similar to: Bayesian constrained regression method?

Hello, I have some discrete pvalues and I would like to sort them. Then add random noise so that they are ordered the same way as the original pvalues. Off course I don't want any pvalues less than 0 or greater than 1. Any ideas on how to do this in R? -- Thanks, Jim. [[alternative HTML version deleted]]

Hello everyone, I have 3 variables Y, X1 and X2. Each variables lies between 0 and 1. I want to do a constrained regression such that a>0 and (1-a) >0 for the model: Y = a*X1 + (1-a)*X2 I tried the help on the constrained regression in R but I concede that it was not helpful. Any help is greatly appreciated -- Thanks, Jim. [[alternative HTML version deleted]]

Hello, R experts, Sorry for asking this question again again since I really want a help! I have a two-factor experiment data and like to calculate estimates of interation contrasts say factor A has levels of a1, a2, and B has levels of b1, b2, b3, b4, and b5 with 3 replicates. I am not sure the constrast estimate I got is right using the script below:

Hello, Is there anywhere I can find some Bayesian Code for 2 by 2 tables or even the non-central hypergeometric distribution in R? Packages would be helpful but the actual coding in R is much better. Thanks, Thanks Jim [[alternative HTML version deleted]]

Hello, Is there any literature there that says that the EM is better/worse than a Baysian model when it comes to differentiating univariate mixture of normal distributions? -- Thanks, Jim. [[alternative HTML version deleted]]

I want to fit some p-values to a beta distribution. But the problem is some of the values have 0s and 1's. I am getting an error if I use the MASS function to do this. Is there anyway to get around this? -- Thanks, Jim. [[alternative HTML version deleted]]

Dear All, I am using a specific approach for my master thesis. In essence, a supervised reclassification is used as an intermediate step to find chemical parameters which are able to reclassify defined groups. These variables will be used in a next step where location and scale estimators of the groups are important. Traditionally linear discriminant analysis is used for reclassification which

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Dear R-ians, I want to perform an linear unmixing of image pixels in fractions of pure endmembers. Therefore I need to perform a constrained linear least-squares problem that looks like : min || Cx - d || ? where sum(x) = 1. I have a 3x3 matrix C, containing the values for endmembers and I have a 3x1 column vector d (for every pixel in the image). In theory my x values should all be in the

Hello when my code executes I receive the message that were some warnings. I want to catch warning messages at run time so to print some local variables and try to understand why this warning happens. I searched on internet and I tried withCallingHandlers( which seems to work but as I used Rkward the result is awful. I get a prompt to copy paste a value only while at the same time my background

Hello R helpers, I am trying to do a linear OLS regression of y on two variables x1 and x2. I want to constrain the coefficients of x1 and x2 to sum up to 1. and therefore run a constrained OLS. Can anybody help with this? (I have seen some answers to similar questions but it was not clear to me what I need to do) - I have tried the lm function with offset but I must not have used it properly.

I have data that ranges from 0.3 to 2 and I want to change the scale to be from 0 to 1. Can this be done in R? -- Thanks, Jim. [[alternative HTML version deleted]]

Hi group, I have this type of data x(predictor), y(response), factor (grouping x into many groups, with 6-20 obs/group) I want to fit a linear regression with one common intercept. 'factor' should only modify the slopes, not the intercept. The intercept is expected to be >0. If I use y~ x + factor, I get a different intercept for each factor level, but one slope only if I use y~ x *

Hi, I am trying to fit a logarithmic trendline to a scatterplot of a species accumulation curve. I've tried abline, lines, curve and scatter.smooth but none of these work. Can anyone help please, Kyran

Hi All, I'm trying to do the following constrained optimization example. Maximize x1*(1-x1) + x2*(1-x2) + x3*(1-x3) s.t. x1 + x2 + x3 = 1 x1 >= 0 and x1 <= 1 x2 >= 0 and x2 <= 1 x3 >= 0 and x3 <= 1 which are the constraints. I'm expecting the answer x1=x2=x3 = 1/3. I tried the "constrOptim" function in R and I'm running into some issues. I first start off

Hello. I have searched the archives but have not found anything. I need to solve a constrained optimisation problem for a nonlinear function (“maximum entropy formalism”). Specifically, Optimise: -1*SUM(p_ilog(p_i)) for a vector p_i of probabilities, conditional on a series of constraints of the form: SUM(T_i*p_i)=k_i for given values of T_i and k_i (these are constraints on

Dear R users, I am pleased to announce the release of a new packaged called `bcrm? (version 0.1), now available on CRAN. The package implements a wide range of Bayesian continuous reassessment method (CRM) designs to be used in Phase I dose-escalation trials. The package is fully documented and highlights include ? A choice of 1-parameter working models or the 2-parameter logistic model.

Dear R users, I am pleased to announce the release of a new packaged called `bcrm? (version 0.1), now available on CRAN. The package implements a wide range of Bayesian continuous reassessment method (CRM) designs to be used in Phase I dose-escalation trials. The package is fully documented and highlights include ? A choice of 1-parameter working models or the 2-parameter logistic model.

I'm relatively new to R, but I'm attempting to do a non-linear maximum likelihood estimation (mle) in R, with the added problem that I have a non-linear constraint. The basic problem is linear in the parameters (a_i) and has only one non-linear component, b, with the problem being linear when b = 0 and non-linear otherwise. Furthermore, f(a_i) <= b <= g(a_i) for some (simple) f

Dear R helpers I have a data frame as given below df = data.frame(A = c(776,827,836,995,855,1026,1203,1363,965,1195),                        B = c(806,953,1049,1056,1243,764,1148,1162,948,1154),                        C = c(959,1155,1193,1163,863,1070,1087,877,1132,944),                        D = c(906,760,978,1170,1009,883,1007,960,828,113)) # Actually the real data has number of vectors and