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Type 'q()' to quit R. > x <- array(list(244752,0,244576,0,241572,0,240541,0,236089,0,236997,0,264579,0,270349,0,269645,0,267037,0,258113,0,262813,0,267413,0,267366,0,264777,0,258863,0,254844,0,254868,0,277267,0,285351,0,286602,0,283042,0,276687,0,277915,0,277128,0,277103,0,275037,0,270150,0,267140,0,264993,0,287259,0,291186,0,292300,0,288186,0,281477,0,282656,0,280190,0,280408,0,276836,0,275216,0,274352,0,271311,0,289802,0,290726,0,292300,0,278506,0,269826,0,265861,0,269034,1,264176,1,255198,1,253353,1,246057,1,235372,1,258556,1,260993,1,254663,1,250643,1,243422,1,247105,1,248541,1,245039,1,237080,1,237085,1,225554,1,226839,1,247934,1,248333,1,246969,1,245098,1,246263,1),dim=c(2,71),dimnames=list(c('WerklozenMannen','Kredietcrisis'),1:71)) > y <- array(NA,dim=c(2,71),dimnames=list(c('WerklozenMannen','Kredietcrisis'),1:71)) > 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) > 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 WerklozenMannen Kredietcrisis 1 244752 0 2 244576 0 3 241572 0 4 240541 0 5 236089 0 6 236997 0 7 264579 0 8 270349 0 9 269645 0 10 267037 0 11 258113 0 12 262813 0 13 267413 0 14 267366 0 15 264777 0 16 258863 0 17 254844 0 18 254868 0 19 277267 0 20 285351 0 21 286602 0 22 283042 0 23 276687 0 24 277915 0 25 277128 0 26 277103 0 27 275037 0 28 270150 0 29 267140 0 30 264993 0 31 287259 0 32 291186 0 33 292300 0 34 288186 0 35 281477 0 36 282656 0 37 280190 0 38 280408 0 39 276836 0 40 275216 0 41 274352 0 42 271311 0 43 289802 0 44 290726 0 45 292300 0 46 278506 0 47 269826 0 48 265861 0 49 269034 1 50 264176 1 51 255198 1 52 253353 1 53 246057 1 54 235372 1 55 258556 1 56 260993 1 57 254663 1 58 250643 1 59 243422 1 60 247105 1 61 248541 1 62 245039 1 63 237080 1 64 237085 1 65 225554 1 66 226839 1 67 247934 1 68 248333 1 69 246969 1 70 245098 1 71 246263 1 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Kredietcrisis 270667 -23132 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -34577.8 -5781.8 398.9 8681.2 21633.2 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 270667 1991 135.972 < 2e-16 *** Kredietcrisis -23132 3497 -6.614 6.61e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 13790 on 69 degrees of freedom Multiple R-squared: 0.388, Adjusted R-squared: 0.3791 F-statistic: 43.74 on 1 and 69 DF, p-value: 6.614e-09 > postscript(file="/var/www/html/rcomp/tmp/1qsji1229866020.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/2mj4q1229866020.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3xtbr1229866020.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4xeue1229866020.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/503td1229866020.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 71 Frequency = 1 1 2 3 4 5 6 -25914.8125 -26090.8125 -29094.8125 -30125.8125 -34577.8125 -33669.8125 7 8 9 10 11 12 -6087.8125 -317.8125 -1021.8125 -3629.8125 -12553.8125 -7853.8125 13 14 15 16 17 18 -3253.8125 -3300.8125 -5889.8125 -11803.8125 -15822.8125 -15798.8125 19 20 21 22 23 24 6600.1875 14684.1875 15935.1875 12375.1875 6020.1875 7248.1875 25 26 27 28 29 30 6461.1875 6436.1875 4370.1875 -516.8125 -3526.8125 -5673.8125 31 32 33 34 35 36 16592.1875 20519.1875 21633.1875 17519.1875 10810.1875 11989.1875 37 38 39 40 41 42 9523.1875 9741.1875 6169.1875 4549.1875 3685.1875 644.1875 43 44 45 46 47 48 19135.1875 20059.1875 21633.1875 7839.1875 -840.8125 -4805.8125 49 50 51 52 53 54 21498.9130 16640.9130 7662.9130 5817.9130 -1478.0870 -12163.0870 55 56 57 58 59 60 11020.9130 13457.9130 7127.9130 3107.9130 -4113.0870 -430.0870 61 62 63 64 65 66 1005.9130 -2496.0870 -10455.0870 -10450.0870 -21981.0870 -20696.0870 67 68 69 70 71 398.9130 797.9130 -566.0870 -2437.0870 -1272.0870 > postscript(file="/var/www/html/rcomp/tmp/61n0c1229866020.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 = 71 Frequency = 1 lag(myerror, k = 1) myerror 0 -25914.8125 NA 1 -26090.8125 -25914.8125 2 -29094.8125 -26090.8125 3 -30125.8125 -29094.8125 4 -34577.8125 -30125.8125 5 -33669.8125 -34577.8125 6 -6087.8125 -33669.8125 7 -317.8125 -6087.8125 8 -1021.8125 -317.8125 9 -3629.8125 -1021.8125 10 -12553.8125 -3629.8125 11 -7853.8125 -12553.8125 12 -3253.8125 -7853.8125 13 -3300.8125 -3253.8125 14 -5889.8125 -3300.8125 15 -11803.8125 -5889.8125 16 -15822.8125 -11803.8125 17 -15798.8125 -15822.8125 18 6600.1875 -15798.8125 19 14684.1875 6600.1875 20 15935.1875 14684.1875 21 12375.1875 15935.1875 22 6020.1875 12375.1875 23 7248.1875 6020.1875 24 6461.1875 7248.1875 25 6436.1875 6461.1875 26 4370.1875 6436.1875 27 -516.8125 4370.1875 28 -3526.8125 -516.8125 29 -5673.8125 -3526.8125 30 16592.1875 -5673.8125 31 20519.1875 16592.1875 32 21633.1875 20519.1875 33 17519.1875 21633.1875 34 10810.1875 17519.1875 35 11989.1875 10810.1875 36 9523.1875 11989.1875 37 9741.1875 9523.1875 38 6169.1875 9741.1875 39 4549.1875 6169.1875 40 3685.1875 4549.1875 41 644.1875 3685.1875 42 19135.1875 644.1875 43 20059.1875 19135.1875 44 21633.1875 20059.1875 45 7839.1875 21633.1875 46 -840.8125 7839.1875 47 -4805.8125 -840.8125 48 21498.9130 -4805.8125 49 16640.9130 21498.9130 50 7662.9130 16640.9130 51 5817.9130 7662.9130 52 -1478.0870 5817.9130 53 -12163.0870 -1478.0870 54 11020.9130 -12163.0870 55 13457.9130 11020.9130 56 7127.9130 13457.9130 57 3107.9130 7127.9130 58 -4113.0870 3107.9130 59 -430.0870 -4113.0870 60 1005.9130 -430.0870 61 -2496.0870 1005.9130 62 -10455.0870 -2496.0870 63 -10450.0870 -10455.0870 64 -21981.0870 -10450.0870 65 -20696.0870 -21981.0870 66 398.9130 -20696.0870 67 797.9130 398.9130 68 -566.0870 797.9130 69 -2437.0870 -566.0870 70 -1272.0870 -2437.0870 71 NA -1272.0870 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -26090.8125 -25914.8125 [2,] -29094.8125 -26090.8125 [3,] -30125.8125 -29094.8125 [4,] -34577.8125 -30125.8125 [5,] -33669.8125 -34577.8125 [6,] -6087.8125 -33669.8125 [7,] -317.8125 -6087.8125 [8,] -1021.8125 -317.8125 [9,] -3629.8125 -1021.8125 [10,] -12553.8125 -3629.8125 [11,] -7853.8125 -12553.8125 [12,] -3253.8125 -7853.8125 [13,] -3300.8125 -3253.8125 [14,] -5889.8125 -3300.8125 [15,] -11803.8125 -5889.8125 [16,] -15822.8125 -11803.8125 [17,] -15798.8125 -15822.8125 [18,] 6600.1875 -15798.8125 [19,] 14684.1875 6600.1875 [20,] 15935.1875 14684.1875 [21,] 12375.1875 15935.1875 [22,] 6020.1875 12375.1875 [23,] 7248.1875 6020.1875 [24,] 6461.1875 7248.1875 [25,] 6436.1875 6461.1875 [26,] 4370.1875 6436.1875 [27,] -516.8125 4370.1875 [28,] -3526.8125 -516.8125 [29,] -5673.8125 -3526.8125 [30,] 16592.1875 -5673.8125 [31,] 20519.1875 16592.1875 [32,] 21633.1875 20519.1875 [33,] 17519.1875 21633.1875 [34,] 10810.1875 17519.1875 [35,] 11989.1875 10810.1875 [36,] 9523.1875 11989.1875 [37,] 9741.1875 9523.1875 [38,] 6169.1875 9741.1875 [39,] 4549.1875 6169.1875 [40,] 3685.1875 4549.1875 [41,] 644.1875 3685.1875 [42,] 19135.1875 644.1875 [43,] 20059.1875 19135.1875 [44,] 21633.1875 20059.1875 [45,] 7839.1875 21633.1875 [46,] -840.8125 7839.1875 [47,] -4805.8125 -840.8125 [48,] 21498.9130 -4805.8125 [49,] 16640.9130 21498.9130 [50,] 7662.9130 16640.9130 [51,] 5817.9130 7662.9130 [52,] -1478.0870 5817.9130 [53,] -12163.0870 -1478.0870 [54,] 11020.9130 -12163.0870 [55,] 13457.9130 11020.9130 [56,] 7127.9130 13457.9130 [57,] 3107.9130 7127.9130 [58,] -4113.0870 3107.9130 [59,] -430.0870 -4113.0870 [60,] 1005.9130 -430.0870 [61,] -2496.0870 1005.9130 [62,] -10455.0870 -2496.0870 [63,] -10450.0870 -10455.0870 [64,] -21981.0870 -10450.0870 [65,] -20696.0870 -21981.0870 [66,] 398.9130 -20696.0870 [67,] 797.9130 398.9130 [68,] -566.0870 797.9130 [69,] -2437.0870 -566.0870 [70,] -1272.0870 -2437.0870 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -26090.8125 -25914.8125 2 -29094.8125 -26090.8125 3 -30125.8125 -29094.8125 4 -34577.8125 -30125.8125 5 -33669.8125 -34577.8125 6 -6087.8125 -33669.8125 7 -317.8125 -6087.8125 8 -1021.8125 -317.8125 9 -3629.8125 -1021.8125 10 -12553.8125 -3629.8125 11 -7853.8125 -12553.8125 12 -3253.8125 -7853.8125 13 -3300.8125 -3253.8125 14 -5889.8125 -3300.8125 15 -11803.8125 -5889.8125 16 -15822.8125 -11803.8125 17 -15798.8125 -15822.8125 18 6600.1875 -15798.8125 19 14684.1875 6600.1875 20 15935.1875 14684.1875 21 12375.1875 15935.1875 22 6020.1875 12375.1875 23 7248.1875 6020.1875 24 6461.1875 7248.1875 25 6436.1875 6461.1875 26 4370.1875 6436.1875 27 -516.8125 4370.1875 28 -3526.8125 -516.8125 29 -5673.8125 -3526.8125 30 16592.1875 -5673.8125 31 20519.1875 16592.1875 32 21633.1875 20519.1875 33 17519.1875 21633.1875 34 10810.1875 17519.1875 35 11989.1875 10810.1875 36 9523.1875 11989.1875 37 9741.1875 9523.1875 38 6169.1875 9741.1875 39 4549.1875 6169.1875 40 3685.1875 4549.1875 41 644.1875 3685.1875 42 19135.1875 644.1875 43 20059.1875 19135.1875 44 21633.1875 20059.1875 45 7839.1875 21633.1875 46 -840.8125 7839.1875 47 -4805.8125 -840.8125 48 21498.9130 -4805.8125 49 16640.9130 21498.9130 50 7662.9130 16640.9130 51 5817.9130 7662.9130 52 -1478.0870 5817.9130 53 -12163.0870 -1478.0870 54 11020.9130 -12163.0870 55 13457.9130 11020.9130 56 7127.9130 13457.9130 57 3107.9130 7127.9130 58 -4113.0870 3107.9130 59 -430.0870 -4113.0870 60 1005.9130 -430.0870 61 -2496.0870 1005.9130 62 -10455.0870 -2496.0870 63 -10450.0870 -10455.0870 64 -21981.0870 -10450.0870 65 -20696.0870 -21981.0870 66 398.9130 -20696.0870 67 797.9130 398.9130 68 -566.0870 797.9130 69 -2437.0870 -566.0870 70 -1272.0870 -2437.0870 > 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/7bs4a1229866020.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/89huy1229866020.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9c2vq1229866020.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/10wiux1229866020.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/111svo1229866020.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/126i0q1229866020.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/130av81229866020.tab") > > system("convert tmp/1qsji1229866020.ps tmp/1qsji1229866020.png") > system("convert tmp/2mj4q1229866020.ps tmp/2mj4q1229866020.png") > system("convert tmp/3xtbr1229866020.ps tmp/3xtbr1229866020.png") > system("convert tmp/4xeue1229866020.ps tmp/4xeue1229866020.png") > system("convert tmp/503td1229866020.ps tmp/503td1229866020.png") > system("convert tmp/61n0c1229866020.ps tmp/61n0c1229866020.png") > system("convert tmp/7bs4a1229866020.ps tmp/7bs4a1229866020.png") > system("convert tmp/89huy1229866020.ps tmp/89huy1229866020.png") > system("convert tmp/9c2vq1229866020.ps tmp/9c2vq1229866020.png") > > > proc.time() user system elapsed 2.044 1.500 2.768