Cutte

Marc Girondot - Laboratoire Ecologie, Systématique, Evolution

Université Paris-Saclay, CNRS, AgroParisTech, France.

CutteR is a scientific method and a software used to model left and right censored and truncated data.

It used a Bayesian MCMC distribution fit using conditional gamma, lognormal, normal, Weibull, or generalized gamma distribution. The priors are based on Gaussian distribution of parameters obtained from maximum likelihood with large standard deviation. The posterior distribution is obtained with 5000 MCMC iterations.

Copy data in the box below, select the required options and click 'Run the analysis' button.

Copy the data here, separated by space, semicolon, tabulation, or return; unknown values must be LDL (Lower the Detection Limit) or UDL (Upper the Detection Limit). Do not use comma to separate values as commas are used as synonyme for decimal point.

To try the method, copy these values and set a Lower limit of detection to 10:

89.77 12.75 44.18 60.66 32.68 34.57 28.09 91.11 13.98 56.95 59.64 76.22 84.64 61.45 27.50 17.27 16.39 22.10 15.81 62.86 86.39 50.86 26.33 29.43 53.95 40.94 64.13 19.49 40.26 44.03 66.70 22.10 21.07 36.57 70.29 50.20 54.13 13.11 22.76 40.04 28.49 LDL 13.98 LDL 62.24 55.50 15.30 11.29 14.39 40.88 26.31 33.04 19.02 47.35 56.16 24.00 18.66 20.52 61.26 LDL 15.45 41.65 77.76 71.66 22.13 LDL 46.61 39.65 58.96 LDL 28.01 83.33 64.28 24.31 22.52 LDL 64.47 62.77 11.45 32.91 39.11 71.29 56.73 67.44 32.42 45.63 32.60 60.96 16.38 LDL 12.26 143.26 18.80 13.28 29.26 50.78 15.98 45.35 46.43 89.13

Lower limit of detection

Upper limit of detection

Let a box being empty if the option is not necessary.

Choose which cut will be fitted

How many mixture should be used

Choose which distribution will be fitted

Be patient, the analysis requires between 30 seconds to 5 minutes to be completed.

This web server version v. 6.1.1 build 1532 is a simplified version of the complete tools available in the HelpersMG R package.

The Virtual Data initiative, run by LABEX P2IO and supported by Université Paris-Saclay, is thanked for providing computing resources on its cloud infrastructure.