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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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(4.3,96.2,4.1,96.8,3.9,109.9,3.8,88,3.7,91.1,3.7,106.4,4.1,68.6,4.1,100.1,3.8,108,3.7,106,3.5,108.6,3.6,91.5,4.1,99.2,3.8,98,3.7,96.6,3.6,102.8,3.3,96.9,3.4,110,3.7,70.5,3.7,101.9,3.4,109.6,3.3,107.8,3,113,3,93.8,3.3,108,3,102.8,2.9,116.3,2.8,89.2,2.5,106.7,2.6,112.1,2.8,74.2,2.7,108.8,2.4,111.5,2.2,118.8,2.1,118.9,2.1,97.6,2.3,116.4,2.1,107.9,2,121.2,1.9,97.9,1.7,113.4,1.8,117.6,2.1,79.6,2,115.9,1.8,115.7,1.7,129.1,1.6,123.3,1.6,96.7,1.8,121.2,1.7,118.2,1.7,102.1,1.5,125.4,1.5,116.7,1.5,121.3,1.8,85.3,1.8,114.2,1.7,124.4,1.7,131,1.8,118.3,2,99.6),dim=c(2,60),dimnames=list(c('unempl','proman'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('unempl','proman'),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
unempl proman
1 4.3 96.2
2 4.1 96.8
3 3.9 109.9
4 3.8 88.0
5 3.7 91.1
6 3.7 106.4
7 4.1 68.6
8 4.1 100.1
9 3.8 108.0
10 3.7 106.0
11 3.5 108.6
12 3.6 91.5
13 4.1 99.2
14 3.8 98.0
15 3.7 96.6
16 3.6 102.8
17 3.3 96.9
18 3.4 110.0
19 3.7 70.5
20 3.7 101.9
21 3.4 109.6
22 3.3 107.8
23 3.0 113.0
24 3.0 93.8
25 3.3 108.0
26 3.0 102.8
27 2.9 116.3
28 2.8 89.2
29 2.5 106.7
30 2.6 112.1
31 2.8 74.2
32 2.7 108.8
33 2.4 111.5
34 2.2 118.8
35 2.1 118.9
36 2.1 97.6
37 2.3 116.4
38 2.1 107.9
39 2.0 121.2
40 1.9 97.9
41 1.7 113.4
42 1.8 117.6
43 2.1 79.6
44 2.0 115.9
45 1.8 115.7
46 1.7 129.1
47 1.6 123.3
48 1.6 96.7
49 1.8 121.2
50 1.7 118.2
51 1.7 102.1
52 1.5 125.4
53 1.5 116.7
54 1.5 121.3
55 1.8 85.3
56 1.8 114.2
57 1.7 124.4
58 1.7 131.0
59 1.8 118.3
60 2.0 99.6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) proman
6.34499 -0.03432
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.6173 -0.5411 -0.1218 0.6640 1.3270
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.344986 0.775903 8.178 3.09e-11 ***
proman -0.034322 0.007272 -4.720 1.54e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7717 on 58 degrees of freedom
Multiple R-squared: 0.2775, Adjusted R-squared: 0.265
F-statistic: 22.27 on 1 and 58 DF, p-value: 1.535e-05
> 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,] 0.0601185455 1.202371e-01 9.398815e-01
[2,] 0.0310502255 6.210045e-02 9.689498e-01
[3,] 0.0100946008 2.018920e-02 9.899054e-01
[4,] 0.0044411913 8.882383e-03 9.955588e-01
[5,] 0.0017225122 3.445024e-03 9.982775e-01
[6,] 0.0008344661 1.668932e-03 9.991655e-01
[7,] 0.0007915510 1.583102e-03 9.992084e-01
[8,] 0.0006363366 1.272673e-03 9.993637e-01
[9,] 0.0005449909 1.089982e-03 9.994550e-01
[10,] 0.0002825907 5.651813e-04 9.997174e-01
[11,] 0.0001788931 3.577861e-04 9.998211e-01
[12,] 0.0001501686 3.003373e-04 9.998498e-01
[13,] 0.0005308193 1.061639e-03 9.994692e-01
[14,] 0.0006800999 1.360200e-03 9.993199e-01
[15,] 0.0005846063 1.169213e-03 9.994154e-01
[16,] 0.0007450583 1.490117e-03 9.992549e-01
[17,] 0.0014980540 2.996108e-03 9.985019e-01
[18,] 0.0039010703 7.802141e-03 9.960989e-01
[19,] 0.0161524113 3.230482e-02 9.838476e-01
[20,] 0.0612735137 1.225470e-01 9.387265e-01
[21,] 0.1451282146 2.902564e-01 8.548718e-01
[22,] 0.3154334959 6.308670e-01 6.845665e-01
[23,] 0.5730151762 8.539696e-01 4.269848e-01
[24,] 0.8186835046 3.626330e-01 1.813165e-01
[25,] 0.9343819428 1.312361e-01 6.561806e-02
[26,] 0.9795116399 4.097672e-02 2.048836e-02
[27,] 0.9959715149 8.056970e-03 4.028485e-03
[28,] 0.9997558750 4.882500e-04 2.441250e-04
[29,] 0.9999736208 5.275845e-05 2.637923e-05
[30,] 0.9999940176 1.196487e-05 5.982436e-06
[31,] 0.9999976767 4.646614e-06 2.323307e-06
[32,] 0.9999990731 1.853771e-06 9.268855e-07
[33,] 0.9999999541 9.175112e-08 4.587556e-08
[34,] 0.9999999850 3.000704e-08 1.500352e-08
[35,] 0.9999999934 1.329062e-08 6.645309e-09
[36,] 0.9999999918 1.643960e-08 8.219802e-09
[37,] 0.9999999823 3.538618e-08 1.769309e-08
[38,] 0.9999999522 9.555647e-08 4.777824e-08
[39,] 0.9999999629 7.414291e-08 3.707145e-08
[40,] 0.9999999801 3.971823e-08 1.985912e-08
[41,] 0.9999999367 1.265098e-07 6.325490e-08
[42,] 0.9999997189 5.621391e-07 2.810696e-07
[43,] 0.9999988218 2.356374e-06 1.178187e-06
[44,] 0.9999983738 3.252444e-06 1.626222e-06
[45,] 0.9999945336 1.093284e-05 5.466421e-06
[46,] 0.9999727279 5.454429e-05 2.727215e-05
[47,] 0.9998965094 2.069813e-04 1.034906e-04
[48,] 0.9996550815 6.898369e-04 3.449185e-04
[49,] 0.9994129476 1.174105e-03 5.870524e-04
[50,] 0.9994520145 1.095971e-03 5.479855e-04
[51,] 0.9998711541 2.576918e-04 1.288459e-04
> postscript(file="/var/www/html/rcomp/tmp/1qj5i1258664730.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2h5nm1258664730.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3liy51258664730.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xgte1258664730.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ni9a1258664730.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
1.25682846 1.07742190 1.32704526 0.47538483 0.48178425 1.00691688
7 8 9 10 11 12
0.10953039 1.19068580 1.16183271 0.99318792 0.88242615 0.39551321
13 14 15 16 17 18
1.15979564 0.81860877 0.67055742 0.78335626 0.28085414 0.83047750
19 20 21 22 23 24
-0.22525706 0.85246611 0.81674854 0.65496823 0.53344468 -0.12554528
25 26 27 28 29 30
0.66183271 0.18335626 0.54670858 -0.48342829 -0.18278640 0.10255452
31 32 33 34 35 36
-0.99826420 0.08929062 -0.11803891 -0.06748544 -0.16405320 -0.89512019
37 38 39 40 41 42
-0.04985918 -0.54159953 -0.18511169 -1.08482347 -0.75282636 -0.50867231
43 44 45 46 47 48
-1.51292328 -0.36702038 -0.57388486 -0.21396478 -0.51303467 -1.42601034
49 50 51 52 53 54
-0.38511169 -0.58807887 -1.14066941 -0.54095764 -0.83956246 -0.68167945
55 56 57 58 59 60
-1.61728563 -0.62536845 -0.37528003 -0.14875223 -0.48464663 -0.92647540
> postscript(file="/var/www/html/rcomp/tmp/67lgf1258664730.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 1.25682846 NA
1 1.07742190 1.25682846
2 1.32704526 1.07742190
3 0.47538483 1.32704526
4 0.48178425 0.47538483
5 1.00691688 0.48178425
6 0.10953039 1.00691688
7 1.19068580 0.10953039
8 1.16183271 1.19068580
9 0.99318792 1.16183271
10 0.88242615 0.99318792
11 0.39551321 0.88242615
12 1.15979564 0.39551321
13 0.81860877 1.15979564
14 0.67055742 0.81860877
15 0.78335626 0.67055742
16 0.28085414 0.78335626
17 0.83047750 0.28085414
18 -0.22525706 0.83047750
19 0.85246611 -0.22525706
20 0.81674854 0.85246611
21 0.65496823 0.81674854
22 0.53344468 0.65496823
23 -0.12554528 0.53344468
24 0.66183271 -0.12554528
25 0.18335626 0.66183271
26 0.54670858 0.18335626
27 -0.48342829 0.54670858
28 -0.18278640 -0.48342829
29 0.10255452 -0.18278640
30 -0.99826420 0.10255452
31 0.08929062 -0.99826420
32 -0.11803891 0.08929062
33 -0.06748544 -0.11803891
34 -0.16405320 -0.06748544
35 -0.89512019 -0.16405320
36 -0.04985918 -0.89512019
37 -0.54159953 -0.04985918
38 -0.18511169 -0.54159953
39 -1.08482347 -0.18511169
40 -0.75282636 -1.08482347
41 -0.50867231 -0.75282636
42 -1.51292328 -0.50867231
43 -0.36702038 -1.51292328
44 -0.57388486 -0.36702038
45 -0.21396478 -0.57388486
46 -0.51303467 -0.21396478
47 -1.42601034 -0.51303467
48 -0.38511169 -1.42601034
49 -0.58807887 -0.38511169
50 -1.14066941 -0.58807887
51 -0.54095764 -1.14066941
52 -0.83956246 -0.54095764
53 -0.68167945 -0.83956246
54 -1.61728563 -0.68167945
55 -0.62536845 -1.61728563
56 -0.37528003 -0.62536845
57 -0.14875223 -0.37528003
58 -0.48464663 -0.14875223
59 -0.92647540 -0.48464663
60 NA -0.92647540
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.07742190 1.25682846
[2,] 1.32704526 1.07742190
[3,] 0.47538483 1.32704526
[4,] 0.48178425 0.47538483
[5,] 1.00691688 0.48178425
[6,] 0.10953039 1.00691688
[7,] 1.19068580 0.10953039
[8,] 1.16183271 1.19068580
[9,] 0.99318792 1.16183271
[10,] 0.88242615 0.99318792
[11,] 0.39551321 0.88242615
[12,] 1.15979564 0.39551321
[13,] 0.81860877 1.15979564
[14,] 0.67055742 0.81860877
[15,] 0.78335626 0.67055742
[16,] 0.28085414 0.78335626
[17,] 0.83047750 0.28085414
[18,] -0.22525706 0.83047750
[19,] 0.85246611 -0.22525706
[20,] 0.81674854 0.85246611
[21,] 0.65496823 0.81674854
[22,] 0.53344468 0.65496823
[23,] -0.12554528 0.53344468
[24,] 0.66183271 -0.12554528
[25,] 0.18335626 0.66183271
[26,] 0.54670858 0.18335626
[27,] -0.48342829 0.54670858
[28,] -0.18278640 -0.48342829
[29,] 0.10255452 -0.18278640
[30,] -0.99826420 0.10255452
[31,] 0.08929062 -0.99826420
[32,] -0.11803891 0.08929062
[33,] -0.06748544 -0.11803891
[34,] -0.16405320 -0.06748544
[35,] -0.89512019 -0.16405320
[36,] -0.04985918 -0.89512019
[37,] -0.54159953 -0.04985918
[38,] -0.18511169 -0.54159953
[39,] -1.08482347 -0.18511169
[40,] -0.75282636 -1.08482347
[41,] -0.50867231 -0.75282636
[42,] -1.51292328 -0.50867231
[43,] -0.36702038 -1.51292328
[44,] -0.57388486 -0.36702038
[45,] -0.21396478 -0.57388486
[46,] -0.51303467 -0.21396478
[47,] -1.42601034 -0.51303467
[48,] -0.38511169 -1.42601034
[49,] -0.58807887 -0.38511169
[50,] -1.14066941 -0.58807887
[51,] -0.54095764 -1.14066941
[52,] -0.83956246 -0.54095764
[53,] -0.68167945 -0.83956246
[54,] -1.61728563 -0.68167945
[55,] -0.62536845 -1.61728563
[56,] -0.37528003 -0.62536845
[57,] -0.14875223 -0.37528003
[58,] -0.48464663 -0.14875223
[59,] -0.92647540 -0.48464663
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.07742190 1.25682846
2 1.32704526 1.07742190
3 0.47538483 1.32704526
4 0.48178425 0.47538483
5 1.00691688 0.48178425
6 0.10953039 1.00691688
7 1.19068580 0.10953039
8 1.16183271 1.19068580
9 0.99318792 1.16183271
10 0.88242615 0.99318792
11 0.39551321 0.88242615
12 1.15979564 0.39551321
13 0.81860877 1.15979564
14 0.67055742 0.81860877
15 0.78335626 0.67055742
16 0.28085414 0.78335626
17 0.83047750 0.28085414
18 -0.22525706 0.83047750
19 0.85246611 -0.22525706
20 0.81674854 0.85246611
21 0.65496823 0.81674854
22 0.53344468 0.65496823
23 -0.12554528 0.53344468
24 0.66183271 -0.12554528
25 0.18335626 0.66183271
26 0.54670858 0.18335626
27 -0.48342829 0.54670858
28 -0.18278640 -0.48342829
29 0.10255452 -0.18278640
30 -0.99826420 0.10255452
31 0.08929062 -0.99826420
32 -0.11803891 0.08929062
33 -0.06748544 -0.11803891
34 -0.16405320 -0.06748544
35 -0.89512019 -0.16405320
36 -0.04985918 -0.89512019
37 -0.54159953 -0.04985918
38 -0.18511169 -0.54159953
39 -1.08482347 -0.18511169
40 -0.75282636 -1.08482347
41 -0.50867231 -0.75282636
42 -1.51292328 -0.50867231
43 -0.36702038 -1.51292328
44 -0.57388486 -0.36702038
45 -0.21396478 -0.57388486
46 -0.51303467 -0.21396478
47 -1.42601034 -0.51303467
48 -0.38511169 -1.42601034
49 -0.58807887 -0.38511169
50 -1.14066941 -0.58807887
51 -0.54095764 -1.14066941
52 -0.83956246 -0.54095764
53 -0.68167945 -0.83956246
54 -1.61728563 -0.68167945
55 -0.62536845 -1.61728563
56 -0.37528003 -0.62536845
57 -0.14875223 -0.37528003
58 -0.48464663 -0.14875223
59 -0.92647540 -0.48464663
> 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/7prth1258664730.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8n60m1258664730.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9dv4z1258664730.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10q17p1258664730.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11stbv1258664730.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/123l701258664730.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/132m9z1258664730.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/146b171258664730.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/15xd1l1258664730.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/16fqz51258664730.tab")
+ }
>
> system("convert tmp/1qj5i1258664730.ps tmp/1qj5i1258664730.png")
> system("convert tmp/2h5nm1258664730.ps tmp/2h5nm1258664730.png")
> system("convert tmp/3liy51258664730.ps tmp/3liy51258664730.png")
> system("convert tmp/4xgte1258664730.ps tmp/4xgte1258664730.png")
> system("convert tmp/5ni9a1258664730.ps tmp/5ni9a1258664730.png")
> system("convert tmp/67lgf1258664730.ps tmp/67lgf1258664730.png")
> system("convert tmp/7prth1258664730.ps tmp/7prth1258664730.png")
> system("convert tmp/8n60m1258664730.ps tmp/8n60m1258664730.png")
> system("convert tmp/9dv4z1258664730.ps tmp/9dv4z1258664730.png")
> system("convert tmp/10q17p1258664730.ps tmp/10q17p1258664730.png")
>
>
> proc.time()
user system elapsed
2.501 1.592 3.579