pfa                  package:StatDA                  R Documentation

_P_r_i_n_c_i_p_a_l _F_a_c_t_o_r _A_n_a_l_y_s_i_s

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

     Computes the principal factor analysis of the input data.

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

     pfa(x, factors, data = NULL, covmat = NULL, n.obs = NA, subset, na.action,
     start = NULL, scores = c("none", "regression", "Bartlett"),
     rotation = "varimax", maxiter = 5, control = NULL, ...)

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

       x: (robustly) scaled input data 

 factors: number of factors 

    data: default value is NULL 

  covmat: (robustly) computed covariance or correlation matrix 

   n.obs: number of observations 

  subset: if a subset is used 

   start: starting values 

  scores: which method should be used to calculate the scores 

rotation: if a rotation should be made 

 maxiter: maximum number of iterations  

 control: default value is NULL 

na.action: what to do with NA values

     ...: arguments for creating a list

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

loadings: A matrix of loadings, one column for each factor. The factors
          are ordered in decreasing order of sums of squares of
          loadings.

uniquness: uniquness

correlation: correlation matrix

criteria: The results of the optimization: the value of the negativ
          log-likelihood and information of the iterations used.

 factors: the factors

     dof: degrees of freedom

  method: "principal"

   n.obs: number of observations if available, or NA

    call: The matched call.

STATISTIC, PVAL: The significance-test statistic and p-value, if can be
          computed

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

     Peter Filzmoser <P.Filzmose@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("Ni","Cu","Mg","Rb","Mn")
     x=log10(moss[,var])

     x.mcd=covMcd(x,cor=TRUE)
     x.rsc=scale(x,x.mcd$cent,sqrt(diag(x.mcd$cov)))
     pfa(x.rsc,factors=2,covmat=x.mcd,scores="regression",rotation="varimax",
         maxit=0,start=rep(0,ncol(x.rsc)))

