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.
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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
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> x <- array(list(101.2,0,100.5,0,98,0,106.6,0,90.1,0,96.9,0,125.9,0,112,0,100,0,123.9,0,79.8,0,83.4,0,113.6,0,112.9,0,104,0,109.9,0,99,0,106.3,0,128.9,0,111.1,0,102.9,0,130,0,87,0,87.5,0,117.6,0,103.4,0,110.8,0,112.6,0,102.5,0,112.4,0,135.6,0,105.1,0,127.7,0,137,0,91,0,90.5,0,122.4,1,123.3,1,124.3,1,120,1,118.1,1,119,1,142.7,1,123.6,1,129.6,1,151.6,1,110.4,1,99.2,1,130.5,1,136.2,1,129.7,1,128,1,121.6,1,135.8,1,143.8,1,147.5,1,136.2,1,156.6,1,123.3,1,100.4,1),dim=c(2,60),dimnames=list(c('y','x'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('y','x'),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
y x M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 101.2 0 1 0 0 0 0 0 0 0 0 0 0 1
2 100.5 0 0 1 0 0 0 0 0 0 0 0 0 2
3 98.0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 106.6 0 0 0 0 1 0 0 0 0 0 0 0 4
5 90.1 0 0 0 0 0 1 0 0 0 0 0 0 5
6 96.9 0 0 0 0 0 0 1 0 0 0 0 0 6
7 125.9 0 0 0 0 0 0 0 1 0 0 0 0 7
8 112.0 0 0 0 0 0 0 0 0 1 0 0 0 8
9 100.0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 123.9 0 0 0 0 0 0 0 0 0 0 1 0 10
11 79.8 0 0 0 0 0 0 0 0 0 0 0 1 11
12 83.4 0 0 0 0 0 0 0 0 0 0 0 0 12
13 113.6 0 1 0 0 0 0 0 0 0 0 0 0 13
14 112.9 0 0 1 0 0 0 0 0 0 0 0 0 14
15 104.0 0 0 0 1 0 0 0 0 0 0 0 0 15
16 109.9 0 0 0 0 1 0 0 0 0 0 0 0 16
17 99.0 0 0 0 0 0 1 0 0 0 0 0 0 17
18 106.3 0 0 0 0 0 0 1 0 0 0 0 0 18
19 128.9 0 0 0 0 0 0 0 1 0 0 0 0 19
20 111.1 0 0 0 0 0 0 0 0 1 0 0 0 20
21 102.9 0 0 0 0 0 0 0 0 0 1 0 0 21
22 130.0 0 0 0 0 0 0 0 0 0 0 1 0 22
23 87.0 0 0 0 0 0 0 0 0 0 0 0 1 23
24 87.5 0 0 0 0 0 0 0 0 0 0 0 0 24
25 117.6 0 1 0 0 0 0 0 0 0 0 0 0 25
26 103.4 0 0 1 0 0 0 0 0 0 0 0 0 26
27 110.8 0 0 0 1 0 0 0 0 0 0 0 0 27
28 112.6 0 0 0 0 1 0 0 0 0 0 0 0 28
29 102.5 0 0 0 0 0 1 0 0 0 0 0 0 29
30 112.4 0 0 0 0 0 0 1 0 0 0 0 0 30
31 135.6 0 0 0 0 0 0 0 1 0 0 0 0 31
32 105.1 0 0 0 0 0 0 0 0 1 0 0 0 32
33 127.7 0 0 0 0 0 0 0 0 0 1 0 0 33
34 137.0 0 0 0 0 0 0 0 0 0 0 1 0 34
35 91.0 0 0 0 0 0 0 0 0 0 0 0 1 35
36 90.5 0 0 0 0 0 0 0 0 0 0 0 0 36
37 122.4 1 1 0 0 0 0 0 0 0 0 0 0 37
38 123.3 1 0 1 0 0 0 0 0 0 0 0 0 38
39 124.3 1 0 0 1 0 0 0 0 0 0 0 0 39
40 120.0 1 0 0 0 1 0 0 0 0 0 0 0 40
41 118.1 1 0 0 0 0 1 0 0 0 0 0 0 41
42 119.0 1 0 0 0 0 0 1 0 0 0 0 0 42
43 142.7 1 0 0 0 0 0 0 1 0 0 0 0 43
44 123.6 1 0 0 0 0 0 0 0 1 0 0 0 44
45 129.6 1 0 0 0 0 0 0 0 0 1 0 0 45
46 151.6 1 0 0 0 0 0 0 0 0 0 1 0 46
47 110.4 1 0 0 0 0 0 0 0 0 0 0 1 47
48 99.2 1 0 0 0 0 0 0 0 0 0 0 0 48
49 130.5 1 1 0 0 0 0 0 0 0 0 0 0 49
50 136.2 1 0 1 0 0 0 0 0 0 0 0 0 50
51 129.7 1 0 0 1 0 0 0 0 0 0 0 0 51
52 128.0 1 0 0 0 1 0 0 0 0 0 0 0 52
53 121.6 1 0 0 0 0 1 0 0 0 0 0 0 53
54 135.8 1 0 0 0 0 0 1 0 0 0 0 0 54
55 143.8 1 0 0 0 0 0 0 1 0 0 0 0 55
56 147.5 1 0 0 0 0 0 0 0 1 0 0 0 56
57 136.2 1 0 0 0 0 0 0 0 0 1 0 0 57
58 156.6 1 0 0 0 0 0 0 0 0 0 1 0 58
59 123.3 1 0 0 0 0 0 0 0 0 0 0 1 59
60 100.4 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) x M1 M2 M3 M4
71.7922 5.8694 30.3783 28.0767 25.6750 27.2333
M5 M6 M7 M8 M9 M10
17.5717 24.8900 45.6883 29.6667 28.5850 48.6233
M11 t
6.6017 0.5017
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.4122 -2.6043 -0.2969 2.5607 12.0783
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 71.79222 3.12868 22.947 < 2e-16 ***
x 5.86944 2.80964 2.089 0.0423 *
M1 30.37833 3.48761 8.710 2.71e-11 ***
M2 28.07667 3.46775 8.097 2.13e-10 ***
M3 25.67500 3.44968 7.443 1.98e-09 ***
M4 27.23333 3.43343 7.932 3.73e-10 ***
M5 17.57167 3.41903 5.139 5.50e-06 ***
M6 24.89000 3.40650 7.307 3.17e-09 ***
M7 45.68833 3.39586 13.454 < 2e-16 ***
M8 29.66667 3.38714 8.759 2.31e-11 ***
M9 28.58500 3.38033 8.456 6.34e-11 ***
M10 48.62333 3.37546 14.405 < 2e-16 ***
M11 6.60167 3.37254 1.957 0.0564 .
t 0.50167 0.08111 6.185 1.53e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.331 on 46 degrees of freedom
Multiple R-squared: 0.9292, Adjusted R-squared: 0.9092
F-statistic: 46.43 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,] 0.19830288 0.39660576 0.8016971
[2,] 0.08905284 0.17810568 0.9109472
[3,] 0.08052589 0.16105178 0.9194741
[4,] 0.12708598 0.25417196 0.8729140
[5,] 0.09828074 0.19656149 0.9017193
[6,] 0.05149971 0.10299942 0.9485003
[7,] 0.02622371 0.05244741 0.9737763
[8,] 0.01836127 0.03672254 0.9816387
[9,] 0.01041662 0.02083324 0.9895834
[10,] 0.09925871 0.19851741 0.9007413
[11,] 0.06339998 0.12679995 0.9366000
[12,] 0.04480419 0.08960838 0.9551958
[13,] 0.02514804 0.05029608 0.9748520
[14,] 0.01560380 0.03120761 0.9843962
[15,] 0.01250962 0.02501924 0.9874904
[16,] 0.15353858 0.30707716 0.8464614
[17,] 0.57318657 0.85362685 0.4268134
[18,] 0.47175571 0.94351141 0.5282443
[19,] 0.59534102 0.80931796 0.4046590
[20,] 0.49401556 0.98803113 0.5059844
[21,] 0.38392464 0.76784928 0.6160754
[22,] 0.31682523 0.63365046 0.6831748
[23,] 0.24310442 0.48620884 0.7568956
[24,] 0.17189775 0.34379549 0.8281023
[25,] 0.13566975 0.27133950 0.8643303
[26,] 0.11190422 0.22380844 0.8880958
[27,] 0.09348133 0.18696265 0.9065187
> postscript(file="/var/www/html/rcomp/tmp/1bplc1227776368.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2glcu1227776368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3knxw1227776368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43uzb1227776368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wa811227776368.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
-1.4722222 -0.3722222 -0.9722222 5.5677778 -1.7722222 -2.7922222
7 8 9 10 11 12
4.9077778 6.5277778 -4.8922222 -1.5322222 -4.1122222 5.5877778
13 14 15 16 17 18
4.9077778 6.0077778 -0.9922222 2.8477778 1.1077778 0.5877778
19 20 21 22 23 24
1.8877778 -0.3922222 -8.0122222 -1.4522222 -2.9322222 3.6677778
25 26 27 28 29 30
2.8877778 -9.5122222 -0.2122222 -0.4722222 -1.4122222 0.6677778
31 32 33 34 35 36
2.5677778 -12.4122222 10.7677778 -0.4722222 -4.9522222 0.6477778
37 38 39 40 41 42
-4.2016667 -1.5016667 1.3983333 -4.9616667 2.2983333 -4.6216667
43 44 45 46 47 48
-2.2216667 -5.8016667 0.7783333 2.2383333 2.5583333 -2.5416667
49 50 51 52 53 54
-2.1216667 5.3783333 0.7783333 -2.9816667 -0.2216667 6.1583333
55 56 57 58 59 60
-7.1416667 12.0783333 1.3583333 1.2183333 9.4383333 -7.3616667
> postscript(file="/var/www/html/rcomp/tmp/68zws1227776368.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 -1.4722222 NA
1 -0.3722222 -1.4722222
2 -0.9722222 -0.3722222
3 5.5677778 -0.9722222
4 -1.7722222 5.5677778
5 -2.7922222 -1.7722222
6 4.9077778 -2.7922222
7 6.5277778 4.9077778
8 -4.8922222 6.5277778
9 -1.5322222 -4.8922222
10 -4.1122222 -1.5322222
11 5.5877778 -4.1122222
12 4.9077778 5.5877778
13 6.0077778 4.9077778
14 -0.9922222 6.0077778
15 2.8477778 -0.9922222
16 1.1077778 2.8477778
17 0.5877778 1.1077778
18 1.8877778 0.5877778
19 -0.3922222 1.8877778
20 -8.0122222 -0.3922222
21 -1.4522222 -8.0122222
22 -2.9322222 -1.4522222
23 3.6677778 -2.9322222
24 2.8877778 3.6677778
25 -9.5122222 2.8877778
26 -0.2122222 -9.5122222
27 -0.4722222 -0.2122222
28 -1.4122222 -0.4722222
29 0.6677778 -1.4122222
30 2.5677778 0.6677778
31 -12.4122222 2.5677778
32 10.7677778 -12.4122222
33 -0.4722222 10.7677778
34 -4.9522222 -0.4722222
35 0.6477778 -4.9522222
36 -4.2016667 0.6477778
37 -1.5016667 -4.2016667
38 1.3983333 -1.5016667
39 -4.9616667 1.3983333
40 2.2983333 -4.9616667
41 -4.6216667 2.2983333
42 -2.2216667 -4.6216667
43 -5.8016667 -2.2216667
44 0.7783333 -5.8016667
45 2.2383333 0.7783333
46 2.5583333 2.2383333
47 -2.5416667 2.5583333
48 -2.1216667 -2.5416667
49 5.3783333 -2.1216667
50 0.7783333 5.3783333
51 -2.9816667 0.7783333
52 -0.2216667 -2.9816667
53 6.1583333 -0.2216667
54 -7.1416667 6.1583333
55 12.0783333 -7.1416667
56 1.3583333 12.0783333
57 1.2183333 1.3583333
58 9.4383333 1.2183333
59 -7.3616667 9.4383333
60 NA -7.3616667
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.3722222 -1.4722222
[2,] -0.9722222 -0.3722222
[3,] 5.5677778 -0.9722222
[4,] -1.7722222 5.5677778
[5,] -2.7922222 -1.7722222
[6,] 4.9077778 -2.7922222
[7,] 6.5277778 4.9077778
[8,] -4.8922222 6.5277778
[9,] -1.5322222 -4.8922222
[10,] -4.1122222 -1.5322222
[11,] 5.5877778 -4.1122222
[12,] 4.9077778 5.5877778
[13,] 6.0077778 4.9077778
[14,] -0.9922222 6.0077778
[15,] 2.8477778 -0.9922222
[16,] 1.1077778 2.8477778
[17,] 0.5877778 1.1077778
[18,] 1.8877778 0.5877778
[19,] -0.3922222 1.8877778
[20,] -8.0122222 -0.3922222
[21,] -1.4522222 -8.0122222
[22,] -2.9322222 -1.4522222
[23,] 3.6677778 -2.9322222
[24,] 2.8877778 3.6677778
[25,] -9.5122222 2.8877778
[26,] -0.2122222 -9.5122222
[27,] -0.4722222 -0.2122222
[28,] -1.4122222 -0.4722222
[29,] 0.6677778 -1.4122222
[30,] 2.5677778 0.6677778
[31,] -12.4122222 2.5677778
[32,] 10.7677778 -12.4122222
[33,] -0.4722222 10.7677778
[34,] -4.9522222 -0.4722222
[35,] 0.6477778 -4.9522222
[36,] -4.2016667 0.6477778
[37,] -1.5016667 -4.2016667
[38,] 1.3983333 -1.5016667
[39,] -4.9616667 1.3983333
[40,] 2.2983333 -4.9616667
[41,] -4.6216667 2.2983333
[42,] -2.2216667 -4.6216667
[43,] -5.8016667 -2.2216667
[44,] 0.7783333 -5.8016667
[45,] 2.2383333 0.7783333
[46,] 2.5583333 2.2383333
[47,] -2.5416667 2.5583333
[48,] -2.1216667 -2.5416667
[49,] 5.3783333 -2.1216667
[50,] 0.7783333 5.3783333
[51,] -2.9816667 0.7783333
[52,] -0.2216667 -2.9816667
[53,] 6.1583333 -0.2216667
[54,] -7.1416667 6.1583333
[55,] 12.0783333 -7.1416667
[56,] 1.3583333 12.0783333
[57,] 1.2183333 1.3583333
[58,] 9.4383333 1.2183333
[59,] -7.3616667 9.4383333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.3722222 -1.4722222
2 -0.9722222 -0.3722222
3 5.5677778 -0.9722222
4 -1.7722222 5.5677778
5 -2.7922222 -1.7722222
6 4.9077778 -2.7922222
7 6.5277778 4.9077778
8 -4.8922222 6.5277778
9 -1.5322222 -4.8922222
10 -4.1122222 -1.5322222
11 5.5877778 -4.1122222
12 4.9077778 5.5877778
13 6.0077778 4.9077778
14 -0.9922222 6.0077778
15 2.8477778 -0.9922222
16 1.1077778 2.8477778
17 0.5877778 1.1077778
18 1.8877778 0.5877778
19 -0.3922222 1.8877778
20 -8.0122222 -0.3922222
21 -1.4522222 -8.0122222
22 -2.9322222 -1.4522222
23 3.6677778 -2.9322222
24 2.8877778 3.6677778
25 -9.5122222 2.8877778
26 -0.2122222 -9.5122222
27 -0.4722222 -0.2122222
28 -1.4122222 -0.4722222
29 0.6677778 -1.4122222
30 2.5677778 0.6677778
31 -12.4122222 2.5677778
32 10.7677778 -12.4122222
33 -0.4722222 10.7677778
34 -4.9522222 -0.4722222
35 0.6477778 -4.9522222
36 -4.2016667 0.6477778
37 -1.5016667 -4.2016667
38 1.3983333 -1.5016667
39 -4.9616667 1.3983333
40 2.2983333 -4.9616667
41 -4.6216667 2.2983333
42 -2.2216667 -4.6216667
43 -5.8016667 -2.2216667
44 0.7783333 -5.8016667
45 2.2383333 0.7783333
46 2.5583333 2.2383333
47 -2.5416667 2.5583333
48 -2.1216667 -2.5416667
49 5.3783333 -2.1216667
50 0.7783333 5.3783333
51 -2.9816667 0.7783333
52 -0.2216667 -2.9816667
53 6.1583333 -0.2216667
54 -7.1416667 6.1583333
55 12.0783333 -7.1416667
56 1.3583333 12.0783333
57 1.2183333 1.3583333
58 9.4383333 1.2183333
59 -7.3616667 9.4383333
> 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/73x7u1227776368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/83fhn1227776368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99dzj1227776368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1008vk1227776368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116a5y1227776368.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/12w9nx1227776368.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/13gztw1227776368.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/14nv1v1227776368.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/15np0n1227776369.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/16tmes1227776369.tab")
+ }
>
> system("convert tmp/1bplc1227776368.ps tmp/1bplc1227776368.png")
> system("convert tmp/2glcu1227776368.ps tmp/2glcu1227776368.png")
> system("convert tmp/3knxw1227776368.ps tmp/3knxw1227776368.png")
> system("convert tmp/43uzb1227776368.ps tmp/43uzb1227776368.png")
> system("convert tmp/5wa811227776368.ps tmp/5wa811227776368.png")
> system("convert tmp/68zws1227776368.ps tmp/68zws1227776368.png")
> system("convert tmp/73x7u1227776368.ps tmp/73x7u1227776368.png")
> system("convert tmp/83fhn1227776368.ps tmp/83fhn1227776368.png")
> system("convert tmp/99dzj1227776368.ps tmp/99dzj1227776368.png")
> system("convert tmp/1008vk1227776368.ps tmp/1008vk1227776368.png")
>
>
> proc.time()
user system elapsed
2.628 1.710 3.297