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(56.6,0,56,0,54.8,0,52.7,0,50.9,0,50.6,0,52.1,0,53.3,0,53.9,0,54.3,0,54.2,0,54.2,0,53.5,0,51.4,0,50.5,0,50.3,0,49.8,0,50.7,0,52.8,0,55.3,0,57.3,0,57.5,0,56.8,0,56.4,0,56.3,0,56.4,0,57,0,57.9,0,58.9,0,58.8,0,56.5,1,51.9,1,47.4,1,44.9,1,43.9,1,43.4,1,42.9,1,42.6,1,42.2,1,41.2,1,40.2,1,39.3,1,38.5,1,38.3,1,37.9,1,37.6,1,37.3,1,36,1,34.5,1,33.5,1,32.9,1,32.9,1,32.8,1,31.9,1,30.5,1,29.2,1,28.7,1,28.4,1,28,1,27.4,1,26.9,1),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 56.6 0
2 56.0 0
3 54.8 0
4 52.7 0
5 50.9 0
6 50.6 0
7 52.1 0
8 53.3 0
9 53.9 0
10 54.3 0
11 54.2 0
12 54.2 0
13 53.5 0
14 51.4 0
15 50.5 0
16 50.3 0
17 49.8 0
18 50.7 0
19 52.8 0
20 55.3 0
21 57.3 0
22 57.5 0
23 56.8 0
24 56.4 0
25 56.3 0
26 56.4 0
27 57.0 0
28 57.9 0
29 58.9 0
30 58.8 0
31 56.5 1
32 51.9 1
33 47.4 1
34 44.9 1
35 43.9 1
36 43.4 1
37 42.9 1
38 42.6 1
39 42.2 1
40 41.2 1
41 40.2 1
42 39.3 1
43 38.5 1
44 38.3 1
45 37.9 1
46 37.6 1
47 37.3 1
48 36.0 1
49 34.5 1
50 33.5 1
51 32.9 1
52 32.9 1
53 32.8 1
54 31.9 1
55 30.5 1
56 29.2 1
57 28.7 1
58 28.4 1
59 28.0 1
60 27.4 1
61 26.9 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
54.37 -16.97
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.50645 -3.77333 -0.07333 2.79355 19.09355
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 54.373 1.015 53.57 <2e-16 ***
X -16.967 1.424 -11.92 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.56 on 59 degrees of freedom
Multiple R-squared: 0.7064, Adjusted R-squared: 0.7015
F-statistic: 142 on 1 and 59 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,] 1.212997e-01 2.425993e-01 0.878700337
[2,] 8.980635e-02 1.796127e-01 0.910193650
[3,] 4.142714e-02 8.285429e-02 0.958572856
[4,] 1.593694e-02 3.187389e-02 0.984063056
[5,] 5.793387e-03 1.158677e-02 0.994206613
[6,] 2.062990e-03 4.125979e-03 0.997937010
[7,] 6.820806e-04 1.364161e-03 0.999317919
[8,] 2.131747e-04 4.263495e-04 0.999786825
[9,] 6.111066e-05 1.222213e-04 0.999938889
[10,] 3.268996e-05 6.537991e-05 0.999967310
[11,] 2.791807e-05 5.583614e-05 0.999972082
[12,] 2.326417e-05 4.652834e-05 0.999976736
[13,] 2.352479e-05 4.704958e-05 0.999976475
[14,] 1.335563e-05 2.671127e-05 0.999986644
[15,] 4.540747e-06 9.081495e-06 0.999995459
[16,] 2.642442e-06 5.284884e-06 0.999997358
[17,] 4.512302e-06 9.024605e-06 0.999995488
[18,] 6.324243e-06 1.264849e-05 0.999993676
[19,] 4.989057e-06 9.978113e-06 0.999995011
[20,] 3.066025e-06 6.132050e-06 0.999996934
[21,] 1.722770e-06 3.445541e-06 0.999998277
[22,] 9.562189e-07 1.912438e-06 0.999999044
[23,] 6.252728e-07 1.250546e-06 0.999999375
[24,] 5.529775e-07 1.105955e-06 0.999999447
[25,] 6.959597e-07 1.391919e-06 0.999999304
[26,] 7.066852e-07 1.413370e-06 0.999999293
[27,] 4.645459e-06 9.290918e-06 0.999995355
[28,] 2.840730e-05 5.681461e-05 0.999971593
[29,] 1.714696e-04 3.429391e-04 0.999828530
[30,] 7.303221e-04 1.460644e-03 0.999269678
[31,] 2.082290e-03 4.164579e-03 0.997917710
[32,] 4.763564e-03 9.527129e-03 0.995236436
[33,] 9.875742e-03 1.975148e-02 0.990124258
[34,] 1.981586e-02 3.963172e-02 0.980184141
[35,] 3.968810e-02 7.937620e-02 0.960311898
[36,] 7.477774e-02 1.495555e-01 0.925222257
[37,] 1.290556e-01 2.581113e-01 0.870944358
[38,] 2.024589e-01 4.049177e-01 0.797541143
[39,] 2.898849e-01 5.797698e-01 0.710115076
[40,] 3.984517e-01 7.969034e-01 0.601548297
[41,] 5.237286e-01 9.525428e-01 0.476271391
[42,] 6.651737e-01 6.696525e-01 0.334826261
[43,] 8.121544e-01 3.756913e-01 0.187845642
[44,] 9.005619e-01 1.988763e-01 0.099438130
[45,] 9.378410e-01 1.243179e-01 0.062158966
[46,] 9.538104e-01 9.237913e-02 0.046189565
[47,] 9.614663e-01 7.706737e-02 0.038533684
[48,] 9.720801e-01 5.583972e-02 0.027919862
[49,] 9.864527e-01 2.709460e-02 0.013547302
[50,] 9.948271e-01 1.034578e-02 0.005172890
[51,] 9.967763e-01 6.447458e-03 0.003223729
[52,] 9.928568e-01 1.428643e-02 0.007143215
> postscript(file="/var/www/html/rcomp/tmp/1ildw1258647230.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/20gts1258647230.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32lk41258647230.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4bjue1258647230.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5of2b1258647230.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
2.22666667 1.62666667 0.42666667 -1.67333333 -3.47333333 -3.77333333
7 8 9 10 11 12
-2.27333333 -1.07333333 -0.47333333 -0.07333333 -0.17333333 -0.17333333
13 14 15 16 17 18
-0.87333333 -2.97333333 -3.87333333 -4.07333333 -4.57333333 -3.67333333
19 20 21 22 23 24
-1.57333333 0.92666667 2.92666667 3.12666667 2.42666667 2.02666667
25 26 27 28 29 30
1.92666667 2.02666667 2.62666667 3.52666667 4.52666667 4.42666667
31 32 33 34 35 36
19.09354839 14.49354839 9.99354839 7.49354839 6.49354839 5.99354839
37 38 39 40 41 42
5.49354839 5.19354839 4.79354839 3.79354839 2.79354839 1.89354839
43 44 45 46 47 48
1.09354839 0.89354839 0.49354839 0.19354839 -0.10645161 -1.40645161
49 50 51 52 53 54
-2.90645161 -3.90645161 -4.50645161 -4.50645161 -4.60645161 -5.50645161
55 56 57 58 59 60
-6.90645161 -8.20645161 -8.70645161 -9.00645161 -9.40645161 -10.00645161
61
-10.50645161
> postscript(file="/var/www/html/rcomp/tmp/6o1jc1258647230.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 2.22666667 NA
1 1.62666667 2.22666667
2 0.42666667 1.62666667
3 -1.67333333 0.42666667
4 -3.47333333 -1.67333333
5 -3.77333333 -3.47333333
6 -2.27333333 -3.77333333
7 -1.07333333 -2.27333333
8 -0.47333333 -1.07333333
9 -0.07333333 -0.47333333
10 -0.17333333 -0.07333333
11 -0.17333333 -0.17333333
12 -0.87333333 -0.17333333
13 -2.97333333 -0.87333333
14 -3.87333333 -2.97333333
15 -4.07333333 -3.87333333
16 -4.57333333 -4.07333333
17 -3.67333333 -4.57333333
18 -1.57333333 -3.67333333
19 0.92666667 -1.57333333
20 2.92666667 0.92666667
21 3.12666667 2.92666667
22 2.42666667 3.12666667
23 2.02666667 2.42666667
24 1.92666667 2.02666667
25 2.02666667 1.92666667
26 2.62666667 2.02666667
27 3.52666667 2.62666667
28 4.52666667 3.52666667
29 4.42666667 4.52666667
30 19.09354839 4.42666667
31 14.49354839 19.09354839
32 9.99354839 14.49354839
33 7.49354839 9.99354839
34 6.49354839 7.49354839
35 5.99354839 6.49354839
36 5.49354839 5.99354839
37 5.19354839 5.49354839
38 4.79354839 5.19354839
39 3.79354839 4.79354839
40 2.79354839 3.79354839
41 1.89354839 2.79354839
42 1.09354839 1.89354839
43 0.89354839 1.09354839
44 0.49354839 0.89354839
45 0.19354839 0.49354839
46 -0.10645161 0.19354839
47 -1.40645161 -0.10645161
48 -2.90645161 -1.40645161
49 -3.90645161 -2.90645161
50 -4.50645161 -3.90645161
51 -4.50645161 -4.50645161
52 -4.60645161 -4.50645161
53 -5.50645161 -4.60645161
54 -6.90645161 -5.50645161
55 -8.20645161 -6.90645161
56 -8.70645161 -8.20645161
57 -9.00645161 -8.70645161
58 -9.40645161 -9.00645161
59 -10.00645161 -9.40645161
60 -10.50645161 -10.00645161
61 NA -10.50645161
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.62666667 2.22666667
[2,] 0.42666667 1.62666667
[3,] -1.67333333 0.42666667
[4,] -3.47333333 -1.67333333
[5,] -3.77333333 -3.47333333
[6,] -2.27333333 -3.77333333
[7,] -1.07333333 -2.27333333
[8,] -0.47333333 -1.07333333
[9,] -0.07333333 -0.47333333
[10,] -0.17333333 -0.07333333
[11,] -0.17333333 -0.17333333
[12,] -0.87333333 -0.17333333
[13,] -2.97333333 -0.87333333
[14,] -3.87333333 -2.97333333
[15,] -4.07333333 -3.87333333
[16,] -4.57333333 -4.07333333
[17,] -3.67333333 -4.57333333
[18,] -1.57333333 -3.67333333
[19,] 0.92666667 -1.57333333
[20,] 2.92666667 0.92666667
[21,] 3.12666667 2.92666667
[22,] 2.42666667 3.12666667
[23,] 2.02666667 2.42666667
[24,] 1.92666667 2.02666667
[25,] 2.02666667 1.92666667
[26,] 2.62666667 2.02666667
[27,] 3.52666667 2.62666667
[28,] 4.52666667 3.52666667
[29,] 4.42666667 4.52666667
[30,] 19.09354839 4.42666667
[31,] 14.49354839 19.09354839
[32,] 9.99354839 14.49354839
[33,] 7.49354839 9.99354839
[34,] 6.49354839 7.49354839
[35,] 5.99354839 6.49354839
[36,] 5.49354839 5.99354839
[37,] 5.19354839 5.49354839
[38,] 4.79354839 5.19354839
[39,] 3.79354839 4.79354839
[40,] 2.79354839 3.79354839
[41,] 1.89354839 2.79354839
[42,] 1.09354839 1.89354839
[43,] 0.89354839 1.09354839
[44,] 0.49354839 0.89354839
[45,] 0.19354839 0.49354839
[46,] -0.10645161 0.19354839
[47,] -1.40645161 -0.10645161
[48,] -2.90645161 -1.40645161
[49,] -3.90645161 -2.90645161
[50,] -4.50645161 -3.90645161
[51,] -4.50645161 -4.50645161
[52,] -4.60645161 -4.50645161
[53,] -5.50645161 -4.60645161
[54,] -6.90645161 -5.50645161
[55,] -8.20645161 -6.90645161
[56,] -8.70645161 -8.20645161
[57,] -9.00645161 -8.70645161
[58,] -9.40645161 -9.00645161
[59,] -10.00645161 -9.40645161
[60,] -10.50645161 -10.00645161
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.62666667 2.22666667
2 0.42666667 1.62666667
3 -1.67333333 0.42666667
4 -3.47333333 -1.67333333
5 -3.77333333 -3.47333333
6 -2.27333333 -3.77333333
7 -1.07333333 -2.27333333
8 -0.47333333 -1.07333333
9 -0.07333333 -0.47333333
10 -0.17333333 -0.07333333
11 -0.17333333 -0.17333333
12 -0.87333333 -0.17333333
13 -2.97333333 -0.87333333
14 -3.87333333 -2.97333333
15 -4.07333333 -3.87333333
16 -4.57333333 -4.07333333
17 -3.67333333 -4.57333333
18 -1.57333333 -3.67333333
19 0.92666667 -1.57333333
20 2.92666667 0.92666667
21 3.12666667 2.92666667
22 2.42666667 3.12666667
23 2.02666667 2.42666667
24 1.92666667 2.02666667
25 2.02666667 1.92666667
26 2.62666667 2.02666667
27 3.52666667 2.62666667
28 4.52666667 3.52666667
29 4.42666667 4.52666667
30 19.09354839 4.42666667
31 14.49354839 19.09354839
32 9.99354839 14.49354839
33 7.49354839 9.99354839
34 6.49354839 7.49354839
35 5.99354839 6.49354839
36 5.49354839 5.99354839
37 5.19354839 5.49354839
38 4.79354839 5.19354839
39 3.79354839 4.79354839
40 2.79354839 3.79354839
41 1.89354839 2.79354839
42 1.09354839 1.89354839
43 0.89354839 1.09354839
44 0.49354839 0.89354839
45 0.19354839 0.49354839
46 -0.10645161 0.19354839
47 -1.40645161 -0.10645161
48 -2.90645161 -1.40645161
49 -3.90645161 -2.90645161
50 -4.50645161 -3.90645161
51 -4.50645161 -4.50645161
52 -4.60645161 -4.50645161
53 -5.50645161 -4.60645161
54 -6.90645161 -5.50645161
55 -8.20645161 -6.90645161
56 -8.70645161 -8.20645161
57 -9.00645161 -8.70645161
58 -9.40645161 -9.00645161
59 -10.00645161 -9.40645161
60 -10.50645161 -10.00645161
> 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/7gqks1258647230.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8cdef1258647230.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9c9ww1258647230.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101ion1258647230.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11b2gl1258647230.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/12za421258647230.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/137iga1258647230.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/14it0c1258647230.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/152upt1258647230.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/16bhmx1258647230.tab")
+ }
>
> system("convert tmp/1ildw1258647230.ps tmp/1ildw1258647230.png")
> system("convert tmp/20gts1258647230.ps tmp/20gts1258647230.png")
> system("convert tmp/32lk41258647230.ps tmp/32lk41258647230.png")
> system("convert tmp/4bjue1258647230.ps tmp/4bjue1258647230.png")
> system("convert tmp/5of2b1258647230.ps tmp/5of2b1258647230.png")
> system("convert tmp/6o1jc1258647230.ps tmp/6o1jc1258647230.png")
> system("convert tmp/7gqks1258647230.ps tmp/7gqks1258647230.png")
> system("convert tmp/8cdef1258647230.ps tmp/8cdef1258647230.png")
> system("convert tmp/9c9ww1258647230.ps tmp/9c9ww1258647230.png")
> system("convert tmp/101ion1258647230.ps tmp/101ion1258647230.png")
>
>
> proc.time()
user system elapsed
2.500 1.578 2.893