vbmp                  package:vbmp                  R Documentation

_V_a_r_i_a_t_i_o_n_a_l _B_a_y_e_s_i_a_n _M_u_l_t_i_n_o_m_i_a_l _P_r_o_b_i_t _R_e_g_r_e_s_s_i_o_n _w_i_t_h _G_a_u_s_s_i_a_n _P_r_o_c_e_s_s _P_r_i_o_r_s.

_D_e_s_c_r_i_p_t_i_o_n:

     Used to fit a Multinomial Probit Regression model, specified by
     giving the  matrix design 'X', the associated response variables
     't.class', kernel type and covariate  scaling parameters.
     Covariance paramters can be inferred from the data.

_U_s_a_g_e:

     vbmp(X, t.class, X.TEST, t.class.TEST, theta, control = list())

_A_r_g_u_m_e_n_t_s:

       X: Feature matrix for parameter 'estimation'

 t.class: Target values, integer number used for class labels. 

  X.TEST: Feature matrix to compute out-of-sample (test) prediction
          errors and likelihoods 

t.class.TEST: Target values for test data 

   theta: The covariance function parameters (e.g. scaling coefficients
          for each dimension) 

 control: A list of control parameters. See Details

_D_e_t_a_i_l_s:

     In this implementation a single covariance function is shared
     across all classes. Compute the predictive posteriors on the test
     set and the associated likelihood  and test errors at each
     iteration.

     The control argument is a list that can supply any of the
     following components:

_I_n_f_o_L_e_v_e_l 0 to suppress tracing ( > 0  to print different levels of
     monitoring information) 

_s_F_I_L_E._T_R_A_C_E File name where to redirect output (default NULL) 

_b_T_h_e_t_a_E_s_t_i_m_a_t_e if covariance parameter estimation switched on. Defaults
     to FALSE (switched off) 

_s_K_e_r_n_e_l_T_y_p_e Kernel function used in training and predicting.  Currently
     implemented kernels are Gaussian ('"gauss"'),  Cauchy
     ('"cauchy"'), Laplace ('"laplace"'), Polynomial ('"poly"'),
     Homogeneous polynomial ('"hpoly"'), 'Thin-plate' spline ('"tps"'),
     'linear' spline ('"lsp"') and Inner product('"iprod"'). Defaults
     to '"gauss"'. 

_m_a_x_I_t_s Maximum number of variational EM steps to take.  Defaults to 50.

_T_h_r_e_s_h Convergence threshold on marginal likelihood lowerbound. 
     Defaults to 1e-4.

_m_e_t_h_o_d Integral computation method: "quadrature" (Gaussian quadrature)
     or "classic"(simple sampler).  Defaults to "quadrature".     

_n_N_o_d_e_s_Q_u_a_d Number of nodes used for quadrature. Defaults to 49.     

_n_S_a_m_p_s_T_G Number of samples used in obtaining mean of truncated 
     Gaussian. Defaults to 1000.

_n_S_a_m_p_s_I_S Number of samples used in the importance sampler.  Defaults to
     1000.

_n_S_m_a_l_l_N_o Small number used to prevent numerical problems 
     (ill-conditioned covariance matrix). Defaults to 1e-10.

_p_a_r_G_a_m_m_a_T_a_u,_p_a_r_G_a_m_m_a_S_i_g_m_a The location and scale parameters of the 
     Gamma prior over covaraince params. Default to 1e-6.

_b_M_o_n_i_t_o_r TRUE to collect monitor convergence diagnostics at each
     iteration. Defaults to FALSE.

_b_P_l_o_t_F_i_t_t_i_n_g TRUE to plot test performance results at each iteration 
     during model estimation (if TRUE it forces bMonitor to TRUE).
     Defaults to FALSE.

_V_a_l_u_e:

     'vbmp' returns an object of class "VBMP.obj".  An object of class
     "VBMP.obj" is a list containing at least the following components:       

      Kc: Number of classes

   Ptest: Matrix of multinomial class predictive posterior
          probabilities for the test data 

       X: Feature matrix

  invPHI: Inverse of the Kernel matrix

       Y: Matrix of auxiliary variables

       M: Matrix of GP random variables

   theta: covariance kernel hyperparameters (estimates computed during 
          model fitting, if inferred

sKernelType : Kernel function used in training and predicting

Test.Err: Out-of-Sample Percent Prediction error estimates computed 
          during model fitting (0-1 error loss).

      PL: Predictive Likelihood estimates computed during model fitting

LOWER.BOUND: Lower bound estimates computed during model fitting

_A_u_t_h_o_r(_s):

     N Lama nicola.lama@unina2.it, MA Girolami girolami@dcs.gla.ac.uk

_R_e_f_e_r_e_n_c_e_s:

     Girolami M, Rogers S, _Variational Bayesian Multinomial Probit
     Regression with Gaussian Process Priors_, Neural Computation 18,
     1790-1817 (2006). Lama N, Girolami M _vbmp: Variational Bayesian
     Multinomial Probit Regression for multi-class classification in
     R_, Bioinformatics 24(1):135-136 (2008).  <URL:
     http://bioinformatics.oxfordjournals.org/cgi/content/short/btm535v1>

_S_e_e _A_l_s_o:

     See Also as 'predictCPP', 'covParams',  'lowerBound', 'predError',
      'predLik', 'predClass'

_E_x_a_m_p_l_e_s:

     ## -----------------------------------------------------------------------------
     ## EXAMPLE 1 - Theta estimate with synthetic data
     ## -----------------------------------------------------------------------------
     ## Samples of 2-D data points drawn from three nonlinearly separable
     ## classes which take the form of two annular rings and one zero-centered
     ## Gaussian are used in this little illustrative example. 
     genSample <- function(n, noiseVar=0) {
         ## class 1 and 2 (x ~ U(0,1))
         u <- 4. * matrix(runif(2*n), nrow=n, ncol=2) - 2.;
         i <- which(((u[, 1]^2 + u[, 2]^2) > .1) & ((u[, 1]^2 + u[, 2]^2) < .5) );
         j <- which(((u[, 1]^2 + u[, 2]^2) > .6) & ((u[, 1]^2 + u[, 2]^2) < 1) );
         X <- u[c(i, j),];
         t.class <- c(rep(1, length(i)),rep(2, length(j)));
         ## class 3 (x ~ N(0,1))
         x <- 0.1 * matrix(rnorm(2*length(i)), ncol=2, nrow=length(i) );
         k <- which((x[, 1]^2 + x[, 2]^2) < 0.1);
         X <- rbind(X, x[k, ]);
         t.class <- c(t.class, rep(3, length(k)));
         ## add random coloumns
         if (noiseVar>0) X <- cbind(X, matrix(rnorm(noiseVar*nrow(X)), ncol=noiseVar, nrow=nrow(X)));
         structure( list( t.class=t.class, X=X), class="MultiNoisyData");
     }

     set.seed(123); ## Init random number generator

     ## Generate training and test samples as an independent 
     ## test set to assess out-of-sample prediction error 
     ## and predictive likelihoods.
     nNoisyInputs <- 0;       ## number of additional noisy input parameters
     Ntest <- Ntrain <- 500;  ## sample sizes
     dataXt.train <- genSample(Ntrain, nNoisyInputs);
     dataXt.test  <- genSample(Ntest,  nNoisyInputs);

     ## Not run: 
      
      theta <- runif(ncol(dataXt.train$X));
      res <- vbmp( dataXt.train$X, dataXt.train$t.class,
             dataXt.test$X, dataXt.test$t.class, theta, 
              control=list(bThetaEstimate = T, 
              bPlotFitting=T, maxIts=50));
     ## End(Not run)

     ## set theta params (previously estimated) 
     theta <- c(0.09488309, 0.16141604);   
     ## Fit the vbmp
     res <- vbmp( dataXt.train$X, dataXt.train$t.class,
             dataXt.test$X, dataXt.test$t.class, theta, 
             control=list(maxIts=5));
     ## print out-of-sample error estimate
     predError(res);

     ## Not run: 
     ## ----------------------------------------------------------
     ## EXAMPLE 2 - BRCA12 genomic data
     ## ----------------------------------------------------------
     ## Leave-one-out (LOO) cross-validation prediction error of the probabilistic 
     ## Gaussian process classifier used in Zsofia Kote-Jarai et al. 
     ## Clin Cancer Res 2006;12(13);3896-3901

       if(any(installed.packages()[,1]=="Biobase")) {
         library("Biobase");
         data("BRCA12");
         brca.y <- BRCA12$Target.class;
         brca.x <- t(exprs(BRCA12));
       } else {
         print("Deprecated.....");
         load(url("http://www.dcs.gla.ac.uk/people/personal/girolami/pubs_2005/VBGP/BRCA12.RData"));
         brca.y <- as.numeric(BRCA12$y);
         brca.x <- as.matrix(BRCA12[,-1]);
       }
       
       sKernelType <- "iprod";  ## Covariance function type
       Thresh <- 1e-8;  ## Iteration threshold
       InfoLevel <- 1;
       theta <- rep(1.0, ncol(brca.x));
       ITER.THETA <- 24;
       n     <- nrow(brca.x) ;
       Kfold <- n; # number of folds , if equal to n then LOO
       samps <- sample(rep(1:Kfold, length=n), n, replace=FALSE); 
       res   <- rep(NA, n);
       print(paste("LOO crossvalidation started...... (",n,"steps)"));
       for (x in 1:Kfold) {
           cat(paste(x,", ",sep="")); flush.console();
           resX <- vbmp( brca.x[samps!=x,], brca.y[samps!=x], 
                         brca.x[samps==x,], brca.y[samps==x], 
                         theta,  control=list(bThetaEstimate=F, 
                         bPlotFitting=F, maxIts=ITER.THETA, 
                         sKernelType=sKernelType, Thresh=Thresh));    
           res[samps==x] <- predClass(resX); 
       }
       print("(end)");
       print(paste("Crossvalidated error rate", round(sum(res!=brca.y)/n,2)));
     ## End(Not run)   

