
[40,] 7.101997441 -2.671790044
[41,] 0.269927991 7.101997441
[42,] -1.686862111 0.269927991
[43,] -0.806104998 -1.686862111
[44,] -1.363320221 -0.806104998
[45,] 2.532438745 -1.363320221
[46,] 3.252124450 2.532438745
[47,] -3.513036261 3.252124450
[48,] -0.006606505 -3.513036261
[49,] -2.813683910 -0.006606505
[50,] -1.745455555 -2.813683910
[51,] 2.125179714 -1.745455555
[52,] 6.471101453 2.125179714
[53,] -5.092840721 6.471101453
[54,] 0.094585368 -5.092840721
[55,] 5.549610883 0.094585368
[56,] 3.075940088 5.549610883
[57,] -1.207950524 3.075940088
[58,] 3.949360966 -1.207950524
[59,] -4.358329425 3.949360966
[60,] 2.041403117 -4.358329425
[61,] 0.489432943 2.041403117
[62,] 4.314629757 0.489432943
[63,] -0.356449244 4.314629757
[64,] 0.272732913 -0.356449244
[65,] 3.026397744 0.272732913
[66,] 2.690376576 3.026397744
[67,] -3.191695090 2.690376576
[68,] 1.653732391 -3.191695090
[69,] 0.988996017 1.653732391
[70,] 1.217673516 0.988996017
[71,] 0.493828760 1.217673516
[72,] 3.138759072 0.493828760
[73,] 1.407224918 3.138759072
[74,] 1.936130499 1.407224918
[75,] -0.316883156 1.936130499
[76,] 1.319620316 -0.316883156
[77,] 1.812990626 1.319620316
[78,] 5.397915097 1.812990626
[79,] -1.656787986 5.397915097
[80,] 3.355925699 -1.656787986
[81,] 6.320522446 3.355925699
[82,] 0.677137069 6.320522446
[83,] 1.899821472 0.677137069
[84,] 3.870680211 1.899821472
[85,] 2.664286318 3.870680211
[86,] -0.909133104 2.664286318
[87,] 4.529924187 -0.909133104
[88,] -0.232405815 4.529924187
[89,] 0.412314192 -0.232405815
[90,] 3.588610701 0.412314192
[91,] 2.171868931 3.588610701
[92,] 1.024955688 2.171868931
[93,] -2.296476893 1.024955688
[94,] 5.557188513 -2.296476893
[95,] 2.536697236 5.557188513
[96,] 3.069768695 2.536697236
[97,] 0.944219615 3.069768695
[98,] 0.208956312 0.944219615
[99,] -1.393094943 0.208956312
[100,] -0.627965205 -1.393094943
[101,] -0.315284018 -0.627965205
[102,] -3.757722006 -0.315284018
[103,] 1.300522827 -3.757722006
[104,] 7.171763117 1.300522827
[105,] -6.695599470 7.171763117
[106,] -3.729444126 -6.695599470
[107,] -4.946034134 -3.729444126
[108,] -0.681413834 -4.946034134
[109,] -0.011366157 -0.681413834
[110,] -0.305898483 -0.011366157
[111,] 5.934746994 -0.305898483
[112,] -2.394316725 5.934746994
[113,] -8.204310994 -2.394316725
[114,] -2.068745417 -8.204310994
[115,] 1.585524122 -2.068745417
[116,] -8.095841961 1.585524122
[117,] -3.916257051 -8.095841961
[118,] 1.808290808 -3.916257051
[119,] -7.404239590 1.808290808
[120,] -3.412912517 -7.404239590
[121,] -5.157997212 -3.412912517
[122,] -4.311860326 -5.157997212
[123,] 2.150042330 -4.311860326
[124,] 0.790472551 2.150042330
[125,] 1.574969730 0.790472551
[126,] 0.388858483 1.574969730
[127,] 2.412967107 0.388858483
[128,] 4.460071426 2.412967107
[129,] 0.831680771 4.460071426
[130,] -4.382329546 0.831680771
[131,] 1.097590252 -4.382329546
[132,] 2.294604869 1.097590252
[133,] 1.002795493 2.294604869
[134,] 3.232776556 1.002795493
[135,] -0.593928553 3.232776556
[136,] -2.412304720 -0.593928553
[137,] -9.125232296 -2.412304720
[138,] 2.527828279 -9.125232296
[139,] -6.611836256 2.527828279
[140,] -9.154851176 -6.611836256
[141,] -2.107333212 -9.154851176
[142,] -7.692380476 -2.107333212
[143,] -0.482075812 -7.692380476
[144,] 0.129237548 -0.482075812
[145,] -1.820397967 0.129237548
[146,] -2.647150763 -1.820397967
[147,] -2.138802992 -2.647150763
[148,] -4.110757109 -2.138802992
[149,] -0.142997515 -4.110757109
[150,] -1.304696137 -0.142997515
[151,] 0.632269860 -1.304696137
[152,] 5.805741217 0.632269860
[153,] -8.959010088 5.805741217
[154,] -0.970669281 -8.959010088
[155,] -0.461327431 -0.970669281
[156,] 0.323823438 -0.461327431
[157,] 0.069595778 0.323823438
[158,] 2.291597543 0.069595778
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.252258162 3.061726966
2 0.647840048 -1.252258162
3 1.018246606 0.647840048
4 -2.824250751 1.018246606
5 6.086544668 -2.824250751
6 0.187798246 6.086544668
7 -1.681526217 0.187798246
8 0.033826565 -1.681526217
9 2.511973231 0.033826565
10 3.769857056 2.511973231
11 -1.729646326 3.769857056
12 3.594553343 -1.729646326
13 -5.713500914 3.594553343
14 -1.517641052 -5.713500914
15 3.681791738 -1.517641052
16 -2.277283113 3.681791738
17 -7.058365567 -2.277283113
18 2.551589271 -7.058365567
19 -1.382217163 2.551589271
20 -2.287122536 -1.382217163
21 -0.612087125 -2.287122536
22 1.756351870 -0.612087125
23 1.923385431 1.756351870
24 6.033298616 1.923385431
25 2.058529900 6.033298616
26 0.512099716 2.058529900
27 3.586802795 0.512099716
28 0.990595532 3.586802795
29 0.270420089 0.990595532
30 4.682864079 0.270420089
31 1.913850964 4.682864079
32 -6.999490779 1.913850964
33 -1.067351794 -6.999490779
34 -1.636555923 -1.067351794
35 -0.751800943 -1.636555923
36 -3.167514908 -0.751800943
37 -0.829827779 -3.167514908
38 -0.586068361 -0.829827779
39 -2.671790044 -0.586068361
40 7.101997441 -2.671790044
41 0.269927991 7.101997441
42 -1.686862111 0.269927991
43 -0.806104998 -1.686862111
44 -1.363320221 -0.806104998
45 2.532438745 -1.363320221
46 3.252124450 2.532438745
47 -3.513036261 3.252124450
48 -0.006606505 -3.513036261
49 -2.813683910 -0.006606505
50 -1.745455555 -2.813683910
51 2.125179714 -1.745455555
52 6.471101453 2.125179714
53 -5.092840721 6.471101453
54 0.094585368 -5.092840721
55 5.549610883 0.094585368
56 3.075940088 5.549610883
57 -1.207950524 3.075940088
58 3.949360966 -1.207950524
59 -4.358329425 3.949360966
60 2.041403117 -4.358329425
61 0.489432943 2.041403117
62 4.314629757 0.489432943
63 -0.356449244 4.314629757
64 0.272732913 -0.356449244
65 3.026397744 0.272732913
66 2.690376576 3.026397744
67 -3.191695090 2.690376576
68 1.653732391 -3.191695090
69 0.988996017 1.653732391
70 1.217673516 0.988996017
71 0.493828760 1.217673516
72 3.138759072 0.493828760
73 1.407224918 3.138759072
74 1.936130499 1.407224918
75 -0.316883156 1.936130499
76 1.319620316 -0.316883156
77 1.812990626 1.319620316
78 5.397915097 1.812990626
79 -1.656787986 5.397915097
80 3.355925699 -1.656787986
81 6.320522446 3.355925699
82 0.677137069 6.320522446
83 1.899821472 0.677137069
84 3.870680211 1.899821472
85 2.664286318 3.870680211
86 -0.909133104 2.664286318
87 4.529924187 -0.909133104
88 -0.232405815 4.529924187
89 0.412314192 -0.232405815
90 3.588610701 0.412314192
91 2.171868931 3.588610701
92 1.024955688 2.171868931
93 -2.296476893 1.024955688
94 5.557188513 -2.296476893
95 2.536697236 5.557188513
96 3.069768695 2.536697236
97 0.944219615 3.069768695
98 0.208956312 0.944219615
99 -1.393094943 0.208956312
100 -0.627965205 -1.393094943
101 -0.315284018 -0.627965205
102 -3.757722006 -0.315284018
103 1.300522827 -3.757722006
104 7.171763117 1.300522827
105 -6.695599470 7.171763117
106 -3.729444126 -6.695599470
107 -4.946034134 -3.729444126
108 -0.681413834 -4.946034134
109 -0.011366157 -0.681413834
110 -0.305898483 -0.011366157
111 5.934746994 -0.305898483
112 -2.394316725 5.934746994
113 -8.204310994 -2.394316725
114 -2.068745417 -8.204310994
115 1.585524122 -2.068745417
116 -8.095841961 1.585524122
117 -3.916257051 -8.095841961
118 1.808290808 -3.916257051
119 -7.404239590 1.808290808
120 -3.412912517 -7.404239590
121 -5.157997212 -3.412912517
122 -4.311860326 -5.157997212
123 2.150042330 -4.311860326
124 0.790472551 2.150042330
125 1.574969730 0.790472551
126 0.388858483 1.574969730
127 2.412967107 0.388858483
128 4.460071426 2.412967107
129 0.831680771 4.460071426
130 -4.382329546 0.831680771
131 1.097590252 -4.382329546
132 2.294604869 1.097590252
133 1.002795493 2.294604869
134 3.232776556 1.002795493
135 -0.593928553 3.232776556
136 -2.412304720 -0.593928553
137 -9.125232296 -2.412304720
138 2.527828279 -9.125232296
139 -6.611836256 2.527828279
140 -9.154851176 -6.611836256
141 -2.107333212 -9.154851176
142 -7.692380476 -2.107333212
143 -0.482075812 -7.692380476
144 0.129237548 -0.482075812
145 -1.820397967 0.129237548
146 -2.647150763 -1.820397967
147 -2.138802992 -2.647150763
148 -4.110757109 -2.138802992
149 -0.142997515 -4.110757109
150 -1.304696137 -0.142997515
151 0.632269860 -1.304696137
152 5.805741217 0.632269860
153 -8.959010088 5.805741217
154 -0.970669281 -8.959010088
155 -0.461327431 -0.970669281
156 0.323823438 -0.461327431
157 0.069595778 0.323823438
158 2.291597543 0.069595778
> 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/7cflj1290178602.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8cflj1290178602.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9cflj1290178602.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10no241290178602.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qpjs1290178602.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/12cpzg1290178602.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/138hf71290178602.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/14thdv1290178602.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/15w0c01290178602.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/160jso1290178602.tab")
+ }
>
> try(system("convert tmp/1y55s1290178602.ps tmp/1y55s1290178602.png",intern=TRUE))
character(0)
> try(system("convert tmp/2y55s1290178602.ps tmp/2y55s1290178602.png",intern=TRUE))
character(0)
> try(system("convert tmp/3re4d1290178602.ps tmp/3re4d1290178602.png",intern=TRUE))
character(0)
> try(system("convert tmp/4re4d1290178602.ps tmp/4re4d1290178602.png",intern=TRUE))
character(0)
> try(system("convert tmp/5re4d1290178602.ps tmp/5re4d1290178602.png",intern=TRUE))
character(0)
> try(system("convert tmp/61nmg1290178602.ps tmp/61nmg1290178602.png",intern=TRUE))
character(0)
> try(system("convert tmp/7cflj1290178602.ps tmp/7cflj1290178602.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cflj1290178602.ps tmp/8cflj1290178602.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cflj1290178602.ps tmp/9cflj1290178602.png",intern=TRUE))
character(0)
> try(system("convert tmp/10no241290178602.ps tmp/10no241290178602.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.449 1.855 17.141