similar to: How to solve a non-linear system of equations using R

Dear R users, I have a response variable in a csv file called "y" and a matrix of predictor variables in a csv file called "mat". I have used the function "nls" I have specified the nonlinear relation between these variable.The code I have witten is called Rprog which begins with the phrase: L.minor.m1<-nls(Y~a ....etc.. The program when I execute the program, I

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

Sorry, I can't reproduce the example below even on the same machine. However, the following example produces the same error as NULL values in prior examples: > setClass("FOOCLASS", +????????? representation("list") + ) > ma = new("FOOCLASS", list(M=matrix(rnorm(300), 30,10))) > isS4(ma) [1] TRUE > data.frame(a=1:3) == ma Error in

Hello, I want to solve a nonlinear 3SLS problem with "nlsystemfit()". The equations are of the form y_it = f_i(x,t,theta) The functions f_i(.) have to be formulated as R-functions. When invoking "nlsystemfit()" I get the error Error in deriv.formula(eqns[[i]], names(parmnames)) : Function 'f1' is not in the derivatives table

Dear all The following is a very basic and beginner's question on loops. Suppose you have data (say, 1000 cases) with two variables named "answer" (string) and "class" (numeric). The latter runs from 1 to 78 and categorizes the data. I need to create a file "answer_1.txt" for the cases with class==1, and so on, up to "answer_78.txt" I have been

The point estimates produced by wilcox.test are perverse (not wrong, just brain damaged). The Hodges-Lehmann estimator that goes with the signed rank test is the median of the Walsh averages. The Hodges-Lehmann estimator that goes with the rank sum test is the median of the pairwise differences. wilcox.test agrees except that it uses the following very peculiar definition of "sample

Hi, I would appreciate if someone could help me on track with this problem. I want to compute some parameters from a system of equations given a number of sample observations. The system looks like this: sum_i( A+b_i>0 & A+b_i>C+d_i) = x sum_i( C+d_i>0 & C+d_i>A+b_i) = y sum_i( exp(E+f_i) * ( A+b_i>0 & A+b_i>C+d_i) = z A, C, E are free variables while the other

I am experiencing some problems at working with lists at high level. In the following "coef" contains the original DWT coefficients organized in a list. Thorugh applying the following two commands: coef.abs <- lapply(unlist(coef,recursive=FALSE,use.names =TRUE),abs) coef.abs.sorted <- sort(unlist(coef.abs),decreasing=TRUE) I get vector "coef.abs.sorted" containing

Hi, I am solving a projection pursuit regression problem, of the form y = \sum_i f_i (a_i^T x), where a_i are unknown directions, while f_i are unknown univariate link functions. The following is known about each f_i: 1. f_i (0) = 0  (that is, each f_i passes through the origin) 2. f_i is monotonic. Is there a way to ensure that the function ppr() in R produces solutions that respect the

Hi All I am trying to use the round function on some columns of a dataframe while leaving others unchanged. I wish to specify those columns to leave unchanged. My attempt is below - here, I would like the column d3 to be left but columns d1, d2 and d4 to be rounded to 0 decimal places. I would welcome any suggestions for a nicer way of doing this. d1= rnorm(10,10) d2= rnorm(10,6) d3=

Hi, I am trying to aggregate the sum of my test data.frame as follow: testDF <- data.frame(v1 = c("a", "a", "a", "a", "a", "b", "b", "b", "b", "b", "c", "c", "c", "c", "c", "d", "d", "d", "d",

Dear Sir, Please excuse my akwardness as I a new to R and computers, but would kindly appreciate help. { a <- sample (1:10,100,replace=T ) b <-sample(10:20,100,replace=T) c <- sample(20:30,100,replace=T) d <- sample(30:40,100,replace=T) e <- sample(40:50,100,replace=T) } d1 <- a d2 <- b d3 <-c d4 <- d d5 <- e data.frame(d1,d2,d3,d4,d5) dd <-

Hi everybody, with the following code I generate a list of functions. Each function reflects a "condition". When I evaluate this list of functions by another lapply/sapply, I get an unexpected result: all values coincide. However, when I uncomment the print(), it works as expected. Is this a bug or a feature? conditions <- 1:4 test <- lapply(conditions, function(mycondition){

I am new to R and have a problem that I haven't been able to find the answer to in the guides or online. I have multiple datasets, D1, D2, D3, D4, D5, D6, D7 and D8, and I would like to produce two plots side by side using mfrow.  The first plot should contain data from D1-D4, the second should contain D5-D8. I can plot these separately using the code, par(mfrow=c(1,1))

Hi R help, Hi R help, Which is the easiest (most elegant) way to force "aov" to treat numerical variables as categorical ? Sincerely, Andrea Bernasconi DG PROBLEM EXAMPLE I consider the latin squares example described at page 157 of the book: Statistics for Experimenters: Design, Innovation, and Discovery by George E. P. Box, J. Stuart Hunter, William G. Hunter. This example use

Hello, I have a longitudinal dataset where each individual has a different number of entries. Thus, it is of the following structure: x <- runif(12) id.var <- factor(c(rep("D1",4),rep("D2",2),rep("D3",3),rep("D4",3))) dat <- as.data.frame(x) dat$id.var <- id.var dat > dat x id.var 1 0.9611269 D1 2 0.6738606 D1 3

May anyone help me solve the problem? I use samba 2.2.2 on Solaris 8 ngnvob02 [** NONE **]/export/home/sitlb $ cd /usr/local/samba/bin ngnvob02 [** NONE **]/usr/local/samba/bin $ ./smbpasswd Old SMB password: New SMB password: Retype new SMB password: machine 127.0.0.1 rejected the tconX on the IPC$ share. Error was : = ERRSRV - ERRbadpw. Failed to change password for sitlb But I can change the

Dear all, Given a LME model (following the notation of Pinheiro and Bates 2000) y_i = X_i*beta + Z_i*b_i + e_i, is it possible to extract the variance-covariance matrix for the estimated beta_i hat and b_i hat from the lme fitted object? The reason for needing this is because I want to have interval prediction on the predicted values (at level = 0:1). The "predict.lme" seems to

From: Daniel Kaschek <daniel.kaschek at physik.uni-freiburg.de> > ... When I evaluate this list of functions by > another lapply/sapply, I get an unexpected result: all values coincide. > However, when I uncomment the print(), it works as expected. Is this a > bug or a feature? > > conditions <- 1:4 > test <- lapply(conditions, function(mycondition){ >

Hi All, I'm new to LLVM and I'm having a coding problem. I'm creating a GlobalVariable that contains a StructType that contains a Function. The function returns i32 and takes two i32's. Here is my code: GlobalVariable* retrieved = module->getGlobalVariable("myGV"); ... Constant* result = ConstantExpr::getGetElementPtr(retrieved, indices); How do I get my Function