# Print results of cutter that best describe distribution Print the estimates of cut distribution. ```r ## S3 method for class 'cutter' print(x, silent = FALSE, ...) ``` ## Arguments - `x`: A result file generated by cutter - `silent`: If TRUE does not show the outpout - `...`: Not used ## Returns Nothing ## Details print.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

print.cutter function