# Plot results of cutter that best describe distribution Plot the estimates of cut distribution. ```r ## S3 method for class 'cutter' plot( x, col.hist = "grey", col.DL = "blue", col.dist = "black", col.unobserved = "green", col.mcmc = rgb(red = 0.6, green = 0, blue = 0, alpha = 0.01), legend = TRUE, show.DL = TRUE, show.plot = TRUE, set = NULL, ... ) ``` ## Arguments - `x`: A result file generated by cutter - `col.hist`: The color of histogram - `col.DL`: The color of below of above samples - `col.dist`: The color of distribution - `col.unobserved`: The color of unobserved states - `col.mcmc`: The color of mcmc outputs - `legend`: If TRUE, a legend is shown - `show.DL`: If TRUE, the limits of DL are shown - `show.plot`: If FALSE, no plot is shown - `set`: In case of mixture, will show and return only this set - `...`: Parameters for plot ## Returns The matrix of all the mcmc curves with invisible state. ## Details plot.cutter plot result of cutter ## Examples ```r ## Not run: library(HelpersMG) # _______________________________________________________________ # right censored distribution with gamma distribution # _______________________________________________________________ # Detection limit DL <- 100 # Generate 100 random data from a gamma distribution obc <- rgamma(100, scale=20, shape=2) # remove the data below the detection limit obc[obc>DL] <- +Inf # search for the parameters the best fit these censored data result <- cutter(observations=obc, upper_detection_limit=DL, cut_method="censored") result plot(result, xlim=c(0, 150), breaks=seq(from=0, to=150, by=10)) # _______________________________________________________________ # The same data seen as truncated data with gamma distribution # _______________________________________________________________ obc <- obc[is.finite(obc)] # search for the parameters the best fit these truncated data result <- cutter(observations=obc, upper_detection_limit=DL, cut_method="truncated") result plot(result, xlim=c(0, 150), breaks=seq(from=0, to=150, by=10)) # _______________________________________________________________ # left censored distribution with gamma distribution # _______________________________________________________________ # Detection limit DL <- 10 # Generate 100 random data from a gamma distribution obc <- rgamma(100, scale=20, shape=2) # remove the data below the detection limit obc[obcUDL] <- +Inf # search for the parameters the best fit these censored data result <- cutter(observations=obc, lower_detection_limit=LDL, upper_detection_limit=UDL, cut_method="censored") result plot(result, xlim=c(0, 150), col.DL=c("black", "grey"), col.unobserved=c("green", "blue"), breaks=seq(from=0, to=150, by=10)) # _______________________________________________________________ # Example with two values for lower detection limits # corresponding at two different methods of detection for example # with gamma distribution # _______________________________________________________________ obc <- rgamma(50, scale=20, shape=2) # Detection limit for sample 1 to 50 LDL1 <- 10 # remove the data below the detection limit obc[obc

plot.cutter function