View source: R/predictProbDistr.R View source: R/predictProbDistr.R
predict.ProbDistr | R Documentation |
This is an utility function to get predictions from the probability distributions models used in Methyl-IT: Weibull, Gamma, and generalized Gamma. Some times, after the nonlinear fit of any of the mentioned modelsm we would like to evaluate the model output.
## S3 method for class 'ProbDistr' predict(nlm, pred = "quant", q = 0.95, dist.name) ## S3 method for class 'ProbDistrList' predict(nlm, pred = "quant", q = 0.95, dist.name, num.cores = 1L, tasks = 0L)
nlm |
An object carrying the best nonlinear fit for a distribution model
obtained with function |
pred |
Type of prediction resquested: *density* ('dens'),*quantiles* ('quant'), *random number* ('rnum') or *probabilities* ('prob'). |
q |
numeric vector of quantiles, probabilities or an interger if pred = 'rnum'. |
dist.name |
name of the distribution to fit: Weibull2P (default: 'Weibull2P'), Weibull three-parameters (Weibull3P), gamma with three-parameter (Gamma3P), gamma with two-parameter (Gamma2P), generalized gamma with three-parameter ('GGamma3P') or four-parameter ('GGamma4P'). |
num.cores, tasks |
Paramaters for parallele computation using package
|
Predictions are based on the best model fit returned by function
nonlinearFitDist
. The possible prediction are: *density*,
*quantiles*, *random number* or *probabilities*.
set.seed(1) num.points <- 1000 HD <- makeGRangesFromDataFrame( data.frame(chr = 'chr1', start = 1:num.points, end = 1:num.points, strand = '*', hdiv = rweibull(1:num.points, shape = 0.75, scale = 1)), keep.extra.columns = TRUE) nlms <- nonlinearFitDist(list(HD), column = 1, verbose = FALSE) x=seq(0.1, 10, 0.05) y <- predict(nlms[[1]], pred='dens', q = x, dist.name='Weibull2P') y1 <- dweibull(x, shape = 0.75, scale = 1) # The maximum difference between the 'theoretical' and estimated densities max(abs(round(y, 2) - round(y1, 2)))
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