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- man page for dlkjcorr - added rlkjcorr function (Ben's code) - ensemble optionally ignores link or sim - map() now catches another corner case in linear model definitions - map2stan plot() method for traceplots now has window and n_cols arguments - added divergent(), which counts number of divergent iteration in map2stan fit. Automatically warns user. - map2stan better handles missing values in outcome variables. Working towards automatic prediction interface through this method. - some file rearrangement for sanity - precis() checks and warns about divergent iterations - minor updates to README
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Richard McElreath
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Apr 3, 2015
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| Package: rethinking | ||
| Type: Package | ||
| Title: Statistical Rethinking book package | ||
| Version: 1.52 | ||
| Date: 2015-03-24 | ||
| Version: 1.53 | ||
| Date: 2015-04-03 | ||
| Author: Richard McElreath | ||
| Maintainer: Richard McElreath <[email protected]> | ||
| Imports: coda, MASS, mvtnorm | ||
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| # extracts n_divergent from stan fit | ||
| divergent <- function( fit , warmup=FALSE ) { | ||
| if ( class(fit)=="map2stan" ) fit <- fit@stanfit | ||
| x <- rstan::get_sampler_params(fit) | ||
| if ( warmup==FALSE ) { | ||
| nwarmup <- fit@stan_args[[1]]$warmup | ||
| niter <- fit@stan_args[[1]]$iter | ||
| n <- sapply( x , function(ch) sum(ch[(nwarmup+1):niter,5]) ) | ||
| } else { | ||
| n <- sapply( x , function(ch) sum(ch[,5]) ) | ||
| } | ||
| sum(n) | ||
| } |
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| # graphical posterior validation checks for map and map2stan | ||
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| postcheck <- function( fit , prob=0.89 , window=20 , n=1000 , col=rangi2 , ... ) { | ||
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| undot <- function( astring ) { | ||
| astring <- gsub( "." , "_" , astring , fixed=TRUE ) | ||
| astring | ||
| } | ||
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| pred <- link(fit,n=n) | ||
| sims <- sim(fit,n=n) | ||
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| if ( class(pred)=="list" ) | ||
| if ( length(pred)>1 ) pred <- pred[[1]] | ||
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| # get outcome variable | ||
| lik <- flist_untag(fit@formula)[[1]] | ||
| dname <- as.character(lik[[3]][[1]]) | ||
| outcome <- as.character(lik[[2]]) | ||
| if ( class(fit)=="map2stan" ) outcome <- undot(outcome) | ||
| y <- fit@data[[ outcome ]] | ||
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| # compute posterior predictions for each case | ||
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| mu <- apply( pred , 2 , mean ) | ||
| mu.PI <- apply( pred , 2 , PI , prob=prob ) | ||
| y.PI <- apply( sims , 2 , PI , prob=prob ) | ||
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| # figure out paging | ||
| if ( length(window)==1 ) { | ||
| window <- min(window,length(mu)) | ||
| num_pages <- ceiling( length(mu)/window ) | ||
| cases_per_page <- window | ||
| } | ||
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| # display | ||
| ny <- length(fit@data[[ outcome ]]) | ||
| ymin <- min(c(as.numeric(y.PI),mu,y)) | ||
| ymax <- max(c(as.numeric(y.PI),mu,y)) | ||
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| # check for aggregated binomial context | ||
| mumax <- max(c(as.numeric(mu.PI))) | ||
| if ( ymax > 1 & mumax <= 1 & dname %in% c("dbinom","dbetabinom") ) { | ||
| # probably aggregated binomial | ||
| size_var <- as.character(lik[[3]][[2]]) | ||
| size_var <- fit@data[[ size_var ]] | ||
| for ( i in 1:ny ) { | ||
| y.PI[,i] <- y.PI[,i]/size_var[i] | ||
| y[i] <- y[i]/size_var[i] | ||
| } | ||
| ymin <- 0 | ||
| ymax <- 1 | ||
| } | ||
| if ( dname=="dordlogit" ) { | ||
| # put mu and mu.PI on outcome scale | ||
| ymin <- 1 | ||
| nlevels <- dim(pred)[3] | ||
| mu <- sapply( 1:dim(pred)[2] , | ||
| function(i) { | ||
| temp <- t(pred[,i,])*1:7 | ||
| mean(apply(temp,2,sum)) | ||
| } ) | ||
| mu.PI <- sapply( 1:dim(pred)[2] , | ||
| function(i) { | ||
| temp <- t(pred[,i,])*1:7 | ||
| PI(apply(temp,2,sum),prob) | ||
| } ) | ||
| } | ||
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| start <- 1 | ||
| end <- cases_per_page | ||
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| for ( i in 1:num_pages ) { | ||
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| end <- min( ny , end ) | ||
| window <- start:end | ||
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| set_nice_margins() | ||
| plot( y[window] , xlab="case" , ylab=outcome , col=col , pch=16 , ylim=c( ymin , ymax ) , xaxt="n" , ... ) | ||
| axis( 1 , at=1:length(window) , labels=window ) | ||
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| points( 1:length(window) , mu[window] ) | ||
| for ( x in 1:length(window) ) { | ||
| lines( c(x,x) , mu.PI[,window[x]] ) | ||
| points( c(x,x) , y.PI[,window[x]] , cex=0.7 , pch=3 ) | ||
| } | ||
| mtext( paste("Posterior validation check") ) | ||
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| if ( num_pages > 1 ) { | ||
| ask_old <- devAskNewPage(ask = TRUE) | ||
| on.exit(devAskNewPage(ask = ask_old), add = TRUE) | ||
| } | ||
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| start <- end + 1 | ||
| end <- end + cases_per_page | ||
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| } | ||
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| # invisible result | ||
| result <- list( | ||
| mean=mu, | ||
| PI=mu.PI, | ||
| outPI=y.PI | ||
| ) | ||
| invisible( result ) | ||
| } | ||
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