loadplot               package:StatDA               R Documentation

_P_l_o_t _t_h_e _L_o_a_d_i_n_g_s _o_f _a _F_A

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

     Makes a Reimann-plot of a loadings matrix.

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

     loadplot(fa.object, titlepl = "Factor Analysis", crit = 0.3, length.varnames = 2)

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

fa.object: the output of factor analysis class 

 titlepl: the title of the plot 

    crit: all loadings smaller than crit will be ignored in the plot 

length.varnames: number of letters for abbreviating the variable names
          in the plot 

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

     Plot of the loadings of a FA therefore a object of factor analysis
     class must be provided.

_A_u_t_h_o_r(_s):

     Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

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

     C. Reimann, P. Filzmoser, R.G. Garrett, and R. Dutter: Statistical
     Data Analysis Explained. Applied Environmental Statistics with R.
     John Wiley and Sons Inc. To appear.

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

     data(moss)
     var=c("Ag","Al","As","B","Ba","Bi","Ca","Cd","Co","Cr","Cu","Fe","Hg","K","Mg","Mn","Mo",
           "Na","Ni","P","Pb","Rb","S","Sb","Si","Sr","Th","Tl","U","V","Zn")
     x=log10(moss[,var])

     x.mcd=covMcd(x,cor=TRUE)
     x.rsc=scale(x,x.mcd$cent,sqrt(diag(x.mcd$cov)))
     res5=pfa(x.rsc,factors=2,covmat=x.mcd,scores="regression",rotation="varimax",
         maxit=0,start=rep(0,ncol(x.rsc)))
     loadplot(res5,titlepl="Robust FA (log-transformed)", crit=0.3)

