RES_variable <- 512
# pdf(file="smoothing_combos.pdf", width=8, height=5.5, family="Palatino")
#choose combinations of the polynomial degree (m) and lambda
poly <-c(2, 3, 4, 5)
lambda <- c(1e-2,1e-3,1e-4,1e-5)
# Generate error vectors for each combination
# Save each plot to a PDF in the working directory
ERR_dat<-polynomial_vs_lambda(Ntae_381, spatial_res= RES_variable, polynomial_m_vec=poly, lambda_vec=lambda)
sampling_location_number = length(Ntae_381$fit_data1$y)
ERR_dat <- ERR_dat[-1] #take off the NA placeholder column
X_median <- retina:::reorder_columns(ERR_dat, median)
X_min <- retina:::reorder_columns(ERR_dat, min)
X_max <- retina:::reorder_columns(ERR_dat, max)
X_mean <- retina:::reorder_columns(ERR_dat, mean)
X_sd <- retina:::reorder_columns(ERR_dat, sd)
X_range_len <- retina:::reorder_columns(ERR_dat, retina:::range_len)
the_types <- c( 'X_median',
'X_min',
'X_max',
'X_mean',
'X_sd',
'X_range_len')
for (e in the_types) {
err_obj <- eval(parse(text=e))
boxplot(err_obj, horizontal=TRUE, las=1, col='lightgray', cex=.2, pch=20, sub=paste("n = ", sampling_location_number), cex.axis=0.4, main=paste0("Ordered by ", e))
}
boxplot(X_sd, horizontal=TRUE, las=1, col='lightgray', asp=1, cex=.2, pch=20, sub=paste("n = ", sampling_location_number), cex.axis=0.4, main=paste0("Ordered by sd"))
barplot(apply(X_sd, 2 ,sd), horiz=TRUE, las=1)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.