Simulating responses from a GAM object
This method can be used to simulate vectors of responses from a gamObject.
## S3 method for class 'gam' simulate( object, nsim = 1, seed = NULL, method = "auto", newdata, u = NULL, w = NULL, offset = NULL, trans = NULL, ... )
object |
the output of a |
nsim |
the number of simulated vectors of responses. A positive integer. |
seed |
currently not used. |
method |
the method used for the simulation. If set to "rd" then |
newdata |
Optional new data frame or list to be passed to predict.gam. |
u |
a matrix where each row is a vector of uniform random variables in (0, 1).
This will be used to simulate responses only if |
w |
vector of prior weights to be used in the simulations. If |
offset |
numeric vector of offsets. For GAMs with multiple linear predictor (see eg gaulss) it
must be a list of vectors. NB: if |
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 |
... |
currently not used. |
A matrix where each row is a vector of simulated responses. The number of columns is equal to the number of responses in the fitted object.
library(mgcViz) set.seed(2) ## simulate some data... dat <- gamSim(1,n=400,dist="normal",scale=2) b <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat) # Simulate three vectors of responses matplot(t(simulate(b, nsim = 3)), pch = 19, col = c(1, 3, 4))
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