RtreemixGPS-class          package:Rtreemix          R Documentation

_C_l_a_s_s "_R_t_r_e_e_m_i_x_G_P_S"

_D_e_s_c_r_i_p_t_i_o_n:

     A class for describing the genetic progression scores (GPS) of a
     given set of patterns resulting from a waiting time simulation
     along the edges of the tree components of a given mutagenetic
     trees mixture model. It also contains GPS confidence intervals
     derived with the bootstrap method.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form 'new("RtreemixGPS",
     Data, Model, SamplingMode, SamplingParam, GPS, gpsCI)'. The
     'RtreemixGPS' class contains the GPS values each assigned to  the
     corresponding pattern from the dataset given by 'Data' (the parent
     class). The GPS values are derived in a waiting time simulation
     for a specified sampling mode and its corresponding sampling
     parameter. Moreover, this class specifies the confidence intervals
     for the GPS values derived with the bootstrap method.

     The 'Data' is an 'RtreemixData' object that specifies the patterns
     for which the GPS values are calculated.

     The 'Model' is an 'RtreemixModel' object that specifies the
     mutagenetic trees mixture model used for deriving the GPS values.

     The 'SamplingMode' is a 'character' that specifies the sampling
     mode ("constant" or "exponential") used in the waiting time
     simulations.

     The 'SamplingParam' is a 'numeric' that specifies the sampling
     parameter corresponding to the sampling mode given by
     'SamplingMode'.

     The 'GPS' is a numeric 'vector' that specifies the GPS value of
     each pattern in the given dataset 'Data'. Its length equals the
     number of patterns in 'Data'.

     The 'gpsCI' is a numeric 'matrix' that specifies the confidence
     intervals for each GPS value in the vector 'GPS'. The number of
     rows equals the number of patients in 'Data' and the number of
     columns equals 2. The first column gives the lower bound and the
     second column gives the upper bound of each confidence interval.

_S_l_o_t_s:


     '_M_o_d_e_l': Object of class '"RtreemixModel"'.

     '_S_a_m_p_l_i_n_g_M_o_d_e': Object of class '"character"'. It can have one of
          the two possible values: "constant" or "exponential".

     '_S_a_m_p_l_i_n_g_P_a_r_a_m': Object of class '"numeric"'.

     '_G_P_S': Object of class '"numeric"'. The length of 'GPS' must be
          equal to the number of patterns in the parent 'RtreemixData'
          object.

     '_g_p_s_C_I': Object of class '"matrix"'. It number of columns has to
          be 2 and the number of rows has to be equal to the length of
          'GPS'.

_E_x_t_e_n_d_s:

     Class '"RtreemixData"', directly.

_M_e_t_h_o_d_s:


     _G_P_S 'signature(object = "RtreemixGPS")': A method for obtaining
          the GPS values corresponding to the patterns in the parent
          'RtreemixData' object.

     _M_o_d_e_l 'signature(object = "RtreemixGPS")': A method for obtaining
          the model used for deriving the GPS values.

     _S_a_m_p_l_i_n_g_M_o_d_e 'signature(object = "RtreemixGPS")': A method for
          obtaining the sampling mode ("constant" or "exponential")
          used for the waiting time simulations.

     _S_a_m_p_l_i_n_g_P_a_r_a_m 'signature(object = "RtreemixGPS")': A method for
          obtaining the sampling parameter corresponding to the
          specified 'SamplingMode'.

     _g_e_t_D_a_t_a 'signature(object = "RtreemixGPS")': A method for
          obtaining the set of patterns for which the GPS values are
          calculated. 

     _g_p_s_C_I 'signature(object = "RtreemixGPS")': A method for obtaining
          the GPS confidence intervals.

_N_o_t_e:

     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.

_A_u_t_h_o_r(_s):

     Jasmina Bogojeska

_R_e_f_e_r_e_n_c_e_s:

     Estimating cancer survival and clinical outcome based on genetic
     tumor progression scores, J. Rahnenf\"urer et al.

_S_e_e _A_l_s_o:

     'RtreemixData-class',  'RtreemixModel-class', 'gps-methods',
     'fit-methods', 'confIntGPS-methods'

_E_x_a_m_p_l_e_s:

     ## Generate a random RtreemixModel object with 3 components and 9 genetic events.
     #mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8))
     #show(mod)
     ## Generate an artificial dataset from the model mod.
     #data <- sim(model = mod, no.draws = 300)
     #show(data)

     ## Create an RtreemixGPS object by calculating the GPS for all possible patterns.
     #modGPS.all <- gps(model = mod, no.sim = 1000)
     #show(modGPS.all)
     ## 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)

     ## See the slots from the RtreemixGPS object. 
     #Model(modGPS)
     #SamplingMode(modGPS)
     #SamplingParam(modGPS)
     #GPS(modGPS)
     ## See data.
     #getData(modGPS)

     ## 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 = 10)
     #show(modGPS2)

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

