predict.qda               package:MASS               R Documentation

_C_l_a_s_s_i_f_y _f_r_o_m _Q_u_a_d_r_a_t_i_c _D_i_s_c_r_i_m_i_n_a_n_t _A_n_a_l_y_s_i_s

_D_e_s_c_r_i_p_t_i_o_n:

     Classify multivariate observations in conjunction with 'qda'

_U_s_a_g_e:

     ## S3 method for class 'qda':
     predict(object, newdata, prior = object$prior,
             method = c("plug-in", "predictive", "debiased", "looCV"), ...)

_A_r_g_u_m_e_n_t_s:

  object: object of class '"qda"' 

 newdata: data frame of cases to be classified or, if 'object' has a
          formula, a data frame with columns of the same names as the
          variables used.  A vector will be interpreted as a row
          vector.  If newdata is missing, an attempt will be made to
          retrieve the data used to fit the 'qda' object. 

   prior: The prior probabilities of the classes, by default the
          proportions in the training set or what was set in the call
          to 'qda'. 

  method: This determines how the parameter estimation is handled. With
          '"plug-in"' (the default) the usual unbiased parameter
          estimates are used and assumed to be correct. With
          '"debiased"' an unbiased estimator of the log posterior
          probabilities is used, and with '"predictive"' the parameter
          estimates are integrated out using a vague prior.  With
          '"looCV"' the leave-one-out cross-validation fits to the
          original dataset are computed and returned. 

     ...: arguments based from or to other methods 

_D_e_t_a_i_l_s:

     This function is a method for the generic function 'predict()' for
     class '"qda"'. It can be invoked by calling 'predict(x)' for an
     object 'x' of the appropriate class, or directly by calling
     'predict.qda(x)' regardless of the class of the object.

     Missing values in 'newdata' are handled by returning 'NA' if the
     quadratic discriminants cannot be evaluated. If 'newdata' is
     omitted and the 'na.action' of the fit omitted cases, these will
     be omitted on the prediction.

_V_a_l_u_e:

     a list with components

   class: The MAP classification (a factor) 

posterior: posterior probabilities for the classes 

_R_e_f_e_r_e_n_c_e_s:

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ Fourth edition.  Springer.

     Ripley, B. D. (1996) _Pattern Recognition and Neural Networks_.
     Cambridge University Press.

_S_e_e _A_l_s_o:

     'qda', 'lda', 'predict.lda'

_E_x_a_m_p_l_e_s:

     tr <- sample(1:50, 25)
     train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3])
     test <- rbind(iris3[-tr,,1], iris3[-tr,,2], iris3[-tr,,3])
     cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
     zq <- qda(train, cl)
     predict(zq, test)$class

