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Data X:
12.9 11 18 4 149 13 12 1 0 21 12.2 19 23 4 139 8 8 1 1 22 12.8 16 22 5 148 14 11 1 0 22 7.4 24 22 4 158 16 13 1 1 18 6.7 15 19 4 128 14 11 1 1 23 12.6 17 25 9 224 13 10 1 1 12 14.8 19 28 8 159 15 7 1 0 20 13.3 19 16 11 105 13 10 1 1 22 11.1 28 28 4 159 20 15 1 1 21 8.2 26 21 4 167 17 12 1 1 19 11.4 15 22 6 165 15 12 1 1 22 6.4 26 24 4 159 16 10 1 1 15 10.6 16 24 8 119 12 10 1 1 20 12 24 26 4 176 17 14 1 0 19 6.3 25 28 4 54 11 6 1 0 18 11.3 22 24 11 91 16 12 0 0 15 11.9 15 20 4 163 16 14 1 1 20 9.3 21 26 4 124 15 11 1 0 21 9.6 22 21 6 137 13 8 0 1 21 10 27 28 6 121 14 12 1 0 15 6.4 26 27 4 153 19 15 1 1 16 13.8 26 23 8 148 16 13 1 1 23 10.8 22 24 5 221 17 11 1 0 21 13.8 21 24 4 188 10 12 1 1 18 11.7 22 22 9 149 15 7 1 1 25 10.9 20 21 4 244 14 11 1 1 9 16.1 21 25 7 148 14 7 0 1 30 13.4 20 20 10 92 16 12 0 0 20 9.9 22 21 4 150 15 12 1 1 23 11.5 21 26 4 153 17 13 1 0 16 8.3 8 23 7 94 14 9 1 0 16 11.7 22 21 12 156 16 11 1 0 19 9 20 27 7 132 15 12 1 1 25 9.7 24 25 5 161 16 15 1 1 18 10.8 17 23 8 105 16 12 1 1 23 10.3 20 25 5 97 10 6 1 1 21 10.4 23 23 4 151 8 5 1 0 10 12.7 20 19 9 131 17 13 0 1 14 9.3 22 22 7 166 14 11 1 1 22 11.8 19 24 4 157 10 6 1 0 26 5.9 15 19 4 111 14 12 1 1 23 11.4 20 21 4 145 12 10 1 1 23 13 22 27 4 162 16 6 1 1 24 10.8 17 25 4 163 16 12 1 1 24 12.3 14 25 7 59 16 11 0 1 18 11.3 24 23 4 187 8 6 1 0 23 11.8 17 17 7 109 16 12 1 1 15 7.9 23 28 4 90 15 12 0 1 19 12.7 25 25 4 105 8 8 1 0 16 12.3 16 20 4 83 13 10 0 1 25 11.6 18 25 4 116 14 11 0 1 23 6.7 20 21 8 42 13 7 0 1 17 10.9 18 24 4 148 16 12 1 1 19 12.1 23 28 4 155 19 13 0 1 21 13.3 24 20 4 125 19 14 1 1 18 10.1 23 19 4 116 14 12 1 1 27 5.7 13 24 7 128 15 6 0 0 21 14.3 20 21 12 138 13 14 1 1 13 8 20 24 4 49 10 10 0 0 8 13.3 19 23 4 96 16 12 0 1 29 9.3 22 18 4 164 15 11 1 1 28 12.5 22 27 5 162 11 10 1 0 23 7.6 15 25 15 99 9 7 1 0 21 15.9 17 20 5 202 16 12 1 1 19 9.2 19 21 10 186 12 7 1 0 19 9.1 20 23 9 66 12 12 0 1 20 11.1 22 27 8 183 14 12 1 0 18 13 21 24 4 214 14 10 1 1 19 14.5 21 27 5 188 13 10 1 1 17 12.2 16 24 4 104 15 12 0 0 19 12.3 20 23 9 177 17 12 1 0 25 11.4 21 24 4 126 14 12 1 0 19 8.8 20 21 10 76 11 8 0 0 22 14.6 23 23 4 99 9 10 0 1 23 12.6 18 27 4 139 7 5 1 0 14 13 16 25 7 162 15 10 1 0 16 12.6 17 19 5 108 12 12 0 1 24 13.2 24 24 4 159 15 11 1 0 20 9.9 13 25 4 74 14 9 0 0 12 7.7 19 23 4 110 16 12 1 1 24 10.5 20 23 4 96 14 11 0 0 22 13.4 22 25 4 116 13 10 0 0 12 10.9 19 26 4 87 16 12 0 0 22 4.3 21 26 6 97 13 10 0 1 20 10.3 15 16 10 127 16 9 0 0 10 11.8 21 23 7 106 16 11 0 1 23 11.2 24 26 4 80 16 12 0 1 17 11.4 22 25 4 74 10 7 0 0 22 8.6 20 23 7 91 12 11 0 0 24 13.2 21 26 4 133 12 12 0 0 18 12.6 19 22 8 74 12 6 0 1 21 5.6 14 20 11 114 12 9 0 1 20 9.9 25 27 6 140 19 15 0 1 20 8.8 11 20 14 95 14 10 0 0 22 7.7 17 22 5 98 13 11 0 1 19 9 22 24 4 121 16 12 0 0 20 7.3 20 21 8 126 15 12 0 1 26 11.4 22 24 9 98 12 12 0 1 23 13.6 15 26 4 95 8 11 0 1 24 7.9 23 24 4 110 10 9 0 1 21 10.7 20 24 5 70 16 11 0 1 21 10.3 22 27 4 102 16 12 0 0 19 8.3 16 25 5 86 10 12 0 1 8 9.6 25 27 4 130 18 14 0 1 17 14.2 18 19 4 96 12 8 0 1 20 8.5 19 22 7 102 16 10 0 0 11 13.5 25 22 10 100 10 9 0 0 8 4.9 21 25 4 94 14 10 0 0 15 6.4 22 23 5 52 12 9 0 0 18 9.6 21 24 4 98 11 10 0 0 18 11.6 22 24 4 118 15 12 0 0 19 11.1 23 23 4 99 7 11 0 1 19 4.35 20 22 6 48 16 9 1 1 23 12.7 6 24 4 50 16 11 1 1 22 18.1 15 19 8 150 16 12 1 1 21 17.85 18 25 5 154 16 12 1 1 25 16.6 24 26 4 109 12 7 0 0 30 12.6 22 18 17 68 15 12 0 1 17 17.1 21 24 4 194 14 12 1 1 27 19.1 23 28 4 158 15 12 1 0 23 16.1 20 23 8 159 16 10 1 1 23 13.35 20 19 4 67 13 15 1 0 18 18.4 18 19 7 147 10 10 1 0 18 14.7 25 27 4 39 17 15 1 1 23 10.6 16 24 4 100 15 10 1 1 19 12.6 20 26 5 111 18 15 1 1 15 16.2 14 21 7 138 16 9 1 1 20 13.6 22 25 4 101 20 15 1 1 16 18.9 26 28 4 131 16 12 0 1 24 14.1 20 19 7 101 17 13 1 1 25 14.5 17 20 11 114 16 12 1 1 25 16.15 22 26 7 165 15 12 1 0 19 14.75 22 27 4 114 13 8 1 1 19 14.8 20 23 4 111 16 9 1 1 16 12.45 17 18 4 75 16 15 1 1 19 12.65 22 23 4 82 16 12 1 1 19 17.35 17 21 4 121 17 12 1 1 23 8.6 22 23 4 32 20 15 1 1 21 18.4 21 22 6 150 14 11 1 0 22 16.1 25 21 8 117 17 12 1 1 19 11.6 11 14 23 71 6 6 0 1 20 17.75 19 24 4 165 16 14 1 1 20 15.25 24 26 8 154 15 12 1 1 3 17.65 17 24 6 126 16 12 1 1 23 16.35 22 22 4 149 16 12 1 0 23 17.65 17 20 7 145 14 11 1 0 20 13.6 26 20 4 120 16 12 1 1 15 14.35 20 18 4 109 16 12 1 0 16 14.75 19 18 4 132 16 12 1 0 7 18.25 21 25 10 172 14 12 1 1 24 9.9 24 28 6 169 14 8 1 0 17 16 21 23 5 114 16 8 1 1 24 18.25 19 20 5 156 16 12 1 1 24 16.85 13 22 4 172 15 12 1 0 19 14.6 24 27 4 68 16 11 0 1 25 13.85 28 24 5 89 16 10 0 1 20 18.95 27 23 5 167 18 11 1 1 28 15.6 22 20 5 113 15 12 1 0 23 14.85 23 22 5 115 16 13 0 0 27 11.75 19 21 4 78 16 12 0 0 18 18.45 18 24 6 118 16 12 0 0 28 15.9 23 26 4 87 17 10 0 1 21 17.1 21 24 4 173 14 10 1 0 19 16.1 22 18 4 2 18 11 1 1 23 19.9 17 17 9 162 9 8 0 0 27 10.95 15 23 18 49 15 12 0 1 22 18.45 21 21 6 122 14 9 0 0 28 15.1 20 21 5 96 15 12 0 1 25 15 26 24 4 100 13 9 0 0 21 11.35 19 22 11 82 16 11 0 0 22 15.95 28 24 4 100 20 15 0 1 28 18.1 21 24 10 115 14 8 0 0 20 14.6 19 24 6 141 12 8 0 1 29 15.4 22 23 8 165 15 11 1 1 25 15.4 21 21 8 165 15 11 1 1 25 17.6 20 24 6 110 15 11 0 1 20 13.35 19 19 8 118 16 13 1 1 20 19.1 11 19 4 158 11 7 1 0 16 15.35 17 23 4 146 16 12 0 1 20 7.6 19 25 9 49 7 8 1 0 20 13.4 20 24 9 90 11 8 0 0 23 13.9 17 21 5 121 9 4 0 0 18 19.1 21 18 4 155 15 11 1 1 25 15.25 21 23 4 104 16 10 0 0 18 12.9 12 20 15 147 14 7 0 1 19 16.1 23 23 10 110 15 12 0 0 25 17.35 22 23 9 108 13 11 0 0 25 13.15 22 23 7 113 13 9 0 0 25 12.15 21 23 9 115 12 10 0 0 24 12.6 20 27 6 61 16 8 0 1 19 10.35 18 19 4 60 14 8 0 1 26 15.4 21 25 7 109 16 11 0 1 10 9.6 24 25 4 68 14 12 0 1 17 18.2 22 21 7 111 15 10 0 0 13 13.6 20 25 4 77 10 10 0 0 17 14.85 17 17 15 73 16 12 0 1 30 14.75 19 22 4 151 14 8 1 0 25 14.1 16 23 9 89 16 11 0 0 4 14.9 19 27 4 78 12 8 0 0 16 16.25 23 27 4 110 16 10 0 0 21 19.25 8 5 28 220 16 14 1 1 23 13.6 22 19 4 65 15 9 0 1 22 13.6 23 24 4 141 14 9 1 0 17 15.65 15 23 4 117 16 10 0 0 20 12.75 17 28 5 122 11 13 1 1 20 14.6 21 25 4 63 15 12 0 0 22 9.85 25 27 4 44 18 13 1 1 16 12.65 18 16 12 52 13 8 0 1 23 19.2 20 25 4 131 7 3 0 0 0 16.6 21 26 6 101 7 8 0 1 18 11.2 21 24 6 42 17 12 0 1 25 15.25 24 23 5 152 18 11 1 1 23 11.9 22 24 4 107 15 9 1 0 12 13.2 22 27 4 77 8 12 0 0 18 16.35 23 25 4 154 13 12 1 0 24 12.4 17 19 10 103 13 12 1 1 11 15.85 15 19 7 96 15 10 0 1 18 18.15 22 24 4 175 18 13 1 1 23 11.15 19 20 7 57 16 9 0 1 24 15.65 18 21 4 112 14 12 0 0 29 17.75 21 28 4 143 15 11 1 0 18 7.65 20 26 12 49 19 14 0 0 15 12.35 19 19 5 110 16 11 1 1 29 15.6 19 23 8 131 12 9 1 1 16 19.3 16 23 6 167 16 12 1 0 19 15.2 18 21 17 56 11 8 0 0 22 17.1 23 26 4 137 16 15 1 0 16 15.6 22 25 5 86 15 12 0 1 23 18.4 23 25 4 121 19 14 1 1 23 19.05 20 24 5 149 15 12 1 0 19 18.55 24 23 5 168 14 9 1 0 4 19.1 25 22 6 140 14 9 1 0 20 13.1 25 27 4 88 17 13 0 1 24 12.85 20 26 4 168 16 13 1 1 20 9.5 23 23 4 94 20 15 1 1 4 4.5 21 22 6 51 16 11 1 1 24 11.85 23 26 8 48 9 7 0 0 22 13.6 23 22 10 145 13 10 1 1 16 11.7 11 17 4 66 15 11 1 1 3 12.4 21 25 5 85 19 14 0 1 15 13.35 27 22 4 109 16 14 1 0 24 11.4 19 28 4 63 17 13 0 0 17 14.9 21 22 4 102 16 12 0 1 20 19.9 16 21 16 162 9 8 0 0 27 11.2 21 24 7 86 11 13 0 1 26 14.6 22 26 4 114 14 9 0 1 23 17.6 16 26 4 164 19 12 1 0 17 14.05 18 24 14 119 13 13 1 1 20 16.1 23 27 5 126 14 11 1 0 22 13.35 24 22 5 132 15 11 1 1 19 11.85 20 23 5 142 15 13 1 1 24 11.95 20 22 5 83 14 12 1 0 19 14.75 18 23 7 94 16 12 0 1 23 15.15 4 15 19 81 17 10 0 0 15 13.2 14 20 16 166 12 9 1 1 27 16.85 22 22 4 110 15 10 0 0 26 7.85 17 25 4 64 17 13 0 1 22 7.7 23 27 7 93 15 13 1 0 22 12.6 20 24 9 104 10 9 0 0 18 7.85 18 21 5 105 16 11 0 1 15 10.95 19 17 14 49 15 12 0 1 22 12.35 20 26 4 88 11 8 0 0 27 9.95 15 20 16 95 16 12 0 1 10 14.9 24 22 10 102 16 12 0 1 20 16.65 21 24 5 99 16 12 0 0 17 13.4 19 23 6 63 14 9 0 1 23 13.95 19 22 4 76 14 12 0 0 19 15.7 27 28 4 109 16 12 0 0 13 16.85 23 21 4 117 16 11 0 1 27 10.95 23 24 5 57 18 12 0 1 23 15.35 20 28 4 120 14 6 0 0 16 12.2 17 25 4 73 20 7 0 1 25 15.1 21 24 5 91 15 10 0 0 2 17.75 23 24 4 108 16 12 0 0 26 15.2 22 21 4 105 16 10 0 1 20 14.6 16 20 5 117 16 12 1 0 23 16.65 20 26 8 119 12 9 0 0 22 8.1 16 16 15 31 8 3 0 1 24
Names of X columns:
TOT AMS.I1 AMS.E1 AMS.A LFM CONFSTATTOT CONFSOFTTOT group gender NUMERACYTOT
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 mywarning <- '' 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 (par5=='') par5 <- 0 par5 <- as.numeric(par5) 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=12)'){ (n <- n - 12) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+12,j] - x[i,j] } } x <- x2 } if (par3 == 'First and Seasonal Differences (s=12)'){ (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 - 12) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+12,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*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep=''))) for (i in 1:(n-par5*12)) { for (j in 1:par5) { x2[i,j] <- x[i+par5*12-j*12,par1] } } x <- cbind(x[(par5*12+1):n,], x2) n <- n - par5*12 } 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' } 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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, mywarning) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,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$sigma,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') if(n < 200) { a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(x[i]-mysum$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') } }
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