R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(3.75,0,3.75,0,3.55,0,3.5,0,3.5,0,3.1,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3.21,0,3.25,0,3.25,0,3.45,0,3.5,0,3.5,0,3.64,0,3.75,0,3.93,0,4,0,4.17,0,4.25,0,4.39,0,4.5,0,4.5,0,4.65,0,4.75,0,4.75,0,4.9,0,5,0,5,0,5,0,5,0,5,0,5,0,5,1,5,1,5,1,5,1,5,1,5,1,5.18,1,5.25,1,5.25,1,4.49,1,3.92,1,3.25,1),dim=c(2,72),dimnames=list(c('Yt','Xt'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('Yt','Xt'),1:72))
> 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 = '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
Yt Xt
1 3.75 0
2 3.75 0
3 3.55 0
4 3.50 0
5 3.50 0
6 3.10 0
7 3.00 0
8 3.00 0
9 3.00 0
10 3.00 0
11 3.00 0
12 3.00 0
13 3.00 0
14 3.00 0
15 3.00 0
16 3.00 0
17 3.00 0
18 3.00 0
19 3.00 0
20 3.00 0
21 3.00 0
22 3.00 0
23 3.00 0
24 3.00 0
25 3.00 0
26 3.00 0
27 3.00 0
28 3.00 0
29 3.00 0
30 3.00 0
31 3.00 0
32 3.00 0
33 3.00 0
34 3.00 0
35 3.00 0
36 3.21 0
37 3.25 0
38 3.25 0
39 3.45 0
40 3.50 0
41 3.50 0
42 3.64 0
43 3.75 0
44 3.93 0
45 4.00 0
46 4.17 0
47 4.25 0
48 4.39 0
49 4.50 0
50 4.50 0
51 4.65 0
52 4.75 0
53 4.75 0
54 4.90 0
55 5.00 0
56 5.00 0
57 5.00 0
58 5.00 0
59 5.00 0
60 5.00 0
61 5.00 1
62 5.00 1
63 5.00 1
64 5.00 1
65 5.00 1
66 5.00 1
67 5.18 1
68 5.25 1
69 5.25 1
70 4.49 1
71 3.92 1
72 3.25 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Xt
3.575 1.204
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.5283 -0.5748 -0.2066 0.4075 1.4252
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.57483 0.09219 38.776 < 2e-16 ***
Xt 1.20350 0.22583 5.329 1.14e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7141 on 70 degrees of freedom
Multiple R-squared: 0.2886, Adjusted R-squared: 0.2785
F-statistic: 28.4 on 1 and 70 DF, p-value: 1.141e-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,] 1.106763e-02 2.213525e-02 0.9889324
[2,] 2.836501e-02 5.673002e-02 0.9716350
[3,] 3.346821e-02 6.693642e-02 0.9665318
[4,] 2.685959e-02 5.371918e-02 0.9731404
[5,] 1.857862e-02 3.715723e-02 0.9814214
[6,] 1.178153e-02 2.356306e-02 0.9882185
[7,] 7.025965e-03 1.405193e-02 0.9929740
[8,] 3.993927e-03 7.987853e-03 0.9960061
[9,] 2.182841e-03 4.365682e-03 0.9978172
[10,] 1.154225e-03 2.308449e-03 0.9988458
[11,] 5.934585e-04 1.186917e-03 0.9994065
[12,] 2.980018e-04 5.960037e-04 0.9997020
[13,] 1.467304e-04 2.934607e-04 0.9998533
[14,] 7.111672e-05 1.422334e-04 0.9999289
[15,] 3.406028e-05 6.812057e-05 0.9999659
[16,] 1.618343e-05 3.236686e-05 0.9999838
[17,] 7.660430e-06 1.532086e-05 0.9999923
[18,] 3.628699e-06 7.257398e-06 0.9999964
[19,] 1.728671e-06 3.457341e-06 0.9999983
[20,] 8.327971e-07 1.665594e-06 0.9999992
[21,] 4.082838e-07 8.165677e-07 0.9999996
[22,] 2.051788e-07 4.103577e-07 0.9999998
[23,] 1.065963e-07 2.131927e-07 0.9999999
[24,] 5.783370e-08 1.156674e-07 0.9999999
[25,] 3.317013e-08 6.634027e-08 1.0000000
[26,] 2.041449e-08 4.082897e-08 1.0000000
[27,] 1.373594e-08 2.747189e-08 1.0000000
[28,] 1.034677e-08 2.069355e-08 1.0000000
[29,] 8.997908e-09 1.799582e-08 1.0000000
[30,] 9.411032e-09 1.882206e-08 1.0000000
[31,] 1.252001e-08 2.504002e-08 1.0000000
[32,] 1.432396e-08 2.864792e-08 1.0000000
[33,] 2.052461e-08 4.104922e-08 1.0000000
[34,] 3.886986e-08 7.773971e-08 1.0000000
[35,] 1.209759e-07 2.419518e-07 0.9999999
[36,] 4.941282e-07 9.882564e-07 0.9999995
[37,] 2.403250e-06 4.806501e-06 0.9999976
[38,] 1.754827e-05 3.509655e-05 0.9999825
[39,] 1.509986e-04 3.019972e-04 0.9998490
[40,] 1.385420e-03 2.770840e-03 0.9986146
[41,] 8.495543e-03 1.699109e-02 0.9915045
[42,] 3.815038e-02 7.630076e-02 0.9618496
[43,] 1.093101e-01 2.186202e-01 0.8906899
[44,] 2.285294e-01 4.570588e-01 0.7714706
[45,] 3.685995e-01 7.371990e-01 0.6314005
[46,] 4.900268e-01 9.800536e-01 0.5099732
[47,] 5.940455e-01 8.119089e-01 0.4059545
[48,] 6.710302e-01 6.579396e-01 0.3289698
[49,] 7.178227e-01 5.643547e-01 0.2821773
[50,] 7.527844e-01 4.944311e-01 0.2472156
[51,] 7.757456e-01 4.485087e-01 0.2242544
[52,] 7.811844e-01 4.376311e-01 0.2188156
[53,] 7.733618e-01 4.532763e-01 0.2266382
[54,] 7.539531e-01 4.920938e-01 0.2460469
[55,] 7.232319e-01 5.535362e-01 0.2767681
[56,] 6.807754e-01 6.384491e-01 0.3192246
[57,] 5.918798e-01 8.162405e-01 0.4081202
[58,] 4.958889e-01 9.917779e-01 0.5041111
[59,] 3.979765e-01 7.959530e-01 0.6020235
[60,] 3.040428e-01 6.080857e-01 0.6959572
[61,] 2.197168e-01 4.394337e-01 0.7802832
[62,] 1.493052e-01 2.986104e-01 0.8506948
[63,] 1.151007e-01 2.302015e-01 0.8848993
> postscript(file="/var/www/html/rcomp/tmp/1mshj1258654141.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23pb81258654141.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3anmj1258654141.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4j41v1258654141.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5fd6o1258654141.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 72
Frequency = 1
1 2 3 4 5 6
0.17516667 0.17516667 -0.02483333 -0.07483333 -0.07483333 -0.47483333
7 8 9 10 11 12
-0.57483333 -0.57483333 -0.57483333 -0.57483333 -0.57483333 -0.57483333
13 14 15 16 17 18
-0.57483333 -0.57483333 -0.57483333 -0.57483333 -0.57483333 -0.57483333
19 20 21 22 23 24
-0.57483333 -0.57483333 -0.57483333 -0.57483333 -0.57483333 -0.57483333
25 26 27 28 29 30
-0.57483333 -0.57483333 -0.57483333 -0.57483333 -0.57483333 -0.57483333
31 32 33 34 35 36
-0.57483333 -0.57483333 -0.57483333 -0.57483333 -0.57483333 -0.36483333
37 38 39 40 41 42
-0.32483333 -0.32483333 -0.12483333 -0.07483333 -0.07483333 0.06516667
43 44 45 46 47 48
0.17516667 0.35516667 0.42516667 0.59516667 0.67516667 0.81516667
49 50 51 52 53 54
0.92516667 0.92516667 1.07516667 1.17516667 1.17516667 1.32516667
55 56 57 58 59 60
1.42516667 1.42516667 1.42516667 1.42516667 1.42516667 1.42516667
61 62 63 64 65 66
0.22166667 0.22166667 0.22166667 0.22166667 0.22166667 0.22166667
67 68 69 70 71 72
0.40166667 0.47166667 0.47166667 -0.28833333 -0.85833333 -1.52833333
> postscript(file="/var/www/html/rcomp/tmp/6abz01258654141.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 0.17516667 NA
1 0.17516667 0.17516667
2 -0.02483333 0.17516667
3 -0.07483333 -0.02483333
4 -0.07483333 -0.07483333
5 -0.47483333 -0.07483333
6 -0.57483333 -0.47483333
7 -0.57483333 -0.57483333
8 -0.57483333 -0.57483333
9 -0.57483333 -0.57483333
10 -0.57483333 -0.57483333
11 -0.57483333 -0.57483333
12 -0.57483333 -0.57483333
13 -0.57483333 -0.57483333
14 -0.57483333 -0.57483333
15 -0.57483333 -0.57483333
16 -0.57483333 -0.57483333
17 -0.57483333 -0.57483333
18 -0.57483333 -0.57483333
19 -0.57483333 -0.57483333
20 -0.57483333 -0.57483333
21 -0.57483333 -0.57483333
22 -0.57483333 -0.57483333
23 -0.57483333 -0.57483333
24 -0.57483333 -0.57483333
25 -0.57483333 -0.57483333
26 -0.57483333 -0.57483333
27 -0.57483333 -0.57483333
28 -0.57483333 -0.57483333
29 -0.57483333 -0.57483333
30 -0.57483333 -0.57483333
31 -0.57483333 -0.57483333
32 -0.57483333 -0.57483333
33 -0.57483333 -0.57483333
34 -0.57483333 -0.57483333
35 -0.36483333 -0.57483333
36 -0.32483333 -0.36483333
37 -0.32483333 -0.32483333
38 -0.12483333 -0.32483333
39 -0.07483333 -0.12483333
40 -0.07483333 -0.07483333
41 0.06516667 -0.07483333
42 0.17516667 0.06516667
43 0.35516667 0.17516667
44 0.42516667 0.35516667
45 0.59516667 0.42516667
46 0.67516667 0.59516667
47 0.81516667 0.67516667
48 0.92516667 0.81516667
49 0.92516667 0.92516667
50 1.07516667 0.92516667
51 1.17516667 1.07516667
52 1.17516667 1.17516667
53 1.32516667 1.17516667
54 1.42516667 1.32516667
55 1.42516667 1.42516667
56 1.42516667 1.42516667
57 1.42516667 1.42516667
58 1.42516667 1.42516667
59 1.42516667 1.42516667
60 0.22166667 1.42516667
61 0.22166667 0.22166667
62 0.22166667 0.22166667
63 0.22166667 0.22166667
64 0.22166667 0.22166667
65 0.22166667 0.22166667
66 0.40166667 0.22166667
67 0.47166667 0.40166667
68 0.47166667 0.47166667
69 -0.28833333 0.47166667
70 -0.85833333 -0.28833333
71 -1.52833333 -0.85833333
72 NA -1.52833333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.17516667 0.17516667
[2,] -0.02483333 0.17516667
[3,] -0.07483333 -0.02483333
[4,] -0.07483333 -0.07483333
[5,] -0.47483333 -0.07483333
[6,] -0.57483333 -0.47483333
[7,] -0.57483333 -0.57483333
[8,] -0.57483333 -0.57483333
[9,] -0.57483333 -0.57483333
[10,] -0.57483333 -0.57483333
[11,] -0.57483333 -0.57483333
[12,] -0.57483333 -0.57483333
[13,] -0.57483333 -0.57483333
[14,] -0.57483333 -0.57483333
[15,] -0.57483333 -0.57483333
[16,] -0.57483333 -0.57483333
[17,] -0.57483333 -0.57483333
[18,] -0.57483333 -0.57483333
[19,] -0.57483333 -0.57483333
[20,] -0.57483333 -0.57483333
[21,] -0.57483333 -0.57483333
[22,] -0.57483333 -0.57483333
[23,] -0.57483333 -0.57483333
[24,] -0.57483333 -0.57483333
[25,] -0.57483333 -0.57483333
[26,] -0.57483333 -0.57483333
[27,] -0.57483333 -0.57483333
[28,] -0.57483333 -0.57483333
[29,] -0.57483333 -0.57483333
[30,] -0.57483333 -0.57483333
[31,] -0.57483333 -0.57483333
[32,] -0.57483333 -0.57483333
[33,] -0.57483333 -0.57483333
[34,] -0.57483333 -0.57483333
[35,] -0.36483333 -0.57483333
[36,] -0.32483333 -0.36483333
[37,] -0.32483333 -0.32483333
[38,] -0.12483333 -0.32483333
[39,] -0.07483333 -0.12483333
[40,] -0.07483333 -0.07483333
[41,] 0.06516667 -0.07483333
[42,] 0.17516667 0.06516667
[43,] 0.35516667 0.17516667
[44,] 0.42516667 0.35516667
[45,] 0.59516667 0.42516667
[46,] 0.67516667 0.59516667
[47,] 0.81516667 0.67516667
[48,] 0.92516667 0.81516667
[49,] 0.92516667 0.92516667
[50,] 1.07516667 0.92516667
[51,] 1.17516667 1.07516667
[52,] 1.17516667 1.17516667
[53,] 1.32516667 1.17516667
[54,] 1.42516667 1.32516667
[55,] 1.42516667 1.42516667
[56,] 1.42516667 1.42516667
[57,] 1.42516667 1.42516667
[58,] 1.42516667 1.42516667
[59,] 1.42516667 1.42516667
[60,] 0.22166667 1.42516667
[61,] 0.22166667 0.22166667
[62,] 0.22166667 0.22166667
[63,] 0.22166667 0.22166667
[64,] 0.22166667 0.22166667
[65,] 0.22166667 0.22166667
[66,] 0.40166667 0.22166667
[67,] 0.47166667 0.40166667
[68,] 0.47166667 0.47166667
[69,] -0.28833333 0.47166667
[70,] -0.85833333 -0.28833333
[71,] -1.52833333 -0.85833333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.17516667 0.17516667
2 -0.02483333 0.17516667
3 -0.07483333 -0.02483333
4 -0.07483333 -0.07483333
5 -0.47483333 -0.07483333
6 -0.57483333 -0.47483333
7 -0.57483333 -0.57483333
8 -0.57483333 -0.57483333
9 -0.57483333 -0.57483333
10 -0.57483333 -0.57483333
11 -0.57483333 -0.57483333
12 -0.57483333 -0.57483333
13 -0.57483333 -0.57483333
14 -0.57483333 -0.57483333
15 -0.57483333 -0.57483333
16 -0.57483333 -0.57483333
17 -0.57483333 -0.57483333
18 -0.57483333 -0.57483333
19 -0.57483333 -0.57483333
20 -0.57483333 -0.57483333
21 -0.57483333 -0.57483333
22 -0.57483333 -0.57483333
23 -0.57483333 -0.57483333
24 -0.57483333 -0.57483333
25 -0.57483333 -0.57483333
26 -0.57483333 -0.57483333
27 -0.57483333 -0.57483333
28 -0.57483333 -0.57483333
29 -0.57483333 -0.57483333
30 -0.57483333 -0.57483333
31 -0.57483333 -0.57483333
32 -0.57483333 -0.57483333
33 -0.57483333 -0.57483333
34 -0.57483333 -0.57483333
35 -0.36483333 -0.57483333
36 -0.32483333 -0.36483333
37 -0.32483333 -0.32483333
38 -0.12483333 -0.32483333
39 -0.07483333 -0.12483333
40 -0.07483333 -0.07483333
41 0.06516667 -0.07483333
42 0.17516667 0.06516667
43 0.35516667 0.17516667
44 0.42516667 0.35516667
45 0.59516667 0.42516667
46 0.67516667 0.59516667
47 0.81516667 0.67516667
48 0.92516667 0.81516667
49 0.92516667 0.92516667
50 1.07516667 0.92516667
51 1.17516667 1.07516667
52 1.17516667 1.17516667
53 1.32516667 1.17516667
54 1.42516667 1.32516667
55 1.42516667 1.42516667
56 1.42516667 1.42516667
57 1.42516667 1.42516667
58 1.42516667 1.42516667
59 1.42516667 1.42516667
60 0.22166667 1.42516667
61 0.22166667 0.22166667
62 0.22166667 0.22166667
63 0.22166667 0.22166667
64 0.22166667 0.22166667
65 0.22166667 0.22166667
66 0.40166667 0.22166667
67 0.47166667 0.40166667
68 0.47166667 0.47166667
69 -0.28833333 0.47166667
70 -0.85833333 -0.28833333
71 -1.52833333 -0.85833333
> 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/79p4d1258654141.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8luzm1258654141.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9skxn1258654141.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10vsb31258654141.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11q2ym1258654141.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/12fdi81258654141.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/13t9el1258654142.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/14oedj1258654142.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/15e2qh1258654142.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/16y0w41258654142.tab")
+ }
>
> system("convert tmp/1mshj1258654141.ps tmp/1mshj1258654141.png")
> system("convert tmp/23pb81258654141.ps tmp/23pb81258654141.png")
> system("convert tmp/3anmj1258654141.ps tmp/3anmj1258654141.png")
> system("convert tmp/4j41v1258654141.ps tmp/4j41v1258654141.png")
> system("convert tmp/5fd6o1258654141.ps tmp/5fd6o1258654141.png")
> system("convert tmp/6abz01258654141.ps tmp/6abz01258654141.png")
> system("convert tmp/79p4d1258654141.ps tmp/79p4d1258654141.png")
> system("convert tmp/8luzm1258654141.ps tmp/8luzm1258654141.png")
> system("convert tmp/9skxn1258654141.ps tmp/9skxn1258654141.png")
> system("convert tmp/10vsb31258654141.ps tmp/10vsb31258654141.png")
>
>
> proc.time()
user system elapsed
2.600 1.542 3.082