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Data X:
1980 197632 1980 238592 1981 716800 1981 348160 1981 471040 1981 307200 1981 302080 1981 295936 1981 195584 1981 155648 1981 141312 1982 266240 1983 323584 1983 276480 1983 194560 1983 179200 1983 168960 1983 134144 1983 121856 1984 286720 1984 276480 1984 174080 1984 163840 1984 143360 1984 136192 1984 122880 1984 120832 1984 306176 1984 289792 1984 219136 1984 204800 1984 185344 1984 168960 1984 161792 1984 143360 1984 121856 1984 110592 1984 81920 1985 72704 1987 92160 1987 61440 1987 46080 1988 40960 1988 33792 1988 27648 1988 30720 1988 16384 1989 54272 1989 36864 1989 12288 1990 9216 1991 7168 1992 4096 1993 2048 1994 972.8 1995 870.4 1995 901.12 1995 829.44 1995 1013.76 1995 1290.24 1995 944.128 1995 905.216 1995 685.056 1995 774.144 1995 702.464 1996 302.08 1996 269.312 1996 265.216 1996 211.968 1996 177.152 1997 185.344 1997 151.552 1997 144.384 1997 156.672 1997 124.928 1997 120.832 1997 120.832 1997 106.496 1997 112.64 1997 106.496 1997 104.448 1997 103.424 1997 99.9424 1997 87.4496 1997 81.92 1997 103.424 1997 101.1712 1997 99.4304 1997 97.5872 1997 95.3344 1998 97.4848 1998 95.8464 1998 88.3712 1998 85.9136 1998 80.384 1998 80.0768 1998 87.7568 1998 87.6544 1998 76.0832 1998 62.5664 1998 79.0528 1998 78.1312 1998 68.096 1998 67.8912 1998 65.3312 1998 77.2096 1998 74.9568 1998 67.6864 1998 64.4096 1998 60.7232 1998 83.5584 1998 70.3488 1998 67.6864 1998 60.3136 1998 61.952 1998 62.464 1998 54.784 1998 56.4224 1998 60.5184 1998 57.0368 1998 54.0672 1998 53.5552 1998 53.5552 1998 55.9104 1998 47.5136 1998 53.1456 1998 51.5072 1998 53.248 1998 48.5376 1998 49.0496 1998 43.6224 1999 44.1344 1999 38.6048 1999 37.376 1999 31.4368 1999 35.4304 1999 34.5088 1999 32.9728 1999 32.8704 1999 32.256 1999 32.1536 1999 30.3104 1999 28.9792 1999 33.0752 1999 28.2624 1999 27.7504 1999 25.088 1999 29.4912 1999 28.0576 1999 26.9312 1999 18.944 1999 21.6064 1999 21.0944 1999 20.3776 1999 22.9376 1999 21.6064 1999 21.0944 1999 21.0944 1999 18.944 1999 22.8352 1999 22.528 1999 21.8112 1999 21.8112 1999 18.8416 1999 17.7152 1999 17.3056 1999 16.896 1999 16.6912 1999 15.36 2000 19.73 2000 16.86 2000 16.83 2000 16.76 2000 16.73 2000 16.45 2000 15.82 2000 15.8 2000 15.44 2000 14.29 2000 11.95 2000 15.65 2000 15.06 2000 14.89 2000 14.72 2000 14.58 2000 14.33 2000 14.29 2000 13.88 2000 13.8 2000 13.63 2000 13.47 2000 12.95 2000 12.74 2000 12.54 2000 12.48 2000 11.81 2000 15.22 2000 12.27 2000 11.5 2000 14.57 2000 12.42 2000 11.24 2000 10.06 2000 10.91 2000 10.82 2000 10.41 2000 9.25 2000 8.02 2000 11.5 2000 10.06 2000 9.58 2000 9.14 2000 8.94 2000 7.45 2000 7.27 2000 7.27 2000 7.14 2000 7.88 2000 7.25 2000 6.9 2001 7.31 2001 7.26 2001 6.84 2001 7.48 2001 6.48 2001 5.72 2001 6.82 2001 6.56 2001 6.49 2001 5.87 2001 6.33 2001 6.33 2001 5.75 2001 4.41 2001 2.99 2001 4.57 2002 4.31 2002 3.71 2002 2.65 2002 2.88 2002 3.74 2002 2.59 2002 2.07 2002 2.59 2002 2.88 2002 2.59 2002 2.68 2003 2.58 2003 1.51 2003 1.93 2003 1.78 2003 1.61 2003 1.51 2003 1.42 2003 1.39 2004 1.94 2004 1.94 2004 1.7 2004 1.57 2004 1.41 2004 1.38 2004 1.24 2004 1.22 2004 1.15 2004 1.24 2004 1.21 2004 1.2 2004 0.671 2004 0.598 2005 0.719 2005 0.719 2005 0.719 2005 0.575 2005 0.598 2007 0.426 2007 0.411 2007 0.377 2007 0.371 2007 0.367 2007 0.35 2007 0.333 2007 0.306 2007 0.302 2007 0.287 2010 0.164 2010 0.134 2010 0.0909 2010 0.0688 2010 0.115 2010 0.113 2010 0.0821
Names of X columns:
Year CPGB
Response Variable (column number)
Explanatory Variable (column number)
Include Intercept Term ?
TRUE
TRUE
FALSE
Chart options
Title:
Label y-axis:
Label x-axis:
R Code
cat1 <- as.numeric(par1) # cat2<- as.numeric(par2) # intercept<-as.logical(par3) x <- t(x) xdf<-data.frame(t(y)) (V1<-dimnames(y)[[1]][cat1]) (V2<-dimnames(y)[[1]][cat2]) xdf <- data.frame(log(xdf[[cat1]]), xdf[[cat2]]) names(xdf)<-c('Y', 'X') if(intercept == FALSE) (lmxdf<-lm(Y~ X - 1, data = xdf) ) else (lmxdf<-lm(Y~ X, data = xdf) ) sumlmxdf<-summary(lmxdf) (aov.xdf<-aov(lmxdf) ) (anova.xdf<-anova(lmxdf) ) load(file='createtable') a<-table.start() nc <- ncol(sumlmxdf$'coefficients') nr <- nrow(sumlmxdf$'coefficients') a<-table.row.start(a) a<-table.element(a,'Linear Regression Model', nc+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, lmxdf$call['formula'],nc+1) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'coefficients:',1,TRUE) a<-table.element(a, ' ',nc,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ',1,TRUE) for(i in 1 : nc){ a<-table.element(a, dimnames(sumlmxdf$'coefficients')[[2]][i],1,TRUE) }#end header a<-table.row.end(a) for(i in 1: nr){ a<-table.element(a,dimnames(sumlmxdf$'coefficients')[[1]][i] ,1,TRUE) for(j in 1 : nc){ a<-table.element(a, round(sumlmxdf$coefficients[i, j], digits=3), 1 ,FALSE) }# end cols a<-table.row.end(a) } #end rows a<-table.row.start(a) a<-table.element(a, '- - - ',1,TRUE) a<-table.element(a, ' ',nc,FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Std. Err. ',1,TRUE) a<-table.element(a, paste(round(sumlmxdf$'sigma', digits=3), ' on ', sumlmxdf$'df'[2], 'df') ,nc, FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R-sq. ',1,TRUE) a<-table.element(a, round(sumlmxdf$'r.squared', digits=3) ,nc, FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-sq. ',1,TRUE) a<-table.element(a, round(sumlmxdf$'adj.r.squared', digits=3) ,nc, FALSE) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'ANOVA Statistics', 5+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ',1,TRUE) a<-table.element(a, 'Df',1,TRUE) a<-table.element(a, 'Sum Sq',1,TRUE) a<-table.element(a, 'Mean Sq',1,TRUE) a<-table.element(a, 'F value',1,TRUE) a<-table.element(a, 'Pr(>F)',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, V2,1,TRUE) a<-table.element(a, anova.xdf$Df[1]) a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3)) a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3)) a<-table.element(a, round(anova.xdf$'F value'[1], digits=3)) a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residuals',1,TRUE) a<-table.element(a, anova.xdf$Df[2]) a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3)) a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3)) a<-table.element(a, ' ') a<-table.element(a, ' ') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') bitmap(file='regressionplot.png') plot(Y~ X, data=xdf, xlab=V2, ylab=V1, main='Regression Solution') if(intercept == TRUE) abline(coef(lmxdf), col='red') if(intercept == FALSE) abline(0.0, coef(lmxdf), col='red') dev.off() library(car) bitmap(file='residualsQQplot.png') qq.plot(resid(lmxdf), main='QQplot of Residuals of Fit') dev.off() bitmap(file='residualsplot.png') plot(xdf$X, resid(lmxdf), main='Scatterplot of Residuals of Model Fit') dev.off() bitmap(file='cooksDistanceLmplot.png') plot.lm(lmxdf, which=4) dev.off()
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