gps-methods {Rtreemix} | R Documentation |
These functions compute the genetic progression score (GPS) of each
sample in the given data
by performing a waiting time
simulation along the branchings of the mixture model model
. The
model has to be specified. If a dataset is missing a GPS for all
possible patterns is calculated. The number of events of the samples
in data
equals the number of genetic events in the model
.
## S4 method for signature 'RtreemixModel, RtreemixData': gps(model, data, ...) ## S4 method for signature 'RtreemixModel, matrix': gps(model, data, ...) ## S4 method for signature 'RtreemixModel, missing': gps(model, data, ...)
model |
An object of the class RtreemixModel specifying
the mutagenetic trees mixture model used for deriving the GPS values.
The model should NOT have more than 20 genetic events. |
data |
An RtreemixData object or a 0-1 matrix
containing the samples (patterns of genetic events) for which the GPS values
are to be calculated. The length of each of them has to be equal
to the number of genetic events in the 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 simulations. Its default value is 10.
seed is a positive integer specifying the random generator
seed. Its default value is (-1) and then the time is used as a
random generator.
|
The function returns an object from the RtreemixGPS
class that
containes the calculated GPS values, 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
.
RtreemixData
object.matrix
.The mixture model used for deriving the GPS values should not have more than 20 genetic events. The reason for this is that the number of all possible patterns for which the GPS values are calculated during a computationally intensive simulations is in this case $2^20$. This demands too much memory. The GPS examples are time consuming. They are commented out because of the time restrictions of the check of the package. For trying out the code please copy it and uncomment it.
Jasmina Bogojeska
Estimating cancer survival and clinical outcome based on genetic tumor progression scores, J. Rahnenf"urer et al.
RtreemixGPS-class
, RtreemixData-class
,
RtreemixModel-class
,
fit-methods
, confIntGPS-methods
## 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 RtreemixModel object by fitting model to the given data. #mod <- fit(data = data, K = 2, equal.edgeweights = TRUE, noise = TRUE) #show(mod) ## Create an RtreemixGPS object by calculating the GPS for all possible patterns. #modGPS.all <- gps(model = mod, no.sim = 1000) ## time consuming copmutations #show(modGPS.all) ## See the GPS values for all possible data. #GPS(modGPS.all) ## time consuming copmutations ## Create an RtreemixGPS object by calculating the GPS for the data based on the model mod. #modGPS <- gps(model = mod, data = data, no.sim = 1000) #show(modGPS) ## time consuming copmutations ## See the GPS values for data. #GPS(modGPS) ## time consuming copmutations