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(11
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+ ,6)
+ ,dim=c(7
+ ,156)
+ ,dimnames=list(c('Maand'
+ ,'Schoolprestaties'
+ ,'Sport'
+ ,'GoingOut'
+ ,'Relation'
+ ,'Friends'
+ ,'Job')
+ ,1:156))
> y <- array(NA,dim=c(7,156),dimnames=list(c('Maand','Schoolprestaties','Sport','GoingOut','Relation','Friends','Job'),1:156))
> 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'
> 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
Schoolprestaties Maand Sport GoingOut Relation Friends Job
1 7 11 3 2 3 7 6
2 7 11 5 6 0 7 7
3 6 11 6 6 0 8 8
4 6 11 6 6 6 9 8
5 8 11 7 8 5 5 9
6 8 11 3 1 0 7 8
7 8 11 2 9 8 8 8
8 5 11 4 4 0 7 7
9 4 11 7 7 0 8 7
10 9 11 4 4 9 8 4
11 6 11 6 6 6 6 6
12 6 11 6 5 6 4 7
13 5 11 7 7 5 8 5
14 6 11 4 5 4 8 8
15 2 11 6 6 0 7 5
16 4 11 5 5 0 9 4
17 2 11 0 2 2 2 9
18 6 11 9 9 6 8 8
19 7 11 4 4 0 8 4
20 8 11 2 4 4 4 6
21 5 11 2 5 5 5 6
22 7 11 7 7 7 7 7
23 5 11 5 5 5 8 3
24 4 11 9 9 4 4 4
25 6 11 6 6 6 6 6
26 6 11 6 6 6 6 6
27 7 11 7 3 0 9 7
28 7 11 3 3 1 7 5
29 8 11 6 5 0 6 8
30 4 11 6 5 4 4 6
31 4 11 4 4 4 8 4
32 7 11 7 7 7 3 9
33 7 11 7 6 7 7 7
34 4 11 2 7 0 4 4
35 7 11 4 4 4 7 6
36 5 11 5 5 5 8 8
37 6 11 6 6 0 6 6
38 5 11 5 5 5 5 5
39 6 11 6 0 1 6 6
40 7 11 6 6 2 9 6
41 6 11 6 5 0 8 4
42 9 11 3 3 9 7 7
43 7 11 3 3 3 3 9
44 4 11 3 3 0 4 8
45 6 11 6 7 6 6 6
46 5 11 7 7 1 8 6
47 5 11 5 1 5 5 5
48 4 11 5 5 0 7 7
49 7 11 5 5 0 7 5
50 6 11 6 6 0 9 8
51 6 11 2 2 6 6 6
52 7 11 6 6 7 8 8
53 5 11 5 5 0 5 5
54 4 11 4 2 4 4 4
55 5 11 7 7 5 8 5
56 5 11 5 5 1 9 6
57 4 12 3 3 4 4 4
58 9 12 6 6 9 8 6
59 8 12 2 2 2 2 9
60 8 12 8 8 8 8 7
61 3 12 3 5 3 7 3
62 6 12 0 2 1 7 6
63 6 12 2 6 0 6 6
64 6 12 8 2 6 6 6
65 5 12 4 1 0 5 5
66 5 12 5 5 0 8 5
67 6 12 6 6 6 4 5
68 7 12 5 2 2 9 9
69 6 12 6 6 1 6 8
70 5 12 2 2 5 5 5
71 5 12 6 6 5 5 6
72 7 12 2 5 5 7 7
73 5 12 5 0 5 8 5
74 6 12 6 2 6 9 6
75 6 12 4 4 6 6 6
76 9 12 6 1 0 6 6
77 8 12 5 5 0 5 6
78 5 12 5 5 1 3 9
79 7 12 4 2 7 7 7
80 7 12 2 2 2 9 9
81 4 12 7 7 4 7 4
82 6 12 5 5 0 8 8
83 5 12 6 2 5 5 5
84 5 12 5 5 5 5 8
85 3 12 3 3 3 8 9
86 6 12 6 6 0 6 6
87 4 12 4 1 4 9 4
88 9 12 5 5 9 5 7
89 8 12 7 7 0 8 8
90 4 12 4 2 4 8 9
91 2 12 6 6 2 7 9
92 7 12 8 8 7 7 7
93 7 12 7 7 7 8 8
94 6 12 6 6 6 4 4
95 5 12 7 7 0 5 6
96 8 12 4 4 5 9 7
97 6 12 0 5 6 6 6
98 3 12 3 2 0 7 7
99 5 12 5 5 5 5 5
100 9 12 6 2 9 2 9
101 7 12 5 5 0 7 7
102 7 12 7 7 7 7 7
103 6 12 6 5 1 6 6
104 3 12 8 8 3 8 6
105 7 12 7 2 7 9 9
106 8 12 8 8 8 8 9
107 3 12 3 3 0 3 8
108 5 12 8 2 5 5 8
109 8 12 3 3 3 7 3
110 7 12 4 5 0 8 6
111 5 12 2 2 5 5 5
112 7 12 7 2 7 9 7
113 6 12 6 6 0 6 6
114 7 12 2 2 0 7 7
115 9 12 7 7 0 7 7
116 6 12 6 6 6 6 6
117 6 12 6 2 0 3 8
118 6 12 6 2 6 9 9
119 6 12 6 5 6 6 6
120 2 12 6 6 2 2 9
121 5 12 4 4 5 5 5
122 5 12 2 5 0 5 6
123 4 12 7 7 4 9 4
124 7 12 6 6 0 7 7
125 6 12 6 6 6 6 6
126 5 12 5 5 5 8 8
127 8 12 8 2 8 8 8
128 7 12 6 6 6 6 9
129 5 12 0 3 5 3 8
130 4 12 4 2 0 7 4
131 8 12 8 8 8 9 6
132 6 12 6 6 0 7 6
133 9 12 4 4 9 4 7
134 5 12 6 6 5 5 9
135 6 12 2 5 0 6 8
136 4 12 4 4 0 4 4
137 6 12 2 2 0 6 6
138 3 12 3 3 3 7 9
139 6 12 6 6 6 6 6
140 5 12 5 5 0 5 5
141 4 12 4 4 4 9 8
142 6 12 6 6 6 6 6
143 5 12 1 1 0 9 6
144 4 12 4 5 4 3 6
145 7 12 4 2 7 7 7
146 6 12 6 6 0 6 7
147 7 12 5 5 5 5 9
148 6 12 9 2 6 6 6
149 6 12 6 6 6 9 6
150 8 12 8 8 8 8 6
151 7 12 7 7 2 7 4
152 7 12 7 7 7 7 7
153 4 12 0 9 0 4 8
154 6 12 2 2 0 8 7
155 5 12 6 6 5 5 9
156 2 12 5 5 0 9 6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Maand Sport GoingOut Relation Friends
4.55612 -0.09910 0.04966 -0.03000 0.16993 0.11909
Job
0.15153
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.0221 -0.8511 0.0072 0.8914 3.7414
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.55612 3.06251 1.488 0.13894
Maand -0.09910 0.25644 -0.386 0.69971
Sport 0.04966 0.07192 0.691 0.49095
GoingOut -0.03000 0.06561 -0.457 0.64811
Relation 0.16993 0.04347 3.909 0.00014 ***
Friends 0.11909 0.06900 1.726 0.08642 .
Job 0.15153 0.07804 1.942 0.05408 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.509 on 149 degrees of freedom
Multiple R-squared: 0.1535, Adjusted R-squared: 0.1194
F-statistic: 4.504 on 6 and 149 DF, p-value: 0.000319
> 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,] 0.57264329 0.85471343 0.42735671
[2,] 0.56966081 0.86067838 0.43033919
[3,] 0.57536493 0.84927013 0.42463507
[4,] 0.45458154 0.90916308 0.54541846
[5,] 0.40459361 0.80918721 0.59540639
[6,] 0.55247691 0.89504618 0.44752309
[7,] 0.45581543 0.91163086 0.54418457
[8,] 0.86840248 0.26319505 0.13159752
[9,] 0.84461754 0.31076492 0.15538246
[10,] 0.88952053 0.22095894 0.11047947
[11,] 0.93593051 0.12813897 0.06406949
[12,] 0.91887474 0.16225053 0.08112526
[13,] 0.88720577 0.22558845 0.11279423
[14,] 0.86849290 0.26301421 0.13150710
[15,] 0.83652357 0.32695285 0.16347643
[16,] 0.78996325 0.42007350 0.21003675
[17,] 0.73724263 0.52551473 0.26275737
[18,] 0.69441750 0.61116501 0.30558250
[19,] 0.68951802 0.62096395 0.31048198
[20,] 0.79192148 0.41615704 0.20807852
[21,] 0.77907109 0.44185782 0.22092891
[22,] 0.80970673 0.38058655 0.19029327
[23,] 0.77441568 0.45116863 0.22558432
[24,] 0.72628702 0.54742596 0.27371298
[25,] 0.69018700 0.61962599 0.30981300
[26,] 0.64944329 0.70111342 0.35055671
[27,] 0.68944627 0.62110746 0.31055373
[28,] 0.66347955 0.67304091 0.33652045
[29,] 0.61824022 0.76351956 0.38175978
[30,] 0.56453927 0.87092146 0.43546073
[31,] 0.53339328 0.93321344 0.46660672
[32,] 0.49786682 0.99573364 0.50213318
[33,] 0.50366357 0.99267287 0.49633643
[34,] 0.47923723 0.95847446 0.52076277
[35,] 0.46493336 0.92986672 0.53506664
[36,] 0.41132086 0.82264171 0.58867914
[37,] 0.36281522 0.72563044 0.63718478
[38,] 0.33918491 0.67836983 0.66081509
[39,] 0.33495062 0.66990125 0.66504938
[40,] 0.37528957 0.75057913 0.62471043
[41,] 0.32975621 0.65951241 0.67024379
[42,] 0.29513112 0.59026224 0.70486888
[43,] 0.25831788 0.51663576 0.74168212
[44,] 0.22499596 0.44999192 0.77500404
[45,] 0.20481300 0.40962600 0.79518700
[46,] 0.18156628 0.36313255 0.81843372
[47,] 0.15999622 0.31999245 0.84000378
[48,] 0.13783418 0.27566836 0.86216582
[49,] 0.16189159 0.32378318 0.83810841
[50,] 0.19027342 0.38054684 0.80972658
[51,] 0.16338220 0.32676441 0.83661780
[52,] 0.22533348 0.45066696 0.77466652
[53,] 0.19560417 0.39120834 0.80439583
[54,] 0.17427350 0.34854700 0.82572650
[55,] 0.15410120 0.30820239 0.84589880
[56,] 0.12702658 0.25405316 0.87297342
[57,] 0.10501738 0.21003477 0.89498262
[58,] 0.08588454 0.17176908 0.91411546
[59,] 0.07569895 0.15139789 0.92430105
[60,] 0.06037917 0.12075833 0.93962083
[61,] 0.05177295 0.10354589 0.94822705
[62,] 0.04433125 0.08866249 0.95566875
[63,] 0.03671167 0.07342333 0.96328833
[64,] 0.03689948 0.07379897 0.96310052
[65,] 0.03107620 0.06215239 0.96892380
[66,] 0.02371624 0.04743249 0.97628376
[67,] 0.08291146 0.16582291 0.91708854
[68,] 0.14510956 0.29021913 0.85489044
[69,] 0.12806745 0.25613490 0.87193255
[70,] 0.10714743 0.21429486 0.89285257
[71,] 0.10052589 0.20105177 0.89947411
[72,] 0.11099166 0.22198332 0.88900834
[73,] 0.09454458 0.18908916 0.90545542
[74,] 0.08296476 0.16592953 0.91703524
[75,] 0.07873675 0.15747350 0.92126325
[76,] 0.17959879 0.35919759 0.82040121
[77,] 0.15904208 0.31808416 0.84095792
[78,] 0.18120229 0.36240459 0.81879771
[79,] 0.22154412 0.44308824 0.77845588
[80,] 0.28022217 0.56044434 0.71977783
[81,] 0.35233240 0.70466480 0.64766760
[82,] 0.61102885 0.77794229 0.38897115
[83,] 0.56635238 0.86729523 0.43364762
[84,] 0.51851028 0.96297944 0.48148972
[85,] 0.47321903 0.94643807 0.52678097
[86,] 0.42476504 0.84953007 0.57523496
[87,] 0.42977745 0.85955490 0.57022255
[88,] 0.38266169 0.76532339 0.61733831
[89,] 0.43808327 0.87616653 0.56191673
[90,] 0.40454110 0.80908220 0.59545890
[91,] 0.48131889 0.96263777 0.51868111
[92,] 0.49099306 0.98198612 0.50900694
[93,] 0.44410403 0.88820806 0.55589597
[94,] 0.40313301 0.80626602 0.59686699
[95,] 0.56469011 0.87061978 0.43530989
[96,] 0.51646385 0.96707231 0.48353615
[97,] 0.48509674 0.97019347 0.51490326
[98,] 0.50171178 0.99657644 0.49828822
[99,] 0.48268893 0.96537786 0.51731107
[100,] 0.57351539 0.85296922 0.42648461
[101,] 0.58482904 0.83034192 0.41517096
[102,] 0.53727378 0.92545244 0.46272622
[103,] 0.48362008 0.96724017 0.51637992
[104,] 0.44189326 0.88378652 0.55810674
[105,] 0.48464784 0.96929568 0.51535216
[106,] 0.75044828 0.49910344 0.24955172
[107,] 0.70402667 0.59194665 0.29597333
[108,] 0.69808799 0.60382402 0.30191201
[109,] 0.65442925 0.69114151 0.34557075
[110,] 0.60048058 0.79903884 0.39951942
[111,] 0.78816063 0.42367874 0.21183937
[112,] 0.75763426 0.48473149 0.24236574
[113,] 0.71071449 0.57857102 0.28928551
[114,] 0.76017640 0.47964720 0.23982360
[115,] 0.80622373 0.38755254 0.19377627
[116,] 0.76517910 0.46964180 0.23482090
[117,] 0.74251091 0.51497818 0.25748909
[118,] 0.71630253 0.56739494 0.28369747
[119,] 0.67781512 0.64436975 0.32218488
[120,] 0.63029155 0.73941689 0.36970845
[121,] 0.59590914 0.80818171 0.40409086
[122,] 0.54926615 0.90146771 0.45073385
[123,] 0.53258642 0.93482715 0.46741358
[124,] 0.58051786 0.83896429 0.41948214
[125,] 0.51814913 0.96370173 0.48185087
[126,] 0.53200459 0.93599081 0.46799541
[127,] 0.50405494 0.99189012 0.49594506
[128,] 0.48514648 0.97029297 0.51485352
[129,] 0.53646178 0.92707644 0.46353822
[130,] 0.44775524 0.89551049 0.55224476
[131,] 0.35460311 0.70920622 0.64539689
[132,] 0.37984524 0.75969047 0.62015476
[133,] 0.28648061 0.57296123 0.71351939
[134,] 0.20204345 0.40408689 0.79795655
[135,] 0.26101720 0.52203440 0.73898280
[136,] 0.16544240 0.33088480 0.83455760
[137,] 0.23036891 0.46073782 0.76963109
> postscript(file="/var/wessaorg/rcomp/tmp/1y2gr1324493917.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/299fi1324493917.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3idf31324493917.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4g0zv1324493917.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5o2ja1324493917.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 156
Frequency = 1
1 2 3 4 5 6
1.192439377 1.571381558 0.251103806 -0.887550427 1.617561433 2.369168219
7 8 9 10 11 12
1.180336062 -0.438961526 -1.617029805 2.367178703 -0.227224754 -0.170568459
13 14 15 16 17 18
-1.163614747 -0.359284898 -3.175228748 -1.242228127 -3.347768245 -0.827435240
19 20 21 22 23 24
1.896522454 2.489452138 -0.769564745 0.312572344 -0.821246574 -1.405113192
25 26 27 28 29 30
-0.227224754 -0.227224754 1.143870731 1.713820714 2.459285339 -1.679189054
31 32 33 34 35 36
-1.783185880 0.485888799 0.282570411 -0.437783254 1.032855378 -1.578872762
37 38 39 40 41 42
0.792337747 -0.767021851 0.442399067 1.095208382 0.827202824 2.051353571
43 44 45 46 47 48
1.244232527 -1.213552717 -0.197222821 -0.635431651 -0.887029582 -1.458620375
49 50 51 52 53 54
1.844430101 0.132012073 -0.148589359 0.061614222 0.082613566 -1.366822814
55 56 57 58 59 60
-1.163614747 -0.715205686 -1.188059068 2.123911562 2.652011224 1.102995711
61 62 63 64 65 66
-2.163878080 0.780376920 1.090081904 -0.347453016 0.111367648 -0.175560600
67 68 69 70 71 72
0.261584981 0.669386751 0.418461220 -0.608944274 -0.839104906 0.939827584
73 74 75 76 77 78
-1.175205681 -0.605406651 -0.088806025 3.741429115 3.030189360 -0.356129971
79 80 81 82 83 84
0.410646056 0.818369095 -1.623969661 0.369863686 -0.807587399 -1.122496533
85 86 87 88 89 90
-3.252125104 0.891438779 -1.893182379 2.349320371 2.330545989 -2.501714902
91 92 93 94 95 96
-4.022082834 0.392014527 0.141056405 0.413110219 -0.009128337 1.572320623
97 98 99 100 101 102
0.139839033 -2.350203579 -0.667920820 2.263878514 1.640480657 0.411673376
103 104 105 106 107 108
0.691509762 -2.895843634 -0.279570229 0.799945235 -1.995359953 -1.361484675
109 110 111 112 113 114
2.776118055 1.722574943 -0.608944274 0.023480246 0.891438779 1.699457203
115 116 117 118 119 120
3.601162960 -0.128123722 0.825655770 -1.059982364 -0.158125655 -3.426624171
121 122 123 124 125 126
-0.648261971 0.179171704 -1.862153126 1.620821808 -0.128123722 -1.479771731
127 128 129 130 131 132
0.771458877 0.417300565 -0.696013026 -0.945288647 1.135429216 0.772347046
133 134 135 136 137 138
2.488070953 -1.293680619 0.757029496 -0.528009583 0.970074173 -3.133033372
139 140 141 142 143 144
-0.128123722 0.181714598 -2.409277531 -0.128123722 -0.367542176 -1.361674727
145 146 147 148 149 150
0.410646056 0.739913541 0.725978230 -0.397113797 -0.485398920 1.254520949
151 152 153 154 155 156
1.715884506 0.411673376 -0.785457745 0.580365470 -1.293680619 -3.446177571
> postscript(file="/var/wessaorg/rcomp/tmp/6si6u1324493917.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 1.192439377 NA
1 1.571381558 1.192439377
2 0.251103806 1.571381558
3 -0.887550427 0.251103806
4 1.617561433 -0.887550427
5 2.369168219 1.617561433
6 1.180336062 2.369168219
7 -0.438961526 1.180336062
8 -1.617029805 -0.438961526
9 2.367178703 -1.617029805
10 -0.227224754 2.367178703
11 -0.170568459 -0.227224754
12 -1.163614747 -0.170568459
13 -0.359284898 -1.163614747
14 -3.175228748 -0.359284898
15 -1.242228127 -3.175228748
16 -3.347768245 -1.242228127
17 -0.827435240 -3.347768245
18 1.896522454 -0.827435240
19 2.489452138 1.896522454
20 -0.769564745 2.489452138
21 0.312572344 -0.769564745
22 -0.821246574 0.312572344
23 -1.405113192 -0.821246574
24 -0.227224754 -1.405113192
25 -0.227224754 -0.227224754
26 1.143870731 -0.227224754
27 1.713820714 1.143870731
28 2.459285339 1.713820714
29 -1.679189054 2.459285339
30 -1.783185880 -1.679189054
31 0.485888799 -1.783185880
32 0.282570411 0.485888799
33 -0.437783254 0.282570411
34 1.032855378 -0.437783254
35 -1.578872762 1.032855378
36 0.792337747 -1.578872762
37 -0.767021851 0.792337747
38 0.442399067 -0.767021851
39 1.095208382 0.442399067
40 0.827202824 1.095208382
41 2.051353571 0.827202824
42 1.244232527 2.051353571
43 -1.213552717 1.244232527
44 -0.197222821 -1.213552717
45 -0.635431651 -0.197222821
46 -0.887029582 -0.635431651
47 -1.458620375 -0.887029582
48 1.844430101 -1.458620375
49 0.132012073 1.844430101
50 -0.148589359 0.132012073
51 0.061614222 -0.148589359
52 0.082613566 0.061614222
53 -1.366822814 0.082613566
54 -1.163614747 -1.366822814
55 -0.715205686 -1.163614747
56 -1.188059068 -0.715205686
57 2.123911562 -1.188059068
58 2.652011224 2.123911562
59 1.102995711 2.652011224
60 -2.163878080 1.102995711
61 0.780376920 -2.163878080
62 1.090081904 0.780376920
63 -0.347453016 1.090081904
64 0.111367648 -0.347453016
65 -0.175560600 0.111367648
66 0.261584981 -0.175560600
67 0.669386751 0.261584981
68 0.418461220 0.669386751
69 -0.608944274 0.418461220
70 -0.839104906 -0.608944274
71 0.939827584 -0.839104906
72 -1.175205681 0.939827584
73 -0.605406651 -1.175205681
74 -0.088806025 -0.605406651
75 3.741429115 -0.088806025
76 3.030189360 3.741429115
77 -0.356129971 3.030189360
78 0.410646056 -0.356129971
79 0.818369095 0.410646056
80 -1.623969661 0.818369095
81 0.369863686 -1.623969661
82 -0.807587399 0.369863686
83 -1.122496533 -0.807587399
84 -3.252125104 -1.122496533
85 0.891438779 -3.252125104
86 -1.893182379 0.891438779
87 2.349320371 -1.893182379
88 2.330545989 2.349320371
89 -2.501714902 2.330545989
90 -4.022082834 -2.501714902
91 0.392014527 -4.022082834
92 0.141056405 0.392014527
93 0.413110219 0.141056405
94 -0.009128337 0.413110219
95 1.572320623 -0.009128337
96 0.139839033 1.572320623
97 -2.350203579 0.139839033
98 -0.667920820 -2.350203579
99 2.263878514 -0.667920820
100 1.640480657 2.263878514
101 0.411673376 1.640480657
102 0.691509762 0.411673376
103 -2.895843634 0.691509762
104 -0.279570229 -2.895843634
105 0.799945235 -0.279570229
106 -1.995359953 0.799945235
107 -1.361484675 -1.995359953
108 2.776118055 -1.361484675
109 1.722574943 2.776118055
110 -0.608944274 1.722574943
111 0.023480246 -0.608944274
112 0.891438779 0.023480246
113 1.699457203 0.891438779
114 3.601162960 1.699457203
115 -0.128123722 3.601162960
116 0.825655770 -0.128123722
117 -1.059982364 0.825655770
118 -0.158125655 -1.059982364
119 -3.426624171 -0.158125655
120 -0.648261971 -3.426624171
121 0.179171704 -0.648261971
122 -1.862153126 0.179171704
123 1.620821808 -1.862153126
124 -0.128123722 1.620821808
125 -1.479771731 -0.128123722
126 0.771458877 -1.479771731
127 0.417300565 0.771458877
128 -0.696013026 0.417300565
129 -0.945288647 -0.696013026
130 1.135429216 -0.945288647
131 0.772347046 1.135429216
132 2.488070953 0.772347046
133 -1.293680619 2.488070953
134 0.757029496 -1.293680619
135 -0.528009583 0.757029496
136 0.970074173 -0.528009583
137 -3.133033372 0.970074173
138 -0.128123722 -3.133033372
139 0.181714598 -0.128123722
140 -2.409277531 0.181714598
141 -0.128123722 -2.409277531
142 -0.367542176 -0.128123722
143 -1.361674727 -0.367542176
144 0.410646056 -1.361674727
145 0.739913541 0.410646056
146 0.725978230 0.739913541
147 -0.397113797 0.725978230
148 -0.485398920 -0.397113797
149 1.254520949 -0.485398920
150 1.715884506 1.254520949
151 0.411673376 1.715884506
152 -0.785457745 0.411673376
153 0.580365470 -0.785457745
154 -1.293680619 0.580365470
155 -3.446177571 -1.293680619
156 NA -3.446177571
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.571381558 1.192439377
[2,] 0.251103806 1.571381558
[3,] -0.887550427 0.251103806
[4,] 1.617561433 -0.887550427
[5,] 2.369168219 1.617561433
[6,] 1.180336062 2.369168219
[7,] -0.438961526 1.180336062
[8,] -1.617029805 -0.438961526
[9,] 2.367178703 -1.617029805
[10,] -0.227224754 2.367178703
[11,] -0.170568459 -0.227224754
[12,] -1.163614747 -0.170568459
[13,] -0.359284898 -1.163614747
[14,] -3.175228748 -0.359284898
[15,] -1.242228127 -3.175228748
[16,] -3.347768245 -1.242228127
[17,] -0.827435240 -3.347768245
[18,] 1.896522454 -0.827435240
[19,] 2.489452138 1.896522454
[20,] -0.769564745 2.489452138
[21,] 0.312572344 -0.769564745
[22,] -0.821246574 0.312572344
[23,] -1.405113192 -0.821246574
[24,] -0.227224754 -1.405113192
[25,] -0.227224754 -0.227224754
[26,] 1.143870731 -0.227224754
[27,] 1.713820714 1.143870731
[28,] 2.459285339 1.713820714
[29,] -1.679189054 2.459285339
[30,] -1.783185880 -1.679189054
[31,] 0.485888799 -1.783185880
[32,] 0.282570411 0.485888799
[33,] -0.437783254 0.282570411
[34,] 1.032855378 -0.437783254
[35,] -1.578872762 1.032855378
[36,] 0.792337747 -1.578872762
[37,] -0.767021851 0.792337747
[38,] 0.442399067 -0.767021851
[39,] 1.095208382 0.442399067
[40,] 0.827202824 1.095208382
[41,] 2.051353571 0.827202824
[42,] 1.244232527 2.051353571
[43,] -1.213552717 1.244232527
[44,] -0.197222821 -1.213552717
[45,] -0.635431651 -0.197222821
[46,] -0.887029582 -0.635431651
[47,] -1.458620375 -0.887029582
[48,] 1.844430101 -1.458620375
[49,] 0.132012073 1.844430101
[50,] -0.148589359 0.132012073
[51,] 0.061614222 -0.148589359
[52,] 0.082613566 0.061614222
[53,] -1.366822814 0.082613566
[54,] -1.163614747 -1.366822814
[55,] -0.715205686 -1.163614747
[56,] -1.188059068 -0.715205686
[57,] 2.123911562 -1.188059068
[58,] 2.652011224 2.123911562
[59,] 1.102995711 2.652011224
[60,] -2.163878080 1.102995711
[61,] 0.780376920 -2.163878080
[62,] 1.090081904 0.780376920
[63,] -0.347453016 1.090081904
[64,] 0.111367648 -0.347453016
[65,] -0.175560600 0.111367648
[66,] 0.261584981 -0.175560600
[67,] 0.669386751 0.261584981
[68,] 0.418461220 0.669386751
[69,] -0.608944274 0.418461220
[70,] -0.839104906 -0.608944274
[71,] 0.939827584 -0.839104906
[72,] -1.175205681 0.939827584
[73,] -0.605406651 -1.175205681
[74,] -0.088806025 -0.605406651
[75,] 3.741429115 -0.088806025
[76,] 3.030189360 3.741429115
[77,] -0.356129971 3.030189360
[78,] 0.410646056 -0.356129971
[79,] 0.818369095 0.410646056
[80,] -1.623969661 0.818369095
[81,] 0.369863686 -1.623969661
[82,] -0.807587399 0.369863686
[83,] -1.122496533 -0.807587399
[84,] -3.252125104 -1.122496533
[85,] 0.891438779 -3.252125104
[86,] -1.893182379 0.891438779
[87,] 2.349320371 -1.893182379
[88,] 2.330545989 2.349320371
[89,] -2.501714902 2.330545989
[90,] -4.022082834 -2.501714902
[91,] 0.392014527 -4.022082834
[92,] 0.141056405 0.392014527
[93,] 0.413110219 0.141056405
[94,] -0.009128337 0.413110219
[95,] 1.572320623 -0.009128337
[96,] 0.139839033 1.572320623
[97,] -2.350203579 0.139839033
[98,] -0.667920820 -2.350203579
[99,] 2.263878514 -0.667920820
[100,] 1.640480657 2.263878514
[101,] 0.411673376 1.640480657
[102,] 0.691509762 0.411673376
[103,] -2.895843634 0.691509762
[104,] -0.279570229 -2.895843634
[105,] 0.799945235 -0.279570229
[106,] -1.995359953 0.799945235
[107,] -1.361484675 -1.995359953
[108,] 2.776118055 -1.361484675
[109,] 1.722574943 2.776118055
[110,] -0.608944274 1.722574943
[111,] 0.023480246 -0.608944274
[112,] 0.891438779 0.023480246
[113,] 1.699457203 0.891438779
[114,] 3.601162960 1.699457203
[115,] -0.128123722 3.601162960
[116,] 0.825655770 -0.128123722
[117,] -1.059982364 0.825655770
[118,] -0.158125655 -1.059982364
[119,] -3.426624171 -0.158125655
[120,] -0.648261971 -3.426624171
[121,] 0.179171704 -0.648261971
[122,] -1.862153126 0.179171704
[123,] 1.620821808 -1.862153126
[124,] -0.128123722 1.620821808
[125,] -1.479771731 -0.128123722
[126,] 0.771458877 -1.479771731
[127,] 0.417300565 0.771458877
[128,] -0.696013026 0.417300565
[129,] -0.945288647 -0.696013026
[130,] 1.135429216 -0.945288647
[131,] 0.772347046 1.135429216
[132,] 2.488070953 0.772347046
[133,] -1.293680619 2.488070953
[134,] 0.757029496 -1.293680619
[135,] -0.528009583 0.757029496
[136,] 0.970074173 -0.528009583
[137,] -3.133033372 0.970074173
[138,] -0.128123722 -3.133033372
[139,] 0.181714598 -0.128123722
[140,] -2.409277531 0.181714598
[141,] -0.128123722 -2.409277531
[142,] -0.367542176 -0.128123722
[143,] -1.361674727 -0.367542176
[144,] 0.410646056 -1.361674727
[145,] 0.739913541 0.410646056
[146,] 0.725978230 0.739913541
[147,] -0.397113797 0.725978230
[148,] -0.485398920 -0.397113797
[149,] 1.254520949 -0.485398920
[150,] 1.715884506 1.254520949
[151,] 0.411673376 1.715884506
[152,] -0.785457745 0.411673376
[153,] 0.580365470 -0.785457745
[154,] -1.293680619 0.580365470
[155,] -3.446177571 -1.293680619
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.571381558 1.192439377
2 0.251103806 1.571381558
3 -0.887550427 0.251103806
4 1.617561433 -0.887550427
5 2.369168219 1.617561433
6 1.180336062 2.369168219
7 -0.438961526 1.180336062
8 -1.617029805 -0.438961526
9 2.367178703 -1.617029805
10 -0.227224754 2.367178703
11 -0.170568459 -0.227224754
12 -1.163614747 -0.170568459
13 -0.359284898 -1.163614747
14 -3.175228748 -0.359284898
15 -1.242228127 -3.175228748
16 -3.347768245 -1.242228127
17 -0.827435240 -3.347768245
18 1.896522454 -0.827435240
19 2.489452138 1.896522454
20 -0.769564745 2.489452138
21 0.312572344 -0.769564745
22 -0.821246574 0.312572344
23 -1.405113192 -0.821246574
24 -0.227224754 -1.405113192
25 -0.227224754 -0.227224754
26 1.143870731 -0.227224754
27 1.713820714 1.143870731
28 2.459285339 1.713820714
29 -1.679189054 2.459285339
30 -1.783185880 -1.679189054
31 0.485888799 -1.783185880
32 0.282570411 0.485888799
33 -0.437783254 0.282570411
34 1.032855378 -0.437783254
35 -1.578872762 1.032855378
36 0.792337747 -1.578872762
37 -0.767021851 0.792337747
38 0.442399067 -0.767021851
39 1.095208382 0.442399067
40 0.827202824 1.095208382
41 2.051353571 0.827202824
42 1.244232527 2.051353571
43 -1.213552717 1.244232527
44 -0.197222821 -1.213552717
45 -0.635431651 -0.197222821
46 -0.887029582 -0.635431651
47 -1.458620375 -0.887029582
48 1.844430101 -1.458620375
49 0.132012073 1.844430101
50 -0.148589359 0.132012073
51 0.061614222 -0.148589359
52 0.082613566 0.061614222
53 -1.366822814 0.082613566
54 -1.163614747 -1.366822814
55 -0.715205686 -1.163614747
56 -1.188059068 -0.715205686
57 2.123911562 -1.188059068
58 2.652011224 2.123911562
59 1.102995711 2.652011224
60 -2.163878080 1.102995711
61 0.780376920 -2.163878080
62 1.090081904 0.780376920
63 -0.347453016 1.090081904
64 0.111367648 -0.347453016
65 -0.175560600 0.111367648
66 0.261584981 -0.175560600
67 0.669386751 0.261584981
68 0.418461220 0.669386751
69 -0.608944274 0.418461220
70 -0.839104906 -0.608944274
71 0.939827584 -0.839104906
72 -1.175205681 0.939827584
73 -0.605406651 -1.175205681
74 -0.088806025 -0.605406651
75 3.741429115 -0.088806025
76 3.030189360 3.741429115
77 -0.356129971 3.030189360
78 0.410646056 -0.356129971
79 0.818369095 0.410646056
80 -1.623969661 0.818369095
81 0.369863686 -1.623969661
82 -0.807587399 0.369863686
83 -1.122496533 -0.807587399
84 -3.252125104 -1.122496533
85 0.891438779 -3.252125104
86 -1.893182379 0.891438779
87 2.349320371 -1.893182379
88 2.330545989 2.349320371
89 -2.501714902 2.330545989
90 -4.022082834 -2.501714902
91 0.392014527 -4.022082834
92 0.141056405 0.392014527
93 0.413110219 0.141056405
94 -0.009128337 0.413110219
95 1.572320623 -0.009128337
96 0.139839033 1.572320623
97 -2.350203579 0.139839033
98 -0.667920820 -2.350203579
99 2.263878514 -0.667920820
100 1.640480657 2.263878514
101 0.411673376 1.640480657
102 0.691509762 0.411673376
103 -2.895843634 0.691509762
104 -0.279570229 -2.895843634
105 0.799945235 -0.279570229
106 -1.995359953 0.799945235
107 -1.361484675 -1.995359953
108 2.776118055 -1.361484675
109 1.722574943 2.776118055
110 -0.608944274 1.722574943
111 0.023480246 -0.608944274
112 0.891438779 0.023480246
113 1.699457203 0.891438779
114 3.601162960 1.699457203
115 -0.128123722 3.601162960
116 0.825655770 -0.128123722
117 -1.059982364 0.825655770
118 -0.158125655 -1.059982364
119 -3.426624171 -0.158125655
120 -0.648261971 -3.426624171
121 0.179171704 -0.648261971
122 -1.862153126 0.179171704
123 1.620821808 -1.862153126
124 -0.128123722 1.620821808
125 -1.479771731 -0.128123722
126 0.771458877 -1.479771731
127 0.417300565 0.771458877
128 -0.696013026 0.417300565
129 -0.945288647 -0.696013026
130 1.135429216 -0.945288647
131 0.772347046 1.135429216
132 2.488070953 0.772347046
133 -1.293680619 2.488070953
134 0.757029496 -1.293680619
135 -0.528009583 0.757029496
136 0.970074173 -0.528009583
137 -3.133033372 0.970074173
138 -0.128123722 -3.133033372
139 0.181714598 -0.128123722
140 -2.409277531 0.181714598
141 -0.128123722 -2.409277531
142 -0.367542176 -0.128123722
143 -1.361674727 -0.367542176
144 0.410646056 -1.361674727
145 0.739913541 0.410646056
146 0.725978230 0.739913541
147 -0.397113797 0.725978230
148 -0.485398920 -0.397113797
149 1.254520949 -0.485398920
150 1.715884506 1.254520949
151 0.411673376 1.715884506
152 -0.785457745 0.411673376
153 0.580365470 -0.785457745
154 -1.293680619 0.580365470
155 -3.446177571 -1.293680619
> 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/74jba1324493917.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/8uuss1324493917.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/9w9j51324493917.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/10jzo11324493917.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11d45t1324493917.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1244pt1324493917.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1378401324493917.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14rbvv1324493917.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/15v65p1324493917.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/161yiy1324493917.tab")
+ }
>
> try(system("convert tmp/1y2gr1324493917.ps tmp/1y2gr1324493917.png",intern=TRUE))
character(0)
> try(system("convert tmp/299fi1324493917.ps tmp/299fi1324493917.png",intern=TRUE))
character(0)
> try(system("convert tmp/3idf31324493917.ps tmp/3idf31324493917.png",intern=TRUE))
character(0)
> try(system("convert tmp/4g0zv1324493917.ps tmp/4g0zv1324493917.png",intern=TRUE))
character(0)
> try(system("convert tmp/5o2ja1324493917.ps tmp/5o2ja1324493917.png",intern=TRUE))
character(0)
> try(system("convert tmp/6si6u1324493917.ps tmp/6si6u1324493917.png",intern=TRUE))
character(0)
> try(system("convert tmp/74jba1324493917.ps tmp/74jba1324493917.png",intern=TRUE))
character(0)
> try(system("convert tmp/8uuss1324493917.ps tmp/8uuss1324493917.png",intern=TRUE))
character(0)
> try(system("convert tmp/9w9j51324493917.ps tmp/9w9j51324493917.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jzo11324493917.ps tmp/10jzo11324493917.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.110 0.680 5.807