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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, 1,0,0,0,...

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 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 -26.210 0.702 26.261 111.511 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 48.62944 0.27999 173.684 < 2e-16 *** x_1 -0.67831 0.08053 -8.423 < 2e-16 *** x_2...

...sers, I?m a graduate students and in my master thesis I must obtain the values of the parameters x_i which maximize this Multinomial log?likelihood function log(n!)-sum_{i=1]^4 log(n_i!)+sum_ {i=1}^4 n_i log(x_i) under the following constraints: a) sum_i x_i=1, x_i>=0, b) x_1<=x_2+x_3+x_4 c)x_2<=x_3+x_4 I have been using the ?ConstrOptim? R-function with the instructions I report below, and I have tried to implement them with different values of ?n?. BUT I have encountered 2 problems: 1) the result of the maximization is the same of the minimization, i.e. the maximum value o...

...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 = 0, X_2 = -c/2, X_3 = c, X_4 = 0 and solutions of the above system are: X_1 = 2/3, X_2 = -1/3-c/2, X_3 = c, X_4 = 0. So the Kernel is 1-D spanned by X_2 = -X_3 /2, (nulliity=1), rank is 3. In R I use solve(): > solve(a,b) Error in solve.default(a, b) : Lapack routine dgesv: system is exactly singular and it gives th...

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 message: Error in cor(nnotes) : 'x' must be numeric. I eliminated the first column of names,...

...lize 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 evaluate which X has the greatest predictive power. I'm fairly certain that R squared is not applicable because of the lack of an intercept but I was wondering what was ? Any references to this particular pr...

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 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

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