pulpfiber          package:robustbase          R Documentation(utf8)

_P_u_l_p _F_i_b_e_r _a_n_d _P_a_p_e_r _D_a_t_a

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

     Measurements of aspects pulp fibers and the paper produced from
     them. Four properties of each are measured in sixty-two samples.

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

     data(pulpfiber)

_F_o_r_m_a_t:

     A data frame with 62 observations on the following 8 variables.

     '_X_1' numeric vector of arithmetic fiber length

     '_X_2' numeric vector of long fiber fraction

     '_X_3' numeric vector of fine fiber fraction

     '_X_4' numeric vector of zero span tensile

     '_Y_1' numeric vector of breaking length

     '_Y_2' numeric vector of elastic modulus

     '_Y_3' numeric vector of stress at failure

     '_Y_4' numeric vector of burst strength

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

     Cited from the reference article: _The dataset contains
     measurements of properties of pulp fibers and the paper made from
     them.  The aim is to investigate relations between pulp fiber
     properties and the resulting paper properties.  The dataset
     contains n = 62 measurements of the following four pulp fiber
     characteristics: arithmetic fiber length, long fiber fraction,
     fine fiber fraction, and zero span tensile.  The four paper
     properties that have been measured are breaking length, elastic
     modulus, stress at failure, and burst strength._

     The goal is to predict the q = 4 paper properties from the p = 4
     fiber characteristics.

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

     port to R and this help page: Martin Maechler

_S_o_u_r_c_e:

     Rousseeuw, P.~J., Van Aelst, S., Van Driessen, K., and Agulló, J.
     (2004) Robust multivariate regression; _Technometrics_ *46*,
     293-305.

     <URL: http://allserv.ugent.be/~svaelst/data/pulpfiber.txt>

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

     Lee, J. (1992) _Relationships Between Properties of Pulp-Fibre and
     Paper_, unpublished doctoral thesis, U. Toronto, Faculty of
     Forestry.

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

     data(pulpfiber)
     str(pulpfiber)

     pairs(pulpfiber, gap=.1)
     ## 2 blocks of 4 ..
     c1 <- cov(pulpfiber)
     cR <- covMcd(pulpfiber)
     ## how different are they: The robust estimate has more clear high correlations:
     symnum(cov2cor(c1))
     symnum(cov2cor(cR$cov))

