Posterior simulation from a GAM object
This method can be used to simulate vectors of responses from the Gaussian posterior approximation of a gamObject.
postSim( o, nsim, newdata, trans = NULL, method = "auto", w = NULL, offset = NULL, savePar = FALSE, ... )
o |
the output of a |
nsim |
the number of simulated vectors of responses. A positive integer. |
newdata |
Optional new data frame used to perform the simulations. To be passed to predict.gam. |
trans |
function used to transform or summarize each vector of simulated responses.
It must take a vector as argument, but it can output a vector or a scalar.
Potentially useful for saving storage (e.g. by transforming each simulated vector
to a scalar). If left to |
method |
the method used for the simulation of responses. See simulate.gam. |
w |
vector of prior weights of each response. See simulate.gam. |
offset |
numeric vector of offsets. For GAMs with multiple linear predictor (see eg gaulss) it
must be a list of vectors. If |
savePar |
if |
... |
arguments to be passed to vcov.gam. |
If savePar == FALSE the function will return a matrix where each row is a vector of
simulated responses or a transformed version of it. If savePar == TRUE it will return
a list where the $simY entry will contain the simulated responses and $simBeta
the simulated parameters.
library(mgcViz)
library(MASS)
b <- gam(accel~s(times, k=20), data=mcycle)
# Simulate list of 10 vectors of responses from posterior, taking into
# account smoothing parameters uncertainty (see ?vcov.gam)
n <- 10
sim <- postSim(o = b, nsim = n, unconditional = TRUE)
# Posterior simulations in grey and data in red
plot(rep(mcycle$times, n), as.vector(t(sim)), col = "grey",
ylab = "Acceleration", xlab = "Times")
points(mcycle$times, mcycle$accel, col = 2)
# There is clear disagreement between simulations' and data's
# conditional variance, which can be solved using flexible GAMLSS model:
b <- gam(list(accel~s(times, k=20), ~s(times)), data=mcycle, family = gaulss)
sim <- postSim(o = b, nsim = n)
plot(rep(mcycle$times, n), as.vector(t(sim)), col = "grey",
ylab = "Acceleration", xlab = "Times")
points(mcycle$times, mcycle$accel, col = 2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.