Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
-0.03086 -0.01025 0.04860 0.04399 -0.03429 0.00779 0.00149 0.01848 0.00338 0.00099 -0.01826 0.04033 -0.03086 -0.01025 0.04860 0.04399 -0.03429 0.01244 0.00149 0.01848 0.00338 0.00099 -0.02352 0.04033 -0.03086 -0.01025 0.04860 0.04399 0.01150 0.01244 0.00149 0.01848 0.00338 0.00573 -0.02352 0.04033 -0.03086 -0.01025 0.04860 -0.00793 0.01150 0.01244 0.00149 0.01848 0.01805 0.00573 -0.02352 0.04033 -0.03086 -0.01025 -0.01514 -0.00793 0.01150 0.01244 0.00149 -0.01887 0.01805 0.00573 -0.02352 0.04033 -0.03086 0.01778 -0.01514 -0.00793 0.01150 0.01244 0.04363 -0.01887 0.01805 0.00573 -0.02352 0.04033 0.00634 0.01778 -0.01514 -0.00793 0.01150 0.02875 0.04363 -0.01887 0.01805 0.00573 -0.02352 0.00770 0.00634 0.01778 -0.01514 -0.00793 -0.00393 0.02875 0.04363 -0.01887 0.01805 0.00573 0.00692 0.00770 0.00634 0.01778 -0.01514 0.05280 -0.00393 0.02875 0.04363 -0.01887 0.01805 0.00029 0.00692 0.00770 0.00634 0.01778 -0.00351 0.05280 -0.00393 0.02875 0.04363 -0.01887 0.02487 0.00029 0.00692 0.00770 0.00634 0.05407 -0.00351 0.05280 -0.00393 0.02875 0.04363 0.01708 0.02487 0.00029 0.00692 0.00770 -0.01299 0.05407 -0.00351 0.05280 -0.00393 0.02875 0.02540 0.01708 0.02487 0.00029 0.00692 0.00747 -0.01299 0.05407 -0.00351 0.05280 -0.00393 0.02935 0.02540 0.01708 0.02487 0.00029 -0.03288 0.00747 -0.01299 0.05407 -0.00351 0.05280 0.02615 0.02935 0.02540 0.01708 0.02487 -0.05013 -0.03288 0.00747 -0.01299 0.05407 -0.00351 0.00424 0.02615 0.02935 0.02540 0.01708 0.03715 -0.05013 -0.03288 0.00747 -0.01299 0.05407 -0.00032 0.00424 0.02615 0.02935 0.02540 0.00205 0.03715 -0.05013 -0.03288 0.00747 -0.01299 -0.02353 -0.00032 0.00424 0.02615 0.02935 0.02912 0.00205 0.03715 -0.05013 -0.03288 0.00747 0.01387 -0.02353 -0.00032 0.00424 0.02615 -0.00832 0.02912 0.00205 0.03715 -0.05013 -0.03288 0.01286 0.01387 -0.02353 -0.00032 0.00424 0.02908 -0.00832 0.02912 0.00205 0.03715 -0.05013 -0.00609 0.01286 0.01387 -0.02353 -0.00032 -0.00942 0.02908 -0.00832 0.02912 0.00205 0.03715 0.00635 -0.00609 0.01286 0.01387 -0.02353 0.04381 -0.00942 0.02908 -0.00832 0.02912 0.00205 0.02049 0.00635 -0.00609 0.01286 0.01387 0.00603 0.04381 -0.00942 0.02908 -0.00832 0.02912 0.00332 0.02049 0.00635 -0.00609 0.01286 0.02253 0.00603 0.04381 -0.00942 0.02908 -0.00832 0.00409 0.00332 0.02049 0.00635 -0.00609 0.05789 0.02253 0.00603 0.04381 -0.00942 0.02908 0.02753 0.00409 0.00332 0.02049 0.00635 -0.03783 0.05789 0.02253 0.00603 0.04381 -0.00942 0.01205 0.02753 0.00409 0.00332 0.02049 -0.03176 -0.03783 0.05789 0.02253 0.00603 0.04381 0.01773 0.01205 0.02753 0.00409 0.00332 -0.00572 -0.03176 -0.03783 0.05789 0.02253 0.00603 -0.00897 0.01773 0.01205 0.02753 0.00409 0.01040 -0.00572 -0.03176 -0.03783 0.05789 0.02253 -0.01226 -0.00897 0.01773 0.01205 0.02753 0.03662 0.01040 -0.00572 -0.03176 -0.03783 0.05789 0.00644 -0.01226 -0.00897 0.01773 0.01205 0.03771 0.03662 0.01040 -0.00572 -0.03176 -0.03783 -0.00059 0.00644 -0.01226 -0.00897 0.01773 0.05981 0.03771 0.03662 0.01040 -0.00572 -0.03176 0.01707 -0.00059 0.00644 -0.01226 -0.00897 -0.03204 0.05981 0.03771 0.03662 0.01040 -0.00572 -0.00104 0.01707 -0.00059 0.00644 -0.01226 0.02837 -0.03204 0.05981 0.03771 0.03662 0.01040 0.01272 -0.00104 0.01707 -0.00059 0.00644 0.05003 0.02837 -0.03204 0.05981 0.03771 0.03662 0.01859 0.01272 -0.00104 0.01707 -0.00059 0.04980 0.05003 0.02837 -0.03204 0.05981 0.03771 0.03238 0.01859 0.01272 -0.00104 0.01707 -0.02299 0.04980 0.05003 0.02837 -0.03204 0.05981 0.03132 0.03238 0.01859 0.01272 -0.00104 0.04030 -0.02299 0.04980 0.05003 0.02837 -0.03204 0.01412 0.03132 0.03238 0.01859 0.01272 0.03176 0.04030 -0.02299 0.04980 0.05003 0.02837 0.00588 0.01412 0.03132 0.03238 0.01859 -0.00135 0.03176 0.04030 -0.02299 0.04980 0.05003 0.05686 0.00588 0.01412 0.03132 0.03238 -0.02473 -0.00135 0.03176 0.04030 -0.02299 0.04980 0.05681 0.05686 0.00588 0.01412 0.03132 -0.00171 -0.02473 -0.00135 0.03176 0.04030 -0.02299 -0.04078 0.05681 0.05686 0.00588 0.01412 -0.01575 -0.00171 -0.02473 -0.00135 0.03176 0.04030 0.02507 -0.04078 0.05681 0.05686 0.00588 -0.02624 -0.01575 -0.00171 -0.02473 -0.00135 0.03176 0.00600 0.02507 -0.04078 0.05681 0.05686 0.06724 -0.02624 -0.01575 -0.00171 -0.02473 -0.00135 0.00249 0.00600 0.02507 -0.04078 0.05681 -0.01362 0.06724 -0.02624 -0.01575 -0.00171 -0.02473 0.01885 0.00249 0.00600 0.02507 -0.04078 -0.00422 -0.01362 0.06724 -0.02624 -0.01575 -0.00171 0.00125 0.01885 0.00249 0.00600 0.02507 0.00754 -0.00422 -0.01362 0.06724 -0.02624 -0.01575 0.00695 0.00125 0.01885 0.00249 0.00600 0.00087 0.00754 -0.00422 -0.01362 0.06724 -0.02624 -0.01563 0.00695 0.00125 0.01885 0.00249 0.02715 0.00087 0.00754 -0.00422 -0.01362 0.06724 0.00814 -0.01563 0.00695 0.00125 0.01885 0.02976 0.02715 0.00087 0.00754 -0.00422 -0.01362 0.02368 0.00814 -0.01563 0.00695 0.00125 0.07946 0.02976 0.02715 0.00087 0.00754 -0.00422 0.04099 0.02368 0.00814 -0.01563 0.00695 0.01909 0.07946 0.02976 0.02715 0.00087 0.00754 0.00731 0.04099 0.02368 0.00814 -0.01563 -0.02483 0.01909 0.07946 0.02976 0.02715 0.00087 -0.01730 0.00731 0.04099 0.02368 0.00814 -0.01870 -0.02483 0.01909 0.07946 0.02976 0.02715 -0.00183 -0.01730 0.00731 0.04099 0.02368 0.09682 -0.01870 -0.02483 0.01909 0.07946 0.02976 -0.03830 -0.00183 -0.01730 0.00731 0.04099 0.03823 0.09682 -0.01870 -0.02483 0.01909 0.07946 -0.01249 -0.03830 -0.00183 -0.01730 0.00731 0.09571 0.03823 0.09682 -0.01870 -0.02483 0.01909 0.01229 -0.01249 -0.03830 -0.00183 -0.01730 -0.04663 0.09571 0.03823 0.09682 -0.01870 -0.02483 -0.01747 0.01229 -0.01249 -0.03830 -0.00183 -0.01359 -0.04663 0.09571 0.03823 0.09682 -0.01870 -0.02645 -0.01747 0.01229 -0.01249 -0.03830 0.05114 -0.01359 -0.04663 0.09571 0.03823 0.09682 0.04038 -0.02645 -0.01747 0.01229 -0.01249 -0.04275 0.05114 -0.01359 -0.04663 0.09571 0.03823 0.02925 0.04038 -0.02645 -0.01747 0.01229 0.05739 -0.04275 0.05114 -0.01359 -0.04663 0.09571 0.02270 0.02925 0.04038 -0.02645 -0.01747 0.01186 0.05739 -0.04275 0.05114 -0.01359 -0.04663 -0.00460 0.02270 0.02925 0.04038 -0.02645 0.01066 0.01186 0.05739 -0.04275 0.05114 -0.01359 -0.01894 -0.00460 0.02270 0.02925 0.04038 -0.07387 0.01066 0.01186 0.05739 -0.04275 0.05114 -0.00966 -0.01894 -0.00460 0.02270 0.02925 -0.04131 -0.07387 0.01066 0.01186 0.05739 -0.04275 0.00392 -0.00966 -0.01894 -0.00460 0.02270 -0.17889 -0.04131 -0.07387 0.01066 0.01186 0.05739 -0.03105 0.00392 -0.00966 -0.01894 -0.00460 -0.12781 -0.17889 -0.04131 -0.07387 0.01066 0.01186 -0.02790 -0.03105 0.00392 -0.00966 -0.01894 -0.26933 -0.12781 -0.17889 -0.04131 -0.07387 0.01066 -0.09625 -0.02790 -0.03105 0.00392 -0.00966 -0.05095 -0.26933 -0.12781 -0.17889 -0.04131 -0.07387 -0.05388 -0.09625 -0.02790 -0.03105 0.00392 -0.01074 -0.05095 -0.26933 -0.12781 -0.17889 -0.04131 -0.05034 -0.05388 -0.09625 -0.02790 -0.03105 0.08172 -0.01074 -0.05095 -0.26933 -0.12781 -0.17889 -0.02846 -0.05034 -0.05388 -0.09625 -0.02790 0.11870 0.08172 -0.01074 -0.05095 -0.26933 -0.12781 -0.01454 -0.02846 -0.05034 -0.05388 -0.09625 0.08475 0.11870 0.08172 -0.01074 -0.05095 -0.26933 0.01284 -0.01454 -0.02846 -0.05034 -0.05388 0.04663 0.08475 0.11870 0.08172 -0.01074 -0.05095 0.03762 0.01284 -0.01454 -0.02846 -0.05034 -0.04415 0.04663 0.08475 0.11870 0.08172 -0.01074 0.01973 0.03762 0.01284 -0.01454 -0.02846 0.00970 -0.04415 0.04663 0.08475 0.11870 0.08172 0.03178 0.01973 0.03762 0.01284 -0.01454 -0.03341 0.00970 -0.04415 0.04663 0.08475 0.11870 0.01329 0.03178 0.01973 0.03762 0.01284 0.04031 -0.03341 0.00970 -0.04415 0.04663 0.08475 0.05094 0.01329 0.03178 0.01973 0.03762 0.01938 0.04031 -0.03341 0.00970 -0.04415 0.04663 -0.00804 0.05094 0.01329 0.03178 0.01973 0.05928 0.01938 0.04031 -0.03341 0.00970 -0.04415 0.01116 -0.00804 0.05094 0.01329 0.03178 0.02343 0.05928 0.01938 0.04031 -0.03341 0.00970 0.01128 0.01116 -0.00804 0.05094 0.01329 -0.04536 0.02343 0.05928 0.01938 0.04031 -0.03341 0.02227 0.01128 0.01116 -0.00804 0.05094 0.03355 -0.04536 0.02343 0.05928 0.01938 0.04031 0.01494 0.02227 0.01128 0.01116 -0.00804 0.05659 0.03355 -0.04536 0.02343 0.05928 0.01938 -0.02514 0.01494 0.02227 0.01128 0.01116 -0.06579 0.05659 0.03355 -0.04536 0.02343 0.05928 0.02975 -0.02514 0.01494 0.02227 0.01128 -0.04267 -0.06579 0.05659 0.03355 -0.04536 0.02343 0.05216 0.02975 -0.02514 0.01494 0.02227 -0.02422 -0.04267 -0.06579 0.05659 0.03355 -0.04536 -0.04459 0.05216 0.02975 -0.02514 0.01494 0.07584 -0.02422 -0.04267 -0.06579 0.05659 0.03355 -0.02212 -0.04459 0.05216 0.02975 -0.02514 -0.00903 0.07584 -0.02422 -0.04267 -0.06579 0.05659 0.03171 -0.02212 -0.04459 0.05216 0.02975 0.06617 -0.00903 0.07584 -0.02422 -0.04267 -0.06579 0.02985 0.03171 -0.02212 -0.04459 0.05216 0.04485 0.06617 -0.00903 0.07584 -0.02422 -0.04267 0.01545 0.02985 0.03171 -0.02212 -0.04459 -0.00665 0.04485 0.06617 -0.00903 0.07584 -0.02422 0.01140 0.01545 0.02985 0.03171 -0.02212
Names of X columns:
(1-B)lnYt (1-B)lnY_[t-1] (1-B)lnY_[t-2] (1-B)lnY_[t-3] (1-B)lnY_[t-4] (1-B)lnY_[t-5] (1-B)lnX_[t-1] (1-B)lnX_[t-2] (1-B)lnX_[t-3] (1-B)lnX_[t-4] (1-B)lnX_[t-5]
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) 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 k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) 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)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z 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() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$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$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.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='mytable5.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='mytable6.tab') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation