Species Abundance Data Transformation

Description

Transforms species abundances according to an arbitrary specified vector

Usage

abundtrans(comm,code,value)

Arguments

Details

Performs a respective substitution to transform specific values in an initial data.frame to other specified values.

Value

a data.frame of transformed abundance data

Note

Vegetation data are often collected in arbitrary abundance schemes (e.g. Braun-Blanquet, Domin, etc.) which have no direct algebraic transformation (e.g. log). This function transforms coded abundances to arbitrary importance values as specified.

Author(s)

David W. Roberts droberts@montana.edu

See Also

decostand, wisconsin

Examples

data(bryceveg)
old <- c(0.2,0.5,1.0,2.0,3.0,4.0,5.0,6.0)
new <- c(0.2,0.5,3.0,15.0,37.5,62.5,85.0,97.5)
midpoint <- abundtrans(bryceveg,old,new)

Abundance/Occurrence Graphical Analysis

Description

Calculates and plots summary statistics about species occurrences in a data frame

Usage

abuocc(comm,minabu=0,panel='all')

Arguments

Details

This functions calculates and plots four data summaries about the occurrence of species:

Plots:

1) the number of samples each species occurs in on a log scale, sorted from maximum to minimum

2) the number of species in each sample plot (species richness) from highest to lowest

3) the mean abundance of non-zero values (on a log scale) as a function of the number of plots a species occurs in

4) the total abundance/sample as a function of the plot-level species richness

The third plot allows you to identify individual species with the mouse; the fourth plot allows you to identify individual sample units with the mouse.

Value

Returns an (invisible) list composed of:

Note

It's common in niche theory analyses to calculate the rank abundances of taxa in a sample. This function is similar, but works on multiple samples simultaneously. The spc.plt vector in the returned list can be used anywhere species richness is desired. The plt.spc vector in the returned list can be used to mask out rare species in calculations of sample similarity using dsvdis among other purposes.

Author(s)

David W. Roberts droberts@montana.edu

See Also

fisherfit, prestonfit, radfit

Examples

data(bryceveg) # produces a data.frame called bryceveg
abuocc(bryceveg)

Convert existing and external ordinations to dsv format

Description

This function updates ordinations from previous versions of labdsv and converts ordinations of class ‘boral’ from package boral, list output objects from package Rtsne, class ‘metaMDS’ objects from package vegan, or class ‘ordiplot’ objects from package vegan into objects of class ‘dsvord’ for plotting and comparison.

Usage

as.dsvord(obj)

Arguments

Details

as.dsvord calls internal format-specific conversion functions to produce an object of class ‘dsvord’ from the given input.

Value

an object of class ‘dsvord’, i.e. a list with items ‘points’ and ‘type’ (optionally more), and attributes ‘call’ and ‘timestamp’ and ‘class’.

Note

LabDSV recently converted all ordination objects to a single class with an ancillary ‘type’ specification to differentiate ordination types.

Author(s)

David W. Roberts droberts@montana.edu

Examples

## Not run: data(bryceveg)
dis.bc <- dsvdis(bryceveg,'bray')
library(vegan)
demo.metaMDS <- metaMDS(bryceveg)
metamds.dsv <- as.dsvord(demo.metaMDS)
demo.ordi <- plot(demo.metaMDS)
ordip.dsv <- as.dsvord(demo.ordi)
library(boral)
demo.boral <- boral(bryceveg,row.eff='random')
boral.dsv <- as.dsvord(demo.boral)

## End(Not run)

Site Data for Bryce Canyon National Park

Description

Environmental variables recorded at or calculated for each of 160 sample plots in Bryce Canyon National Park, Utah, U.S.A.

Usage

data(brycesite)

Format

a data.frame with sample units as rows and site variables as columns. Variables are:

plotcode

= original plot codes

annrad

= annual direct solar radiation in Langleys

asp

= slope aspect in degrees

av

= aspect value = (1+cosd(asp-30))/2

depth

= soil depth = "deep" or "shallow"

east

= UTM easting in meters

elev

= elevation in feet

grorad

= growing season radiation in Langleys

north

= UTM northing in meters

pos

= topographic position

quad

= USGS 7.5 minute quad sheet

slope

= percent slope

Bryce Canyon Vegetation Data

Description

Estimates of cover class for all non-tree vascular plant species in 160 375m^2 circular sample plots. Species codes are first three letters of genus + first three letters of specific epithet.

Usage

data(bryceveg)

Format

a data.frame of 160 sample units (rows) and 169 species (columns). Cover is estimated in codes as follows:

0.2

present in the stand but not the plot

0.5

0-1%

1.0

1-5%

2.0

5-25%

3.0

25-50%

4.0

50-75%

5.0

75-95%

6.0

95-100%

Calculate fitted environmental attributes in an ordination

Description

Fits a Generalized Additive Model (GAM) for each environmental variable in a data.frame against an ordination.

Usage

## S3 method for class 'dsvord'
calibrate(ord,site,dims=1:ncol(ord$points),
           family='gaussian',gamma=1,keep.models=FALSE,...)

Arguments

Details

The calibrate function sequentially and independently fits a GAM model for each environmental variable as a function of ordination coordinates, using the family and gamma specifiers supplied in the function call, or their defaults. The model fits two or three dimensional models; if the length of dims is greater than three the dimensions are truncated to the first three chosen.

Value

A list object with vector elements aic, dev.expl, adj.rsq, and fitted value matrix. Optionally, if keep.models is TRUE, a list with all of the GAM models fitted. List element aic gives the model AICs for each variable, dev.expl gives the deviance explained, adj.rsq gives the adjusted r-Squared, and fitted gives the expected value of each variable in each sample unit.

Author(s)

David W. Roberts droberts@montana.edu

See Also

predict for the complementary function that fits GAM models for species

Examples

data(bryceveg)
dis.man <- dist(bryceveg,method="manhattan")
demo.nmds <- nmds(dis.man,k=4)
## Not run: res <- calibrate(demo.nmds,brycesite[,c(2,4,7,12)],minocc=10)

Community Composition Modeling

Description

Compares the composition of modeled communities to real data using Bray-Curtis similarity

Usage

ccm(model,data)

Arguments

Details

The algorithm sweeps through the fitted values and data one sample unit at time calculating the similarity to the simulated community to the real community. The calculation is similarity, not dissimilarity, and results in a vector of length equal to the number of sample units.

The diverse matrix has the diversity of the data in the first column, and the diversity of the simulated or fitted data in the second column.

Value

A list object with two components:

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg) 
bryceveg <- dropspc(bryceveg,4)
bryce.bc <- dsvdis(bryceveg,'bray')
bryce.nmds <- nmds(bryce.bc)
## Not run: bryce.preds <- predict(bryce.nmds,bryceveg)
## Not run: bryce.ccm <- ccm(bryceveg,bryce.preds$fitted)
## Not run: summary(bryce.ccm$sim)
## Not run: boxplot(bryce.ccm$diverse)

Compositional Specificity Analysis

Description

Calculates the mean similarity of all plots in which each species occurs

Usage

compspec(comm, dis, numitr=100, drop=FALSE, progress=FALSE)
## S3 method for class 'compspec'
plot(x,spc=NULL,pch=1,type='p',col=1,...)

Arguments

Value

a list with several data.frames: ‘vals’ with species name, mean similarity, number of occurrences, and probability of observing as high a mean similarity as observed, and ‘quantiles’ with the distribution of the quantiles of mean similarity for given numbers of occurrences. If drop=TRUE, results specific to dropping out each species in turn are added to the list by species name.

Note

One measure of the habitat specificity of a species is the degree to which a species only occurs in communities that are similar to each other. This function calculates the mean similarity of all samples in which each species occurs, and compares that value to the distribution of mean similarities for randomly generated sets of the same size. The mean similarity of species which only occur once is set to 0, rather than NA.

If drop=TRUE each species is deleted in turn and a new dissimilarity matrix minus that species is calculated for the analysis. This eliminates the bias that part of the similarity of communities being analyzed is due to the known joint occurrence of the species being analyzed.

Author(s)

David W. Roberts droberts@montana.edu

See Also

indval,isamic

Examples

data(bryceveg) # returns a vegetation data.frame
dis.bc <- dsvdis(bryceveg,'bray/curtis')
    # returns a Bray/Curtis dissimilarity matrix
compspec(bryceveg,dis.bc)

Constancy-Coverage Table for Ecological Community Data

Description

Produces a table of combined species constancy and importance

Usage

concov(comm,clustering,digits=1,width=5,typical=TRUE,thresh=10)

Arguments

Details

concov calls const and importance and then combines the output in a single table.

Value

a data.frame with factors (combined constancy and coverage) as columns

Note

Constancy-coverage tables are an informative and concise representation of species in classified types. The output format [constancy(mean cover)] follows the convention of the US Forest Service vegetation classifications.

Author(s)

David W. Roberts droberts@montana.edu

See Also

const, importance

Examples

data(bryceveg)  # returns a vegetation data.frame
data(brycesite) # returns a site data.frame
## Not run: concov(bryceveg,brycesite$quad) # calculates the constancy 
                                         # and coverage by USGS quad
## End(Not run)

Constancy Table

Description

For a classified set of vegetation samples, lists for each species the fraction of samples in each class the species occurs in.

Usage

const(comm, clustering, minval = 0, show = minval, digits = 2, 
             sort = FALSE, spcord = NULL)

Arguments

Details

Produces a table with species as rows, and species constancy in clusters as columns.

The ‘clustering’ vector represents a classification of the samples that the table summarizes. It may result from a cluster analysis, partitioning an ordination, subjective partitioning of a vegetation table, or other source.

The ‘minval’ argument is used to emphasize the dominant species and suppress the rare species. Vegetation tables are often very sparse, and this argument simplifies making them more compact.

The ‘digits’ argument limits the reported precision of the calculations. Generally, relatively low precision is adequate and perhaps more realistic.

The ‘spcord’ argument specifies the order species are listed in a table. You can use the reverse of the number of occurrences to get dominant species at the top to rarer at the bottom, use fidelity values for the ordered clusters, or possibly the order of species centroids in an ordination.

Value

a data.frame with species as rows, classes as columns, with fraction of occurrence of species in classes.

Note

Constancy tables are often used in vegetation classification to calculate or present characteristic species for specific classes or types. ‘const’ may be combined with ‘importance’ and ‘vegtab’ to achieve a vegetation table-oriented analysis.

Author(s)

David W. Roberts droberts@montana.edu

See Also

importance, vegtab, vegemite

Examples

data(bryceveg) # returns a data.frame called bryceveg
data(brycesite)
class <- cut(brycesite$elev,10,labels=FALSE)
const(bryceveg,class,minval=0.25)

Convex Data Transformation

Description

Calculates a convex data transformation for a given number of desired classes.

Usage

convex(n,b=2,stand=FALSE)

Arguments

Details

Calculates a series of values where the difference between adjacent values is 1/b the previous difference. With the default b=2 you get an octave scale.

Value

a vector of numeric values

Author(s)

David W. Roberts droberts@montana.edu

See Also

spcmax, samptot, abundtrans, hellinger

Examples

convex(5,2)

Change Factors in Data.frames to Character Vectors

Description

Looks at each column in a data.frame, and converts factors to character vectors.

Usage

defactorize(df)

Arguments

Details

The function simply scans each column in a data.frame looking for factor columns. For each factor column it calls the ‘as.character()’ function to convert the column to a character vector.

Value

Returns a data.frame where every factor column has been converted to a character vector.

Note

This function simplifies editing data.frames by allowing users to edit character columns (which have no levels constraints) and then converting the results to factors for modeling. It is often used in a cycle of

defactorize(df)

edit the columns as necessary to correct errors or simplify

factorize(df)

Author(s)

David W. Roberts droberts@montana.edu

See Also

factorize

Examples

data(brycesite)
brycesite <- defactorize(brycesite)
brycesite$quad[brycesite$quad=='bp'] <- 'BP'
brycesite <- factorize(brycesite)

Create Three Column Database Form Data Frame from Sparse Data Frames

Description

Takes a sparse matrix data frame (typical of ecological abundance data) and converts it into three column database format.

Usage

dematrify(comm, filename, sep = ",", thresh = 0)

Arguments

Details

The routine is pure R code to convert data from sparse matrix form to three column database form for export or reduced storage

Value

a data.frame with the first column the sample ID, the second column the taxon ID, and the third column the abundance.

Note

Typically, large ecological data sets are characterized by sparse matrices of taxon abundance in samples with many zeros in the matrix. Because these datasets may be many columns wide, they are difficult to work with in text editors or spreadsheets, and require excessive amount of space for storage. The reduced three column form is suitable for input to databases, and more easily edited.

Author(s)

David W. Roberts droberts@montana.edu

See Also

matrify

Examples

library(labdsv)
data(bryceveg)
x <- dematrify(bryceveg)

Direct Gradient Analysis

Description

Direct gradient analysis is a graphical representation of the abundance distribution of (typically) species along opposing environmental gradients

Usage

dga(z,x,y,step=50,pres="+",abs="-",labcex=1,
    xlab = deparse(substitute(x)), ylab = deparse(substitute(y)),
    pch = 1, title = "", ...)

Arguments

Details

‘dga’ interpolates a grid of x,y values from the supplied data and fits a GAM (from mgcv) of the z variable to the grid. For presence/absence data (enterd as a logical) it employs a binomial family, for species abundances a negative binomial is employed. The GAM surface is then represented by a contour map and abundance symbols as described above.

Value

a graph of the distribution of the z variable on a grid of x and y is displayed on the current active device.

Note

Direct gradient analysis was promoted by Robert Whittaker and followers as a preferred method of vegetation analysis.

Author(s)

David W. Roberts droberts@montana.edu

See Also

gam

Examples

data(bryceveg) # returns a data.frame called bryceveg
x <- c(0.2,0.5,1.0,2.0,3.0,4.0,5.0,6.0)
y <- c(0.2,0.5,3.0,15.0,37.5,62.5,85.0,97.5)
cover <- abundtrans(bryceveg,x,y)
data(brycesite)
dga(round(cover$arcpat),brycesite$elev,brycesite$av)

Dissimilarity Analysis

Description

Dissimilarity analysis is a graphical analysis of the distribution of values in a dissimilarity matrix

Usage

disana(x, panel='all')

Arguments

Details

Calculates three vectors: the minimum, mean, and maximum dissimilarity for each sample in a dissimilarity matrix. By default it produces three plots: the sorted dissimilarity values, the sorted min, mean, and maximum dissimilarity for each sample, and the mean dissimilarity versus the minimum dissimilarity for each sample. Optionally, you can identify sample plots in the last panel with the mouse.

Value

Plots three graphs to the current graphical device, and returns an (invisible) list with four components:

Note

Dissimilarity matrices are often large, and difficult to visualize directly. ‘disana’ is designed to highlight aspects of interest in these large matrices. If the first panel shows a long limb of constant maximum value, you should consider recalculating the dissimilarity with a step-across adjustment. The third panel is useful for identifying outliers, which are plots more than 0.5 dissimilar to their nearest neighbor.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg) # returns a data.frame called veg
dis.bc <- dsvdis(bryceveg,'bray/curtis')
disana(dis.bc)

Dropping Plots with Missing Values From Taxon and Site Data Frames

Description

Looks for plots which have missing values in site or environment data, and deletes those plots from both the community and site data frames.

Usage

dropplt(comm,site,which=NULL)

Arguments

Details

First looks to see that the row names of the community data frame and the site or environment data frame are identical. If not, it prints an error message and exits. If which is NULL, it then looks at the site or environment data frame for plots or samples that have missing values, and deletes those plots from both the community and site data frames. Alternatively, if which is a numeric scalar or vector it deletes the specified plots from both the community and site data.frames.

Value

produces a list with two components:

Note

This is a VERY heavy-handed approach to managing missing values. Most R routines (including most of the labdsv package functions) have ways of handling missing values that are fairly graceful. This function simply maintains the correspondence between the community and site data frames while eliminating ALL missing values, and all plots that have missing values.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg)  # returns a data frame called bryceveg
data(brycesite) # returns a data frame called brycesite
demo <- dropplt(bryceveg,brycesite)
newcomm <- demo$comm
newsite <- demo$site

Dropping Species with Few Occurrences

Description

Eliminates species from the community data frame that occur fewer than or equal to a threshold number of occurrences.

Usage

dropspc(comm,minocc=0,minabu=0)

Arguments

Details

The function is useful for eliminating species (columns) from community data frames which never occur, which often happens if you eliminate plots, and those plots are the only ones that contain that species. In addition, many species are rare in data frames, and some algorithms (especially dissimilarity functions and table sorting routines) benefit from smaller, simpler data frames.

Value

Produces a new community data frame

Note

This is a heavy-handed approach to managing rare species in data.frames. It is often possible to write a mask (logical vector) that suppresses the influence of rare species and keeps the original data.frame intact, but this function simplifies data management for some purposes.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg) # returns a data frame called bryceveg
newveg <- dropspc(bryceveg,5) # deletes species which 
                              # occur 5 or fewer times

Dissimilarity Indices and Distance Measures

Description

This function provides a set of alternative dissimilarity indices and distance metrics for classification and ordination, including weighting by species (columns) and shortest-path adjustment for dissimilarity indices.

Usage

dsvdis(x,index,weight=rep(1,ncol(x)),step=0.0,
       diag=FALSE, upper=FALSE)

Arguments

Details

The function calculates dissimilarity or distance between rows of a matrix of observations according to a specific index. Three indices convert the data to presence/absence automatically. In contingency table notation, they are:

Others are quantitative. For variable i in samples x and y:

The weight argument allows the assignment of weights to individual species in the calculation of plot-to-plot similarity. The weights can be assigned by life-form, indicator value, or for other investigator specific reasons. For the presence/absence indices the weights should be integers; for the quantitative indices the weights should be in the interval [0,1]. The default (rep(1,ncol(x)) is to set all species = 1.

The threshold dissimilarity ‘step’ sets all values greater than or equal to "step" to 9999.9 and then solves for the shortest path distance connecting plots to other non-9999.9 values in the matrix. Step = 0.0 (the default) is a flag for "no shortest-path correction".

Value

Returns an object of class "dist", equivalent to that from dist.

Note

Ecologists have spent a great deal of time and effort examining the properties of different dissimilarity indices and distances for ecological data. Many of these indices should have more general application however. Dissimilarity indices are bounded [0,1], so that samples with no attributes in common cannot be more dissimilar than 1.0, regardless of their separation along hypothetical or real gradients. The shortest-path adjustment provides a partial solution. Pairs of samples more dissimilar than a specified threshold are set to 9999.9, and the algorithm solves for their actual dissimilarity from the transitive closure of the triangle inequality. Essentially, the dissimilarity is replaced by the shortest connected path between points less than the threshold apart. In this way it is possible to obtain dissimilarities much greater than 1.0.

The chi-square distance is not usually employed directly in cluster analysis or ordination, but is provided so that you can calculate correspondence analysis as a principal coordinates analysis (using cmdscale) from a simple distance matrix.

Author(s)

David W. Roberts droberts@montana.edu

See Also

dist, vegdist

Examples

data(bryceveg)   # returns a data.frame called "bryceveg"
dis.ochiai <- dsvdis(bryceveg,index="ochiai")
dis.bc <- dsvdis(bryceveg,index="bray/curtis")

LabDSV Object ls() Command

Description

The function searches through all the objects in the specified environment, and determines which ones have specific meaning in LabDSV. It then produces an output of a summary of every known LabDSV object sorted by type.

Usage

dsvls(frame=NULL,opt='full')

Arguments

Value

Prints output to the console

Note

It's common that after a while the number of objects in your workspace can get large, and even with disciplined naming of objects the list can get overwhelming. dsvls() attempts to organize and report on the objects LabDSV understands.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg)
dis.bc <- dsvdis(bryceveg,'bray')
nmds.bc <- nmds(dis.bc,2)
dsvls()

Environmental Distribution Test

Description

Calculates whether the value of a specified environmental variable has an improbable distribution with respect to a specified vector

Usage

envrtest(set,env,numitr=1000,minval=0,replace=FALSE,
     plotit = TRUE, main = paste(deparse(substitute(set)),
     " on ", deparse(substitute(env))))

Arguments

Details

Calculates the maximum within-set difference in the values of vector ‘env’, and the distribution of the permuted random within-set differences. It then plots the observed difference as a red line, and the sorted permuted differences as a black line and prints the probability of getting such a limited distribution. The probability is calculated by permuting numitr-1 times, counting the number of times the permuted maximum difference is as small or smaller than observed (n), and calculating (n+1)/numitr. To get three-digit probabilities, set numitr=1000 (the default)

Value

Produces a plot on the current graphics device, and an invisible list with the components observed within-set difference and the p-value.

Note

The plot is based on the concept of constraint, or limiting value, and checks to see whether the distribution of a particular variable within a cluster is constrained in an improbable way.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg) # returns a vegetation data.frame
data(brycesite) # returns and environmental data.frame
envrtest(bryceveg$berrep>0,brycesite$elev)

Nearest Euclidean Space Representation of a Dissimilarity Object

Description

Calculates the nearest Euclidean space representation of a dissimilarity object by iterating the transitive closure of the triangle inequality

Usage

euclidify(dis,upper=FALSE,diag=FALSE)
as.euclidean(dis,upper=FALSE,diag=FALSE)

Arguments

Details

Implements a constrained iteration of the transitive closure of Pythagoras' theorem, such that the squared distance between any two objects is less than or equal to the sum of the squared distances from the two objects to all possible third objects.

Value

An object of class ‘dist’

Note

Many multivariate statistical methods are designed for euclidean spaces, and yet the direct calculation of euclidean distance is often inappropriate due to problems with joint absences. euclidify takes any dissimilarity matrix and converts it to the closest euclidean representation, generally to avoid negative eigenvalues in an eigenanalysis of the matrix.

Author(s)

David W. Roberts droberts@montana.edu

See Also

metrify

Examples

data(bryceveg) # returns a vegetation data.frame
dis.bc <- dsvdis(bryceveg,'bray/curtis') # calculate a Bray/Curtis
                                         # dissimilarity matrix
dis.euc <- euclidify(dis.bc) # calculate the nearest euclidean 
                             # representation 
## Not run: plot(dis.bc,dis.euc)

Change Character Vectors in Data.frames to Factors

Description

Looks at each column in a data.frame, and converts character vector columns to factors.

Usage

factorize(df)

Arguments

Details

The function simply scans each column in a data.frame looking for character vector columns. For each character column it calls the ‘factor()’ function to convert the column to a factor.

Value

Returns a data.frame where every character column has been converted to a factor

Note

This function simplifies editing data.frames by allowing users to edit character columns (which have no levels constraints) and then converting the results to factors for modeling. It is often used in a cycle of

defactorize(df)

edit the columns as necessary to correct errors or simplify

factorize(df)

Author(s)

David W. Roberts droberts@montana.edu

See Also

defactorize

Examples

data(brycesite)
brycesite <- defactorize(brycesite)
brycesite$quad[brycesite$quad=='bp'] <- 'BP'
brycesite <- factorize(brycesite)

Global Search and Replace for Data.frames

Description

Performs in-place editing of data.frames that have factor columns while correcting for the change to levels.

Usage

gsr(field,old,new)

Arguments

Details

The function temporarily converts a vector or vector column in a data.frame to a character vector, and then loops through the ‘old’ vector looking for values to replace with the respective value in the ‘new’ vector. The column is then converted back to a factor.

Value

a factor vector

Note

The function is designed to make simple editing changes to data.frames or factor vectors, resetting the levels appropriately.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(brycesite)
brycesite$quad <- gsr(brycesite$quad,
    old=c('bp','bc','pc','rp','tc','tr'),
    new=c('BP','BC','PC','RP','TC','TR'))

Hellinger Data Transformation

Description

Performs the Hellinger data transformation (square root of sample total standardized data).

Usage

hellinger(comm)

Arguments

Details

Calculates a sample total standardization (all values in a row are divided by the row sum), and then takes the square root of the values.

Value

A community data.frame

Note

Hellinger standardization is a convex standardization that simultaneously helps minimize effects of vastly different sample total abundances.

Author(s)

David W. Roberts droberts@montana.edu

See Also

spcmax, samptot, abundtrans

Examples

data(bryceveg)
hellveg <- hellinger(bryceveg)

Homoteneity Analysis of Classified Ecological Communities

Description

Homoteneity is defined as ‘the mean constancy of the S most constant species, expressed as a fraction, where S is the mean species richness of a type.’

Usage

homoteneity(comm,clustering)

Arguments

Value

A data.frame of homoteneity values

Note

This function was adapted from the Virginia Heritage Program at

http://www.dcr.virginia.gov/natural_heritage/ncstatistics.shtml

Author(s)

David W. Roberts droberts@montana.edu

See Also

const, concov

Examples

data(bryceveg) # returns a data.frame of species in sample plots   
data(brycesite) # returns a data.frame of site variables
homoteneity(bryceveg,brycesite$quad) # analysis of species constancy
                                     # by USGS quad location

Importance Table

Description

For a classified set of vegetation samples, a importance table lists for each species the average or typical abundance of each species in each class.

Usage

importance(comm,clustering,minval=0,digits=2,show=minval,
       sort=FALSE,typical=TRUE,spcord,dots=TRUE)

Arguments

Value

a data.frame with species as rows, classes as columns, with average abundance of species in classes.

Note

Importance tables are often used in vegetation classification to calculate or present characteristic species for specific classes or types. Importance may be combined with const, concov and vegtab to achieve a vegetation table-oriented analysis.

Author(s)

David W. Roberts droberts@montana.edu

See Also

const, vegtab, concov

Examples

data(bryceveg) # returns a data.frame called bryceveg
data(brycesite)
class <- cut(brycesite$elev,10,labels=FALSE)
importance(bryceveg,class,minval=0.25)

Dufrene-Legendre Indicator Species Analysis

Description

Calculates the indicator value (fidelity and relative abundance) of species in clusters or types.

Usage

indval(x, ...)
## Default S3 method:
indval(x,clustering,numitr=1000,...)
## S3 method for class 'stride'
indval(x,comm,numitr=1,...)
## S3 method for class 'indval'
summary(object, p=0.05, type='short', digits=2, show=p,
       sort=FALSE, too.many=100, ...)

Arguments

Details

Calculates the indicator value ‘d’ of species as the product of the relative frequency and relative average abundance in clusters. Specifically,

where:
p_{ij} = presence/absence (1/0) of species i in sample j;
x_{ij} = abundance of species i in sample j;
n_c = number of samples in cluster c;
for cluster c \in K;

f_{ic} = {\sum_{j \in c} p_{ij} \over n_c}

a_{ic} = {\sum_{j \in c} x_{ij} / n_c \over \sum_{k=1}^K (\sum_{j \in k} x_{ij} / n_k)}

d_{ic} = f_{ic} \times a_{ic}

Calculated on a ‘stride’ the function calculates the indicator values of species for each of the separate partitions in the stride.

Value

The default function returns a list of class ‘indval’ with components:

The stride-based function returns a data.frame with the number of clusters in the first column and the mean indicator value in the second.

The ‘summary’ function has two options. In ‘short’ mode it presents a table of indicator species whose probability is less then ‘p’, giving their indicator value and the identity of the cluster they indicate, along with the sum of probabilities for the entire data set. In ‘long’ mode, the indicator value of each species in each class is shown, with values less than ‘show’ replaced by a place-holder dot to emphasize larger values.

If ‘sort==TRUE’, a prompt is given to re-order the rows of the matrix interactively.

Note

Indicator value analysis was proposed by Dufrene and Legendre (1997) as a possible stopping rule for clustering, but has been used by ecologists for a variety of analyses. Dufrene and Legendre's nomenclature in the paper is somewhat ambiguous, but the equations above are taken from the worked example in the paper, not the equations on page 350 which appear to be in error. Dufrene and Legendre, however, multiply d by 100; this function does not.

Author(s)

David W. Roberts droberts@montana.edu

References

Dufrene, M. and Legendre, P. 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67(3):345-366.

See Also

isamic

Examples

data(bryceveg) # returns a vegetation data.frame
data(brycesite)
clust <- cut(brycesite$elev,5,labels=FALSE)
summary(indval(bryceveg,clust))

Indicator Species Analysis Minimizing Intermediate Occurrences

Description

Calculates the degree to which species are either always present or always absent within clusters or types.

Usage

isamic(comm,clustering,sort=FALSE)

Arguments

Details

Calculates the constancy (fractional occurrence of each species in every type), and then calculates twice the the sum of the absolute values of the constancy - 0.5, normalized to the number of clusters (columns).

Value

A data.frame of species indicator values

Author(s)

David W. Roberts droberts@montana.edu

References

Aho, K., D.W. Roberts, and T.W. Weaver. 2008. Using geometric and non-geometric internal evaluators to compare eight vegetation classification methods. J. Veg. Sci. 19(4):549-562.

See Also

indval

Examples

data(bryceveg)
data(brycesite)
clust <- cut(brycesite$elev,5,labels=FALSE)
isamic(bryceveg,clust)

LabDSV Internal Functions

Description

These functions establish several generic functions, and are not intended to be called directly

Create Taxon Data.frames From Three Column Database Form

Description

Takes a data.frame in three column form (sample.id, taxon, abundance) and converts it into full matrix form, and then exports it as a data.frame with the appropriate row.names and column names.

Usage

matrify(data, strata=FALSE, base=100)

Arguments

Details

The routine is pure R code to convert data from database form to the sparse matrix form required by multivariate analyses in packages ‘labdsv’ and ‘vegan’, as well as dist and other routines. If TRUE, the strata argument specifies calculating individual species abundances as independent overlap of strata. The base function is useful for converting percent to a fraction.

Value

A data.frame with samples as rows, taxa as columns, and abundance values for taxa in samples.

Note

Typically, the source of the data will be an ASCII file or a dBase database or a CSV file from an Excel file in three column format. That file can be read into a data.frame with read.table or read.csv and then that data.frame can be matrified by this function.

Author(s)

David W. Roberts droberts@montana.edu

See Also

dematrify

Examples

x <- cbind(c('a','a','b','b','b','c','c'),
           c('x','y','x','z','w','y','z'),
           c(1,2,1,3,2,2,1))
matrify(x) 

Nearest Metric Space Representation of a Dissimilarity Object

Description

Calculates the nearest metric space representation of a dissimilarity object by iterating the transitive closure of the triangle inequality rule

Usage

metrify(dis,upper=FALSE,diag=FALSE)
as.metric(dis,upper=FALSE,diag=FALSE)
is.metric(dis)

Arguments

Details

Implements a constrained iteration of the transitive closure of the triangle inequality, such that the distance between any two objects is less than or equal to the sum of the distances from the two objects to a third.

Value

For metrify and as.metric, an object of class ‘dist’. For is.metric returns TRUE or FALSE.

Note

Many multivariate statistical methods are designed for metric spaces, and yet the direct calculation of distance is often inappropriate due to problems with joint absences. metrify takes any dissimilarity matrix and converts it to the closest metric space representation, generally to avoid negative eigenvalues in an eigenanalysis of the matrix.

Author(s)

David W. Roberts droberts@montana.edu

See Also

euclidify

Examples

data(bryceveg) # returns a vegetation data.frame
dis.bc <- dsvdis(bryceveg,'bray/curtis') # calculate a Bray/Curtis
            #  dissimilarity matrix
dis.met <- metrify(dis.bc) # calculate the nearest euclidean
            #  representation

Neighbors

Description

Calculates the nearest neighbors in a distance/dissimilarity matrix

Usage

neighbors(dis,numnbr)

Arguments

Details

For each sample unit in a dissimilarity matrix finds the ‘numnbr’ nearest neighbors and returns them in order.

Value

Returns a data.frame with sample units as rows and neighbors as columns, listed in order of proximity to the sample unit.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg) # returns a data.frame called veg
dis.bc <- dsvdis(bryceveg,'bray/curtis')
neighbors(dis.bc,5)

Nonmetric Multidimensional Scaling

Description

This function is simply a wrapper for the isoMDS function in the MASS package by Venables and Ripley. The purpose is to convert the output to class ‘dsvord’ to simplify plotting and additional graphical analysis as well as to provide a summary method.

Usage

nmds(dis,k=2,y=cmdscale(d=dis,k=k),maxit=50,trace=FALSE)
bestnmds(dis,k=2,itr=20,maxit=100,trace=FALSE,pbar=TRUE)

Arguments

Details

The nmds function simply calls the isoMDS function of the MASS library, but converts the result from a list to an object of class ‘dsvord’. The only purpose for the function is to allow ‘plot’, ‘identify’, ‘surf’, and other additional methods to be defined for the class, to simplify the analysis of the result.

The ‘bestnmds’ function runs one run from a PCO solution and ‘itr-1’ number of random initial locations and returns the best result of the set.

Value

An object of class ‘dsvord’, with components:

Note

nmds is included as part of the LabDSV package to provide a consistent interface and utility for vegetation ordination methods. Other analyses included with the same interface at present include principal components analysis (pca), principal coordinates analysis (pco), and t-distributed neighborhood embedding (t-SNE).

Author(s)

Venables and Ripley for the original isoMDS function included in the MASS package.

David W. Roberts droberts@montana.edu

References

Kruskal, J.B. (1964) Multidimensional scaling by optimizing goodness of fit to nonmetric hypothesis. Psychometrics 29:1-27.

Kruskal, J.B. (1964) Nonmetric multidimensional scaling: a numerical method. Psychometrics 29:115-129.

T.F. Cox and M.A.A. Cox. (1994) Multidimensional Scaling. Chapman and Hall.

See Also

isoMDS for the original function

plot.dsvord for the ‘plot’ method, the ‘plotid’ method to identify points with a mouse, the ‘points’ method to identify points meeting a logical condition, the ‘hilight’ method to color-code points according to a factor, the ‘chullord’ method to add convex hulls for a factor, or the the ‘surf’ method to add surface contours for continuous variables.

initMDS for an alternative way to automate random starts

postMDS for a post-solution rescaling

metaMDS for a full treatment of variations

Examples

data(bryceveg)
data(brycesite)
dis.man <- dist(bryceveg,method="manhattan")
demo.nmds <- nmds(dis.man,k=4)
plot(demo.nmds)
points(demo.nmds,brycesite$elev>8000)
plotid(demo.nmds,ids=row.names(brycesite))

Re-Order the Rows and Columns of a Taxon Data Frame

Description

Allows analysts to interactively re-order a community data frame to achieve a ‘structured’ table following phytosociological principles.

Usage

ordcomm(comm,site)

Arguments

Details

Prints a copy of the community data frame, and then prompts for plots to move in front of another plot. It then prompts for species to move in front of a specified species. Multiple plots or species can be moved in a single move, with plot or species IDs separated by commas with no blanks. The program cycles between prompting for plots to move, and then species to move, until both prompts are responded to with blank lines.

Value

produces a list with two components:

Note

This is a a fairly simple means to sort a table. For large tables, it is often possible (and preferable) to sort the tables with ordination coordinates or other indices, but this function allows analysts to order the table arbitrarily into any form.

Author(s)

David W. Roberts droberts@montana.edu

See Also

summary.indval,const,importance

Examples

## Not run: data(bryceveg) # returns a data frame called bryceveg
## Not run: data(brycesite) # returns a data frame called brycesite
## Not run: demo <- ordcomm(bryceveg,brycesite)
## Not run: newveg <- demo$taxon
## Not run: newsite <- demo$site

Ordination to Dissimilarity Comparison

Description

Plots the distribution of pair-wise distances of all points in an ordination over the distances in the dissimilarity or distance matrix the ordination was calculated from. Prints the correlation between the two on the graph.

Usage

ordcomp(x,dis,dim,xlab="Computed Distance",
        ylab="Ordination Distance",title="",pch=1)

Arguments

Value

A plot is created on the current graphics device. Returns the (invisible) correlation.

Note

Ordinations are low dimensional representations of multidimensional spaces. This function attempts to portray how well the low dimensional solution approximates the full dimensional space.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg) # produces a vegetation data.frame
dis.bc <- dsvdis(bryceveg,'bray/curtis') # creates a Bray/Curtis 
                                         # dissimilarity matrix
pco.bc <- pco(dis.bc,2) # produces a two-dimensional Principal 
                        # Coordinates Ordination object
ordcomp(pco.bc,dis.bc)

Ordination Point Pair-Wise Distance Calculation

Description

Calculates the pair-wise distances of all points in an ordination. The function is simply a wrapper for the ‘dist’ function, but simplifies managing ordinations that store their coordinates under different names, as well as managing the desired dimensionality of the calculations.

Usage

orddist(x,dim)

Arguments

Value

An object of class ‘dist’ is produced

Note

Ordinations are low dimensional representations of multidimensional spaces. This function produces data on the low-dimensional distances for other analyses.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg) # produces a vegetation data.frame
dis.bc <- dsvdis(bryceveg,'bray/curtis') # creates a Bray/Curtis 
                                         #dissimilarity matrix
pco.bc <- pco(dis.bc,2) # produces a two-dimensional Principal 
                        # Coordinates Ordination object
orddist(pco.bc,dim=2)

Nearest Neighbors Plotted in Ordination Space

Description

For each sample unit in an ordination, for each of n nearest neighbors, draws an arrow from the sample unit to its n neighbors.

Usage

ordneighbors(ord,dis,numnbr=1,ax=1,ay=2,digits=5,length=0.1)

Arguments

Value

Additional information is plotted on an existing ordination and summary information is printed. Returns an (invisible) list of summary values.

Note

Ordinations are low dimensional representations of multidimensional spaces. This function attempts to portray how well the low dimensional solution approximates the neighborhood relations of the full dimensional space.

If numnbr = 1 and there are ties the function plots arrows for all tied values. If n > 1 the function draws arrows for all values with rank <= n.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg) # produces a vegetation data.frame
dis.bc <- dsvdis(bryceveg,'bray/curtis') # creates a Bray/Curtis 
                                         # dissimilarity matrix
pco.bc <- pco(dis.bc,2) # produces a two-dimensional Principal 
                        # Coordinates Ordination object
plot(pco.bc)
ordneighbors(pco.bc,dis.bc)

Ordination Partitioning

Description

This function allows users to partition or classify the points in an ordination by identifying clusters of points with a mouse

Usage

ordpart(ord, ax = 1, ay = 2)

Arguments

Details

Given a plot of an ordination, you assign plots to clusters by drawing a polygon with the first mouse button to include all points in a given cluster. To end that cluster, click the right mouse button to close the polygon. Plots included in that cluster will be color-coded to indicate membership. Start the next cluster by drawing another polygon. To end, click the right mouse button again after closing the last polygon. Plots within more than one polygon are assigned membership in the last polygon which includes them; plots which are not within any polygon are assigned membership in cluster zero.

Value

A integer vector of cluster membership values

Note

Although the routine could easily be adapted for any scatter plot, it is currently only designed for objects of class ‘dsvord’.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg)
data(brycesite)
dis.bc <- dsvdis(bryceveg,'bray/curtis')
nmds.1 <- nmds(dis.bc,5)
plot(nmds.1)
## Not run: clustering <- ordpart(nmds.1)

Ordination Distribution Test

Description

Testing the distribution of points in an ordination

Usage

ordtest(ord, var, dim=1:ncol(ord$points), index = 'euclidean',
   nitr = 1000)

Arguments

Details

Calculates the sum of within-set pair-wise distances and compares to ‘nitr’ permutations of the same distribution to calculate the probability of observing clusters as tight as observed or tighter. The p-value is calculated by running nitr-1 permutations and counting the number of cases where the sum of pair-wise distances is as small as smaller than observed. That count is increased by one and divided by nitr to estimate p.

Value

Produces a list with components:

Author(s)

David W. Roberts droberts@montana.edu

See Also

anosim

Examples

data(bryceveg)
data(brycesite)
dis.bc <- dsvdis(bryceveg,'bray/curtis')
pco.bc <- pco(dis.bc)
plot(pco.bc)
demo <- ordtest(pco.bc,brycesite$quad)
demo$p

Principal Components Analysis

Description

Principal components analysis is a eigenanalysis of a correlation or covariance matrix used to project a high-dimensional system to fewer dimensions.

Usage

pca(mat, cor = FALSE, dim = min(nrow(mat),ncol(mat)))
## S3 method for class 'pca'
summary(object, dim = length(object$sdev), ...)
## S3 method for class 'pca'
scores(x, labels = NULL, dim = length(x$sdev), ...)
## S3 method for class 'pca'
loadings(x, dim = length(x$sdev), digits = 3, cutoff = 0.1, ...)
## S3 method for class 'pca'
varplot(x, dim=length(x$sdev),...)

Arguments

Details

PCA is a common multivariate technique. The version here is simply a wrapper for the prcomp function to make its use and plotting consistent with the other LabDSV functions.

Value

an object of class "pca", a list with components:

Note

The current version of pca is based on the prcomp function, as opposed to the princomp function. Nonetheless, it maintains the more conventional labels "scores" and "loadings", rather than x and rotation. prcomp is based on a singular value decomposition algorithm, as has worked better in my experience. In the rare cases where it fails, you may want to try princomp.

Author(s)

David W. Roberts droberts@montana.edu

See Also

princomp, prcomp, pco, nmds, fso, cca

Examples

data(bryceveg) # returns a vegetation data.frame
data(brycesite)
x <- pca(bryceveg,dim=10)  # returns the first 10 eigenvectors 
                           # and loadings
plot(x)
surf(x,brycesite$elev)
points(x,brycesite$depth=='deep')

Principal Coordinates Analysis

Description

Principal coordinates analysis is an eigenanalysis of distance or metric dissimilarity matrices.

Usage

pco(dis, k=2)

Arguments

Details

pco is simply a wrapper for the cmdscale function of Venebles and Ripley to make plotting of the function similar to other LabDSV functions

Value

An object of class ‘pco’ with components:

Note

Principal Coordinates Analysis was pioneered by Gower (1966) as an alternative to PCA better suited to ecological datasets.

Author(s)

of the ‘cmdscale’ function: Venebles and Ripley

of the wrapper function David W. Roberts droberts@montana.edu

References

Gower, J.C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53:325-328.

See Also

cmdscale, pca, nmds, cca

Examples

data(bryceveg) # returns a vegetation data.frame
dis.bc <- dsvdis(bryceveg,'bray/curtis')
                  # returns an object of class dist'
veg.pco <- pco(dis.bc,k=4) # returns first 4 dimensions
plot(veg.pco)

Plotting Routines For LabDSV Ordinations

Description

A set of routines for plotting, highlighting points, or adding fitted surfaces to ordinations.

Usage

## S3 method for class 'dsvord'
plot(x, ax = 1, ay = 2, col = 1, title = "", pch = 1,
                     xlab = paste(x$type, ax), ylab = paste(x$type, ay), ...)
## S3 method for class 'dsvord'
points(x, which, ax = 1, ay = 2, col = 2, pch = 1, cex = 1, 
                      breaks=FALSE, ...)
## S3 method for class 'dsvord'
plotid(ord, ids = seq(1:nrow(ord$points)), ax = 1, ay = 2,
       col = 1, ...)
## S3 method for class 'dsvord'
hilight(ord, overlay, ax = 1, ay = 2, title="", 
        cols=c(2,3,4,5,6,7), glyph=c(1,3,5), ...)
## S3 method for class 'dsvord'
chullord(ord, overlay, ax = 1, ay = 2, cols=c(2,3,4,5,6,7), 
        ltys = c(1,2,3), ...)
## S3 method for class 'dsvord'
ellip(ord, overlay, ax = 1, ay = 2, cols=c(2,3,4,5,6,7),
        ltys = c(1,2,3), ...)
## S3 method for class 'dsvord'
surf(ord, var, ax = 1, ay = 2, thinplate = TRUE, col = 2, 
        labcex = 0.8, lty = 1, family = gaussian, gamma=1, grid=50, ...)
## S3 method for class 'dsvord'
thull(ord,var,grain,ax=1,ay=2,col=2,grid=51,nlevels=5,
        levels=NULL,lty=1,
     numitr=100,...)
## S3 method for class 'dsvord'
density(ord, overlay, ax = 1, ay = 2, cols = c(2, 3, 4, 5,
    6, 7), ltys = c(1, 2, 3), numitr, ...)

Arguments

Details

Function ‘plot’ produces a scatter plot of sample scores for the specified axes, erasing or over-plotting on the current graphic device. Axes dimensions are controlled to produce a graph with the correct aspect ratio. Functions ‘points’, ‘plotid’, and ‘surf’ add detail to an existing plot. The axes specified must match the underlying plot exactly.

Function ‘plotid’ identifies and labels samples (optionally with values from a third vector) in the ordination, and requires interaction with the mouse: left button identifies, right button exits.

Function ‘points’ is passed a logical vector to identify a set of samples by color of glyph. It can be used to identify a single set meeting almost any criterion that can be stated as a logical expression.

Function ‘hilight’ is passed a factor vector or integer vector, and identifies factor values by color and glyph.

Function ‘chullord’ is passed a factor vector or integer vector, and plots a convex hull around all points in each factor class. By specifying values for arguments ‘cols’ and ‘ltys’ it is possible to control the sequence of colors and linetypes of the convex hulls.

Function ‘ellip’ is passed a factor vector or integer vector, and plots minimal volume ellipses containingg all points within a class. By specifying values for arguments ‘cols’ and ‘ltys’ it is possible to control the sequence of colors and linetypes of the ellipses.

Function ‘density’ calculates the fraction of points within the convex hull that belong to the specified type.

Function ‘surf’ calculates and plots fitted surfaces for logical or quantitative variables. The function employs the gam function to fit a variable to the ordination coordinates, and to predict the values at all grid points. The grid is established with the ‘expand.grid’ function, and the grid is then specified in a call to ‘predict.gam’. The predicted values are trimmed to the the convex hull of the data, and the contours are fit by ‘contour’. The default link function for fitting the GAMs is ‘gaussian’, suitable for unbounded continuous variables. For logical variables you should specify ‘family = binomial’ to get a logistic GAM, and for integer counts you should specify ‘family = poisson’ to get a Poisson GAM or ‘family='nb'’ to get a negative binomial fit.

Function ‘thull’ calculates a tensioned hull for a specific variable on the ordination. A tensioned hull is a minimum volume container. The grain size must be specified as a fraction of the units of the NMDS, with larger values generating smoother representations, and smaller numbers a more resolved container. ‘thull’ returns an invisible object of class ‘thull’ which has an associated plot function. Plotting the thull object produces a colored surface representation of the thull with optional contour lines.

Value

Function ‘plotid’ returns a vector of row numbers of identified plots

Note

The contouring routine using predict.gam follows ordisurf as suggested by Jari Oksanen.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg)
data(brycesite)
dis.bc <- dsvdis(bryceveg,'bray/curtis')
nmds.1 <- nmds(dis.bc,5)
plot(nmds.1)
points(nmds.1,brycesite$elev>8000)
surf(nmds.1,brycesite$elev)
## Not run: plotid(nmds.1,ids=row.names(bryceveg))

Plotting a Tensioned Hull

Description

A tensioned hull is a minimum volume container for specified elements of an ordination. A ‘thull’ object is returned as an invisible object by plotting a thull of an NMDS or PCO (or MFSO). Subsequently plotting the returned thull results in an ‘image’ of the representation.

Usage

## S3 method for class 'thull'
plot(x,col=rainbow(20),levels=NULL,cont=TRUE,
          xlab=x$xlab,ylab=x$ylab,main=x$main,...)

Arguments

Details

Tensioned hull analysis fits a minimum volume envelope to specific points in an ordination. A tensioned hull object is returned from function thull of a ordination of class ‘dsvord’. This function plots the resulting tensioned hull as an image, with optional overlays of contours.

Value

Produces a plot on the current graphic device.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg) # returns a data.frame called bryceveg
dis.bc <- dsvdis(bryceveg,'bray') # calculates a Bray-Curtis 
                                  # dissimilarity matrix
nmds.bc <- nmds(dis.bc) # calculates an NMDS ordination
plot(nmds.bc) # plots the ordination on the current device
demo.thull <- thull(nmds.bc,bryceveg$arcpat,0.25) # calculates 
                        # the tensioned hull representing the 
                        # distributtion of a species
plot(demo.thull) # portrays the image version of the tensioned hull

Predict species abundances in an ordination

Description

This function fits a Generalized Additive Model (GAM) for each species in a data.frame against an ordination.

Usage

## S3 method for class 'dsvord'
predict(object,comm,minocc=5,dims=1:ncol(object$points),
                         family='nb',gamma=1,keep.models=FALSE,...)

Arguments

Details

The predict function sequentially and independently fits a GAM model of each species distribution as a function of ordination coordinates, using the family and gamma specifiers supplied in the function call, or their defaults. The function fits two or three dimensional models; if the length of dims is greater than three the dimensions are truncated to the first three chosen.

Value

A list object with vector elements aic, dev.expl, adj.rsq, and matrix fitted. Optionally, if keep.models is TRUE, a list with all of the GAM models fitted. list element aic gives the model AICs for each species, dev.expl gives the deviance explained, adj.rsq gives the adjusted r-Squared, and fitted gives the expected abundance of each species in each sample unit.

Author(s)

David W. Roberts droberts@montana.edu

See Also

calibrate for the complementary function that fits GAM models for environment variables

Examples

data(bryceveg)
dis.man <- dist(bryceveg,method="manhattan")
demo.nmds <- nmds(dis.man,k=4)
## Not run: res <- predict(demo.nmds,bryceveg,minocc=10)

Identify Rare Taxa in a Data Set

Description

Identifies the distribution of rare taxa in a community data.frame, using a specified rareness threshold.

Usage

raretaxa(comm,min=1,log=FALSE,type='b', panel='all')

Arguments

Details

Rare species are an issue in ecological data sets. This function produces three graphs identifying (1) the distribution of rare species/plot, (2) the mean abundance (when present) of rare species, and (3) the total abundance or rare species/plot.

Value

Produces only graphs and returns no output

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg)
raretaxa(bryceveg,min=3,log=TRUE)

Reconcile Community and Site Data.Frames

Description

reconcile takes two data frames (comm and site) and sorts both into the same order, and then deletes any rows unique to either of the two data.frames, achieving perfect correspondence of the two.

Usage

reconcile(comm,site,exlist)

Arguments

Details

reconcile sorts each data.frame alphabetically by row.name, and then compares the list of row.names to identify sample plots common to both data.frames. Sample plots which occur in only one of the data.frames are deleted.

Value

A list object with two elements: comm and site, which are the sorted and reconciled data.frames.

Note

Package labdsv (and many other packages in ecological data analysis) require two data.frames to structure the data. One contains the abundance of species within samples with samples as rows and species as columns. This data.frame I refer to as the sQuotecomm data.frame. The other data.frame contains all the environmental or site data collected at the same samples. This data.frame I refer to as the ‘site’ data.frame. Due to independent subsampling, sorting or editing of the data (often outside of R) the two data.frames often lose the necessary requirement of the identical number of rows, with the rows in exactly the same order. The reconcile() function is a simple remedy to correct this situation while maintaining the maximum amount of data.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg)   # returns a data.frame of taxon abundance
data(brycesite)  # returns a data.frame of site variables
test <- reconcile(bryceveg,brycesite)

Randomize a Community Data.Frame

Description

Permutes a vegetation (or other) data.frame to establish a basis for null model tests in vegetation ecology.

Usage

rndcomm(comm,replace=FALSE,species=FALSE,plots=FALSE)

Arguments

Details

Permutes or bootstraps a vegetation data frame for input to dist, vegdist, dsvdis, or other routines. Can randomize by columns (species=TRUE), samples (plots=TRUE), or fully (neither species nor plots = TRUE).

Value

a data.frame with samples as rows and species as columns of the same dimensions as entered.

Note

Randomizing vegetation often leads to unrealistic data distributions, but this function attempts to preserve either species occurrence distributions or plot-level species richness. It is probably worth examining the output of this function with abuocc to see its characteristics before engaging in extensive analysis.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg) # returns a vegetation data.frame called bryceveg
test <- rndcomm(bryceveg,species=TRUE) # preserves species abundance
                                       # distribution
test2 <- rndcomm(bryceveg,plots=TRUE) # preserves plot-level 
                                      # species richness

Random Distance

Description

Calculates a random distance matrix for use in null model analysis.

Usage

rnddist(size, method='metric', sat = 1.0, upper=FALSE, 
       diag=FALSE)

Arguments

Details

Generates a matrix of size^2 uniform random numbers and passes the matrix to metrify or euclidify to ensure the metric or euclidean properties of the distances. Values are normalized to a maximum of 1.0.

Value

A dissimilarity object of class ‘dist’

Author(s)

David W. Roberts droberts@montana.edu

See Also

metrify, euclidify

Examples

x <- rnddist(100)
pco.x <- pco(x)
plot(pco.x)

Sample total standardization

Description

Standardizes a community data set to a sample total standardization.

Usage

samptot(comm)

Arguments

Details

This function simply calculates row sums for the community matrix and then divides all values in that row by the appropriate sum so that all samples total to 1.0.

Value

A data frame of sample total standardized community data.

Author(s)

David W. Roberts droberts@montana.edu

See Also

spcmax, abundtrans

Examples

    data(bryceveg)
    stveg <- samptot(bryceveg)
    apply(stveg,1,sum)

Species Discrimination Analysis

Description

Calculates the degree to which species are restricted to certain classes of classified vegetation

Usage

spcdisc(x,sort=FALSE)

Arguments

Details

Calculates a Shannon-Weiner information statistic on the relative abundance of species within classes.

Value

A vector of discrimination values.

Author(s)

David W. Roberts droberts@montana.edu

See Also

const, importance, indval, isamic

Examples

data(bryceveg)
data(brycesite)
test <- const(bryceveg,brycesite$quad)
spcdisc(test)

Species Maximum Standardization

Description

Standardizes a community data.frame by dividing the abundance of each species by the maximum value obtained for that species.

Usage

spcmax(comm)

Arguments

Details

This is a simple standardization to make each species abundance scaled from 0 to 1, essentially relativizing abundance by species and making each species equal in the calculation of distance or dissimilarity or other analyses.

Value

A data.frame of standardized community data.

Author(s)

David W. Roberts droberts@montana.edu

See Also

samptot, abundtrans, hellinger

Examples

data(bryceveg)
smveg <- spcmax(bryceveg)
apply(smveg,2,max)

Step-Across Distance

Description

Solves for the shortest-path step-across distance for a given distance matrix

Usage

stepdist(dis,alpha)

Arguments

Details

The function takes the dist object and converts all values >= alpha to 9999.9 and then solves for new distances by calculating the transitive closure of the triangle inequality.

Value

an object of class ‘dist’

Note

The ‘dsvdis’ function includes a step-across function in the initial calculation of a distance or dissimilarity matrix. This function simply allows the conversion to take place at a later time, or on distance metrics that ‘dsvdis’ doesn't support.

Author(s)

David W. Roberts droberts@montana.edu

Examples

data(bryceveg)
dis.bc <- dsvdis(bryceveg,'bray')
dis.bcx <- stepdist(dis.bc,1.00)
disana(dis.bcx)

t-Distributed Stochastic Neighbor Embedding

Description

This function is a wrapper for the Rtsne function in the Rtsne package by Krijthe and van der Maaten. The purpose is to convert the output to class ‘dsvord’ to simplify plotting and additional graphical analysis as well as to provide a summary method.

Usage

tsne(dis,k=2,perplexity=30,theta= 0.0,eta=200)
besttsne(dis,k=2,itr=100,perplexity=30,theta=0.0,eta = 200,pbar=TRUE)

Arguments

Details

The tsne function simply calls the Rtsne function of the Rtsne package with a specified distance/dissimilarity matrix rather than the community matrix. By convention, t-SNE employs a PCA on the input data matrix, and calculates distances among the first 50 eigenvectors of the PCA. Rtsne, however, allows the submission of a pre-calculated distance/dissimilarity matrix in place of the PCA. Given the long history of research into the use of PCA in ecological community analysis, tsne allows the simple use of any of a vast number of distance/dissimilarity matrices known to work better with ecological data.

In addition, the tsne function converts the output to an object of class ‘dsvord’ to simplify plotting and analyses using the many functions defined for objects of class ‘dsvord’. (see plot.dsvord for more details.)

The ‘besttsne’ function runs one run from a PCO solution as the initial configuration and ‘itr-1’ number of random initial locations and returns the best result of the set.

Value

an object of class ‘dsvord’, with components:

Note

tsne is included as part of the LabDSV package to provide a consistent interface and utility for ecological community ordination methods. Other analyses included with the same interface at present include nonmetric multidimensional scaling (NMDS), principal components analysis (pca), and principal coordinates analysis (pco).

Author(s)

Jesse H. Krijthe for the original Rtsne R code, adapted from C++ code from Laurens van der Maaten.

David W. Roberts droberts@montana.edu

References

van der Maaten, L. 2014. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research, 15, p.3221-3245.

van der Maaten, L.J.P. & Hinton, G.E., 2008. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research, 9, pp.2579-2605.

Krijthe, J,H, 2015. Rtsne: T-Distributed Stochastic Neighbor Embedding using a Barnes-Hut Implementation, URL: https://github.com/jkrijthe/Rtsne

See Also

Rtsne for the original function

plot.dsvord for the ‘plot’ method, the ‘plotid’ method to identify points with a mouse, the ‘points’ method to identify points meeting a logical condition, the ‘hilight’ method to color-code points according to a factor, the ‘chullord’ method to add convex hulls for a factor, or the the ‘surf’ method to add surface contours for continuous variables.

Examples

data(bryceveg)
data(brycesite)
dis.man <- dist(bryceveg,method="manhattan")
demo.tsne <- tsne(dis.man,k=2)
plot(demo.tsne)
points(demo.tsne,brycesite$elev>8000)
plotid(demo.tsne,ids=row.names(brycesite))

Vegetation Table

Description

Produces an ordered table of abundance of species in samples, sub-sampled by (an optional) classification of the samples

Usage

vegtab(comm,set,minval=1,pltord,spcord,pltlbl,trans=FALSE)

Arguments

Details

Subsets a vegetation data.frame according to specified plots or minimum species abundances, optionally ordering in arbitrary order.

Value

a data.frame with specified rows, columns, and row.names

Note

Vegetation tables are a common tool in vegetation analysis. In recent years analysis has tended to become more quantitative, and less oriented to sorted tables, but even still presenting the results from these analyses often involves a sorted vegetation table.

Author(s)

David W. Roberts droberts@montana.edu

See Also

vegemite

Examples

data(bryceveg)  # returns a vegetation data frame called bryceveg
data(brycesite) # returns an environmental data frame called 
                # brycesite
vegtab(bryceveg,minval=10,pltord=brycesite$elev)
        # produces a sorted table for species whose abundance sums
        # to 10, with rows in order of elevation.