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Data X:
0 17.080 9 0 17.240 8 0 17.200 7 0 15.580 9 0 18.950 9 0 23.360 8 0 19.190 6 0 14.150 6 0 19.870 8 0 19.420 9 0 16.020 6 0 17.350 8 0 21.930 8 0 21.180 8 0 22.580 8 0 19.320 7 0 14.720 5 0 18.780 6 0 23.520 9 0 26.040 9 0 14.000 5 0 12.560 5 0 0.839 4 0 25.780 7 0 29.880 9 0 14.040 3 0 23.480 9 0 17.550 5 0 29.800 8 0 21.000 9 0 12.820 5 0 30.000 9 0 26.730 8 0 20.930 7 0 16.120 5 0 21.750 8 0 27.250 9 0 20.710 8 0 15.470 8 0 20.040 8 0 31.350 9 0 24.200 8 0 17.760 5 0 19.310 8 0 13.430 5 0 20.760 9 0 16.240 7 0 13.440 8 0 16.500 6 0 27.320 8 0 20.170 5 0 27.970 9 0 35.560 9 0 17.030 8 0 16.340 6 0 25.700 9 0 30.160 9 0 24.190 7 0 15.690 4 0 16.980 8 0 21.230 8 0 24.810 8 0 14.810 6 0 15.770 4 0 19.400 8 0 17.470 6 0 20.690 9 0 16.310 7 0 15.360 5 0 25.600 9 0 19.620 8 0 25.310 8 0 27.150 9 0 24.570 9 0 20.900 9 0 17.890 7 0 18.580 5 0 14.520 5 0 38.420 9 0 17.190 6 0 21.110 7 0 16.950 6 0 22.110 8 0 17.940 8 0 19.170 7 0 21.440 8 0 12.530 7 0 26.590 9 0 15.800 5 0 21.260 9 0 30.290 9 0 29.640 9 0 16.110 7 0 22.150 8 0 23.880 8 0 21.960 9 0 17.510 9 0 21.650 9 0 16.820 7 0 15.230 8 0 12.920 8 0 16.490 7 0 25.880 9 0 0.796 4 0 25.740 9 0 19.790 6 0 23.540 8 0 17.180 6 0 17.420 7 0 16.030 7 0 26.390 8 0 18.290 7 0 20.840 7 0 22.200 7 0 14.730 7 0 23.410 8 0 16.980 7 0 11.960 5 0 18.720 8 0 22.190 7 0 24.200 9 0 18.270 7 0 14.610 7 0 13.380 6 0 20.900 8 0 16.970 8 0 15.620 8 0 20.400 9 0 16.090 7 0 24.580 8 0 26.500 9 0 14.290 8 0 16.750 8 0 19.470 9 0 20.690 8 0 15.720 6 0 13.480 6 0 22.880 8 0 17.730 9 0 0.791 5 0 19.050 7 0 24.630 9 0 14.310 6 0 26.310 9 0 31.140 9 0 21.350 9 0 15.270 6 0 22.930 8 0 30.420 9 0 29.270 8 0 26.650 8 0 23.010 9 0 24.600 9 0 25.920 9 0 17.500 7 0 17.590 8 0 15.360 6 0 22.590 9 0 20.480 9 0 25.710 9 0 20.460 7 0 17.800 8 0 15.520 5 0 19.530 8 0 28.930 9 0 17.130 6 0 28.510 9 0 16.240 6 0 26.310 8 0 18.190 5 0 16.580 7 0 21.580 7 0 17.890 4 0 30.040 9 0 25.030 8 0 19.330 9 0 20.910 9 0 23.160 9 0 17.040 5 0 16.060 9 0 11.650 7 0 21.020 6 0 23.200 9 0 22.300 9 0 17.160 9 0 17.900 7 0 11.460 5 0 21.870 8 0 27.170 9 0 17.960 7 1 19.530 9 0 13.350 8 0 21.190 9 0 16.660 6 0 18.260 6 0 27.090 8 0 28.710 9 0 10.920 5 0 22.620 6 0 21.040 6 0 21.660 9 0 16.900 7 0 29.730 9 0 21.450 8 0 19.710 5 0 20.950 7 0 16.970 6 0 24.550 9 0 19.200 7 0 21.640 9 0 21.300 9 0 29.930 8 0 25.290 9 0 17.260 7 0 24.420 9 0 11.020 4 0 20.560 9 0 18.080 5 0 23.050 8 0 19.690 9 0 15.560 8 0 10.720 3 0 20.420 9 0 15.120 8 0 14.230 6 0 36.810 9 0 19.910 8 0 18.970 8 0 13.700 7 0 13.380 6 0 20.160 8 0 26.390 9 0 13.890 4 0 16.120 7 0 21.350 8 0 26.810 8 0 32.230 9 0 17.960 6 0 20.100 8 0 15.230 6 0 17.440 8 0 24.850 9 0 23.350 8 0 14.150 7 0 20.760 9 0 24.350 8 0 17.280 7 0 28.500 9 0 18.440 8 0 17.540 9 0 13.430 6 0 23.030 8 0 22.460 9 0 24.760 8 0 32.390 9 0 24.570 9 0 23.820 8 0 16.400 7 0 15.890 5 0 20.560 7 0 22.260 8 0 18.860 9 0 28.330 9 0 17.150 6 0 26.310 8 0 25.500 7 0 19.120 9 0 18.770 7 0 19.350 7 0 15.390 5 0 28.030 9 0 29.230 9 0 23.580 8 0 20.940 8 0 18.550 9 0 15.350 6 0 21.350 7 0 19.300 5 0 21.820 9 0 13.590 5 0 20.020 7 0 16.990 6 0 25.000 8 0 23.660 7 0 20.690 8 0 14.180 4 0 23.330 8 0 15.140 5 0 17.580 8 0 25.350 7 0 25.640 7 0 24.870 9 0 15.910 9 0 16.240 8 0 27.980 9 0 16.910 6 0 19.990 8 0 18.690 9 0 10.040 4 0 14.270 6 0 18.260 7 0 26.880 9 0 16.570 8 0 16.720 6 0 20.150 8 0 23.710 7 0 21.150 5 0 23.280 8 0 14.950 7 0 28.840 11 0 23.280 10 0 33.810 14 0 21.700 11 0 34.700 11 0 30.580 12 0 18.110 10 0 25.240 11 0 26.420 10 0 37.410 14 0 43.360 13 0 48.420 14 0 45.500 12 0 28.410 12 0 31.660 10 0 38.160 13 0 25.610 10 0 36.540 11 0 24.810 10 0 26.650 11 0 32.030 10 0 35.490 13 1 22.360 14 0 32.220 11 0 31.110 10 0 34.900 11 0 31.470 13 0 25.200 10 0 22.920 10 0 28.890 12 0 22.460 10 0 19.370 10 0 26.460 10 0 29.570 11 0 40.070 11 0 23.860 11 0 32.510 10 0 27.620 11 0 30.110 11 0 43.050 13 0 39.060 13 0 35.830 11 0 32.360 11 0 34.360 14 0 30.580 11 0 30.070 10 0 34.890 10 0 28.640 10 1 34.280 14 0 28.190 13 0 22.500 10 0 46.830 14 0 23.520 10 0 31.080 11 0 39.940 13 0 43.930 12 1 32.080 13 0 25.920 10 0 31.930 13 1 16.940 11 1 39.570 14 0 23.460 11 1 47.890 13 0 35.150 11 0 27.540 11 0 27.200 10 0 24.630 11 0 26.330 11 0 30.480 10 0 31.110 11 0 37.450 13 1 23.840 12 0 20.940 10 0 31.830 10 1 30.740 14 0 39.770 11 0 33.540 10 0 34.110 11 1 23.870 10 0 31.710 11 0 38.870 13 0 26.460 13 0 25.040 10 0 35.870 11 0 38.450 11 0 29.710 12 0 28.910 10 0 18.230 10 0 24.170 11 0 21.750 10 0 27.350 11 0 42.730 14 0 29.760 13 1 38.350 12 0 40.650 11 0 23.180 11 0 35.960 11 0 33.950 14 0 27.510 12 0 26.730 10 0 25.560 12 0 25.420 11 0 26.080 10 0 23.540 11 1 25.990 13 0 14.580 10 0 37.950 10 0 24.910 11 0 30.600 13 0 25.450 10 0 29.930 11 0 33.050 10 1 47.560 13 0 37.740 11 0 28.550 10 0 29.880 11 0 24.980 11 0 31.690 14 0 28.870 11 0 27.040 13 0 35.150 11 0 34.250 11 0 22.870 10 0 24.340 13 0 23.650 10 1 30.860 13 0 26.960 10 0 28.680 12 0 28.130 10 1 43.090 14 0 32.550 12 1 34.130 10 0 45.930 11 0 41.110 14 0 19.160 12 0 18.580 10 1 29.750 10 0 33.500 10 0 29.010 10 0 22.410 12 0 42.250 13 0 32.230 11 0 52.240 12 0 40.730 11 0 40.800 12 0 26.060 11 1 31.690 11 0 44.110 12 0 37.910 12 0 30.890 13 0 24.650 11 1 33.430 12 0 32.000 10 0 29.130 12 0 48.770 13 0 23.580 10 0 32.790 12 0 25.810 10 0 23.470 12 0 26.910 10 0 28.270 11 0 18.730 10 1 37.510 12 0 25.380 14 0 27.580 10 0 30.500 10 0 30.790 12 0 22.010 10 0 18.580 10 1 22.160 13 0 34.030 12 0 35.010 12 0 25.780 11 1 30.780 13 1 31.860 12 0 16.650 10 0 20.810 11 0 29.740 11 1 32.970 13 0 40.730 12 0 44.480 13 0 39.840 13 0 22.500 10 0 27.520 12 1 23.040 12 0 36.800 14 1 31.020 11 0 28.620 10 1 26.770 13 0 30.230 11 0 36.810 11 0 32.550 13 0 36.920 12 0 23.560 10 0 45.910 10 0 30.820 12 1 32.970 13 0 32.580 11 0 22.160 10 0 32.470 11 0 43.240 11 0 23.620 11 0 25.630 11 0 32.060 11 0 35.850 14 0 47.200 12 0 33.310 13 0 50.830 13 1 34.980 10 0 24.170 12 0 23.640 10 0 23.410 10 1 27.590 12 1 29.530 11 0 32.310 12 0 30.780 11 0 33.690 11 0 35.290 12 0 28.660 12 0 28.910 14 0 30.220 11 0 31.270 10 0 28.660 11 0 26.050 12 0 30.560 13 0 25.690 12 0 25.010 11 0 33.200 11 0 21.230 11 0 37.800 14 0 38.470 11 1 37.850 13 0 39.240 12 0 21.320 10 0 27.520 12 0 24.490 13 0 34.560 10 0 30.730 10 0 26.880 10 0 33.290 10 0 42.710 14 0 35.300 12 0 29.280 11 0 26.890 11 0 23.320 12 0 29.340 14 1 22.760 14 0 31.100 10 0 28.940 11 1 46.370 11 0 24.350 10 0 28.380 10 0 30.350 12 0 48.310 12 0 28.120 11 0 27.140 12 0 30.860 10 0 35.190 12 0 42.320 13 0 27.700 10 0 33.410 12 0 30.900 10 0 25.310 13 0 28.220 12 1 30.380 10 0 29.350 12 0 25.680 10 0 23.870 11 0 24.990 12 0 41.300 11 0 30.010 12 0 31.320 10 0 35.770 13 0 32.220 12 0 32.800 11 0 26.590 11 0 28.220 11 0 21.400 11 0 42.030 12 0 29.970 14 1 31.200 11 0 25.620 11 0 30.820 12 0 38.060 14 1 33.390 11 1 31.520 13 0 24.580 11 0 23.910 10 0 31.410 13 0 25.790 12 1 31.040 11 1 40.450 13 1 47.630 14 0 21.000 10 1 30.690 11 0 27.850 11 0 42.840 15 1 45.060 15 0 29.060 18 0 51.020 19 1 35.190 19 1 36.880 16 0 44.290 17 0 42.790 15 0 45.000 15 0 26.350 15 1 26.790 15 1 21.980 15 1 33.450 19 0 30.820 18 0 33.870 16 1 30.820 17 1 29.030 16 1 30.040 15 0 57.930 15 0 39.850 15 0 42.200 18 0 47.240 17 0 37.310 15 1 34.060 17 0 35.000 17 0 36.740 16 0 56.330 17 1 31.220 15 1 33.300 15 1 26.080 16 0 36.450 16 1 37.990 15 1 40.860 18 0 28.870 15 1 40.700 16 0 39.600 17 0 42.990 16 0 29.810 16 1 22.640 15 1 44.040 18 1 22.780 15 0 45.040 16 0 56.380 17 1 48.720 16 1 42.700 16 1 37.270 15 0 28.530 18 1 27.950 16 0 32.110 15
Names of X columns:
Smoke FEV Age
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
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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