R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(12300.00,0.00,12092.80,0.00,12380.80,0.00,12196.90,0.00,9455.00,0.00,13168.00,0.00,13427.90,0.00,11980.50,0.00,11884.80,0.00,11691.70,0.00,12233.80,0.00,14341.40,0.00,13130.70,0.00,12421.10,0.00,14285.80,0.00,12864.60,0.00,11160.20,0.00,14316.20,0.00,14388.70,0.00,14013.90,0.00,13419.00,0.00,12769.60,0.00,13315.50,0.00,15332.90,0.00,14243.00,0.00,13824.40,0.00,14962.90,0.00,13202.90,0.00,12199.00,0.00,15508.90,0.00,14199.80,0.00,15169.60,0.00,14058.00,0.00,13786.20,0.00,14147.90,0.00,16541.70,0.00,13587.50,0.00,15582.40,0.00,15802.80,0.00,14130.50,0.00,12923.20,0.00,15612.20,1.00,16033.70,1.00,16036.60,1.00,14037.80,1.00,15330.60,1.00,15038.30,1.00,17401.80,1.00,14992.50,1.00,16043.70,1.00,16929.60,1.00,15921.30,1.00,14417.20,1.00,15961.00,1.00,17851.90,1.00,16483.90,1.00,14215.50,1.00,17429.70,1.00,17839.50,1.00,17629.20,1.00),dim=c(2,60),dimnames=list(c('x','y'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('x','y'),1:60))
> 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
x y M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 12300.0 0 1 0 0 0 0 0 0 0 0 0 0
2 12092.8 0 0 1 0 0 0 0 0 0 0 0 0
3 12380.8 0 0 0 1 0 0 0 0 0 0 0 0
4 12196.9 0 0 0 0 1 0 0 0 0 0 0 0
5 9455.0 0 0 0 0 0 1 0 0 0 0 0 0
6 13168.0 0 0 0 0 0 0 1 0 0 0 0 0
7 13427.9 0 0 0 0 0 0 0 1 0 0 0 0
8 11980.5 0 0 0 0 0 0 0 0 1 0 0 0
9 11884.8 0 0 0 0 0 0 0 0 0 1 0 0
10 11691.7 0 0 0 0 0 0 0 0 0 0 1 0
11 12233.8 0 0 0 0 0 0 0 0 0 0 0 1
12 14341.4 0 0 0 0 0 0 0 0 0 0 0 0
13 13130.7 0 1 0 0 0 0 0 0 0 0 0 0
14 12421.1 0 0 1 0 0 0 0 0 0 0 0 0
15 14285.8 0 0 0 1 0 0 0 0 0 0 0 0
16 12864.6 0 0 0 0 1 0 0 0 0 0 0 0
17 11160.2 0 0 0 0 0 1 0 0 0 0 0 0
18 14316.2 0 0 0 0 0 0 1 0 0 0 0 0
19 14388.7 0 0 0 0 0 0 0 1 0 0 0 0
20 14013.9 0 0 0 0 0 0 0 0 1 0 0 0
21 13419.0 0 0 0 0 0 0 0 0 0 1 0 0
22 12769.6 0 0 0 0 0 0 0 0 0 0 1 0
23 13315.5 0 0 0 0 0 0 0 0 0 0 0 1
24 15332.9 0 0 0 0 0 0 0 0 0 0 0 0
25 14243.0 0 1 0 0 0 0 0 0 0 0 0 0
26 13824.4 0 0 1 0 0 0 0 0 0 0 0 0
27 14962.9 0 0 0 1 0 0 0 0 0 0 0 0
28 13202.9 0 0 0 0 1 0 0 0 0 0 0 0
29 12199.0 0 0 0 0 0 1 0 0 0 0 0 0
30 15508.9 0 0 0 0 0 0 1 0 0 0 0 0
31 14199.8 0 0 0 0 0 0 0 1 0 0 0 0
32 15169.6 0 0 0 0 0 0 0 0 1 0 0 0
33 14058.0 0 0 0 0 0 0 0 0 0 1 0 0
34 13786.2 0 0 0 0 0 0 0 0 0 0 1 0
35 14147.9 0 0 0 0 0 0 0 0 0 0 0 1
36 16541.7 0 0 0 0 0 0 0 0 0 0 0 0
37 13587.5 0 1 0 0 0 0 0 0 0 0 0 0
38 15582.4 0 0 1 0 0 0 0 0 0 0 0 0
39 15802.8 0 0 0 1 0 0 0 0 0 0 0 0
40 14130.5 0 0 0 0 1 0 0 0 0 0 0 0
41 12923.2 0 0 0 0 0 1 0 0 0 0 0 0
42 15612.2 1 0 0 0 0 0 1 0 0 0 0 0
43 16033.7 1 0 0 0 0 0 0 1 0 0 0 0
44 16036.6 1 0 0 0 0 0 0 0 1 0 0 0
45 14037.8 1 0 0 0 0 0 0 0 0 1 0 0
46 15330.6 1 0 0 0 0 0 0 0 0 0 1 0
47 15038.3 1 0 0 0 0 0 0 0 0 0 0 1
48 17401.8 1 0 0 0 0 0 0 0 0 0 0 0
49 14992.5 1 1 0 0 0 0 0 0 0 0 0 0
50 16043.7 1 0 1 0 0 0 0 0 0 0 0 0
51 16929.6 1 0 0 1 0 0 0 0 0 0 0 0
52 15921.3 1 0 0 0 1 0 0 0 0 0 0 0
53 14417.2 1 0 0 0 0 1 0 0 0 0 0 0
54 15961.0 1 0 0 0 0 0 1 0 0 0 0 0
55 17851.9 1 0 0 0 0 0 0 1 0 0 0 0
56 16483.9 1 0 0 0 0 0 0 0 1 0 0 0
57 14215.5 1 0 0 0 0 0 0 0 0 1 0 0
58 17429.7 1 0 0 0 0 0 0 0 0 0 1 0
59 17839.5 1 0 0 0 0 0 0 0 0 0 0 1
60 17629.2 1 0 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) y M1 M2 M3 M4
15270.1 2448.1 -2109.0 -1766.9 -887.4 -2096.5
M5 M6 M7 M8 M9 M10
-3728.9 -1336.1 -1069.0 -1512.5 -2726.4 -2047.8
M11
-1734.4
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2086.29 -766.99 -15.88 588.15 2079.15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15270.1 510.0 29.940 < 2e-16 ***
y 2448.1 313.9 7.799 5.09e-10 ***
M1 -2109.0 701.9 -3.005 0.004253 **
M2 -1766.9 701.9 -2.517 0.015293 *
M3 -887.4 701.9 -1.264 0.212360
M4 -2096.5 701.9 -2.987 0.004466 **
M5 -3728.9 701.9 -5.313 2.90e-06 ***
M6 -1336.1 699.1 -1.911 0.062077 .
M7 -1069.0 699.1 -1.529 0.132929
M8 -1512.5 699.1 -2.164 0.035615 *
M9 -2726.4 699.1 -3.900 0.000305 ***
M10 -2047.8 699.1 -2.929 0.005226 **
M11 -1734.4 699.1 -2.481 0.016738 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1105 on 47 degrees of freedom
Multiple R-squared: 0.7021, Adjusted R-squared: 0.626
F-statistic: 9.229 on 12 and 47 DF, p-value: 8.837e-09
> 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,] 0.6470593 0.7058814 0.3529407
[2,] 0.7429579 0.5140842 0.2570421
[3,] 0.6965941 0.6068118 0.3034059
[4,] 0.6316363 0.7367274 0.3683637
[5,] 0.7343391 0.5313218 0.2656609
[6,] 0.7308522 0.5382955 0.2691478
[7,] 0.7413846 0.5172309 0.2586154
[8,] 0.7362554 0.5274893 0.2637446
[9,] 0.7001410 0.5997180 0.2998590
[10,] 0.7233313 0.5533374 0.2766687
[11,] 0.7690049 0.4619902 0.2309951
[12,] 0.7823737 0.4352525 0.2176263
[13,] 0.7540572 0.4918857 0.2459428
[14,] 0.7864400 0.4271201 0.2135600
[15,] 0.8179486 0.3641029 0.1820514
[16,] 0.8051141 0.3897718 0.1948859
[17,] 0.8181101 0.3637798 0.1818899
[18,] 0.8406024 0.3187951 0.1593976
[19,] 0.8463488 0.3073025 0.1536512
[20,] 0.8362451 0.3275098 0.1637549
[21,] 0.7994428 0.4011144 0.2005572
[22,] 0.7155764 0.5688471 0.2844236
[23,] 0.7501365 0.4997271 0.2498635
[24,] 0.6973760 0.6052480 0.3026240
[25,] 0.6048944 0.7902113 0.3951056
[26,] 0.5192858 0.9614285 0.4807142
[27,] 0.3864883 0.7729766 0.6135117
[28,] 0.3698290 0.7396579 0.6301710
[29,] 0.2344662 0.4689324 0.7655338
> postscript(file="/var/www/html/freestat/rcomp/tmp/1gph11227551246.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/20d021227551246.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3l5qa1227551246.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/48ccx1227551246.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5fuic1227551246.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 = 60
Frequency = 1
1 2 3 4 5 6
-861.114839 -1410.454839 -2001.954839 -976.714839 -2086.294839 -766.009677
7 8 9 10 11 12
-773.249677 -1777.149677 -658.969677 -1530.609677 -1301.949677 -928.749677
13 14 15 16 17 18
-30.414839 -1082.154839 -96.954839 -309.014839 -381.094839 382.190323
19 20 21 22 23 24
187.550323 256.250323 875.230323 -452.709677 -220.249677 62.750323
25 26 27 28 29 30
1081.885161 321.145161 580.145161 29.285161 657.705161 1574.890323
31 32 33 34 35 36
-1.349677 1411.950323 1514.230323 563.890323 612.150323 1271.550323
37 38 39 40 41 42
426.385161 2079.145161 1420.045161 956.885161 1381.905161 -769.935484
43 44 45 46 47 48
-615.575484 -169.175484 -954.095484 -339.835484 -945.575484 -316.475484
49 50 51 52 53 54
-616.740645 92.319355 98.719355 299.559355 427.779355 -421.135484
55 56 57 58 59 60
1202.624516 278.124516 -776.395484 1759.264516 1855.624516 -89.075484
> postscript(file="/var/www/html/freestat/rcomp/tmp/6frso1227551246.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -861.114839 NA
1 -1410.454839 -861.114839
2 -2001.954839 -1410.454839
3 -976.714839 -2001.954839
4 -2086.294839 -976.714839
5 -766.009677 -2086.294839
6 -773.249677 -766.009677
7 -1777.149677 -773.249677
8 -658.969677 -1777.149677
9 -1530.609677 -658.969677
10 -1301.949677 -1530.609677
11 -928.749677 -1301.949677
12 -30.414839 -928.749677
13 -1082.154839 -30.414839
14 -96.954839 -1082.154839
15 -309.014839 -96.954839
16 -381.094839 -309.014839
17 382.190323 -381.094839
18 187.550323 382.190323
19 256.250323 187.550323
20 875.230323 256.250323
21 -452.709677 875.230323
22 -220.249677 -452.709677
23 62.750323 -220.249677
24 1081.885161 62.750323
25 321.145161 1081.885161
26 580.145161 321.145161
27 29.285161 580.145161
28 657.705161 29.285161
29 1574.890323 657.705161
30 -1.349677 1574.890323
31 1411.950323 -1.349677
32 1514.230323 1411.950323
33 563.890323 1514.230323
34 612.150323 563.890323
35 1271.550323 612.150323
36 426.385161 1271.550323
37 2079.145161 426.385161
38 1420.045161 2079.145161
39 956.885161 1420.045161
40 1381.905161 956.885161
41 -769.935484 1381.905161
42 -615.575484 -769.935484
43 -169.175484 -615.575484
44 -954.095484 -169.175484
45 -339.835484 -954.095484
46 -945.575484 -339.835484
47 -316.475484 -945.575484
48 -616.740645 -316.475484
49 92.319355 -616.740645
50 98.719355 92.319355
51 299.559355 98.719355
52 427.779355 299.559355
53 -421.135484 427.779355
54 1202.624516 -421.135484
55 278.124516 1202.624516
56 -776.395484 278.124516
57 1759.264516 -776.395484
58 1855.624516 1759.264516
59 -89.075484 1855.624516
60 NA -89.075484
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1410.454839 -861.114839
[2,] -2001.954839 -1410.454839
[3,] -976.714839 -2001.954839
[4,] -2086.294839 -976.714839
[5,] -766.009677 -2086.294839
[6,] -773.249677 -766.009677
[7,] -1777.149677 -773.249677
[8,] -658.969677 -1777.149677
[9,] -1530.609677 -658.969677
[10,] -1301.949677 -1530.609677
[11,] -928.749677 -1301.949677
[12,] -30.414839 -928.749677
[13,] -1082.154839 -30.414839
[14,] -96.954839 -1082.154839
[15,] -309.014839 -96.954839
[16,] -381.094839 -309.014839
[17,] 382.190323 -381.094839
[18,] 187.550323 382.190323
[19,] 256.250323 187.550323
[20,] 875.230323 256.250323
[21,] -452.709677 875.230323
[22,] -220.249677 -452.709677
[23,] 62.750323 -220.249677
[24,] 1081.885161 62.750323
[25,] 321.145161 1081.885161
[26,] 580.145161 321.145161
[27,] 29.285161 580.145161
[28,] 657.705161 29.285161
[29,] 1574.890323 657.705161
[30,] -1.349677 1574.890323
[31,] 1411.950323 -1.349677
[32,] 1514.230323 1411.950323
[33,] 563.890323 1514.230323
[34,] 612.150323 563.890323
[35,] 1271.550323 612.150323
[36,] 426.385161 1271.550323
[37,] 2079.145161 426.385161
[38,] 1420.045161 2079.145161
[39,] 956.885161 1420.045161
[40,] 1381.905161 956.885161
[41,] -769.935484 1381.905161
[42,] -615.575484 -769.935484
[43,] -169.175484 -615.575484
[44,] -954.095484 -169.175484
[45,] -339.835484 -954.095484
[46,] -945.575484 -339.835484
[47,] -316.475484 -945.575484
[48,] -616.740645 -316.475484
[49,] 92.319355 -616.740645
[50,] 98.719355 92.319355
[51,] 299.559355 98.719355
[52,] 427.779355 299.559355
[53,] -421.135484 427.779355
[54,] 1202.624516 -421.135484
[55,] 278.124516 1202.624516
[56,] -776.395484 278.124516
[57,] 1759.264516 -776.395484
[58,] 1855.624516 1759.264516
[59,] -89.075484 1855.624516
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1410.454839 -861.114839
2 -2001.954839 -1410.454839
3 -976.714839 -2001.954839
4 -2086.294839 -976.714839
5 -766.009677 -2086.294839
6 -773.249677 -766.009677
7 -1777.149677 -773.249677
8 -658.969677 -1777.149677
9 -1530.609677 -658.969677
10 -1301.949677 -1530.609677
11 -928.749677 -1301.949677
12 -30.414839 -928.749677
13 -1082.154839 -30.414839
14 -96.954839 -1082.154839
15 -309.014839 -96.954839
16 -381.094839 -309.014839
17 382.190323 -381.094839
18 187.550323 382.190323
19 256.250323 187.550323
20 875.230323 256.250323
21 -452.709677 875.230323
22 -220.249677 -452.709677
23 62.750323 -220.249677
24 1081.885161 62.750323
25 321.145161 1081.885161
26 580.145161 321.145161
27 29.285161 580.145161
28 657.705161 29.285161
29 1574.890323 657.705161
30 -1.349677 1574.890323
31 1411.950323 -1.349677
32 1514.230323 1411.950323
33 563.890323 1514.230323
34 612.150323 563.890323
35 1271.550323 612.150323
36 426.385161 1271.550323
37 2079.145161 426.385161
38 1420.045161 2079.145161
39 956.885161 1420.045161
40 1381.905161 956.885161
41 -769.935484 1381.905161
42 -615.575484 -769.935484
43 -169.175484 -615.575484
44 -954.095484 -169.175484
45 -339.835484 -954.095484
46 -945.575484 -339.835484
47 -316.475484 -945.575484
48 -616.740645 -316.475484
49 92.319355 -616.740645
50 98.719355 92.319355
51 299.559355 98.719355
52 427.779355 299.559355
53 -421.135484 427.779355
54 1202.624516 -421.135484
55 278.124516 1202.624516
56 -776.395484 278.124516
57 1759.264516 -776.395484
58 1855.624516 1759.264516
59 -89.075484 1855.624516
> 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/freestat/rcomp/tmp/7xhd21227551246.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8g6l61227551246.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9m8ko1227551246.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/109onp1227551246.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11d7s31227551246.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/freestat/rcomp/tmp/128tln1227551246.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/freestat/rcomp/tmp/13rs481227551246.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/freestat/rcomp/tmp/1424341227551246.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/freestat/rcomp/tmp/15f0301227551246.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/freestat/rcomp/tmp/1654pt1227551246.tab")
+ }
>
> system("convert tmp/1gph11227551246.ps tmp/1gph11227551246.png")
> system("convert tmp/20d021227551246.ps tmp/20d021227551246.png")
> system("convert tmp/3l5qa1227551246.ps tmp/3l5qa1227551246.png")
> system("convert tmp/48ccx1227551246.ps tmp/48ccx1227551246.png")
> system("convert tmp/5fuic1227551246.ps tmp/5fuic1227551246.png")
> system("convert tmp/6frso1227551246.ps tmp/6frso1227551246.png")
> system("convert tmp/7xhd21227551246.ps tmp/7xhd21227551246.png")
> system("convert tmp/8g6l61227551246.ps tmp/8g6l61227551246.png")
> system("convert tmp/9m8ko1227551246.ps tmp/9m8ko1227551246.png")
> system("convert tmp/109onp1227551246.ps tmp/109onp1227551246.png")
>
>
> proc.time()
user system elapsed
3.634 2.475 4.060