Q.iid {ar.matrix} | R Documentation |
Functions for creating precision matricies and observations of a independent identically distributed GMRF process.
Q.iid(M, sigma, sparse=FALSE, vcov=FALSE) r.iid(n, M, sigma)
M |
int > 0, number of elements in the process. |
sigma |
float > 0, standard deviat |
sparse |
bool Should the matrix be of class 'dsCMatrix' |
vcov |
bool If the vcov matrix should be returned instead of the precision matrix. |
n |
int > 0, number of observations to simulate from the GMRF. |
Q.iid returns either a precision or variance-covariance function with iid structure.
r.iid retrurns a matrix with n rows which are the n observations of a Gaussian Markov random field iid process.
require("leaflet") require("sp") # simulate iid data and attach to spatial polygons data frame US.df@data$data <- c(r.iid(1, M=nrow(US.graph), sigma=1)) # color palette of data pal <- colorNumeric(palette="YlGnBu", domain=US.df@data$data) # see map map1<-leaflet() %>% addProviderTiles("CartoDB.Positron") %>% addPolygons(data=US.df, fillColor=~pal(data), color="#b2aeae", fillOpacity=0.7, weight=0.3, smoothFactor=0.2) %>% addLegend("bottomright", pal=pal, values=US.df$data, title="", opacity=1) map1