similar to: MAXIMIZATION WITH CONSTRAINTS

Dear R-users, i try to solve to following linear programm in R 0 * x_1 + 2/3 * x_2 + 1/3 * x_3 + 1/3 * x_4 = 0.3 x_1 + x_2 + x_3 + x_4 = 1 x_1, x_2, x_3, x_4 > 0, x_1, x_2, x_3, x_4 < 1 as you can see i have no objective function here besides that i use the following code. library(lpSolve) f.obj<-c(1,1,1,1) f.con<-matrix(c(0,2/3,1/3,1/3, 1,1,1,1,

I have a simple system of linear equations to solve for X, aX=b: > a [,1] [,2] [,3] [,4] [1,] 1 2 1 1 [2,] 3 0 0 4 [3,] 1 -4 -2 -2 [4,] 0 0 0 0 > b [,1] [1,] 0 [2,] 2 [3,] 2 [4,] 0 (This is ex Ch1, 2.2 of Artin, Algebra). So, 3 eqs in 4 unknowns. One can easily use row-reductions to find a homogeneous solution(b=0) of: X_1

Hello folks, I am on learning phase of R. I have developed Regression Model over six predictor variables. while development, i found my all data are not very linear. So, may because of this the prediction of my model is not exact. Here is the summary of model : Call: lm(formula = y ~ x_1 + x_2 + x_3 + x_4 + x_5 + x_6) Residuals: Min 1Q Median 3Q Max -125.302

Hi, I was wondering if I can have some advice on the following problem. Let's say that I have a problem in which I want to predict a binary outcome and I use logistic regression for that purpose. In addition, suppose that my model includes predictors that will not be used in scoring new observations but must be used during model training to absorb certain effects that could bias the

I realize that the following has been talked about on this list many times before in some related way but I am going to ask for help anyway because I still don't know what to do. Suppose I have no intercept models such as the following : Y = B*X_1 + error Y = B*X_2 + error Y = B*X_3 + error Y = B*X_4 + error and I run regressions on each ( over the same sample of Y ) and now I want to

Dear R users, I have a txt file named 'data_1.txt' whose first column contains the names of the individuals and the other columns contain the values of four variables X_1,X_2,X_3 and X_4. I read it with R from its location and called it data. I'd like to do a normalized principal component analysis. I started by calculating the correlation matrix: cor(data) I got the following

Dear all, I have a dataset where the interaction is more than obvious, but I was asked to give a p-value, so I ran a logistic regression using glm. Very funny, in the outcome the interaction term is NOT significant, although that's completely counterintuitive. There are 3 variables : spot (binary response), constr (gene construct) and vernalized (growth conditions). Only for the FLC construct

Hi All Apologies if this is not the correct list for this question. The Rglpk package offers the following example in its documentation library(Rglpk) ## Simple mixed integer linear program. ## maximize: 3 x_1 + 1 x_2 + 3 x_3 ## subject to: -1 x_1 + 2 x_2 + x_3 <= 4 ## 4 x_2 - 3 x_3 <= 2 ## x_1 - 3 x_2 + 2 x_3 <= 3 ## x_1, x_3 are non-negative integers ## x_2 is a non-negative real

Dear experts! I have a minimization problem with non-linear constraint and Objective function(theta)=lambda*(Constr)^2-f(x,theta). Theta is a vector of parameters. I'd like to stop the optimization after the value of the constraint is less or equal some constant value, say d, and save the last computed value of the function. For this purpose, I thought to define the Objective function like

Dear List: I cannot seem to find a way to plot my data correctly. I have a small data frame with 6 total variables (x_1 ... x_6). I am trying to plot x_1 against x_2 and x_3. I have tried plot(x_2, x_1) #obviously works fine plot(x_3, x_1, add=TRUE) # Does not work. I keep getting error messages. I would also like to add ablines to this plot. I have experimented with a number of other

# Hi all, # consider the following code (please, run it: # it's fully working and requires just few minutes # to finish): require(CreditMetrics) require(clusterGeneration) install.packages("Rdonlp2", repos= c("http://R-Forge.R-project.org", getOption("repos"))) install.packages("Rsolnp2", repos= c("http://R-Forge.R-project.org",

Dear all, Is there any method in R to find all possible nonnegative integer solutions to the linear equation with unit coefficients as follow: X1+X2+...+Xk=N Thank you, Amin Zollanvari

Dear all I want to compute Monte Carlo p-values in lmer-models based on sampled data sets. To speed up calculations, I've tried to use internal functions from the Matrix package (as suggested ealier on the list by Doug Bates). So I did: fm2 <- lmer(resistance ~ ET + position + (1|Grp), Semiconductor,method='ML') simdata<-simulate(fm2,nsim=1) ynew <- simdata[,1] mer

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Hi Daniel, Sorry for taking so long to respond. I spoke with a colleague more familiar with llvm who thought he could clear up my confusion, but we both came out of the conversation confused. I will try my best to explain the ambiguity. In an DAG, alias queries would be completely unambiguous. Every instruction would only be executed once, and every SSA value really would have a single static

Buenas tardes, Estoy interesado en generar observaciones de una distribucion binomial bivariada en la que hay _cierto_ grado de correlacion (denotemoslo rho). Podria por favor alguien indicarme como hacerlo en R? Este es el contexto. Supongamos que se tienen dos experimentos en los que la variable respuesta _sigue_ una distribucion binomial, i.e., X_i ~Binomial(n_i, p_i), i=1,2 y que, por ahora,

Hi Hal, Thank you for your email, that makes a lot of sense to me. I am working on some tools to use memory profiling to speculatively replace memory loads and stores with value forwarding in hardware implementations. I'd like to compare the profiled data to static alias analysis, so it would be super useful if there was a way to answer the questions about aliasing across backedges that

Dear All, Which package/function could i use to solve following linear least square problem? A over determined system of linear equations is given. The nnls-function may would be a possibility BUT: The solving is constrained with a inequality that all unknowns are >= 0 and a equality that the sum of all unknowns is 1 The influence of the equations according to the solving process is

Hello everyone, I am working with plm package and I have problem with random and within models, which are giving errors which says "empty model". However, the model is not empty. In the source code for plm.fit, where the error originates it says something like (writing from the top of my head...) X <- model.matrix(formula,data, lhs=1,...) if (ncol(X) == 0) stop("empty