1 Type Package Title Multinomial Diversity Model Version 1.3 Date Package MDM February 19, 2015 Author Glenn De'ath Code for mdm was adapted from multinom in the nnet package (Brian Ripley and Bill Venables Maintainer Glenn De'ath Depends R (>= 2.9.0), nnet The multinomial diversity model is a toolbox for relating diversity to complex predictors. It is based on (1) Shannon diversity; (2) the multinomial logit model, and (3) the link between Shannon diversity and the log-likelihood of the MLM. License GPL-2 GPL-3 Repository CRAN Date/Publication :12:01 NeedsCompilation no R topics documented: MDM-package anova.mdm dev2div ed ed eds eds entropy.plot mdm simdata spider y2p Index 15 1
2 2 anova.mdm MDM-package Multinomial Diversity Model Details The multinomial diversity model is a toolbox for relating diversity to complex predictors. It is based on (1) Shannon diversity; (2) the multinomial logit model, and (3) the link between Shannon diversity and the log-likelihood of the MLM. Package: MDM Type: Package Version: 1.0 Date: License: GPL (version 2 or newer) LazyLoad: yes Author(s) Glenn De ath: References De ath, G. (2011) The Multinomial Diversity Model: Linking Shannon Diversity To Multiple Predictors library(mdm) fit0 <- mdm(y2p(spider6[,1:6])~1,data=spider6) fit1 <- mdm(y2p(spider6[,1:6])~water,data=spider6) fit2 <- mdm(y2p(spider6[,1:6])~water+herbs,data=spider6) fit3 <- mdm(y2p(spider6[,1:6])~site,data=spider6,alpha=true) anova(fit0,fit1,fit2,fit3) anova.mdm Analysis of Deviance, Entropy and Diversity Tables
3 anova.mdm 3 Provides an analysis of deviance, entropy and diversity for a collection of diversity models (outputs from mdm). ## S3 method for class mdm anova(object,..., topnote = TRUE, cols = c("df","dev","ent","div")[1:4]) object,... topnote cols objects of class mdm, usually, a result of a call to mdm. If TRUE then model descriptions appear above the anova table, if FALSE they appear as the first column of the table The list of colums to print out. Defaults to all columns. Details Specifying a single object gives a sequential analysis of deviance table for that fit. That is, the reductions in the residual sum of deviances, plus the residual deviance The deviances are converted into entropies and diversities, and differences in deviances are converted into differnces in entropies and diversities. If more than one object is specified, the table has a row for the residual degrees of freedom and sum of deviances for each model. For all but the first model, the change in degrees of freedom and deviances. This only makes statistical sense if the models are nested. It is conventional to list the models from smallest to largest, but this is up to the user. The analysis of deviance, entropy and diversity for a collection of diversity models. Note mdm is a modifed version of multinom in the nnet package. References De ath, G. (2011) The Multinomial Diversity Model: Linking Shannon Diversity To Multiple Predictors. Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge. Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer. multinom, nnet
4 4 dev2div library(mdm) fit0 <- mdm(y2p(spider6[,1:6])~1,data=spider6) fit1 <- mdm(y2p(spider6[,1:6])~water,data=spider6) fit2 <- mdm(y2p(spider6[,1:6])~water+herbs,data=spider6) fit3 <- mdm(y2p(spider6[,1:6])~site,data=spider6) anova(fit0,fit1,fit2,fit3) dev2div Converts deviances to diversities Takes either (1) a mdm or (2) a scaler, vector or matrix of deviances, and extracts or converts then to diverities. The relationship between deviance (dev) and diversity (d) is given by div = exp(dev/2/n) where n is the number of units (typically rows of a matrix) over which deviance is being averaged. dev2div(x, n) x n a mdm or a scaler, vector or matrix of deviances. if x is not a mdm, then the divisor in the conversion as defined as above. The diversity of x. ed, ed1, eds, eds1 x <- c(5,10,15) dev2div(x,n=10)
5 ed 5 ed Calculate alpha, beta and gamma true entropies and diversities Calculates alpha, beta and gamma true diversities of a data set. The matrix or data frame is automatically scaled to row sums of one. ed(x, q = 1, w = 1, retq = TRUE) x the input matrix or data frame. q the order of diversity; typically 0, 1 or 2. w retq weights if required. if TRUE then diversities are returned; if FALSE the entropies for alpha and gamma are returned. a vector of entropies or diversities dev2div, ed1, eds, eds1 ed(spider6[,1:6]) ed(spider6[,1:6],q=0) ed(spider6[,1:6],q=2) ed(spider6[,1:6],retq=false) ed(spider6[,1:6])
6 6 eds ed1 Calculate alpha, beta and gamma true entropies and diversities Calculates true diversities of individual sites of a data set ed1(x, q = 1, retq = TRUE) x the input matrix or data frame. q the order of diversity; typically 0, 1 or 2. retq a vector of entropies or diversities dev2div, ed1, eds, eds1 ed1(spider6[,1:6]) ed1(spider6[,1:6],q=0) ed1(spider6[,1:6],q=2) ed1(spider6[,1:6],retq=false) ed1(spider6[,1:6]) if TRUE then diversities are returned; if FALSE the entropies for alpha and gamma are returned. eds Calculate alpha, beta and gamma parametric entropies and diversities Calculates alpha, beta and gamma parametric diversities of a data set. The matrix or data frame is automatically scaled to row sums of one. Unlike true diversities where weighting is done through the function ed, weighting for parameterized diversity is done within the MDM, or more genrelaly by using case weights.
7 eds1 7 eds(x, q = 1, w = 1, retq = TRUE) x w the input matrix or data frame. weights if required. q the order of diversity; typically 0, 1 or 2. retq a vector of entropies or diversities dev2div, ed1, ed, eds1 eds(spider6[,1:6]) eds(spider6[,1:6],q=0) eds(spider6[,1:6],q=2) eds(spider6[,1:6],retq=false) eds(spider6[,1:6]) if TRUE then parametric diversities are returned; if FALSE the entropies for alpha and gamma are returned. eds1 Calculate alpha, beta and gamma parametric entropies and diversities Calculates parametric diversities of individual sites of a data set eds1(x, q = 1, retq = TRUE) x the input matrix or data frame. q the order of diversity; typically 0, 1 or 2. retq if TRUE then parametric diversities are returned; if FALSE the entropies for alpha and gamma are returned.
8 8 entropy.plot a vector of entropies or diversities dev2div, ed1, eds, eds1 eds1(spider6[,1:6]) eds1(spider6[,1:6],q=0) eds1(spider6[,1:6],q=2) eds1(spider6[,1:6],retq=false) eds1(spider6[,1:6]) entropy.plot Entropy plot Plots components of entropy by species or sites. entropy.plot(lst, y, x, ord=true, type=c("species","sites"), labs, segs = TRUE, wid.seg=0.25, pchs = c(15,19,0,2,6,2,15,17,21,17),...) lst y x ord type labs segs wid.seg pchs a list of mdm fitted models optional locations of the components on the vertical axis optional used to provide the range of the horizontal axis if TRUE orders the components by total entropy of each fitted model. should the plot break down entropy by species (columns = default) or sites (rows). an (optional) vector of labels for species (or sites if type == "sites"). use segments to plot end of bars as vertical lines widths of bar ends some nice plotting characters... other arguements passed to the plot function (e.g. cex).
9 mdm 9 a list of plotted values of the length of lst, each component of which is of length equal to the number of species (sites). mdm fit0 <- mdm(y2p(spider6[,1:6])~1,data=spider6) fit1 <- mdm(y2p(spider6[,1:6])~water+herbs,data=spider6) fit2 <- mdm(y2p(spider6[,1:6])~site,data=spider6,alpha=true) anova(fit0,fit1,fit2) entropy.plot(list(fit0,fit2,fit1)) mdm Fits the parameteric diversity model The parametric diversity model (mdm) is a new method for directly relating diversity to environmental predictors. It is based on three components: (1) parametric diversity, a new parametric model of diversity that can represent any configuration of species proportional abundances, (2) the multinomial logit model (MLM) that can relate species proportional abundances to complex predictors and (3) the link between parametric diversity and the likelihood function of the MLM. The mdm is fitted using the multinom package from nnet. Parametric diversities and true diversities can also be calculated for data sets using the functions eds, eds1, ed, ed1. mdm(formula, data, weights, subset, na.action, MaxNWts, maxit = 1000, contrasts = NULL, Hess = FALSE, censored = FALSE, model = TRUE, use.shortcut = TRUE,...) formula data a formula expression as for regression models, of the form response ~ predictors. The response should be a matrix with K columns comprising proportions for each of K classes. A log-linear model is fitted, with coefficients zero for the first class. An offset can be included: it should be a numeric matrix with K columns. See the documentation of formula() for other details. an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which mdm is called.
10 10 mdm weights subset na.action MaxNWts optional case weights in fitting. expression saying which subset of the rows of the data should be used in the fit. All observations are included by default. a function to filter missing data. The maximum allowable number of weights. There is no limit in the code, but MaxNWts is set to the exact number of required as specified in the formula. Thus it should not need to be changed when fitting mdm. maxit maximum number of iterations. Default contrasts Hess Details censored model use.shortcut a list of contrasts to be used for some or all of the factors appearing as variables in the model formula. logical for whether the Hessian (the O/E information matrix) should be returned. If Y is a matrix with K > 2 columns, interpret the entries as one for possible classes, zero for impossible classes, rather than as counts. logical. If true, the model frame is saved as component model of the returned object. logical. If true, and the model is ~1 (a constant) or ~sites (a factor with one level for each site) then the model is not fitted since the fitted values are known in each case. The first (alpha) model has fitted values equal to the input data and the second (gamma) model fits the row means. Fitting the alpha-model using nnet can be prohibitively expensive in computational time and is unneccessary. The returned models when use.shortcut == TRUE has the same components as when use.shortcut == FALSE, and hence can be used in anova tables and plotting. Using use.shortcut == TRUE can result in saving > 99% of computational time for a collection of models.... additional arguments for nnet. mdm calls multinom which calls nnet. The variables on the rhs of the formula should be roughly scaled to [0,1] or the fit will be slow or may not converge at all. A nnet object with additional components: deviance edf AIC Hessian model entropy diversity the residual deviance, compared to the full saturated model (that explains individual observations exactly). Also, minus twice log-likelihood the (effective) number of degrees of freedom used by the model the AIC for this fit if Hess is true if model is true the entropy of the fitted values the diversity of the fitted values
11 simdata 11 Note mdm is a modifed version of multinom in the nnet package. References De ath, G. (2011) The Multinomial Diversity Model: Linking Shannon Diversity To Multiple Predictors. Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge. Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer. multinom, nnet library(mdm) fit0 <- mdm(y2p(spider6[,1:6])~1,data=spider6) fit1 <- mdm(y2p(spider6[,1:6])~water,data=spider6) fit2 <- mdm(y2p(spider6[,1:6])~water+herbs,data=spider6) fit3 <- mdm(y2p(spider6[,1:6])~site,data=spider6) anova(fit0,fit1,fit2,fit3) simdata Species abundance data simulator Simulates species abundance data along a one-dimensional gradient simdata(d, p = 10, n = 100, strip0 = TRUE, extreme = 0, ret = TRUE, k.rand = FALSE, d.rand = TRUE, mu.rand = TRUE, s = rep((4:8)/10, length = p), amp = c(sample(1:5), rep = TRUE, length = p), skew = 1, ampfun, lst = FALSE, err = 1, err.type = c("p", "n"), as.df = TRUE, plotit = TRUE, ptype = "l", plty = 1, pcols = rainbow(p), add.rug = FALSE,...)
12 12 simdata d the (optional) locations of the species along the 1-D gradient. If d is given then it will define both the number of species and also the locations on the gradient e.g. d = rep(1:10,each=3) will generate species at locations 1,1,1,2,2,2,...,10,10,10. If d is not specified then d.rand = TRUE will randomly allocate the species modes along a gradient on [0, 1], but if d.rand = FALSE will uniformly distribute the species modes along a gradient. p number of species. n number of sites. strip0 if TRUE the sites with zero total abundance are omitted. extreme number typically in the range -1 to +1 with larger numbers reducing the range of species. ret if TRUE the generated data are returned k.rand should the be random (TRUE) or fixed d.rand should the be random (TRUE) or fixed mu.rand should the be random (TRUE) or fixed s the spans of the species response curves; s is the standard deviation of the spread amp the amplitudes of the species response curves skew the skewness of the distribution; range (>0 to 5), 1 = symmetric. ampfun any function to modify the amplitude lst if lst == TRUE then both the systematic and random values are returned err if err == 0 then the values are systematic with no random variation err.type type of error; p = poison, g = gaussian as.df if return returns a data frame, otherwise a matrix plotit if TRUE then the data are plotted ptype species plot types e.g. "l" gives lines plty species plot line types pcols species plot colours add.rug should a rug be added?... other arguments passed to plot. if lst == FALSE then a data frame with variables "Locations", "Taxa.1" "Taxa.N" where N is number of species. if lst == TRUE then two data frames "x" and "xs" with variables "Locations", "Taxa.1" "Taxa.N" and additionally, components "sigma", "amp" and "mu" that represent the spans, amplitudes and locations of the N species along the 1-D gradient. mydata <- simdata() summary(mydata) mydata <- simdata(p=5, n=50, amp=1, err=0, d.rand=false, mu.rand=false, plotit = TRUE) summary(mydata)
13 spider6 13 spider6 The spider data set Data set on abundances of spiders and environmental predictors. This is a subset of a larger data comprising 12 species and 6 environmental predictors. All variables are rated on a 0-9 scale. Format A data frame with 28 observations on the following 9 variables. Pard.lugu a numeric vector Pard.pull a numeric vector Troc.terr a numeric vector Pard.mont a numeric vector Alop.acce a numeric vector Alop.fabr a numeric vector Water a numeric vector Herbs a numeric vector Site a factor with 28 levels Source package mvpart References De ath G. (2002) Multivariate regression trees: A new technique for modeling species-environment relationships. Ecology, 2002, 83: summary(spider6) fit0 <- mdm(y2p(spider6[,1:6])~1,data=spider6) fit1 <- mdm(y2p(spider6[,1:6])~water+herbs,data=spider6) fit2 <- mdm(y2p(spider6[,1:6])~site,data=spider6,alpha=true) anova(fit0,fit1,fit2)
14 14 y2p y2p Scale rows of a species-sites data set The response matrix for mdm diversity analyses requires that species (columns of the data matrix) are scaled to proportions i.e. that they sum to one for each site (row of the data matrix). Two matrices are particularly useful; these correspond to alpha and gamma diversities. The former is particularly useful for mdm anovas that include the alpha model which is computationally expensive using the function mdm. y2p(y, mean=false) y mean matrix or data frame of numeric values to be transformed if mean = TRUE then each row is replaced by the species means scaled to row sums of one A matrix of the same dimensions as the input matrix. Each row of the matrix will sum to one. mydata <- matrix(0:8,nrow=3,ncol=3) mydata y2p(mydata)
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