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Type 'q()' to quit R. > x <- array(list(6.3 + ,2.0 + ,4.5 + ,1.000 + ,6.600 + ,42.0 + ,3 + ,1 + ,3 + ,2.1 + ,1.8 + ,69.0 + ,2547.000 + ,44.500 + ,624.0 + ,3 + ,5 + ,4 + ,9.1 + ,0.7 + ,27.0 + ,10.55 + ,179.500 + ,180.0 + ,4 + ,4 + ,4 + ,15.8 + ,3.9 + ,19.0 + ,0.023 + ,0.300 + ,35.0 + ,1 + ,1 + ,1 + ,5.2 + ,1.0 + ,30.4 + ,160.000 + ,169.000 + ,392.0 + ,4 + ,5 + ,4 + ,10.9 + ,3.6 + ,28.0 + ,3.300 + ,25.600 + ,63.0 + ,1 + ,2 + ,1 + ,8.3 + ,1.4 + ,50.0 + ,52.16 + ,440.000 + ,230.0 + ,1 + ,1 + ,1 + ,11.0 + ,1.5 + ,7.0 + ,0.425 + ,6.400 + ,112.0 + ,5 + ,4 + ,4 + ,3.2 + ,0.7 + ,30.0 + ,465.000 + ,423.000 + ,281.0 + ,5 + ,5 + ,5 + ,6.3 + ,2.1 + ,3.5 + ,0.075 + ,1.200 + ,42.0 + ,1 + ,1 + ,1 + ,6.6 + ,4.1 + ,6.0 + ,0.785 + ,3.500 + ,42.0 + ,2 + ,2 + ,2 + ,9.5 + ,1.2 + ,10.4 + ,0.200 + ,5.000 + ,120.0 + ,2 + ,2 + ,2 + ,3.3 + ,0.5 + ,20.0 + ,27.66 + ,115.000 + ,148.0 + ,5 + ,5 + ,5 + ,11.0 + ,3.4 + ,3.9 + ,0.120 + ,1.000 + ,16.0 + ,3 + ,1 + ,2 + ,4.7 + ,1.5 + ,41.0 + ,85.000 + ,325.000 + ,310.0 + ,1 + ,3 + ,1 + ,10.4 + ,3.4 + ,9.0 + ,0.101 + ,4.000 + ,28.0 + ,5 + ,1 + ,3 + ,7.4 + ,0.8 + ,7.6 + ,1.040 + ,5.500 + ,68.0 + ,5 + ,3 + ,4 + ,2.1 + ,0.8 + ,46.0 + ,521.000 + ,655.000 + ,336.0 + ,5 + ,5 + ,5 + ,17.9 + ,2.0 + ,24.0 + ,0.010 + ,0.250 + ,50.0 + ,1 + ,1 + ,1 + ,6.1 + ,1.9 + ,100.0 + ,62.000 + ,1320.000 + ,267.0 + ,1 + ,1 + ,1 + ,11.9 + ,1.3 + ,3.2 + ,0.023 + ,0.400 + ,19.0 + ,4 + ,1 + ,3 + ,13.8 + ,5.6 + ,5.0 + ,1.700 + ,6.300 + ,12.0 + ,2 + ,1 + ,1 + ,14.3 + ,3.1 + ,6.5 + ,3.500 + ,10.800 + ,120.0 + ,2 + ,1 + ,1 + ,15.2 + ,1.8 + ,12.0 + ,0.480 + ,15.500 + ,140.0 + ,2 + ,2 + ,2 + ,10.0 + ,0.9 + ,20.2 + ,10.000 + ,115.000 + ,170.0 + ,4 + ,4 + ,4 + ,11.9 + ,1.8 + ,13.0 + ,1.620 + ,11.400 + ,17.0 + ,2 + ,1 + ,2 + ,6.5 + ,1.9 + ,27.0 + ,192.000 + ,180.000 + ,115.0 + ,4 + ,4 + ,4 + ,7.5 + ,0.9 + ,18.0 + ,2.500 + ,12.300 + ,31.0 + ,5 + ,5 + ,5 + ,10.6 + ,2.6 + ,4.7 + ,0.280 + ,1.900 + ,21.0 + ,3 + ,1 + ,3 + ,7.4 + ,2.4 + ,9.8 + ,4.235 + ,50.400 + ,52.0 + ,1 + ,1 + ,1 + ,8.4 + ,1.2 + ,29.0 + ,6.800 + ,179.000 + ,164.0 + ,2 + ,3 + ,2 + ,5.7 + ,0.9 + ,7.0 + ,0.750 + ,12.300 + ,225.0 + ,2 + ,2 + ,2 + ,4.9 + ,0.5 + ,6.0 + ,3.600 + ,21.000 + ,225.0 + ,3 + ,2 + ,3 + ,3.2 + ,0.6 + ,20.0 + ,55.500 + ,175.000 + ,151.0 + ,5 + ,5 + ,5 + ,11.0 + ,2.3 + ,4.5 + ,0.900 + ,2.600 + ,60.0 + ,2 + ,1 + ,2 + ,4.9 + ,0.5 + ,7.5 + ,2.000 + ,12.300 + ,200.0 + ,3 + ,1 + ,3 + ,13.2 + ,2.6 + ,2.3 + ,0.104 + ,2.500 + ,46.0 + ,3 + ,2 + ,2 + ,9.7 + ,0.6 + ,24.0 + ,4.190 + ,58.000 + ,210.0 + ,4 + ,3 + ,4 + ,12.8 + ,6.6 + ,3.0 + ,3.500 + ,3.900 + ,14.0 + ,2 + ,1 + ,1) + ,dim=c(9 + ,39) + ,dimnames=list(c('SWS' + ,'PS' + ,'L' + ,'Wb' + ,'Wbr' + ,'Tg' + ,'P' + ,'S' + ,'D') + ,1:39)) > y <- array(NA,dim=c(9,39),dimnames=list(c('SWS','PS','L','Wb','Wbr','Tg','P','S','D'),1:39)) > 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 SWS PS L Wb Wbr Tg P S D 1 6.3 2.0 4.5 1.000 6.60 42 3 1 3 2 2.1 1.8 69.0 2547.000 44.50 624 3 5 4 3 9.1 0.7 27.0 10.550 179.50 180 4 4 4 4 15.8 3.9 19.0 0.023 0.30 35 1 1 1 5 5.2 1.0 30.4 160.000 169.00 392 4 5 4 6 10.9 3.6 28.0 3.300 25.60 63 1 2 1 7 8.3 1.4 50.0 52.160 440.00 230 1 1 1 8 11.0 1.5 7.0 0.425 6.40 112 5 4 4 9 3.2 0.7 30.0 465.000 423.00 281 5 5 5 10 6.3 2.1 3.5 0.075 1.20 42 1 1 1 11 6.6 4.1 6.0 0.785 3.50 42 2 2 2 12 9.5 1.2 10.4 0.200 5.00 120 2 2 2 13 3.3 0.5 20.0 27.660 115.00 148 5 5 5 14 11.0 3.4 3.9 0.120 1.00 16 3 1 2 15 4.7 1.5 41.0 85.000 325.00 310 1 3 1 16 10.4 3.4 9.0 0.101 4.00 28 5 1 3 17 7.4 0.8 7.6 1.040 5.50 68 5 3 4 18 2.1 0.8 46.0 521.000 655.00 336 5 5 5 19 17.9 2.0 24.0 0.010 0.25 50 1 1 1 20 6.1 1.9 100.0 62.000 1320.00 267 1 1 1 21 11.9 1.3 3.2 0.023 0.40 19 4 1 3 22 13.8 5.6 5.0 1.700 6.30 12 2 1 1 23 14.3 3.1 6.5 3.500 10.80 120 2 1 1 24 15.2 1.8 12.0 0.480 15.50 140 2 2 2 25 10.0 0.9 20.2 10.000 115.00 170 4 4 4 26 11.9 1.8 13.0 1.620 11.40 17 2 1 2 27 6.5 1.9 27.0 192.000 180.00 115 4 4 4 28 7.5 0.9 18.0 2.500 12.30 31 5 5 5 29 10.6 2.6 4.7 0.280 1.90 21 3 1 3 30 7.4 2.4 9.8 4.235 50.40 52 1 1 1 31 8.4 1.2 29.0 6.800 179.00 164 2 3 2 32 5.7 0.9 7.0 0.750 12.30 225 2 2 2 33 4.9 0.5 6.0 3.600 21.00 225 3 2 3 34 3.2 0.6 20.0 55.500 175.00 151 5 5 5 35 11.0 2.3 4.5 0.900 2.60 60 2 1 2 36 4.9 0.5 7.5 2.000 12.30 200 3 1 3 37 13.2 2.6 2.3 0.104 2.50 46 3 2 2 38 9.7 0.6 24.0 4.190 58.00 210 4 3 4 39 12.8 6.6 3.0 3.500 3.90 14 2 1 1 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) PS L Wb Wbr Tg 10.15919 0.19638 0.16770 -0.00252 -0.01304 -0.01110 P S D 1.96413 -0.23619 -2.57507 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -3.5295 -2.1296 -0.0603 1.8407 6.1262 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 10.159193 2.727578 3.725 0.000809 *** PS 0.196377 0.560304 0.350 0.728425 L 0.167700 0.069236 2.422 0.021681 * Wb -0.002520 0.002376 -1.061 0.297365 Wbr -0.013038 0.004686 -2.782 0.009242 ** Tg -0.011098 0.007921 -1.401 0.171442 P 1.964131 1.109124 1.771 0.086741 . S -0.236195 0.644834 -0.366 0.716721 D -2.575065 1.514544 -1.700 0.099435 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.684 on 30 degrees of freedom Multiple R-squared: 0.6389, Adjusted R-squared: 0.5426 F-statistic: 6.635 on 8 and 30 DF, p-value: 5.479e-05 > 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,] 0.7364308 0.5271384 0.2635692 [2,] 0.7459161 0.5081678 0.2540839 [3,] 0.6428553 0.7142895 0.3571447 [4,] 0.6628078 0.6743843 0.3371922 [5,] 0.7301842 0.5396317 0.2698158 [6,] 0.6738305 0.6523390 0.3261695 [7,] 0.6249780 0.7500440 0.3750220 [8,] 0.7334654 0.5330693 0.2665346 [9,] 0.6440623 0.7118754 0.3559377 [10,] 0.5405960 0.9188081 0.4594040 [11,] 0.4627012 0.9254025 0.5372988 [12,] 0.4080149 0.8160298 0.5919851 [13,] 0.7863016 0.4273969 0.2136984 [14,] 0.8665197 0.2669606 0.1334803 [15,] 0.7499798 0.5000404 0.2500202 [16,] 0.5926763 0.8146475 0.4073237 > postscript(file="/var/www/rcomp/tmp/1cxz11292249041.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/25oy41292249041.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/35oy41292249041.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/45oy41292249041.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5fyf71292249041.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 = 39 Frequency = 1 1 2 3 4 5 6 -2.38292623 -0.47109809 2.02845239 2.92814989 0.32801095 -2.53718779 7 8 9 10 11 12 -1.25135150 2.12418253 1.91339787 -3.52946814 -3.16256770 0.45275035 13 14 15 16 17 18 -2.86420031 -0.79607109 -3.41808841 -3.43230092 -2.17365238 1.88687891 19 20 21 22 23 24 4.72854668 -0.02568947 1.26987121 0.80518713 2.80634126 6.12616282 25 26 27 28 29 30 3.07622362 1.00666514 -1.06474802 -1.10819532 1.46956204 -2.78196159 31 32 33 34 35 36 -0.75673269 -1.45633128 -1.27853297 -2.09810273 1.79458154 -2.16118410 37 38 39 2.41799421 1.64778131 -0.06034913 > postscript(file="/var/www/rcomp/tmp/6fyf71292249041.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 = 39 Frequency = 1 lag(myerror, k = 1) myerror 0 -2.38292623 NA 1 -0.47109809 -2.38292623 2 2.02845239 -0.47109809 3 2.92814989 2.02845239 4 0.32801095 2.92814989 5 -2.53718779 0.32801095 6 -1.25135150 -2.53718779 7 2.12418253 -1.25135150 8 1.91339787 2.12418253 9 -3.52946814 1.91339787 10 -3.16256770 -3.52946814 11 0.45275035 -3.16256770 12 -2.86420031 0.45275035 13 -0.79607109 -2.86420031 14 -3.41808841 -0.79607109 15 -3.43230092 -3.41808841 16 -2.17365238 -3.43230092 17 1.88687891 -2.17365238 18 4.72854668 1.88687891 19 -0.02568947 4.72854668 20 1.26987121 -0.02568947 21 0.80518713 1.26987121 22 2.80634126 0.80518713 23 6.12616282 2.80634126 24 3.07622362 6.12616282 25 1.00666514 3.07622362 26 -1.06474802 1.00666514 27 -1.10819532 -1.06474802 28 1.46956204 -1.10819532 29 -2.78196159 1.46956204 30 -0.75673269 -2.78196159 31 -1.45633128 -0.75673269 32 -1.27853297 -1.45633128 33 -2.09810273 -1.27853297 34 1.79458154 -2.09810273 35 -2.16118410 1.79458154 36 2.41799421 -2.16118410 37 1.64778131 2.41799421 38 -0.06034913 1.64778131 39 NA -0.06034913 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.47109809 -2.38292623 [2,] 2.02845239 -0.47109809 [3,] 2.92814989 2.02845239 [4,] 0.32801095 2.92814989 [5,] -2.53718779 0.32801095 [6,] -1.25135150 -2.53718779 [7,] 2.12418253 -1.25135150 [8,] 1.91339787 2.12418253 [9,] -3.52946814 1.91339787 [10,] -3.16256770 -3.52946814 [11,] 0.45275035 -3.16256770 [12,] -2.86420031 0.45275035 [13,] -0.79607109 -2.86420031 [14,] -3.41808841 -0.79607109 [15,] -3.43230092 -3.41808841 [16,] -2.17365238 -3.43230092 [17,] 1.88687891 -2.17365238 [18,] 4.72854668 1.88687891 [19,] -0.02568947 4.72854668 [20,] 1.26987121 -0.02568947 [21,] 0.80518713 1.26987121 [22,] 2.80634126 0.80518713 [23,] 6.12616282 2.80634126 [24,] 3.07622362 6.12616282 [25,] 1.00666514 3.07622362 [26,] -1.06474802 1.00666514 [27,] -1.10819532 -1.06474802 [28,] 1.46956204 -1.10819532 [29,] -2.78196159 1.46956204 [30,] -0.75673269 -2.78196159 [31,] -1.45633128 -0.75673269 [32,] -1.27853297 -1.45633128 [33,] -2.09810273 -1.27853297 [34,] 1.79458154 -2.09810273 [35,] -2.16118410 1.79458154 [36,] 2.41799421 -2.16118410 [37,] 1.64778131 2.41799421 [38,] -0.06034913 1.64778131 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.47109809 -2.38292623 2 2.02845239 -0.47109809 3 2.92814989 2.02845239 4 0.32801095 2.92814989 5 -2.53718779 0.32801095 6 -1.25135150 -2.53718779 7 2.12418253 -1.25135150 8 1.91339787 2.12418253 9 -3.52946814 1.91339787 10 -3.16256770 -3.52946814 11 0.45275035 -3.16256770 12 -2.86420031 0.45275035 13 -0.79607109 -2.86420031 14 -3.41808841 -0.79607109 15 -3.43230092 -3.41808841 16 -2.17365238 -3.43230092 17 1.88687891 -2.17365238 18 4.72854668 1.88687891 19 -0.02568947 4.72854668 20 1.26987121 -0.02568947 21 0.80518713 1.26987121 22 2.80634126 0.80518713 23 6.12616282 2.80634126 24 3.07622362 6.12616282 25 1.00666514 3.07622362 26 -1.06474802 1.00666514 27 -1.10819532 -1.06474802 28 1.46956204 -1.10819532 29 -2.78196159 1.46956204 30 -0.75673269 -2.78196159 31 -1.45633128 -0.75673269 32 -1.27853297 -1.45633128 33 -2.09810273 -1.27853297 34 1.79458154 -2.09810273 35 -2.16118410 1.79458154 36 2.41799421 -2.16118410 37 1.64778131 2.41799421 38 -0.06034913 1.64778131 > 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/rcomp/tmp/7qpxa1292249041.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/8qpxa1292249041.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/91ged1292249041.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') Warning messages: 1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced 2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/101ged1292249041.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/rcomp/tmp/11mhc11292249041.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/rcomp/tmp/128zbp1292249041.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/rcomp/tmp/137asd1292249042.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/rcomp/tmp/14iksg1292249042.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/rcomp/tmp/15l2841292249042.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/rcomp/tmp/16hc6d1292249042.tab") + } > > try(system("convert tmp/1cxz11292249041.ps tmp/1cxz11292249041.png",intern=TRUE)) character(0) > try(system("convert tmp/25oy41292249041.ps tmp/25oy41292249041.png",intern=TRUE)) character(0) > try(system("convert tmp/35oy41292249041.ps tmp/35oy41292249041.png",intern=TRUE)) character(0) > try(system("convert tmp/45oy41292249041.ps tmp/45oy41292249041.png",intern=TRUE)) character(0) > try(system("convert tmp/5fyf71292249041.ps tmp/5fyf71292249041.png",intern=TRUE)) character(0) > try(system("convert tmp/6fyf71292249041.ps tmp/6fyf71292249041.png",intern=TRUE)) character(0) > try(system("convert tmp/7qpxa1292249041.ps tmp/7qpxa1292249041.png",intern=TRUE)) character(0) > try(system("convert tmp/8qpxa1292249041.ps tmp/8qpxa1292249041.png",intern=TRUE)) character(0) > try(system("convert tmp/91ged1292249041.ps tmp/91ged1292249041.png",intern=TRUE)) character(0) > try(system("convert tmp/101ged1292249041.ps tmp/101ged1292249041.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.980 1.580 4.532