| predict.ProbDistr {MethylIT.utils} | 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)))