Rtreemix (version 1.34.0)

confIntGPS-methods: Method for calculating GPS values and their 95% bootstrap confidence intervals

Description

The method first calculates the genetic progression score (GPS) for the patterns in a given dataset data based on a fitted mutagenetic trees mixture model with K components. The data and K have to be specified. Then, it derives a 95% confidence intervals for the GPS values with bootstrap analysis.

Usage

confIntGPS(data, K, ...)

Arguments

data

An RtreemixData object containing the samples (patterns of genetic events) for which the GPS values and their bootstrap confidence intervals are to be calculated. The number of genetic events should NOT be greater than 20.

K

An integer larger than 0 specifying the number of branchings in the mixture model.

...

sampling.mode is a character that specifies the sampling mode ("constant" or "exponential") used in the waiting time simulations. Its default value is "exponential". sampling.param is a numeric that specifies the sampling parameter corresponding to the sampling mode given by sampling.mode. Its default value is 1. no.sim is an integer larger than 0 giving the number of iterations for the waiting time simulation. Its default values is 10000. B is an integer larger than 0 specifying the number of bootstrap samples used in the bootstrap analysis. Its default value is 1000. equal.star is a logical specifying whether to use equal edge weights in the noise component. The default value is TRUE. When you have few data samples always use its default value (TRUE) to ensure nonzero probabilities for all possible patterns (sets of events).

Value

The function returns an object from the RtreemixGPS class that containes the calculated GPS values, their 95% confidence intervals, the model used for the computation, the data, and so on (see RtreemixGPS-class). The GPS values are represented as a numeric vector with length equal to the number of samples in data. Their corresponding confidence intervals are given in a matrix with two columns.

See Also

RtreemixGPS-class, gps-methods, RtreemixData-class, RtreemixModel-class, fit-methods

Examples

## Create an RtreemixData object from a randomly generated RtreemixModel object.
#rand.mod <- generate(K = 2, no.events = 7, noise.tree = TRUE, prob = c(0.2, 0.8))
#data <- sim(model = rand.mod, no.draws = 400)

## Create an RtreemixGPS object by calculating GPS values for a given dataset
## and their 95% confidence intervals using the bootstrap method.
#modGPS2 <- confIntGPS(data = data, K = 2, B = 100) ## time consuming computation
#show(modGPS2)

## See the GPS values for the object modGPS2 and their confidence intervals.
#GPS(modGPS2)
#gpsCI(modGPS2)

## See data.
#getData(modGPS2)