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Type 'q()' to quit R. > x <- array(list(1.1608,0,1.1208,0,1.0883,0,1.0704,0,1.0628,0,1.0378,0,1.0353,0,1.0604,0,1.0501,0,1.0706,0,1.0338,0,1.011,0,1.0137,0,0.9834,0,0.9643,0,0.947,0,0.906,0,0.9492,0,0.9397,0,0.9041,0,0.8721,0,0.8552,0,0.8564,0,0.8973,0,0.9383,0,0.9217,0,0.9095,0,0.892,0,0.8742,0,0.8532,0,0.8607,0,0.9005,0,0.9111,0,0.9059,1,0.8883,1,0.8924,1,0.8833,1,0.87,1,0.8758,1,0.8858,1,0.917,1,0.9554,1,0.9922,1,0.9778,1,0.9808,1,0.9811,1,1.0014,1,1.0183,1),dim=c(2,48),dimnames=list(c('koers','dummy'),1:48)) > y <- array(NA,dim=c(2,48),dimnames=list(c('koers','dummy'),1:48)) > 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 koers dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 1.1608 0 1 0 0 0 0 0 0 0 0 0 0 1 2 1.1208 0 0 1 0 0 0 0 0 0 0 0 0 2 3 1.0883 0 0 0 1 0 0 0 0 0 0 0 0 3 4 1.0704 0 0 0 0 1 0 0 0 0 0 0 0 4 5 1.0628 0 0 0 0 0 1 0 0 0 0 0 0 5 6 1.0378 0 0 0 0 0 0 1 0 0 0 0 0 6 7 1.0353 0 0 0 0 0 0 0 1 0 0 0 0 7 8 1.0604 0 0 0 0 0 0 0 0 1 0 0 0 8 9 1.0501 0 0 0 0 0 0 0 0 0 1 0 0 9 10 1.0706 0 0 0 0 0 0 0 0 0 0 1 0 10 11 1.0338 0 0 0 0 0 0 0 0 0 0 0 1 11 12 1.0110 0 0 0 0 0 0 0 0 0 0 0 0 12 13 1.0137 0 1 0 0 0 0 0 0 0 0 0 0 13 14 0.9834 0 0 1 0 0 0 0 0 0 0 0 0 14 15 0.9643 0 0 0 1 0 0 0 0 0 0 0 0 15 16 0.9470 0 0 0 0 1 0 0 0 0 0 0 0 16 17 0.9060 0 0 0 0 0 1 0 0 0 0 0 0 17 18 0.9492 0 0 0 0 0 0 1 0 0 0 0 0 18 19 0.9397 0 0 0 0 0 0 0 1 0 0 0 0 19 20 0.9041 0 0 0 0 0 0 0 0 1 0 0 0 20 21 0.8721 0 0 0 0 0 0 0 0 0 1 0 0 21 22 0.8552 0 0 0 0 0 0 0 0 0 0 1 0 22 23 0.8564 0 0 0 0 0 0 0 0 0 0 0 1 23 24 0.8973 0 0 0 0 0 0 0 0 0 0 0 0 24 25 0.9383 0 1 0 0 0 0 0 0 0 0 0 0 25 26 0.9217 0 0 1 0 0 0 0 0 0 0 0 0 26 27 0.9095 0 0 0 1 0 0 0 0 0 0 0 0 27 28 0.8920 0 0 0 0 1 0 0 0 0 0 0 0 28 29 0.8742 0 0 0 0 0 1 0 0 0 0 0 0 29 30 0.8532 0 0 0 0 0 0 1 0 0 0 0 0 30 31 0.8607 0 0 0 0 0 0 0 1 0 0 0 0 31 32 0.9005 0 0 0 0 0 0 0 0 1 0 0 0 32 33 0.9111 0 0 0 0 0 0 0 0 0 1 0 0 33 34 0.9059 1 0 0 0 0 0 0 0 0 0 1 0 34 35 0.8883 1 0 0 0 0 0 0 0 0 0 0 1 35 36 0.8924 1 0 0 0 0 0 0 0 0 0 0 0 36 37 0.8833 1 1 0 0 0 0 0 0 0 0 0 0 37 38 0.8700 1 0 1 0 0 0 0 0 0 0 0 0 38 39 0.8758 1 0 0 1 0 0 0 0 0 0 0 0 39 40 0.8858 1 0 0 0 1 0 0 0 0 0 0 0 40 41 0.9170 1 0 0 0 0 1 0 0 0 0 0 0 41 42 0.9554 1 0 0 0 0 0 1 0 0 0 0 0 42 43 0.9922 1 0 0 0 0 0 0 1 0 0 0 0 43 44 0.9778 1 0 0 0 0 0 0 0 1 0 0 0 44 45 0.9808 1 0 0 0 0 0 0 0 0 1 0 0 45 46 0.9811 1 0 0 0 0 0 0 0 0 0 1 0 46 47 1.0014 1 0 0 0 0 0 0 0 0 0 0 1 47 48 1.0183 1 0 0 0 0 0 0 0 0 0 0 0 48 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) dummy M1 M2 M3 M4 1.0942237 0.1309692 0.0018659 -0.0163521 -0.0240202 -0.0278632 M5 M6 M7 M8 M9 M10 -0.0298313 -0.0140994 0.0008076 0.0113645 0.0110215 -0.0152139 M11 t -0.0166069 -0.0068319 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.090977 -0.034281 0.004045 0.031343 0.121040 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.0942237 0.0380537 28.755 < 2e-16 *** dummy 0.1309692 0.0332954 3.934 0.000392 *** M1 0.0018659 0.0441253 0.042 0.966517 M2 -0.0163521 0.0439975 -0.372 0.712452 M3 -0.0240202 0.0438979 -0.547 0.587827 M4 -0.0278632 0.0438266 -0.636 0.529189 M5 -0.0298313 0.0437838 -0.681 0.500276 M6 -0.0140994 0.0437695 -0.322 0.749328 M7 0.0008076 0.0437838 0.018 0.985392 M8 0.0113645 0.0438266 0.259 0.796962 M9 0.0110215 0.0438979 0.251 0.803271 M10 -0.0152139 0.0435491 -0.349 0.728981 M11 -0.0166069 0.0435060 -0.382 0.705047 t -0.0068319 0.0011185 -6.108 6.23e-07 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.06151 on 34 degrees of freedom Multiple R-squared: 0.5581, Adjusted R-squared: 0.3891 F-statistic: 3.303 on 13 and 34 DF, p-value: 0.002564 > 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,] 0.02089193 0.04178387 0.97910807 [2,] 0.05876985 0.11753971 0.94123015 [3,] 0.06063814 0.12127629 0.93936186 [4,] 0.05168008 0.10336016 0.94831992 [5,] 0.06421197 0.12842395 0.93578803 [6,] 0.14510443 0.29020886 0.85489557 [7,] 0.10027138 0.20054275 0.89972862 [8,] 0.06654007 0.13308015 0.93345993 [9,] 0.13756455 0.27512909 0.86243545 [10,] 0.32150839 0.64301678 0.67849161 [11,] 0.62421898 0.75156203 0.37578102 [12,] 0.87403719 0.25192563 0.12596281 [13,] 0.91689909 0.16620183 0.08310091 [14,] 0.83308301 0.33383399 0.16691699 [15,] 0.90118535 0.19762930 0.09881465 > postscript(file="/var/www/html/rcomp/tmp/12xkr1227520397.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2lrls1227520397.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3386v1227520397.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4t7511227520397.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5ideh1227520397.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 = 48 Frequency = 1 1 2 3 4 5 6 0.071542308 0.056592308 0.038592308 0.031367308 0.032567308 -0.001332692 7 8 9 10 11 12 -0.011907692 0.009467308 0.006342308 0.059909615 0.031334615 -0.001240385 13 14 15 16 17 18 0.006425641 0.001175641 -0.003424359 -0.010049359 -0.042249359 -0.007949359 19 20 21 22 23 24 -0.025524359 -0.064849359 -0.089674359 -0.073507051 -0.064082051 -0.032957051 25 26 27 28 29 30 0.013008974 0.021458974 0.023758974 0.016933974 0.007933974 -0.021966026 31 32 33 34 35 36 -0.022541026 0.013533974 0.031308974 -0.071792949 -0.081167949 -0.086842949 37 38 39 40 41 42 -0.090976923 -0.079226923 -0.058926923 -0.038251923 0.001748077 0.031248077 43 44 45 46 47 48 0.059973077 0.041848077 0.052023077 0.085390385 0.113915385 0.121040385 > postscript(file="/var/www/html/rcomp/tmp/6ag0g1227520397.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 = 48 Frequency = 1 lag(myerror, k = 1) myerror 0 0.071542308 NA 1 0.056592308 0.071542308 2 0.038592308 0.056592308 3 0.031367308 0.038592308 4 0.032567308 0.031367308 5 -0.001332692 0.032567308 6 -0.011907692 -0.001332692 7 0.009467308 -0.011907692 8 0.006342308 0.009467308 9 0.059909615 0.006342308 10 0.031334615 0.059909615 11 -0.001240385 0.031334615 12 0.006425641 -0.001240385 13 0.001175641 0.006425641 14 -0.003424359 0.001175641 15 -0.010049359 -0.003424359 16 -0.042249359 -0.010049359 17 -0.007949359 -0.042249359 18 -0.025524359 -0.007949359 19 -0.064849359 -0.025524359 20 -0.089674359 -0.064849359 21 -0.073507051 -0.089674359 22 -0.064082051 -0.073507051 23 -0.032957051 -0.064082051 24 0.013008974 -0.032957051 25 0.021458974 0.013008974 26 0.023758974 0.021458974 27 0.016933974 0.023758974 28 0.007933974 0.016933974 29 -0.021966026 0.007933974 30 -0.022541026 -0.021966026 31 0.013533974 -0.022541026 32 0.031308974 0.013533974 33 -0.071792949 0.031308974 34 -0.081167949 -0.071792949 35 -0.086842949 -0.081167949 36 -0.090976923 -0.086842949 37 -0.079226923 -0.090976923 38 -0.058926923 -0.079226923 39 -0.038251923 -0.058926923 40 0.001748077 -0.038251923 41 0.031248077 0.001748077 42 0.059973077 0.031248077 43 0.041848077 0.059973077 44 0.052023077 0.041848077 45 0.085390385 0.052023077 46 0.113915385 0.085390385 47 0.121040385 0.113915385 48 NA 0.121040385 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.056592308 0.071542308 [2,] 0.038592308 0.056592308 [3,] 0.031367308 0.038592308 [4,] 0.032567308 0.031367308 [5,] -0.001332692 0.032567308 [6,] -0.011907692 -0.001332692 [7,] 0.009467308 -0.011907692 [8,] 0.006342308 0.009467308 [9,] 0.059909615 0.006342308 [10,] 0.031334615 0.059909615 [11,] -0.001240385 0.031334615 [12,] 0.006425641 -0.001240385 [13,] 0.001175641 0.006425641 [14,] -0.003424359 0.001175641 [15,] -0.010049359 -0.003424359 [16,] -0.042249359 -0.010049359 [17,] -0.007949359 -0.042249359 [18,] -0.025524359 -0.007949359 [19,] -0.064849359 -0.025524359 [20,] -0.089674359 -0.064849359 [21,] -0.073507051 -0.089674359 [22,] -0.064082051 -0.073507051 [23,] -0.032957051 -0.064082051 [24,] 0.013008974 -0.032957051 [25,] 0.021458974 0.013008974 [26,] 0.023758974 0.021458974 [27,] 0.016933974 0.023758974 [28,] 0.007933974 0.016933974 [29,] -0.021966026 0.007933974 [30,] -0.022541026 -0.021966026 [31,] 0.013533974 -0.022541026 [32,] 0.031308974 0.013533974 [33,] -0.071792949 0.031308974 [34,] -0.081167949 -0.071792949 [35,] -0.086842949 -0.081167949 [36,] -0.090976923 -0.086842949 [37,] -0.079226923 -0.090976923 [38,] -0.058926923 -0.079226923 [39,] -0.038251923 -0.058926923 [40,] 0.001748077 -0.038251923 [41,] 0.031248077 0.001748077 [42,] 0.059973077 0.031248077 [43,] 0.041848077 0.059973077 [44,] 0.052023077 0.041848077 [45,] 0.085390385 0.052023077 [46,] 0.113915385 0.085390385 [47,] 0.121040385 0.113915385 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.056592308 0.071542308 2 0.038592308 0.056592308 3 0.031367308 0.038592308 4 0.032567308 0.031367308 5 -0.001332692 0.032567308 6 -0.011907692 -0.001332692 7 0.009467308 -0.011907692 8 0.006342308 0.009467308 9 0.059909615 0.006342308 10 0.031334615 0.059909615 11 -0.001240385 0.031334615 12 0.006425641 -0.001240385 13 0.001175641 0.006425641 14 -0.003424359 0.001175641 15 -0.010049359 -0.003424359 16 -0.042249359 -0.010049359 17 -0.007949359 -0.042249359 18 -0.025524359 -0.007949359 19 -0.064849359 -0.025524359 20 -0.089674359 -0.064849359 21 -0.073507051 -0.089674359 22 -0.064082051 -0.073507051 23 -0.032957051 -0.064082051 24 0.013008974 -0.032957051 25 0.021458974 0.013008974 26 0.023758974 0.021458974 27 0.016933974 0.023758974 28 0.007933974 0.016933974 29 -0.021966026 0.007933974 30 -0.022541026 -0.021966026 31 0.013533974 -0.022541026 32 0.031308974 0.013533974 33 -0.071792949 0.031308974 34 -0.081167949 -0.071792949 35 -0.086842949 -0.081167949 36 -0.090976923 -0.086842949 37 -0.079226923 -0.090976923 38 -0.058926923 -0.079226923 39 -0.038251923 -0.058926923 40 0.001748077 -0.038251923 41 0.031248077 0.001748077 42 0.059973077 0.031248077 43 0.041848077 0.059973077 44 0.052023077 0.041848077 45 0.085390385 0.052023077 46 0.113915385 0.085390385 47 0.121040385 0.113915385 > 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/7jews1227520397.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8cvri1227520397.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9tehc1227520397.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/108uba1227520397.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11myad1227520397.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/123rkx1227520397.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/13rujc1227520397.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/14racg1227520397.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/150njw1227520397.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/16mkub1227520397.tab") + } > > system("convert tmp/12xkr1227520397.ps tmp/12xkr1227520397.png") > system("convert tmp/2lrls1227520397.ps tmp/2lrls1227520397.png") > system("convert tmp/3386v1227520397.ps tmp/3386v1227520397.png") > system("convert tmp/4t7511227520397.ps tmp/4t7511227520397.png") > system("convert tmp/5ideh1227520397.ps tmp/5ideh1227520397.png") > system("convert tmp/6ag0g1227520397.ps tmp/6ag0g1227520397.png") > system("convert tmp/7jews1227520397.ps tmp/7jews1227520397.png") > system("convert tmp/8cvri1227520397.ps tmp/8cvri1227520397.png") > system("convert tmp/9tehc1227520397.ps tmp/9tehc1227520397.png") > system("convert tmp/108uba1227520397.ps tmp/108uba1227520397.png") > > > proc.time() user system elapsed 2.271 1.543 5.910