R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(24
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+ ,31
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+ ,20)
+ ,dim=c(6
+ ,159)
+ ,dimnames=list(c('ConcernoverMistakes'
+ ,'Doubtsaboutactions'
+ ,'ParentalExpectations'
+ ,'ParentalCriticism'
+ ,'PersonalStandards'
+ ,'Organization')
+ ,1:159))
> y <- array(NA,dim=c(6,159),dimnames=list(c('ConcernoverMistakes','Doubtsaboutactions','ParentalExpectations','ParentalCriticism','PersonalStandards','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 = '3'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
ParentalExpectations ConcernoverMistakes Doubtsaboutactions
1 11 24 14
2 7 25 11
3 17 17 6
4 10 18 12
5 12 18 8
6 12 16 10
7 11 20 10
8 11 16 11
9 12 18 16
10 13 17 11
11 14 23 13
12 16 30 12
13 11 23 8
14 10 18 12
15 11 15 11
16 15 12 4
17 9 21 9
18 11 15 8
19 17 20 8
20 17 31 14
21 11 27 15
22 18 34 16
23 14 21 9
24 10 31 14
25 11 19 11
26 15 16 8
27 15 20 9
28 13 21 9
29 16 22 9
30 13 17 9
31 9 24 10
32 18 25 16
33 18 26 11
34 12 25 8
35 17 17 9
36 9 32 16
37 9 33 11
38 12 13 16
39 18 32 12
40 12 25 12
41 18 29 14
42 14 22 9
43 15 18 10
44 16 17 9
45 10 20 10
46 11 15 12
47 14 20 14
48 9 33 14
49 12 29 10
50 17 23 14
51 5 26 16
52 12 18 9
53 12 20 10
54 6 11 6
55 24 28 8
56 12 26 13
57 12 22 10
58 14 17 8
59 7 12 7
60 13 14 15
61 12 17 9
62 13 21 10
63 14 19 12
64 8 18 13
65 11 10 10
66 9 29 11
67 11 31 8
68 13 19 9
69 10 9 13
70 11 20 11
71 12 28 8
72 9 19 9
73 15 30 9
74 18 29 15
75 15 26 9
76 12 23 10
77 13 13 14
78 14 21 12
79 10 19 12
80 13 28 11
81 13 23 14
82 11 18 6
83 13 21 12
84 16 20 8
85 8 23 14
86 16 21 11
87 11 21 10
88 9 15 14
89 16 28 12
90 12 19 10
91 14 26 14
92 8 10 5
93 9 16 11
94 15 22 10
95 11 19 9
96 21 31 10
97 14 31 16
98 18 29 13
99 12 19 9
100 13 22 10
101 15 23 10
102 12 15 7
103 19 20 9
104 15 18 8
105 11 23 14
106 11 25 14
107 10 21 8
108 13 24 9
109 15 25 14
110 12 17 14
111 12 13 8
112 16 28 8
113 9 21 8
114 18 25 7
115 8 9 6
116 13 16 8
117 17 19 6
118 9 17 11
119 15 25 14
120 8 20 11
121 7 29 11
122 12 14 11
123 14 22 14
124 6 15 8
125 8 19 20
126 17 20 11
127 10 15 8
128 11 20 11
129 14 18 10
130 11 33 14
131 13 22 11
132 12 16 9
133 11 17 9
134 9 16 8
135 12 21 10
136 20 26 13
137 12 18 13
138 13 18 12
139 12 17 8
140 12 22 13
141 9 30 14
142 15 30 12
143 24 24 14
144 7 21 15
145 17 21 13
146 11 29 16
147 17 31 9
148 11 20 9
149 12 16 9
150 14 22 8
151 11 20 7
152 16 28 16
153 21 38 11
154 14 22 9
155 20 20 11
156 13 17 9
157 11 28 14
158 15 22 13
159 19 31 16
ParentalCriticism PersonalStandards Organization
1 12 24 26
2 8 25 23
3 8 30 25
4 8 19 23
5 9 22 19
6 7 22 29
7 4 25 25
8 11 23 21
9 7 17 22
10 7 21 25
11 12 19 24
12 10 19 18
13 10 15 22
14 8 16 15
15 8 23 22
16 4 27 28
17 9 22 20
18 8 14 12
19 7 22 24
20 11 23 20
21 9 23 21
22 11 21 20
23 13 19 21
24 8 18 23
25 8 20 28
26 9 23 24
27 6 25 24
28 9 19 24
29 9 24 23
30 6 22 23
31 6 25 29
32 16 26 24
33 5 29 18
34 7 32 25
35 9 25 21
36 6 29 26
37 6 28 22
38 5 17 22
39 12 28 22
40 7 29 23
41 10 26 30
42 9 25 23
43 8 14 17
44 5 25 23
45 8 26 23
46 8 20 25
47 10 18 24
48 6 32 24
49 8 25 23
50 7 25 21
51 4 23 24
52 8 21 24
53 8 20 28
54 4 15 16
55 20 30 20
56 8 24 29
57 8 26 27
58 6 24 22
59 4 22 28
60 8 14 16
61 9 24 25
62 6 24 24
63 7 24 28
64 9 24 24
65 5 19 23
66 5 31 30
67 8 22 24
68 8 27 21
69 6 19 25
70 8 25 25
71 7 20 22
72 7 21 23
73 9 27 26
74 11 23 23
75 6 25 25
76 8 20 21
77 6 21 25
78 9 22 24
79 8 23 29
80 6 25 22
81 10 25 27
82 8 17 26
83 8 19 22
84 10 25 24
85 5 19 27
86 7 20 24
87 5 26 24
88 8 23 29
89 14 27 22
90 7 17 21
91 8 17 24
92 6 19 24
93 5 17 23
94 6 22 20
95 10 21 27
96 12 32 26
97 9 21 25
98 12 21 21
99 7 18 21
100 8 18 19
101 10 23 21
102 6 19 21
103 10 20 16
104 10 21 22
105 10 20 29
106 5 17 15
107 7 18 17
108 10 19 15
109 11 22 21
110 6 15 21
111 7 14 19
112 12 18 24
113 11 24 20
114 11 35 17
115 11 29 23
116 5 21 24
117 8 25 14
118 6 20 19
119 9 22 24
120 4 13 13
121 4 26 22
122 7 17 16
123 11 25 19
124 6 20 25
125 7 19 25
126 8 21 23
127 4 22 24
128 8 24 26
129 9 21 26
130 8 26 25
131 11 24 18
132 8 16 21
133 5 23 26
134 4 18 23
135 8 16 23
136 10 26 22
137 6 19 20
138 9 21 13
139 9 21 24
140 13 22 15
141 9 23 14
142 10 29 22
143 20 21 10
144 5 21 24
145 11 23 22
146 6 27 24
147 9 25 19
148 7 21 20
149 9 10 13
150 10 20 20
151 9 26 22
152 8 24 24
153 7 29 29
154 6 19 12
155 13 24 20
156 6 19 21
157 8 24 24
158 10 22 22
159 16 17 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ConcernoverMistakes Doubtsaboutactions
6.16888 0.09104 -0.12497
ParentalCriticism PersonalStandards Organization
0.66542 0.11661 -0.08837
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9072 -1.8280 0.0789 1.8148 7.2694
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.16888 1.77134 3.483 0.000648 ***
ConcernoverMistakes 0.09104 0.04811 1.893 0.060307 .
Doubtsaboutactions -0.12497 0.08722 -1.433 0.153941
ParentalCriticism 0.66542 0.08630 7.710 1.49e-12 ***
PersonalStandards 0.11661 0.06322 1.845 0.067032 .
Organization -0.08837 0.06186 -1.429 0.155164
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.695 on 153 degrees of freedom
Multiple R-squared: 0.4074, Adjusted R-squared: 0.388
F-statistic: 21.04 on 5 and 153 DF, p-value: 5.71e-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,] 0.55814712 0.88370576 0.44185288
[2,] 0.41762238 0.83524476 0.58237762
[3,] 0.61551337 0.76897326 0.38448663
[4,] 0.79238563 0.41522873 0.20761437
[5,] 0.80349834 0.39300332 0.19650166
[6,] 0.74311935 0.51376131 0.25688065
[7,] 0.66132527 0.67734947 0.33867473
[8,] 0.62350503 0.75298995 0.37649497
[9,] 0.66920332 0.66159336 0.33079668
[10,] 0.58954787 0.82090426 0.41045213
[11,] 0.69693394 0.60613213 0.30306606
[12,] 0.78168841 0.43662319 0.21831159
[13,] 0.73923862 0.52152275 0.26076138
[14,] 0.79886134 0.40227732 0.20113866
[15,] 0.75403734 0.49192531 0.24596266
[16,] 0.78032681 0.43934639 0.21967319
[17,] 0.73126633 0.53746735 0.26873367
[18,] 0.70780116 0.58439768 0.29219884
[19,] 0.68885938 0.62228124 0.31114062
[20,] 0.62850935 0.74298129 0.37149065
[21,] 0.60071040 0.79857920 0.39928960
[22,] 0.54924937 0.90150127 0.45075063
[23,] 0.62909243 0.74181513 0.37090757
[24,] 0.61586546 0.76826907 0.38413454
[25,] 0.68127567 0.63744865 0.31872433
[26,] 0.72626083 0.54747834 0.27373917
[27,] 0.74011987 0.51976026 0.25988013
[28,] 0.77750552 0.44498897 0.22249448
[29,] 0.83049657 0.33900685 0.16950343
[30,] 0.82727426 0.34545148 0.17272574
[31,] 0.81515701 0.36968599 0.18484299
[32,] 0.78632901 0.42734197 0.21367099
[33,] 0.84243798 0.31512404 0.15756202
[34,] 0.80790553 0.38418893 0.19209447
[35,] 0.82033585 0.35932831 0.17966415
[36,] 0.87207945 0.25584109 0.12792055
[37,] 0.88621680 0.22756640 0.11378320
[38,] 0.86265669 0.27468661 0.13734331
[39,] 0.83961325 0.32077349 0.16038675
[40,] 0.86698324 0.26603352 0.13301676
[41,] 0.84665030 0.30669941 0.15334970
[42,] 0.89544062 0.20911875 0.10455938
[43,] 0.93638441 0.12723118 0.06361559
[44,] 0.92039820 0.15920360 0.07960180
[45,] 0.90040197 0.19919606 0.09959803
[46,] 0.92083741 0.15832519 0.07916259
[47,] 0.90600813 0.18798374 0.09399187
[48,] 0.88449498 0.23101003 0.11550502
[49,] 0.86285915 0.27428171 0.13714085
[50,] 0.85529789 0.28940422 0.14470211
[51,] 0.85256865 0.29486271 0.14743135
[52,] 0.83478050 0.33043899 0.16521950
[53,] 0.81271910 0.37456179 0.18728090
[54,] 0.79016049 0.41967903 0.20983951
[55,] 0.78628772 0.42742455 0.21371228
[56,] 0.86182965 0.27634071 0.13817035
[57,] 0.84575730 0.30848539 0.15424270
[58,] 0.84203114 0.31593773 0.15796886
[59,] 0.84170612 0.31658775 0.15829388
[60,] 0.81440963 0.37118073 0.18559037
[61,] 0.78962780 0.42074441 0.21037220
[62,] 0.76740080 0.46519839 0.23259920
[63,] 0.73942001 0.52115999 0.26057999
[64,] 0.74217714 0.51564572 0.25782286
[65,] 0.70977079 0.58045843 0.29022921
[66,] 0.72814555 0.54370891 0.27185445
[67,] 0.73571589 0.52856822 0.26428411
[68,] 0.70049720 0.59900561 0.29950280
[69,] 0.73115066 0.53769868 0.26884934
[70,] 0.69769957 0.60460086 0.30230043
[71,] 0.67280403 0.65439194 0.32719597
[72,] 0.63398527 0.73202947 0.36601473
[73,] 0.59120183 0.81759635 0.40879817
[74,] 0.55541801 0.88916398 0.44458199
[75,] 0.51405927 0.97188145 0.48594073
[76,] 0.48268927 0.96537854 0.51731073
[77,] 0.45384294 0.90768587 0.54615706
[78,] 0.53672363 0.92655274 0.46327637
[79,] 0.49072090 0.98144179 0.50927910
[80,] 0.46903179 0.93806359 0.53096821
[81,] 0.44676626 0.89353252 0.55323374
[82,] 0.40250020 0.80500039 0.59749980
[83,] 0.38403275 0.76806550 0.61596725
[84,] 0.37541611 0.75083222 0.62458389
[85,] 0.33241135 0.66482269 0.66758865
[86,] 0.35284859 0.70569717 0.64715141
[87,] 0.34913333 0.69826667 0.65086667
[88,] 0.38318855 0.76637709 0.61681145
[89,] 0.34473285 0.68946569 0.65526715
[90,] 0.32327788 0.64655576 0.67672212
[91,] 0.28155947 0.56311895 0.71844053
[92,] 0.24245193 0.48490386 0.75754807
[93,] 0.20683405 0.41366810 0.79316595
[94,] 0.17956476 0.35912951 0.82043524
[95,] 0.23990773 0.47981547 0.76009227
[96,] 0.20916923 0.41833845 0.79083077
[97,] 0.19990805 0.39981610 0.80009195
[98,] 0.16761501 0.33523002 0.83238499
[99,] 0.16010469 0.32020937 0.83989531
[100,] 0.14658370 0.29316740 0.85341630
[101,] 0.11967153 0.23934307 0.88032847
[102,] 0.11174092 0.22348183 0.88825908
[103,] 0.09381494 0.18762989 0.90618506
[104,] 0.08206789 0.16413578 0.91793211
[105,] 0.22221754 0.44443508 0.77778246
[106,] 0.19228112 0.38456223 0.80771888
[107,] 0.40341428 0.80682856 0.59658572
[108,] 0.40197055 0.80394109 0.59802945
[109,] 0.42879016 0.85758031 0.57120984
[110,] 0.38999908 0.77999815 0.61000092
[111,] 0.35366890 0.70733780 0.64633110
[112,] 0.31254199 0.62508399 0.68745801
[113,] 0.36355078 0.72710156 0.63644922
[114,] 0.33868335 0.67736670 0.66131665
[115,] 0.29104933 0.58209866 0.70895067
[116,] 0.40726560 0.81453120 0.59273440
[117,] 0.36517300 0.73034600 0.63482700
[118,] 0.45168905 0.90337811 0.54831095
[119,] 0.39601389 0.79202778 0.60398611
[120,] 0.36914535 0.73829069 0.63085465
[121,] 0.31479783 0.62959566 0.68520217
[122,] 0.36530075 0.73060150 0.63469925
[123,] 0.35072593 0.70145187 0.64927407
[124,] 0.29181665 0.58363331 0.70818335
[125,] 0.23833951 0.47667902 0.76166049
[126,] 0.18941538 0.37883076 0.81058462
[127,] 0.15236005 0.30472009 0.84763995
[128,] 0.24237182 0.48474365 0.75762818
[129,] 0.23034119 0.46068238 0.76965881
[130,] 0.20415287 0.40830575 0.79584713
[131,] 0.18459020 0.36918040 0.81540980
[132,] 0.22638273 0.45276546 0.77361727
[133,] 0.43033508 0.86067017 0.56966492
[134,] 0.41834202 0.83668403 0.58165798
[135,] 0.33911052 0.67822104 0.66088948
[136,] 0.31443214 0.62886429 0.68556786
[137,] 0.28133447 0.56266893 0.71866553
[138,] 0.27141906 0.54283812 0.72858094
[139,] 0.19516328 0.39032655 0.80483672
[140,] 0.14267815 0.28535631 0.85732185
[141,] 0.08214864 0.16429728 0.91785136
[142,] 0.04394662 0.08789324 0.95605338
> postscript(file="/var/wessaorg/rcomp/tmp/1kgjd1322162357.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/wessaorg/rcomp/tmp/2aydt1322162357.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/30ooi1322162357.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4s23u1322162357.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5v6ns1322162357.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 159
Frequency = 1
1 2 3 4 5 6
-4.090293312 -6.276282104 3.420870673 -1.814362151 -1.682999043 0.963595838
7 8 9 10 11 12
0.892392020 -3.396709242 1.495808723 1.760653284 -0.717922304 1.320436052
13 14 15 16 17 18
-2.722250589 -2.171504542 -1.221021859 4.902769700 -4.742776932 -1.430174356
19 20 21 22 23 24
4.907624876 1.524224134 -2.567414962 2.734268338 -1.966277120 -2.631340116
25 26 27 28 29 30
-0.705130827 1.824330730 3.348196224 -0.039463439 2.198076963 1.882776929
31 32 33 34 35 36
-2.449137648 -0.003046509 5.720657186 -1.625304178 3.359931301 -3.159171724
37 38 39 40 41 42
-4.111961986 3.281864963 1.111504344 -0.952320156 3.905616654 0.081467564
43 44 45 46 47 48
2.988507668 5.198373658 -3.062659736 -0.481105072 1.127635399 -4.026733082
49 50 51 52 53 54
-1.765421829 4.769406584 -4.759160459 -0.334133480 0.078853278 -3.417382879
55 56 57 58 59 60
1.242410114 -0.470536803 -0.891256995 2.436212189 -2.139259449 1.889174542
61 62 63 64 65 66
-1.169974005 1.498738964 2.618831121 -4.849488134 1.660293611 -2.725229559
67 68 69 70 71 72
-2.759254097 -0.389945124 0.637576462 -1.644333072 -0.764229188 -2.848121153
73 74 75 76 77 78
0.385032521 3.096395277 2.890319886 -0.812869818 3.165167173 0.985632216
79 80 81 82 83 84
-1.841613083 0.693072597 -0.696640256 -1.065877089 0.824142695 1.561521896
85 86 87 88 89 90
-1.669859223 4.424726252 -0.069054907 -2.227498740 -1.738571004 0.566548415
91 92 93 94 95 96
2.028846992 -2.541633082 -0.527760634 3.287431191 -2.490910641 3.839644082
97 98 99 100 101 102
0.780098642 2.237497265 0.324964398 0.334647610 0.506452129 0.987995798
103 104 105 106 107 108
4.562572921 1.033299400 -1.936850612 0.320821138 -2.335578069 -1.773354111
109 110 111 112 113 114
0.275452518 2.147173167 1.035932391 0.318607621 -6.431820204 0.531222706
115 116 117 118 119 120
-6.907207142 2.719249238 2.849750555 -1.987540331 1.871416344 -1.643776454
121 122 123 124 125 126
-4.183728225 0.704872799 -0.977994496 -4.650153689 -2.063438910 4.645361878
127 128 129 130 131 132
0.359106043 -1.439352349 1.302158858 -2.569555217 -2.324680274 0.165882100
133 134 135 136 137 138
0.696706428 -0.353868960 0.012392980 5.346795277 1.376348351 -0.596719114
139 140 141 142 143 144
-1.033491748 -4.437476066 -5.584112676 -0.492172646 2.522194287 -2.861134821
145 146 147 148 149 150
2.486404933 -0.829571742 1.908610777 -1.204276401 -0.506857021 -0.390998956
151 152 153 154 155 156
-3.191379841 3.280447878 7.269396568 1.805314182 3.703295072 2.055862480
157 158 159
-1.969501358 1.177397983 1.146705129
> postscript(file="/var/wessaorg/rcomp/tmp/6vkqv1322162357.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -4.090293312 NA
1 -6.276282104 -4.090293312
2 3.420870673 -6.276282104
3 -1.814362151 3.420870673
4 -1.682999043 -1.814362151
5 0.963595838 -1.682999043
6 0.892392020 0.963595838
7 -3.396709242 0.892392020
8 1.495808723 -3.396709242
9 1.760653284 1.495808723
10 -0.717922304 1.760653284
11 1.320436052 -0.717922304
12 -2.722250589 1.320436052
13 -2.171504542 -2.722250589
14 -1.221021859 -2.171504542
15 4.902769700 -1.221021859
16 -4.742776932 4.902769700
17 -1.430174356 -4.742776932
18 4.907624876 -1.430174356
19 1.524224134 4.907624876
20 -2.567414962 1.524224134
21 2.734268338 -2.567414962
22 -1.966277120 2.734268338
23 -2.631340116 -1.966277120
24 -0.705130827 -2.631340116
25 1.824330730 -0.705130827
26 3.348196224 1.824330730
27 -0.039463439 3.348196224
28 2.198076963 -0.039463439
29 1.882776929 2.198076963
30 -2.449137648 1.882776929
31 -0.003046509 -2.449137648
32 5.720657186 -0.003046509
33 -1.625304178 5.720657186
34 3.359931301 -1.625304178
35 -3.159171724 3.359931301
36 -4.111961986 -3.159171724
37 3.281864963 -4.111961986
38 1.111504344 3.281864963
39 -0.952320156 1.111504344
40 3.905616654 -0.952320156
41 0.081467564 3.905616654
42 2.988507668 0.081467564
43 5.198373658 2.988507668
44 -3.062659736 5.198373658
45 -0.481105072 -3.062659736
46 1.127635399 -0.481105072
47 -4.026733082 1.127635399
48 -1.765421829 -4.026733082
49 4.769406584 -1.765421829
50 -4.759160459 4.769406584
51 -0.334133480 -4.759160459
52 0.078853278 -0.334133480
53 -3.417382879 0.078853278
54 1.242410114 -3.417382879
55 -0.470536803 1.242410114
56 -0.891256995 -0.470536803
57 2.436212189 -0.891256995
58 -2.139259449 2.436212189
59 1.889174542 -2.139259449
60 -1.169974005 1.889174542
61 1.498738964 -1.169974005
62 2.618831121 1.498738964
63 -4.849488134 2.618831121
64 1.660293611 -4.849488134
65 -2.725229559 1.660293611
66 -2.759254097 -2.725229559
67 -0.389945124 -2.759254097
68 0.637576462 -0.389945124
69 -1.644333072 0.637576462
70 -0.764229188 -1.644333072
71 -2.848121153 -0.764229188
72 0.385032521 -2.848121153
73 3.096395277 0.385032521
74 2.890319886 3.096395277
75 -0.812869818 2.890319886
76 3.165167173 -0.812869818
77 0.985632216 3.165167173
78 -1.841613083 0.985632216
79 0.693072597 -1.841613083
80 -0.696640256 0.693072597
81 -1.065877089 -0.696640256
82 0.824142695 -1.065877089
83 1.561521896 0.824142695
84 -1.669859223 1.561521896
85 4.424726252 -1.669859223
86 -0.069054907 4.424726252
87 -2.227498740 -0.069054907
88 -1.738571004 -2.227498740
89 0.566548415 -1.738571004
90 2.028846992 0.566548415
91 -2.541633082 2.028846992
92 -0.527760634 -2.541633082
93 3.287431191 -0.527760634
94 -2.490910641 3.287431191
95 3.839644082 -2.490910641
96 0.780098642 3.839644082
97 2.237497265 0.780098642
98 0.324964398 2.237497265
99 0.334647610 0.324964398
100 0.506452129 0.334647610
101 0.987995798 0.506452129
102 4.562572921 0.987995798
103 1.033299400 4.562572921
104 -1.936850612 1.033299400
105 0.320821138 -1.936850612
106 -2.335578069 0.320821138
107 -1.773354111 -2.335578069
108 0.275452518 -1.773354111
109 2.147173167 0.275452518
110 1.035932391 2.147173167
111 0.318607621 1.035932391
112 -6.431820204 0.318607621
113 0.531222706 -6.431820204
114 -6.907207142 0.531222706
115 2.719249238 -6.907207142
116 2.849750555 2.719249238
117 -1.987540331 2.849750555
118 1.871416344 -1.987540331
119 -1.643776454 1.871416344
120 -4.183728225 -1.643776454
121 0.704872799 -4.183728225
122 -0.977994496 0.704872799
123 -4.650153689 -0.977994496
124 -2.063438910 -4.650153689
125 4.645361878 -2.063438910
126 0.359106043 4.645361878
127 -1.439352349 0.359106043
128 1.302158858 -1.439352349
129 -2.569555217 1.302158858
130 -2.324680274 -2.569555217
131 0.165882100 -2.324680274
132 0.696706428 0.165882100
133 -0.353868960 0.696706428
134 0.012392980 -0.353868960
135 5.346795277 0.012392980
136 1.376348351 5.346795277
137 -0.596719114 1.376348351
138 -1.033491748 -0.596719114
139 -4.437476066 -1.033491748
140 -5.584112676 -4.437476066
141 -0.492172646 -5.584112676
142 2.522194287 -0.492172646
143 -2.861134821 2.522194287
144 2.486404933 -2.861134821
145 -0.829571742 2.486404933
146 1.908610777 -0.829571742
147 -1.204276401 1.908610777
148 -0.506857021 -1.204276401
149 -0.390998956 -0.506857021
150 -3.191379841 -0.390998956
151 3.280447878 -3.191379841
152 7.269396568 3.280447878
153 1.805314182 7.269396568
154 3.703295072 1.805314182
155 2.055862480 3.703295072
156 -1.969501358 2.055862480
157 1.177397983 -1.969501358
158 1.146705129 1.177397983
159 NA 1.146705129
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -6.276282104 -4.090293312
[2,] 3.420870673 -6.276282104
[3,] -1.814362151 3.420870673
[4,] -1.682999043 -1.814362151
[5,] 0.963595838 -1.682999043
[6,] 0.892392020 0.963595838
[7,] -3.396709242 0.892392020
[8,] 1.495808723 -3.396709242
[9,] 1.760653284 1.495808723
[10,] -0.717922304 1.760653284
[11,] 1.320436052 -0.717922304
[12,] -2.722250589 1.320436052
[13,] -2.171504542 -2.722250589
[14,] -1.221021859 -2.171504542
[15,] 4.902769700 -1.221021859
[16,] -4.742776932 4.902769700
[17,] -1.430174356 -4.742776932
[18,] 4.907624876 -1.430174356
[19,] 1.524224134 4.907624876
[20,] -2.567414962 1.524224134
[21,] 2.734268338 -2.567414962
[22,] -1.966277120 2.734268338
[23,] -2.631340116 -1.966277120
[24,] -0.705130827 -2.631340116
[25,] 1.824330730 -0.705130827
[26,] 3.348196224 1.824330730
[27,] -0.039463439 3.348196224
[28,] 2.198076963 -0.039463439
[29,] 1.882776929 2.198076963
[30,] -2.449137648 1.882776929
[31,] -0.003046509 -2.449137648
[32,] 5.720657186 -0.003046509
[33,] -1.625304178 5.720657186
[34,] 3.359931301 -1.625304178
[35,] -3.159171724 3.359931301
[36,] -4.111961986 -3.159171724
[37,] 3.281864963 -4.111961986
[38,] 1.111504344 3.281864963
[39,] -0.952320156 1.111504344
[40,] 3.905616654 -0.952320156
[41,] 0.081467564 3.905616654
[42,] 2.988507668 0.081467564
[43,] 5.198373658 2.988507668
[44,] -3.062659736 5.198373658
[45,] -0.481105072 -3.062659736
[46,] 1.127635399 -0.481105072
[47,] -4.026733082 1.127635399
[48,] -1.765421829 -4.026733082
[49,] 4.769406584 -1.765421829
[50,] -4.759160459 4.769406584
[51,] -0.334133480 -4.759160459
[52,] 0.078853278 -0.334133480
[53,] -3.417382879 0.078853278
[54,] 1.242410114 -3.417382879
[55,] -0.470536803 1.242410114
[56,] -0.891256995 -0.470536803
[57,] 2.436212189 -0.891256995
[58,] -2.139259449 2.436212189
[59,] 1.889174542 -2.139259449
[60,] -1.169974005 1.889174542
[61,] 1.498738964 -1.169974005
[62,] 2.618831121 1.498738964
[63,] -4.849488134 2.618831121
[64,] 1.660293611 -4.849488134
[65,] -2.725229559 1.660293611
[66,] -2.759254097 -2.725229559
[67,] -0.389945124 -2.759254097
[68,] 0.637576462 -0.389945124
[69,] -1.644333072 0.637576462
[70,] -0.764229188 -1.644333072
[71,] -2.848121153 -0.764229188
[72,] 0.385032521 -2.848121153
[73,] 3.096395277 0.385032521
[74,] 2.890319886 3.096395277
[75,] -0.812869818 2.890319886
[76,] 3.165167173 -0.812869818
[77,] 0.985632216 3.165167173
[78,] -1.841613083 0.985632216
[79,] 0.693072597 -1.841613083
[80,] -0.696640256 0.693072597
[81,] -1.065877089 -0.696640256
[82,] 0.824142695 -1.065877089
[83,] 1.561521896 0.824142695
[84,] -1.669859223 1.561521896
[85,] 4.424726252 -1.669859223
[86,] -0.069054907 4.424726252
[87,] -2.227498740 -0.069054907
[88,] -1.738571004 -2.227498740
[89,] 0.566548415 -1.738571004
[90,] 2.028846992 0.566548415
[91,] -2.541633082 2.028846992
[92,] -0.527760634 -2.541633082
[93,] 3.287431191 -0.527760634
[94,] -2.490910641 3.287431191
[95,] 3.839644082 -2.490910641
[96,] 0.780098642 3.839644082
[97,] 2.237497265 0.780098642
[98,] 0.324964398 2.237497265
[99,] 0.334647610 0.324964398
[100,] 0.506452129 0.334647610
[101,] 0.987995798 0.506452129
[102,] 4.562572921 0.987995798
[103,] 1.033299400 4.562572921
[104,] -1.936850612 1.033299400
[105,] 0.320821138 -1.936850612
[106,] -2.335578069 0.320821138
[107,] -1.773354111 -2.335578069
[108,] 0.275452518 -1.773354111
[109,] 2.147173167 0.275452518
[110,] 1.035932391 2.147173167
[111,] 0.318607621 1.035932391
[112,] -6.431820204 0.318607621
[113,] 0.531222706 -6.431820204
[114,] -6.907207142 0.531222706
[115,] 2.719249238 -6.907207142
[116,] 2.849750555 2.719249238
[117,] -1.987540331 2.849750555
[118,] 1.871416344 -1.987540331
[119,] -1.643776454 1.871416344
[120,] -4.183728225 -1.643776454
[121,] 0.704872799 -4.183728225
[122,] -0.977994496 0.704872799
[123,] -4.650153689 -0.977994496
[124,] -2.063438910 -4.650153689
[125,] 4.645361878 -2.063438910
[126,] 0.359106043 4.645361878
[127,] -1.439352349 0.359106043
[128,] 1.302158858 -1.439352349
[129,] -2.569555217 1.302158858
[130,] -2.324680274 -2.569555217
[131,] 0.165882100 -2.324680274
[132,] 0.696706428 0.165882100
[133,] -0.353868960 0.696706428
[134,] 0.012392980 -0.353868960
[135,] 5.346795277 0.012392980
[136,] 1.376348351 5.346795277
[137,] -0.596719114 1.376348351
[138,] -1.033491748 -0.596719114
[139,] -4.437476066 -1.033491748
[140,] -5.584112676 -4.437476066
[141,] -0.492172646 -5.584112676
[142,] 2.522194287 -0.492172646
[143,] -2.861134821 2.522194287
[144,] 2.486404933 -2.861134821
[145,] -0.829571742 2.486404933
[146,] 1.908610777 -0.829571742
[147,] -1.204276401 1.908610777
[148,] -0.506857021 -1.204276401
[149,] -0.390998956 -0.506857021
[150,] -3.191379841 -0.390998956
[151,] 3.280447878 -3.191379841
[152,] 7.269396568 3.280447878
[153,] 1.805314182 7.269396568
[154,] 3.703295072 1.805314182
[155,] 2.055862480 3.703295072
[156,] -1.969501358 2.055862480
[157,] 1.177397983 -1.969501358
[158,] 1.146705129 1.177397983
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -6.276282104 -4.090293312
2 3.420870673 -6.276282104
3 -1.814362151 3.420870673
4 -1.682999043 -1.814362151
5 0.963595838 -1.682999043
6 0.892392020 0.963595838
7 -3.396709242 0.892392020
8 1.495808723 -3.396709242
9 1.760653284 1.495808723
10 -0.717922304 1.760653284
11 1.320436052 -0.717922304
12 -2.722250589 1.320436052
13 -2.171504542 -2.722250589
14 -1.221021859 -2.171504542
15 4.902769700 -1.221021859
16 -4.742776932 4.902769700
17 -1.430174356 -4.742776932
18 4.907624876 -1.430174356
19 1.524224134 4.907624876
20 -2.567414962 1.524224134
21 2.734268338 -2.567414962
22 -1.966277120 2.734268338
23 -2.631340116 -1.966277120
24 -0.705130827 -2.631340116
25 1.824330730 -0.705130827
26 3.348196224 1.824330730
27 -0.039463439 3.348196224
28 2.198076963 -0.039463439
29 1.882776929 2.198076963
30 -2.449137648 1.882776929
31 -0.003046509 -2.449137648
32 5.720657186 -0.003046509
33 -1.625304178 5.720657186
34 3.359931301 -1.625304178
35 -3.159171724 3.359931301
36 -4.111961986 -3.159171724
37 3.281864963 -4.111961986
38 1.111504344 3.281864963
39 -0.952320156 1.111504344
40 3.905616654 -0.952320156
41 0.081467564 3.905616654
42 2.988507668 0.081467564
43 5.198373658 2.988507668
44 -3.062659736 5.198373658
45 -0.481105072 -3.062659736
46 1.127635399 -0.481105072
47 -4.026733082 1.127635399
48 -1.765421829 -4.026733082
49 4.769406584 -1.765421829
50 -4.759160459 4.769406584
51 -0.334133480 -4.759160459
52 0.078853278 -0.334133480
53 -3.417382879 0.078853278
54 1.242410114 -3.417382879
55 -0.470536803 1.242410114
56 -0.891256995 -0.470536803
57 2.436212189 -0.891256995
58 -2.139259449 2.436212189
59 1.889174542 -2.139259449
60 -1.169974005 1.889174542
61 1.498738964 -1.169974005
62 2.618831121 1.498738964
63 -4.849488134 2.618831121
64 1.660293611 -4.849488134
65 -2.725229559 1.660293611
66 -2.759254097 -2.725229559
67 -0.389945124 -2.759254097
68 0.637576462 -0.389945124
69 -1.644333072 0.637576462
70 -0.764229188 -1.644333072
71 -2.848121153 -0.764229188
72 0.385032521 -2.848121153
73 3.096395277 0.385032521
74 2.890319886 3.096395277
75 -0.812869818 2.890319886
76 3.165167173 -0.812869818
77 0.985632216 3.165167173
78 -1.841613083 0.985632216
79 0.693072597 -1.841613083
80 -0.696640256 0.693072597
81 -1.065877089 -0.696640256
82 0.824142695 -1.065877089
83 1.561521896 0.824142695
84 -1.669859223 1.561521896
85 4.424726252 -1.669859223
86 -0.069054907 4.424726252
87 -2.227498740 -0.069054907
88 -1.738571004 -2.227498740
89 0.566548415 -1.738571004
90 2.028846992 0.566548415
91 -2.541633082 2.028846992
92 -0.527760634 -2.541633082
93 3.287431191 -0.527760634
94 -2.490910641 3.287431191
95 3.839644082 -2.490910641
96 0.780098642 3.839644082
97 2.237497265 0.780098642
98 0.324964398 2.237497265
99 0.334647610 0.324964398
100 0.506452129 0.334647610
101 0.987995798 0.506452129
102 4.562572921 0.987995798
103 1.033299400 4.562572921
104 -1.936850612 1.033299400
105 0.320821138 -1.936850612
106 -2.335578069 0.320821138
107 -1.773354111 -2.335578069
108 0.275452518 -1.773354111
109 2.147173167 0.275452518
110 1.035932391 2.147173167
111 0.318607621 1.035932391
112 -6.431820204 0.318607621
113 0.531222706 -6.431820204
114 -6.907207142 0.531222706
115 2.719249238 -6.907207142
116 2.849750555 2.719249238
117 -1.987540331 2.849750555
118 1.871416344 -1.987540331
119 -1.643776454 1.871416344
120 -4.183728225 -1.643776454
121 0.704872799 -4.183728225
122 -0.977994496 0.704872799
123 -4.650153689 -0.977994496
124 -2.063438910 -4.650153689
125 4.645361878 -2.063438910
126 0.359106043 4.645361878
127 -1.439352349 0.359106043
128 1.302158858 -1.439352349
129 -2.569555217 1.302158858
130 -2.324680274 -2.569555217
131 0.165882100 -2.324680274
132 0.696706428 0.165882100
133 -0.353868960 0.696706428
134 0.012392980 -0.353868960
135 5.346795277 0.012392980
136 1.376348351 5.346795277
137 -0.596719114 1.376348351
138 -1.033491748 -0.596719114
139 -4.437476066 -1.033491748
140 -5.584112676 -4.437476066
141 -0.492172646 -5.584112676
142 2.522194287 -0.492172646
143 -2.861134821 2.522194287
144 2.486404933 -2.861134821
145 -0.829571742 2.486404933
146 1.908610777 -0.829571742
147 -1.204276401 1.908610777
148 -0.506857021 -1.204276401
149 -0.390998956 -0.506857021
150 -3.191379841 -0.390998956
151 3.280447878 -3.191379841
152 7.269396568 3.280447878
153 1.805314182 7.269396568
154 3.703295072 1.805314182
155 2.055862480 3.703295072
156 -1.969501358 2.055862480
157 1.177397983 -1.969501358
158 1.146705129 1.177397983
> 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/wessaorg/rcomp/tmp/7gzrk1322162357.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/wessaorg/rcomp/tmp/8u8rf1322162357.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/wessaorg/rcomp/tmp/9oasz1322162357.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/wessaorg/rcomp/tmp/1096s91322162357.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11vgsn1322162357.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12ivie1322162357.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13257x1322162357.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14qxt61322162357.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/wessaorg/rcomp/tmp/15wkqr1322162357.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/wessaorg/rcomp/tmp/16si6h1322162357.tab")
+ }
>
> try(system("convert tmp/1kgjd1322162357.ps tmp/1kgjd1322162357.png",intern=TRUE))
character(0)
> try(system("convert tmp/2aydt1322162357.ps tmp/2aydt1322162357.png",intern=TRUE))
character(0)
> try(system("convert tmp/30ooi1322162357.ps tmp/30ooi1322162357.png",intern=TRUE))
character(0)
> try(system("convert tmp/4s23u1322162357.ps tmp/4s23u1322162357.png",intern=TRUE))
character(0)
> try(system("convert tmp/5v6ns1322162357.ps tmp/5v6ns1322162357.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vkqv1322162357.ps tmp/6vkqv1322162357.png",intern=TRUE))
character(0)
> try(system("convert tmp/7gzrk1322162357.ps tmp/7gzrk1322162357.png",intern=TRUE))
character(0)
> try(system("convert tmp/8u8rf1322162357.ps tmp/8u8rf1322162357.png",intern=TRUE))
character(0)
> try(system("convert tmp/9oasz1322162357.ps tmp/9oasz1322162357.png",intern=TRUE))
character(0)
> try(system("convert tmp/1096s91322162357.ps tmp/1096s91322162357.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.038 0.562 5.673