pamr.makeclasses            package:pamr            R Documentation

_A _f_u_n_c_t_i_o_n _t_o _i_n_t_e_r_a_c_t_i_v_e_l_y _d_e_f_i_n_e _c_l_a_s_s_e_s _f_r_o_m _a _c_l_u_s_t_e_r_i_n_g _t_r_e_e

_D_e_s_c_r_i_p_t_i_o_n:

     function to interactively define classes from a clustering tree

_U_s_a_g_e:

     pamr.makeclasses(data,  sort.by.class=F,...)

_A_r_g_u_m_e_n_t_s:

    data: The input data. A list with components: x- an expression
          genes in the rows, samples in the columns, and y-  a vector
          of the class labels for each sample, and batchlabels- a
          vector of batch labels for each sample. This object if the
          same form as that produced by pamr.from.excel.

sort.by.class: Optional argument. If true, the clustering tree is
          forced to put all samples in the same class  (as defined by
          the class labels y in `data') together in the tree. This is
          useful if a regrouping of classes is desired. Eg: given
          classes 1,2,3,4 you want to define new classes [1,3] vs [2,4]
           or 2 vs [1,3]

     ...: 

     {Any additional arguments to be passed to hclust}

_D_e_t_a_i_l_s:

     'pamr.makeclasses'  Using this function the user interactively
     defines a new set of classes, to be used in pamr.train, pamr.cv
     etc.  After invoking pamr.makeclasses, a clustering tree is drawn.
     This callss the R function 'hclust', and any arguments for
     'hclust' can be passed to it. Using the left button, the user
     clicks at the junction point defining the subgroup 1. More groups
     can be added to class 1 by clicking on further junction points.
     The user ends the definition of class 1 by clicking on the
     rightmost button [in Windows,  an additional menu appears and he
     chooses Stop] . This process is continued for classes 2,3 etc.
     Note that some sample may be left out of the new classes. Two
     consecutive clicks of the right button ends the definition for all
     classes.

     At the end, the clustering is redrawn, with the new class labels
     shown.

     Note: this function is "fragile". The user must click close to the
     junction point, to avoid confusion with other junction points.
     Classes 1,2,3.. cannot have samples in common (if they do, an
     Error message will appear). If the function is confused about the
     desired choices, it will complain and ask the user to rerun
     pamr.makeclasses. The user should also check that the labels on
     the  final redrawn cluster tree agrees with the desired  classes.

_V_a_l_u_e:

     A vector of class labels 1,2,3... If a component is NA (missing),
     then the sample is not assigned to any class. This vector should
     be assigned to  the newy component of data, for use in pamr.train
     etc. Note that pamr.train uses the class labels in the component
     ``newy'' if it is present. Otherwise it uses the data labels
     ``y''.

_A_u_t_h_o_r(_s):

     Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, and
     Gilbert Chu

_R_e_f_e_r_e_n_c_e_s:

_E_x_a_m_p_l_e_s:

     set.seed(120)
     #generate some data
     x <- matrix(rnorm(1000*20),ncol=20)
     y <- sample(c(1:4),size=20,replace=TRUE)
     batchlabels <- sample(c(1:5),size=20,replace=TRUE)
     mydata <- list(x=x,y=factor(y),batchlabels=factor(batchlabels))

     # mydata$newy <- pamr.makeclasses(mydata)    Run this and defien some new classes

     train <- pamr.train(mydata)
     results <- pamr.cv(train, mydata)

