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Type 'q()' to quit R. > x <- array(list(4.1,24.3,1.7,19,8,37.2,5,27.3,6,21.5,4.8,19.2,3.8,16.8,2.7,14.9,1,18.2,8.4,34.8,6.1,29.6,4.8,25.3,2.2,17.5,0.6,13.7,2.8,17.9,7.4,24.8,6.3,25.3,2.3,21.7,1.1,13.8,3.4,17.2,6.1,26.7,2.9,17.9,1.9,15.8,0.6,16.3,7.2,26.8,1.6,17.8,2.5,17.2,3.5,18.3,4,16.9,0.9,11.9,1.7,13.8,8.9,27.9,5.2,21.4,3.6,18.6,0.7,12.6,1.4,14.2,3.7,19.6,4.8,18.9,5.6,26.4,6.2,31.1,1.4,16.2,1.8,18.9,8.6,35.8,7.2,24.2,2.7,18.2,2.9,19.6,3.6,22.8,1.7,12,0.5,9,0.4,11,7.4,34.3,6.2,27.3,5.8,24.8,4.1,21,3.6,17.2,8.4,30,2.8,14,9,38.9,2.9,14.1,4.1,19.9),dim=c(2,60),dimnames=list(c('afstand','schadebedrag'),1:60)) > y <- array(NA,dim=c(2,60),dimnames=list(c('afstand','schadebedrag'),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 = 'No Linear Trend' > par2 = 'Do not include Seasonal 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 > 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 afstand schadebedrag 1 4.1 24.3 2 1.7 19.0 3 8.0 37.2 4 5.0 27.3 5 6.0 21.5 6 4.8 19.2 7 3.8 16.8 8 2.7 14.9 9 1.0 18.2 10 8.4 34.8 11 6.1 29.6 12 4.8 25.3 13 2.2 17.5 14 0.6 13.7 15 2.8 17.9 16 7.4 24.8 17 6.3 25.3 18 2.3 21.7 19 1.1 13.8 20 3.4 17.2 21 6.1 26.7 22 2.9 17.9 23 1.9 15.8 24 0.6 16.3 25 7.2 26.8 26 1.6 17.8 27 2.5 17.2 28 3.5 18.3 29 4.0 16.9 30 0.9 11.9 31 1.7 13.8 32 8.9 27.9 33 5.2 21.4 34 3.6 18.6 35 0.7 12.6 36 1.4 14.2 37 3.7 19.6 38 4.8 18.9 39 5.6 26.4 40 6.2 31.1 41 1.4 16.2 42 1.8 18.9 43 8.6 35.8 44 7.2 24.2 45 2.7 18.2 46 2.9 19.6 47 3.6 22.8 48 1.7 12.0 49 0.5 9.0 50 0.4 11.0 51 7.4 34.3 52 6.2 27.3 53 5.8 24.8 54 4.1 21.0 55 3.6 17.2 56 8.4 30.0 57 2.8 14.0 58 9.0 38.9 59 2.9 14.1 60 4.1 19.9 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) schadebedrag -2.7160 0.3205 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -2.1164 -0.7066 -0.1323 0.6818 2.6751 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.71600 0.45652 -5.949 1.65e-07 *** schadebedrag 0.32046 0.02068 15.493 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.094 on 58 degrees of freedom Multiple R-squared: 0.8054, Adjusted R-squared: 0.802 F-statistic: 240 on 1 and 58 DF, p-value: < 2.2e-16 > 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,] 0.8947777 0.21044456 0.10522228 [2,] 0.8996085 0.20078296 0.10039148 [3,] 0.8514098 0.29718030 0.14859015 [4,] 0.7729483 0.45410341 0.22705171 [5,] 0.9333375 0.13332492 0.06666246 [6,] 0.9118209 0.17635814 0.08817907 [7,] 0.8708014 0.25839729 0.12919865 [8,] 0.8200902 0.35981962 0.17990981 [9,] 0.7774995 0.44500110 0.22250055 [10,] 0.7685787 0.46284270 0.23142135 [11,] 0.6952907 0.60941857 0.30470929 [12,] 0.8876957 0.22460868 0.11230434 [13,] 0.8775094 0.24498112 0.12249056 [14,] 0.9327018 0.13459639 0.06729819 [15,] 0.9096493 0.18070140 0.09035070 [16,] 0.8865025 0.22699498 0.11349749 [17,] 0.8488755 0.30224909 0.15112455 [18,] 0.7983648 0.40327039 0.20163519 [19,] 0.7472373 0.50552530 0.25276265 [20,] 0.8435490 0.31290193 0.15645096 [21,] 0.8652091 0.26958189 0.13479094 [22,] 0.8860226 0.22795488 0.11397744 [23,] 0.8498024 0.30039520 0.15019760 [24,] 0.8089601 0.38207987 0.19103994 [25,] 0.8304019 0.33919622 0.16959811 [26,] 0.7817349 0.43653026 0.21826513 [27,] 0.7234066 0.55318682 0.27659341 [28,] 0.9382458 0.12350835 0.06175418 [29,] 0.9363628 0.12727447 0.06363724 [30,] 0.9109320 0.17813600 0.08906800 [31,] 0.8929446 0.21411086 0.10705543 [32,] 0.8650984 0.26980315 0.13490158 [33,] 0.8171720 0.36565599 0.18282799 [34,] 0.8513515 0.29729706 0.14864853 [35,] 0.7995156 0.40096875 0.20048437 [36,] 0.7932736 0.41345271 0.20672636 [37,] 0.8112184 0.37756324 0.18878162 [38,] 0.8987737 0.20245252 0.10122626 [39,] 0.8540006 0.29199876 0.14599938 [40,] 0.9662226 0.06755471 0.03377735 [41,] 0.9549755 0.09004895 0.04502448 [42,] 0.9531859 0.09362824 0.04681412 [43,] 0.9704201 0.05915973 0.02957987 [44,] 0.9481625 0.10367502 0.05183751 [45,] 0.9208692 0.15826166 0.07913083 [46,] 0.9661961 0.06760784 0.03380392 [47,] 0.9637668 0.07246647 0.03623323 [48,] 0.9274124 0.14517529 0.07258764 [49,] 0.8606609 0.27867821 0.13933910 [50,] 0.8039632 0.39207359 0.19603680 [51,] 0.6581968 0.68360641 0.34180321 > postscript(file="/var/wessaorg/rcomp/tmp/1j3sj1321356232.ps",horizontal=F,onefile=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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2qnyh1321356232.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/32e5y1321356232.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4j1sh1321356232.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5abc91321356232.ps",horizontal=F,onefile=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 -0.971269483 -1.672811025 -1.205253278 -1.032661063 1.826029325 1.363096203 7 8 9 10 11 12 1.132209467 0.641090801 -2.116439937 -0.036140014 -0.669727942 -0.591733343 13 14 15 16 17 18 -0.692115235 -1.074352567 -0.220300779 2.168498587 0.908266657 -1.938063447 19 20 21 22 23 24 -0.606398953 0.604023923 0.259617253 -0.120300779 -0.447326673 -1.907558603 25 26 27 28 29 30 1.327570867 -1.388254393 -0.295976077 0.351513677 1.300163081 -0.197517619 31 32 33 34 35 36 -0.006398953 2.675060621 1.058075711 0.355374519 -0.621842321 -0.434584497 37 38 39 40 41 42 0.134910659 1.459235361 -0.144243589 -1.050423732 -1.075512217 -1.540764639 43 44 45 46 47 48 -0.156603874 2.160776903 -0.416439937 -0.665089341 -0.990573693 0.570435995 49 50 51 52 53 54 0.331827576 -0.409100144 -0.875908084 0.167338937 0.568498587 0.086261255 55 56 57 58 59 60 0.804023923 1.502086514 1.029508275 -0.750041840 1.097461889 0.438771501 > postscript(file="/var/wessaorg/rcomp/tmp/6iflc1321356232.ps",horizontal=F,onefile=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 -0.971269483 NA 1 -1.672811025 -0.971269483 2 -1.205253278 -1.672811025 3 -1.032661063 -1.205253278 4 1.826029325 -1.032661063 5 1.363096203 1.826029325 6 1.132209467 1.363096203 7 0.641090801 1.132209467 8 -2.116439937 0.641090801 9 -0.036140014 -2.116439937 10 -0.669727942 -0.036140014 11 -0.591733343 -0.669727942 12 -0.692115235 -0.591733343 13 -1.074352567 -0.692115235 14 -0.220300779 -1.074352567 15 2.168498587 -0.220300779 16 0.908266657 2.168498587 17 -1.938063447 0.908266657 18 -0.606398953 -1.938063447 19 0.604023923 -0.606398953 20 0.259617253 0.604023923 21 -0.120300779 0.259617253 22 -0.447326673 -0.120300779 23 -1.907558603 -0.447326673 24 1.327570867 -1.907558603 25 -1.388254393 1.327570867 26 -0.295976077 -1.388254393 27 0.351513677 -0.295976077 28 1.300163081 0.351513677 29 -0.197517619 1.300163081 30 -0.006398953 -0.197517619 31 2.675060621 -0.006398953 32 1.058075711 2.675060621 33 0.355374519 1.058075711 34 -0.621842321 0.355374519 35 -0.434584497 -0.621842321 36 0.134910659 -0.434584497 37 1.459235361 0.134910659 38 -0.144243589 1.459235361 39 -1.050423732 -0.144243589 40 -1.075512217 -1.050423732 41 -1.540764639 -1.075512217 42 -0.156603874 -1.540764639 43 2.160776903 -0.156603874 44 -0.416439937 2.160776903 45 -0.665089341 -0.416439937 46 -0.990573693 -0.665089341 47 0.570435995 -0.990573693 48 0.331827576 0.570435995 49 -0.409100144 0.331827576 50 -0.875908084 -0.409100144 51 0.167338937 -0.875908084 52 0.568498587 0.167338937 53 0.086261255 0.568498587 54 0.804023923 0.086261255 55 1.502086514 0.804023923 56 1.029508275 1.502086514 57 -0.750041840 1.029508275 58 1.097461889 -0.750041840 59 0.438771501 1.097461889 60 NA 0.438771501 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -1.672811025 -0.971269483 [2,] -1.205253278 -1.672811025 [3,] -1.032661063 -1.205253278 [4,] 1.826029325 -1.032661063 [5,] 1.363096203 1.826029325 [6,] 1.132209467 1.363096203 [7,] 0.641090801 1.132209467 [8,] -2.116439937 0.641090801 [9,] -0.036140014 -2.116439937 [10,] -0.669727942 -0.036140014 [11,] -0.591733343 -0.669727942 [12,] -0.692115235 -0.591733343 [13,] -1.074352567 -0.692115235 [14,] -0.220300779 -1.074352567 [15,] 2.168498587 -0.220300779 [16,] 0.908266657 2.168498587 [17,] -1.938063447 0.908266657 [18,] -0.606398953 -1.938063447 [19,] 0.604023923 -0.606398953 [20,] 0.259617253 0.604023923 [21,] -0.120300779 0.259617253 [22,] -0.447326673 -0.120300779 [23,] -1.907558603 -0.447326673 [24,] 1.327570867 -1.907558603 [25,] -1.388254393 1.327570867 [26,] -0.295976077 -1.388254393 [27,] 0.351513677 -0.295976077 [28,] 1.300163081 0.351513677 [29,] -0.197517619 1.300163081 [30,] -0.006398953 -0.197517619 [31,] 2.675060621 -0.006398953 [32,] 1.058075711 2.675060621 [33,] 0.355374519 1.058075711 [34,] -0.621842321 0.355374519 [35,] -0.434584497 -0.621842321 [36,] 0.134910659 -0.434584497 [37,] 1.459235361 0.134910659 [38,] -0.144243589 1.459235361 [39,] -1.050423732 -0.144243589 [40,] -1.075512217 -1.050423732 [41,] -1.540764639 -1.075512217 [42,] -0.156603874 -1.540764639 [43,] 2.160776903 -0.156603874 [44,] -0.416439937 2.160776903 [45,] -0.665089341 -0.416439937 [46,] -0.990573693 -0.665089341 [47,] 0.570435995 -0.990573693 [48,] 0.331827576 0.570435995 [49,] -0.409100144 0.331827576 [50,] -0.875908084 -0.409100144 [51,] 0.167338937 -0.875908084 [52,] 0.568498587 0.167338937 [53,] 0.086261255 0.568498587 [54,] 0.804023923 0.086261255 [55,] 1.502086514 0.804023923 [56,] 1.029508275 1.502086514 [57,] -0.750041840 1.029508275 [58,] 1.097461889 -0.750041840 [59,] 0.438771501 1.097461889 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -1.672811025 -0.971269483 2 -1.205253278 -1.672811025 3 -1.032661063 -1.205253278 4 1.826029325 -1.032661063 5 1.363096203 1.826029325 6 1.132209467 1.363096203 7 0.641090801 1.132209467 8 -2.116439937 0.641090801 9 -0.036140014 -2.116439937 10 -0.669727942 -0.036140014 11 -0.591733343 -0.669727942 12 -0.692115235 -0.591733343 13 -1.074352567 -0.692115235 14 -0.220300779 -1.074352567 15 2.168498587 -0.220300779 16 0.908266657 2.168498587 17 -1.938063447 0.908266657 18 -0.606398953 -1.938063447 19 0.604023923 -0.606398953 20 0.259617253 0.604023923 21 -0.120300779 0.259617253 22 -0.447326673 -0.120300779 23 -1.907558603 -0.447326673 24 1.327570867 -1.907558603 25 -1.388254393 1.327570867 26 -0.295976077 -1.388254393 27 0.351513677 -0.295976077 28 1.300163081 0.351513677 29 -0.197517619 1.300163081 30 -0.006398953 -0.197517619 31 2.675060621 -0.006398953 32 1.058075711 2.675060621 33 0.355374519 1.058075711 34 -0.621842321 0.355374519 35 -0.434584497 -0.621842321 36 0.134910659 -0.434584497 37 1.459235361 0.134910659 38 -0.144243589 1.459235361 39 -1.050423732 -0.144243589 40 -1.075512217 -1.050423732 41 -1.540764639 -1.075512217 42 -0.156603874 -1.540764639 43 2.160776903 -0.156603874 44 -0.416439937 2.160776903 45 -0.665089341 -0.416439937 46 -0.990573693 -0.665089341 47 0.570435995 -0.990573693 48 0.331827576 0.570435995 49 -0.409100144 0.331827576 50 -0.875908084 -0.409100144 51 0.167338937 -0.875908084 52 0.568498587 0.167338937 53 0.086261255 0.568498587 54 0.804023923 0.086261255 55 1.502086514 0.804023923 56 1.029508275 1.502086514 57 -0.750041840 1.029508275 58 1.097461889 -0.750041840 59 0.438771501 1.097461889 > 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/wessaorg/rcomp/tmp/7u5co1321356232.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8y2ki1321356232.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/96d2w1321356232.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/1000d21321356232.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/wessaorg/rcomp/tmp/11fi1y1321356232.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/wessaorg/rcomp/tmp/12kugv1321356233.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/wessaorg/rcomp/tmp/135t4o1321356233.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/wessaorg/rcomp/tmp/148p021321356233.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/wessaorg/rcomp/tmp/15udrt1321356233.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/wessaorg/rcomp/tmp/16fbcl1321356233.tab") + } > > try(system("convert tmp/1j3sj1321356232.ps tmp/1j3sj1321356232.png",intern=TRUE)) character(0) > try(system("convert tmp/2qnyh1321356232.ps tmp/2qnyh1321356232.png",intern=TRUE)) character(0) > try(system("convert tmp/32e5y1321356232.ps tmp/32e5y1321356232.png",intern=TRUE)) character(0) > try(system("convert tmp/4j1sh1321356232.ps tmp/4j1sh1321356232.png",intern=TRUE)) character(0) > try(system("convert tmp/5abc91321356232.ps tmp/5abc91321356232.png",intern=TRUE)) character(0) > try(system("convert tmp/6iflc1321356232.ps tmp/6iflc1321356232.png",intern=TRUE)) character(0) > try(system("convert tmp/7u5co1321356232.ps tmp/7u5co1321356232.png",intern=TRUE)) character(0) > try(system("convert tmp/8y2ki1321356232.ps tmp/8y2ki1321356232.png",intern=TRUE)) character(0) > try(system("convert tmp/96d2w1321356232.ps tmp/96d2w1321356232.png",intern=TRUE)) character(0) > try(system("convert tmp/1000d21321356232.ps tmp/1000d21321356232.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.087 0.474 3.609