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Last data update: 2014.03.03

R: Estimate PLC/FLC distributions for all states

estimate_LC_pdfs

R Documentation

Estimate PLC/FLC distributions for all states

Description

estimate_LC_pdfs estimates the PLC and FLC distributions for each state k = 1, …, K. It iteratively applies estimate_LC.pdf.state.

estimate_LC.pdf.state estimates the PLC and FLC distributions using weighted maximum likelihood (cov.wt) and nonparametric kernel density estimation (wKDE) for one (!) state.

Usage

estimate_LC_pdfs(LCs, weight.matrix = NULL, method = c("nonparametric", "normal", 
    "huge"), eval.LCs = NULL)

estimate_LC_pdf_state(state, states = NULL, weights = NULL, LCs = NULL, eval.LCs = NULL, 
    method = c("nonparametric", "normal", "huge"))

Arguments

`LCs`	matrix of PLCs/FLCs. This matrix has N rows and n_p or n_f columns (depending on the PLC/FLC dimensionality)
`weight.matrix`	N \times K weight matrix
`states`	vector of length N with entry i being the label k = 1, …, K of PLC i
`method`	type of estimation: either a (multivariate) Normal distribution (`"normal"`) or nonparametric with a kernel density estimator (`method = "nonparametric"`). For multivariate distributions (as usual for PLCs) only `'normal'` should be used due to computational efficiency and statistical accuracy.
`eval.LCs`	on what LCs should the estimate be evaluated? If `NULL` then densities will be evaluated on the training data `LCs`
`state`	integer; which state-conditional density should be estimated
`weights`	weights of the samples. Either a i) length N vector with the weights for each observation; ii) N \times K matrix, where the `state` column of that matrix is used as a weight-vector.

Value

estimate_LC_pdfs returns an N \times K matrix.

estimate_LC.pdf.state returns a vector of length N with the state-conditional density evaluated at eval.LCs.

Examples

set.seed(10)
WW <- matrix(runif(10000), ncol = 10)
WW <- normalize(WW)
temp_flcs <- cbind(sort(rnorm(nrow(WW))))
temp_flc_pdfs <- estimate_LC_pdfs(temp_flcs, WW)
matplot(temp_flcs, temp_flc_pdfs, col = 1:ncol(WW), type = "l", xlab = "FLCs", 
    ylab = "pdf", lty = 1)
###################### one state only ###
temp_flcs <- temp_flcs[order(temp_flcs)]
temp_flc_pdf <- estimate_LC_pdf_state(state = 3, LCs = temp_flcs, weights = WW)

plot(temp_flcs, temp_flc_pdf, type = "l", xlab = "FLC", ylab = "pdf")

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(LICORS)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/LICORS/estimate_LC_pdfs.Rd_%03d_medium.png", width=480, height=480)
> ### Name: estimate_LC_pdfs
> ### Title: Estimate PLC/FLC distributions for all states
> ### Aliases: estimate_LC_pdfs estimate_LC_pdf_state estimate_LC.pdf.state
> ### Keywords: distribution multivariate nonparametric
> 
> ### ** Examples
> 
> set.seed(10)
> WW <- matrix(runif(10000), ncol = 10)
> WW <- normalize(WW)
> temp_flcs <- cbind(sort(rnorm(nrow(WW))))
> temp_flc_pdfs <- estimate_LC_pdfs(temp_flcs, WW)
> matplot(temp_flcs, temp_flc_pdfs, col = 1:ncol(WW), type = "l", xlab = "FLCs", 
+     ylab = "pdf", lty = 1)
> ###################### one state only ###
> temp_flcs <- temp_flcs[order(temp_flcs)]
> temp_flc_pdf <- estimate_LC_pdf_state(state = 3, LCs = temp_flcs, weights = WW)
> 
> plot(temp_flcs, temp_flc_pdf, type = "l", xlab = "FLC", ylab = "pdf")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>