Description Usage Arguments Value Author(s) See Also Examples
Computes the matrix products between a sparse matrix F
containing the temporal trends and a list of land-use-regression components.
See the examples for details.
1 | calc.FX(F, LUR, loc.ind)
|
F |
A (number of obs.) - by - (number of temporal trends) matrix
containing the temporal trends. Usually |
LUR |
A list of matrices, usually |
loc.ind |
A vector indicating which location each row in |
Returns a matrix
Johan Lindstrom and Adam Szpiro
Other block matrix functions: blockMult
,
calc.FXtF2
, calc.mu.B
,
calc.tFXF
, calc.tFX
,
makeCholBlock
, makeSigmaB
,
makeSigmaNu
Other temporal trend functions: calc.FXtF2
,
calc.tFXF
, calc.tFX
,
expandF
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 57 | ##This starts with a couple of simple examples, more elaborate examples
##with real data can be found further down.
require(Matrix)
##create a trend
trend <- cbind(1:5,sin(1:5))
##an index of locations
idx <- c(rep(1:3,3),1:2,2:3)
##a list of time points for each location/observation
T <- c(rep(1:3,each=3),4,4,5,5)
##create a list of matrices
X <- list(matrix(1,3,1), matrix(runif(6),3,2))
##expand the F matrix to match the locations/times in idx/T.
F <- trend[T,]
##compute F %*% X
FX <- calc.FX(F, X, idx)
##alternatievly this can be computed as block matrix
##times each (expanded) temporal trend
Fexp <- expandF(F, idx)
FX.alt <- Fexp %*% bdiag(X)
##compare results
##some examples using real data
data(mesa.model)
##Some information about the size(s) of the model.
dim <- loglikeSTdim(mesa.model)
##compute F %*% X
FX <- calc.FX(mesa.model$F, mesa.model$LUR, mesa.model$obs$idx)
##The resulting matrix is
##(number of time points) - by - (number of land use covariates)
##where the number of land use covariates are computed over all the
##two + intercept temporal trends.
##Each column contains the temporal trend for the observations
##multiplied by the corresponding LUR-covariate
par(mfrow=c(3,1))
plot(FX[,2])
points(mesa.model$LUR[[1]][mesa.model$obs$idx,2] * mesa.model$F[,1], col=2, pch=3)
plot(FX[,dim$p[1]+1])
points(mesa.model$LUR[[2]][mesa.model$obs$idx,1] *
mesa.model$F[,2], col=2, pch=3)
plot(FX[,dim$p[1]+dim$p[2]+2])
points(mesa.model$LUR[[3]][mesa.model$obs$idx,2] *
mesa.model$F[,3], col=2, pch=3)
##If the regression parameters, alpha, are known (or estimated)
##The intercept part of the model is given by FX %*% alpha
|
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