Displaying 4 results from an estimated 4 matches for "model_function".
Latin Hypercube Sampling when parameters are defined according to specific probability distributions
2017 May 27
2
Latin Hypercube Sampling when parameters are defined according to specific probability distributions
...now, transform the dispersal_distance paramter to an exponential sample
Y <- X
Y[,"dispersal_distance"] <- qexp(X[,"dispersal_distance"],
rate=exponential_rate)
hist(Y[,1], breaks=10)
# you can transform the other marginals as required and then assess
function sensitivity
model_function <- function(z) z[1]*z[2] + z[3]
apply(Y, 1, model_function)
# now, trying to use pse
library(pse)
q <- list("qexp", "qunif", "qunif")
q.arg <- list(list(rate=exponential_rate), list(min=0, max=1),
list(min=0, max=1))
uncoupledLHS <- LHS(model=model_functio...
Latin Hypercube Sampling when parameters are defined according to specific probability distributions
2017 Jun 01
1
Latin Hypercube Sampling when parameters are defined according to specific probability distributions
...uld have a value generated by the LHS for all distance classes at the first line of the data frame.
library(pse)
q <- list("qexp", "qunif", "qunif")
q.arg <- list(list(rate=exponential_rate), list(min=0, max=1),
list(min=0, max=1))
uncoupledLHS <- LHS(model=model_function, input_parameters, N, q, q.arg)
hist(uncoupledLHS$data$dispersal_distance, breaks=10)
tabLHS <- get.data(uncoupledLHS)
Sorry, it?s the first time that I perform a sensitivity analysis using the LHS.
Thank you very much for your time.
Have a nice day
Nell
_______________________________...
Latin Hypercube Sampling when parameters are defined according to specific probability distributions
2017 Jun 01
0
Latin Hypercube Sampling when parameters are defined according to specific probability distributions
...for all distance classes at the first line of the data frame.
>
>
>
> library(pse)
> q <- list("qexp", "qunif", "qunif")
> q.arg <- list(list(rate=exponential_rate), list(min=0, max=1),
> list(min=0, max=1))
> uncoupledLHS <- LHS(model=model_function, input_parameters, N, q, q.arg)
> hist(uncoupledLHS$data$dispersal_distance, breaks=10)
>
> tabLHS <- get.data(uncoupledLHS)
>
>
>
> Sorry, it?s the first time that I perform a sensitivity analysis using the LHS.
>
>
> Thank you very much for your time.
>
> Ha...
2010 Oct 03
5
How to iterate through different arguments?
...(y~x1) and I want to use a for loop to change the
number of explanatory variables, how would I do this?
So for example I want to store the model objects in a list.
model1 = lm(y~x1)
model2 = lm(y~x1+x2)
model3 = lm(y~x1+x2+x3)
model4 = lm(y~x1+x2+x3+x4)
model5 = lm(y~x1+x2+x3+x4+x5)...
model10.
model_function = function(x){
for(i in 1:x) {
}
If x =1, then the list will only add model1. If x =2, then the list will add
both model1 and model2. If x=3, then the list will add model1 model 2 and
model3 and so on. How do I translate this into code?
--
View this message in context: http://r.789695.n4.nabble.co...