Description Usage Arguments Value Author(s) References See Also Examples
View source: R/createSparseGrid.R
Creates nodes and weights that can be used for sparse grid integration. Based on Matlab code by Florian Heiss and Viktor Winschel, available from http://www.sparsegrids.de
1  createSparseGrid(type, dimension, k, sym = FALSE)

type 
String or function for type of 1D integration rule, can take on values

dimension 
dimension of the integration problem. 
k 
Accuracy level. The rule will be exact for polynomial up to total order 2k1. 
sym 
(optional) only used for own 1D quadrature rule (type not "KPU",...). If sym is supplied and not FALSE, the code will run faster but will produce incorrect results if 1D quadrature rule is asymmetric. 
The return value contains a list with nodes and weights
nodes 
matrix with a node in each row 
weights 
vector with corresponding weights 
Jelmer Ypma
Florian Heiss, Viktor Winschel, Likelihood approximation by numerical integration on sparse grids, Journal of Econometrics, Volume 144, Issue 1, May 2008, Pages 6280, http://www.sparsegrids.de
createProductRuleGrid
createMonteCarloGrid
createIntegrationGrid
integrate
pmvnorm
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56  # load library
library('SparseGrid')
# define function to be integrated
# g(x) = x[1] * x[2] * ... * x[n]
g < function( x ) {
return( prod( x ) )
}
#
# Create sparse integration grid to approximate integral of a function with uniform weights
#
sp.grid < createSparseGrid( 'KPU', dimension=3, k=5 )
# number of nodes and weights
length( sp.grid$weights )
# evaluate function g in nodes
gx.sp < apply( sp.grid$nodes, 1, g )
# take weighted sum to get approximation for the integral
val.sp < gx.sp %*% sp.grid$weights
#
# Create integration grid to approximate integral of a function with uniform weights
#
pr.grid < createProductRuleGrid( 'KPU', dimension=3, k=5 )
# number of nodes and weights
length( pr.grid$weights )
# evaluate function g in nodes
gx.pr < apply( pr.grid$nodes, 1, g )
# take weighted sum to get approximation for the integral
val.pr < gx.pr %*% pr.grid$weights
#
# Create integration grid to approximation integral using Monte Carlo simulation
#
set.seed( 3141 )
mc.grid < createMonteCarloGrid( runif, dimension=3, num.sim=1000 )
# number of nodes and weights
length( mc.grid$weights )
# evaluate function g in MC nodes
gx.mc < apply( mc.grid$nodes, 1, g )
# take weighted sum to get approximation for the integral
# the weights are all equal to 1/1000 in this case
val.mc < gx.mc %*% mc.grid$weights
val.sp
val.pr
val.mc

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