R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'license()' or 'licence()' for distribution details.
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> x <- array(list(15,0,14.4,0,13,0,13.7,0,13.6,0,15.2,0,12.9,0,14,0,14.1,0,13.2,0,11.3,0,13.3,0,14.4,0,13.3,0,11.6,0,13.2,0,13.1,0,14.6,0,14,0,14.3,0,13.8,0,13.7,0,11,0,14.4,0,15.6,0,13.7,0,12.6,0,13.2,0,13.3,0,14.3,0,14,0,13.4,0,13.9,0,13.7,0,10.5,0,14.5,0,15,0,13.5,0,13.5,0,13.2,0,13.8,0,16.2,0,14.7,0,13.9,0,16,0,14.4,0,12.3,0,15.9,0,15.9,0,15.5,0,15.1,0,14.5,0,15.1,0,17.4,0,16.2,0,15.6,0,17.2,0,14.9,0,13.8,0,17.5,0,16.2,0,17.5,0,16.6,0,16.2,0,16.6,0,19.6,0,15.9,0,18,0,18.3,0,16.3,0,14.9,0,18.2,0,18.4,0,18.5,0,16,0,17.4,0,17.2,0,19.6,0,17.2,0,18.3,0,19.3,0,18.1,0,16.2,0,18.4,0,20.5,0,19,0,16.5,0,18.7,0,19,0,19.2,0,20.5,0,19.3,0,20.6,0,20.1,0,16.1,0,20.4,0,19.7,1,15.6,1,14.4,1,13.7,1,14.1,1,15,1,14.2,1,13.6,1,15.4,1,14.8,1,12.5,1,16.2,1,16.1,1,16,1,15.8,1,15.2,1,15.7,1,18.9,1,17.4,1,17,1,19.8,1,17.7,1,16,1,19.6,1,19.7,1),dim=c(2,121),dimnames=list(c('uitvoercijfer','X'),1:121))
> y <- array(NA,dim=c(2,121),dimnames=list(c('uitvoercijfer','X'),1:121))
> 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 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
uitvoercijfer X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 15.0 0 1 0 0 0 0 0 0 0 0 0 0
2 14.4 0 0 1 0 0 0 0 0 0 0 0 0
3 13.0 0 0 0 1 0 0 0 0 0 0 0 0
4 13.7 0 0 0 0 1 0 0 0 0 0 0 0
5 13.6 0 0 0 0 0 1 0 0 0 0 0 0
6 15.2 0 0 0 0 0 0 1 0 0 0 0 0
7 12.9 0 0 0 0 0 0 0 1 0 0 0 0
8 14.0 0 0 0 0 0 0 0 0 1 0 0 0
9 14.1 0 0 0 0 0 0 0 0 0 1 0 0
10 13.2 0 0 0 0 0 0 0 0 0 0 1 0
11 11.3 0 0 0 0 0 0 0 0 0 0 0 1
12 13.3 0 0 0 0 0 0 0 0 0 0 0 0
13 14.4 0 1 0 0 0 0 0 0 0 0 0 0
14 13.3 0 0 1 0 0 0 0 0 0 0 0 0
15 11.6 0 0 0 1 0 0 0 0 0 0 0 0
16 13.2 0 0 0 0 1 0 0 0 0 0 0 0
17 13.1 0 0 0 0 0 1 0 0 0 0 0 0
18 14.6 0 0 0 0 0 0 1 0 0 0 0 0
19 14.0 0 0 0 0 0 0 0 1 0 0 0 0
20 14.3 0 0 0 0 0 0 0 0 1 0 0 0
21 13.8 0 0 0 0 0 0 0 0 0 1 0 0
22 13.7 0 0 0 0 0 0 0 0 0 0 1 0
23 11.0 0 0 0 0 0 0 0 0 0 0 0 1
24 14.4 0 0 0 0 0 0 0 0 0 0 0 0
25 15.6 0 1 0 0 0 0 0 0 0 0 0 0
26 13.7 0 0 1 0 0 0 0 0 0 0 0 0
27 12.6 0 0 0 1 0 0 0 0 0 0 0 0
28 13.2 0 0 0 0 1 0 0 0 0 0 0 0
29 13.3 0 0 0 0 0 1 0 0 0 0 0 0
30 14.3 0 0 0 0 0 0 1 0 0 0 0 0
31 14.0 0 0 0 0 0 0 0 1 0 0 0 0
32 13.4 0 0 0 0 0 0 0 0 1 0 0 0
33 13.9 0 0 0 0 0 0 0 0 0 1 0 0
34 13.7 0 0 0 0 0 0 0 0 0 0 1 0
35 10.5 0 0 0 0 0 0 0 0 0 0 0 1
36 14.5 0 0 0 0 0 0 0 0 0 0 0 0
37 15.0 0 1 0 0 0 0 0 0 0 0 0 0
38 13.5 0 0 1 0 0 0 0 0 0 0 0 0
39 13.5 0 0 0 1 0 0 0 0 0 0 0 0
40 13.2 0 0 0 0 1 0 0 0 0 0 0 0
41 13.8 0 0 0 0 0 1 0 0 0 0 0 0
42 16.2 0 0 0 0 0 0 1 0 0 0 0 0
43 14.7 0 0 0 0 0 0 0 1 0 0 0 0
44 13.9 0 0 0 0 0 0 0 0 1 0 0 0
45 16.0 0 0 0 0 0 0 0 0 0 1 0 0
46 14.4 0 0 0 0 0 0 0 0 0 0 1 0
47 12.3 0 0 0 0 0 0 0 0 0 0 0 1
48 15.9 0 0 0 0 0 0 0 0 0 0 0 0
49 15.9 0 1 0 0 0 0 0 0 0 0 0 0
50 15.5 0 0 1 0 0 0 0 0 0 0 0 0
51 15.1 0 0 0 1 0 0 0 0 0 0 0 0
52 14.5 0 0 0 0 1 0 0 0 0 0 0 0
53 15.1 0 0 0 0 0 1 0 0 0 0 0 0
54 17.4 0 0 0 0 0 0 1 0 0 0 0 0
55 16.2 0 0 0 0 0 0 0 1 0 0 0 0
56 15.6 0 0 0 0 0 0 0 0 1 0 0 0
57 17.2 0 0 0 0 0 0 0 0 0 1 0 0
58 14.9 0 0 0 0 0 0 0 0 0 0 1 0
59 13.8 0 0 0 0 0 0 0 0 0 0 0 1
60 17.5 0 0 0 0 0 0 0 0 0 0 0 0
61 16.2 0 1 0 0 0 0 0 0 0 0 0 0
62 17.5 0 0 1 0 0 0 0 0 0 0 0 0
63 16.6 0 0 0 1 0 0 0 0 0 0 0 0
64 16.2 0 0 0 0 1 0 0 0 0 0 0 0
65 16.6 0 0 0 0 0 1 0 0 0 0 0 0
66 19.6 0 0 0 0 0 0 1 0 0 0 0 0
67 15.9 0 0 0 0 0 0 0 1 0 0 0 0
68 18.0 0 0 0 0 0 0 0 0 1 0 0 0
69 18.3 0 0 0 0 0 0 0 0 0 1 0 0
70 16.3 0 0 0 0 0 0 0 0 0 0 1 0
71 14.9 0 0 0 0 0 0 0 0 0 0 0 1
72 18.2 0 0 0 0 0 0 0 0 0 0 0 0
73 18.4 0 1 0 0 0 0 0 0 0 0 0 0
74 18.5 0 0 1 0 0 0 0 0 0 0 0 0
75 16.0 0 0 0 1 0 0 0 0 0 0 0 0
76 17.4 0 0 0 0 1 0 0 0 0 0 0 0
77 17.2 0 0 0 0 0 1 0 0 0 0 0 0
78 19.6 0 0 0 0 0 0 1 0 0 0 0 0
79 17.2 0 0 0 0 0 0 0 1 0 0 0 0
80 18.3 0 0 0 0 0 0 0 0 1 0 0 0
81 19.3 0 0 0 0 0 0 0 0 0 1 0 0
82 18.1 0 0 0 0 0 0 0 0 0 0 1 0
83 16.2 0 0 0 0 0 0 0 0 0 0 0 1
84 18.4 0 0 0 0 0 0 0 0 0 0 0 0
85 20.5 0 1 0 0 0 0 0 0 0 0 0 0
86 19.0 0 0 1 0 0 0 0 0 0 0 0 0
87 16.5 0 0 0 1 0 0 0 0 0 0 0 0
88 18.7 0 0 0 0 1 0 0 0 0 0 0 0
89 19.0 0 0 0 0 0 1 0 0 0 0 0 0
90 19.2 0 0 0 0 0 0 1 0 0 0 0 0
91 20.5 0 0 0 0 0 0 0 1 0 0 0 0
92 19.3 0 0 0 0 0 0 0 0 1 0 0 0
93 20.6 0 0 0 0 0 0 0 0 0 1 0 0
94 20.1 0 0 0 0 0 0 0 0 0 0 1 0
95 16.1 0 0 0 0 0 0 0 0 0 0 0 1
96 20.4 0 0 0 0 0 0 0 0 0 0 0 0
97 19.7 1 1 0 0 0 0 0 0 0 0 0 0
98 15.6 1 0 1 0 0 0 0 0 0 0 0 0
99 14.4 1 0 0 1 0 0 0 0 0 0 0 0
100 13.7 1 0 0 0 1 0 0 0 0 0 0 0
101 14.1 1 0 0 0 0 1 0 0 0 0 0 0
102 15.0 1 0 0 0 0 0 1 0 0 0 0 0
103 14.2 1 0 0 0 0 0 0 1 0 0 0 0
104 13.6 1 0 0 0 0 0 0 0 1 0 0 0
105 15.4 1 0 0 0 0 0 0 0 0 1 0 0
106 14.8 1 0 0 0 0 0 0 0 0 0 1 0
107 12.5 1 0 0 0 0 0 0 0 0 0 0 1
108 16.2 1 0 0 0 0 0 0 0 0 0 0 0
109 16.1 1 1 0 0 0 0 0 0 0 0 0 0
110 16.0 1 0 1 0 0 0 0 0 0 0 0 0
111 15.8 1 0 0 1 0 0 0 0 0 0 0 0
112 15.2 1 0 0 0 1 0 0 0 0 0 0 0
113 15.7 1 0 0 0 0 1 0 0 0 0 0 0
114 18.9 1 0 0 0 0 0 1 0 0 0 0 0
115 17.4 1 0 0 0 0 0 0 1 0 0 0 0
116 17.0 1 0 0 0 0 0 0 0 1 0 0 0
117 19.8 1 0 0 0 0 0 0 0 0 1 0 0
118 17.7 1 0 0 0 0 0 0 0 0 0 1 0
119 16.0 1 0 0 0 0 0 0 0 0 0 0 1
120 19.6 1 0 0 0 0 0 0 0 0 0 0 0
121 19.7 1 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
1.674e+01 5.144e-01 7.713e-02 -1.140e+00 -2.330e+00 -1.940e+00
M5 M6 M7 M8 M9 M10
-1.690e+00 1.600e-01 -1.140e+00 -1.100e+00 3.243e-16 -1.150e+00
M11
-3.380e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.4371 -1.7371 -0.6142 1.6029 4.9029
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.674e+01 6.955e-01 24.063 < 2e-16 ***
X 5.144e-01 4.896e-01 1.051 0.295740
M1 7.713e-02 9.521e-01 0.081 0.935584
M2 -1.140e+00 9.739e-01 -1.171 0.244333
M3 -2.330e+00 9.739e-01 -2.393 0.018458 *
M4 -1.940e+00 9.739e-01 -1.992 0.048885 *
M5 -1.690e+00 9.739e-01 -1.735 0.085527 .
M6 1.600e-01 9.739e-01 0.164 0.869805
M7 -1.140e+00 9.739e-01 -1.171 0.244333
M8 -1.100e+00 9.739e-01 -1.130 0.261176
M9 3.243e-16 9.739e-01 3.33e-16 1.000000
M10 -1.150e+00 9.739e-01 -1.181 0.240246
M11 -3.380e+00 9.739e-01 -3.471 0.000747 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.178 on 108 degrees of freedom
Multiple R-squared: 0.2137, Adjusted R-squared: 0.1263
F-statistic: 2.446 on 12 and 108 DF, p-value: 0.007401
> 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,] 6.208409e-02 1.241682e-01 0.9379159
[2,] 2.107247e-02 4.214495e-02 0.9789275
[3,] 7.412421e-03 1.482484e-02 0.9925876
[4,] 4.019489e-03 8.038978e-03 0.9959805
[5,] 1.216296e-03 2.432591e-03 0.9987837
[6,] 3.856769e-04 7.713538e-04 0.9996143
[7,] 1.257864e-04 2.515728e-04 0.9998742
[8,] 3.727405e-05 7.454810e-05 0.9999627
[9,] 2.587381e-05 5.174763e-05 0.9999741
[10,] 1.511696e-05 3.023392e-05 0.9999849
[11,] 4.583675e-06 9.167350e-06 0.9999954
[12,] 1.467643e-06 2.935285e-06 0.9999985
[13,] 4.358049e-07 8.716098e-07 0.9999996
[14,] 1.228093e-07 2.456185e-07 0.9999999
[15,] 6.060315e-08 1.212063e-07 0.9999999
[16,] 2.475143e-08 4.950287e-08 1.0000000
[17,] 1.508576e-08 3.017151e-08 1.0000000
[18,] 6.163475e-09 1.232695e-08 1.0000000
[19,] 2.157077e-09 4.314155e-09 1.0000000
[20,] 1.588426e-09 3.176851e-09 1.0000000
[21,] 1.095310e-09 2.190619e-09 1.0000000
[22,] 4.314056e-10 8.628113e-10 1.0000000
[23,] 2.232888e-10 4.465776e-10 1.0000000
[24,] 4.156881e-10 8.313763e-10 1.0000000
[25,] 1.787120e-10 3.574240e-10 1.0000000
[26,] 9.543141e-11 1.908628e-10 1.0000000
[27,] 5.864430e-10 1.172886e-09 1.0000000
[28,] 8.535911e-10 1.707182e-09 1.0000000
[29,] 5.177311e-10 1.035462e-09 1.0000000
[30,] 1.352836e-08 2.705673e-08 1.0000000
[31,] 1.512323e-08 3.024647e-08 1.0000000
[32,] 4.010123e-08 8.020247e-08 1.0000000
[33,] 2.400280e-07 4.800559e-07 0.9999998
[34,] 3.457395e-07 6.914789e-07 0.9999997
[35,] 1.327537e-06 2.655073e-06 0.9999987
[36,] 1.035360e-05 2.070719e-05 0.9999896
[37,] 1.301354e-05 2.602708e-05 0.9999870
[38,] 2.434526e-05 4.869052e-05 0.9999757
[39,] 8.994324e-05 1.798865e-04 0.9999101
[40,] 2.431576e-04 4.863151e-04 0.9997568
[41,] 4.049920e-04 8.099840e-04 0.9995950
[42,] 1.584905e-03 3.169810e-03 0.9984151
[43,] 2.547481e-03 5.094962e-03 0.9974525
[44,] 6.069185e-03 1.213837e-02 0.9939308
[45,] 1.585442e-02 3.170885e-02 0.9841456
[46,] 3.204606e-02 6.409211e-02 0.9679539
[47,] 7.142529e-02 1.428506e-01 0.9285747
[48,] 1.238209e-01 2.476419e-01 0.8761791
[49,] 1.523337e-01 3.046673e-01 0.8476663
[50,] 1.868924e-01 3.737847e-01 0.8131076
[51,] 2.901598e-01 5.803196e-01 0.7098402
[52,] 3.241087e-01 6.482175e-01 0.6758913
[53,] 4.021096e-01 8.042191e-01 0.5978904
[54,] 4.710722e-01 9.421444e-01 0.5289278
[55,] 5.351654e-01 9.296691e-01 0.4648346
[56,] 5.813865e-01 8.372270e-01 0.4186135
[57,] 6.266222e-01 7.467557e-01 0.3733778
[58,] 7.066353e-01 5.867294e-01 0.2933647
[59,] 7.397267e-01 5.205466e-01 0.2602733
[60,] 7.331487e-01 5.337025e-01 0.2668513
[61,] 7.401483e-01 5.197033e-01 0.2598517
[62,] 7.366313e-01 5.267375e-01 0.2633687
[63,] 7.357817e-01 5.284365e-01 0.2642183
[64,] 7.511179e-01 4.977643e-01 0.2488821
[65,] 7.458439e-01 5.083121e-01 0.2541561
[66,] 7.586263e-01 4.827474e-01 0.2413737
[67,] 7.710140e-01 4.579719e-01 0.2289860
[68,] 7.697759e-01 4.604481e-01 0.2302241
[69,] 7.919958e-01 4.160085e-01 0.2080042
[70,] 8.109402e-01 3.781197e-01 0.1890598
[71,] 7.951702e-01 4.096597e-01 0.2048298
[72,] 7.860656e-01 4.278689e-01 0.2139344
[73,] 7.785416e-01 4.429168e-01 0.2214584
[74,] 7.683945e-01 4.632111e-01 0.2316055
[75,] 7.295298e-01 5.409403e-01 0.2704702
[76,] 7.487863e-01 5.024274e-01 0.2512137
[77,] 7.229239e-01 5.541523e-01 0.2770761
[78,] 6.906643e-01 6.186713e-01 0.3093357
[79,] 6.817626e-01 6.364748e-01 0.3182374
[80,] 6.217887e-01 7.564226e-01 0.3782113
[81,] 5.683265e-01 8.633470e-01 0.4316735
[82,] 5.104676e-01 9.790648e-01 0.4895324
[83,] 4.268579e-01 8.537158e-01 0.5731421
[84,] 3.497571e-01 6.995142e-01 0.6502429
[85,] 2.804429e-01 5.608858e-01 0.7195571
[86,] 2.140052e-01 4.280104e-01 0.7859948
[87,] 2.245873e-01 4.491746e-01 0.7754127
[88,] 2.008483e-01 4.016966e-01 0.7991517
[89,] 1.842207e-01 3.684414e-01 0.8157793
[90,] 2.204829e-01 4.409658e-01 0.7795171
> postscript(file="/var/www/html/rcomp/tmp/1v3z41292771504.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2v3z41292771504.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/36uy71292771504.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/46uy71292771504.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/56uy71292771504.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 = 121
Frequency = 1
1 2 3 4 5 6
-1.81424632 -1.19711397 -1.40711397 -1.09711397 -1.44711397 -1.69711397
7 8 9 10 11 12
-2.69711397 -1.63711397 -2.63711397 -2.38711397 -2.05711397 -3.43711397
13 14 15 16 17 18
-2.41424632 -2.29711397 -2.80711397 -1.59711397 -1.94711397 -2.29711397
19 20 21 22 23 24
-1.59711397 -1.33711397 -2.93711397 -1.88711397 -2.35711397 -2.33711397
25 26 27 28 29 30
-1.21424632 -1.89711397 -1.80711397 -1.59711397 -1.74711397 -2.59711397
31 32 33 34 35 36
-1.59711397 -2.23711397 -2.83711397 -1.88711397 -2.85711397 -2.23711397
37 38 39 40 41 42
-1.81424632 -2.09711397 -0.90711397 -1.59711397 -1.24711397 -0.69711397
43 44 45 46 47 48
-0.89711397 -1.73711397 -0.73711397 -1.18711397 -1.05711397 -0.83711397
49 50 51 52 53 54
-0.91424632 -0.09711397 0.69288603 -0.29711397 0.05288603 0.50288603
55 56 57 58 59 60
0.60288603 -0.03711397 0.46288603 -0.68711397 0.44288603 0.76288603
61 62 63 64 65 66
-0.61424632 1.90288603 2.19288603 1.40288603 1.55288603 2.70288603
67 68 69 70 71 72
0.30288603 2.36288603 1.56288603 0.71288603 1.54288603 1.46288603
73 74 75 76 77 78
1.58575368 2.90288603 1.59288603 2.60288603 2.15288603 2.70288603
79 80 81 82 83 84
1.60288603 2.66288603 2.56288603 2.51288603 2.84288603 1.66288603
85 86 87 88 89 90
3.68575368 3.40288603 2.09288603 3.90288603 3.95288603 2.30288603
91 92 93 94 95 96
4.90288603 3.66288603 3.86288603 4.51288603 2.74288603 3.66288603
97 98 99 100 101 102
2.37132353 -0.51154412 -0.52154412 -1.61154412 -1.46154412 -2.41154412
103 104 105 106 107 108
-1.91154412 -2.55154412 -1.85154412 -1.30154412 -1.37154412 -1.05154412
109 110 111 112 113 114
-1.22867647 -0.11154412 0.87845588 -0.11154412 0.13845588 1.48845588
115 116 117 118 119 120
1.28845588 0.84845588 2.54845588 1.59845588 2.12845588 2.34845588
121
2.37132353
> postscript(file="/var/www/html/rcomp/tmp/64p4g1292771504.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 = 121
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.81424632 NA
1 -1.19711397 -1.81424632
2 -1.40711397 -1.19711397
3 -1.09711397 -1.40711397
4 -1.44711397 -1.09711397
5 -1.69711397 -1.44711397
6 -2.69711397 -1.69711397
7 -1.63711397 -2.69711397
8 -2.63711397 -1.63711397
9 -2.38711397 -2.63711397
10 -2.05711397 -2.38711397
11 -3.43711397 -2.05711397
12 -2.41424632 -3.43711397
13 -2.29711397 -2.41424632
14 -2.80711397 -2.29711397
15 -1.59711397 -2.80711397
16 -1.94711397 -1.59711397
17 -2.29711397 -1.94711397
18 -1.59711397 -2.29711397
19 -1.33711397 -1.59711397
20 -2.93711397 -1.33711397
21 -1.88711397 -2.93711397
22 -2.35711397 -1.88711397
23 -2.33711397 -2.35711397
24 -1.21424632 -2.33711397
25 -1.89711397 -1.21424632
26 -1.80711397 -1.89711397
27 -1.59711397 -1.80711397
28 -1.74711397 -1.59711397
29 -2.59711397 -1.74711397
30 -1.59711397 -2.59711397
31 -2.23711397 -1.59711397
32 -2.83711397 -2.23711397
33 -1.88711397 -2.83711397
34 -2.85711397 -1.88711397
35 -2.23711397 -2.85711397
36 -1.81424632 -2.23711397
37 -2.09711397 -1.81424632
38 -0.90711397 -2.09711397
39 -1.59711397 -0.90711397
40 -1.24711397 -1.59711397
41 -0.69711397 -1.24711397
42 -0.89711397 -0.69711397
43 -1.73711397 -0.89711397
44 -0.73711397 -1.73711397
45 -1.18711397 -0.73711397
46 -1.05711397 -1.18711397
47 -0.83711397 -1.05711397
48 -0.91424632 -0.83711397
49 -0.09711397 -0.91424632
50 0.69288603 -0.09711397
51 -0.29711397 0.69288603
52 0.05288603 -0.29711397
53 0.50288603 0.05288603
54 0.60288603 0.50288603
55 -0.03711397 0.60288603
56 0.46288603 -0.03711397
57 -0.68711397 0.46288603
58 0.44288603 -0.68711397
59 0.76288603 0.44288603
60 -0.61424632 0.76288603
61 1.90288603 -0.61424632
62 2.19288603 1.90288603
63 1.40288603 2.19288603
64 1.55288603 1.40288603
65 2.70288603 1.55288603
66 0.30288603 2.70288603
67 2.36288603 0.30288603
68 1.56288603 2.36288603
69 0.71288603 1.56288603
70 1.54288603 0.71288603
71 1.46288603 1.54288603
72 1.58575368 1.46288603
73 2.90288603 1.58575368
74 1.59288603 2.90288603
75 2.60288603 1.59288603
76 2.15288603 2.60288603
77 2.70288603 2.15288603
78 1.60288603 2.70288603
79 2.66288603 1.60288603
80 2.56288603 2.66288603
81 2.51288603 2.56288603
82 2.84288603 2.51288603
83 1.66288603 2.84288603
84 3.68575368 1.66288603
85 3.40288603 3.68575368
86 2.09288603 3.40288603
87 3.90288603 2.09288603
88 3.95288603 3.90288603
89 2.30288603 3.95288603
90 4.90288603 2.30288603
91 3.66288603 4.90288603
92 3.86288603 3.66288603
93 4.51288603 3.86288603
94 2.74288603 4.51288603
95 3.66288603 2.74288603
96 2.37132353 3.66288603
97 -0.51154412 2.37132353
98 -0.52154412 -0.51154412
99 -1.61154412 -0.52154412
100 -1.46154412 -1.61154412
101 -2.41154412 -1.46154412
102 -1.91154412 -2.41154412
103 -2.55154412 -1.91154412
104 -1.85154412 -2.55154412
105 -1.30154412 -1.85154412
106 -1.37154412 -1.30154412
107 -1.05154412 -1.37154412
108 -1.22867647 -1.05154412
109 -0.11154412 -1.22867647
110 0.87845588 -0.11154412
111 -0.11154412 0.87845588
112 0.13845588 -0.11154412
113 1.48845588 0.13845588
114 1.28845588 1.48845588
115 0.84845588 1.28845588
116 2.54845588 0.84845588
117 1.59845588 2.54845588
118 2.12845588 1.59845588
119 2.34845588 2.12845588
120 2.37132353 2.34845588
121 NA 2.37132353
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.19711397 -1.81424632
[2,] -1.40711397 -1.19711397
[3,] -1.09711397 -1.40711397
[4,] -1.44711397 -1.09711397
[5,] -1.69711397 -1.44711397
[6,] -2.69711397 -1.69711397
[7,] -1.63711397 -2.69711397
[8,] -2.63711397 -1.63711397
[9,] -2.38711397 -2.63711397
[10,] -2.05711397 -2.38711397
[11,] -3.43711397 -2.05711397
[12,] -2.41424632 -3.43711397
[13,] -2.29711397 -2.41424632
[14,] -2.80711397 -2.29711397
[15,] -1.59711397 -2.80711397
[16,] -1.94711397 -1.59711397
[17,] -2.29711397 -1.94711397
[18,] -1.59711397 -2.29711397
[19,] -1.33711397 -1.59711397
[20,] -2.93711397 -1.33711397
[21,] -1.88711397 -2.93711397
[22,] -2.35711397 -1.88711397
[23,] -2.33711397 -2.35711397
[24,] -1.21424632 -2.33711397
[25,] -1.89711397 -1.21424632
[26,] -1.80711397 -1.89711397
[27,] -1.59711397 -1.80711397
[28,] -1.74711397 -1.59711397
[29,] -2.59711397 -1.74711397
[30,] -1.59711397 -2.59711397
[31,] -2.23711397 -1.59711397
[32,] -2.83711397 -2.23711397
[33,] -1.88711397 -2.83711397
[34,] -2.85711397 -1.88711397
[35,] -2.23711397 -2.85711397
[36,] -1.81424632 -2.23711397
[37,] -2.09711397 -1.81424632
[38,] -0.90711397 -2.09711397
[39,] -1.59711397 -0.90711397
[40,] -1.24711397 -1.59711397
[41,] -0.69711397 -1.24711397
[42,] -0.89711397 -0.69711397
[43,] -1.73711397 -0.89711397
[44,] -0.73711397 -1.73711397
[45,] -1.18711397 -0.73711397
[46,] -1.05711397 -1.18711397
[47,] -0.83711397 -1.05711397
[48,] -0.91424632 -0.83711397
[49,] -0.09711397 -0.91424632
[50,] 0.69288603 -0.09711397
[51,] -0.29711397 0.69288603
[52,] 0.05288603 -0.29711397
[53,] 0.50288603 0.05288603
[54,] 0.60288603 0.50288603
[55,] -0.03711397 0.60288603
[56,] 0.46288603 -0.03711397
[57,] -0.68711397 0.46288603
[58,] 0.44288603 -0.68711397
[59,] 0.76288603 0.44288603
[60,] -0.61424632 0.76288603
[61,] 1.90288603 -0.61424632
[62,] 2.19288603 1.90288603
[63,] 1.40288603 2.19288603
[64,] 1.55288603 1.40288603
[65,] 2.70288603 1.55288603
[66,] 0.30288603 2.70288603
[67,] 2.36288603 0.30288603
[68,] 1.56288603 2.36288603
[69,] 0.71288603 1.56288603
[70,] 1.54288603 0.71288603
[71,] 1.46288603 1.54288603
[72,] 1.58575368 1.46288603
[73,] 2.90288603 1.58575368
[74,] 1.59288603 2.90288603
[75,] 2.60288603 1.59288603
[76,] 2.15288603 2.60288603
[77,] 2.70288603 2.15288603
[78,] 1.60288603 2.70288603
[79,] 2.66288603 1.60288603
[80,] 2.56288603 2.66288603
[81,] 2.51288603 2.56288603
[82,] 2.84288603 2.51288603
[83,] 1.66288603 2.84288603
[84,] 3.68575368 1.66288603
[85,] 3.40288603 3.68575368
[86,] 2.09288603 3.40288603
[87,] 3.90288603 2.09288603
[88,] 3.95288603 3.90288603
[89,] 2.30288603 3.95288603
[90,] 4.90288603 2.30288603
[91,] 3.66288603 4.90288603
[92,] 3.86288603 3.66288603
[93,] 4.51288603 3.86288603
[94,] 2.74288603 4.51288603
[95,] 3.66288603 2.74288603
[96,] 2.37132353 3.66288603
[97,] -0.51154412 2.37132353
[98,] -0.52154412 -0.51154412
[99,] -1.61154412 -0.52154412
[100,] -1.46154412 -1.61154412
[101,] -2.41154412 -1.46154412
[102,] -1.91154412 -2.41154412
[103,] -2.55154412 -1.91154412
[104,] -1.85154412 -2.55154412
[105,] -1.30154412 -1.85154412
[106,] -1.37154412 -1.30154412
[107,] -1.05154412 -1.37154412
[108,] -1.22867647 -1.05154412
[109,] -0.11154412 -1.22867647
[110,] 0.87845588 -0.11154412
[111,] -0.11154412 0.87845588
[112,] 0.13845588 -0.11154412
[113,] 1.48845588 0.13845588
[114,] 1.28845588 1.48845588
[115,] 0.84845588 1.28845588
[116,] 2.54845588 0.84845588
[117,] 1.59845588 2.54845588
[118,] 2.12845588 1.59845588
[119,] 2.34845588 2.12845588
[120,] 2.37132353 2.34845588
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.19711397 -1.81424632
2 -1.40711397 -1.19711397
3 -1.09711397 -1.40711397
4 -1.44711397 -1.09711397
5 -1.69711397 -1.44711397
6 -2.69711397 -1.69711397
7 -1.63711397 -2.69711397
8 -2.63711397 -1.63711397
9 -2.38711397 -2.63711397
10 -2.05711397 -2.38711397
11 -3.43711397 -2.05711397
12 -2.41424632 -3.43711397
13 -2.29711397 -2.41424632
14 -2.80711397 -2.29711397
15 -1.59711397 -2.80711397
16 -1.94711397 -1.59711397
17 -2.29711397 -1.94711397
18 -1.59711397 -2.29711397
19 -1.33711397 -1.59711397
20 -2.93711397 -1.33711397
21 -1.88711397 -2.93711397
22 -2.35711397 -1.88711397
23 -2.33711397 -2.35711397
24 -1.21424632 -2.33711397
25 -1.89711397 -1.21424632
26 -1.80711397 -1.89711397
27 -1.59711397 -1.80711397
28 -1.74711397 -1.59711397
29 -2.59711397 -1.74711397
30 -1.59711397 -2.59711397
31 -2.23711397 -1.59711397
32 -2.83711397 -2.23711397
33 -1.88711397 -2.83711397
34 -2.85711397 -1.88711397
35 -2.23711397 -2.85711397
36 -1.81424632 -2.23711397
37 -2.09711397 -1.81424632
38 -0.90711397 -2.09711397
39 -1.59711397 -0.90711397
40 -1.24711397 -1.59711397
41 -0.69711397 -1.24711397
42 -0.89711397 -0.69711397
43 -1.73711397 -0.89711397
44 -0.73711397 -1.73711397
45 -1.18711397 -0.73711397
46 -1.05711397 -1.18711397
47 -0.83711397 -1.05711397
48 -0.91424632 -0.83711397
49 -0.09711397 -0.91424632
50 0.69288603 -0.09711397
51 -0.29711397 0.69288603
52 0.05288603 -0.29711397
53 0.50288603 0.05288603
54 0.60288603 0.50288603
55 -0.03711397 0.60288603
56 0.46288603 -0.03711397
57 -0.68711397 0.46288603
58 0.44288603 -0.68711397
59 0.76288603 0.44288603
60 -0.61424632 0.76288603
61 1.90288603 -0.61424632
62 2.19288603 1.90288603
63 1.40288603 2.19288603
64 1.55288603 1.40288603
65 2.70288603 1.55288603
66 0.30288603 2.70288603
67 2.36288603 0.30288603
68 1.56288603 2.36288603
69 0.71288603 1.56288603
70 1.54288603 0.71288603
71 1.46288603 1.54288603
72 1.58575368 1.46288603
73 2.90288603 1.58575368
74 1.59288603 2.90288603
75 2.60288603 1.59288603
76 2.15288603 2.60288603
77 2.70288603 2.15288603
78 1.60288603 2.70288603
79 2.66288603 1.60288603
80 2.56288603 2.66288603
81 2.51288603 2.56288603
82 2.84288603 2.51288603
83 1.66288603 2.84288603
84 3.68575368 1.66288603
85 3.40288603 3.68575368
86 2.09288603 3.40288603
87 3.90288603 2.09288603
88 3.95288603 3.90288603
89 2.30288603 3.95288603
90 4.90288603 2.30288603
91 3.66288603 4.90288603
92 3.86288603 3.66288603
93 4.51288603 3.86288603
94 2.74288603 4.51288603
95 3.66288603 2.74288603
96 2.37132353 3.66288603
97 -0.51154412 2.37132353
98 -0.52154412 -0.51154412
99 -1.61154412 -0.52154412
100 -1.46154412 -1.61154412
101 -2.41154412 -1.46154412
102 -1.91154412 -2.41154412
103 -2.55154412 -1.91154412
104 -1.85154412 -2.55154412
105 -1.30154412 -1.85154412
106 -1.37154412 -1.30154412
107 -1.05154412 -1.37154412
108 -1.22867647 -1.05154412
109 -0.11154412 -1.22867647
110 0.87845588 -0.11154412
111 -0.11154412 0.87845588
112 0.13845588 -0.11154412
113 1.48845588 0.13845588
114 1.28845588 1.48845588
115 0.84845588 1.28845588
116 2.54845588 0.84845588
117 1.59845588 2.54845588
118 2.12845588 1.59845588
119 2.34845588 2.12845588
120 2.37132353 2.34845588
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/74p4g1292771504.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8rvxd1292771504.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rvxd1292771504.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rvxd1292771504.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11o5c41292771504.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12gwc71292771504.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/135f901292771504.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14y6ql1292771504.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/1517or1292771504.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16fymi1292771504.tab")
+ }
>
> try(system("convert tmp/1v3z41292771504.ps tmp/1v3z41292771504.png",intern=TRUE))
character(0)
> try(system("convert tmp/2v3z41292771504.ps tmp/2v3z41292771504.png",intern=TRUE))
character(0)
> try(system("convert tmp/36uy71292771504.ps tmp/36uy71292771504.png",intern=TRUE))
character(0)
> try(system("convert tmp/46uy71292771504.ps tmp/46uy71292771504.png",intern=TRUE))
character(0)
> try(system("convert tmp/56uy71292771504.ps tmp/56uy71292771504.png",intern=TRUE))
character(0)
> try(system("convert tmp/64p4g1292771504.ps tmp/64p4g1292771504.png",intern=TRUE))
character(0)
> try(system("convert tmp/74p4g1292771504.ps tmp/74p4g1292771504.png",intern=TRUE))
character(0)
> try(system("convert tmp/8rvxd1292771504.ps tmp/8rvxd1292771504.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rvxd1292771504.ps tmp/9rvxd1292771504.png",intern=TRUE))
character(0)
> try(system("convert tmp/10rvxd1292771504.ps tmp/10rvxd1292771504.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.324 1.707 9.589