1 ## compar.gee.R (2008-01-14)
3 ## Comparative Analysis with GEEs
5 ## Copyright 2002-2008 Emmanuel Paradis
7 ## This file is part of the R-package `ape'.
8 ## See the file ../COPYING for licensing issues.
10 compar.gee <- function(formula, data = NULL, family = "gaussian", phy,
11 scale.fix = FALSE, scale.value = 1)
13 require(gee, quietly = TRUE)
14 if (is.null(data)) data <- parent.frame() else {
15 if(!any(is.na(match(rownames(data), phy$tip.label))))
16 data <- data[phy$tip.label, ]
17 else warning("the rownames of the data.frame and the tip labels of the tree
18 do not match: the former were ignored in the analysis.")
20 effect.assign <- attr(model.matrix(formula, data = data), "assign")
21 for (i in all.vars(formula)) {
22 if (any(is.na(eval(parse(text = i), envir = data))))
23 stop("the present method cannot (yet) be used directly with missing data: you may consider removing the species with missing data from your tree with the function `drop.tip'.")
25 if (is.null(phy$edge.length))
26 stop("the tree has no branch lengths.")
27 R <- vcv.phylo(phy, cor = TRUE)
28 id <- rep(1, dim(R)[1])
29 geemod <- do.call("gee", list(formula, id, data = data, family = family, R = R,
30 corstr = "fixed", scale.fix = scale.fix,
31 scale.value = scale.value))
32 W <- geemod$naive.variance
33 if (family == "binomial")
34 W <- summary(glm(formula, family = quasibinomial, data = data))$cov.scaled
36 dfP <- sum(phy$edge.length)*N / sum(diag(vcv.phylo(phy)))
37 obj <- list(call = geemod$call,
38 effect.assign = effect.assign,
40 coefficients = geemod$coefficients,
41 residuals = geemod$residuals,
42 family = geemod$family$family,
43 link = geemod$family$link,
47 class(obj) <- "compar.gee"
51 print.compar.gee <- function(x, ...)
56 coef <- matrix(rep(coef, 4), ncol = 4)
57 dimnames(coef) <- list(cnames,
58 c("Estimate", "S.E.", "t", "Pr(T > |t|)"))
59 df <- x$dfP - dim(coef)[1]
60 coef[, 2] <- sqrt(diag(x$W))
61 coef[, 3] <- coef[, 1]/coef[, 2]
63 warning("not enough degrees of freedom to compute P-values.")
65 } else coef[, 4] <- 2 * (1 - pt(abs(coef[, 3]), df))
66 residu <- quantile(as.vector(x$residuals))
67 names(residu) <- c("Min", "1Q", "Median", "3Q", "Max")
71 cat("\nNumber of observations: ", x$nobs, "\n")
73 cat(" Link: ", x$link, "\n")
74 cat(" Variance to Mean Relation:", x$family, "\n")
75 cat("\nSummary of Residuals:\n")
78 cat("\n\nCoefficients: (", sum(nas), " not defined because of singularities)\n",
80 else cat("\n\nCoefficients:\n")
82 cat("\nEstimated Scale Parameter: ", x$scale)
83 cat("\n\"Phylogenetic\" df (dfP): ", x$dfP, "\n")
86 drop1.compar.gee <- function(object, scope, quiet = FALSE, ...)
88 fm <- formula(object$call)
90 z <- attr(trm, "term.labels")
91 ind <- object$effect.assign
93 ans <- matrix(NA, n, 3)
96 ans[i, 1] <- length(wh)
97 ans[i, 2] <- t(object$coefficients[wh]) %*%
98 solve(object$W[wh, wh]) %*% object$coefficients[wh]
100 df <- object$dfP - length(object$coefficients)
101 if (df < 0) warning("not enough degrees of freedom to compute P-values.")
102 else ans[, 3] <- pf(ans[, 2], ans[, 1], df, lower.tail = FALSE)
103 colnames(ans) <- c("df", "F", "Pr(>F)")
105 if (any(attr(trm, "order") > 1) && !quiet)
106 warning("there is at least one interaction term in your model:
107 you should be careful when interpreting the significance of the main effects.")
108 class(ans) <- "anova"
109 attr(ans, "heading") <- c("Single term deletions\n\nModel:\n",
110 as.character(as.expression(fm)))