R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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(-820.8,0,993.3,0,741.7,0,603.6,0,-145.8,0,-35.1,0,395.1,0,523.1,0,462.3,0,183.4,0,791.5,0,344.8,0,-217.0,0,406.7,0,228.6,0,-580.1,0,-1550.4,0,-1447.5,0,-40.1,0,-1033.5,0,-925.6,0,-347.8,0,-447.7,0,-102.6,0,-2062.2,0,-929.7,1,-720.7,1,-1541.8,1,-1432.3,1,-1216.2,1,-212.8,1,-378.2,1,76.9,1,-101.3,1,220.4,1,495.6,1,-1035.2,1,61.8,1,-734.8,1,-6.9,1,-1061.1,1,-854.6,1,-186.5,1,244.0,1,-992.6,1,-335.2,1,316.8,1,477.6,1,-572.1,1,1115.2,1),dim=c(2,50),dimnames=list(c('Totaal','Dummy'),1:50))
> y <- array(NA,dim=c(2,50),dimnames=list(c('Totaal','Dummy'),1:50))
> 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
Totaal Dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 -820.8 0 1 0 0 0 0 0 0 0 0 0 0
2 993.3 0 0 1 0 0 0 0 0 0 0 0 0
3 741.7 0 0 0 1 0 0 0 0 0 0 0 0
4 603.6 0 0 0 0 1 0 0 0 0 0 0 0
5 -145.8 0 0 0 0 0 1 0 0 0 0 0 0
6 -35.1 0 0 0 0 0 0 1 0 0 0 0 0
7 395.1 0 0 0 0 0 0 0 1 0 0 0 0
8 523.1 0 0 0 0 0 0 0 0 1 0 0 0
9 462.3 0 0 0 0 0 0 0 0 0 1 0 0
10 183.4 0 0 0 0 0 0 0 0 0 0 1 0
11 791.5 0 0 0 0 0 0 0 0 0 0 0 1
12 344.8 0 0 0 0 0 0 0 0 0 0 0 0
13 -217.0 0 1 0 0 0 0 0 0 0 0 0 0
14 406.7 0 0 1 0 0 0 0 0 0 0 0 0
15 228.6 0 0 0 1 0 0 0 0 0 0 0 0
16 -580.1 0 0 0 0 1 0 0 0 0 0 0 0
17 -1550.4 0 0 0 0 0 1 0 0 0 0 0 0
18 -1447.5 0 0 0 0 0 0 1 0 0 0 0 0
19 -40.1 0 0 0 0 0 0 0 1 0 0 0 0
20 -1033.5 0 0 0 0 0 0 0 0 1 0 0 0
21 -925.6 0 0 0 0 0 0 0 0 0 1 0 0
22 -347.8 0 0 0 0 0 0 0 0 0 0 1 0
23 -447.7 0 0 0 0 0 0 0 0 0 0 0 1
24 -102.6 0 0 0 0 0 0 0 0 0 0 0 0
25 -2062.2 0 1 0 0 0 0 0 0 0 0 0 0
26 -929.7 1 0 1 0 0 0 0 0 0 0 0 0
27 -720.7 1 0 0 1 0 0 0 0 0 0 0 0
28 -1541.8 1 0 0 0 1 0 0 0 0 0 0 0
29 -1432.3 1 0 0 0 0 1 0 0 0 0 0 0
30 -1216.2 1 0 0 0 0 0 1 0 0 0 0 0
31 -212.8 1 0 0 0 0 0 0 1 0 0 0 0
32 -378.2 1 0 0 0 0 0 0 0 1 0 0 0
33 76.9 1 0 0 0 0 0 0 0 0 1 0 0
34 -101.3 1 0 0 0 0 0 0 0 0 0 1 0
35 220.4 1 0 0 0 0 0 0 0 0 0 0 1
36 495.6 1 0 0 0 0 0 0 0 0 0 0 0
37 -1035.2 1 1 0 0 0 0 0 0 0 0 0 0
38 61.8 1 0 1 0 0 0 0 0 0 0 0 0
39 -734.8 1 0 0 1 0 0 0 0 0 0 0 0
40 -6.9 1 0 0 0 1 0 0 0 0 0 0 0
41 -1061.1 1 0 0 0 0 1 0 0 0 0 0 0
42 -854.6 1 0 0 0 0 0 1 0 0 0 0 0
43 -186.5 1 0 0 0 0 0 0 1 0 0 0 0
44 244.0 1 0 0 0 0 0 0 0 1 0 0 0
45 -992.6 1 0 0 0 0 0 0 0 0 1 0 0
46 -335.2 1 0 0 0 0 0 0 0 0 0 1 0
47 316.8 1 0 0 0 0 0 0 0 0 0 0 1
48 477.6 1 0 0 0 0 0 0 0 0 0 0 0
49 -572.1 1 1 0 0 0 0 0 0 0 0 0 0
50 1115.2 1 0 1 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) Dummy M1 M2 M3 M4
434.75 -261.80 -1271.49 51.79 -425.15 -685.15
M5 M6 M7 M8 M9 M10
-1351.25 -1192.20 -314.92 -465.00 -648.60 -454.07
M11
-83.60
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1225.46 -329.39 39.64 489.06 890.46
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 434.75 328.08 1.325 0.19325
Dummy -261.80 179.25 -1.461 0.15259
M1 -1271.49 423.80 -3.000 0.00481 **
M2 51.79 423.80 0.122 0.90340
M3 -425.15 446.33 -0.953 0.34700
M4 -685.15 446.33 -1.535 0.13327
M5 -1351.25 446.33 -3.027 0.00447 **
M6 -1192.20 446.33 -2.671 0.01117 *
M7 -314.92 446.33 -0.706 0.48486
M8 -465.00 446.33 -1.042 0.30425
M9 -648.60 446.33 -1.453 0.15460
M10 -454.07 446.33 -1.017 0.31559
M11 -83.60 446.33 -0.187 0.85244
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 631.2 on 37 degrees of freedom
Multiple R-squared: 0.4416, Adjusted R-squared: 0.2605
F-statistic: 2.439 on 12 and 37 DF, p-value: 0.01879
> 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,] 0.7740533 0.4518934 0.22594672
[2,] 0.8770343 0.2459315 0.12296574
[3,] 0.9144806 0.1710389 0.08551943
[4,] 0.8857948 0.2284105 0.11420524
[5,] 0.9219903 0.1560194 0.07800970
[6,] 0.9251485 0.1497030 0.07485148
[7,] 0.8987360 0.2025280 0.10126399
[8,] 0.8844566 0.2310868 0.11554340
[9,] 0.8367984 0.3264031 0.16320156
[10,] 0.8529475 0.2941050 0.14705248
[11,] 0.9277632 0.1444736 0.07223681
[12,] 0.8770690 0.2458620 0.12293101
[13,] 0.9520885 0.0958231 0.04791155
[14,] 0.9272338 0.1455324 0.07276622
[15,] 0.8875975 0.2248050 0.11240251
[16,] 0.8127782 0.3744435 0.18722177
[17,] 0.7674401 0.4651198 0.23255988
[18,] 0.8492237 0.3015526 0.15077630
[19,] 0.7252546 0.5494908 0.27474542
> postscript(file="/var/www/html/rcomp/tmp/1d3oj1291325784.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2d3oj1291325784.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/36c541291325784.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/46c541291325784.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/56c541291325784.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 50
Frequency = 1
1 2 3 4 5 6
15.94194 506.76290 732.10242 854.00242 770.70242 722.35242
7 8 9 10 11 12
275.27742 553.35242 676.15242 202.72742 440.35242 -89.94758
13 14 15 16 17 18
619.74194 -79.83710 219.00242 -329.69758 -633.89758 -690.04758
19 20 21 22 23 24
-159.92258 -1003.24758 -711.74758 -328.47258 -798.84758 -537.34758
25 26 27 28 29 30
-1225.45806 -1154.44194 -468.50242 -1029.60242 -254.00242 -196.95242
31 32 33 34 35 36
-70.82742 -86.15242 552.54758 179.82258 131.04758 322.64758
37 38 39 40 41 42
63.33710 -162.94194 -482.60242 505.29758 117.19758 164.64758
43 44 45 46 47 48
-44.52742 536.04758 -516.95242 -54.07742 227.44758 304.64758
49 50
526.43710 890.45806
> postscript(file="/var/www/html/rcomp/tmp/6rdpk1291325785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 50
Frequency = 1
lag(myerror, k = 1) myerror
0 15.94194 NA
1 506.76290 15.94194
2 732.10242 506.76290
3 854.00242 732.10242
4 770.70242 854.00242
5 722.35242 770.70242
6 275.27742 722.35242
7 553.35242 275.27742
8 676.15242 553.35242
9 202.72742 676.15242
10 440.35242 202.72742
11 -89.94758 440.35242
12 619.74194 -89.94758
13 -79.83710 619.74194
14 219.00242 -79.83710
15 -329.69758 219.00242
16 -633.89758 -329.69758
17 -690.04758 -633.89758
18 -159.92258 -690.04758
19 -1003.24758 -159.92258
20 -711.74758 -1003.24758
21 -328.47258 -711.74758
22 -798.84758 -328.47258
23 -537.34758 -798.84758
24 -1225.45806 -537.34758
25 -1154.44194 -1225.45806
26 -468.50242 -1154.44194
27 -1029.60242 -468.50242
28 -254.00242 -1029.60242
29 -196.95242 -254.00242
30 -70.82742 -196.95242
31 -86.15242 -70.82742
32 552.54758 -86.15242
33 179.82258 552.54758
34 131.04758 179.82258
35 322.64758 131.04758
36 63.33710 322.64758
37 -162.94194 63.33710
38 -482.60242 -162.94194
39 505.29758 -482.60242
40 117.19758 505.29758
41 164.64758 117.19758
42 -44.52742 164.64758
43 536.04758 -44.52742
44 -516.95242 536.04758
45 -54.07742 -516.95242
46 227.44758 -54.07742
47 304.64758 227.44758
48 526.43710 304.64758
49 890.45806 526.43710
50 NA 890.45806
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 506.76290 15.94194
[2,] 732.10242 506.76290
[3,] 854.00242 732.10242
[4,] 770.70242 854.00242
[5,] 722.35242 770.70242
[6,] 275.27742 722.35242
[7,] 553.35242 275.27742
[8,] 676.15242 553.35242
[9,] 202.72742 676.15242
[10,] 440.35242 202.72742
[11,] -89.94758 440.35242
[12,] 619.74194 -89.94758
[13,] -79.83710 619.74194
[14,] 219.00242 -79.83710
[15,] -329.69758 219.00242
[16,] -633.89758 -329.69758
[17,] -690.04758 -633.89758
[18,] -159.92258 -690.04758
[19,] -1003.24758 -159.92258
[20,] -711.74758 -1003.24758
[21,] -328.47258 -711.74758
[22,] -798.84758 -328.47258
[23,] -537.34758 -798.84758
[24,] -1225.45806 -537.34758
[25,] -1154.44194 -1225.45806
[26,] -468.50242 -1154.44194
[27,] -1029.60242 -468.50242
[28,] -254.00242 -1029.60242
[29,] -196.95242 -254.00242
[30,] -70.82742 -196.95242
[31,] -86.15242 -70.82742
[32,] 552.54758 -86.15242
[33,] 179.82258 552.54758
[34,] 131.04758 179.82258
[35,] 322.64758 131.04758
[36,] 63.33710 322.64758
[37,] -162.94194 63.33710
[38,] -482.60242 -162.94194
[39,] 505.29758 -482.60242
[40,] 117.19758 505.29758
[41,] 164.64758 117.19758
[42,] -44.52742 164.64758
[43,] 536.04758 -44.52742
[44,] -516.95242 536.04758
[45,] -54.07742 -516.95242
[46,] 227.44758 -54.07742
[47,] 304.64758 227.44758
[48,] 526.43710 304.64758
[49,] 890.45806 526.43710
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 506.76290 15.94194
2 732.10242 506.76290
3 854.00242 732.10242
4 770.70242 854.00242
5 722.35242 770.70242
6 275.27742 722.35242
7 553.35242 275.27742
8 676.15242 553.35242
9 202.72742 676.15242
10 440.35242 202.72742
11 -89.94758 440.35242
12 619.74194 -89.94758
13 -79.83710 619.74194
14 219.00242 -79.83710
15 -329.69758 219.00242
16 -633.89758 -329.69758
17 -690.04758 -633.89758
18 -159.92258 -690.04758
19 -1003.24758 -159.92258
20 -711.74758 -1003.24758
21 -328.47258 -711.74758
22 -798.84758 -328.47258
23 -537.34758 -798.84758
24 -1225.45806 -537.34758
25 -1154.44194 -1225.45806
26 -468.50242 -1154.44194
27 -1029.60242 -468.50242
28 -254.00242 -1029.60242
29 -196.95242 -254.00242
30 -70.82742 -196.95242
31 -86.15242 -70.82742
32 552.54758 -86.15242
33 179.82258 552.54758
34 131.04758 179.82258
35 322.64758 131.04758
36 63.33710 322.64758
37 -162.94194 63.33710
38 -482.60242 -162.94194
39 505.29758 -482.60242
40 117.19758 505.29758
41 164.64758 117.19758
42 -44.52742 164.64758
43 536.04758 -44.52742
44 -516.95242 536.04758
45 -54.07742 -516.95242
46 227.44758 -54.07742
47 304.64758 227.44758
48 526.43710 304.64758
49 890.45806 526.43710
> 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/7jno51291325785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jno51291325785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jno51291325785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ueo81291325785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11xw4w1291325785.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/121fl11291325785.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/13pyid1291325785.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/14iphy1291325785.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/1548f41291325785.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/16ihdv1291325785.tab")
+ }
>
> try(system("convert tmp/1d3oj1291325784.ps tmp/1d3oj1291325784.png",intern=TRUE))
character(0)
> try(system("convert tmp/2d3oj1291325784.ps tmp/2d3oj1291325784.png",intern=TRUE))
character(0)
> try(system("convert tmp/36c541291325784.ps tmp/36c541291325784.png",intern=TRUE))
character(0)
> try(system("convert tmp/46c541291325784.ps tmp/46c541291325784.png",intern=TRUE))
character(0)
> try(system("convert tmp/56c541291325784.ps tmp/56c541291325784.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rdpk1291325785.ps tmp/6rdpk1291325785.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jno51291325785.ps tmp/7jno51291325785.png",intern=TRUE))
character(0)
> try(system("convert tmp/8jno51291325785.ps tmp/8jno51291325785.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jno51291325785.ps tmp/9jno51291325785.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ueo81291325785.ps tmp/10ueo81291325785.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.392 1.621 11.538