Estiamtes regression coefficients for tip species curves from raw data. May be useful if wanting to perform methods without first performing ancestral curve reconstruction via the phylocurve() function.

1 2 | ```
get.tip.coefficients(formula, tree, data, ymin = 0.01, ymax = 0.99,
ylength = 30, species.identifier = "species", verbose = FALSE)
``` |

`formula` |
Formula for function-valued trait (currently only supports models of the form Y~X) |

`tree` |
A phylogenetic tree of class "phylo" |

`data` |
A data frame with data for tip curve estimation, where each row contains a single data point. A column named "species" has the species names corresponding to each data point, a predictor (X) variable and the response (Y) variable, which must be scaled between 0 and 1. |

`ymin` |
Because 0 and 1 are undefined for inverse logit functions, the minimum and maximum values are defaulted to .01 and .99. Can be adjusted as needed. |

`ymax` |
Because 0 and 1 are undefined for inverse logit functions, the minimum and maximum values are defaulted to .01 and .99. Can be adjusted as needed. |

`ylength` |
How many landmarks (points on the curve) to evaluate. |

`species.identifier` |
Default is "species". Can be changed if the column in data has a different species identifier name. |

`verbose` |
ether to print progress during tip curve coefficient estimation. |

Estimated regression coefficients of tip curves.

1 2 3 4 5 6 7 8 9 | ```
# simulate evolution of a function-valued trait (glm with logit link)
sim_data <- sim.curves()
# get tip coefficients and aligned function data
tip.coefficients <- get.tip.coefficients(formula = y~x,tree = sim_data$tree,data = sim_data$data)
data <- get.aligned.function.data(tip.coefficients)
# estimate evolutionary rates
evo.model.fitted <- evo.model(sim_data$tree,data)
``` |

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