# R/procD.lm.r In geomorph: Geometric Morphometric Analyses of 2D/3D Landmark Data

#### Documented in procD.lm

#' Procrustes ANOVA/regression for shape data
#'
#' Function performs Procrustes ANOVA with permutation procedures to assess statistical hypotheses describing
#'   patterns of shape variation and covariation for a set of Procrustes-aligned coordinates
#'
#' The function quantifies the relative amount of shape variation attributable to one or more factors in a
#'   linear model and estimates the probability of this variation ("significance") for a null model, via distributions generated
#'   from resampling permutations. Data input is specified by a formula (e.g.,
#'   y~X), where 'y' specifies the response variables (shape data), and 'X' contains one or more independent
#'   variables (discrete or continuous). The response matrix 'y' can be either in the form of a two-dimensional data
#'   matrix of dimension (n x [p x k]), or a 3D array (p x n x k).  It is assumed that  -if the data based
#'   on landmark coordinates - the landmarks have previously been aligned using Generalized Procrustes Analysis (GPA)
#'   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  \code{\link{lm}} function)
#'   can also be input in place of a formula.  Arguments for \code{\link{lm}} can also be passed on via this function.
#'
#'   The function \code{\link{two.d.array}} can be used to obtain a two-dimensional data matrix from a 3D array of landmark
#'   coordinates; however this step is no longer necessary, as procD.lm can receive 3D arrays as dependent variables.  It is also
#'   recommended that \code{\link{geomorph.data.frame}} is used to create and input a data frame.  This will reduce problems caused
#'   by conflicts between the global and function environments.  In the absence of a specified data frame, procD.lm will attempt to
#'   coerce input data into a data frame, but success is not guaranteed.
#'
#'   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 observed SS are 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). Two possible resampling procedures are provided. First, if RRPP=FALSE,
#'   the rows of the matrix of shape variables are randomized relative to the design matrix.
#'   This is analogous to a 'full' randomization. Second, if RRPP=TRUE, a residual randomization permutation procedure is utilized
#'   (Collyer et al. 2015). Here, residual shape values from a reduced model are
#'   obtained, and are randomized with respect to the linear model under consideration. These are then added to
#'   predicted values from the remaining effects to obtain pseudo-values from which SS are calculated. NOTE: for
#'   single-factor designs, the two approaches are identical.  However, when evaluating factorial models it has been
#'   shown that RRPP attains higher statistical power and thus has greater ability to identify patterns in data should
#'   they be present (see Anderson and terBraak 2003).
#'
#'   Effect-sizes (Z scores) are computed as standard deviates of either the SS,
#'   F, or Cohen's f-squared sampling distributions generated, which might be more intuitive for P-values than F-values
#'   (see Collyer et al. 2015).  Values from these distributions are log-transformed prior to effect size estimation,
#'   to assure normally distributed data.  The SS type will influence how Cohen's f-squared values are calculated.
#'   Cohen's f-squared values are based on partial eta-squared values that can be calculated sequentially or marginally, as with SS.
#'
#'   In the case that multiple factor or factor-covariate interactions are used in the model
#'   formula, one can specify whether all main effects should be added to the
#'   model first, or interactions should precede subsequent main effects
#'   (i.e., Y ~ a + b + c + a:b + ..., or Y ~ a + b + a:b + c + ..., respectively.)
#'
#'   The generic function, \code{\link{plot}} has several options for plotting, using \code{\link{plot.procD.lm}}.  Diagnostics plots,
#'   principal component plots (rotated to first PC of covariance matrix of fitted values), and regression plots can be performed.  The
#'   latter is fundamentally similar to the plotting options for \code{\link{procD.allometry}}.  One must provide a linear predictor, and
#'   can choose among common regression component (CRC), predicted values (PredLine), or regression scores (RegScore).  See \code{\link{procD.allometry}}
#'   for details. In these plotting optons, the predictor does not need to be size, and fitted values and residuals from the procD.lm fit are used rather
#'   than mean-centered values.
#'
#'  \subsection{Notes for geomorph 3.0.4 and subsequent versions}{
#'  Compared to previous versions of geomorph, users might notice differences in effect sizes.  Previous versions used z-scores calculated with
#'  expected values of statistics from null hypotheses (sensu Collyer et al. 2015); however Adams and Collyer (2016) showed that expected values
#'  for some statistics can vary with sample size and variable number, and recommended finding the expected value, empirically, as the mean from the set
#'  of random outcomes.  Geomorph 3.0.4 and subsequent versions now center z-scores on their empirically estimated expected values and where appropriate,
#'  log-transform values to assure statistics are normally distributed.  This can result in negative effect sizes, when statistics are smaller than
#'  expected compared to the average random outcome.  For ANOVA-based functions, the option to choose among different statistics to measure effect size
#'  is now a function argument.
#' }
#'
#' @param f1 A formula for the linear model (e.g., y~x1+x2)
#' @param iter Number of iterations for significance testing
#' @param 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.
#' @param RRPP A logical value indicating whether residual randomization should be used for significance testing
#' @param effect.type One of "F", "SS", or "cohen", to choose from which random distribution to estimate effect size.
#' (The option, "cohen", is for Cohen's f-squared values.  The default is "F".  Values are log-transformed before z-score calculation to
#' assure normally distributed data.)
#' @param int.first A logical value to indicate if interactions of first main effects should precede subsequent main effects
#' @param data A data frame for the function environment, see \code{\link{geomorph.data.frame}}
#' @param print.progress A logical value to indicate whether a progress bar should be printed to the screen.
#' This is helpful for long-running analyses.
#' @param ... Arguments passed on to procD.fit (typically associated with the lm function,
#' such as weights or offset).  The function procD.fit can also currently
#' handle either type I, type II, or type III sums of squares and cross-products (SSCP) calculations.  Choice of SSCP type can be made with the argument,
#' SS.type; i.e., SS.type = "I" or SS.type = "III".  Only advanced users should consider using these additional arguments, as such arguments
#' are experimental in nature.
#' @keywords analysis
#' @export
#' @author Dean Adams and Michael Collyer
#' @return An object of class "procD.lm" is a list containing the following
#' \item{aov.table}{An analysis of variance table; the same as the summary.}
#' \item{call}{The matched call.}
#' \item{coefficients}{A vector or matrix of linear model coefficients.}
#' \item{Y}{The response data, in matrix form.}
#' \item{X}{The model matrix.}
#' \item{QR}{The QR decompositions of the model matrix.}
#' \item{fitted}{The fitted values.}
#' \item{residuals}{The residuals (observed responses - fitted responses).}
#' \item{weights}{The weights used in weighted least-squares fitting.  If no weights are used,
#' NULL is returned.}
#' \item{Terms}{The results of the \code{\link{terms}} function applied to the model matrix}
#' \item{term.labels}{The terms used in constructing the aov.table.}
#' \item{data}{The data frame for the model.}
#' \item{SS}{The sums of squares for each term, model residuals, and the total.}
#' \item{SS.type}{The type of sums of squares.  One of type I or type III.}
#' \item{df}{The degrees of freedom for each SS.}
#' \item{R2}{The coefficient of determination for each model term.}
#' \item{F}{The F values for each model term.}
#' \item{permutations}{The number of random permutations (including observed) used.}
#' \item{random.SS}{A matrix or vector of random SS found via the resampling procedure used.}
#' \item{random.F}{A matrix or vector of random F values found via the resampling procedure used.}
#' \item{random.cohenf}{A matrix or vector of random Cohen's f-squared values
#'  found via the resampling procedure used.}
#' \item{permutations}{The number of random permutations (including observed) used.}
#' \item{effect.type}{The distribution used to estimate effect-size.}
#' \item{perm.method}{A value indicating whether "Raw" values were shuffled or "RRPP" performed.}
#' @references Anderson MJ. 2001. A new method for non-parametric multivariate analysis of variance.
#'    Austral Ecology 26: 32-46.
#' @references Anderson MJ. and C.J.F. terBraak. 2003. Permutation tests for multi-factorial analysis of variance.
#'    Journal of Statistical Computation and Simulation 73: 85-113.
#' @references Goodall, C.R. 1991. Procrustes methods in the statistical analysis of shape. Journal of the
#'    Royal Statistical Society B 53:285-339.
#' @references 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.
#' @references Adams, D.C. and M.L. Collyer. 2016.  On the comparison of the strength of morphological integration across morphometric
#' datasets. Evolution. 70:2623-2631.
#' @examples
#' ### MANOVA example for Goodall's F test (multivariate shape vs. factors)
#' data(plethodon)
#' Y.gpa <- gpagen(plethodonland) #GPA-alignment #' gdf <- geomorph.data.frame(shape = Y.gpacoords,
#' site = plethodon$site, species = plethodon$species) # geomorph data frame
#'
#' procD.lm(shape ~ species * site, data = gdf, iter = 999, RRPP = FALSE) # randomize raw values
#' procD.lm(shape ~ species * site, data = gdf, iter = 999, RRPP = TRUE) # randomize residuals
#'
#' ### Regression example
#' data(ratland)
#' rat.gpa<-gpagen(ratland)         #GPA-alignment
#' gdf <- geomorph.data.frame(rat.gpa) # geomorph data frame is easy without additional input
#'
#' procD.lm(coords ~ Csize, data = gdf, iter = 999, RRPP = FALSE) # randomize raw values
#' procD.lm(coords ~ Csize, data = gdf, iter = 999, RRPP = TRUE) # randomize raw values
#' # Outcomes should be exactly the same
#'
#' ### Extracting objects and plotting options
#' rat.anova <- procD.lm(coords ~ Csize, data = gdf, iter = 999, RRPP = TRUE)
#' summary(rat.anova)
#' # diagnostic plots
#' plot(rat.anova, type = "diagnostics")
#' # diagnostic plots, including plotOutliers
#' plot(rat.anova, type = "diagnostics", outliers = TRUE)
#' # PC plot rotated to major axis of fitted values
#' plot(rat.anova, type = "PC", pch = 19, col = "blue")
#' # Uses residuals from model to find the commonom regression component
#' # for a predictor from the model
#' plot(rat.anova, type = "regression", predictor = gdf$Csize, reg.type = "CRC", #' pch = 19, col = "green") #' # Uses residuals from model to find the projected regression scores #' rat.plot <- plot(rat.anova, type = "regression", predictor = gdf$Csize, reg.type = "RegScore",
#' pch = 21, bg = "yellow")
#'
#' # TPS grids for min and max scores in previous plot
#' preds <- shape.predictor(gdf$coords, x = rat.plot$RegScore,
#'                         predmin = min(rat.plot$RegScore), #' predmax = max(rat.plot$RegScore))
#' M <- rat.gpa$consensus #' plotRefToTarget(M, preds$predmin, mag=3)
#' plotRefToTarget(M, preds$predmax, mag=3) #' #' attributes(rat.anova) #' rat.anova$fitted # just the fitted values
procD.lm<- function(f1, iter = 999, seed=NULL, RRPP = TRUE, effect.type = c("F", "SS", "cohen"),
int.first = FALSE,  data=NULL, print.progress = TRUE, ...){
if(int.first) ko = TRUE else ko = FALSE
if(!is.null(data)) data <- droplevels(data)
pfit <- procD.fit(f1, data=data, keep.order=ko,  pca=FALSE, ...)
Y <- pfit$Y n <- dim(pfit$Y)[[1]]
p <- dim(pfit$Y)[[2]] k <- length(pfit$term.labels)
if(p > n) pfitr <- procD.fit(f1, data=data, keep.order=ko,  pca=TRUE, ...) else
pfitr <- pfit
if(k > 0) {
if(print.progress == TRUE){
if(RRPP == TRUE) P <- SS.iter(pfitr, Yalt="RRPP", iter=iter, seed=seed) else
P <- SS.iter(pfitr, Yalt="resample", iter=iter, seed=seed)
} else {
if(RRPP == TRUE) P <- .SS.iter(pfitr, Yalt="RRPP", iter=iter, seed=seed) else
P <- .SS.iter(pfitr, Yalt="resample", iter=iter, seed=seed)
}
SS <-P$SS SSE <- P$SSE
SSY <- P$SSY anova.parts.obs <- anova.parts(pfitr, P) anova.tab <-anova.parts.obs$anova.table
df <- anova.parts.obs$df effect.type <- match.arg(effect.type) SS.type <- pfit$SS.type
if(is.matrix(SS)){
MSE <- SSE/df[k+1]
SSE.mat <- matrix(SSE, k, length(SSE), byrow = TRUE)
MSE.mat <- matrix(MSE, k, length(MSE), byrow = TRUE)
Fs <- (SS[1:k,]/df[1:k])/MSE.mat
if(SS.type == "III") {
etas <- SS/(SS+SSE.mat)
cohenf <- etas/(1-etas)
} else {
etas <- SS/SSY
unexp <- 1 - apply(etas, 2, cumsum)
cohenf <- etas/unexp
}
P.val <- apply(Fs, 1, pval)
if(effect.type == "SS") Z <- apply(log(SS), 1, effect.size) else
if(effect.type == "F") Z <- apply(log(Fs), 1, effect.size) else
Z <- apply(log(cohenf), 1, effect.size)
rownames(SS) <- rownames(Fs) <- rownames(cohenf) <- pfit$term.labels colnames(SS) <- colnames(Fs) <- colnames(cohenf) <- c("obs", paste("iter", 1:iter, sep=":")) } else { MSE <- SSE/df[2] Fs <- (SS/df[1])/MSE etas <- SS/SSY cohenf <- etas/(1-etas) P.val <- pval(Fs) if(effect.type == "SS") Z <- effect.size(log(P)) else if(effect.type == "F") Z <- effect.size(log(Fs)) else Z <- effect.size(log(cohenf)) names(SS) <- names(Fs) <- names(cohenf) <- c("obs", paste("iter", 1:iter, sep=":")) } tab <- data.frame(anova.tab, Z = c(Z, NA, NA), Pr = c(P.val, NA, NA)) colnames(tab)[1] <- "Df" colnames(tab)[ncol(tab)] <- "Pr(>F)" class(tab) <- c("anova", class(tab)) out <- list(aov.table = tab, call = match.call(), coefficients=pfit$wCoefficients.full[[k]],
Y=pfit$Y, X=pfit$X,
QR = pfit$wQRs.full[[k]], fitted=pfit$wFitted.full[[k]],
residuals = pfit$wResiduals.full[[k]], weights = pfit$weights, Terms = pfit$Terms, term.labels = pfit$term.labels,
data = pfit$data, SS = anova.parts.obs$SS, SS.type = SS.type, df = anova.parts.obs$df, R2 = anova.parts.obs$R2[1:k], F = anova.parts.obs$Fs[1:k], permutations = iter+1, random.SS = SS, random.F = Fs, random.cohenf = cohenf, effect.type=effect.type, perm.method = ifelse(RRPP==TRUE,"RRPP", "Raw")) } else { Y <- pfit$wY
SSY <- sum(center(Y)^2)
n <- NROW(Y)
df <- n - 1
tab <- data.frame(Df = df,SS = SSY,
MS = SSY/df, Rsq = NA,
F = NA, P = NA)
rownames(tab) <- "Residuals"
colnames(tab)[NCOL(tab)] <- "Pr(>F)"
class(tab) = c("anova", class(tab))
out <- list(aov.table = tab, call = match.call(),
coefficients=pfit$wCoefficients.full[[1]], Y=pfit$Y,  X=pfit$X, QR = pfit$wQRs.full[[1]], fitted=pfit$wFitted.full[[1]], residuals = pfit$wResiduals.full[[1]],
weights = pfit$weights, Terms = pfit$Terms, term.labels = pfit$term.labels, data = pfit$data)
}
class(out) <- "procD.lm"
out
}


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geomorph documentation built on Aug. 10, 2017, 1:11 a.m.