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(3353,1,3186,1,3902,1,4164,1,3499,1,4145,1,3796,1,3711,1,3949,1,3740,1,3243,1,4407,1,4814,1,3908,1,5250,1,3937,1,4004,1,5560,1,3922,1,3759,1,4138,1,4634,1,3996,1,4308,1,4143,0,4429,0,5219,0,4929,0,5755,0,5592,0,4163,0,4962,0,5208,0,4755,0,4491,0,5732,0,5731,0,5040,0,6102,0,4904,0,5369,0,5578,0,4619,0,4731,0,5011,0,5299,0,4146,0,4625,0,4736,0,4219,0,5116,0,4205,0,4121,0,5103,1,4300,1,4578,1,3809,1,5526,1,4247,1,3830,1,4394,1),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 3353 1
2 3186 1
3 3902 1
4 4164 1
5 3499 1
6 4145 1
7 3796 1
8 3711 1
9 3949 1
10 3740 1
11 3243 1
12 4407 1
13 4814 1
14 3908 1
15 5250 1
16 3937 1
17 4004 1
18 5560 1
19 3922 1
20 3759 1
21 4138 1
22 4634 1
23 3996 1
24 4308 1
25 4143 0
26 4429 0
27 5219 0
28 4929 0
29 5755 0
30 5592 0
31 4163 0
32 4962 0
33 5208 0
34 4755 0
35 4491 0
36 5732 0
37 5731 0
38 5040 0
39 6102 0
40 4904 0
41 5369 0
42 5578 0
43 4619 0
44 4731 0
45 5011 0
46 5299 0
47 4146 0
48 4625 0
49 4736 0
50 4219 0
51 5116 0
52 4205 0
53 4121 0
54 5103 1
55 4300 1
56 4578 1
57 3809 1
58 5526 1
59 4247 1
60 3830 1
61 4394 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
4928.6 -768.9
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-973.75 -363.75 -24.62 290.38 1400.25
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4928.6 108.1 45.586 < 2e-16 ***
X -768.9 149.3 -5.151 3.14e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 582.2 on 59 degrees of freedom
Multiple R-squared: 0.3102, Adjusted R-squared: 0.2985
F-statistic: 26.53 on 1 and 59 DF, p-value: 3.139e-06
> 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,] 0.42182845 0.8436569 0.5781716
[2,] 0.37813358 0.7562672 0.6218664
[3,] 0.24713968 0.4942794 0.7528603
[4,] 0.15242525 0.3048505 0.8475748
[5,] 0.09835470 0.1967094 0.9016453
[6,] 0.05627188 0.1125438 0.9437281
[7,] 0.07382301 0.1476460 0.9261770
[8,] 0.11764506 0.2352901 0.8823549
[9,] 0.29929605 0.5985921 0.7007039
[10,] 0.22943053 0.4588611 0.7705695
[11,] 0.59935315 0.8012937 0.4006468
[12,] 0.52276203 0.9544759 0.4772380
[13,] 0.44582441 0.8916488 0.5541756
[14,] 0.83219366 0.3356127 0.1678063
[15,] 0.78716203 0.4256759 0.2128380
[16,] 0.75759485 0.4848103 0.2424051
[17,] 0.69837187 0.6032563 0.3016281
[18,] 0.67654159 0.6469168 0.3234584
[19,] 0.61935367 0.7612927 0.3806463
[20,] 0.55428805 0.8914239 0.4457119
[21,] 0.53625750 0.9274850 0.4637425
[22,] 0.49303400 0.9860680 0.5069660
[23,] 0.49990288 0.9998058 0.5000971
[24,] 0.43463473 0.8692695 0.5653653
[25,] 0.53751045 0.9249791 0.4624896
[26,] 0.55676738 0.8864652 0.4432326
[27,] 0.61298236 0.7740353 0.3870176
[28,] 0.53907478 0.9218504 0.4609252
[29,] 0.48094390 0.9618878 0.5190561
[30,] 0.41212009 0.8242402 0.5878799
[31,] 0.37896996 0.7579399 0.6210300
[32,] 0.44596798 0.8919360 0.5540320
[33,] 0.51711174 0.9657765 0.4828883
[34,] 0.44383887 0.8876777 0.5561611
[35,] 0.71347283 0.5730543 0.2865272
[36,] 0.64651394 0.7069721 0.3534861
[37,] 0.64737460 0.7052508 0.3526254
[38,] 0.74019508 0.5196098 0.2598049
[39,] 0.68090379 0.6381924 0.3190962
[40,] 0.61048152 0.7790370 0.3895185
[41,] 0.56379052 0.8724190 0.4362095
[42,] 0.62483514 0.7503297 0.3751649
[43,] 0.61006448 0.7798710 0.3899355
[44,] 0.52861414 0.9427717 0.4713859
[45,] 0.45464949 0.9092990 0.5453505
[46,] 0.39783539 0.7956708 0.6021646
[47,] 0.45538558 0.9107712 0.5446144
[48,] 0.37156982 0.7431396 0.6284302
[49,] 0.28908458 0.5781692 0.7109154
[50,] 0.32760550 0.6552110 0.6723945
[51,] 0.21407303 0.4281461 0.7859270
[52,] 0.12792019 0.2558404 0.8720798
> postscript(file="/var/www/html/rcomp/tmp/1y10m1258617442.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2q0y61258617442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3b5u21258617442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4t58r1258617442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xfer1258617442.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
-806.7500000 -973.7500000 -257.7500000 4.2500000 -660.7500000 -14.7500000
7 8 9 10 11 12
-363.7500000 -448.7500000 -210.7500000 -419.7500000 -916.7500000 247.2500000
13 14 15 16 17 18
654.2500000 -251.7500000 1090.2500000 -222.7500000 -155.7500000 1400.2500000
19 20 21 22 23 24
-237.7500000 -400.7500000 -21.7500000 474.2500000 -163.7500000 148.2500000
25 26 27 28 29 30
-785.6206897 -499.6206897 290.3793103 0.3793103 826.3793103 663.3793103
31 32 33 34 35 36
-765.6206897 33.3793103 279.3793103 -173.6206897 -437.6206897 803.3793103
37 38 39 40 41 42
802.3793103 111.3793103 1173.3793103 -24.6206897 440.3793103 649.3793103
43 44 45 46 47 48
-309.6206897 -197.6206897 82.3793103 370.3793103 -782.6206897 -303.6206897
49 50 51 52 53 54
-192.6206897 -709.6206897 187.3793103 -723.6206897 -807.6206897 943.2500000
55 56 57 58 59 60
140.2500000 418.2500000 -350.7500000 1366.2500000 87.2500000 -329.7500000
61
234.2500000
> postscript(file="/var/www/html/rcomp/tmp/6xlzy1258617442.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 -806.7500000 NA
1 -973.7500000 -806.7500000
2 -257.7500000 -973.7500000
3 4.2500000 -257.7500000
4 -660.7500000 4.2500000
5 -14.7500000 -660.7500000
6 -363.7500000 -14.7500000
7 -448.7500000 -363.7500000
8 -210.7500000 -448.7500000
9 -419.7500000 -210.7500000
10 -916.7500000 -419.7500000
11 247.2500000 -916.7500000
12 654.2500000 247.2500000
13 -251.7500000 654.2500000
14 1090.2500000 -251.7500000
15 -222.7500000 1090.2500000
16 -155.7500000 -222.7500000
17 1400.2500000 -155.7500000
18 -237.7500000 1400.2500000
19 -400.7500000 -237.7500000
20 -21.7500000 -400.7500000
21 474.2500000 -21.7500000
22 -163.7500000 474.2500000
23 148.2500000 -163.7500000
24 -785.6206897 148.2500000
25 -499.6206897 -785.6206897
26 290.3793103 -499.6206897
27 0.3793103 290.3793103
28 826.3793103 0.3793103
29 663.3793103 826.3793103
30 -765.6206897 663.3793103
31 33.3793103 -765.6206897
32 279.3793103 33.3793103
33 -173.6206897 279.3793103
34 -437.6206897 -173.6206897
35 803.3793103 -437.6206897
36 802.3793103 803.3793103
37 111.3793103 802.3793103
38 1173.3793103 111.3793103
39 -24.6206897 1173.3793103
40 440.3793103 -24.6206897
41 649.3793103 440.3793103
42 -309.6206897 649.3793103
43 -197.6206897 -309.6206897
44 82.3793103 -197.6206897
45 370.3793103 82.3793103
46 -782.6206897 370.3793103
47 -303.6206897 -782.6206897
48 -192.6206897 -303.6206897
49 -709.6206897 -192.6206897
50 187.3793103 -709.6206897
51 -723.6206897 187.3793103
52 -807.6206897 -723.6206897
53 943.2500000 -807.6206897
54 140.2500000 943.2500000
55 418.2500000 140.2500000
56 -350.7500000 418.2500000
57 1366.2500000 -350.7500000
58 87.2500000 1366.2500000
59 -329.7500000 87.2500000
60 234.2500000 -329.7500000
61 NA 234.2500000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -973.7500000 -806.7500000
[2,] -257.7500000 -973.7500000
[3,] 4.2500000 -257.7500000
[4,] -660.7500000 4.2500000
[5,] -14.7500000 -660.7500000
[6,] -363.7500000 -14.7500000
[7,] -448.7500000 -363.7500000
[8,] -210.7500000 -448.7500000
[9,] -419.7500000 -210.7500000
[10,] -916.7500000 -419.7500000
[11,] 247.2500000 -916.7500000
[12,] 654.2500000 247.2500000
[13,] -251.7500000 654.2500000
[14,] 1090.2500000 -251.7500000
[15,] -222.7500000 1090.2500000
[16,] -155.7500000 -222.7500000
[17,] 1400.2500000 -155.7500000
[18,] -237.7500000 1400.2500000
[19,] -400.7500000 -237.7500000
[20,] -21.7500000 -400.7500000
[21,] 474.2500000 -21.7500000
[22,] -163.7500000 474.2500000
[23,] 148.2500000 -163.7500000
[24,] -785.6206897 148.2500000
[25,] -499.6206897 -785.6206897
[26,] 290.3793103 -499.6206897
[27,] 0.3793103 290.3793103
[28,] 826.3793103 0.3793103
[29,] 663.3793103 826.3793103
[30,] -765.6206897 663.3793103
[31,] 33.3793103 -765.6206897
[32,] 279.3793103 33.3793103
[33,] -173.6206897 279.3793103
[34,] -437.6206897 -173.6206897
[35,] 803.3793103 -437.6206897
[36,] 802.3793103 803.3793103
[37,] 111.3793103 802.3793103
[38,] 1173.3793103 111.3793103
[39,] -24.6206897 1173.3793103
[40,] 440.3793103 -24.6206897
[41,] 649.3793103 440.3793103
[42,] -309.6206897 649.3793103
[43,] -197.6206897 -309.6206897
[44,] 82.3793103 -197.6206897
[45,] 370.3793103 82.3793103
[46,] -782.6206897 370.3793103
[47,] -303.6206897 -782.6206897
[48,] -192.6206897 -303.6206897
[49,] -709.6206897 -192.6206897
[50,] 187.3793103 -709.6206897
[51,] -723.6206897 187.3793103
[52,] -807.6206897 -723.6206897
[53,] 943.2500000 -807.6206897
[54,] 140.2500000 943.2500000
[55,] 418.2500000 140.2500000
[56,] -350.7500000 418.2500000
[57,] 1366.2500000 -350.7500000
[58,] 87.2500000 1366.2500000
[59,] -329.7500000 87.2500000
[60,] 234.2500000 -329.7500000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -973.7500000 -806.7500000
2 -257.7500000 -973.7500000
3 4.2500000 -257.7500000
4 -660.7500000 4.2500000
5 -14.7500000 -660.7500000
6 -363.7500000 -14.7500000
7 -448.7500000 -363.7500000
8 -210.7500000 -448.7500000
9 -419.7500000 -210.7500000
10 -916.7500000 -419.7500000
11 247.2500000 -916.7500000
12 654.2500000 247.2500000
13 -251.7500000 654.2500000
14 1090.2500000 -251.7500000
15 -222.7500000 1090.2500000
16 -155.7500000 -222.7500000
17 1400.2500000 -155.7500000
18 -237.7500000 1400.2500000
19 -400.7500000 -237.7500000
20 -21.7500000 -400.7500000
21 474.2500000 -21.7500000
22 -163.7500000 474.2500000
23 148.2500000 -163.7500000
24 -785.6206897 148.2500000
25 -499.6206897 -785.6206897
26 290.3793103 -499.6206897
27 0.3793103 290.3793103
28 826.3793103 0.3793103
29 663.3793103 826.3793103
30 -765.6206897 663.3793103
31 33.3793103 -765.6206897
32 279.3793103 33.3793103
33 -173.6206897 279.3793103
34 -437.6206897 -173.6206897
35 803.3793103 -437.6206897
36 802.3793103 803.3793103
37 111.3793103 802.3793103
38 1173.3793103 111.3793103
39 -24.6206897 1173.3793103
40 440.3793103 -24.6206897
41 649.3793103 440.3793103
42 -309.6206897 649.3793103
43 -197.6206897 -309.6206897
44 82.3793103 -197.6206897
45 370.3793103 82.3793103
46 -782.6206897 370.3793103
47 -303.6206897 -782.6206897
48 -192.6206897 -303.6206897
49 -709.6206897 -192.6206897
50 187.3793103 -709.6206897
51 -723.6206897 187.3793103
52 -807.6206897 -723.6206897
53 943.2500000 -807.6206897
54 140.2500000 943.2500000
55 418.2500000 140.2500000
56 -350.7500000 418.2500000
57 1366.2500000 -350.7500000
58 87.2500000 1366.2500000
59 -329.7500000 87.2500000
60 234.2500000 -329.7500000
> 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/7x33c1258617442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hnir1258617442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/935kb1258617442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/104a8q1258617442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11h1zn1258617442.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/12onol1258617442.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/137nwx1258617442.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/14ae3w1258617442.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/15a29n1258617442.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/16oqxp1258617442.tab")
+ }
>
> system("convert tmp/1y10m1258617442.ps tmp/1y10m1258617442.png")
> system("convert tmp/2q0y61258617442.ps tmp/2q0y61258617442.png")
> system("convert tmp/3b5u21258617442.ps tmp/3b5u21258617442.png")
> system("convert tmp/4t58r1258617442.ps tmp/4t58r1258617442.png")
> system("convert tmp/5xfer1258617442.ps tmp/5xfer1258617442.png")
> system("convert tmp/6xlzy1258617442.ps tmp/6xlzy1258617442.png")
> system("convert tmp/7x33c1258617442.ps tmp/7x33c1258617442.png")
> system("convert tmp/8hnir1258617442.ps tmp/8hnir1258617442.png")
> system("convert tmp/935kb1258617442.ps tmp/935kb1258617442.png")
> system("convert tmp/104a8q1258617442.ps tmp/104a8q1258617442.png")
>
>
> proc.time()
user system elapsed
2.444 1.533 3.574