Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/advanced.procD.lm.r

The function quantifies the relative amount of shape variation explained by a suite of factors and covariates in a "full" model, after accounting for variation in a "reduced" model. Inputs are formulae for full and reduced models (order is not important, but it is better to list the model with the most terms first or use a geomorph data frame), plus indication if means or slopes are to be compared among groups, with appropriate formulae to define how they should be compared.

1 2 3 |

`f1` |
A formula for a linear model, containing the response matrix (e.g., y ~ x1 + x2) |

`f2` |
A formula for another linear model (e.g., ~ x1 + x2 + x3 + a*b). f1 and f2 should be nested. |

`groups` |
A formula for grouping factors (e.g., ~ a, or ~ a*b) |

`slope` |
A formula with one covariate (e.g., ~ x3) |

`angle.type` |
A value specifying whether differences between slopes should be represented by vector correlations (r), radians (rad) or degrees (deg) |

`pc.shape` |
An argument for whether analysis should be performed on the principal component scores fo shape. This is a useful option if the data are high-dimensional (many more variables that observations) but will not affect results |

`iter` |
Number of iterations for significance testing |

`seed` |
An optional argument for setting the seed for random permutations of the resampling procedure. If left NULL (the default), the exact same P-values will be found for repeated runs of the analysis (with the same number of iterations). If seed = "random", a random seed will be used, and P-values will vary. One can also specify an integer for specific seed values, which might be of interest for advanced users. |

`print.progress` |
A logical value to indicate whether a progress bar should be printed to the screen. This is helpful for long-running analyses. |

`data` |
A data frame for the function environment; see |

`...` |
Arguments passed on to procD.fit (typically associated with the lm function) |

The response matrix 'y' can be in the form of a two-dimensional data
matrix of dimension (n x [p x k]) or a 3D array (p x k x n). It is assumed that the landmarks have previously
been aligned using Generalized Procrustes Analysis (GPA) [e.g., with `gpagen`

]. The names specified for the
independent (x) variables in the formula represent one or more
vectors containing continuous data or factors. It is assumed that the order of the specimens in the
shape matrix matches the order of values in the independent variables. Linear model fits (using the `lm`

function)
can also be input in place of a formula. Arguments for `lm`

can also be passed on via this function.

The function performs statistical assessment of the terms in the model using Procrustes distances among
specimens, rather than explained covariance matrices among variables. With this approach, the sum-of-squared
Procrustes distances are used as a measure of SS (see Goodall 1991). The SS between models is evaluated through
permutation. In morphometrics this approach is known as a Procrustes ANOVA (Goodall 1991), which is equivalent
to distance-based anova designs (Anderson 2001). Unlike `procD.lm`

, this function is strictly for comparison
of two nested models. (Use of `procD.lm`

will be more suitable in most cases.)
A residual randomization permutation procedure (RRPP) is utilized
for reduced model residuals to evaluate the SS between models (Collyer et al. 2015). Effect-sizes (Z-scores) are
computed as standard deviates of the SS sampling
distributions generated, which might be more intuitive for P-values than F-values (see Collyer et al. 2015).

Pairwise tests are only performed if formulae are provided to compute such results.
The generic functions, `print`

, `summary`

, and `plot`

all work with `advanced.procD.lm`

.
The generic function, `plot`

, produces diagnostic plots for Procrustes residuals of the linear fit.

Function returns an ANOVA table of statistical results for model comparison: error df (for each model), SS, MS,
F ratio, Z, and Prand. A list of essentially the same components as `procD.lm`

is also returned, and additionally
LS means or slopes, pairwise differences comparisons of these, effect sizes, and P-values may also be returned. If a group formula
is provided but slope formula is null, pairwise differences are Procrustes distances between least squares (LS) means for the
defined groups. If a slope formula is provided, two sets of pairwise differences, plus effect sizes and P-values, are provided.
The first is for differences in slope vector length (magnitude). The length of the slope vector corresponds to the amount of shape
change per unit of covariate change. Large differences correspond to differences in the amount of shape change between groups.
The second is for slope vector orientation differences. Differences in the direction of shape change (covariance of shape variables)
can be summarized as a vector correlation or angle between vectors. See `summary.advanced.procD.lm`

for summary options.

Michael Collyer

Collyer, M.L., D.J. Sekora, and D.C. Adams. 2015. A method for analysis of phenotypic change for phenotypes described by high-dimensional data. Heredity. 115:357-365.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ```
data(plethodon)
Y.gpa<-gpagen(plethodon$land) #GPA-alignment
gdf <- geomorph.data.frame(Y.gpa, species = plethodon$species, site = plethodon$site)
# Example of a nested model comparison (as with ANOVA with RRPP)
advanced.procD.lm(coords ~ log(Csize) + species,
~ log(Csize)*species*site, iter=499, data = gdf)
# Example of a test of a factor interaction, plus pairwise comparisons
advanced.procD.lm(coords ~ site*species, ~ site + species, groups = ~site*species,
iter=499, data = gdf)
# Example of a test of a factor interaction, plus pairwise comparisons,
# accounting for a common allometry
advanced.procD.lm(coords ~ Csize + site*species,
~ log(Csize) + site + species,
groups = ~ site*species, slope = ~log(Csize), iter = 499, data = gdf)
# Example of a test of homogeneity of slopes, plus pairwise slopes comparisons
gdf$group <- factor(paste(gdf$species, gdf$site, sep="."))
advanced.procD.lm(coords ~ log(Csize) + group,
~ log(Csize) * group,
groups = ~ group,
slope = ~ log(Csize), angle.type = "deg", iter = 499, data = gdf)
# Example of partial pairwise comparisons, given greater model complexity.
# Plus, working with class advanced.procD.lm objects.
aov.pleth <- advanced.procD.lm(coords ~ log(Csize)*site*species,
~ log(Csize) + site*species,
groups = ~ species, slope = ~ log(Csize), angle.type = "deg", iter = 499, data = gdf)
summary(aov.pleth) # ANOVA plus pairwise tests
plot(aov.pleth) # diagnostic plots
aov.pleth$slopes # extract the slope vectors
``` |

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