R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(13
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+ ,13)
+ ,dim=c(6
+ ,150)
+ ,dimnames=list(c('Learning'
+ ,'Concern'
+ ,'Doubts'
+ ,'Expectations'
+ ,'Standards'
+ ,'Organization')
+ ,1:150))
> y <- array(NA,dim=c(6,150),dimnames=list(c('Learning','Concern','Doubts','Expectations','Standards','Organization'),1:150))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = '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
> 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
Learning Concern Doubts Expectations Standards Organization t
1 13 26 9 15 25 25 1
2 16 20 9 15 25 24 2
3 19 21 9 14 19 21 3
4 15 31 14 10 18 23 4
5 14 21 8 10 18 17 5
6 13 18 8 12 22 19 6
7 19 26 11 18 29 18 7
8 15 22 10 12 26 27 8
9 14 22 9 14 25 23 9
10 15 29 15 18 23 23 10
11 16 15 14 9 23 29 11
12 16 16 11 11 23 21 12
13 16 24 14 11 24 26 13
14 17 17 6 17 30 25 14
15 15 19 20 8 19 25 15
16 15 22 9 16 24 23 16
17 20 31 10 21 32 26 17
18 18 28 8 24 30 20 18
19 16 38 11 21 29 29 19
20 16 26 14 14 17 24 20
21 19 25 11 7 25 23 21
22 16 25 16 18 26 24 22
23 17 29 14 18 26 30 23
24 17 28 11 13 25 22 24
25 16 15 11 11 23 22 25
26 15 18 12 13 21 13 26
27 14 21 9 13 19 24 27
28 15 25 7 18 35 17 28
29 12 23 13 14 19 24 29
30 14 23 10 12 20 21 30
31 16 19 9 9 21 23 31
32 14 18 9 12 21 24 32
33 7 18 13 8 24 24 33
34 10 26 16 5 23 24 34
35 14 18 12 10 19 23 35
36 16 18 6 11 17 26 36
37 16 28 14 11 24 24 37
38 16 17 14 12 15 21 38
39 14 29 10 12 25 23 39
40 20 12 4 15 27 28 40
41 14 25 12 12 29 23 41
42 14 28 12 16 27 22 42
43 11 20 14 14 18 24 43
44 15 17 9 17 25 21 44
45 16 17 9 13 22 23 45
46 14 20 10 10 26 23 46
47 16 31 14 17 23 20 47
48 14 21 10 12 16 23 48
49 12 19 9 13 27 21 49
50 16 23 14 13 25 27 50
51 9 15 8 11 14 12 51
52 14 24 9 13 19 15 52
53 16 28 8 12 20 22 53
54 16 16 9 12 16 21 54
55 15 19 9 12 18 21 55
56 16 21 9 9 22 20 56
57 12 21 15 7 21 24 57
58 16 20 8 17 22 24 58
59 16 16 10 12 22 29 59
60 14 25 8 12 32 25 60
61 16 30 14 9 23 14 61
62 17 29 11 9 31 30 62
63 18 22 10 13 18 19 63
64 18 19 12 10 23 29 64
65 12 33 14 11 26 25 65
66 16 17 9 12 24 25 66
67 10 9 13 10 19 25 67
68 14 14 15 13 14 16 68
69 18 15 8 6 20 25 69
70 18 12 7 7 22 28 70
71 16 21 10 13 24 24 71
72 16 20 10 11 25 25 72
73 16 29 13 18 21 21 73
74 13 33 11 9 28 22 74
75 16 21 8 9 24 20 75
76 16 15 12 11 20 25 76
77 20 19 9 11 21 27 77
78 16 23 10 15 23 21 78
79 15 20 11 8 13 13 79
80 15 20 11 11 24 26 80
81 16 18 10 14 21 26 81
82 14 31 16 14 21 25 82
83 15 18 16 12 17 22 83
84 12 13 8 12 14 19 84
85 17 9 6 8 29 23 85
86 16 20 11 11 25 25 86
87 15 18 12 10 16 15 87
88 13 23 14 17 25 21 88
89 16 17 9 16 25 23 89
90 16 17 11 13 21 25 90
91 16 16 8 15 23 24 91
92 16 31 8 11 22 24 92
93 14 15 7 12 19 21 93
94 16 28 16 16 24 24 94
95 16 26 13 20 26 22 95
96 20 20 8 16 25 24 96
97 15 19 11 11 20 28 97
98 16 25 14 15 22 21 98
99 13 18 10 15 14 17 99
100 17 20 10 12 20 28 100
101 16 33 14 9 32 24 101
102 12 24 14 24 21 10 102
103 16 22 10 15 22 20 103
104 16 32 12 18 28 22 104
105 17 31 9 17 25 19 105
106 13 13 16 12 17 22 106
107 12 18 8 15 21 22 107
108 18 17 9 11 23 26 108
109 14 29 16 11 27 24 109
110 14 22 13 15 22 22 110
111 13 18 13 12 19 20 111
112 16 22 8 14 20 20 112
113 13 25 14 11 17 15 113
114 16 20 11 20 24 20 114
115 13 20 9 11 21 20 115
116 16 17 8 12 21 24 116
117 15 21 13 17 23 22 117
118 16 26 13 12 24 29 118
119 15 10 10 11 19 23 119
120 17 15 8 10 22 24 120
121 15 20 7 11 26 22 121
122 12 14 11 12 17 16 122
123 16 16 11 9 17 23 123
124 10 23 14 8 19 27 124
125 16 11 6 6 15 16 125
126 14 19 10 12 17 21 126
127 15 30 9 15 27 26 127
128 13 21 12 13 19 22 128
129 15 20 11 17 21 23 129
130 11 22 14 14 25 19 130
131 12 30 12 16 19 18 131
132 8 25 14 15 22 24 132
133 16 28 8 16 18 24 133
134 15 23 14 11 20 29 134
135 17 23 8 11 15 22 135
136 16 21 11 16 20 24 136
137 10 30 12 15 29 22 137
138 18 22 9 14 19 12 138
139 13 32 16 9 29 26 139
140 15 22 11 13 24 18 140
141 16 15 11 11 23 22 141
142 16 21 12 14 22 24 142
143 14 27 15 11 23 21 143
144 10 22 13 12 22 15 144
145 17 9 6 8 29 23 145
146 13 29 11 7 26 22 146
147 15 20 7 11 26 22 147
148 16 16 8 13 21 24 148
149 12 16 8 9 18 23 149
150 13 16 9 12 10 13 150
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Concern Doubts Expectations Standards
13.191792 0.005070 -0.278495 0.102340 0.021824
Organization t
0.149072 -0.005812
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.3910 -1.2394 0.2616 1.2648 4.3689
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.191792 1.538649 8.574 1.49e-14 ***
Concern 0.005070 0.037499 0.135 0.89263
Doubts -0.278495 0.068266 -4.080 7.47e-05 ***
Expectations 0.102340 0.054077 1.892 0.06045 .
Standards 0.021824 0.048563 0.449 0.65382
Organization 0.149072 0.048734 3.059 0.00265 **
t -0.005812 0.003953 -1.470 0.14371
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.05 on 143 degrees of freedom
Multiple R-squared: 0.219, Adjusted R-squared: 0.1862
F-statistic: 6.682 on 6 and 143 DF, p-value: 2.984e-06
> 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,] 0.90986621 0.18026758 0.09013379
[2,] 0.83352448 0.33295105 0.16647552
[3,] 0.73681285 0.52637430 0.26318715
[4,] 0.67342158 0.65315685 0.32657842
[5,] 0.65525574 0.68948853 0.34474426
[6,] 0.60154410 0.79691179 0.39845590
[7,] 0.50963267 0.98073465 0.49036733
[8,] 0.58392024 0.83215952 0.41607976
[9,] 0.50309794 0.99380412 0.49690206
[10,] 0.42833033 0.85666065 0.57166967
[11,] 0.35700409 0.71400817 0.64299591
[12,] 0.45430970 0.90861939 0.54569030
[13,] 0.42395599 0.84791198 0.57604401
[14,] 0.35440524 0.70881048 0.64559476
[15,] 0.29854982 0.59709964 0.70145018
[16,] 0.24677159 0.49354319 0.75322841
[17,] 0.24220081 0.48440163 0.75779919
[18,] 0.20666577 0.41333154 0.79333423
[19,] 0.29488897 0.58977795 0.70511103
[20,] 0.37032888 0.74065776 0.62967112
[21,] 0.31459694 0.62919388 0.68540306
[22,] 0.28252232 0.56504464 0.71747768
[23,] 0.23955462 0.47910924 0.76044538
[24,] 0.88924327 0.22151346 0.11075673
[25,] 0.91691534 0.16616932 0.08308466
[26,] 0.89662249 0.20675502 0.10337751
[27,] 0.89589215 0.20821570 0.10410785
[28,] 0.89146322 0.21707356 0.10853678
[29,] 0.89031931 0.21936137 0.10968069
[30,] 0.86799722 0.26400556 0.13200278
[31,] 0.90489917 0.19020166 0.09510083
[32,] 0.88216040 0.23567919 0.11783960
[33,] 0.86167362 0.27665277 0.13832638
[34,] 0.90624488 0.18751023 0.09375512
[35,] 0.88430021 0.23139957 0.11569979
[36,] 0.86384081 0.27231838 0.13615919
[37,] 0.83923425 0.32153150 0.16076575
[38,] 0.82586223 0.34827555 0.17413777
[39,] 0.79836163 0.40327674 0.20163837
[40,] 0.83919869 0.32160261 0.16080131
[41,] 0.82281895 0.35436209 0.17718105
[42,] 0.92039986 0.15920028 0.07960014
[43,] 0.90967330 0.18065341 0.09032670
[44,] 0.90294643 0.19410714 0.09705357
[45,] 0.89676484 0.20647031 0.10323516
[46,] 0.88046980 0.23906040 0.11953020
[47,] 0.87899491 0.24201019 0.12100509
[48,] 0.86817800 0.26364399 0.13182200
[49,] 0.84551934 0.30896132 0.15448066
[50,] 0.82017587 0.35964825 0.17982413
[51,] 0.83640549 0.32718902 0.16359451
[52,] 0.87791550 0.24416900 0.12208450
[53,] 0.86369539 0.27260923 0.13630461
[54,] 0.89685520 0.20628960 0.10314480
[55,] 0.90988121 0.18023758 0.09011879
[56,] 0.92803915 0.14392170 0.07196085
[57,] 0.91292137 0.17415726 0.08707863
[58,] 0.96863315 0.06273370 0.03136685
[59,] 0.96126090 0.07747820 0.03873910
[60,] 0.96855029 0.06289941 0.03144971
[61,] 0.96693641 0.06612718 0.03306359
[62,] 0.95770061 0.08459878 0.04229939
[63,] 0.94680327 0.10639346 0.05319673
[64,] 0.93475225 0.13049550 0.06524775
[65,] 0.94053741 0.11892518 0.05946259
[66,] 0.92894807 0.14210385 0.07105193
[67,] 0.91421840 0.17156319 0.08578160
[68,] 0.94605978 0.10788044 0.05394022
[69,] 0.93145698 0.13708604 0.06854302
[70,] 0.92448416 0.15103167 0.07551584
[71,] 0.90927380 0.18145240 0.09072620
[72,] 0.88802941 0.22394118 0.11197059
[73,] 0.86526203 0.26947593 0.13473797
[74,] 0.85276809 0.29446381 0.14723191
[75,] 0.90063918 0.19872164 0.09936082
[76,] 0.88491473 0.23017054 0.11508527
[77,] 0.85968590 0.28062820 0.14031410
[78,] 0.84299272 0.31401456 0.15700728
[79,] 0.83624475 0.32751049 0.16375525
[80,] 0.80768162 0.38463675 0.19231838
[81,] 0.77212842 0.45574316 0.22787158
[82,] 0.74282641 0.51434718 0.25717359
[83,] 0.70467746 0.59064509 0.29532254
[84,] 0.73532823 0.52934354 0.26467177
[85,] 0.72522279 0.54955443 0.27477721
[86,] 0.68548710 0.62902580 0.31451290
[87,] 0.73178276 0.53643449 0.26821724
[88,] 0.69688803 0.60622395 0.30311197
[89,] 0.68929467 0.62141066 0.31070533
[90,] 0.67977453 0.64045094 0.32022547
[91,] 0.63739063 0.72521875 0.36260937
[92,] 0.61976280 0.76047440 0.38023720
[93,] 0.59164659 0.81670681 0.40835341
[94,] 0.54410708 0.91178583 0.45589292
[95,] 0.50773296 0.98453408 0.49226704
[96,] 0.49223281 0.98446562 0.50776719
[97,] 0.43984584 0.87969168 0.56015416
[98,] 0.61834185 0.76331631 0.38165815
[99,] 0.60697947 0.78604106 0.39302053
[100,] 0.59260304 0.81479392 0.40739696
[101,] 0.54042055 0.91915890 0.45957945
[102,] 0.48853312 0.97706625 0.51146688
[103,] 0.43281997 0.86563994 0.56718003
[104,] 0.39773027 0.79546055 0.60226973
[105,] 0.35856876 0.71713752 0.64143124
[106,] 0.34627844 0.69255689 0.65372156
[107,] 0.29299233 0.58598466 0.70700767
[108,] 0.26331169 0.52662339 0.73668831
[109,] 0.26578081 0.53156162 0.73421919
[110,] 0.21719076 0.43438152 0.78280924
[111,] 0.19140631 0.38281262 0.80859369
[112,] 0.15504289 0.31008578 0.84495711
[113,] 0.13931993 0.27863986 0.86068007
[114,] 0.13217586 0.26435171 0.86782414
[115,] 0.17685034 0.35370068 0.82314966
[116,] 0.14370897 0.28741794 0.85629103
[117,] 0.11632419 0.23264838 0.88367581
[118,] 0.08791315 0.17582630 0.91208685
[119,] 0.07271716 0.14543431 0.92728284
[120,] 0.05081116 0.10162231 0.94918884
[121,] 0.05119745 0.10239489 0.94880255
[122,] 0.04255166 0.08510331 0.95744834
[123,] 0.43363291 0.86726581 0.56636709
[124,] 0.34766006 0.69532011 0.65233994
[125,] 0.28522727 0.57045453 0.71477273
[126,] 0.23040076 0.46080153 0.76959924
[127,] 0.17563898 0.35127796 0.82436102
[128,] 0.73371304 0.53257393 0.26628696
[129,] 0.65798004 0.68403992 0.34201996
[130,] 0.54006800 0.91986399 0.45993200
[131,] 0.38492767 0.76985534 0.61507233
> postscript(file="/var/www/rcomp/tmp/17gy61292443905.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/27gy61292443905.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/37gy61292443905.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4z7f91292443905.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5z7f91292443905.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 = 150
Frequency = 1
1 2 3 4 5 6
-3.61884617 -0.43353997 3.24770196 0.72832605 -0.99169890 -2.56079533
7 8 9 10 11 12
3.62220599 -1.29233414 -2.15158454 0.12399752 0.94892333 1.10207561
13 14 15 16 17 18
1.13562704 -0.64694191 2.40878679 -1.29375503 2.81141256 0.90650633
19 20 21 22 23 24
-1.31570400 1.31006491 4.17631724 1.27797501 0.81208283 1.71357696
25 26 27 28 29 30
1.03363101 1.48334450 -1.95768453 -1.34652431 -2.94455895 -1.14416211
31 32 33 34 35 36
0.59048659 -1.85472170 -7.39104193 -3.26146357 -0.60439982 -0.77546766
37 38 39 40 41 42
1.55297955 2.15585890 -1.52954004 1.79546857 -1.02794003 -1.25397711
43 44 45 46 47 48
-3.54765923 -0.93168470 0.25081401 -1.26036724 1.59996393 -1.24025153
49 50 51 52 53 54
-3.54705491 0.98016910 -4.96360424 -0.48594560 0.25810112 0.83962129
55 56 57 58 59 60
-0.21342590 1.15103977 -1.54196032 -0.52576574 -0.17634350 -2.39510853
61 62 63 64 65 66
3.39955200 1.01520334 3.29215953 2.57735174 -2.50235419 0.13341506
67 68 69 70 71 72
-4.39242864 1.28877231 2.58383047 1.73315440 0.46742189 0.51208737
73 74 75 76 77 78
1.27495993 -1.67728481 0.93932566 1.22679885 4.05687513 0.76232669
79 80 81 82 83 84
2.18903935 -0.29016845 0.20574250 -0.03431547 1.77660279 -2.90750846
85 86 87 88 89 90
1.04730291 0.87195192 1.95587665 -1.31389817 0.13405449 0.79302858
91 92 93 94 95 96
-0.13083063 0.23010855 -1.55110040 1.92956108 0.95516507 3.73196056
97 98 99 100 101 102
-0.39714031 2.00423340 -1.29756290 1.23439058 1.92968721 -1.22688803
103 104 105 106 107 108
1.08359516 0.85958718 1.65001123 -0.06436760 -3.70618585 2.35261411
109 110 111 112 113 114
0.45789750 -0.33837800 -0.64164946 0.72490081 0.50432543 0.88081808
115 116 117 118 119 120
-1.68383219 0.36006796 0.48087365 0.90770375 0.26504732 1.57631203
121 122 123 124 125 126
-0.61321509 -1.47448981 1.78469630 -3.94709444 1.82336603 -0.50044868
127 128 129 130 131 132
-1.09952570 -1.23703416 -0.10672567 -2.45955772 -1.97596184 -6.24537187
133 134 135 136 137 138
0.05921344 0.48403952 1.97150357 0.90398022 -4.65327884 4.36891042
139 140 141 142 143 144
-0.52006422 1.03631248 1.70783231 1.37837907 0.92166592 -2.79024456
145 146 147 148 149 150
1.39602772 -0.99020626 -0.46210100 0.44878530 -2.92150000 -0.27889839
> postscript(file="/var/www/rcomp/tmp/6sgec1292443905.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 = 150
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.61884617 NA
1 -0.43353997 -3.61884617
2 3.24770196 -0.43353997
3 0.72832605 3.24770196
4 -0.99169890 0.72832605
5 -2.56079533 -0.99169890
6 3.62220599 -2.56079533
7 -1.29233414 3.62220599
8 -2.15158454 -1.29233414
9 0.12399752 -2.15158454
10 0.94892333 0.12399752
11 1.10207561 0.94892333
12 1.13562704 1.10207561
13 -0.64694191 1.13562704
14 2.40878679 -0.64694191
15 -1.29375503 2.40878679
16 2.81141256 -1.29375503
17 0.90650633 2.81141256
18 -1.31570400 0.90650633
19 1.31006491 -1.31570400
20 4.17631724 1.31006491
21 1.27797501 4.17631724
22 0.81208283 1.27797501
23 1.71357696 0.81208283
24 1.03363101 1.71357696
25 1.48334450 1.03363101
26 -1.95768453 1.48334450
27 -1.34652431 -1.95768453
28 -2.94455895 -1.34652431
29 -1.14416211 -2.94455895
30 0.59048659 -1.14416211
31 -1.85472170 0.59048659
32 -7.39104193 -1.85472170
33 -3.26146357 -7.39104193
34 -0.60439982 -3.26146357
35 -0.77546766 -0.60439982
36 1.55297955 -0.77546766
37 2.15585890 1.55297955
38 -1.52954004 2.15585890
39 1.79546857 -1.52954004
40 -1.02794003 1.79546857
41 -1.25397711 -1.02794003
42 -3.54765923 -1.25397711
43 -0.93168470 -3.54765923
44 0.25081401 -0.93168470
45 -1.26036724 0.25081401
46 1.59996393 -1.26036724
47 -1.24025153 1.59996393
48 -3.54705491 -1.24025153
49 0.98016910 -3.54705491
50 -4.96360424 0.98016910
51 -0.48594560 -4.96360424
52 0.25810112 -0.48594560
53 0.83962129 0.25810112
54 -0.21342590 0.83962129
55 1.15103977 -0.21342590
56 -1.54196032 1.15103977
57 -0.52576574 -1.54196032
58 -0.17634350 -0.52576574
59 -2.39510853 -0.17634350
60 3.39955200 -2.39510853
61 1.01520334 3.39955200
62 3.29215953 1.01520334
63 2.57735174 3.29215953
64 -2.50235419 2.57735174
65 0.13341506 -2.50235419
66 -4.39242864 0.13341506
67 1.28877231 -4.39242864
68 2.58383047 1.28877231
69 1.73315440 2.58383047
70 0.46742189 1.73315440
71 0.51208737 0.46742189
72 1.27495993 0.51208737
73 -1.67728481 1.27495993
74 0.93932566 -1.67728481
75 1.22679885 0.93932566
76 4.05687513 1.22679885
77 0.76232669 4.05687513
78 2.18903935 0.76232669
79 -0.29016845 2.18903935
80 0.20574250 -0.29016845
81 -0.03431547 0.20574250
82 1.77660279 -0.03431547
83 -2.90750846 1.77660279
84 1.04730291 -2.90750846
85 0.87195192 1.04730291
86 1.95587665 0.87195192
87 -1.31389817 1.95587665
88 0.13405449 -1.31389817
89 0.79302858 0.13405449
90 -0.13083063 0.79302858
91 0.23010855 -0.13083063
92 -1.55110040 0.23010855
93 1.92956108 -1.55110040
94 0.95516507 1.92956108
95 3.73196056 0.95516507
96 -0.39714031 3.73196056
97 2.00423340 -0.39714031
98 -1.29756290 2.00423340
99 1.23439058 -1.29756290
100 1.92968721 1.23439058
101 -1.22688803 1.92968721
102 1.08359516 -1.22688803
103 0.85958718 1.08359516
104 1.65001123 0.85958718
105 -0.06436760 1.65001123
106 -3.70618585 -0.06436760
107 2.35261411 -3.70618585
108 0.45789750 2.35261411
109 -0.33837800 0.45789750
110 -0.64164946 -0.33837800
111 0.72490081 -0.64164946
112 0.50432543 0.72490081
113 0.88081808 0.50432543
114 -1.68383219 0.88081808
115 0.36006796 -1.68383219
116 0.48087365 0.36006796
117 0.90770375 0.48087365
118 0.26504732 0.90770375
119 1.57631203 0.26504732
120 -0.61321509 1.57631203
121 -1.47448981 -0.61321509
122 1.78469630 -1.47448981
123 -3.94709444 1.78469630
124 1.82336603 -3.94709444
125 -0.50044868 1.82336603
126 -1.09952570 -0.50044868
127 -1.23703416 -1.09952570
128 -0.10672567 -1.23703416
129 -2.45955772 -0.10672567
130 -1.97596184 -2.45955772
131 -6.24537187 -1.97596184
132 0.05921344 -6.24537187
133 0.48403952 0.05921344
134 1.97150357 0.48403952
135 0.90398022 1.97150357
136 -4.65327884 0.90398022
137 4.36891042 -4.65327884
138 -0.52006422 4.36891042
139 1.03631248 -0.52006422
140 1.70783231 1.03631248
141 1.37837907 1.70783231
142 0.92166592 1.37837907
143 -2.79024456 0.92166592
144 1.39602772 -2.79024456
145 -0.99020626 1.39602772
146 -0.46210100 -0.99020626
147 0.44878530 -0.46210100
148 -2.92150000 0.44878530
149 -0.27889839 -2.92150000
150 NA -0.27889839
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.43353997 -3.61884617
[2,] 3.24770196 -0.43353997
[3,] 0.72832605 3.24770196
[4,] -0.99169890 0.72832605
[5,] -2.56079533 -0.99169890
[6,] 3.62220599 -2.56079533
[7,] -1.29233414 3.62220599
[8,] -2.15158454 -1.29233414
[9,] 0.12399752 -2.15158454
[10,] 0.94892333 0.12399752
[11,] 1.10207561 0.94892333
[12,] 1.13562704 1.10207561
[13,] -0.64694191 1.13562704
[14,] 2.40878679 -0.64694191
[15,] -1.29375503 2.40878679
[16,] 2.81141256 -1.29375503
[17,] 0.90650633 2.81141256
[18,] -1.31570400 0.90650633
[19,] 1.31006491 -1.31570400
[20,] 4.17631724 1.31006491
[21,] 1.27797501 4.17631724
[22,] 0.81208283 1.27797501
[23,] 1.71357696 0.81208283
[24,] 1.03363101 1.71357696
[25,] 1.48334450 1.03363101
[26,] -1.95768453 1.48334450
[27,] -1.34652431 -1.95768453
[28,] -2.94455895 -1.34652431
[29,] -1.14416211 -2.94455895
[30,] 0.59048659 -1.14416211
[31,] -1.85472170 0.59048659
[32,] -7.39104193 -1.85472170
[33,] -3.26146357 -7.39104193
[34,] -0.60439982 -3.26146357
[35,] -0.77546766 -0.60439982
[36,] 1.55297955 -0.77546766
[37,] 2.15585890 1.55297955
[38,] -1.52954004 2.15585890
[39,] 1.79546857 -1.52954004
[40,] -1.02794003 1.79546857
[41,] -1.25397711 -1.02794003
[42,] -3.54765923 -1.25397711
[43,] -0.93168470 -3.54765923
[44,] 0.25081401 -0.93168470
[45,] -1.26036724 0.25081401
[46,] 1.59996393 -1.26036724
[47,] -1.24025153 1.59996393
[48,] -3.54705491 -1.24025153
[49,] 0.98016910 -3.54705491
[50,] -4.96360424 0.98016910
[51,] -0.48594560 -4.96360424
[52,] 0.25810112 -0.48594560
[53,] 0.83962129 0.25810112
[54,] -0.21342590 0.83962129
[55,] 1.15103977 -0.21342590
[56,] -1.54196032 1.15103977
[57,] -0.52576574 -1.54196032
[58,] -0.17634350 -0.52576574
[59,] -2.39510853 -0.17634350
[60,] 3.39955200 -2.39510853
[61,] 1.01520334 3.39955200
[62,] 3.29215953 1.01520334
[63,] 2.57735174 3.29215953
[64,] -2.50235419 2.57735174
[65,] 0.13341506 -2.50235419
[66,] -4.39242864 0.13341506
[67,] 1.28877231 -4.39242864
[68,] 2.58383047 1.28877231
[69,] 1.73315440 2.58383047
[70,] 0.46742189 1.73315440
[71,] 0.51208737 0.46742189
[72,] 1.27495993 0.51208737
[73,] -1.67728481 1.27495993
[74,] 0.93932566 -1.67728481
[75,] 1.22679885 0.93932566
[76,] 4.05687513 1.22679885
[77,] 0.76232669 4.05687513
[78,] 2.18903935 0.76232669
[79,] -0.29016845 2.18903935
[80,] 0.20574250 -0.29016845
[81,] -0.03431547 0.20574250
[82,] 1.77660279 -0.03431547
[83,] -2.90750846 1.77660279
[84,] 1.04730291 -2.90750846
[85,] 0.87195192 1.04730291
[86,] 1.95587665 0.87195192
[87,] -1.31389817 1.95587665
[88,] 0.13405449 -1.31389817
[89,] 0.79302858 0.13405449
[90,] -0.13083063 0.79302858
[91,] 0.23010855 -0.13083063
[92,] -1.55110040 0.23010855
[93,] 1.92956108 -1.55110040
[94,] 0.95516507 1.92956108
[95,] 3.73196056 0.95516507
[96,] -0.39714031 3.73196056
[97,] 2.00423340 -0.39714031
[98,] -1.29756290 2.00423340
[99,] 1.23439058 -1.29756290
[100,] 1.92968721 1.23439058
[101,] -1.22688803 1.92968721
[102,] 1.08359516 -1.22688803
[103,] 0.85958718 1.08359516
[104,] 1.65001123 0.85958718
[105,] -0.06436760 1.65001123
[106,] -3.70618585 -0.06436760
[107,] 2.35261411 -3.70618585
[108,] 0.45789750 2.35261411
[109,] -0.33837800 0.45789750
[110,] -0.64164946 -0.33837800
[111,] 0.72490081 -0.64164946
[112,] 0.50432543 0.72490081
[113,] 0.88081808 0.50432543
[114,] -1.68383219 0.88081808
[115,] 0.36006796 -1.68383219
[116,] 0.48087365 0.36006796
[117,] 0.90770375 0.48087365
[118,] 0.26504732 0.90770375
[119,] 1.57631203 0.26504732
[120,] -0.61321509 1.57631203
[121,] -1.47448981 -0.61321509
[122,] 1.78469630 -1.47448981
[123,] -3.94709444 1.78469630
[124,] 1.82336603 -3.94709444
[125,] -0.50044868 1.82336603
[126,] -1.09952570 -0.50044868
[127,] -1.23703416 -1.09952570
[128,] -0.10672567 -1.23703416
[129,] -2.45955772 -0.10672567
[130,] -1.97596184 -2.45955772
[131,] -6.24537187 -1.97596184
[132,] 0.05921344 -6.24537187
[133,] 0.48403952 0.05921344
[134,] 1.97150357 0.48403952
[135,] 0.90398022 1.97150357
[136,] -4.65327884 0.90398022
[137,] 4.36891042 -4.65327884
[138,] -0.52006422 4.36891042
[139,] 1.03631248 -0.52006422
[140,] 1.70783231 1.03631248
[141,] 1.37837907 1.70783231
[142,] 0.92166592 1.37837907
[143,] -2.79024456 0.92166592
[144,] 1.39602772 -2.79024456
[145,] -0.99020626 1.39602772
[146,] -0.46210100 -0.99020626
[147,] 0.44878530 -0.46210100
[148,] -2.92150000 0.44878530
[149,] -0.27889839 -2.92150000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.43353997 -3.61884617
2 3.24770196 -0.43353997
3 0.72832605 3.24770196
4 -0.99169890 0.72832605
5 -2.56079533 -0.99169890
6 3.62220599 -2.56079533
7 -1.29233414 3.62220599
8 -2.15158454 -1.29233414
9 0.12399752 -2.15158454
10 0.94892333 0.12399752
11 1.10207561 0.94892333
12 1.13562704 1.10207561
13 -0.64694191 1.13562704
14 2.40878679 -0.64694191
15 -1.29375503 2.40878679
16 2.81141256 -1.29375503
17 0.90650633 2.81141256
18 -1.31570400 0.90650633
19 1.31006491 -1.31570400
20 4.17631724 1.31006491
21 1.27797501 4.17631724
22 0.81208283 1.27797501
23 1.71357696 0.81208283
24 1.03363101 1.71357696
25 1.48334450 1.03363101
26 -1.95768453 1.48334450
27 -1.34652431 -1.95768453
28 -2.94455895 -1.34652431
29 -1.14416211 -2.94455895
30 0.59048659 -1.14416211
31 -1.85472170 0.59048659
32 -7.39104193 -1.85472170
33 -3.26146357 -7.39104193
34 -0.60439982 -3.26146357
35 -0.77546766 -0.60439982
36 1.55297955 -0.77546766
37 2.15585890 1.55297955
38 -1.52954004 2.15585890
39 1.79546857 -1.52954004
40 -1.02794003 1.79546857
41 -1.25397711 -1.02794003
42 -3.54765923 -1.25397711
43 -0.93168470 -3.54765923
44 0.25081401 -0.93168470
45 -1.26036724 0.25081401
46 1.59996393 -1.26036724
47 -1.24025153 1.59996393
48 -3.54705491 -1.24025153
49 0.98016910 -3.54705491
50 -4.96360424 0.98016910
51 -0.48594560 -4.96360424
52 0.25810112 -0.48594560
53 0.83962129 0.25810112
54 -0.21342590 0.83962129
55 1.15103977 -0.21342590
56 -1.54196032 1.15103977
57 -0.52576574 -1.54196032
58 -0.17634350 -0.52576574
59 -2.39510853 -0.17634350
60 3.39955200 -2.39510853
61 1.01520334 3.39955200
62 3.29215953 1.01520334
63 2.57735174 3.29215953
64 -2.50235419 2.57735174
65 0.13341506 -2.50235419
66 -4.39242864 0.13341506
67 1.28877231 -4.39242864
68 2.58383047 1.28877231
69 1.73315440 2.58383047
70 0.46742189 1.73315440
71 0.51208737 0.46742189
72 1.27495993 0.51208737
73 -1.67728481 1.27495993
74 0.93932566 -1.67728481
75 1.22679885 0.93932566
76 4.05687513 1.22679885
77 0.76232669 4.05687513
78 2.18903935 0.76232669
79 -0.29016845 2.18903935
80 0.20574250 -0.29016845
81 -0.03431547 0.20574250
82 1.77660279 -0.03431547
83 -2.90750846 1.77660279
84 1.04730291 -2.90750846
85 0.87195192 1.04730291
86 1.95587665 0.87195192
87 -1.31389817 1.95587665
88 0.13405449 -1.31389817
89 0.79302858 0.13405449
90 -0.13083063 0.79302858
91 0.23010855 -0.13083063
92 -1.55110040 0.23010855
93 1.92956108 -1.55110040
94 0.95516507 1.92956108
95 3.73196056 0.95516507
96 -0.39714031 3.73196056
97 2.00423340 -0.39714031
98 -1.29756290 2.00423340
99 1.23439058 -1.29756290
100 1.92968721 1.23439058
101 -1.22688803 1.92968721
102 1.08359516 -1.22688803
103 0.85958718 1.08359516
104 1.65001123 0.85958718
105 -0.06436760 1.65001123
106 -3.70618585 -0.06436760
107 2.35261411 -3.70618585
108 0.45789750 2.35261411
109 -0.33837800 0.45789750
110 -0.64164946 -0.33837800
111 0.72490081 -0.64164946
112 0.50432543 0.72490081
113 0.88081808 0.50432543
114 -1.68383219 0.88081808
115 0.36006796 -1.68383219
116 0.48087365 0.36006796
117 0.90770375 0.48087365
118 0.26504732 0.90770375
119 1.57631203 0.26504732
120 -0.61321509 1.57631203
121 -1.47448981 -0.61321509
122 1.78469630 -1.47448981
123 -3.94709444 1.78469630
124 1.82336603 -3.94709444
125 -0.50044868 1.82336603
126 -1.09952570 -0.50044868
127 -1.23703416 -1.09952570
128 -0.10672567 -1.23703416
129 -2.45955772 -0.10672567
130 -1.97596184 -2.45955772
131 -6.24537187 -1.97596184
132 0.05921344 -6.24537187
133 0.48403952 0.05921344
134 1.97150357 0.48403952
135 0.90398022 1.97150357
136 -4.65327884 0.90398022
137 4.36891042 -4.65327884
138 -0.52006422 4.36891042
139 1.03631248 -0.52006422
140 1.70783231 1.03631248
141 1.37837907 1.70783231
142 0.92166592 1.37837907
143 -2.79024456 0.92166592
144 1.39602772 -2.79024456
145 -0.99020626 1.39602772
146 -0.46210100 -0.99020626
147 0.44878530 -0.46210100
148 -2.92150000 0.44878530
149 -0.27889839 -2.92150000
> 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/rcomp/tmp/7sgec1292443905.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/837vx1292443905.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/937vx1292443905.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10ezd01292443905.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11zzb61292443905.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/rcomp/tmp/1220sb1292443905.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/rcomp/tmp/13gr7k1292443905.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/rcomp/tmp/14ks6q1292443905.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/rcomp/tmp/15ntne1292443905.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/rcomp/tmp/16rbl21292443905.tab")
+ }
>
> try(system("convert tmp/17gy61292443905.ps tmp/17gy61292443905.png",intern=TRUE))
character(0)
> try(system("convert tmp/27gy61292443905.ps tmp/27gy61292443905.png",intern=TRUE))
character(0)
> try(system("convert tmp/37gy61292443905.ps tmp/37gy61292443905.png",intern=TRUE))
character(0)
> try(system("convert tmp/4z7f91292443905.ps tmp/4z7f91292443905.png",intern=TRUE))
character(0)
> try(system("convert tmp/5z7f91292443905.ps tmp/5z7f91292443905.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sgec1292443905.ps tmp/6sgec1292443905.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sgec1292443905.ps tmp/7sgec1292443905.png",intern=TRUE))
character(0)
> try(system("convert tmp/837vx1292443905.ps tmp/837vx1292443905.png",intern=TRUE))
character(0)
> try(system("convert tmp/937vx1292443905.ps tmp/937vx1292443905.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ezd01292443905.ps tmp/10ezd01292443905.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.46 1.76 6.20