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| # | |
| # jorgensen-1997.R, 2 Nov 16 | |
| # Data from: | |
| # Effort Estimation: Software Effort Estimation by Analogy and “Regression Toward the Mean” | |
| # Magne Jørgensen, Ulf Indahl, Dag Sjøberg | |
| # | |
| # Example from: | |
| # Empirical Software Engineering using R | |
| # Derek M. Jones | |
| source("ESEUR_config.r") | |
| library("simex") | |
| pal_col=rainbow(3) | |
| jorg=read.csv(paste0(ESEUR_dir, "regression/jorgensen-1997.csv.xz"), as.is=TRUE) | |
| jorg$l_Size=log(jorg$Size) | |
| jorg$l_Effort=log(jorg$Effort) | |
| plot(jorg$Size, jorg$Effort, log="xy", col=point_col, | |
| xlab="Size (function points)", ylab="Effort (hours)\n") | |
| # t=loess.smooth(log(jorg$Size), log(jorg$Effort), span=0.3) | |
| # lines(exp(t$x), exp(t$y), col=loess_col) | |
| # j_mod=glm(log(Effort) ~ l_Size+I(l_Size^2), data=jorg) | |
| jes_mod=glm(l_Effort ~ l_Size, data=jorg) | |
| xbounds=seq(20, 10000, by=20) | |
| es_pred=predict(jes_mod, newdata=data.frame(l_Size=log(xbounds)), se.fit=TRUE) | |
| lines(xbounds, exp(es_pred$fit), col=pal_col[1]) | |
| lines(xbounds, exp(es_pred$fit+1.96*es_pred$se.fit), col=pal_col[2]) | |
| lines(xbounds, exp(es_pred$fit-1.96*es_pred$se.fit), col=pal_col[2]) | |
| jse_mod=glm(l_Size ~ l_Effort, data=jorg) | |
| se_pred=predict(jse_mod, newdata=data.frame(l_Effort=log(xbounds))) | |
| # plot(jorg$Effort, jorg$Size, log="xy", col=point_col, | |
| # xlab="Effort (hours)", ylab="Size (function points)\n") | |
| # lines(xbounds, exp(se_pred), col=pal_col[3]) | |
| lines(exp(se_pred), xbounds, col=pal_col[3]) | |
| jes_sim_mod=simex(jes_mod, SIMEXvariable="l_Size", measurement.error=jorg$l_Size/10, asymptotic=FALSE) | |
| summary(jes_sim_mod) | |
| jse_sim_mod=simex(jse_mod, SIMEXvariable="l_Effort", measurement.error=jorg$l_Effort/20, asymptotic=FALSE) | |
| summary(jse_sim_mod) | |
| # | |
| # plot_layout(1, 2) | |
| # | |
| # x = log(jorg$Size) | |
| # y = log(jorg$Effort) | |
| # | |
| # yx_line = glm(y ~ x) # Assume x values do not contain any error | |
| # xy_line = glm(x ~ y) # Assume y values do not contain any error | |
| # | |
| # plot(y ~ x, col=point_col, | |
| # xlab="Size", ylab="Effort\n") | |
| # yx_pred=predict(yx_line) | |
| # lines(x, yx_pred, col=pal_col[1]) | |
| # | |
| # # Draw error lines to fitted line | |
| # segments(x, y, x, fitted(yx_line), lty = 2, col =pal_col[1]) # vertical line | |
| # | |
| # plot(y ~ x, col=point_col, | |
| # xlab="Size", ylab="\n") | |
| # | |
| # yx_pred=predict(yx_line) | |
| # lines(x, yx_pred, col=pal_col[1]) | |
| # | |
| # # To plot the line fitted to x ~ y on the same axis as the y ~ x plot | |
| # # we need to map the intercept and slope from y = a + bx to x = -a/b + y/b | |
| # # xy_line.coef=xy_line$coefficients | |
| # # abline(coef=c(-xy_line.coef[1]/xy_line.coef[2], 1/xy_line.coef[2]), col=pal_col[2]) | |
| # | |
| # # Or use predict | |
| # xy_pred=predict(xy_line) | |
| # lines(xy_pred, y, col=pal_col[2]) | |
| # | |
| # segments(x, y, fitted(xy_line), y, lty = 2, col =pal_col[2]) # horizontal line | |
| # |