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...ch as Rmpi/snow/doParallel? Thank you! ##################### library(data.table) library(mgcv) library(reshape2) library(dplyr) library(tidyr) library(lubridate) library(DataCombine) # gam_max_count_wk <- gam(count_pop ~ factor(citycode) + factor(year) + factor(week) + s(lnincome) + s(tmax) + s(hmax),data=cont,na.action="na.omit", method="ML") # # Historic temp_hist <- read.csv("/work/sd00815/giss_historic/giss_temp_hist.csv") humid_hist <- read.csv("/work/sd00815/giss_historic/giss_hum_hist.csv") # temp_hist <- as.data.table(temp_hist) humid_h...

I'm solving the differential equation dy/dx = xy-1 with y(0) = sqrt(pi/2). This can be used in computing the tail of the normal distribution. (The actual solution is y(x) = exp(x^2/2) * Integral_x_inf {exp(-t^2/2) dt} = Integral_0_inf {exp (-xt - t^2/2) dt}. For large x, y ~ 1/x, starting around x~2.) I'm testing both lsoda and rk4 from the package odesolve. rk4 is accurate using step

..., are there any problem with the codes that i used? What does these warnings mean? Q2: As we know, the span is an important role for smoothing, so i try to choose the optimal span by the AIC criterion. But it seemed that i can't run it successfully, My codes are listed below. hmin<-0.05; hmax<-0.95; hstart<-hmin; nh<-100 hstep<-(hmax-hmin)/(nh-1) aic.h<- matrix(NA,nrow=nh,ncol=2) for (i in 1:nh) { htry<-hstart+(i-1)*hstep m.gam <-gam(mark~lo(x,y,span=htry)+lo(lstday2004,span=htry)+lo(slope,span=htry)+lo(ndvi2004,span=htry)+lo(elevation,span=htry)+disbinary,fam...

Hello, I wanted to test the odesolve package and tried to use compiled C-code. But when I do: erg <- lsoda(y, times, "mond", parms, rtol, atol, tcrit=NULL, jacfunc=NULL, verbose=FALSE, dllname="mond", hmin=0, hmax=Inf) I get the error message: Error in lsoda(y, times, "mond", parms, rtol, atol, tcrit = NULL, jacfunc = NULL, : Unable to find mond in mond The C code is: #include <math.h> void mond(long int *neq, double *t, double *y, double *ydot) { doubl...

Hi - I am getting different results when I run the numerical integrator function lsoda (odesolve package) on a Mac and a PC. I am trying to simulating a system of 10 ODE's with two exogenous pulsed inputs to the system, and have had reasonably good success with many model parameter sets. Under some parameter sets, however, the simulations fail on the Mac (see error message below). The

...x* is for the quantities for one sample (y* is the response predicted from the model derived; i used one to test my R traduction compared to the R pls module ) Calculate the covariance matrix C=XXT and c*=Xx* for prediction y is centered and become y1 y*1=0 For h =1,2,3...hmax s=Cyh center s working scalar for prediction sample s*=c*Tyh orthogonalize s to previous t: for g=1,...(h-1), s=s-(tgTs/tgTtg)tg orthogonalize s* to previous t*: for g=1,...(h-1), s*=s*-(tgTs/tgTtg)t*g t*h=s* th=s th2=tTt...

...* y[2]*y[3] > if(y[3] < .4) yd3 <- -y[3] ### These if,else statements are new > else yd3 <- p["k3"] * y[2]^2 > list(c(yd1,-yd1-yd3,yd3),c(massbalance=sum(y))) > } > out <- lsoda(c(.5,0,.5),times,lsexamp, parms, rtol=1e-4, atol= my.atol, > hmax=.1) > matplot(out[,1],out[,2:5], type="l") > out[dim(out)[1],] # The intent of my could was to cause population 1 to > fall to zero as soon as it reached < 0.4. However, the populations 1 > and 2 reach approximations of 0 (4e-281 and 5e-11). > So, I have two questions:...

Hello, I am using odesolve to simulate a group of people moving through time and transmitting infections to one another. In Matlab, there is a NonNegative option which tells the Matlab solver to keep the vector elements of the ODE solution non-negative at all times. What is the right way to do this in R? Thanks, Jeremy P.S., Below is a simplified version of the code I use to try to do this,