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copied!<p>Responding to @jbssm comment, @BenBolker solution seems to give very reasonable results:</p> <pre><code>vCenter_slope <- 600.7472 flN <- transform(flN,w=1/as.numeric(timeError_4_8)^2) # slope fixed m1 <- lm(as.numeric(arrivalTime_4_8) ~ 1 + offset(vCenter*vCenter_slope), data = flN, weights=w) # slope chosen by lm(...) m0 <- lm(as.numeric(arrivalTime_4_8) ~ vCenter, data=flN, weights=w) library(ggplot2) ggp <- ggplot(flN) ggp <- ggp + geom_point(aes(x=vCenter,y=arrivalTime_4_8)) ggp <- ggp + geom_errorbar(aes(x=vCenter, ymin=arrivalTime_4_8-timeError_4_8,ymax=arrivalTime_4_8+timeError_4_8),width=0) ggp <- ggp + geom_errorbarh(aes(x=vCenter, y=arrivalTime_4_8, xmax=vHigh, xmin=vLow),height=0) ggp <- ggp + geom_abline(slope=600.7472, intercept=coef(m1)[1]) ggp <- ggp + geom_abline(slope=coef(m0)[2], intercept=coef(m0)[1],color="red") ggp </code></pre> <p><img src="https://i.stack.imgur.com/PEis4.png" alt=""></p> <p>The red line allows <code>lm(...)</code> to set the slope, the black line uses fixed slope. Note that the black line is further away from the first two <code>vCenter</code> points than you might expect, because you're weighting inversely to error in <code>vCenter</code>. If this is your whole dataset, then using weights is highly questionable.</p> <p>Finally, you can read up on "errors-in-variables" models <a href="http://en.wikipedia.org/wiki/Errors-in-variables_models" rel="nofollow noreferrer">here</a> and <a href="http://personalpages.manchester.ac.uk/staff/Graham.Dunn/MSc%20Biostatistics/Linear%20Regression%20with%20errors.pdf" rel="nofollow noreferrer">here</a>. After reading these, you might want to look at the <a href="http://cran.r-project.org/web/packages/MethComp/MethComp.pdf" rel="nofollow noreferrer">MethComp</a> package, which supports `Deming Regression' (one type of errors-in-variables regression).</p>

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