(1) and the cut-off value for that limit obtained by solving for X when Y = 50%. Thus, the cut-off values obtained from selleck chemicals the upper prediction limit help distinguish between fly lines with sensitive and normal responses, and those from the lower prediction limit are used to distinguish between flies with normal and resistant response. In addition, we have incorporated in HEPB the option of generating 500 values of the response variable, using simulation, within the observed range of the explanatory variable, based on the regression parameters estimated for the original data.
The implementation of this project was done using the Embarcadero ® Delphi ® XE language (Embarcadero ® RAD Studio XE Version 15.0.3953.35171). For the purposes of demonstration of our programs, a dataset from the Call laboratory is used where 809 flies from 6 separate experiments were assayed for their response to 1% isoflurane using the inebriometer (Dawson et al., Sirolimus research buy 2013). The data needs to be formatted in two columns, the first (X) is the independent variable or the dose associated with a desired response (e.g., time taken for a given fly to be fully anesthetized, as manifested by falling through the entire inebriometer column), and the second (Y) is the response variable (e.g., the percentage of flies that were anesthetized in a given time). The analysis
to estimate the parameters c and d and compute the regression was
done using the Excel template (available and from the authors). The instructions to enable the use of macros and Solver are given in the Initial Instructions worksheet. The X and Y variables need to be entered into the corresponding columns in the Regression worksheet, following which, the graph will auto-populate with the raw data (blue dots; Fig. 2). In this process, the user has the option to change any or all of the four parameter values (that is, set the range limits for a and b and starting values for c and d). A warning message alerts the user if the range limits for a and b are set to be within the corresponding limits in the observed data. A button then allows the user to assign a and b to the minimum and maximum values of the current dataset. The data are analyzed by pressing the Perform Regression button. This runs Solver, which begins the optimization process by means of iteration. When this process is complete, the Excel spreadsheet displays the final Hill equation fit to the data and the values of c and d (called EC50 and Hill slope in the template), along with the R2 value. The regression line is plotted in red in the graph with the original data ( Fig. 4). The analysis on the example dataset yielded a c value (EC50) of 342.701 and a d value (Hill slope) of 4.859, with a R2 value of 0.970.