predict: Prediction and simulation method for 'mobility.model' class
In COVID-19-Mobility-Data-Network/mobility: Tools for Analyzing Human Mobility Data

Description Usage Arguments Details Value Author(s) See Also Examples

This function uses a fitted mobility.model object to simulate a connectivity matrix based on estimated parameters.

1	predict(object, newdata, nsim, seed, ...)

`object`	a `mobility.model` object containing a fitted mobility model and its associated data
`newdata`	a list containing new data to used in model prediction. If `NULL` (the default) the function will simulate the model with the data used for fitting. `D` a named matrix of distances between all ij location pairs `N` a named vector of population sizes for all locations (either N or both N_orig and N_dest must be supplied) `N_orig` a named vector of population sizes for each origin `N_dest` a named vector of population sizes for each destination
`nsim`	number of simulations (default = 1).
`seed`	optional integer specifying the call to `set.seed` prior to model simulation (default = `NULL`)
`...`	further arguments passed to or from other methods

When nsim = 1, the prediction matrix is calculated using the mean point estimate of parameter values. If nsim > 1 then returns and array that contains nsim number of simulated replications based on the posterior distributions of each parameter.

a vector, matrix, or array containing predited or simulated mobility values.

John Giles

Other model: check(), compare(), fit_jags(), fit_prob_travel(), mobility(), residuals(), summary()

mod <- mobility(data=mobility_matrices,
                model='gravity',
                type='transport')

mod <- mobility(data=mobility_matrices,
                model='departure-diffusion',
                type='power',
                hierarchical=TRUE)

predict(object=mod)

n <- 5
ids <- letters[1:n]

# Distance matrix
D <- get_distance_matrix(x=rnorm(n, -100, 2),
                         y=rnorm(n, 20, 1),
                         id=ids)*111.35

# Vector of population sizes
N <- rnbinom(n, size=5, mu=5000)
names(N) <- ids

# Predict mobility model using new data
predict(object=mod, newdata=list(D=D, N=N))