cond.prob: This function calculates conditional probability values and...

In kevinlzheng/RegR: Collective Functions to Perform Generalized Linear Model Graphs

Description

It also uses bootstrapping techniques to calculate confidence interval of change points

Usage

cond.prob(my.data, xindex, yindex, weight = NULL, goodvar = 0,
  goodmetric = 1, CI = TRUE, j, timeboot = 99,
  xlabel = names(my.data[xindex]), ylabel = names(my.data[yindex]),
  cpplot = FALSE, log.flag = "", change.pt = FALSE, rounder1 = 3,
  rounder2 = 1, sequencecode = "", ...)

Arguments

my.data	data frame
xindex	xvariable at column name or index
yindex	xvariable at column name or index
weight	is also optional, when there is a site weight column in mydata, enter the column number
goodvar	is a flag for good or bad variables, goodvar is 1 if a predictor increase a good metric, e.g., habitat score
goodmetric	is a flag for good or bad metrics, e.g., EPT metric is good(1), HBI is bad (0).
j	x is stressor, y is biological response, j is the criteria
cpplot	is a optional flag to determine if conditional prob should be plotted conditional prob with confidence intervals should be plotted.
change.pt	is an option flag to choose if changepoint analysis should be performed
rounder1	is number of decimal for change points displaying in graphs
rounder2	is biocriteria rounder

kevinlzheng/RegR documentation built on May 20, 2019, 9:07 a.m.