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(3016,0,2155,0,2172,0,2150,0,2533,0,2058,0,2160,0,2260,0,2498,0,2695,0,2799,0,2946,0,2930,0,2318,0,2540,0,2570,0,2669,0,2450,0,2842,0,3440,0,2678,0,2981,0,2260,0,2844,0,2546,0,2456,0,2295,0,2379,0,2479,0,2057,0,2280,0,2351,0,2276,0,2548,1,2311,1,2201,1,2725,1,2408,1,2139,1,1898,1,2537,1,2068,1,2063,1,2520,1,2434,1,2190,1,2794,1,2070,1,2615,1,2265,1,2139,1,2428,1,2137,1,1823,1,2063,1,1806,1,1758,1,2243,1,1993,1,1932,1,2465,1),dim=c(2,61),dimnames=list(c('y','x'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('y','x'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
y x
1 3016 0
2 2155 0
3 2172 0
4 2150 0
5 2533 0
6 2058 0
7 2160 0
8 2260 0
9 2498 0
10 2695 0
11 2799 0
12 2946 0
13 2930 0
14 2318 0
15 2540 0
16 2570 0
17 2669 0
18 2450 0
19 2842 0
20 3440 0
21 2678 0
22 2981 0
23 2260 0
24 2844 0
25 2546 0
26 2456 0
27 2295 0
28 2379 0
29 2479 0
30 2057 0
31 2280 0
32 2351 0
33 2276 0
34 2548 1
35 2311 1
36 2201 1
37 2725 1
38 2408 1
39 2139 1
40 1898 1
41 2537 1
42 2068 1
43 2063 1
44 2520 1
45 2434 1
46 2190 1
47 2794 1
48 2070 1
49 2615 1
50 2265 1
51 2139 1
52 2428 1
53 2137 1
54 1823 1
55 2063 1
56 1806 1
57 1758 1
58 2243 1
59 1993 1
60 1932 1
61 2465 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x
2517.7 -282.9
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-476.75 -237.67 -38.67 199.25 922.33
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2517.67 53.14 47.374 < 2e-16 ***
x -282.92 78.44 -3.607 0.000639 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 305.3 on 59 degrees of freedom
Multiple R-squared: 0.1807, Adjusted R-squared: 0.1668
F-statistic: 13.01 on 1 and 59 DF, p-value: 0.0006392
> 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,] 0.8915952 0.21680951 0.10840475
[2,] 0.8809719 0.23805626 0.11902813
[3,] 0.8341074 0.33178528 0.16589264
[4,] 0.7584856 0.48302885 0.24151443
[5,] 0.6850136 0.62997290 0.31498645
[6,] 0.6850415 0.62991692 0.31495846
[7,] 0.7232065 0.55358700 0.27679350
[8,] 0.8156211 0.36875771 0.18437886
[9,] 0.8592267 0.28154659 0.14077330
[10,] 0.8204424 0.35911515 0.17955757
[11,] 0.7572196 0.48556072 0.24278036
[12,] 0.6871551 0.62568978 0.31284489
[13,] 0.6283490 0.74330191 0.37165095
[14,] 0.5486450 0.90271004 0.45135502
[15,] 0.5557218 0.88855642 0.44427821
[16,] 0.9472491 0.10550171 0.05275086
[17,] 0.9311475 0.13770497 0.06885248
[18,] 0.9619038 0.07619236 0.03809618
[19,] 0.9548882 0.09022365 0.04511183
[20,] 0.9651107 0.06977869 0.03488934
[21,] 0.9530047 0.09399059 0.04699529
[22,] 0.9359468 0.12810647 0.06405323
[23,] 0.9192299 0.16154020 0.08077010
[24,] 0.8938658 0.21226849 0.10613425
[25,] 0.8679558 0.26408835 0.13204417
[26,] 0.8820965 0.23580698 0.11790349
[27,] 0.8526838 0.29463231 0.14731616
[28,] 0.8119533 0.37609342 0.18804671
[29,] 0.7704610 0.45907792 0.22953896
[30,] 0.7502455 0.49950903 0.24975452
[31,] 0.6962260 0.60754801 0.30377401
[32,] 0.6353000 0.72939998 0.36469999
[33,] 0.7206131 0.55877381 0.27938691
[34,] 0.6748103 0.65037940 0.32518970
[35,] 0.6214941 0.75701171 0.37850585
[36,] 0.6477588 0.70448245 0.35224122
[37,] 0.6474773 0.70504544 0.35252272
[38,] 0.5936878 0.81262443 0.40631221
[39,] 0.5357210 0.92855793 0.46427896
[40,] 0.5305290 0.93894208 0.46947104
[41,] 0.4909786 0.98195724 0.50902138
[42,] 0.4059859 0.81197178 0.59401411
[43,] 0.6848023 0.63039548 0.31519774
[44,] 0.6089430 0.78211396 0.39105698
[45,] 0.7657314 0.46853714 0.23426857
[46,] 0.7121878 0.57562436 0.28781218
[47,] 0.6209021 0.75819582 0.37909791
[48,] 0.6911823 0.61763544 0.30881772
[49,] 0.5960015 0.80799700 0.40399850
[50,] 0.5487678 0.90246444 0.45123222
[51,] 0.4072238 0.81444754 0.59277623
[52,] 0.3574214 0.71484274 0.64257863
> postscript(file="/var/www/html/rcomp/tmp/12b5q1261270163.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2a0ub1261270163.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/322ob1261270163.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4z5ac1261270163.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5e66n1261270163.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 = 61
Frequency = 1
1 2 3 4 5 6 7
498.33333 -362.66667 -345.66667 -367.66667 15.33333 -459.66667 -357.66667
8 9 10 11 12 13 14
-257.66667 -19.66667 177.33333 281.33333 428.33333 412.33333 -199.66667
15 16 17 18 19 20 21
22.33333 52.33333 151.33333 -67.66667 324.33333 922.33333 160.33333
22 23 24 25 26 27 28
463.33333 -257.66667 326.33333 28.33333 -61.66667 -222.66667 -138.66667
29 30 31 32 33 34 35
-38.66667 -460.66667 -237.66667 -166.66667 -241.66667 313.25000 76.25000
36 37 38 39 40 41 42
-33.75000 490.25000 173.25000 -95.75000 -336.75000 302.25000 -166.75000
43 44 45 46 47 48 49
-171.75000 285.25000 199.25000 -44.75000 559.25000 -164.75000 380.25000
50 51 52 53 54 55 56
30.25000 -95.75000 193.25000 -97.75000 -411.75000 -171.75000 -428.75000
57 58 59 60 61
-476.75000 8.25000 -241.75000 -302.75000 230.25000
> postscript(file="/var/www/html/rcomp/tmp/6rxu21261270163.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 498.33333 NA
1 -362.66667 498.33333
2 -345.66667 -362.66667
3 -367.66667 -345.66667
4 15.33333 -367.66667
5 -459.66667 15.33333
6 -357.66667 -459.66667
7 -257.66667 -357.66667
8 -19.66667 -257.66667
9 177.33333 -19.66667
10 281.33333 177.33333
11 428.33333 281.33333
12 412.33333 428.33333
13 -199.66667 412.33333
14 22.33333 -199.66667
15 52.33333 22.33333
16 151.33333 52.33333
17 -67.66667 151.33333
18 324.33333 -67.66667
19 922.33333 324.33333
20 160.33333 922.33333
21 463.33333 160.33333
22 -257.66667 463.33333
23 326.33333 -257.66667
24 28.33333 326.33333
25 -61.66667 28.33333
26 -222.66667 -61.66667
27 -138.66667 -222.66667
28 -38.66667 -138.66667
29 -460.66667 -38.66667
30 -237.66667 -460.66667
31 -166.66667 -237.66667
32 -241.66667 -166.66667
33 313.25000 -241.66667
34 76.25000 313.25000
35 -33.75000 76.25000
36 490.25000 -33.75000
37 173.25000 490.25000
38 -95.75000 173.25000
39 -336.75000 -95.75000
40 302.25000 -336.75000
41 -166.75000 302.25000
42 -171.75000 -166.75000
43 285.25000 -171.75000
44 199.25000 285.25000
45 -44.75000 199.25000
46 559.25000 -44.75000
47 -164.75000 559.25000
48 380.25000 -164.75000
49 30.25000 380.25000
50 -95.75000 30.25000
51 193.25000 -95.75000
52 -97.75000 193.25000
53 -411.75000 -97.75000
54 -171.75000 -411.75000
55 -428.75000 -171.75000
56 -476.75000 -428.75000
57 8.25000 -476.75000
58 -241.75000 8.25000
59 -302.75000 -241.75000
60 230.25000 -302.75000
61 NA 230.25000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -362.66667 498.33333
[2,] -345.66667 -362.66667
[3,] -367.66667 -345.66667
[4,] 15.33333 -367.66667
[5,] -459.66667 15.33333
[6,] -357.66667 -459.66667
[7,] -257.66667 -357.66667
[8,] -19.66667 -257.66667
[9,] 177.33333 -19.66667
[10,] 281.33333 177.33333
[11,] 428.33333 281.33333
[12,] 412.33333 428.33333
[13,] -199.66667 412.33333
[14,] 22.33333 -199.66667
[15,] 52.33333 22.33333
[16,] 151.33333 52.33333
[17,] -67.66667 151.33333
[18,] 324.33333 -67.66667
[19,] 922.33333 324.33333
[20,] 160.33333 922.33333
[21,] 463.33333 160.33333
[22,] -257.66667 463.33333
[23,] 326.33333 -257.66667
[24,] 28.33333 326.33333
[25,] -61.66667 28.33333
[26,] -222.66667 -61.66667
[27,] -138.66667 -222.66667
[28,] -38.66667 -138.66667
[29,] -460.66667 -38.66667
[30,] -237.66667 -460.66667
[31,] -166.66667 -237.66667
[32,] -241.66667 -166.66667
[33,] 313.25000 -241.66667
[34,] 76.25000 313.25000
[35,] -33.75000 76.25000
[36,] 490.25000 -33.75000
[37,] 173.25000 490.25000
[38,] -95.75000 173.25000
[39,] -336.75000 -95.75000
[40,] 302.25000 -336.75000
[41,] -166.75000 302.25000
[42,] -171.75000 -166.75000
[43,] 285.25000 -171.75000
[44,] 199.25000 285.25000
[45,] -44.75000 199.25000
[46,] 559.25000 -44.75000
[47,] -164.75000 559.25000
[48,] 380.25000 -164.75000
[49,] 30.25000 380.25000
[50,] -95.75000 30.25000
[51,] 193.25000 -95.75000
[52,] -97.75000 193.25000
[53,] -411.75000 -97.75000
[54,] -171.75000 -411.75000
[55,] -428.75000 -171.75000
[56,] -476.75000 -428.75000
[57,] 8.25000 -476.75000
[58,] -241.75000 8.25000
[59,] -302.75000 -241.75000
[60,] 230.25000 -302.75000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -362.66667 498.33333
2 -345.66667 -362.66667
3 -367.66667 -345.66667
4 15.33333 -367.66667
5 -459.66667 15.33333
6 -357.66667 -459.66667
7 -257.66667 -357.66667
8 -19.66667 -257.66667
9 177.33333 -19.66667
10 281.33333 177.33333
11 428.33333 281.33333
12 412.33333 428.33333
13 -199.66667 412.33333
14 22.33333 -199.66667
15 52.33333 22.33333
16 151.33333 52.33333
17 -67.66667 151.33333
18 324.33333 -67.66667
19 922.33333 324.33333
20 160.33333 922.33333
21 463.33333 160.33333
22 -257.66667 463.33333
23 326.33333 -257.66667
24 28.33333 326.33333
25 -61.66667 28.33333
26 -222.66667 -61.66667
27 -138.66667 -222.66667
28 -38.66667 -138.66667
29 -460.66667 -38.66667
30 -237.66667 -460.66667
31 -166.66667 -237.66667
32 -241.66667 -166.66667
33 313.25000 -241.66667
34 76.25000 313.25000
35 -33.75000 76.25000
36 490.25000 -33.75000
37 173.25000 490.25000
38 -95.75000 173.25000
39 -336.75000 -95.75000
40 302.25000 -336.75000
41 -166.75000 302.25000
42 -171.75000 -166.75000
43 285.25000 -171.75000
44 199.25000 285.25000
45 -44.75000 199.25000
46 559.25000 -44.75000
47 -164.75000 559.25000
48 380.25000 -164.75000
49 30.25000 380.25000
50 -95.75000 30.25000
51 193.25000 -95.75000
52 -97.75000 193.25000
53 -411.75000 -97.75000
54 -171.75000 -411.75000
55 -428.75000 -171.75000
56 -476.75000 -428.75000
57 8.25000 -476.75000
58 -241.75000 8.25000
59 -302.75000 -241.75000
60 230.25000 -302.75000
> 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/73owk1261270163.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8dt0h1261270163.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ofby1261270163.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10cyba1261270163.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ra5e1261270163.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/12v7n11261270163.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/13t5j71261270163.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/14sly91261270163.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/15zabf1261270163.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/16ma651261270163.tab")
+ }
>
> try(system("convert tmp/12b5q1261270163.ps tmp/12b5q1261270163.png",intern=TRUE))
character(0)
> try(system("convert tmp/2a0ub1261270163.ps tmp/2a0ub1261270163.png",intern=TRUE))
character(0)
> try(system("convert tmp/322ob1261270163.ps tmp/322ob1261270163.png",intern=TRUE))
character(0)
> try(system("convert tmp/4z5ac1261270163.ps tmp/4z5ac1261270163.png",intern=TRUE))
character(0)
> try(system("convert tmp/5e66n1261270163.ps tmp/5e66n1261270163.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rxu21261270163.ps tmp/6rxu21261270163.png",intern=TRUE))
character(0)
> try(system("convert tmp/73owk1261270163.ps tmp/73owk1261270163.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dt0h1261270163.ps tmp/8dt0h1261270163.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ofby1261270163.ps tmp/9ofby1261270163.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cyba1261270163.ps tmp/10cyba1261270163.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.488 1.587 3.580