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 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(8.2,1.4,8.0,1.2,7.5,1.0,6.8,1.7,6.5,2.4,6.6,2.0,7.6,2.1,8.0,2.0,8.1,1.8,7.7,2.7,7.5,2.3,7.6,1.9,7.8,2.0,7.8,2.3,7.8,2.8,7.5,2.4,7.5,2.3,7.1,2.7,7.5,2.7,7.5,2.9,7.6,3.0,7.7,2.2,7.7,2.3,7.9,2.8,8.1,2.8,8.2,2.8,8.2,2.2,8.2,2.6,7.9,2.8,7.3,2.5,6.9,2.4,6.6,2.3,6.7,1.9,6.9,1.7,7.0,2.0,7.1,2.1,7.2,1.7,7.1,1.8,6.9,1.8,7.0,1.8,6.8,1.3,6.4,1.3,6.7,1.3,6.6,1.2,6.4,1.4,6.3,2.2,6.2,2.9,6.5,3.1,6.8,3.5,6.8,3.6,6.4,4.4,6.1,4.1,5.8,5.1,6.1,5.8,7.2,5.9,7.3,5.4,6.9,5.5,6.1,4.8,5.8,3.2,6.2,2.7,7.1,2.1,7.7,1.9,7.9,0.6,7.7,0.7),dim=c(2,64),dimnames=list(c('Y','X'),1:64))
> y <- array(NA,dim=c(2,64),dimnames=list(c('Y','X'),1:64))
> 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 8.2 1.4
2 8.0 1.2
3 7.5 1.0
4 6.8 1.7
5 6.5 2.4
6 6.6 2.0
7 7.6 2.1
8 8.0 2.0
9 8.1 1.8
10 7.7 2.7
11 7.5 2.3
12 7.6 1.9
13 7.8 2.0
14 7.8 2.3
15 7.8 2.8
16 7.5 2.4
17 7.5 2.3
18 7.1 2.7
19 7.5 2.7
20 7.5 2.9
21 7.6 3.0
22 7.7 2.2
23 7.7 2.3
24 7.9 2.8
25 8.1 2.8
26 8.2 2.8
27 8.2 2.2
28 8.2 2.6
29 7.9 2.8
30 7.3 2.5
31 6.9 2.4
32 6.6 2.3
33 6.7 1.9
34 6.9 1.7
35 7.0 2.0
36 7.1 2.1
37 7.2 1.7
38 7.1 1.8
39 6.9 1.8
40 7.0 1.8
41 6.8 1.3
42 6.4 1.3
43 6.7 1.3
44 6.6 1.2
45 6.4 1.4
46 6.3 2.2
47 6.2 2.9
48 6.5 3.1
49 6.8 3.5
50 6.8 3.6
51 6.4 4.4
52 6.1 4.1
53 5.8 5.1
54 6.1 5.8
55 7.2 5.9
56 7.3 5.4
57 6.9 5.5
58 6.1 4.8
59 5.8 3.2
60 6.2 2.7
61 7.1 2.1
62 7.7 1.9
63 7.9 0.6
64 7.7 0.7
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
7.637 -0.186
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.24152 -0.56592 0.00736 0.52478 1.08408
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.63677 0.18691 40.857 < 2e-16 ***
X -0.18602 0.06694 -2.779 0.00721 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6294 on 62 degrees of freedom
Multiple R-squared: 0.1108, Adjusted R-squared: 0.09641
F-statistic: 7.722 on 1 and 62 DF, p-value: 0.00721
> 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,] 0.46893820 0.93787640 0.5310618
[2,] 0.33924379 0.67848758 0.6607562
[3,] 0.42065431 0.84130862 0.5793457
[4,] 0.54222109 0.91555781 0.4577789
[5,] 0.57287761 0.85424479 0.4271224
[6,] 0.58584900 0.82830199 0.4141510
[7,] 0.48895681 0.97791361 0.5110432
[8,] 0.39393746 0.78787492 0.6060625
[9,] 0.33525611 0.67051223 0.6647439
[10,] 0.29510193 0.59020386 0.7048981
[11,] 0.27233063 0.54466125 0.7276694
[12,] 0.20681036 0.41362072 0.7931896
[13,] 0.15274846 0.30549691 0.8472515
[14,] 0.11669769 0.23339537 0.8833023
[15,] 0.08433046 0.16866092 0.9156695
[16,] 0.06066213 0.12132425 0.9393379
[17,] 0.04634909 0.09269819 0.9536509
[18,] 0.03446404 0.06892809 0.9655360
[19,] 0.02593985 0.05187970 0.9740601
[20,] 0.02820857 0.05641714 0.9717914
[21,] 0.04512926 0.09025853 0.9548707
[22,] 0.08727144 0.17454287 0.9127286
[23,] 0.15342540 0.30685079 0.8465746
[24,] 0.28699225 0.57398449 0.7130078
[25,] 0.37667834 0.75335669 0.6233217
[26,] 0.36499606 0.72999212 0.6350039
[27,] 0.38845988 0.77691976 0.6115401
[28,] 0.47986510 0.95973020 0.5201349
[29,] 0.51513267 0.96973465 0.4848673
[30,] 0.49033173 0.98066345 0.5096683
[31,] 0.45266290 0.90532580 0.5473371
[32,] 0.41057461 0.82114922 0.5894254
[33,] 0.36288514 0.72577027 0.6371149
[34,] 0.31729357 0.63458714 0.6827064
[35,] 0.28028803 0.56057607 0.7197120
[36,] 0.23819273 0.47638546 0.7618073
[37,] 0.20007203 0.40014406 0.7999280
[38,] 0.21889130 0.43778260 0.7811087
[39,] 0.18330022 0.36660043 0.8166998
[40,] 0.16029961 0.32059923 0.8397004
[41,] 0.17587214 0.35174429 0.8241279
[42,] 0.24049619 0.48099238 0.7595038
[43,] 0.36144107 0.72288214 0.6385589
[44,] 0.37196844 0.74393687 0.6280316
[45,] 0.32358455 0.64716911 0.6764154
[46,] 0.26919678 0.53839356 0.7308032
[47,] 0.24293982 0.48587965 0.7570602
[48,] 0.26065106 0.52130212 0.7393489
[49,] 0.30974664 0.61949328 0.6902534
[50,] 0.26192680 0.52385361 0.7380732
[51,] 0.26288398 0.52576795 0.7371160
[52,] 0.36608078 0.73216155 0.6339192
[53,] 0.65714780 0.68570440 0.3428522
[54,] 0.78094521 0.43810958 0.2190548
[55,] 0.71794415 0.56411171 0.2820559
> postscript(file="/var/www/html/rcomp/tmp/1ojds1258657857.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2aey11258657857.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3kji61258657857.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ph2o1258657857.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xc4a1258657857.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 = 64
Frequency = 1
1 2 3 4 5 6
0.82365280 0.58644943 0.04924606 -0.52054215 -0.69033036 -0.66473710
7 8 9 10 11 12
0.35386459 0.73526290 0.79805953 0.56547469 0.29106795 0.31666122
13 14 15 16 17 18
0.53526290 0.59106795 0.68407637 0.30966964 0.29106795 -0.03452531
19 20 21 22 23 24
0.36547469 0.40267806 0.52127974 0.47246627 0.49106795 0.78407637
25 26 27 28 29 30
0.98407637 1.08407637 0.97246627 1.04687301 0.78407637 0.12827132
31 32 33 34 35 36
-0.29033036 -0.60893205 -0.58333878 -0.42054215 -0.26473710 -0.14613541
37 38 39 40 41 42
-0.12054215 -0.20194047 -0.40194047 -0.30194047 -0.59494889 -0.99494889
43 44 45 46 47 48
-0.69494889 -0.81355057 -0.97634720 -0.92753373 -0.89732194 -0.56011857
49 50 51 52 53 54
-0.18571184 -0.16711015 -0.41829668 -0.77410173 -0.88808489 -0.45787310
55 56 57 58 59 60
0.66072858 0.66772016 0.28632184 -0.64388994 -1.24151689 -0.93452531
61 62 63 64
-0.14613541 0.41666122 0.37483933 0.19344101
> postscript(file="/var/www/html/rcomp/tmp/63o2d1258657857.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 = 64
Frequency = 1
lag(myerror, k = 1) myerror
0 0.82365280 NA
1 0.58644943 0.82365280
2 0.04924606 0.58644943
3 -0.52054215 0.04924606
4 -0.69033036 -0.52054215
5 -0.66473710 -0.69033036
6 0.35386459 -0.66473710
7 0.73526290 0.35386459
8 0.79805953 0.73526290
9 0.56547469 0.79805953
10 0.29106795 0.56547469
11 0.31666122 0.29106795
12 0.53526290 0.31666122
13 0.59106795 0.53526290
14 0.68407637 0.59106795
15 0.30966964 0.68407637
16 0.29106795 0.30966964
17 -0.03452531 0.29106795
18 0.36547469 -0.03452531
19 0.40267806 0.36547469
20 0.52127974 0.40267806
21 0.47246627 0.52127974
22 0.49106795 0.47246627
23 0.78407637 0.49106795
24 0.98407637 0.78407637
25 1.08407637 0.98407637
26 0.97246627 1.08407637
27 1.04687301 0.97246627
28 0.78407637 1.04687301
29 0.12827132 0.78407637
30 -0.29033036 0.12827132
31 -0.60893205 -0.29033036
32 -0.58333878 -0.60893205
33 -0.42054215 -0.58333878
34 -0.26473710 -0.42054215
35 -0.14613541 -0.26473710
36 -0.12054215 -0.14613541
37 -0.20194047 -0.12054215
38 -0.40194047 -0.20194047
39 -0.30194047 -0.40194047
40 -0.59494889 -0.30194047
41 -0.99494889 -0.59494889
42 -0.69494889 -0.99494889
43 -0.81355057 -0.69494889
44 -0.97634720 -0.81355057
45 -0.92753373 -0.97634720
46 -0.89732194 -0.92753373
47 -0.56011857 -0.89732194
48 -0.18571184 -0.56011857
49 -0.16711015 -0.18571184
50 -0.41829668 -0.16711015
51 -0.77410173 -0.41829668
52 -0.88808489 -0.77410173
53 -0.45787310 -0.88808489
54 0.66072858 -0.45787310
55 0.66772016 0.66072858
56 0.28632184 0.66772016
57 -0.64388994 0.28632184
58 -1.24151689 -0.64388994
59 -0.93452531 -1.24151689
60 -0.14613541 -0.93452531
61 0.41666122 -0.14613541
62 0.37483933 0.41666122
63 0.19344101 0.37483933
64 NA 0.19344101
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.58644943 0.82365280
[2,] 0.04924606 0.58644943
[3,] -0.52054215 0.04924606
[4,] -0.69033036 -0.52054215
[5,] -0.66473710 -0.69033036
[6,] 0.35386459 -0.66473710
[7,] 0.73526290 0.35386459
[8,] 0.79805953 0.73526290
[9,] 0.56547469 0.79805953
[10,] 0.29106795 0.56547469
[11,] 0.31666122 0.29106795
[12,] 0.53526290 0.31666122
[13,] 0.59106795 0.53526290
[14,] 0.68407637 0.59106795
[15,] 0.30966964 0.68407637
[16,] 0.29106795 0.30966964
[17,] -0.03452531 0.29106795
[18,] 0.36547469 -0.03452531
[19,] 0.40267806 0.36547469
[20,] 0.52127974 0.40267806
[21,] 0.47246627 0.52127974
[22,] 0.49106795 0.47246627
[23,] 0.78407637 0.49106795
[24,] 0.98407637 0.78407637
[25,] 1.08407637 0.98407637
[26,] 0.97246627 1.08407637
[27,] 1.04687301 0.97246627
[28,] 0.78407637 1.04687301
[29,] 0.12827132 0.78407637
[30,] -0.29033036 0.12827132
[31,] -0.60893205 -0.29033036
[32,] -0.58333878 -0.60893205
[33,] -0.42054215 -0.58333878
[34,] -0.26473710 -0.42054215
[35,] -0.14613541 -0.26473710
[36,] -0.12054215 -0.14613541
[37,] -0.20194047 -0.12054215
[38,] -0.40194047 -0.20194047
[39,] -0.30194047 -0.40194047
[40,] -0.59494889 -0.30194047
[41,] -0.99494889 -0.59494889
[42,] -0.69494889 -0.99494889
[43,] -0.81355057 -0.69494889
[44,] -0.97634720 -0.81355057
[45,] -0.92753373 -0.97634720
[46,] -0.89732194 -0.92753373
[47,] -0.56011857 -0.89732194
[48,] -0.18571184 -0.56011857
[49,] -0.16711015 -0.18571184
[50,] -0.41829668 -0.16711015
[51,] -0.77410173 -0.41829668
[52,] -0.88808489 -0.77410173
[53,] -0.45787310 -0.88808489
[54,] 0.66072858 -0.45787310
[55,] 0.66772016 0.66072858
[56,] 0.28632184 0.66772016
[57,] -0.64388994 0.28632184
[58,] -1.24151689 -0.64388994
[59,] -0.93452531 -1.24151689
[60,] -0.14613541 -0.93452531
[61,] 0.41666122 -0.14613541
[62,] 0.37483933 0.41666122
[63,] 0.19344101 0.37483933
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.58644943 0.82365280
2 0.04924606 0.58644943
3 -0.52054215 0.04924606
4 -0.69033036 -0.52054215
5 -0.66473710 -0.69033036
6 0.35386459 -0.66473710
7 0.73526290 0.35386459
8 0.79805953 0.73526290
9 0.56547469 0.79805953
10 0.29106795 0.56547469
11 0.31666122 0.29106795
12 0.53526290 0.31666122
13 0.59106795 0.53526290
14 0.68407637 0.59106795
15 0.30966964 0.68407637
16 0.29106795 0.30966964
17 -0.03452531 0.29106795
18 0.36547469 -0.03452531
19 0.40267806 0.36547469
20 0.52127974 0.40267806
21 0.47246627 0.52127974
22 0.49106795 0.47246627
23 0.78407637 0.49106795
24 0.98407637 0.78407637
25 1.08407637 0.98407637
26 0.97246627 1.08407637
27 1.04687301 0.97246627
28 0.78407637 1.04687301
29 0.12827132 0.78407637
30 -0.29033036 0.12827132
31 -0.60893205 -0.29033036
32 -0.58333878 -0.60893205
33 -0.42054215 -0.58333878
34 -0.26473710 -0.42054215
35 -0.14613541 -0.26473710
36 -0.12054215 -0.14613541
37 -0.20194047 -0.12054215
38 -0.40194047 -0.20194047
39 -0.30194047 -0.40194047
40 -0.59494889 -0.30194047
41 -0.99494889 -0.59494889
42 -0.69494889 -0.99494889
43 -0.81355057 -0.69494889
44 -0.97634720 -0.81355057
45 -0.92753373 -0.97634720
46 -0.89732194 -0.92753373
47 -0.56011857 -0.89732194
48 -0.18571184 -0.56011857
49 -0.16711015 -0.18571184
50 -0.41829668 -0.16711015
51 -0.77410173 -0.41829668
52 -0.88808489 -0.77410173
53 -0.45787310 -0.88808489
54 0.66072858 -0.45787310
55 0.66772016 0.66072858
56 0.28632184 0.66772016
57 -0.64388994 0.28632184
58 -1.24151689 -0.64388994
59 -0.93452531 -1.24151689
60 -0.14613541 -0.93452531
61 0.41666122 -0.14613541
62 0.37483933 0.41666122
63 0.19344101 0.37483933
> 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/7bwcj1258657857.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8x1dv1258657857.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9sm821258657857.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10iugg1258657857.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111p4h1258657857.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/12lb441258657857.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/1310bi1258657858.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/14oxmo1258657858.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/15hul81258657858.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/16b5a31258657858.tab")
+ }
>
> system("convert tmp/1ojds1258657857.ps tmp/1ojds1258657857.png")
> system("convert tmp/2aey11258657857.ps tmp/2aey11258657857.png")
> system("convert tmp/3kji61258657857.ps tmp/3kji61258657857.png")
> system("convert tmp/4ph2o1258657857.ps tmp/4ph2o1258657857.png")
> system("convert tmp/5xc4a1258657857.ps tmp/5xc4a1258657857.png")
> system("convert tmp/63o2d1258657857.ps tmp/63o2d1258657857.png")
> system("convert tmp/7bwcj1258657857.ps tmp/7bwcj1258657857.png")
> system("convert tmp/8x1dv1258657857.ps tmp/8x1dv1258657857.png")
> system("convert tmp/9sm821258657857.ps tmp/9sm821258657857.png")
> system("convert tmp/10iugg1258657857.ps tmp/10iugg1258657857.png")
>
>
> proc.time()
user system elapsed
2.547 1.609 3.022