# Plot Log-Likelihood Contours Creates a set of contour plots, such that there is a contour plot for every pair of parameters varying. ```r dc.PlotLogLikelihoodContours( loglikelihood.fcn, predicted.params, ..., n.divs = 2, multiple.screens = FALSE, num.contour.lines = 10, zoom.percent = 0.9, allow.neg.params = FALSE, param.names = c("param 1", "param 2", "param 3", "param 4") ) ``` ## Arguments - `loglikelihood.fcn`: log-likelihood function to plot. - `predicted.params`: estimated parameters. - `...`: all additional arguments required by the log-likelihood function. For example, `bgbb.rf.matrix.LL` requires rf.matrix; `pnbd.cbs.LL` requires cal.cbs and hardie (defaults to TRUE); and `bgnbd.cbs.LL` requires cal.cbs. - `n.divs`: integer representing how fine-grained the contour plot is. A higher value will produce a higher resolution plot with smoother contour lines, but will take longer to plot. n.divs also affects the boundaries of the contour plot; see details. - `multiple.screens`: if TRUE, plots each contour plot on a separate R graphics window. - `num.contour.lines`: number of contour lines to plot in the window. - `zoom.percent`: determines boundaries of contour plot. See details. - `allow.neg.params`: if FALSE, the contour plot will not include negative values (see details). This should be set to false for the BG/BB and Pareto/NBD models. - `param.names`: a vector containing parameter names. ## Details For each contour plot, the non-varying parameters are kept constant at the predicted values. The contour will extend out by (n.divs * zoom.percent) in both directions and both dimensions from the estimated parameter values. The exception is if allow.neg.params is FALSE. In this case, the contour plot will end at zero if it would have extended into negative parameter values. The estimated parameter values will be indicated by the intersection of two red lines. ## Examples ```r # **Example for BG/BB model: data(donationsSummary) rf.matrix <- donationsSummary$rf.matrix # starting-point parameters bgbb.startingparams <- c(1, 1, 0.5, 3) # estimated parameters bgbb.est.params <- bgbb.EstimateParameters(rf.matrix, bgbb.startingparams) # set up parameter names for a more descriptive result bgbb.param.names <- c("alpha", "beta", "gamma", "delta") # plot-log likelihood contours: dc.PlotLogLikelihoodContours(bgbb.rf.matrix.LL, bgbb.est.params, rf.matrix = rf.matrix, n.divs = 5, num.contour.lines = 8, zoom.percent = 0.3, allow.neg.params = FALSE, param.names = bgbb.param.names) # **Example for Pareto/NBD model: data(cdnowSummary) cbs <- cdnowSummary$cbs # Speed up calculations: cbs <- dc.compress.cbs(cbs) # parameters estimated using pnbd.EstimateParameters pnbd.est.params <- cdnowSummary$est.params # set up parameter names for a more descriptive result pnbd.param.names <- c("r", "alpha", "s", "beta") # plot log-likelihood contours: dc.PlotLogLikelihoodContours(pnbd.cbs.LL, pnbd.est.params, cal.cbs = cbs, hardie = TRUE, n.divs = 5, num.contour.lines = 15, zoom.percent = 0.3, allow.neg.params = FALSE, param.names = pnbd.param.names) # **Example for BG/NBD model: data(cdnowSummary) cbs <- cdnowSummary$cbs # parameters estimated using bgnbd.EstimateParameters bgnbd.est.params <- cdnowSummary$est.params # set up parameter names for a more descriptive result bgnbd.param.names <- c("r", "alpha", "s", "beta") # plot log-likelihood contours: dc.PlotLogLikelihoodContours(bgnbd.cbs.LL, bgnbd.est.params, cal.cbs = cbs, n.divs = 5, num.contour.lines = 15, zoom.percent = 0.3, allow.neg.params = FALSE, param.names = bgnbd.param.names) ``` ## See Also `dc.PlotLogLikelihoodContour`

dc.PlotLogLikelihoodContours function