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 = '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 t
1 12300.0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 12092.8 0 0 1 0 0 0 0 0 0 0 0 0 2
3 12380.8 0 0 0 1 0 0 0 0 0 0 0 0 3
4 12196.9 0 0 0 0 1 0 0 0 0 0 0 0 4
5 9455.0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 13168.0 0 0 0 0 0 0 1 0 0 0 0 0 6
7 13427.9 0 0 0 0 0 0 0 1 0 0 0 0 7
8 11980.5 0 0 0 0 0 0 0 0 1 0 0 0 8
9 11884.8 0 0 0 0 0 0 0 0 0 1 0 0 9
10 11691.7 0 0 0 0 0 0 0 0 0 0 1 0 10
11 12233.8 0 0 0 0 0 0 0 0 0 0 0 1 11
12 14341.4 0 0 0 0 0 0 0 0 0 0 0 0 12
13 13130.7 0 1 0 0 0 0 0 0 0 0 0 0 13
14 12421.1 0 0 1 0 0 0 0 0 0 0 0 0 14
15 14285.8 0 0 0 1 0 0 0 0 0 0 0 0 15
16 12864.6 0 0 0 0 1 0 0 0 0 0 0 0 16
17 11160.2 0 0 0 0 0 1 0 0 0 0 0 0 17
18 14316.2 0 0 0 0 0 0 1 0 0 0 0 0 18
19 14388.7 0 0 0 0 0 0 0 1 0 0 0 0 19
20 14013.9 0 0 0 0 0 0 0 0 1 0 0 0 20
21 13419.0 0 0 0 0 0 0 0 0 0 1 0 0 21
22 12769.6 0 0 0 0 0 0 0 0 0 0 1 0 22
23 13315.5 0 0 0 0 0 0 0 0 0 0 0 1 23
24 15332.9 0 0 0 0 0 0 0 0 0 0 0 0 24
25 14243.0 0 1 0 0 0 0 0 0 0 0 0 0 25
26 13824.4 0 0 1 0 0 0 0 0 0 0 0 0 26
27 14962.9 0 0 0 1 0 0 0 0 0 0 0 0 27
28 13202.9 0 0 0 0 1 0 0 0 0 0 0 0 28
29 12199.0 0 0 0 0 0 1 0 0 0 0 0 0 29
30 15508.9 0 0 0 0 0 0 1 0 0 0 0 0 30
31 14199.8 0 0 0 0 0 0 0 1 0 0 0 0 31
32 15169.6 0 0 0 0 0 0 0 0 1 0 0 0 32
33 14058.0 0 0 0 0 0 0 0 0 0 1 0 0 33
34 13786.2 0 0 0 0 0 0 0 0 0 0 1 0 34
35 14147.9 0 0 0 0 0 0 0 0 0 0 0 1 35
36 16541.7 0 0 0 0 0 0 0 0 0 0 0 0 36
37 13587.5 0 1 0 0 0 0 0 0 0 0 0 0 37
38 15582.4 0 0 1 0 0 0 0 0 0 0 0 0 38
39 15802.8 0 0 0 1 0 0 0 0 0 0 0 0 39
40 14130.5 0 0 0 0 1 0 0 0 0 0 0 0 40
41 12923.2 0 0 0 0 0 1 0 0 0 0 0 0 41
42 15612.2 1 0 0 0 0 0 1 0 0 0 0 0 42
43 16033.7 1 0 0 0 0 0 0 1 0 0 0 0 43
44 16036.6 1 0 0 0 0 0 0 0 1 0 0 0 44
45 14037.8 1 0 0 0 0 0 0 0 0 1 0 0 45
46 15330.6 1 0 0 0 0 0 0 0 0 0 1 0 46
47 15038.3 1 0 0 0 0 0 0 0 0 0 0 1 47
48 17401.8 1 0 0 0 0 0 0 0 0 0 0 0 48
49 14992.5 1 1 0 0 0 0 0 0 0 0 0 0 49
50 16043.7 1 0 1 0 0 0 0 0 0 0 0 0 50
51 16929.6 1 0 0 1 0 0 0 0 0 0 0 0 51
52 15921.3 1 0 0 0 1 0 0 0 0 0 0 0 52
53 14417.2 1 0 0 0 0 1 0 0 0 0 0 0 53
54 15961.0 1 0 0 0 0 0 1 0 0 0 0 0 54
55 17851.9 1 0 0 0 0 0 0 1 0 0 0 0 55
56 16483.9 1 0 0 0 0 0 0 0 1 0 0 0 56
57 14215.5 1 0 0 0 0 0 0 0 0 1 0 0 57
58 17429.7 1 0 0 0 0 0 0 0 0 0 1 0 58
59 17839.5 1 0 0 0 0 0 0 0 0 0 0 1 59
60 17629.2 1 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) y M1 M2 M3 M4
13325.20 16.94 -1703.84 -1442.74 -644.28 -1934.46
M5 M6 M7 M8 M9 M10
-3647.82 -849.90 -663.80 -1188.34 -2483.26 -1885.76
M11 t
-1653.36 81.04
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1262.63 -453.77 93.11 333.34 1369.39
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13325.204 339.128 39.293 < 2e-16 ***
y 16.943 292.055 0.058 0.953988
M1 -1703.838 390.609 -4.362 7.20e-05 ***
M2 -1442.737 389.903 -3.700 0.000574 ***
M3 -644.277 389.353 -1.655 0.104787
M4 -1934.456 388.960 -4.973 9.61e-06 ***
M5 -3647.815 388.724 -9.384 2.95e-12 ***
M6 -849.904 389.923 -2.180 0.034438 *
M7 -663.803 389.059 -1.706 0.094719 .
M8 -1188.342 388.350 -3.060 0.003687 **
M9 -2483.262 387.798 -6.403 7.18e-08 ***
M10 -1885.761 387.404 -4.868 1.37e-05 ***
M11 -1653.361 387.167 -4.270 9.67e-05 ***
t 81.039 7.824 10.358 1.31e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 612 on 46 degrees of freedom
Multiple R-squared: 0.9106, Adjusted R-squared: 0.8853
F-statistic: 36.04 on 13 and 46 DF, p-value: < 2.2e-16
> 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,] 0.442251436 0.88450287 0.5577486
[2,] 0.277006390 0.55401278 0.7229936
[3,] 0.159072779 0.31814556 0.8409272
[4,] 0.170697679 0.34139536 0.8293023
[5,] 0.134034607 0.26806921 0.8659654
[6,] 0.083142550 0.16628510 0.9168575
[7,] 0.046280094 0.09256019 0.9537199
[8,] 0.024432639 0.04886528 0.9755674
[9,] 0.022332710 0.04466542 0.9776673
[10,] 0.011780020 0.02356004 0.9882200
[11,] 0.005502995 0.01100599 0.9944970
[12,] 0.012541969 0.02508394 0.9874580
[13,] 0.006971655 0.01394331 0.9930283
[14,] 0.006929885 0.01385977 0.9930701
[15,] 0.037679077 0.07535815 0.9623209
[16,] 0.033466252 0.06693250 0.9665337
[17,] 0.072199783 0.14439957 0.9278002
[18,] 0.059852960 0.11970592 0.9401470
[19,] 0.044436082 0.08887216 0.9555639
[20,] 0.037047433 0.07409487 0.9629526
[21,] 0.110616733 0.22123347 0.8893833
[22,] 0.130114196 0.26022839 0.8698858
[23,] 0.085112923 0.17022585 0.9148871
[24,] 0.055428248 0.11085650 0.9445718
[25,] 0.028106453 0.05621291 0.9718935
[26,] 0.019715549 0.03943110 0.9802845
[27,] 0.011581718 0.02316344 0.9884183
> postscript(file="/var/www/html/rcomp/tmp/1ra9y1227551398.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2hytu1227551398.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3cg461227551398.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xpl61227551398.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52jje1227551398.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
597.59457 48.25457 -543.24543 481.99457 -627.58543 206.46326
7 8 9 10 11 12
199.22326 -804.67674 313.50326 -558.13674 -329.47674 43.72326
13 14 15 16 17 18
455.82163 -595.91837 389.28163 177.22163 105.14163 382.19032
19 20 21 22 23 24
187.55032 256.25032 875.23032 -452.70968 -220.24968 62.75032
25 26 27 28 29 30
595.64869 -165.09131 93.90869 -456.95131 171.46869 602.41738
31 32 33 34 35 36
-973.82262 439.47738 541.75738 -408.58262 -360.32262 299.07738
37 38 39 40 41 42
-1032.32425 620.43575 -38.66425 -501.82425 -76.80425 -283.69901
43 44 45 46 47 48
-129.33901 317.06099 -467.85901 146.40099 -459.33901 169.76099
49 50 51 52 53 54
-616.74065 92.31935 98.71935 299.55935 427.77935 -907.37195
55 56 57 58 59 60
716.38805 -208.11195 -1262.63195 1273.02805 1369.38805 -575.31195
> postscript(file="/var/www/html/rcomp/tmp/6z4j51227551398.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
0 597.59457 NA
1 48.25457 597.59457
2 -543.24543 48.25457
3 481.99457 -543.24543
4 -627.58543 481.99457
5 206.46326 -627.58543
6 199.22326 206.46326
7 -804.67674 199.22326
8 313.50326 -804.67674
9 -558.13674 313.50326
10 -329.47674 -558.13674
11 43.72326 -329.47674
12 455.82163 43.72326
13 -595.91837 455.82163
14 389.28163 -595.91837
15 177.22163 389.28163
16 105.14163 177.22163
17 382.19032 105.14163
18 187.55032 382.19032
19 256.25032 187.55032
20 875.23032 256.25032
21 -452.70968 875.23032
22 -220.24968 -452.70968
23 62.75032 -220.24968
24 595.64869 62.75032
25 -165.09131 595.64869
26 93.90869 -165.09131
27 -456.95131 93.90869
28 171.46869 -456.95131
29 602.41738 171.46869
30 -973.82262 602.41738
31 439.47738 -973.82262
32 541.75738 439.47738
33 -408.58262 541.75738
34 -360.32262 -408.58262
35 299.07738 -360.32262
36 -1032.32425 299.07738
37 620.43575 -1032.32425
38 -38.66425 620.43575
39 -501.82425 -38.66425
40 -76.80425 -501.82425
41 -283.69901 -76.80425
42 -129.33901 -283.69901
43 317.06099 -129.33901
44 -467.85901 317.06099
45 146.40099 -467.85901
46 -459.33901 146.40099
47 169.76099 -459.33901
48 -616.74065 169.76099
49 92.31935 -616.74065
50 98.71935 92.31935
51 299.55935 98.71935
52 427.77935 299.55935
53 -907.37195 427.77935
54 716.38805 -907.37195
55 -208.11195 716.38805
56 -1262.63195 -208.11195
57 1273.02805 -1262.63195
58 1369.38805 1273.02805
59 -575.31195 1369.38805
60 NA -575.31195
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 48.25457 597.59457
[2,] -543.24543 48.25457
[3,] 481.99457 -543.24543
[4,] -627.58543 481.99457
[5,] 206.46326 -627.58543
[6,] 199.22326 206.46326
[7,] -804.67674 199.22326
[8,] 313.50326 -804.67674
[9,] -558.13674 313.50326
[10,] -329.47674 -558.13674
[11,] 43.72326 -329.47674
[12,] 455.82163 43.72326
[13,] -595.91837 455.82163
[14,] 389.28163 -595.91837
[15,] 177.22163 389.28163
[16,] 105.14163 177.22163
[17,] 382.19032 105.14163
[18,] 187.55032 382.19032
[19,] 256.25032 187.55032
[20,] 875.23032 256.25032
[21,] -452.70968 875.23032
[22,] -220.24968 -452.70968
[23,] 62.75032 -220.24968
[24,] 595.64869 62.75032
[25,] -165.09131 595.64869
[26,] 93.90869 -165.09131
[27,] -456.95131 93.90869
[28,] 171.46869 -456.95131
[29,] 602.41738 171.46869
[30,] -973.82262 602.41738
[31,] 439.47738 -973.82262
[32,] 541.75738 439.47738
[33,] -408.58262 541.75738
[34,] -360.32262 -408.58262
[35,] 299.07738 -360.32262
[36,] -1032.32425 299.07738
[37,] 620.43575 -1032.32425
[38,] -38.66425 620.43575
[39,] -501.82425 -38.66425
[40,] -76.80425 -501.82425
[41,] -283.69901 -76.80425
[42,] -129.33901 -283.69901
[43,] 317.06099 -129.33901
[44,] -467.85901 317.06099
[45,] 146.40099 -467.85901
[46,] -459.33901 146.40099
[47,] 169.76099 -459.33901
[48,] -616.74065 169.76099
[49,] 92.31935 -616.74065
[50,] 98.71935 92.31935
[51,] 299.55935 98.71935
[52,] 427.77935 299.55935
[53,] -907.37195 427.77935
[54,] 716.38805 -907.37195
[55,] -208.11195 716.38805
[56,] -1262.63195 -208.11195
[57,] 1273.02805 -1262.63195
[58,] 1369.38805 1273.02805
[59,] -575.31195 1369.38805
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 48.25457 597.59457
2 -543.24543 48.25457
3 481.99457 -543.24543
4 -627.58543 481.99457
5 206.46326 -627.58543
6 199.22326 206.46326
7 -804.67674 199.22326
8 313.50326 -804.67674
9 -558.13674 313.50326
10 -329.47674 -558.13674
11 43.72326 -329.47674
12 455.82163 43.72326
13 -595.91837 455.82163
14 389.28163 -595.91837
15 177.22163 389.28163
16 105.14163 177.22163
17 382.19032 105.14163
18 187.55032 382.19032
19 256.25032 187.55032
20 875.23032 256.25032
21 -452.70968 875.23032
22 -220.24968 -452.70968
23 62.75032 -220.24968
24 595.64869 62.75032
25 -165.09131 595.64869
26 93.90869 -165.09131
27 -456.95131 93.90869
28 171.46869 -456.95131
29 602.41738 171.46869
30 -973.82262 602.41738
31 439.47738 -973.82262
32 541.75738 439.47738
33 -408.58262 541.75738
34 -360.32262 -408.58262
35 299.07738 -360.32262
36 -1032.32425 299.07738
37 620.43575 -1032.32425
38 -38.66425 620.43575
39 -501.82425 -38.66425
40 -76.80425 -501.82425
41 -283.69901 -76.80425
42 -129.33901 -283.69901
43 317.06099 -129.33901
44 -467.85901 317.06099
45 146.40099 -467.85901
46 -459.33901 146.40099
47 169.76099 -459.33901
48 -616.74065 169.76099
49 92.31935 -616.74065
50 98.71935 92.31935
51 299.55935 98.71935
52 427.77935 299.55935
53 -907.37195 427.77935
54 716.38805 -907.37195
55 -208.11195 716.38805
56 -1262.63195 -208.11195
57 1273.02805 -1262.63195
58 1369.38805 1273.02805
59 -575.31195 1369.38805
> 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/78n541227551398.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8docv1227551398.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9fn5s1227551398.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ywyh1227551398.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/115aay1227551398.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/1276dt1227551398.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/133qyp1227551398.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/14tlhu1227551398.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/155mom1227551398.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/16ieva1227551398.tab")
+ }
>
> system("convert tmp/1ra9y1227551398.ps tmp/1ra9y1227551398.png")
> system("convert tmp/2hytu1227551398.ps tmp/2hytu1227551398.png")
> system("convert tmp/3cg461227551398.ps tmp/3cg461227551398.png")
> system("convert tmp/4xpl61227551398.ps tmp/4xpl61227551398.png")
> system("convert tmp/52jje1227551398.ps tmp/52jje1227551398.png")
> system("convert tmp/6z4j51227551398.ps tmp/6z4j51227551398.png")
> system("convert tmp/78n541227551398.ps tmp/78n541227551398.png")
> system("convert tmp/8docv1227551398.ps tmp/8docv1227551398.png")
> system("convert tmp/9fn5s1227551398.ps tmp/9fn5s1227551398.png")
> system("convert tmp/10ywyh1227551398.ps tmp/10ywyh1227551398.png")
>
>
> proc.time()
user system elapsed
2.415 1.588 3.971