search for: lars52r

Dear All, I couple of weeks ago, I’ve asked for a package recommendation for nonlinear optimization. In my problem I have a fairly complicated non-linear objective function subject to one non-linear equality constrain. I’ve been suggested to use the *Rdonlp2* package, but I did not get any results after running the program for 5 hrs. Is it normal to run this type of programs for hours? Also,

Dear experts, I'm attempting to solve a constrained optimization problem using the Rdonlp2 package. I created a Lagrange function (L=f(x)-lambda(g(x)-c)), where x is a vector of 16 parameters. This is what I'm using as objective function in the code below. In addition, I set bounds on these parameters (par.u and par.l). When I run the code, I get the error message shown below. Any idea

Hi, Maybe I'm missing the point here...but let's suppose you are working with "large" data sets and using functions that take a significant amount of time to run in R. I woulnd't like to run these functions every time I call Sweave("myfile.Rnw") within R. What is the "common" practice to use Sweave in these situations. I would just run the function once,

Hello, I'd like to produce a ggplot where the order of factors within facets is based on the average of another variable. Here's a reproducible example. My problem is that the factors are ordered similarly in both facets. I would like to have, within each facet of `f1', boxplots for 'x' within each factor `f2', where the boxplots are ordered based on the average of x

...X1:X4)%>% group_by(f1,f2,X_name)%>% do(describe(.$X_value))%>% select(mean)%>%# You select only mean value spread(X_name,mean)# Vincent Med venlig hilsen/ Best regards Edjabou Maklawe Essonanawe Vincent Mobile: +45 31 95 99 33 On Sat, Sep 9, 2017 at 12:30 PM, Lars Bishop <lars52r at gmail.com> wrote: > Dear group, > > Is there a way I could avoid the sort of duplication illustrated below? > i.e., I have the same dplyr::summarise function on different group_by > arguments. So I'd like to create a single summarise function that could be > applied to...

Dear List, Several times I use this package I get the error message shown below. When I work out simple examples, it turns out to be fine, but when working with real and moderate size data sets I always get the same error. Do you know what could be the cause of the problem? Error in apply(mod.matrix[, components], 1, prod) : subscript out of bounds Error in

Dear group, Is there a way I could avoid the sort of duplication illustrated below? i.e., I have the same dplyr::summarise function on different group_by arguments. So I'd like to create a single summarise function that could be applied to both. My attempt below fails. df <- data.frame(matrix(rnorm(40), 10, 4), f1 = gl(3, 10, labels = letters[1:3]), f2 =

...scribe(.$X_value))%>% > select(mean)%>%# You select only mean value > spread(X_name,mean)# > > Vincent > > Med venlig hilsen/ Best regards > > Edjabou Maklawe Essonanawe Vincent > Mobile: +45 31 95 99 33 > > On Sat, Sep 9, 2017 at 12:30 PM, Lars Bishop <lars52r at gmail.com> wrote: > > > Dear group, > > > > Is there a way I could avoid the sort of duplication illustrated below? > > i.e., I have the same dplyr::summarise function on different group_by > > arguments. So I'd like to create a single summarise function t...

Hi, I couldn't find a package to fit a penalized (lasso/ridge) Gamma regression model. Does anybody know any? Thanks in advance, Lars. [[alternative HTML version deleted]]

Dear R users, Let's say I have a list with components being 'm' matrices (as exemplified in the "mylist" object below). Now, I'd like to subset this list based on an index vector, which will partition each matrix 'm' in 2 sub-matrices. My questions are: 1. Is there an elegant way to have the results shown in mylist2 for an arbitrary number of matrices in mylist?

Dear All, I need to write 'n' functions on 'm' variables. The functions should be constructed according to the values of an (nxm) matrix of '1/0' values as follows. For example, if row1 is equal to ,say [1 0 .......0 0] then f1 <- (1+x1) if row 2 is equal to, say [1 1 1 0...0 1] then f2 <-(1+x1)*(1+x2)*(1+x3)*(1+xm) if row n is equal to [0 1 0 1 1 0 ..... 0] then

Hi, The "penalized" documentation says that "Unordered factors are turned into as many dummy variables as the factor has levels". This is done by a function in the package called contr.none. I'm trying to figure out how exactly is a model matrix created with this contrast option when the user calls the function with a formula. I typed "library(penalized) ;

Hello, Is it possible to execute functions (outside the ui and server shiny environments) after reading data using reactiveFileReader() ? For example, I'd like to fit a linear model on data read using reactiveFileReader() outside ui/server. library(shiny) library(dplyr) bigData <- reactiveFileReader(1000, NULL, 'data.csv', read.csv) fit <- lm(y ~., data = bigData()) ui

Dear users, The company where I work is considering getting a license for Revolution Enterprise - Windows 64-bit. I'll appreciate for those familiar with the product if can share your experiences with it? In particular, how does it compare to the "free" version of R 64-bit? Thanks in advance. Regards, Lars. [[alternative HTML version deleted]]

Let's say I have the data frame 'dd' below. I'd like to select one column from this data frame (say 'a') and keep its name in the resulting data frame. That can be done as in #2. However, what if I want to make my selection based on a vector of names (and again keep those names in the resulting data frame). My attempt is #4 but doesn't work. dd <- data.frame(a =

Dear List, I'd appreciate you guidance for obtaining the desired result shown below, by combining tapply(x, g, mean) and g in the example. Basically, I'm trying to create a vector whose values are based on the result from tapply(x, g, mean) but that follow the pattern and length given by the factor g. Of course I'm looking for a generic solution (i.e, not something that just work

Dear R users, I’m trying to understand how correlated predictors impact the Relative Importance measure in Stochastic Boosting Trees (J. Friedman). As Friedman described “ …with single decision trees (referring to Brieman’s CART algorithm), the relative importance measure is augmented by a strategy involving surrogate splits intended to uncover the masking of influential variables by others

Hi, I'm new in Latex and I'm trying to include an R chart into a Latex document. This is what I'm doing: 1) In R: save the chart as a a Postcript in a folder C:/xxx/Density.eps 2) In Latex (using TexWorks on windows xp) : In the preambule: \documentclass[11pt]{article} \usepackage{graphicx} \begin{document} blah..blah…blah \begin{figure} \centering

Hi, I need to extract the second largest element from each row of a matrix. Below is my solution, but I think there should be a more efficient way to accomplish the same, or not? set.seed(1) a <- matrix(rnorm(9), 3 ,3) sec.large <- as.vector(apply(a, 1, order, decreasing=T)[2,]) ans <- sapply(1:length(sec.large), function(i) a[i, sec.large[i]]) ans Thanks in advance for your

Dear R users, Is there any package for Latent Class Analysis (to be used in a clustering application) which supports mixed indicator variables (categorical and continuous)? Alternatively, is there any other clustering algorithm available that supports this type of data? Thanks in advance for your help. Regards, Lars. [[alternative HTML version deleted]]