summary.bsts {bsts}  R Documentation 
Print a summary of a bsts
object.
## S3 method for class 'bsts' summary(object, burn = SuggestBurn(.1, object), ...)
object 
An object of class 
burn 
The number of MCMC iterations to discard as burnin. 
... 
Additional arguments passed to

Returns a list with the following elements.
residual.sd 
The posterior mean of the residual standard deviation parameter. 
prediction.sd 
The standard deviation of the onestepahead prediction errors for the training data. 
rsquare 
Proportion by which the residual variance is less than the variance of the original observations. 
relative.gof 
Harvey's goodness of fit statistic. Let nu denote the one step ahead prediction errors, n denote the length of the series, and y denote the original series. The goodness of fit statistic is 1  sum(nu^2) / (n2) * var(diff(y)). This statistic is analogous to rsquare in a regression model, but the reduction in sum of squared errors is relative to a random walk with a constant drift, y[t+1] = y[t] + beta + epsilon[t], which Harvey (1989, equation 5.5.14) argues is a more relevant baseline than a simple mean. Unlike a traditional Rsquare statistic, this can be negative. 
size 
Distribution of the number of nonzero coefficients appearing in the model 
coefficients 
If

Harvey's goodness of fit statistic is from Harvey (1989) Forecasting, structural time series models, and the Kalman filter. Page 268.
bsts
, plot.bsts
, summary.lm.spike
data(AirPassengers) y < log(AirPassengers) ss < AddLocalLinearTrend(list(), y) ss < AddSeasonal(ss, y, nseasons = 12) model < bsts(y, state.specification = ss, niter = 100) summary(model, burn = 20)