kde                    package:ks                    R Documentation

_K_e_r_n_e_l _d_e_n_s_i_t_y _e_s_t_i_m_a_t_e _f_o_r _m_u_l_t_i_v_a_r_i_a_t_e _d_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     Kernel density estimate for 2- to 6-dimensional data

_U_s_a_g_e:

      kde(x, H, gridsize, supp=3.7, eval.points, eval.levels)

_A_r_g_u_m_e_n_t_s:

       x: matrix of data values

       H: bandwidth matrix

gridsize: vector of number of grid points

    supp: effective support for standard normal is ['-supp, supp']

eval.points: points that density estimate is evaluated at (required for
          dimensions > 3)

eval.levels: levels at which to draw the level surfaces for
          3-dimensiona data

_D_e_t_a_i_l_s:

     The kernel density estimate is computed exactly i.e. binning is
     not used. 

     If 'gridsize' is not set to a specific value, then it defaults to
     50 grid points in each co-ordinate direction i.e. 'rep(50, d)'.
     Not required to be set if specifying 'eval.points'.

     If 'eval.points' is not specified, then the density estimate is
     automatically computed over a grid whose resolution is controlled
     by 'gridsize' (a grid is required for plotting).

_V_a_l_u_e:

     Kernel density estimate is an object of class 'kde' which is a
     list with 4 fields 

       x: data points - same as input

eval.points: points that density estimate is evaluated at

estimate: density estimate at 'eval.points'

       H: bandwidth matrix

_R_e_f_e_r_e_n_c_e_s:

     Wand, M.P. & Jones, M.C. (1995) _Kernel Smoothing_. Chapman &
     Hall. London.

_S_e_e _A_l_s_o:

     'plot.kde'

_E_x_a_m_p_l_e_s:

     ### bivariate example
     data(unicef)
     H.pi <- Hpi(unicef, nstage=1)
     fhat <- kde(unicef, H.pi)

     ### trivariate example
     mus <- rbind(c(0,0,0), c(2,2,2))
     Sigma <- matrix(c(1, 0.7, 0.7, 0.7, 1, 0.7, 0.7, 0.7, 1), nr=3, nc=3) 
     Sigmas <- rbind(Sigma, Sigma)
     props <- c(1/2, 1/2)
     x <- rmvnorm.mixt(n=100, mus=mus, Sigmas=Sigmas, props=props)
     H.pi <- Hpi(x)
     fhat <- kde(x, H.pi, eval.levels=seq(-3,3, length=9)) 

     ### 4-variate example
     library(MASS)
     data(iris)
     ir <- iris[,1:4][iris[,5]=="setosa",]
     H.scv <- Hscv(ir)
     fhat <- kde(ir, H.scv, eval.points=ir)  

