bimac                  package:ouch                  R Documentation

_A_n_o_l_i_s _b_i_m_a_c_u_l_a_t_u_s _l_i_z_a_r_d _s_i_z_e _d_a_t_a.

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

     This is the Anolis bimaculatus dataset used in Butler & King 2004.
     It is used to test a hypothesis of character displacement using an
     interspecific dataset of body sizes and current data on
     sympatry/allopatry.  The data frame consists of the following
     columns: 'species' which are species names, 'size' which is the
     phenotypic data, and the variables 'ancestor' and 'time' which
     specify the topology of the phylogeny and the location of the
     nodes in time, respectively. The columns 'OU.1', 'OU.3', 'OU.4',
     and 'OU.LP' specify the hypothesized selective regimes (there are
     four alternatives in this example).  Explanations of the data are
     given below.  Mean species lengths.

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

     data(bimac)

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

     A data frame with 45 observations on the following 8 variables.

     _s_p_e_c_i_e_s Species names for extant species.

     _s_i_z_e Body size (head length in mm) of extant species.

     _a_n_c_e_s_t_o_r Ancestral node.

     _t_i_m_e Time of node.

     _O_U._1 a factor with levels 'ns'

     _O_U._3 a factor with levels 'small' 'medium' 'large'

     _O_U._4 a factor with levels 'small' 'medium' 'large' 'anc'

     _O_U._L_P a factor with levels 'small' 'medium' 'large'

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

_B_o_d_y _s_i_z_e. We use the phenotypic data and phylogeny of Losos (1990),
     which employed the head lengths (of males) as a proxy for body
     size. In this group of lizards, head length correlates very
     strongly with snout-to-vent length and the cube root of mass,
     which are standard measures of body size. The data are head
     lengths in mm, note that we use the log of this value in analyses.       

_T_r_e_e _t_o_p_o_l_o_g_y The tree topology is encoded via two vectors: 'ancestor'
     and 'time'.  Each node of the phylogenetic tree has a
     corresponding row in the data frame, numbered from 1 to 45. The
     columns 'ancestor' and 'time' specify the phylogeny. The
     'ancestor' variable specifies the topology: it is a list
     indicating the ancestor of each node.  The root node has ancestor
     0. The variable 'time' specifies the temporal location of each
     node, with the root node being at time 0.

_S_p_e_c_i_f_i_c_a_t_i_o_n_s _o_f _s_e_l_e_c_t_i_v_e _r_e_g_i_m_e_s. (Columns 'OU.1', 'OU.3', 'OU.4',
     'OU.LP').  These columns are factors, the levels of which
     correspond to the ``paintings'' of the respective adaptive regime
     hypotheses onto the phylogeny. Each selective regime is named
     (small, medium, large, etc.).  Put the corresponding name on each
     branch segment to indicate which selective regime it belongs to.
     Each column corresponds to a different painting of the selective
     regimes, and thus to a different hypothesis.  In this example,
     there are 3 alternative models (see Butler & King 2004): 'OU.4' is
     4-regime model, 'OU.3' is 3-regime model (all ancestors are
     medium), 'OU.LP' is linear parsimony model.

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

     Aaron A. King <king at tiem dot utk dot edu>

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

     Butler, M.A. and A.A. King (2004) Phylogenetic comparative
     analysis: a modeling approach for adaptive evolution. American
     Naturalist, in press.

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

     Lazell, J. D. 1972. The anoles (Sauria: Iguanidae) of the Lesser
     Antilles. Bull. Mus. Comp. Zool., 143:1-115.

     Losos, J. B. 1990. A phylogenetic analysis of character
     displacement in Caribbean Anolis lizards. Evolution, 44:558-569.

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

     data(bimac)
     attach(bimac)
     tree.plot(ancestor,time,names=species,regimes=OU.LP)
     brown.fit(log(size),ancestor,time/max(time))
     hansen.fit(log(size),ancestor,time/max(time),OU.1)
     hansen.fit(log(size),ancestor,time/max(time),OU.3)
     hansen.fit(log(size),ancestor,time/max(time),OU.4)
     hansen.fit(log(size),ancestor,time/max(time),OU.LP)

