similar to: Multi-line input to rsympy

Hi all, I'm having major difficulty using the rSymPy package this week, but I have used it without problems before now. Here is an example of what's happening: > library(rSymPy) Loading required package: rJava > sympy("var('a b c d')") Error in .jcheck() : No running detected. Maybe .jinit() would help. And I have no idea how to fix it. I Have tried re-installing

I am experimenting with rSymPy, and it seems to work nice. However, I dislike the need to wrap all sympy expressions within quotes, it leads to ugly calls like library(rSymPy) Var("x,y,z") sympy("(x+y)**2") and so on. Inspired by the function cq from mvbutiles package: library(mvbutils) > cq function (...) { as.character(sapply(as.list(match.call(expand.dots =

Dear R People: I have been experimenting with rPython, rSymPy, and rJython. Here is my latest snag: > library(rJython) Loading required package: rJava Loading required package: rjson > library(rSymPy) > rJython <- rJython() > x <- "x" > y <- "y" > rJython$exec("from sympy import *") Error in .jcall("RJavaTools",

2009/8/30 Uwe Ligges <ligges at statistik.tu-dortmund.de>: [snip] > Guido van Steen wrote: [snip] >> Something that interests me too: What about R's policy with respect to >> including binary files? I saw that developers should include a file [snip] > Please do not include binary files and carefully watch for licenses of those > files (e.g. if GPL'ed, you need to

Dear R Users: I would like to use symbolic integration from rSymPy and pass in the actual function. So I have > sympy("x=Symbol('x')") and if I use > sympy("integrate(cos(x),x)") it works fine. But what I want to do is something like > y <- "cos(x)" > sympy("integrate(y,x)") "cos(x)*x" Has anyone run into this, please?

I am working on a project that requires me to do very large factorial evaluations. On R the built in factorial function and the one I created both are not able to do factorials over 170. The first gives an error and mine return Inf. Is there a way to have R do these larger calculations (the calculator in accessories can do 10000 factorial and Maple can do even larger) -- View this message in

Hello everyone, I am trying to solve 2D differential equations using finite difference scheme in R. I have been able to work with the equations with only one spatial dimensions but I want to extend it to the two dimensional problem. For example i can simulate one dimensional diffusion using a code like the following. But I want to write a similar code for,say, a two dimensional diffusion

Hi all R users, I have problems with my second loop for drawing the three curves in the same graphic. I need help please Thank you in advance ######################################################################### simulation <- function(k, n){ conc <- seq(0,100,by=0.5) #choixg <- seq(1, length(conc)) choixg <- rep(0,length(conc)) for (i in 1:length(conc)){ choixg[i] <- (k

Hello all R users, My simulation function works correctly, but I have problems with plot function. You will find the following code using it. Thank you for your help ##################################################" simulation <- function(k, n){ conc <- seq(0,10,by=0.5) #choixg <- seq(1, length(conc)) choixg <- rep(0,length(conc)) for (i in 1:length(conc)){ choixg[i]

#how do I break these up into first two letters (RM), number, and then the last part #is there an easily accessible regex tutorial on the internet? v = (structure(1:122, .Label = c("RM215Temp", "RM215SpCond", "RM215DO.Conc", "RM215Depth", "RM215pH", "RM215ORP", "RM215Turbidity.", "RM215Battery", "RM215DO.",

Hi, I think you're misunderstanding which set of variables go on either side of the formula. Is this what you're looking for? > aggregate(OD ~ Time + Target + Conc, data = df, FUN = "mean") Time Target Conc OD 1 1 BACT 1 765.3333 2 1 BACT 2 745.3333 3 1 BACT 3 675.0000 > aggregate(ODnorm ~ Time + Target + Conc, data = df, FUN =

Hallo Everybody How do you specify arguments for a function used within another function? Here is my problem: I am reconstructing a calculator for the burden of disease due to air pollution from publications and tools published by the WHO. The calculations make use of published dose-response relationships for particular health end-points. This is then applied to populations with known or

Also, > aggregate(cbind(OD, ODnorm) ~ Time + Target + Conc, data = df, FUN = "mean") Time Target Conc OD ODnorm 1 1 BACT 1 765.3333 108.33333 2 1 BACT 2 745.3333 88.33333 3 1 BACT 3 675.0000 18.00000 (You might wish for "cbind(OD,ODnorm) ~ . - Well", but aggregate.formula is not smart enough for that.) -pd > On 24 Oct 2023, at

I have used Sweave a lot the latest year, but never really used any long function calls. If I have code which look like this ------------------------------------------------------------- gof <- benthic.flux(ID="Gulf of Finland", meas.conc=conc, bw.conc=bw.conc, time=times,

Hi, Is there a way to populate customized settings of the drivers (printing related TDB files) from one Print Server to another? Here is the background: I am working on centralized driver distribution. Besides pushing all the drivers from a central location to the edges, the administrator should also be allowed to customize the drivers settings and the distribution should send those

I am having trouble fitting correlation structures within nlme. I would like to fit corCAR1, corGaus and corExp correlation structures to my data. I either get the error "step halving reduced below minimum in pnls step" or alternatively R crashes. My dataset is similar to the CO2 example in the nlme package. The one major difference is that in my case the 'conc' steps are

Hi I have measured the UV absorbance (abs) of 10 solutions of a substance at known concentrations (conc) and have used a linear model to plot a calibration graph with confidence limits. I now want to predict the concentration of solutions with UV absorbance results given in the new.abs data.frame, however predict.lm only appears to work for new "conc" variables not new "abs"

my reproducible example test<-structure(list(site = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L), .Label = c("A", "B", "C", "D", "E"), class = "factor"),

Hi all, I have a large set of descriptors, which are stored as the vectors, each one containing about 450 elements. Now I have to perform some algebraical operations on this set to eliminate the redundant ones. The problem is, that not all vales in the vectors are known. Are there any norm defined how should I process such vectors? Simple example: having two vectors: a b 3 4 2

Dear R experts! I'd to fit data by 'nls' with me-supplied function 'fcn'. 1) I'd like 'fcn' to accept arbitrary arguments, i.e. I defined it as f(...) {<body>}. (Ok, that's not actually impotant). 2) Second, I would NOT like to supply every parameter in the formula. To illustrate this, let's look at the last example of 'nls' help