![]() ![]() > For example, should I sample N values from a uniform distribution for each > distribution, how can I perform a LHS ? > When a parameter is defined according to a specific probability > distribution for the parameter dispersal distance: >Here is the code to represent an exponential probability > In the model, the user thus sets a default probability value for each > distance which is defined according to an exponential probability > Among the input parameters in the model, I have a parameter dispersal > I would like to perform a sensitivity analysis using a Latin Hypercube > parameters are >defined according to specific probability distributions > 11:41am Nelly Reduan Latin Hypercube Sampling when > according to specific probability distributions > À : r-help at > Objet : Re: Latin Hypercube Sampling when parameters are defined > With this condition, is it possible to perform a LHS? > x > barplot(y/100, names.arg=x, ylab="Probability", xlab="Distance (km)") The sum of the probabilities of all distance Must equal one, I am going to treat distance as a class, instead of as a When you say that the sum of the probabilities of all distance classes I still might not be interpreting your question correctly, but I will try. Objet : Re: Latin Hypercube Sampling when parameters are defined according to specific probability distributions ![]() But, it’s not clear for me how to do this when parameters are defined from probability distributions? In particular, how can I use your code to apply my model to each of the 50 rows of the data frame “tabLHS”? Given that one row corresponds to one model simulation, I should have a value generated by the LHS for all distance classes at the first line of the data frame. Then, I apply my model to each row of the data frame. When all parameters are defined by a single value, I need first to create a data frame in which each column represents a different parameter, and each row represents a different combination of parameter values. It’s correct for me to treat distance as a class. Indeed, I have multiple parameters in my model: parameters which are defined by a single value (like “temperature", "pressure”) and parameters which are defined by probability distributions (like “dispersal distance”). ![]() I have some difficulties to understand how to apply my agent-based model to each parameter combination generated by the LHS, in particular when parameters are defined by probability distributions.
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