predict.mcmc_output  R Documentation 
Draw samples from the posterior predictive distribution for future
time points given the posterior draws of hyperparameters θ and
latent state alpha_{n+1} returned by run_mcmc
.
Function can also be used to draw samples from the posterior predictive
distribution p(\tilde y_1, …, \tilde y_n  y_1,…, y_n).
## S3 method for class 'mcmc_output' predict( object, model, nsim, type = "response", future = TRUE, seed = sample(.Machine$integer.max, size = 1), ... )
object 
Results object of class 
model 
A 
nsim 
Positive integer defining number of samples to draw. Should be
less than or equal to 
type 
Type of predictions. Possible choices are

future 
Default is 
seed 
Seed for the C++ RNG (positive integer). Note that this affects
only the C++ side, and 
... 
Ignored. 
A data.frame consisting of samples from the predictive posterior distribution.
fitted
for insample predictions.
library("graphics") y < log10(JohnsonJohnson) prior < uniform(0.01, 0, 1) model < bsm_lg(window(y, end = c(1974, 4)), sd_y = prior, sd_level = prior, sd_slope = prior, sd_seasonal = prior) mcmc_results < run_mcmc(model, iter = 5000) future_model < model future_model$y < ts(rep(NA, 25), start = tsp(model$y)[2] + 2 * deltat(model$y), frequency = frequency(model$y)) # use "state" for illustrative purposes, we could use type = "mean" directly pred < predict(mcmc_results, model = future_model, type = "state", nsim = 1000) library("dplyr") sumr_fit < as.data.frame(mcmc_results, variable = "states") %>% group_by(time, iter) %>% mutate(signal = value[variable == "level"] + value[variable == "seasonal_1"]) %>% group_by(time) %>% summarise(mean = mean(signal), lwr = quantile(signal, 0.025), upr = quantile(signal, 0.975)) sumr_pred < pred %>% group_by(time, sample) %>% mutate(signal = value[variable == "level"] + value[variable == "seasonal_1"]) %>% group_by(time) %>% summarise(mean = mean(signal), lwr = quantile(signal, 0.025), upr = quantile(signal, 0.975)) # If we used type = "mean", we could do # sumr_pred < pred %>% # group_by(time) %>% # summarise(mean = mean(value), # lwr = quantile(value, 0.025), # upr = quantile(value, 0.975)) library("ggplot2") rbind(sumr_fit, sumr_pred) %>% ggplot(aes(x = time, y = mean)) + geom_ribbon(aes(ymin = lwr, ymax = upr), fill = "#92f0a8", alpha = 0.25) + geom_line(colour = "#92f0a8") + theme_bw() + geom_point(data = data.frame( mean = log10(JohnsonJohnson), time = time(JohnsonJohnson))) # Posterior predictions for past observations: yrep < predict(mcmc_results, model = model, type = "response", future = FALSE, nsim = 1000) meanrep < predict(mcmc_results, model = model, type = "mean", future = FALSE, nsim = 1000) sumr_yrep < yrep %>% group_by(time) %>% summarise(earnings = mean(value), lwr = quantile(value, 0.025), upr = quantile(value, 0.975)) %>% mutate(interval = "Observations") sumr_meanrep < meanrep %>% group_by(time) %>% summarise(earnings = mean(value), lwr = quantile(value, 0.025), upr = quantile(value, 0.975)) %>% mutate(interval = "Mean") rbind(sumr_meanrep, sumr_yrep) %>% mutate(interval = factor(interval, levels = c("Observations", "Mean"))) %>% ggplot(aes(x = time, y = earnings)) + geom_ribbon(aes(ymin = lwr, ymax = upr, fill = interval), alpha = 0.75) + theme_bw() + geom_point(data = data.frame( earnings = model$y, time = time(model$y)))
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