Dear R/S users I hope the answer to this question turns out to be simple, since the question can be formulated in a simple way: If I have a sausage which is curved, which transformation do I need to apply to straigthen it out? More concretely: Let an experiment produce 1,000 data points with every point in the range(1, 10,000) and let us assume a gaussian distribution. Now I repeat the experiment 8 times. For every data point I get a mean with standard deviation. Due to experimental limits, data points with low values have usually a higher standard deviation. If I draw the data of any two experiments on a Log-Log-plot, the data will be scattered around the diagonal. If e.g. an instrument has a lower sensitivity, the cloud seen in the scatterplot would be shifted and/or rotated. To correct for this I could do a linear transformation such as e.g. "lsfit" or "ltsreg". (is this correct?) However, when the instrument introduces some kind of non- linearity, e.g. saturation effects at high values, so that the data look like a curved sausage in 8-dim space, what is the best way to fit these data? Thank you in advance for your help Best regards Christian Stratowa, Vienna, Austria -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._