ICEcolor
(Package: ICEinfer) :
Compute Preference Colors for Outcomes in a Bootstrap ICE Scatter within a Confidence Wedge
Assuming ICEw is an object of class ICEwedge, ICEcolor uses the value of lambda given by lfact * (ICEw item lambda) and the ICE Preference Map with parameters beta and gamma to compute the Economic Preference value for only the points in a Bootstrap Distribution of ICE Uncertainty that also happen to fall within the ICE confidence wedge. When the overall level of confidence (statistical size of the wedge) is held fixed, the points to be colored are always the very same points for all choices of lambda. However, the numerical value of preference (and thus the color) for each such point as well as potential overall asymmetry in the resulting ICE map do depend greatly upon choice of lambda.
plot.ICEcolor
(Package: ICEinfer) :
Add Economic Preference Colors to Bootstrap Uncertainty Scatters within a Confidence Wedge
Assuming x is an object of class ICEcolor, the default invocation of plot(x) recolors the default alias display of the points within the bootstrap distribution of ICE uncertainty that are within its statistical confidence wedge. An invocation of the form plot(x, alibi=TRUE) recolors the alibi display. When ready, the user should click within this graphics window to display a Histogram of all the Economic Preference values falling within the ICE Statistical Confidence Wedge.
plot.ICEepmap
(Package: ICEinfer) :
Display Indifference Curves on a standardized ICE Economic Preference Map
Display plots of the Indifference Curves of an ICE Economic Preference Map using the contourplot() and expand.grid() functions from the lattice R-package.
ICEscale() computes Summary Statistics for 2-sample, 2-variable inference where one variable is a measure of effectiveness (higher values are better) and the other variable is a measure of cost (lower values are better). The 2 samples are of patients receiving only 1 of the 2 possible treatments. The treatment called new is the one with the higher numerical level for the specified treatment indicator variable, while the treatment called std corresponds to the lower numerical level. The pivotal statistic for inference is (DeltaEffe, DeltaCost), which are the head-to-head mean differences for new treatment minus std treatment. Each sample is assumed to provide unbiased estimates of the overall expected effectiveness and cost for that treatment.
ICEepmap() and ICEomega() set numerical values for lambda (the full, fair shadow price of health) and for the two so-called power-parameters of a parametric ICE Preference Map. These functions return a value, epm, that is an output list object of class ICEepmap for display using print(epm) or plot(epm, xygrid). The primary purpose of such plots is to allow the user to more easily visualize the profound effects that changing numerical values for lambda, beta and either gamma or eta = gamma / beta can have on the iso-preference contours (level curves) of an ICE map.
Assuming x is an output list object of class ICEuncrt, the default invocation of plot(x) graphically displays the bootstrap distrib of ICE uncertainty currently stored in x. An invocation of the form x10 <- plot(x, lfact=10) increases the value of x item lambda by a factor of 10, displays that transformed bootstrap distribution, and stores it in object x10. When the x item unit is cost, an invocation of the form xs <- plot(x, swu=TRUE) displays the bootstrap distribution stored in x using effe units and stores the transformed distribution in object xs.
ICEalice
(Package: ICEinfer) :
Functions to compute and display ICE Acceptability Curves
ICEalice() computes statistics for the VAGR Acceptability Curve and for the Buckingham ALICE curve. Plots for the resulting ICEalice object are of two types: [1] a VAGR curve where the horizontal axis is the Willingness to Pay (WTP) ICE Ratio, and [2] a monotone ALICE curve where the horizontal axis is the Absolute Value of the ICE Polar Angle, which varies from +45 degrees to +135 degrees. Printing an ICEalice object yields a 13 x 5 table (matrix) of numerical values for Absolute ICEangle, WTP, VAGR Acceptability, WTA and ALICE acceptability, respectively.
ICEuncrt
(Package: ICEinfer) :
Compute Bootstrap Distribution of ICE Uncertainty for given Shadow Price of Health, lambda
ICEuncrt() uses bootstrap resampling (with replacement) to compute the distribution of uncertainty for 2-sample, 2-variable statistical inference. The 2 variables must be measures of effectiveness (higher values are better) and cost (lower values are better). The 2 samples are of patients receiving only 1 of the 2 possible treatments. The treatment called new is the one with the higher numerical level for the specified treatment indicator variable, while the treatment called std corresponds to the lower numerical level. The pivotal statistic for inference is (DeltaEffe, DeltaCost), which are the head-to-head mean differences for new treatment minus std treatment. Each sample is assumed to provide unbiased estimates of the overall expected effectiveness and cost for that treatment.
ICEwedge
(Package: ICEinfer) :
Equivariant Wedge-Shaped ICE Region with Confidence Level from 0.50 to 0.99
ICEwedge() uses the Bootstrap Distribution of ICE Uncertainty generated by ICEuncrt() to calculate and sort ICE Angle Order Statistics around a circle. ICEwedge() then counts outwards the same number of ICE Angle Order Statistics, floor(R*conf/2), both Counter-Clockwise and Clockwise from the so-called center Order Statistic (the one nearest to the Observed ICE Ratio) to define a pair of ICE Ray Endpoints at ICE Angle Order Statistics (reported as numbers jlo and kup, respectively) that subtend an ICE Polar Angle reported as being of subangle in degrees.
Assuming x is an output list object of class ICEuncrt, the default invocations of x or print(x) describe the bootstrap distribution of ICE uncertainty currently stored in x. An invocation of the form x10 <- print(x, lfact=10) increases the value of x item lambda by a factor of 10, describes that transformed bootstrap distribution, and stores it in object x10. When x item unit is cost, an invocation of the form xs <- print(x, swu=TRUE) describes the bootstrap distribution stored in x using effe units and stores the transformed distribution in object xs.
providing 
.