similar to: Hypercube in R

Dear R useRs, I have a vector with positive and negative numbers: A=c(0,1,2,3,0,4,5) Now if i-th element in vector A is > 0, then i-th element in vector B is a+1 else i-th element in vector b=a (or 0) vector A: 0 1 2 3 0 4 5 vector B: 0 2 3 4 0 5 6 What's the right way to do this. I still have some problems with for and if statements... Cheers, Andrej

Dear useRs, I have two different length vectors: one column (1...m) and one row vector (1...n): 20 40 20 60 5 4 2 Now I have to calculate ratios between column vector elements and each row vector elements: 4 5 10 8 10 20 4 5 20 15 12 30 Thank's in advance for any suggestions, Andrej

Dear R useRs, I have big (23000 rows), vertical bar delimited file: e.g. A00001|Text a,Text b, Text c|345 A00002|Text bla|456 ... .. . Try using A <- read.table('filename.txt', header=FALSE,sep='\|') process stop at line 11975 with warning message: number of items read is not a multiple of the number of columns I have no problems with processing similar file, which is

Dear all, Suppose I plot two normal distributions (A and B) side by side and add vertical line which hipotheticaly represent alpha value; e.g.: x <- seq(-3.5,5, length=1000) y <- dnorm(x) # Plot distribution A plot(y~x, type='l',axes=F,xlab="",ylab="",lwd=2) # Plot distribution B y2 <- dnorm(x-1.5) lines(y2~x,lwd=2) # Plot vertical line for alpha value

Dear R useRs, is there any simple, build in function to match specific regular expression in data file and write it to a vector. I have the following text file: *NEW RECORD *ID-001 *AB-text *NEW RECORD *ID-002 *AB-text etc. Now I have to match all ID fields and print them to a vector: 001 002 etc. I know that this is very simple with Perl or R-Perl interface, but if possible, I want to do

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

Dear all, I try to install the latest R version using checkinstall (v. 1.6.2) on Ubuntu 11.10. After solving all the dependencies (installed using apt-get build-dep r-base) checkinstall fails to build and install R package with the following error (the same commands build and install R-2.13.2 on the same machine without any problem): wget

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

Dear all, I'm working on ability estimates using Rasch model. Using the "ltm" package, the procedure is quite simple: ## Factor Scores for the Rasch model fit <- rasch(LSAT) factor.scores(fit) What about Partial Credit Model (PCM)? For PCM I use PCM function from eRm package. Is there any similar function like factor.scores to estimate ability scores using PCM model? Best,

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

>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

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]

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",

Dear useRs, I'm working on multiple comparison design on two factor (2 3 levels) ANOVA. Each of the tests I have tried (Tukey, multcomp package) seem to do only with one factor at a time. fm1 <- aov(breaks ~ wool * tension, data = warpbreaks) tHSD <- TukeyHSD(fm1, "tension", ordered = FALSE) $tension diff lwr upr p adj M-L -10.000000 -19.35342

> 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

Hi all, I am attempting to use latin hypercube sampling to sample different variable functions in a series of simultaneous differential equations. There is very little code online about lhs or clhs, so from different other help threads I have seen, it seems I need to create a probability density function for each variable function, and then use latin hypercube sampling on this pdf. So far, I

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)

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 (

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