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...al. Using the B Spline function from the 'splines' package, it is possible to fit a model of some property with depth based on the bs() function: #natual, B-Splines library(splines) #fit a b-spline model: fm <- lm(y ~ bs(x, df=5) ) I am able to predict a soil property with depth, at unsampled locations with this model with: new_x <- seq(range(x)[1], range(x)[2], len = 200) #predict attribute at unsampled depths: new_y <- predict(fm, data.frame(x=new_x) ) #plot the predicted attribute at the unsampled depths lines(new_x, new_y, col='red') This tends to work fairly w...

...ger version. And I must confess that although my R skills are improving, they are not so highly developed. I am using 10 fold cross validation with 3 repeats in the train function of the caret() package to identify an optimal nnet (neural network) model to predict daily river water temperature at unsampled sites. I am also withholding data from 10% of sites to have a better understanding of generalization error.?However, the focus on predictions at other sites is turning out to be not easily facilitated ? as far as I can see. ?My data structure (example at bottom of email) consists of columns ident...

Dear Helpers   I have a sample frame and i have sampled from it using three methods and now i want to calculate the statistics but i only get the population parameters.   H <- matrix(rnorm(100, mean=50000, sd=5000)) sampleframe=data.frame(type=c(rep("H",100)),value=c(H)) sampleframe   str=strata(sampleframe,c("type"),size=c(20,), method="srswor")

Dear all, We are analysing some survey data and we are not sure if we are using the correct syntax for our design. The population of interest is a set of 4416 polygons with different sizes ranging from 0.003 to 45.6 ha, 7460 ha in total. Each polygon has a binary attribute (presence/absence) and we want to estimate the probability of presence in the population. We used sampling with replacement