R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(8.6,0,8.5,0,8.3,0,7.8,0,7.8,0,8,0,8.6,0,8.9,0,8.9,0,8.6,0,8.3,0,8.3,0,8.3,0,8.4,0,8.5,0,8.4,0,8.6,0,8.5,0,8.5,0,8.5,0,8.5,0,8.5,0,8.5,0,8.5,0,8.5,0,8.5,0,8.5,0,8.5,0,8.6,0,8.4,0,8.1,0,8,0,8,0,8,0,8,0,7.9,0,7.8,0,7.8,0,7.9,0,8.1,0,8,0,7.6,0,7.3,0,7,0,6.8,0,7,0,7.1,0,7.2,0,7.1,1,6.9,1,6.7,1,6.7,1,6.6,1,6.9,1,7.3,1,7.5,1,7.3,1,7.1,1,6.9,1,7.1,1),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 = '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 8.6 0
2 8.5 0
3 8.3 0
4 7.8 0
5 7.8 0
6 8.0 0
7 8.6 0
8 8.9 0
9 8.9 0
10 8.6 0
11 8.3 0
12 8.3 0
13 8.3 0
14 8.4 0
15 8.5 0
16 8.4 0
17 8.6 0
18 8.5 0
19 8.5 0
20 8.5 0
21 8.5 0
22 8.5 0
23 8.5 0
24 8.5 0
25 8.5 0
26 8.5 0
27 8.5 0
28 8.5 0
29 8.6 0
30 8.4 0
31 8.1 0
32 8.0 0
33 8.0 0
34 8.0 0
35 8.0 0
36 7.9 0
37 7.8 0
38 7.8 0
39 7.9 0
40 8.1 0
41 8.0 0
42 7.6 0
43 7.3 0
44 7.0 0
45 6.8 0
46 7.0 0
47 7.1 0
48 7.2 0
49 7.1 1
50 6.9 1
51 6.7 1
52 6.7 1
53 6.6 1
54 6.9 1
55 7.3 1
56 7.5 1
57 7.3 1
58 7.1 1
59 6.9 1
60 7.1 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
8.144 -1.135
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.3438 -0.2437 0.1240 0.3563 0.7563
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.14375 0.06856 118.777 < 2e-16 ***
X -1.13542 0.15331 -7.406 6.09e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.475 on 58 degrees of freedom
Multiple R-squared: 0.486, Adjusted R-squared: 0.4772
F-statistic: 54.85 on 1 and 58 DF, p-value: 6.092e-10
> 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,] 0.511600002 0.976799996 0.48840000
[2,] 0.363224270 0.726448540 0.63677573
[3,] 0.333151711 0.666303421 0.66684829
[4,] 0.452247713 0.904495426 0.54775229
[5,] 0.514153079 0.971693843 0.48584692
[6,] 0.435193416 0.870386832 0.56480658
[7,] 0.335806122 0.671612244 0.66419388
[8,] 0.249591268 0.499182536 0.75040873
[9,] 0.178858884 0.357717767 0.82114112
[10,] 0.124698361 0.249396721 0.87530164
[11,] 0.089564276 0.179128552 0.91043572
[12,] 0.059557037 0.119114073 0.94044296
[13,] 0.047225775 0.094451551 0.95277422
[14,] 0.033041581 0.066083162 0.96695842
[15,] 0.023086330 0.046172660 0.97691367
[16,] 0.016201483 0.032402966 0.98379852
[17,] 0.011497100 0.022994199 0.98850290
[18,] 0.008316547 0.016633093 0.99168345
[19,] 0.006192076 0.012384151 0.99380792
[20,] 0.004802295 0.009604590 0.99519770
[21,] 0.003937779 0.007875559 0.99606222
[22,] 0.003479098 0.006958197 0.99652090
[23,] 0.003394183 0.006788366 0.99660582
[24,] 0.003776435 0.007552871 0.99622356
[25,] 0.007004529 0.014009058 0.99299547
[26,] 0.009366650 0.018733299 0.99063335
[27,] 0.012332904 0.024665808 0.98766710
[28,] 0.018052613 0.036105225 0.98194739
[29,] 0.025221058 0.050442116 0.97477894
[30,] 0.034659538 0.069319075 0.96534046
[31,] 0.048119765 0.096239531 0.95188023
[32,] 0.069062058 0.138124117 0.93093794
[33,] 0.099803132 0.199606264 0.90019687
[34,] 0.135438517 0.270877035 0.86456148
[35,] 0.187441837 0.374883674 0.81255816
[36,] 0.378628353 0.757256707 0.62137165
[37,] 0.713247591 0.573504818 0.28675241
[38,] 0.871122367 0.257755266 0.12887763
[39,] 0.932387303 0.135225394 0.06761270
[40,] 0.963438813 0.073122374 0.03656119
[41,] 0.985024127 0.029951747 0.01497587
[42,] 0.986110327 0.027779346 0.01388967
[43,] 0.982706948 0.034586104 0.01729305
[44,] 0.974454320 0.051091360 0.02554568
[45,] 0.952872704 0.094254593 0.04712730
[46,] 0.917693034 0.164613931 0.08230697
[47,] 0.898691162 0.202617676 0.10130884
[48,] 0.886310509 0.227378982 0.11368949
[49,] 0.940886976 0.118226049 0.05911302
[50,] 0.920229630 0.159540741 0.07977037
[51,] 0.831684607 0.336630785 0.16831539
> postscript(file="/var/www/html/rcomp/tmp/1h49w1258714442.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2hjue1258714442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/381981258714442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kp6b1258714442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53rgw1258714442.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
0.45625000 0.35625000 0.15625000 -0.34375000 -0.34375000 -0.14375000
7 8 9 10 11 12
0.45625000 0.75625000 0.75625000 0.45625000 0.15625000 0.15625000
13 14 15 16 17 18
0.15625000 0.25625000 0.35625000 0.25625000 0.45625000 0.35625000
19 20 21 22 23 24
0.35625000 0.35625000 0.35625000 0.35625000 0.35625000 0.35625000
25 26 27 28 29 30
0.35625000 0.35625000 0.35625000 0.35625000 0.45625000 0.25625000
31 32 33 34 35 36
-0.04375000 -0.14375000 -0.14375000 -0.14375000 -0.14375000 -0.24375000
37 38 39 40 41 42
-0.34375000 -0.34375000 -0.24375000 -0.04375000 -0.14375000 -0.54375000
43 44 45 46 47 48
-0.84375000 -1.14375000 -1.34375000 -1.14375000 -1.04375000 -0.94375000
49 50 51 52 53 54
0.09166667 -0.10833333 -0.30833333 -0.30833333 -0.40833333 -0.10833333
55 56 57 58 59 60
0.29166667 0.49166667 0.29166667 0.09166667 -0.10833333 0.09166667
> postscript(file="/var/www/html/rcomp/tmp/65y621258714442.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 0.45625000 NA
1 0.35625000 0.45625000
2 0.15625000 0.35625000
3 -0.34375000 0.15625000
4 -0.34375000 -0.34375000
5 -0.14375000 -0.34375000
6 0.45625000 -0.14375000
7 0.75625000 0.45625000
8 0.75625000 0.75625000
9 0.45625000 0.75625000
10 0.15625000 0.45625000
11 0.15625000 0.15625000
12 0.15625000 0.15625000
13 0.25625000 0.15625000
14 0.35625000 0.25625000
15 0.25625000 0.35625000
16 0.45625000 0.25625000
17 0.35625000 0.45625000
18 0.35625000 0.35625000
19 0.35625000 0.35625000
20 0.35625000 0.35625000
21 0.35625000 0.35625000
22 0.35625000 0.35625000
23 0.35625000 0.35625000
24 0.35625000 0.35625000
25 0.35625000 0.35625000
26 0.35625000 0.35625000
27 0.35625000 0.35625000
28 0.45625000 0.35625000
29 0.25625000 0.45625000
30 -0.04375000 0.25625000
31 -0.14375000 -0.04375000
32 -0.14375000 -0.14375000
33 -0.14375000 -0.14375000
34 -0.14375000 -0.14375000
35 -0.24375000 -0.14375000
36 -0.34375000 -0.24375000
37 -0.34375000 -0.34375000
38 -0.24375000 -0.34375000
39 -0.04375000 -0.24375000
40 -0.14375000 -0.04375000
41 -0.54375000 -0.14375000
42 -0.84375000 -0.54375000
43 -1.14375000 -0.84375000
44 -1.34375000 -1.14375000
45 -1.14375000 -1.34375000
46 -1.04375000 -1.14375000
47 -0.94375000 -1.04375000
48 0.09166667 -0.94375000
49 -0.10833333 0.09166667
50 -0.30833333 -0.10833333
51 -0.30833333 -0.30833333
52 -0.40833333 -0.30833333
53 -0.10833333 -0.40833333
54 0.29166667 -0.10833333
55 0.49166667 0.29166667
56 0.29166667 0.49166667
57 0.09166667 0.29166667
58 -0.10833333 0.09166667
59 0.09166667 -0.10833333
60 NA 0.09166667
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.35625000 0.45625000
[2,] 0.15625000 0.35625000
[3,] -0.34375000 0.15625000
[4,] -0.34375000 -0.34375000
[5,] -0.14375000 -0.34375000
[6,] 0.45625000 -0.14375000
[7,] 0.75625000 0.45625000
[8,] 0.75625000 0.75625000
[9,] 0.45625000 0.75625000
[10,] 0.15625000 0.45625000
[11,] 0.15625000 0.15625000
[12,] 0.15625000 0.15625000
[13,] 0.25625000 0.15625000
[14,] 0.35625000 0.25625000
[15,] 0.25625000 0.35625000
[16,] 0.45625000 0.25625000
[17,] 0.35625000 0.45625000
[18,] 0.35625000 0.35625000
[19,] 0.35625000 0.35625000
[20,] 0.35625000 0.35625000
[21,] 0.35625000 0.35625000
[22,] 0.35625000 0.35625000
[23,] 0.35625000 0.35625000
[24,] 0.35625000 0.35625000
[25,] 0.35625000 0.35625000
[26,] 0.35625000 0.35625000
[27,] 0.35625000 0.35625000
[28,] 0.45625000 0.35625000
[29,] 0.25625000 0.45625000
[30,] -0.04375000 0.25625000
[31,] -0.14375000 -0.04375000
[32,] -0.14375000 -0.14375000
[33,] -0.14375000 -0.14375000
[34,] -0.14375000 -0.14375000
[35,] -0.24375000 -0.14375000
[36,] -0.34375000 -0.24375000
[37,] -0.34375000 -0.34375000
[38,] -0.24375000 -0.34375000
[39,] -0.04375000 -0.24375000
[40,] -0.14375000 -0.04375000
[41,] -0.54375000 -0.14375000
[42,] -0.84375000 -0.54375000
[43,] -1.14375000 -0.84375000
[44,] -1.34375000 -1.14375000
[45,] -1.14375000 -1.34375000
[46,] -1.04375000 -1.14375000
[47,] -0.94375000 -1.04375000
[48,] 0.09166667 -0.94375000
[49,] -0.10833333 0.09166667
[50,] -0.30833333 -0.10833333
[51,] -0.30833333 -0.30833333
[52,] -0.40833333 -0.30833333
[53,] -0.10833333 -0.40833333
[54,] 0.29166667 -0.10833333
[55,] 0.49166667 0.29166667
[56,] 0.29166667 0.49166667
[57,] 0.09166667 0.29166667
[58,] -0.10833333 0.09166667
[59,] 0.09166667 -0.10833333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.35625000 0.45625000
2 0.15625000 0.35625000
3 -0.34375000 0.15625000
4 -0.34375000 -0.34375000
5 -0.14375000 -0.34375000
6 0.45625000 -0.14375000
7 0.75625000 0.45625000
8 0.75625000 0.75625000
9 0.45625000 0.75625000
10 0.15625000 0.45625000
11 0.15625000 0.15625000
12 0.15625000 0.15625000
13 0.25625000 0.15625000
14 0.35625000 0.25625000
15 0.25625000 0.35625000
16 0.45625000 0.25625000
17 0.35625000 0.45625000
18 0.35625000 0.35625000
19 0.35625000 0.35625000
20 0.35625000 0.35625000
21 0.35625000 0.35625000
22 0.35625000 0.35625000
23 0.35625000 0.35625000
24 0.35625000 0.35625000
25 0.35625000 0.35625000
26 0.35625000 0.35625000
27 0.35625000 0.35625000
28 0.45625000 0.35625000
29 0.25625000 0.45625000
30 -0.04375000 0.25625000
31 -0.14375000 -0.04375000
32 -0.14375000 -0.14375000
33 -0.14375000 -0.14375000
34 -0.14375000 -0.14375000
35 -0.24375000 -0.14375000
36 -0.34375000 -0.24375000
37 -0.34375000 -0.34375000
38 -0.24375000 -0.34375000
39 -0.04375000 -0.24375000
40 -0.14375000 -0.04375000
41 -0.54375000 -0.14375000
42 -0.84375000 -0.54375000
43 -1.14375000 -0.84375000
44 -1.34375000 -1.14375000
45 -1.14375000 -1.34375000
46 -1.04375000 -1.14375000
47 -0.94375000 -1.04375000
48 0.09166667 -0.94375000
49 -0.10833333 0.09166667
50 -0.30833333 -0.10833333
51 -0.30833333 -0.30833333
52 -0.40833333 -0.30833333
53 -0.10833333 -0.40833333
54 0.29166667 -0.10833333
55 0.49166667 0.29166667
56 0.29166667 0.49166667
57 0.09166667 0.29166667
58 -0.10833333 0.09166667
59 0.09166667 -0.10833333
> 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/73n4u1258714442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hd1u1258714442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99n9x1258714442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10qela1258714442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11dw6p1258714443.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/12fh6d1258714443.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/13u9rh1258714443.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/141i3n1258714443.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/15eksu1258714443.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/169zrn1258714443.tab")
+ }
>
> system("convert tmp/1h49w1258714442.ps tmp/1h49w1258714442.png")
> system("convert tmp/2hjue1258714442.ps tmp/2hjue1258714442.png")
> system("convert tmp/381981258714442.ps tmp/381981258714442.png")
> system("convert tmp/4kp6b1258714442.ps tmp/4kp6b1258714442.png")
> system("convert tmp/53rgw1258714442.ps tmp/53rgw1258714442.png")
> system("convert tmp/65y621258714442.ps tmp/65y621258714442.png")
> system("convert tmp/73n4u1258714442.ps tmp/73n4u1258714442.png")
> system("convert tmp/8hd1u1258714442.ps tmp/8hd1u1258714442.png")
> system("convert tmp/99n9x1258714442.ps tmp/99n9x1258714442.png")
> system("convert tmp/10qela1258714442.ps tmp/10qela1258714442.png")
>
>
> proc.time()
user system elapsed
2.417 1.531 3.426