R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1.472,1.475,1.553,1.575,1.556,1.555,1.577,1.498,1.437,1.332,1.273,1.345,1.324,1.279,1.305,1.319,1.365,1.402,1.409,1.427,1.456,1.482,1.491,1.461,1.427,1.369,1.357,1.341,1.257,1.221,1.277,1.289,1.307,1.390,1.366,1.322,1.336,1.365,1.400,1.444,1.435,1.439,1.426,1.434,1.377,1.371,1.356,1.318,1.291,1.322,1.320,1.316,1.279,1.253,1.229,1.240,1.286,1.297,1.283),dim=c(1,59),dimnames=list(c('Dollar'),1:59))
> y <- array(NA,dim=c(1,59),dimnames=list(c('Dollar'),1:59))
> 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'
> 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, 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
Dollar M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1.472 1 0 0 0 0 0 0 0 0 0 0
2 1.475 0 1 0 0 0 0 0 0 0 0 0
3 1.553 0 0 1 0 0 0 0 0 0 0 0
4 1.575 0 0 0 1 0 0 0 0 0 0 0
5 1.556 0 0 0 0 1 0 0 0 0 0 0
6 1.555 0 0 0 0 0 1 0 0 0 0 0
7 1.577 0 0 0 0 0 0 1 0 0 0 0
8 1.498 0 0 0 0 0 0 0 1 0 0 0
9 1.437 0 0 0 0 0 0 0 0 1 0 0
10 1.332 0 0 0 0 0 0 0 0 0 1 0
11 1.273 0 0 0 0 0 0 0 0 0 0 1
12 1.345 0 0 0 0 0 0 0 0 0 0 0
13 1.324 1 0 0 0 0 0 0 0 0 0 0
14 1.279 0 1 0 0 0 0 0 0 0 0 0
15 1.305 0 0 1 0 0 0 0 0 0 0 0
16 1.319 0 0 0 1 0 0 0 0 0 0 0
17 1.365 0 0 0 0 1 0 0 0 0 0 0
18 1.402 0 0 0 0 0 1 0 0 0 0 0
19 1.409 0 0 0 0 0 0 1 0 0 0 0
20 1.427 0 0 0 0 0 0 0 1 0 0 0
21 1.456 0 0 0 0 0 0 0 0 1 0 0
22 1.482 0 0 0 0 0 0 0 0 0 1 0
23 1.491 0 0 0 0 0 0 0 0 0 0 1
24 1.461 0 0 0 0 0 0 0 0 0 0 0
25 1.427 1 0 0 0 0 0 0 0 0 0 0
26 1.369 0 1 0 0 0 0 0 0 0 0 0
27 1.357 0 0 1 0 0 0 0 0 0 0 0
28 1.341 0 0 0 1 0 0 0 0 0 0 0
29 1.257 0 0 0 0 1 0 0 0 0 0 0
30 1.221 0 0 0 0 0 1 0 0 0 0 0
31 1.277 0 0 0 0 0 0 1 0 0 0 0
32 1.289 0 0 0 0 0 0 0 1 0 0 0
33 1.307 0 0 0 0 0 0 0 0 1 0 0
34 1.390 0 0 0 0 0 0 0 0 0 1 0
35 1.366 0 0 0 0 0 0 0 0 0 0 1
36 1.322 0 0 0 0 0 0 0 0 0 0 0
37 1.336 1 0 0 0 0 0 0 0 0 0 0
38 1.365 0 1 0 0 0 0 0 0 0 0 0
39 1.400 0 0 1 0 0 0 0 0 0 0 0
40 1.444 0 0 0 1 0 0 0 0 0 0 0
41 1.435 0 0 0 0 1 0 0 0 0 0 0
42 1.439 0 0 0 0 0 1 0 0 0 0 0
43 1.426 0 0 0 0 0 0 1 0 0 0 0
44 1.434 0 0 0 0 0 0 0 1 0 0 0
45 1.377 0 0 0 0 0 0 0 0 1 0 0
46 1.371 0 0 0 0 0 0 0 0 0 1 0
47 1.356 0 0 0 0 0 0 0 0 0 0 1
48 1.318 0 0 0 0 0 0 0 0 0 0 0
49 1.291 1 0 0 0 0 0 0 0 0 0 0
50 1.322 0 1 0 0 0 0 0 0 0 0 0
51 1.320 0 0 1 0 0 0 0 0 0 0 0
52 1.316 0 0 0 1 0 0 0 0 0 0 0
53 1.279 0 0 0 0 1 0 0 0 0 0 0
54 1.253 0 0 0 0 0 1 0 0 0 0 0
55 1.229 0 0 0 0 0 0 1 0 0 0 0
56 1.240 0 0 0 0 0 0 0 1 0 0 0
57 1.286 0 0 0 0 0 0 0 0 1 0 0
58 1.297 0 0 0 0 0 0 0 0 0 1 0
59 1.283 0 0 0 0 0 0 0 0 0 0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
1.3615 0.0085 0.0005 0.0255 0.0375 0.0169
M6 M7 M8 M9 M10 M11
0.0125 0.0221 0.0161 0.0111 0.0129 -0.0077
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.1546 -0.0782 -0.0034 0.0568 0.1934
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.36150 0.05043 27.000 <2e-16 ***
M1 0.00850 0.06765 0.126 0.901
M2 0.00050 0.06765 0.007 0.994
M3 0.02550 0.06765 0.377 0.708
M4 0.03750 0.06765 0.554 0.582
M5 0.01690 0.06765 0.250 0.804
M6 0.01250 0.06765 0.185 0.854
M7 0.02210 0.06765 0.327 0.745
M8 0.01610 0.06765 0.238 0.813
M9 0.01110 0.06765 0.164 0.870
M10 0.01290 0.06765 0.191 0.850
M11 -0.00770 0.06765 -0.114 0.910
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1009 on 47 degrees of freedom
Multiple R-squared: 0.01654, Adjusted R-squared: -0.2136
F-statistic: 0.07185 on 11 and 47 DF, p-value: 1
> 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,] 0.9605993 0.07880132 0.03940066
[2,] 0.9823961 0.03520774 0.01760387
[3,] 0.9822953 0.03540932 0.01770466
[4,] 0.9796741 0.04065183 0.02032592
[5,] 0.9790554 0.04188929 0.02094464
[6,] 0.9699250 0.06015002 0.03007501
[7,] 0.9608996 0.07820077 0.03910038
[8,] 0.9647319 0.07053625 0.03526812
[9,] 0.9821884 0.03562318 0.01781159
[10,] 0.9835351 0.03292983 0.01646492
[11,] 0.9791257 0.04174867 0.02087434
[12,] 0.9638179 0.07236419 0.03618209
[13,] 0.9439628 0.11207442 0.05603721
[14,] 0.9252728 0.14945434 0.07472717
[15,] 0.9435308 0.11293837 0.05646919
[16,] 0.9710136 0.05797278 0.02898639
[17,] 0.9706623 0.05867549 0.02933775
[18,] 0.9632079 0.07358411 0.03679206
[19,] 0.9466031 0.10679380 0.05339690
[20,] 0.9180915 0.16381694 0.08190847
[21,] 0.8759191 0.24816188 0.12408094
[22,] 0.8176250 0.36474995 0.18237498
[23,] 0.7479193 0.50416136 0.25208068
[24,] 0.6556568 0.68868648 0.34434324
[25,] 0.5669130 0.86617402 0.43308701
[26,] 0.5124714 0.97505728 0.48752864
[27,] 0.4926098 0.98521969 0.50739015
[28,] 0.5335100 0.93298003 0.46649002
[29,] 0.6362842 0.72743155 0.36371577
[30,] 0.8272919 0.34541621 0.17270810
> postscript(file="/var/wessaorg/rcomp/tmp/1ifd61355423420.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2gb2f1355423420.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3u38n1355423420.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4mm9e1355423420.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5xd4j1355423420.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 = 59
Frequency = 1
1 2 3 4 5 6 7 8 9 10
0.1020 0.1130 0.1660 0.1760 0.1776 0.1810 0.1934 0.1204 0.0644 -0.0424
11 12 13 14 15 16 17 18 19 20
-0.0808 -0.0165 -0.0460 -0.0830 -0.0820 -0.0800 -0.0134 0.0280 0.0254 0.0494
21 22 23 24 25 26 27 28 29 30
0.0834 0.1076 0.1372 0.0995 0.0570 0.0070 -0.0300 -0.0580 -0.1214 -0.1530
31 32 33 34 35 36 37 38 39 40
-0.1066 -0.0886 -0.0656 0.0156 0.0122 -0.0395 -0.0340 0.0030 0.0130 0.0450
41 42 43 44 45 46 47 48 49 50
0.0566 0.0650 0.0424 0.0564 0.0044 -0.0034 0.0022 -0.0435 -0.0790 -0.0400
51 52 53 54 55 56 57 58 59
-0.0670 -0.0830 -0.0994 -0.1210 -0.1546 -0.1376 -0.0866 -0.0774 -0.0708
> postscript(file="/var/wessaorg/rcomp/tmp/6yo8k1355423420.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 = 59
Frequency = 1
lag(myerror, k = 1) myerror
0 0.1020 NA
1 0.1130 0.1020
2 0.1660 0.1130
3 0.1760 0.1660
4 0.1776 0.1760
5 0.1810 0.1776
6 0.1934 0.1810
7 0.1204 0.1934
8 0.0644 0.1204
9 -0.0424 0.0644
10 -0.0808 -0.0424
11 -0.0165 -0.0808
12 -0.0460 -0.0165
13 -0.0830 -0.0460
14 -0.0820 -0.0830
15 -0.0800 -0.0820
16 -0.0134 -0.0800
17 0.0280 -0.0134
18 0.0254 0.0280
19 0.0494 0.0254
20 0.0834 0.0494
21 0.1076 0.0834
22 0.1372 0.1076
23 0.0995 0.1372
24 0.0570 0.0995
25 0.0070 0.0570
26 -0.0300 0.0070
27 -0.0580 -0.0300
28 -0.1214 -0.0580
29 -0.1530 -0.1214
30 -0.1066 -0.1530
31 -0.0886 -0.1066
32 -0.0656 -0.0886
33 0.0156 -0.0656
34 0.0122 0.0156
35 -0.0395 0.0122
36 -0.0340 -0.0395
37 0.0030 -0.0340
38 0.0130 0.0030
39 0.0450 0.0130
40 0.0566 0.0450
41 0.0650 0.0566
42 0.0424 0.0650
43 0.0564 0.0424
44 0.0044 0.0564
45 -0.0034 0.0044
46 0.0022 -0.0034
47 -0.0435 0.0022
48 -0.0790 -0.0435
49 -0.0400 -0.0790
50 -0.0670 -0.0400
51 -0.0830 -0.0670
52 -0.0994 -0.0830
53 -0.1210 -0.0994
54 -0.1546 -0.1210
55 -0.1376 -0.1546
56 -0.0866 -0.1376
57 -0.0774 -0.0866
58 -0.0708 -0.0774
59 NA -0.0708
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.1130 0.1020
[2,] 0.1660 0.1130
[3,] 0.1760 0.1660
[4,] 0.1776 0.1760
[5,] 0.1810 0.1776
[6,] 0.1934 0.1810
[7,] 0.1204 0.1934
[8,] 0.0644 0.1204
[9,] -0.0424 0.0644
[10,] -0.0808 -0.0424
[11,] -0.0165 -0.0808
[12,] -0.0460 -0.0165
[13,] -0.0830 -0.0460
[14,] -0.0820 -0.0830
[15,] -0.0800 -0.0820
[16,] -0.0134 -0.0800
[17,] 0.0280 -0.0134
[18,] 0.0254 0.0280
[19,] 0.0494 0.0254
[20,] 0.0834 0.0494
[21,] 0.1076 0.0834
[22,] 0.1372 0.1076
[23,] 0.0995 0.1372
[24,] 0.0570 0.0995
[25,] 0.0070 0.0570
[26,] -0.0300 0.0070
[27,] -0.0580 -0.0300
[28,] -0.1214 -0.0580
[29,] -0.1530 -0.1214
[30,] -0.1066 -0.1530
[31,] -0.0886 -0.1066
[32,] -0.0656 -0.0886
[33,] 0.0156 -0.0656
[34,] 0.0122 0.0156
[35,] -0.0395 0.0122
[36,] -0.0340 -0.0395
[37,] 0.0030 -0.0340
[38,] 0.0130 0.0030
[39,] 0.0450 0.0130
[40,] 0.0566 0.0450
[41,] 0.0650 0.0566
[42,] 0.0424 0.0650
[43,] 0.0564 0.0424
[44,] 0.0044 0.0564
[45,] -0.0034 0.0044
[46,] 0.0022 -0.0034
[47,] -0.0435 0.0022
[48,] -0.0790 -0.0435
[49,] -0.0400 -0.0790
[50,] -0.0670 -0.0400
[51,] -0.0830 -0.0670
[52,] -0.0994 -0.0830
[53,] -0.1210 -0.0994
[54,] -0.1546 -0.1210
[55,] -0.1376 -0.1546
[56,] -0.0866 -0.1376
[57,] -0.0774 -0.0866
[58,] -0.0708 -0.0774
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.1130 0.1020
2 0.1660 0.1130
3 0.1760 0.1660
4 0.1776 0.1760
5 0.1810 0.1776
6 0.1934 0.1810
7 0.1204 0.1934
8 0.0644 0.1204
9 -0.0424 0.0644
10 -0.0808 -0.0424
11 -0.0165 -0.0808
12 -0.0460 -0.0165
13 -0.0830 -0.0460
14 -0.0820 -0.0830
15 -0.0800 -0.0820
16 -0.0134 -0.0800
17 0.0280 -0.0134
18 0.0254 0.0280
19 0.0494 0.0254
20 0.0834 0.0494
21 0.1076 0.0834
22 0.1372 0.1076
23 0.0995 0.1372
24 0.0570 0.0995
25 0.0070 0.0570
26 -0.0300 0.0070
27 -0.0580 -0.0300
28 -0.1214 -0.0580
29 -0.1530 -0.1214
30 -0.1066 -0.1530
31 -0.0886 -0.1066
32 -0.0656 -0.0886
33 0.0156 -0.0656
34 0.0122 0.0156
35 -0.0395 0.0122
36 -0.0340 -0.0395
37 0.0030 -0.0340
38 0.0130 0.0030
39 0.0450 0.0130
40 0.0566 0.0450
41 0.0650 0.0566
42 0.0424 0.0650
43 0.0564 0.0424
44 0.0044 0.0564
45 -0.0034 0.0044
46 0.0022 -0.0034
47 -0.0435 0.0022
48 -0.0790 -0.0435
49 -0.0400 -0.0790
50 -0.0670 -0.0400
51 -0.0830 -0.0670
52 -0.0994 -0.0830
53 -0.1210 -0.0994
54 -0.1546 -0.1210
55 -0.1376 -0.1546
56 -0.0866 -0.1376
57 -0.0774 -0.0866
58 -0.0708 -0.0774
> 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/wessaorg/rcomp/tmp/7yocp1355423420.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8maze1355423420.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9cs3b1355423420.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/108kvg1355423420.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/112cyn1355423420.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/wessaorg/rcomp/tmp/12hvll1355423420.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/wessaorg/rcomp/tmp/134tyn1355423420.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/wessaorg/rcomp/tmp/14yvng1355423420.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/wessaorg/rcomp/tmp/15se8u1355423420.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/wessaorg/rcomp/tmp/16tse51355423420.tab")
+ }
>
> try(system("convert tmp/1ifd61355423420.ps tmp/1ifd61355423420.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gb2f1355423420.ps tmp/2gb2f1355423420.png",intern=TRUE))
character(0)
> try(system("convert tmp/3u38n1355423420.ps tmp/3u38n1355423420.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mm9e1355423420.ps tmp/4mm9e1355423420.png",intern=TRUE))
character(0)
> try(system("convert tmp/5xd4j1355423420.ps tmp/5xd4j1355423420.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yo8k1355423420.ps tmp/6yo8k1355423420.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yocp1355423420.ps tmp/7yocp1355423420.png",intern=TRUE))
character(0)
> try(system("convert tmp/8maze1355423420.ps tmp/8maze1355423420.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cs3b1355423420.ps tmp/9cs3b1355423420.png",intern=TRUE))
character(0)
> try(system("convert tmp/108kvg1355423420.ps tmp/108kvg1355423420.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.978 1.340 8.381