similar to: latin hypercube sampling

Hi all, I am a begginer in R and I have been trying to use the Conditioned Latin Hypercube to choose sample points only in areas close to roads due to the difficult thorough access in the study area. I could use a code to create the points throughout the area, but I need to create a code to make the R only comes up with points close to this road. I've been using the packages

Hi everybody, I'm trying to create a numerical data frame on which to perform PRCC. So far I have created a data frame that consists of function/vector output that displays in numerical form, but when I try and run PRCC (from epiR package) I get the following error message: "Error in solve.default(C) : Lapack routine dgesv: system is exactly singular" It appears this is because

Dear R-users I was running into problems with my R code trying to run clh sampling (clhs package) in parallel mode (=on various data sets simultaneously). Here is the code (which I developed with some help:)): ****************************************** library("clhs") library("snow") a <- as.data.frame(replicate(1000, rnorm(20))) b <- as.data.frame(replicate(1000,

Hello, I am performing a sensitivity analysis using a Latin Hypercube sampling. However, I have difficulty to draw a Hypercube sample for one variable. I?ve generated this variable from a Poisson distribution as follows: set.seed(5) mortality_probability <- round(ppois(seq(0, 7, by = 1), lambda = 0.9), 2) barplot(mortality_probability, names.arg = seq(0, 7, by = 1), xlab = "Age

Hello, I got one question on the Latin hypercube sampling. suppose there are three variables a, b, c, all of them follow the normal distribution. the mean value and standard deviation for each are  a(32, 2), b(35,5), c(37,3). I would like to use Latin hypercube sampling to random generate 1000 samples. but it needs to satisfy the condition that a<b<c. How can I implement this

> How can I draw a Hypercube sample for the variable mortality_probability so > that this variable exhibits the same pattern as the observed distribution? One simple way is to use the uniform random output of randomLHS as input to the quantile function for your desired distribution(s). For example: q <- randomLHS(1000, 3) colnames(q) <- c("A", "B",

> However, my variable is simulated from the cumulative distribution function > of the Poisson distribution. Then I am afraid I don't know what you're trying to achieve. Or why. However, the principle holds; write a function that maps [0,1] to the 'pattern' you want, do that and apply it to the result from randomLHS. It happens that for generating data that follow a given

Hello all, I am trying to do a Latin Hypercube Sampling (LHS) to a 5-parameter design matrix. I start as follows: library(lhs) p1<-randomLHS(1000, 5) If I check the distribution of each parameter (column), they are perfectly uniformly distributed (as expected).For example, hist(p1[,1]) Now the hard (maybe strange) question. I want the combination of the first three parameters to sum up to

Hi, I am interested in using R to perform Monte Carlo simulation using MC sampling, Latin Hypercube Sampling (LHS), and importance sampling. While I'm sure I can code R to do LHS, I would be surprised if others had not done so. Is there R code publically available for this purpose? (I have searched CRAN and the WWW extensively with no luck). Thanks in advance. Dan Loughlin

Hi, I am running a simulation and have to perform ANOVA to determine the rank of factors. Used the aov() function and it works great for full factorial design. 1. For a massive set of data, I tried using biglm, while it can create the linear model, all the residuals (for assumption validation) are not recorded and the sum of squares are not there, just the estimated regression coefficient, 95%

Hi everyone, I emailed yesterday thinking I had a problem with reading matrix information as numerical output into epiR. After working on the data frame today, I realise it's a problem with the data itself rather than the syntax. An original data frame of min max T1 1.500000e+01 3.999954e+01 SE1 0.000000e+00 1.000000e+00 PRE

Hi I want to du a sensitivity analysis using Latin Hypercubes. But my parameters have to fulfill two conditions: 1) ranging from 0 to 1 2) have to sum up to 1 So far I am using the lhs package and am doing the following: library(lhs) ws <- improvedLHS(1000, 7) wsSums <- rowSums(ws) wss <- ws / wsSums but I think I can't do that, as after the normalization > min(wss) [1]

Thanks for your answer. I have attached the plot for representing the variable. I think that I need to draw a Hypercube sample for each age class (i.e., for 0, 1, 2, 3, 4, 5, 6, 7) in a given simulation (i.e., N = 1) and the LHS values for all age classes should be like the observed cumulative distribution (see attached figure). Thus, the output of randomLHS should be a matrix with 100 rows (N =

Thanks for your answer. However, my variable is simulated from the cumulative distribution function of the Poisson distribution. So, the pattern obtained from the function "qpois" is not the same as the observed pattern (i.e., obtained from the function "ppois") set.seed(5) mortality_probability <- round(ppois(seq(0, 7, by = 1), lambda = 0.9), 2)

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

Hi, I'd like to use Latin Hypercube Sampling (LHC) in the the context of uncertainty / sensitivity analysis of a complex model with approximately 10 input variables. With the LHC approach I'd like to generate parameter combinations for my model input variables. Therefore I came across an simple example here on the mailing list (

>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

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

Hello All! I calculate a variogram using the function variog (package geoR) afterwards I use variofit to fit a spherical model (see code below). Now I just changed the units of the variable (in this case MPa to kPa just a factor of 1000). If I do so, I get a different fit and therefore different ranges etc. Why? The semi-variance is of course 6 orders of magnitude higher but the values

# lucreate -n B85 Analyzing system configuration. Hi, after typing # lucreate -n B85 I get the following error: No name for current boot environment. INFORMATION: The current boot environment is not named - assigning name <BE1>. Current boot environment is named <BE1>. Creating initial configuration for primary boot environment <BE1>. ERROR: Unable to determine major and