Latin Hypercube Sampling when parameters are defined according to specific probability distributions
I think you should take this conversation private or seek local statistical expertise. This is about strategies for analysis, not about programming in R. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Wed, May 31, 2017 at 9:49 PM, Nelly Reduan <nell.redu at hotmail.fr> wrote:
Thank you very much Rob for your answer. 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. 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?). It?s correct for me to treat distance as a class. 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. Then, I apply my model to each row of the data frame. 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.
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
________________________________
De : Rob C <bertcarnell at gmail.com>
Envoy? : mardi 30 mai 2017 16:26:08
? : Nelly Reduan
Cc : r-help at r-project.org
Objet : Re: [R] Latin Hypercube Sampling when parameters are defined 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("dispersal_distance", "temperature", "pressure")
N <- 1000
plot(1:25, distance_class_probabilities, type="h", lwd=5)
set.seed(1)
require(lhs)
X <- randomLHS(N, length(input_parameters))
dimnames(X) <- list(NULL, input_parameters)
Y <- X
Y[,"dispersal_distance"] <-
approx(x=cumsum(distance_class_probabilities), y=1:25,
xout=X[,"dispersal_distance"], method="constant", yleft=0)$y + 1
hist(Y[,"dispersal_distance"], breaks=c(seq(0.5, 25.5, by=1)))
plot(table(distance_classes[Y[,"dispersal_distance"]]))
Is it still a Latin hypercube?
This is a more difficult question. In some ways, the sample is still a Latin
hypercube since it was drawn that way. But once the sample has been discretized
into the distance classes, then it loses the latin property of having only one
sample per "row". It might be close enough for your purposes though.
Rob
On Tue, May 30, 2017 at 10:59 AM, Nelly Reduan <nell.redu at hotmail.fr> wrote:
Thanks a lot Rob for your answer. I need to add a condition for the
parameter ?dispersal distance?. The sum of the probabilities of all distance
classes must be equal to 1:
y <- 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)
x <- seq(1, 25, by = 1)
barplot(y/100, names.arg=x, ylab="Probability", xlab="Distance (km)")
With this condition, is it possible to perform a LHS?
Thanks a lot for your time.
Nell
________________________________
De : R-help <r-help-bounces at r-project.org> de la part de Rob C
<bertcarnell at gmail.com>
Envoy? : samedi 27 mai 2017 13:32:23
? : r-help at r-project.org
Objet : Re: [R] Latin Hypercube Sampling when parameters are defined
according to specific probability distributions
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
distribution.
In the model, the user thus sets a default probability value for each
distance class.
For example, for distances ([0 2]; ]2 4]; ]4 6]; ]6 8]; ]8 10];; ]48
50],
respective probabilities are 0.055; 0.090; 0.065; 0.035; 0.045;; 0.005.
>Here is the code to represent an exponential probability
distribution for the parameter dispersal distance:
set.seed(0)
foo <- rexp(100, rate = 1/10)
hist(foo, prob=TRUE, breaks=20, ylim=c(0,0.1), xlab ="Distance (km)")
lines(dexp(seq(1, 100, by = 1), rate = 1/mean(foo)),col="red")
1/mean(foo)
When a parameter is defined according to a specific probability
distribution, how can I perform a LHS ?
For example, should I sample N values from a uniform distribution for each
distance class (i.e., [0 ? 2]; ]2 ? 4]; ]4 ? 6]; ]6 ? 8]; ]8 ? 10];??; ]48 ?
50])
or sample N values from exponential distributions with different rates ?
Here is the code used to perform a LHS when the parameter ?dispersal
distance? is defined by one default value in the model:
library(pse)
factors <- c("distance")
q <- c("qexp")
q.arg <- list( list(rate=1/30) )
uncoupledLHS <- LHS(model=NULL, factors, 50, q, q.arg)
head(uncoupledLHS)
Thanks a lot for your time.
Have a nice day
Nell
Nell,
I would like to suggest a slightly different method for generating the
sample using the lhs library, then I will try using the pse library.
Generally when you have a package specific
question, you should try to contact the package maintainer first.
set.seed(1)
# I don't think your model has only one parameter, so I will include
multiple
input_parameters <- c("dispersal_distance", "temperature", "pressure")
N <- 50
exponential_rate <- 1/30
library(lhs)
X <- randomLHS(N, length(input_parameters))
dimnames(X) <- list(NULL, input_parameters)
# X is now a uniformly distributed Latin hypercube
head(X)
hist(X[,1], breaks=5)
hist(X[,2], breaks=5)
hist(X[,3], breaks=5)
# 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_function, input_parameters, N, q, q.arg)
hist(uncoupledLHS$data$dispersal_distance, breaks=10)
Rob
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and provide commented, minimal, self-contained, reproducible code.