search for: distance_class_prob

...d according to specific probability distributions Nell, I still might not be interpreting your question correctly, but I will try. When you say that the sum of the probabilities of all distance classes must equal one, I am going to treat distance as a class, instead of as a continuous variable. distance_class_probabilities <- c(9, 11, 10, 8.9, 8, 7, 6, 5.8, 5.1, 4, 3.9, 3.7, 3.4, 3.1, 2, 1.9, 1.6, 1.4, 1, 0.9, 0.8, 0.7, 0.4, 0.3, 0.1)/100 sum(distance_class_probabilities) distance_classes <- factor(paste0("d", 1:25), ordered = TRUE, levels=paste0("d", 1:25)) input_parameters <- c...

...ons > > Nell, > > I still might not be interpreting your question correctly, but I will try. > > When you say that the sum of the probabilities of all distance classes > must equal one, I am going to treat distance as a class, instead of as a > continuous variable. > > distance_class_probabilities <- c(9, 11, 10, 8.9, 8, 7, 6, 5.8, 5.1, > 4, 3.9, 3.7, 3.4, 3.1, 2, 1.9, 1.6, 1.4, 1, 0.9, 0.8, 0.7, 0.4, 0.3, > 0.1)/100 > sum(distance_class_probabilities) > distance_classes <- factor(paste0("d", 1:25), ordered = TRUE, > levels=paste0("d", 1:25))...

>May 26, 2017; 11:41am Nelly Reduan Latin Hypercube Sampling when parameters are >defined according to specific probability distributions >Hello, > I would like to perform a sensitivity analysis using a Latin Hypercube Sampling (LHS). >Among the input parameters in the model, I have a parameter dispersal distance which is defined according to an exponential probability