hspider                 package:VGAM                 R Documentation

_H_u_n_t_i_n_g _S_p_i_d_e_r _D_a_t_a

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

     Abundance of hunting spiders in a Dutch dune area.

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

     data(hspider)

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

     A data frame with 28 observations (sites) on the following 18
     variables.

     _W_a_t_e_r_C_o_n Log percentage of soil dry mass.

     _B_a_r_e_S_a_n_d Log percentage cover of bare sand.

     _F_a_l_l_T_w_i_g Log percentage cover of fallen leaves and twigs.

     _C_o_v_e_M_o_s_s Log percentage cover of the moss layer.

     _C_o_v_e_H_e_r_b Log percentage cover of the herb layer.

     _R_e_f_l_L_u_x Reflection of the soil surface with cloudless sky.

     _A_l_o_p_a_c_c_e Abundance of _Alopecosa accentuata_.

     _A_l_o_p_c_u_n_e Abundance of _Alopecosa cuneata_.

     _A_l_o_p_f_a_b_r Abundance of _Alopecosa fabrilis_.

     _A_r_c_t_l_u_t_e Abundance of _Arctosa lutetiana_.

     _A_r_c_t_p_e_r_i Abundance of _Arctosa perita_.

     _A_u_l_o_a_l_b_i Abundance of _Aulonia albimana_.

     _P_a_r_d_l_u_g_u Abundance of _Pardosa lugubris_.

     _P_a_r_d_m_o_n_t Abundance of _Pardosa monticola_.

     _P_a_r_d_n_i_g_r Abundance of _Pardosa nigriceps_.

     _P_a_r_d_p_u_l_l Abundance of _Pardosa pullata_.

     _T_r_o_c_t_e_r_r Abundance of _Trochosa terricola_.

     _Z_o_r_a_s_p_i_n Abundance of _Zora spinimana_.

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

     The data, which originally came from Van der Aart and
     Smeek-Enserink (1975) consists of abundances (numbers trapped over
     a 60 week period) and 6 environmental variables.  There were 28
     sites.

     This data set has been often used to illustrate ordination, e.g.,
     using canonical correspondence analysis (CCA).  In the example
     below, the data is used for constrained quadratic ordination (CQO;
     formerly called canonical Gaussian ordination or CGO), a
     numerically intensive method that has many superior qualities. 
     See 'cqo' for details.

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

     Van der Aart, P. J. M. and Smeek-Enserink, N. (1975) Correlations
     between distributions of hunting spiders (Lycosidae, Ctenidae) and
     environmental characteristics in a dune area. _Netherlands Journal
     of Zoology_, *25*, 1-45.

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

     data(hspider)
     str(hspider)

     ## Not run: 
     set.seed(111)  # This leads to the global solution
     hspider[,1:6]=scale(hspider[,1:6]) # Standardize the environmental variables
     p1 = cqo(cbind(Alopacce, Alopcune, Alopfabr, Arctlute, Arctperi, Auloalbi,
                    Pardlugu, Pardmont, Pardnigr, Pardpull, Trocterr, Zoraspin) ~
              WaterCon + BareSand + FallTwig + CoveMoss + CoveHerb + ReflLux,
              fam = poissonff, data = hspider, Crow1posit=FALSE)
     nos = ncol(p1@y)
     lvplot(p1, y=TRUE, lcol=1:nos, pch=1:nos, pcol=1:nos) 
     Coef(p1)
     summary(p1)
     ## End(Not run)

