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Type 'q()' to quit R. > x <- array(list(91.2,0,99.2,0,108.2,0,101.5,0,106.9,0,104.4,0,77.9,0,60,0,99.5,0,95,0,105.6,0,102.5,0,93.3,0,97.3,0,127,0,111.7,0,96.4,0,133,0,72.2,0,95.8,0,124.1,0,127.6,0,110.7,0,104.6,0,112.7,0,115.3,0,139.4,0,119,0,97.4,0,154,0,81.5,0,88.8,0,127.7,1,105.1,1,114.9,1,106.4,1,104.5,1,121.6,1,141.4,1,99,1,126.7,1,134.1,1,81.3,1,88.6,1,132.7,1,132.9,1,134.4,1,103.7,1,119.7,1,115,1,132.9,1,108.5,1,113.9,1,142,1,97.7,1,92.2,1,128.8,1,134.9,1,128.2,1,114.8,1),dim=c(2,60),dimnames=list(c('Transportmiddelen','Conjunctuur'),1:60)) > y <- array(NA,dim=c(2,60),dimnames=list(c('Transportmiddelen','Conjunctuur'),1:60)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Include Monthly 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 Transportmiddelen Conjunctuur M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 91.2 0 1 0 0 0 0 0 0 0 0 0 0 1 2 99.2 0 0 1 0 0 0 0 0 0 0 0 0 2 3 108.2 0 0 0 1 0 0 0 0 0 0 0 0 3 4 101.5 0 0 0 0 1 0 0 0 0 0 0 0 4 5 106.9 0 0 0 0 0 1 0 0 0 0 0 0 5 6 104.4 0 0 0 0 0 0 1 0 0 0 0 0 6 7 77.9 0 0 0 0 0 0 0 1 0 0 0 0 7 8 60.0 0 0 0 0 0 0 0 0 1 0 0 0 8 9 99.5 0 0 0 0 0 0 0 0 0 1 0 0 9 10 95.0 0 0 0 0 0 0 0 0 0 0 1 0 10 11 105.6 0 0 0 0 0 0 0 0 0 0 0 1 11 12 102.5 0 0 0 0 0 0 0 0 0 0 0 0 12 13 93.3 0 1 0 0 0 0 0 0 0 0 0 0 13 14 97.3 0 0 1 0 0 0 0 0 0 0 0 0 14 15 127.0 0 0 0 1 0 0 0 0 0 0 0 0 15 16 111.7 0 0 0 0 1 0 0 0 0 0 0 0 16 17 96.4 0 0 0 0 0 1 0 0 0 0 0 0 17 18 133.0 0 0 0 0 0 0 1 0 0 0 0 0 18 19 72.2 0 0 0 0 0 0 0 1 0 0 0 0 19 20 95.8 0 0 0 0 0 0 0 0 1 0 0 0 20 21 124.1 0 0 0 0 0 0 0 0 0 1 0 0 21 22 127.6 0 0 0 0 0 0 0 0 0 0 1 0 22 23 110.7 0 0 0 0 0 0 0 0 0 0 0 1 23 24 104.6 0 0 0 0 0 0 0 0 0 0 0 0 24 25 112.7 0 1 0 0 0 0 0 0 0 0 0 0 25 26 115.3 0 0 1 0 0 0 0 0 0 0 0 0 26 27 139.4 0 0 0 1 0 0 0 0 0 0 0 0 27 28 119.0 0 0 0 0 1 0 0 0 0 0 0 0 28 29 97.4 0 0 0 0 0 1 0 0 0 0 0 0 29 30 154.0 0 0 0 0 0 0 1 0 0 0 0 0 30 31 81.5 0 0 0 0 0 0 0 1 0 0 0 0 31 32 88.8 0 0 0 0 0 0 0 0 1 0 0 0 32 33 127.7 1 0 0 0 0 0 0 0 0 1 0 0 33 34 105.1 1 0 0 0 0 0 0 0 0 0 1 0 34 35 114.9 1 0 0 0 0 0 0 0 0 0 0 1 35 36 106.4 1 0 0 0 0 0 0 0 0 0 0 0 36 37 104.5 1 1 0 0 0 0 0 0 0 0 0 0 37 38 121.6 1 0 1 0 0 0 0 0 0 0 0 0 38 39 141.4 1 0 0 1 0 0 0 0 0 0 0 0 39 40 99.0 1 0 0 0 1 0 0 0 0 0 0 0 40 41 126.7 1 0 0 0 0 1 0 0 0 0 0 0 41 42 134.1 1 0 0 0 0 0 1 0 0 0 0 0 42 43 81.3 1 0 0 0 0 0 0 1 0 0 0 0 43 44 88.6 1 0 0 0 0 0 0 0 1 0 0 0 44 45 132.7 1 0 0 0 0 0 0 0 0 1 0 0 45 46 132.9 1 0 0 0 0 0 0 0 0 0 1 0 46 47 134.4 1 0 0 0 0 0 0 0 0 0 0 1 47 48 103.7 1 0 0 0 0 0 0 0 0 0 0 0 48 49 119.7 1 1 0 0 0 0 0 0 0 0 0 0 49 50 115.0 1 0 1 0 0 0 0 0 0 0 0 0 50 51 132.9 1 0 0 1 0 0 0 0 0 0 0 0 51 52 108.5 1 0 0 0 1 0 0 0 0 0 0 0 52 53 113.9 1 0 0 0 0 1 0 0 0 0 0 0 53 54 142.0 1 0 0 0 0 0 1 0 0 0 0 0 54 55 97.7 1 0 0 0 0 0 0 1 0 0 0 0 55 56 92.2 1 0 0 0 0 0 0 0 1 0 0 0 56 57 128.8 1 0 0 0 0 0 0 0 0 1 0 0 57 58 134.9 1 0 0 0 0 0 0 0 0 0 1 0 58 59 128.2 1 0 0 0 0 0 0 0 0 0 0 1 59 60 114.8 1 0 0 0 0 0 0 0 0 0 0 0 60 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Conjunctuur M1 M2 M3 M4 86.858 -10.089 3.683 8.372 27.761 5.210 M5 M6 M7 M8 M9 M10 4.819 29.348 -22.743 -20.494 18.293 14.122 M11 t 13.071 0.711 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -16.07222 -5.88847 0.06167 5.87986 16.46444 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 86.8583 5.0166 17.314 < 2e-16 *** Conjunctuur -10.0889 4.8273 -2.090 0.042176 * M1 3.6829 5.8544 0.629 0.532406 M2 8.3719 5.8395 1.434 0.158425 M3 27.7610 5.8278 4.764 1.93e-05 *** M4 5.2100 5.8195 0.895 0.375304 M5 4.8190 5.8145 0.829 0.411498 M6 29.3481 5.8128 5.049 7.46e-06 *** M7 -22.7429 5.8145 -3.911 0.000300 *** M8 -20.4939 5.8195 -3.522 0.000981 *** M9 18.2929 5.8078 3.150 0.002870 ** M10 14.1219 5.7994 2.435 0.018826 * M11 13.0710 5.7944 2.256 0.028882 * t 0.7110 0.1394 5.102 6.24e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 9.159 on 46 degrees of freedom Multiple R-squared: 0.8197, Adjusted R-squared: 0.7687 F-statistic: 16.08 on 13 and 46 DF, p-value: 6.003e-13 > 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,] 0.6805080 0.6389839 0.31949196 [2,] 0.8426476 0.3147048 0.15735241 [3,] 0.8196504 0.3606992 0.18034958 [4,] 0.9376618 0.1246764 0.06233822 [5,] 0.9205782 0.1588435 0.07942175 [6,] 0.9307141 0.1385719 0.06928595 [7,] 0.9122170 0.1755660 0.08778301 [8,] 0.8761630 0.2476741 0.12383703 [9,] 0.8144147 0.3711705 0.18558526 [10,] 0.7381961 0.5236078 0.26180390 [11,] 0.6523732 0.6952535 0.34762676 [12,] 0.6606767 0.6786466 0.33932330 [13,] 0.8399937 0.3200125 0.16000627 [14,] 0.9100750 0.1798499 0.08992497 [15,] 0.8807070 0.2385859 0.11929296 [16,] 0.8248673 0.3502654 0.17513270 [17,] 0.7608009 0.4783983 0.23919913 [18,] 0.9044188 0.1911625 0.09558124 [19,] 0.9047808 0.1904383 0.09521916 [20,] 0.8472456 0.3055088 0.15275439 [21,] 0.8508284 0.2983432 0.14917161 [22,] 0.8111018 0.3777964 0.18889820 [23,] 0.7903156 0.4193687 0.20968436 [24,] 0.7756688 0.4486623 0.22433116 [25,] 0.8643215 0.2713569 0.13567847 [26,] 0.7661072 0.4677856 0.23389278 [27,] 0.8180920 0.3638159 0.18190795 > postscript(file="/var/www/html/rcomp/tmp/1yp211229094301.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/27mgh1229094301.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3laqx1229094301.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4ellr1229094301.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5z9q51229094301.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 = 60 Frequency = 1 1 2 3 4 5 6 -0.05222222 2.54777778 -8.55222222 6.58777778 11.66777778 -16.07222222 7 8 9 10 11 12 8.80777778 -12.05222222 -12.05000000 -13.09000000 -2.15000000 7.11000000 13 14 15 16 17 18 -6.48388889 -7.88388889 1.71611111 8.25611111 -7.36388889 3.99611111 19 20 21 22 23 24 -5.42388889 15.21611111 4.01833333 10.97833333 -5.58166667 0.67833333 25 26 27 28 29 30 4.38444444 1.58444444 5.58444444 7.02444444 -14.89555556 16.46444444 31 32 33 34 35 36 -4.65555556 -0.31555556 9.17555556 -9.96444444 0.17555556 4.03555556 37 38 39 40 41 42 -2.25833333 9.44166667 9.14166667 -11.41833333 15.96166667 -1.87833333 43 44 45 46 47 48 -3.29833333 1.04166667 5.64388889 9.30388889 11.14388889 -7.19611111 49 50 51 52 53 54 4.41000000 -5.69000000 -7.89000000 -10.45000000 -5.37000000 -2.51000000 55 56 57 58 59 60 4.57000000 -3.89000000 -6.78777778 2.77222222 -3.58777778 -4.62777778 > postscript(file="/var/www/html/rcomp/tmp/6d4pz1229094301.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 = 60 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.05222222 NA 1 2.54777778 -0.05222222 2 -8.55222222 2.54777778 3 6.58777778 -8.55222222 4 11.66777778 6.58777778 5 -16.07222222 11.66777778 6 8.80777778 -16.07222222 7 -12.05222222 8.80777778 8 -12.05000000 -12.05222222 9 -13.09000000 -12.05000000 10 -2.15000000 -13.09000000 11 7.11000000 -2.15000000 12 -6.48388889 7.11000000 13 -7.88388889 -6.48388889 14 1.71611111 -7.88388889 15 8.25611111 1.71611111 16 -7.36388889 8.25611111 17 3.99611111 -7.36388889 18 -5.42388889 3.99611111 19 15.21611111 -5.42388889 20 4.01833333 15.21611111 21 10.97833333 4.01833333 22 -5.58166667 10.97833333 23 0.67833333 -5.58166667 24 4.38444444 0.67833333 25 1.58444444 4.38444444 26 5.58444444 1.58444444 27 7.02444444 5.58444444 28 -14.89555556 7.02444444 29 16.46444444 -14.89555556 30 -4.65555556 16.46444444 31 -0.31555556 -4.65555556 32 9.17555556 -0.31555556 33 -9.96444444 9.17555556 34 0.17555556 -9.96444444 35 4.03555556 0.17555556 36 -2.25833333 4.03555556 37 9.44166667 -2.25833333 38 9.14166667 9.44166667 39 -11.41833333 9.14166667 40 15.96166667 -11.41833333 41 -1.87833333 15.96166667 42 -3.29833333 -1.87833333 43 1.04166667 -3.29833333 44 5.64388889 1.04166667 45 9.30388889 5.64388889 46 11.14388889 9.30388889 47 -7.19611111 11.14388889 48 4.41000000 -7.19611111 49 -5.69000000 4.41000000 50 -7.89000000 -5.69000000 51 -10.45000000 -7.89000000 52 -5.37000000 -10.45000000 53 -2.51000000 -5.37000000 54 4.57000000 -2.51000000 55 -3.89000000 4.57000000 56 -6.78777778 -3.89000000 57 2.77222222 -6.78777778 58 -3.58777778 2.77222222 59 -4.62777778 -3.58777778 60 NA -4.62777778 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 2.5477778 -0.05222222 [2,] -8.5522222 2.54777778 [3,] 6.5877778 -8.55222222 [4,] 11.6677778 6.58777778 [5,] -16.0722222 11.66777778 [6,] 8.8077778 -16.07222222 [7,] -12.0522222 8.80777778 [8,] -12.0500000 -12.05222222 [9,] -13.0900000 -12.05000000 [10,] -2.1500000 -13.09000000 [11,] 7.1100000 -2.15000000 [12,] -6.4838889 7.11000000 [13,] -7.8838889 -6.48388889 [14,] 1.7161111 -7.88388889 [15,] 8.2561111 1.71611111 [16,] -7.3638889 8.25611111 [17,] 3.9961111 -7.36388889 [18,] -5.4238889 3.99611111 [19,] 15.2161111 -5.42388889 [20,] 4.0183333 15.21611111 [21,] 10.9783333 4.01833333 [22,] -5.5816667 10.97833333 [23,] 0.6783333 -5.58166667 [24,] 4.3844444 0.67833333 [25,] 1.5844444 4.38444444 [26,] 5.5844444 1.58444444 [27,] 7.0244444 5.58444444 [28,] -14.8955556 7.02444444 [29,] 16.4644444 -14.89555556 [30,] -4.6555556 16.46444444 [31,] -0.3155556 -4.65555556 [32,] 9.1755556 -0.31555556 [33,] -9.9644444 9.17555556 [34,] 0.1755556 -9.96444444 [35,] 4.0355556 0.17555556 [36,] -2.2583333 4.03555556 [37,] 9.4416667 -2.25833333 [38,] 9.1416667 9.44166667 [39,] -11.4183333 9.14166667 [40,] 15.9616667 -11.41833333 [41,] -1.8783333 15.96166667 [42,] -3.2983333 -1.87833333 [43,] 1.0416667 -3.29833333 [44,] 5.6438889 1.04166667 [45,] 9.3038889 5.64388889 [46,] 11.1438889 9.30388889 [47,] -7.1961111 11.14388889 [48,] 4.4100000 -7.19611111 [49,] -5.6900000 4.41000000 [50,] -7.8900000 -5.69000000 [51,] -10.4500000 -7.89000000 [52,] -5.3700000 -10.45000000 [53,] -2.5100000 -5.37000000 [54,] 4.5700000 -2.51000000 [55,] -3.8900000 4.57000000 [56,] -6.7877778 -3.89000000 [57,] 2.7722222 -6.78777778 [58,] -3.5877778 2.77222222 [59,] -4.6277778 -3.58777778 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 2.5477778 -0.05222222 2 -8.5522222 2.54777778 3 6.5877778 -8.55222222 4 11.6677778 6.58777778 5 -16.0722222 11.66777778 6 8.8077778 -16.07222222 7 -12.0522222 8.80777778 8 -12.0500000 -12.05222222 9 -13.0900000 -12.05000000 10 -2.1500000 -13.09000000 11 7.1100000 -2.15000000 12 -6.4838889 7.11000000 13 -7.8838889 -6.48388889 14 1.7161111 -7.88388889 15 8.2561111 1.71611111 16 -7.3638889 8.25611111 17 3.9961111 -7.36388889 18 -5.4238889 3.99611111 19 15.2161111 -5.42388889 20 4.0183333 15.21611111 21 10.9783333 4.01833333 22 -5.5816667 10.97833333 23 0.6783333 -5.58166667 24 4.3844444 0.67833333 25 1.5844444 4.38444444 26 5.5844444 1.58444444 27 7.0244444 5.58444444 28 -14.8955556 7.02444444 29 16.4644444 -14.89555556 30 -4.6555556 16.46444444 31 -0.3155556 -4.65555556 32 9.1755556 -0.31555556 33 -9.9644444 9.17555556 34 0.1755556 -9.96444444 35 4.0355556 0.17555556 36 -2.2583333 4.03555556 37 9.4416667 -2.25833333 38 9.1416667 9.44166667 39 -11.4183333 9.14166667 40 15.9616667 -11.41833333 41 -1.8783333 15.96166667 42 -3.2983333 -1.87833333 43 1.0416667 -3.29833333 44 5.6438889 1.04166667 45 9.3038889 5.64388889 46 11.1438889 9.30388889 47 -7.1961111 11.14388889 48 4.4100000 -7.19611111 49 -5.6900000 4.41000000 50 -7.8900000 -5.69000000 51 -10.4500000 -7.89000000 52 -5.3700000 -10.45000000 53 -2.5100000 -5.37000000 54 4.5700000 -2.51000000 55 -3.8900000 4.57000000 56 -6.7877778 -3.89000000 57 2.7722222 -6.78777778 58 -3.5877778 2.77222222 59 -4.6277778 -3.58777778 > 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/7w0g51229094301.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8kedk1229094301.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9v77h1229094301.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10xglv1229094301.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11p3si1229094302.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/12yqvb1229094302.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/13oenk1229094302.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/14mv2h1229094302.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/15cqra1229094302.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/16h3yy1229094302.tab") + } > > system("convert tmp/1yp211229094301.ps tmp/1yp211229094301.png") > system("convert tmp/27mgh1229094301.ps tmp/27mgh1229094301.png") > system("convert tmp/3laqx1229094301.ps tmp/3laqx1229094301.png") > system("convert tmp/4ellr1229094301.ps tmp/4ellr1229094301.png") > system("convert tmp/5z9q51229094301.ps tmp/5z9q51229094301.png") > system("convert tmp/6d4pz1229094301.ps tmp/6d4pz1229094301.png") > system("convert tmp/7w0g51229094301.ps tmp/7w0g51229094301.png") > system("convert tmp/8kedk1229094301.ps tmp/8kedk1229094301.png") > system("convert tmp/9v77h1229094301.ps tmp/9v77h1229094301.png") > system("convert tmp/10xglv1229094301.ps tmp/10xglv1229094301.png") > > > proc.time() user system elapsed 2.496 1.668 5.529