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Data X:
30277 694 355 30277 694 355 47262 1486 670 110000 2974 1910 101353 2642 1000 70367 2052 920 70367 2052 920 70367 2056 920 70367 2052 920 110239 3700 1150 110000 2974 1160 46052 1452 660 70367 2052 920 70367 2052 920 86000 2124 930 110000 2974 1160 88500 2124 1030 70367 2052 920 88500 2124 930 70367 2052 920 88500 2124 1029 101509 2758 1000 110000 2974 1160 101509 2758 1150 70606 1770 858 91000 2032 999 77713 1890 909 91000 2032 999 77713 1882 909 91000 2032 999 122000 2850 670 91000 2032 999 2329 94 60 47225 1366 670 28430 808 400 85619 2114 920 52926 1302 617 53872 1494 636 105000 2720 1068 105000 2720 1068 25000 776 385 86000 2114 920 53049 1344 600 112000 3800 1090 75166 1928 766 68000 1080 636 51004 940 545 70327 1791 921 151400 2620 1253 90000 2000 900 83338 1750 945 83000 1750 945 61000 1380 600 86000 2104 800 55451 1264 557 33920 1214 530 81769 1848 842 38000 749 460 59652 1320 644 55451 1266 588 55451 1266 588 55451 1266 588 63000 1440 561 53872 1494 612 63000 1440 531 85000 1848 800 58600 1566 700 133500 3959 1313 58825 1560 700 35143 1250 535 89600 2550 987 59058 1700 740 16852 952 297 58600 1566 760 34250 1052 470 90000 2240 1100 50760 1748 614 93000 2394 1109 91000 2244 1100 38000 1056 438 77104 2002 800 81000 2144 1000 42000 1504 630 75338 1956 1300 28000 1150 380 77104 2002 959 50760 1748 614 30277 684 400 30277 684 400 30277 684 400 22080 826 350 85000 1968 869 45000 1178 520 76000 1874 850 77000 2016 900 69153 1882 794 115000 3574 1220 116000 2600 1100 91627 1974 900 116000 3100 1200 77499 1950 900 113000 2674 1238 113000 3782 1200 108865 2758 1100 108806 2600 1110 91627 1974 900 30277 686 373 69845 1590 696 44348 1200 520 113000 2674 1238 77499 1950 900 108977 2602 1200 77499 1950 900 30277 688 373 12500 394 146 50000 700 445 33000 490 324 19200 320 211 46000 700 447 138000 3114 1185 90090 2501 848 48563 2020 671 74137 1950 760 138000 3114 1176 158000 4370 1360 74137 1950 760 160000 3634 1360 90090 2501 869 70000 1800 720 158000 4370 1360 73941 2744 822 138000 3114 1185 73941 2744 822 138000 3114 1185 220000 5400 2100 90090 2501 868 78491 2435 765 90090 2501 858 73192 2852 808 70000 2076 720 78491 2435 660 138000 3114 1176 10000 208 160 10000 208 160 10000 208 160 16800 296 210 25000 382 295 25000 388 287 16800 296 197 3341 66 59 19093 800 470 42000 1480 680 40053 1287 750 3341 66 59 76800 1960 1200 5350 158 88 5350 167 88 14745 308 180
Names of X columns:
a b c
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
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
par6 <- '12' par5 <- '0' par4 <- '0' par3 <- 'No Linear Trend' par2 <- 'Do not include Seasonal Dummies' par1 <- '1' library(lattice) library(lmtest) library(car) library(MASS) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test mywarning <- '' par6 <- as.numeric(par6) if(is.na(par6)) { par6 <- 12 mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.' } par1 <- as.numeric(par1) if(is.na(par1)) { par1 <- 1 mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.' } if (par4=='') par4 <- 0 par4 <- as.numeric(par4) if (!is.numeric(par4)) par4 <- 0 if (par5=='') par5 <- 0 par5 <- as.numeric(par5) if (!is.numeric(par5)) par5 <- 0 x <- na.omit(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'){ (n <- n -1) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par3 == 'Seasonal Differences (s)'){ (n <- n - par6) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+par6,j] - x[i,j] } } x <- x2 } if (par3 == 'First and Seasonal Differences (s)'){ (n <- n -1) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 (n <- n - par6) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+par6,j] - x[i,j] } } x <- x2 } if(par4 > 0) { x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep=''))) for (i in 1:(n-par4)) { for (j in 1:par4) { x2[i,j] <- x[i+par4-j,par1] } } x <- cbind(x[(par4+1):n,], x2) n <- n - par4 } if(par5 > 0) { x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep=''))) for (i in 1:(n-par5*par6)) { for (j in 1:par5) { x2[i,j] <- x[i+par5*par6-j*par6,par1] } } x <- cbind(x[(par5*par6+1):n,], x2) n <- n - par5*par6 } if (par2 == 'Include Seasonal Dummies'){ x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep =''))) for (i in 1:(par6-1)){ x2[seq(i,n,par6),i] <- 1 } x <- cbind(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[n,])) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } print(x) (k <- length(x[n,])) head(x) 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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') sresid <- studres(mylm) hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals') xfit<-seq(min(sresid),max(sresid),length=40) yfit<-dnorm(xfit) lines(xfit, yfit) grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqPlot(mylm, main='QQ Plot') grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) print(z) plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$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, signif(mysum$coefficients[i,1],6), sep=' ') if (rownames(mysum$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.row.start(a) a<-table.element(a, mywarning) 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,'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$coefficients)[i],header=TRUE) a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+')) a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+')) a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' ')) 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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[2],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') myr <- as.numeric(mysum$resid) myr a <-table.start() a <- table.row.start(a) a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE) a <- table.row.end(a) a <- table.row.start(a) a <- table.element(a,'Description',1,TRUE) a <- table.element(a,'Link',1,TRUE) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Histogram',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'QQ Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Kernel Density Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Spectral Analysis',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <- table.element(a,'Summary Statistics',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a<-table.end(a) table.save(a,file='mytable7.tab') if(n < 200) { a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' ')) 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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' ')) 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,signif(numsignificant1,6)) a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' ')) 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,signif(numsignificant5,6)) a<-table.element(a,signif(numsignificant5/numgqtests,6)) 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,signif(numsignificant10,6)) a<-table.element(a,signif(numsignificant10/numgqtests,6)) 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') } } a<-table.start() a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_fitted <- resettest(mylm,power=2:3,type='fitted') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_fitted'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_regressors <- resettest(mylm,power=2:3,type='regressor') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_regressors'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_principal_components'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable8.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) vif <- vif(mylm) a<-table.element(a,paste('<pre>',RC.texteval('vif'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable9.tab')
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Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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