CorCompare              package:StatDA              R Documentation

_C_o_m_p_a_r_e_s _C_o_r_r_e_l_a_t_i_o_n _M_a_t_r_i_c_e_s

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

     This function compares two correlation matrices numerically and
     graphically.

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

     CorCompare(cor1, cor2, labels1, labels2, method1, method2, ndigits = 4,
     lty1 = 1, lty2 = 2, col1 = 1, col2 = 2, lwd1 = 1.1, lwd2 = 1.1,
     cex.label = 1.1, cex.legend = 1.2, lwd.legend = 1.2, cex.cor = 1, ...)

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

cor1,cor2: two correlation matrices based on different estimation
          methods 

labels1, labels2: labels for the two estimation methods 

method1, method2: description of the estimation methods 

 ndigits: number of digits to be used for plotting the numbers 

lty1, lty2, col1,col2, lwd1, lwd2, cex.label, cex.cor: other graphics
          parameters 

cex.legend, lwd.legend: graphical parameters for the legend

     ...: further graphical parameters for the ellipses

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

     The ellipses are plotted with the function do.ellipses. Therefore
     the radius is calculated with singular value decomposition.

_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(chorizon)
     x=chorizon[,c("Ca","Cu","Mg","Na","P","Sr","Zn")]
     par(mfrow=c(1,1),mar=c(4,4,2,0))
     R=covMcd(log10(x),cor=TRUE)$cor
     P=cor(log10(x))

     CorCompare(R,P,labels1=dimnames(x)[[2]],labels2=dimnames(x)[[2]],
     method1="Robust",method2="Pearson",ndigits=2, cex.label=1.2)

