rstan

问题 Error in compileCode(f, code, language = language, verbose = verbose) : Compilation ERROR, function(s)/method(s) not created! Error in .shlib_internal(commandArgs(TRUE)) : C++14 standard requested but CXX14 is not defined Calls: <Anonymous> -> .shlib_internal Execution halted In addition: Warning message: In system(cmd, intern = !verbose) : running command 'C:/PROGRA~1/R/R-36~1.0/bin/x64/R CMD SHLIB file1a1860a0379.cpp 2> file1a1860a0379.cpp.err.txt' had status 1 Error in sink(type = "output"

问题 Error in compileCode(f, code, language = language, verbose = verbose) : Compilation ERROR, function(s)/method(s) not created! Error in .shlib_internal(commandArgs(TRUE)) : C++14 standard requested but CXX14 is not defined Calls: <Anonymous> -> .shlib_internal Execution halted In addition: Warning message: In system(cmd, intern = !verbose) : running command 'C:/PROGRA~1/R/R-36~1.0/bin/x64/R CMD SHLIB file1a1860a0379.cpp 2> file1a1860a0379.cpp.err.txt' had status 1 Error in sink(type = "output"

问题 I have a simple .stan program for multilevel model that was running fine in Windows. But I was getting this strange error in Linux when I was running it. 'prep_call_sampler not found' 回答1: I tried to find the source of the problem, updated packages and looked up online for solutions. Finally, I saw a blog with a post describing a similar problem in the STAN forum The solution was to simply replace the a*b with a * b in the .stan program and the model works fine in linux. Just thought of

问题 I want to graph the histograms of parameter estimates from a stan model against the priors for those parameters. I have tried doing this by running a model in stan, graphing it with ggplot2, then overlaying an approximation of the prior distribution using R's random generator function (e.g. rnorm() , rbinom() ) but I have run into many scaling issues that make the graphs impossible to get looking right. I was thinking a better way to do it would be simply to sample directly from the prior

问题 I am trying to get the marginal effects, according to this post: http://andrewgelman.com/2016/01/14/rstanarm-and-more/ td <- readRDS("some data") CHAINS <- 1 CORES <- 1 SEED <- 42 ITERATIONS <- 2000 MAX_TREEDEPTH <- 9 md <- td[,.(y,x1,x2)] # selection the columns i need. y is binary glm1 <- stan_glm(y~x1+x2, data = md, family = binomial(link="logit"), prior = NULL, prior_intercept = NULL, chains = CHAINS, cores = CORES, seed = SEED, iter = ITERATIONS, control=list(max_treedepth=MAX_TREEDEPTH)

问题 Walter Zucchini in his book Hidden Markov Models for Time Series An Introduction Using R, in chapter 8 page 129, adjusts a Poisson HMM using R2OpenBUGS, then I show the code. I am interested in adjusting this same model but with rstan, but since I am new using this package, I am not clear about the syntax any suggestion. data dat <- read.table("http://www.hmms-for-time-series.de/second/data/earthquakes.txt") RJAGS library(R2jags) library(rjags) HMM <- function(){ for(i in 1:m){ delta[i] <- 1

问题 I've been using prophet in R on windows for several months now and have recently started seeing random RStudio crashes. Prior to a few days ago, I was having no issues. I know that's not much to go off of, but I'm hoping someone can recognize what might be going on with my system. I've noticed that it crashes more frequently when n.changepoints is higher than the default of 25. I see the crashes on both R 3.4 and 3.5. I have Rtools installed properly and can compile cpp code inline as

问题 Given the sample data sampleDT and the brms models brm.fit and brm.fit.distr below, I would like to: estimate, extract and add to the data frame the values of the standard deviations for each observation from the distributional model brm.fit.distr . I can do this using brm.fit , but my approach fails when I use brm.fit.distr . Sample data sampleDT<-structure(list(id = 1:10, N = c(10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L), A = c(62L, 96L, 17L, 41L, 212L, 143L, 143L, 143L, 73L, 73L), B

问题 The following model is model 1 of Preece and Baines (1978, Annals of Human Biology ), and is used to describe human growth. My Stan code for this model is as follows: ```{stan output.var="test"} data { int<lower=1> n; ordered[n] t; // age ordered[n] y; // height of human } parameters { positive_ordered[2] h; real<lower=0, upper=t[n-1]>theta; positive_ordered[2] s; real<lower=0>sigma; } model { h[1] ~ uniform(0, y[n]); h[2] ~ normal(180, 20); sigma ~ student_t(2, 0, 1); s[1] ~ normal(5, 5); s

问题 If my observed dataset has weights (for example tracking multiplicity) is it possible to provide this either to pystan or pymc3, similar to the function signature (http://mc-stan.org/rstanarm/reference/stan_glm.html) in the rstanarm package: stan_glm(formula, family = gaussian(), data, weights, subset, na.action = NULL, offset = NULL, model = TRUE, x = FALSE, y = TRUE, contrasts = NULL, ..., prior = normal(), prior_intercept = normal(), prior_aux = exponential(), prior_PD = FALSE, algorithm =