list.quantile.regs: Quantile Regressions for Bias Correction
In PhHermann/LDJump: Estimating Variable Recombination Rates from Population Genetic Data
Description
Usage
Format
Examples
Description
This data set contains a list of quantile regression models (rq) for bias correction. In total nine regression models are saved in a list form, where we recommend to use the 0.35 for the correction.
Usage
1
data("list.quantile.regs")
Format
A list object of length 9, containing quantiles in sequences between 0.1 and 0.5 with 0.05 distances.
Examples
1
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3
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5
6
data(list.quantile.regs)
##### Do not run these examples #####
##### In get_smuce the function is called as follows #####
##### pr1 = predict(mod.full,help); pr1[is.na(pr1)] = -1/gam; #####
##### ind.q = which(seq(0.1, 0.5, by = 0.05) == quant) #####
##### pr.cor = predict(list.quantile.regs[[ind.q]], data.frame(x = pr1) #####
PhHermann/LDJump documentation built on Nov. 16, 2019, 12:53 p.m.
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Description Usage Format Examples
Description
This data set contains a list of quantile regression models (rq) for bias correction. In total nine regression models are saved in a list form, where we recommend to use the 0.35 for the correction.
Usage
1 | data("list.quantile.regs")
|
Format
A list object of length 9, containing quantiles in sequences between 0.1 and 0.5 with 0.05 distances.
Examples
1 2 3 4 5 6 | data(list.quantile.regs)
##### Do not run these examples #####
##### In get_smuce the function is called as follows #####
##### pr1 = predict(mod.full,help); pr1[is.na(pr1)] = -1/gam; #####
##### ind.q = which(seq(0.1, 0.5, by = 0.05) == quant) #####
##### pr.cor = predict(list.quantile.regs[[ind.q]], data.frame(x = pr1) #####
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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