msss_pred<-function(locations,results,design_mat=NULL,model_used=1:100,type="standard")
{
nu=results$params$nu
niu=results$params$kernel_width
spatial_dimension=results$params$spatial_dimension
pred_val=matrix(0,length(model_used),dim(locations)[1])
for(modelno in model_used)
{
#from 1 model set up knots
#first set up data frame with resolution and location of each knot
#extract knots and the resolutions for modelno
knots_dataframe=data.frame(results$cpp_run$top_models_knots[,,modelno])
knots_dataframe=knots_dataframe[complete.cases(knots_dataframe),]
knots_dataframe=as.matrix(knots_dataframe)
knot_resolutions=results$cpp_run$top_model_knot_res[modelno,]
knot_resolutions=knot_resolutions[which(knot_resolutions!=0)]
knot_resolutions=(knot_resolutions)
#create column fn
r1_knot_mindist=min(dist(knots_dataframe[which(knot_resolutions==1),]))
maxdist=niu*r1_knot_mindist*((.5)^((0):99))
design_matrix=create_column(knots_dataframe[1,1:results$params$spatial_dimension,drop=F],knot_resolutions[1],locations,maxdist,nu)
for(i in 2:dim(knots_dataframe)[1])
{
design_matrix=cbind.spam(design_matrix,create_column(knots_dataframe[i,1:results$params$spatial_dimension,drop=F],knot_resolutions[i],locations,maxdist,nu))
}
if(type=="standard")
{
if(is.null(design_mat)==F)
{
design_matrix=cbind.spam(design_matrix,as.spam(design_mat))
}
muu=results$cpp_run$top_model_mus[modelno,]
muu=muu[1:(dim(design_matrix)[2])]
pred_val[modelno,]=as.matrix(design_matrix%*%muu)
}
if(type=="rescount")
{
useful=design_matrix[]>0
useful=useful*t(replicate(nrow(design_matrix),knot_resolutions))
pred_val[modelno,]=apply(useful,1,max)
}
}
logweights=results$cpp_run$top_models_log_likelihood[model_used]-max(results$cpp_run$top_models_log_likelihood[model_used])
weightss=exp(logweights)/sum(exp(logweights))
summary_pred=t(weightss)%*%pred_val
return(summary_pred)
}
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