PRIM.example.data          package:rfcdmin          R Documentation

_P_R_I_M _e_x_a_m_p_l_e: _S_i_m_u_l_a_t_e_d _c_l_u_s_t_e_r_e_d _d_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     *PRIM-2Dimensional Dataset:*

     The simulated data comprises of three actual normally distributed
     clusters, denoted by 1, 2, and 3, which are true category statuses
     denoted by the vector 'true.Y.status.PRIM'.  In reality, the truth
     is unknown, and thus, the information about the true category
     status is hidden.  The rules obtained from PRIM will be compared
     to the true cluster categories, from which the data was generated.

     The stochastic structure of the data is a mixture model of
     normals. In our test dataset there were 1000 observations.

     Each of the 1000 'Y.PRIM' response comes from independent
     bernoulli distributions with a probability of 0.5.

     The 'X.PRIM' observations with 2 column variables and 1000 row
     observations was made up of the following distributions
     conditional on the response 'Y.PRIM':

[_T_r_u_e _C_l_u_s_t_e_r _1] 1/4 of the observations when the response Y.PRIM=1,
     came from the following Multivariate Normal distribution.

     X.PRIM | Y.PRIM=1 ~ N([500,500],[[625, 0],[0, 625]])   

[_T_r_u_e _C_l_u_s_t_e_r _2] 1/4 of the observations when the response Y.PRIM=1
     came from the following Multivariate Normal distribution.

     X.PRIM | Y.PRIM=1 ~ N([400,100],[[625, 0],[0, 625]])

[_T_r_u_e _C_l_u_s_t_e_r _3] 1/4 of the observations when the response Y.PRIM=1
     came from the following Multivariate Normal distribution.

     X.PRIM | Y.PRIM=1 ~ N([200,900],[[625, 0],[0, 625]])


[_N_o_i_s_e] Each of the two column variables for 1/4 of the  observations
     when the response Y.PRIM=1 and all observations when Y.PRIM=0 came
     from the following uniform distribution with a range of integer 
     values between 0 and 1023.

     X1|Y.PRIM=1,Y.PRIM=0 ~ U(0,1023)

     X2|Y.PRIM=1,Y.PRIM=0 ~ U(0,1023)


     *PRIM-4Dimensional Dataset:*

     The simulated data comprises of three actual clusters, denoted by
     1, 2, and 3, that are normally distributed.  These values for true
     category status are denoted by the vector true.Y.status.PRIM4D. 
     In reality, the truth is unknown, and thus, the information about
     the true category status is hidden.  The rules obtained will be
     compared to the true cluster categories, from which the data was
     generated.

     The stochastic structure of the data is a mixture model of
     normals. In our test dataset there were 2000 observations.

     Each of the 2000 Y.PRIM4D response comes from independent
     bernoulli distributions with a probability of 0.5.

     The X.PRIM4D matrix observations with 4 column variables and 2000
     row observations was made up of the following distributions
     conditional on the response Y.PRIM4D:

[_T_r_u_e _C_l_u_s_t_e_r _1] 1/4 of the observations when the response Y.PRIM4D=1,
     came from the following Multivariate Normal distribution.

     X.PRIM4D | Y.PRIM4D=1 ~ N([500,500,500,500], [[625,
     0,0,0],[0,625,0,0], [0,0,625,0],[0,0,0,625]])   


[_T_r_u_e _C_l_u_s_t_e_r _2] 1/4 of the observations when the response Y.PRIM4D=1
     came from the following Multivariate Normal distribution.

     X.PRIM4D | Y.PRIM4D=1 ~ N([200,1000,200,800], [[700,
     0,0,0],[0,200,0,0], [0,0,100,0],[0,0,0,700]])   


[_T_r_u_e _C_l_u_s_t_e_r _3] 1/4 of the observations when the response Y.PRIM4D=1
     came from the following Multivariate Normal distribution.

     X.PRIM4D | Y.PRIM4D=1 ~ N([1000,100,900,100], [[400,
     0,0,0],[0,500,0,0], [0,0,600,0],[0,0,0,300]])


[_N_o_i_s_e] Each of the two column variables for 1/4 of the  observations
     when the response Y.PRIM4D=1 and all observations when Y.PRIM4D=0
     came from the following uniform distribution with a range of
     integer  values between 0 and 1023.  Let Xi denote the i-th column
     of X.PRIM4D.

     X1|Y.PRIM4D=1,Y.PRIM4D=0 ~ U(0,1023)

     X2|Y.PRIM4D=1,Y.PRIM4D=0 ~ U(0,1023)

     X3|Y.PRIM4D=1,Y.PRIM4D=0 ~ U(0,1023)

     X4|Y.PRIM4D=1,Y.PRIM4D=0 ~ U(0,1023) 

_U_s_a_g_e:

     data(PRIM.example.data)

_F_o_r_m_a_t:

_X._P_R_I_M: A matrix of 2 columns named "X1" and "X2" with 1000 row
     observations.

_Y._P_R_I_M A vector denoting the observed response corresponding to each
     row observation of 'X.PRIM' and having a length of 1000

_t_r_u_e._Y._s_t_a_t_u_s._P_R_I_M A vector denoting the true cluster category
     corresponding to each row observation of 'X.PRIM' and having a
     length of 1000


_X._P_R_I_M_4_D A matrix of 4 columns named "X1", "X2", "X3", and "X4" with
     2000 row observations.

_Y._P_R_I_M_4_D A vector denoting the observed response corresponding to each
     row observation of 'X.PRIM' and having a length of 2000

_t_r_u_e._Y._s_t_a_t_u_s._P_R_I_M_4_D A vector denoting the true cluster category
     corresponding to each row observation of 'X.PRIM' and having a
     length of 2000 

_S_o_u_r_c_e:

     A dataset generated with the following code in the "example"
     section.

_R_e_f_e_r_e_n_c_e_s:

     See also 'rfcprim' for examples using this simulated data.

_E_x_a_m_p_l_e_s:

     data(PRIM.example.data)

      ## The following is the source R code
      ## setting the seed will generate the same dataset
     if (FALSE){
       if (require(MASS)){
         set.seed(20)
         ## Y.PRIM response binary data
         Y.PRIM <- rbinom(1000, 1, 0.5)

         ## X.PRIM matrix, rows corresponding to Y.PRIM
         ##           columns are the variables

         ## when Y.PRIM==1 there is a MVN distribution of the X.PRIM
         ## There is simulation for 3 clusters

         Sigma <- matrix(c(25,0,0,25),2,2)^2

         X1.y1 <- mvrnorm(n=ceiling(length(Y.PRIM[Y.PRIM==1])/4),
                    c(500, 500), Sigma, empirical = FALSE)

         X2.y1 <- mvrnorm(n=ceiling(length(Y.PRIM[Y.PRIM==1])/4),
                    c(400, 100), Sigma, empirical = FALSE)
      
         X3.y1 <- mvrnorm(n=ceiling(length(Y.PRIM[Y.PRIM==1])/4),
                   c(200, 900), Sigma, empirical = FALSE)

         X4.1.y1 <-  runif(ceiling(length(Y.PRIM[Y.PRIM==1])/4), 0, 1023)
      
         X4.2.y1 <-  runif(ceiling(length(Y.PRIM[Y.PRIM==1])/4), 0, 1023)
         X4.y1 <- cbind(X4.1.y1, X4.2.y1)

         X.y1 <- rbind(X1.y1, X2.y1, X3.y1, X4.y1)[1:length(Y.PRIM[Y.PRIM==1]),]

         ## when Y.PRIM==0 there is only a uniform distribution of X's
         X1.y0 <- runif(length(Y.PRIM[Y.PRIM==0]), 0, 1023)
         X2.y0 <- runif(length(Y.PRIM[Y.PRIM==0]), 0, 1023)
         X.y0 <- cbind(X1.y0, X2.y0)

         ## true Y.PRIM cluster status:
         ## 0, 4 is noise
         ## 1, 2, 3, are the true clusters

         true.Y.status.PRIM <- c(rep(2, dim(X1.y1)[1]),
         rep(1, dim(X2.y1)[1]), rep(3, dim(X3.y1)[1]),
         rep(4, dim(X4.y1)[1]))[1:length(Y.PRIM[Y.PRIM==1])]

         true.Y.status.PRIM <- c(true.Y.status.PRIM, rep(0,dim(X.y0)[1]))

         ## sort the Y.PRIM to correspond with the X.PRIM matrix
         Y.PRIM<- sort(Y.PRIM, decreasing=TRUE)
         X.PRIM <- rbind(X.y1, X.y0)
         colnames(X.PRIM) <- c("X1", "X2") 
       }
        ## 4D Dataset

       if (require(MASS)==TRUE){
         Y.PRIM4D <- rbinom(2000, 1, 0.5)
         ## when Y.PRIM4D==1 there is a MVN distribution of the X
         Sigma1 <- rbind(c(625,0,0,0),
                         c(0, 625, 0, 0),
                         c(0, 0, 625, 0),
                         c(0, 0, 0, 625))
         Sigma2 <- rbind(c(700, 0, 0, 0),
                         c(0, 200, 0, 0),
                         c(0, 0, 100,0),
                         c(0, 0, 0, 700))
         Sigma3 <- rbind(c(400,0,0,0),
                         c(0,500,0,0),
                         c(0,0,600,0),
                         c(0, 0,0,300))

         X1.y1 <- mvrnorm(n=ceiling(length(Y.PRIM4D[Y.PRIM4D==1])/4),
                        c(500, 500, 500, 500), Sigma1, empirical = FALSE)
         X2.y1 <- mvrnorm(n=ceiling(length(Y.PRIM4D[Y.PRIM4D==1])/4),
                        c(200, 1000, 200, 800), Sigma2, empirical = FALSE)
         X3.y1 <- mvrnorm(n=ceiling(length(Y.PRIM4D[Y.PRIM4D==1])/4),
                        c(1000, 100, 900, 100), Sigma3, empirical = FALSE)

         X4.1.y1 <-  runif(ceiling(length(Y.PRIM4D[Y.PRIM4D==1])/4), 0, 1023)
         X4.2.y1 <-  runif(ceiling(length(Y.PRIM4D[Y.PRIM4D==1])/4), 0, 1023)
         X4.3.y1 <-  runif(ceiling(length(Y.PRIM4D[Y.PRIM4D==1])/4), 0, 1023)
         X4.4.y1 <-  runif(ceiling(length(Y.PRIM4D[Y.PRIM4D==1])/4), 0, 1023)

         X4.y1 <- cbind(X4.1.y1, X4.2.y1, X4.3.y1, X4.4.y1)

         X.y1 <- rbind(X1.y1, X2.y1, X3.y1, X4.y1)[1:length(Y.PRIM4D[Y.PRIM4D==1]),]
         ## when Y.PRIM4D==0 there is only a uniform distribution of X's
         X1.y0 <- runif(length(Y.PRIM4D[Y.PRIM4D==0]), 0, 1023)
         X2.y0 <- runif(length(Y.PRIM4D[Y.PRIM4D==0]), 0, 1023)
         X3.y0 <- runif(length(Y.PRIM4D[Y.PRIM4D==0]), 0, 1023)
         X4.y0 <- runif(length(Y.PRIM4D[Y.PRIM4D==0]), 0, 1023)
         X.y0 <- cbind(X1.y0, X2.y0, X3.y0, X4.y0)
         ## true if in a cluster otherwise FALSE
         true.Y.status.PRIM4D <- c(rep(2, dim(X1.y1)[1]), rep(1, dim(X2.y1)[1]), rep(3, dim(X3.y1)[1]),  
                            rep(4, dim(X4.y1)[1]))[1:length(Y.PRIM4D[Y.PRIM4D==1])]
         ## NOTE: true.Y.status.PRIM4D=0,4 is uniform, random noise; true.Y.status.PRIM4D=1,2,3
         ## denotes the real cluster category
         true.Y.status.PRIM4D <- c(true.Y.status.PRIM4D, rep(0,dim(X.y0)[1]))
         Y.PRIM4D <- sort(Y.PRIM4D, decreasing=TRUE)
         X.PRIM4D <- rbind(X.y1, X.y0)
         colnames(X.PRIM4D) <- c("X1", "X2", "X3", "X4")
         }
       }

