Hi everyone, I would like to fit a predictive model to my data in order to compare absorbance readings to a toxin standard. This data was obtained by exposing red blood cells to different toxin concentrations, which lead to the lysis of the red blood cells, increasing the absorbance (hemoglobin is freed). The data has a sigmoid shape (see below), so I thought about fitting a logistic model to the data so that I will be able to determine the toxin equivalent of new absorbance readings. http://r.789695.n4.nabble.com/file/n3595812/Unbenannt.jpg The data points for this curve are: http://r.789695.n4.nabble.com/file/n3595812/qweqwe.jpg I must admit that I am totally lost. I have done a fair bit of reading on logistic regression, but most seem to focus on binary outcomes or multinomial analysis. Do I have to somehow assign 'pass' or 'fail' to this data, maybe 0 and 100% lysis? Or is the logistic model not suitable for what I am planning. All I want to do is to fit a predictive model to this data and to graphically represent the 'best fit'. Any help will be greatly appreciated.
Thanks in advance, Andreas -- View this message in context: http://r.789695.n4.nabble.com/predictive-logistic-model-cell-biology-non-dichotomous-data-tp3595812p3595812.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
