, Ermias T. Azeria
[ctb]| Title: | Likelihood Based Optimal Partitioning and Indicator Species Analysis |
|---|---|
| Description: | Likelihood based optimal partitioning and indicator species analysis. Finding the best binary partition for each species based on model selection, with the possibility to take into account modifying/confounding variables as described in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>. The package implements binary and multi-level response models, various measures of uncertainty, Lorenz-curve based thresholding, with native support for parallel computations. |
| Authors: | Peter Solymos [cre, aut]
, Ermias T. Azeria
[ctb] |
| Maintainer: | Peter Solymos <[email protected]> |
| License: | GPL-2 |
| Version: | 0.1-3 |
| Built: | 2024-11-17 04:55:09 UTC |
| Source: | https://github.com/psolymos/opticut |
Likelihood based optimal partitioning and indicator species analysis. Finding the best binary partition for each species based on model selection, with the possibility to take into account modifying/confounding variables as described in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>. The package implements binary and multi-level response models, various measures of uncertainty, Lorenz-curve based thresholding, with native support for parallel computations.
The DESCRIPTION file:
| Package: | opticut |
| Type: | Package |
| Title: | Likelihood Based Optimal Partitioning and Indicator Species Analysis |
| Version: | 0.1-3 |
| Date: | 2024-05-21 |
| Author: | Peter Solymos [cre, aut] (<https://orcid.org/0000-0001-7337-1740>), Ermias T. Azeria [ctb] |
| Maintainer: | Peter Solymos <[email protected]> |
| Description: | Likelihood based optimal partitioning and indicator species analysis. Finding the best binary partition for each species based on model selection, with the possibility to take into account modifying/confounding variables as described in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>. The package implements binary and multi-level response models, various measures of uncertainty, Lorenz-curve based thresholding, with native support for parallel computations. |
| URL: | https://github.com/psolymos/opticut |
| BugReports: | https://github.com/psolymos/opticut/issues |
| Depends: | R (>= 3.1.0), pbapply (>= 1.3-0) |
| Imports: | MASS, pscl, betareg, ResourceSelection (>= 0.3-2), parallel, mefa4 |
| License: | GPL-2 |
| LazyLoad: | yes |
| LazyData: | true |
| Repository: | https://psolymos.r-universe.dev |
| RemoteUrl: | https://github.com/psolymos/opticut |
| RemoteRef: | HEAD |
| RemoteSha: | 5e8e4fe774148d56fe151fd9e2c22eeaa83be4af |
Index of help topics:
allComb Finding All Possible Binary Partitions
bestmodel Best model, Partition, and MLE
beta2i Scaling for the Indicator Potential
birdrec Bird Species Detections
dolina Land Snail Data Set
lorenz Lorenz Curve Based Thresholds and Partitions
multicut Multi-level Response Model
occolors Color Palettes for the opticut Package
ocoptions Options for the opticut Package
opticut Optimal Binary Response Model
opticut-package Likelihood Based Optimal Partitioning and
Indicator Species Analysis
optilevels Optimal Number of Factor Levels
rankComb Ranking Based Binary Partitions
uncertainty Quantifying Uncertainty for Fitted Objects
warblers Warblers Data Set
The main user interface are the opticut
and multicut functions
to find the optimal binary or multi-level response models.
Make sure to evaluate uncertainty.
optilevels finds the optimal number of factor levels.
Peter Solymos [cre, aut] (<https://orcid.org/0000-0001-7337-1740>), Ermias T. Azeria [ctb]
Maintainer: Peter Solymos <[email protected]>
Kemencei, Z., Farkas, R., Pall-Gergely, B., Vilisics, F., Nagy, A., Hornung, E. & Solymos, P., 2014. Microhabitat associations of land snails in forested dolinas: implications for coarse filter conservation. Community Ecology 15:180–186. <doi:10.1556/ComEc.15.2014.2.6>
## community data y <- cbind( Sp1=c(4,6,3,5, 5,6,3,4, 4,1,3,2), Sp2=c(0,0,0,0, 1,0,0,1, 4,2,3,4), Sp3=c(0,0,3,0, 2,3,0,5, 5,6,3,4)) ## stratification g <- c(1,1,1,1, 2,2,2,2, 3,3,3,3) ## find optimal partitions for each species oc <- opticut(formula = y ~ 1, strata = g, dist = "poisson") summary(oc) ## visualize the results plot(oc, cut = -Inf) ## quantify uncertainty uc <- uncertainty(oc, type = "asymp", B = 999) summary(uc) ## go beyond binary partitions mc <- multicut(formula = y ~ 1, strata = g, dist = "poisson") summary(mc) ol <- optilevels(y[,"Sp2"], as.factor(g)) ol[c("delta", "coef", "rank", "levels")]## community data y <- cbind( Sp1=c(4,6,3,5, 5,6,3,4, 4,1,3,2), Sp2=c(0,0,0,0, 1,0,0,1, 4,2,3,4), Sp3=c(0,0,3,0, 2,3,0,5, 5,6,3,4)) ## stratification g <- c(1,1,1,1, 2,2,2,2, 3,3,3,3) ## find optimal partitions for each species oc <- opticut(formula = y ~ 1, strata = g, dist = "poisson") summary(oc) ## visualize the results plot(oc, cut = -Inf) ## quantify uncertainty uc <- uncertainty(oc, type = "asymp", B = 999) summary(uc) ## go beyond binary partitions mc <- multicut(formula = y ~ 1, strata = g, dist = "poisson") summary(mc) ol <- optilevels(y[,"Sp2"], as.factor(g)) ol[c("delta", "coef", "rank", "levels")]
These functions are used to find all possible binary partitions. Finding all combinations require a classification vector with K > 1 strata.
allComb(x, collapse) kComb(k) checkComb(x)allComb(x, collapse) kComb(k) checkComb(x)
x |
a vector for |
collapse |
character, what to paste between levels.
Defaults to |
k |
numeric, number of levels (strata) in a given classification (K > 1). |
kComb returns a contrast matrix corresponding to
all possible binary partitions of the factor with K levels.
Complements are not counted twice, i.e.
(0,0,1,1) is equivalent to (1,1,0,0).
The number of such possible combinations is M = 2^(K - 1) - 1.
allComb takes a classification vector with at least 2 levels
and returns a model matrix with binary partitions.
checkComb checks if combinations are unique and non-complementary
(misfits are returned as attributes). Returns a logical value.
Peter Solymos <[email protected]>
opticut for the user interface.
rankComb and lorenz
for alternative partitioning algorithms.
kComb(k = 2) kComb(k = 3) kComb(k = 4) ## finding all combinations (f <- rep(LETTERS[1:4], each=2)) (mc <- allComb(f, collapse = "_")) ## checking for complementary entries checkComb(mc) # TRUE ## adding complementary entries to the matrix mc2 <- cbind(z = 1 - mc[,1], mc[,c(1:ncol(mc), 1)]) colnames(mc2) <- 1:ncol(mc2) mc2 checkComb(mc2) # FALSEkComb(k = 2) kComb(k = 3) kComb(k = 4) ## finding all combinations (f <- rep(LETTERS[1:4], each=2)) (mc <- allComb(f, collapse = "_")) ## checking for complementary entries checkComb(mc) # TRUE ## adding complementary entries to the matrix mc2 <- cbind(z = 1 - mc[,1], mc[,c(1:ncol(mc), 1)]) colnames(mc2) <- 1:ncol(mc2) mc2 checkComb(mc2) # FALSE
Generic functions for accessing best model, best partition, and Maximum Likelihood Estimate from fitted objects.
bestmodel(object, ...) bestpart(object, ...) getMLE(object, ...)bestmodel(object, ...) bestpart(object, ...) getMLE(object, ...)
object |
fitted model object. |
... |
other arguments passed to the underlying functions. |
bestmodel returns the best supported model for further
manipulation (e.g. prediction).
bestpart returns a matrix with the best supported
partitions for each species (species as columns).
getMLE returns a named list corresponding to the best supported
model. The list has the following elements:
coef is the Maximum Likelihood Estimate (MLE),
vcov is the variance-covariance matrix for the MLE,
dist is the distribution inherited from input object.
Peter Solymos <[email protected]>
opticut, multicut, uncertainty.
Transformation of estimated contrasts to indicator potential.
beta2i(x, scale = 1)beta2i(x, scale = 1)
x |
numeric, real valued coefficients. |
scale |
numeric, scaling constant. |
Returns a numeric vector (I = abs(tanh(x * scale))).
Peter Solymos <[email protected]>
opticut and multicut
use the scaled I values as indicator potential.
ocoptions for setting value for the default scaling factor.
x <- seq(-5, 5, 0.1) Col <- occolors(c("red", "blue"))(10) plot(x, beta2i(x), type = "n") s <- seq(1, 0.1, -0.1) for (i in 1:10) { lines(x, beta2i(x, scale = s[i]), col = Col[i]) text(1.5 - 0.2, beta2i(1.5, scale = s[i]), s[i], col = Col[i]) }x <- seq(-5, 5, 0.1) Col <- occolors(c("red", "blue"))(10) plot(x, beta2i(x), type = "n") s <- seq(1, 0.1, -0.1) for (i in 1:10) { lines(x, beta2i(x, scale = s[i]), col = Col[i]) text(1.5 - 0.2, beta2i(1.5, scale = s[i]), s[i], col = Col[i]) }
Data set listing 156 species (mostly birds, few amphibians and mammals) detected at 127 sites (367 point locations) in Alberta, Canada in 2015, using autonomous recording technology (ARU; Wildlife Acoustic Song Meter) for sound recordings.
data("birdrec")data("birdrec")
A list with 3 elements with matching ordering:
xtab is a sample x species matrix with number of detections,
samp is a data frame with sample level attributes.
taxa is a data frame with species level attributes.
Multiple random recordings at each location were selected
according to a stratified random design
(based on combination of TOY and TOD).
These recordings were listened to by trained analysts
and species were identified based on auditory cues.
This data set lists detections from the first 1-minute segment of each
recording. Dates for the 3967 1-minute segments
range between 2015-03-31 and 2015-07-29.
Variables in birdrec$samp are the following:
PKEY: primary key for location/time combinations.
POINT: unique spatial location IDs, each point had
its own ARU unit.
SITE: site ID (1-4 ARU units deployed per site).
YEAR: year, 2015.
MONTH: month from 3 (March) to 7 (July).
MDAY: day of month, 1-31.
HOUR: 24-hour of day, values between 0-12.
MINUTE: minute, 0-59.
YDAY: ordinal day of the year, 89-209.
RAIN, WIND, INDUSTRY, NOISE:
level of rain, wind, industrial noise, and background noise.
0 = no; 1 = light; 2 = moderate; 3 = heavy.
MICROPHONE:
Every recording contains a certain level of background static due to the
pre-amplifiers; however, problems, such as, electrostatic discharge on the
microphones, faulty wiring, poorly installed microphones and/or
missing microphones can occur causing excess static or dead channels.
0 = no microphone related issues;
1 = left microphone cuts out intermittently;
2 = right microphone cuts out intermittently;
3 = both microphones cut out intermittently;
4 = left channel failed;
5 = right channel failed;
6 = both channels failed (no cases in the data set);
7 = left side extra static;
8 = right side extra static;
9 = both sides extra static;
10 = other issues;
11 = unbalanced channels.
TOY: time of year intervals used for stratified random
selection of dates. 8 intervals divided into 3 major units
(early, mid, and late breeding season; YDAY 140 and 180 were used
as threshold between the major units).
TOD: time of day, midnight (HOUR = 0) or
morning (HOUR > 0).
Variables in birdrec$taxa are the following:
Species, CommonName, ScientificName,
Family, Order, Class,
and MigratoryBehaviour.
Methodology and metadata is described in ABMI (2016), and Lankau et al. (2015).
Alberta Biodiversity Monitoring Institute (ABMI, www.abmi.ca)
Alberta Biodiversity Monitoring Institute (ABMI), 2016. Terrestrial field data collection protocols (abridged version) 2016-05-18. Alberta Biodiversity Monitoring Institute; Edmonton, Alberta, Canada.
Lankau, H.E., MacPhail, A., Knaggs, M. & Bayne, E., 2015. Acoustic recording analysis protocol. Bioacoustic Unit, University of Alberta, and Alberta Biodiversity Monitoring Institute; Edmonton, Alberta, Canada.
data(birdrec) str(birdrec) aggregate(rowSums(birdrec$xtab), list(TOY=birdrec$samp$TOY, TOD=birdrec$samp$TOD), mean) boxplot(rowSums(birdrec$xtab) ~ TOD + TOY, birdrec$samp, col=c("gold", "tomato"), ylab="# detections") ## Not run: y <- ifelse(birdrec$xtab > 0, 1, 0) g <- paste0(gsub("[[:digit:]]", "", as.character(birdrec$samp$TOY)), substr(as.character(birdrec$samp$TOD), 1, 4)) g <- factor(g, levels=c("EarlyMorn", "MidMorn", "LateMorn", "EarlyMidn", "MidMidn", "LateMidn")) ## binary response model oc <- opticut(y ~ 1, strata=g, dist="binomial") ## multi-level response model mc <- multicut(y ~ 1, strata=g, dist="binomial") ## testing equality of labels splito <- as.character(summary(oc)$summary$split) splitm <- as.character(summary(mc)$summary$split) table(splito == splitm) ## seeing how much those differ bpo <- summary(oc)$bestpart bpm <- summary(mc)$bestpart rs <- rowSums(abs(bpo-bpm)) table(rs) 10 * bpo[rs > 0,] + bpm[rs > 0,] ## End(Not run)data(birdrec) str(birdrec) aggregate(rowSums(birdrec$xtab), list(TOY=birdrec$samp$TOY, TOD=birdrec$samp$TOD), mean) boxplot(rowSums(birdrec$xtab) ~ TOD + TOY, birdrec$samp, col=c("gold", "tomato"), ylab="# detections") ## Not run: y <- ifelse(birdrec$xtab > 0, 1, 0) g <- paste0(gsub("[[:digit:]]", "", as.character(birdrec$samp$TOY)), substr(as.character(birdrec$samp$TOD), 1, 4)) g <- factor(g, levels=c("EarlyMorn", "MidMorn", "LateMorn", "EarlyMidn", "MidMidn", "LateMidn")) ## binary response model oc <- opticut(y ~ 1, strata=g, dist="binomial") ## multi-level response model mc <- multicut(y ~ 1, strata=g, dist="binomial") ## testing equality of labels splito <- as.character(summary(oc)$summary$split) splitm <- as.character(summary(mc)$summary$split) table(splito == splitm) ## seeing how much those differ bpo <- summary(oc)$bestpart bpm <- summary(mc)$bestpart rs <- rowSums(abs(bpo-bpm)) table(rs) 10 * bpo[rs > 0,] + bpm[rs > 0,] ## End(Not run)
A comprehensive and micro-scale land snail data set from 16 dolinas of the Aggtelek Karst Area, Hungary. Data set containing land snail counts as described in Kemecei et al. 2014.
data("dolina")data("dolina")
A list with 3 elements: xtab is a sample x species matrix with counts,
samp is a data frame with sample level attributes,
taxa is a data frame with scientific names and families for the species.
Land snails were sampled during daylight hours between
16 and 18 of August, 2007. Samples were taken from four
microhabitat types (dolina$samp$microhab, dolina$samp$mhab):
litter (LI), trunks of live trees (TL), dead
wood (also known as coarse woody debris; DW), and rock (RO).
In each of the 16 dolina (dolina$samp$dolina),
seven samples were collected in the litter microhabitat
along a north-south transect. In the case of the other three
microhabitat types, samples were collected from three random
locations per microhabitat type in each dolina.
A total of 256 samples (dolina$samp$sample) were
collected, each consisting 2 sub-samples collected
by 2 sampling methods (dolina$samp$method):
litter samples (Q) and timed search (T).
One liter of litter samples including topsoil were collected to be examined later in the laboratory. Litter samples were collected adjacent to live wood, dead wood and rocks, and not from the wood or rocks themselves. Litter samples in the litter microhabitat were not collected near wood or rocks (minimum distance of 2 meters). During 5 minutes per site of time-restricted direct search we investigated microhabitats in a 1 meter radius circle around the litter sample location, but also including tree or rock surfaces for those microhabitats.
The vertical zone (dolina$samp$stratum,
bottom, middle or edge of the dolinas),
aspect of these sample locations (dolina$samp$aspect),
along with litter depth (dolina$samp$lthick, cm),
and litter moisture (dolina$samp$lmoist,
scored on an ordinal scale: 1=dry,
2=fresh, 3=moist) were also recorded.
Distinction of live animals versus fresh empty shells was not feasible due to the method of sorting dry material and the delay in litter sample processing, so these were combined and constituted the 'fresh' group. Whitened, disintegrating and broken shells constituted the 'broken' group. This 'broken' group was excluded from the data set presented here.
Solymos et al. 2016 and Kemencei et al. 2014.
Kemencei, Z., Farkas, R., Pall-Gergely, B., Vilisics, F., Nagy, A., Hornung, E. & Solymos, P., 2014. Microhabitat associations of land snails in forested dolinas: implications for coarse filter conservation. Community Ecology 15:180–186. <doi:10.1556/ComEc.15.2014.2.6>
Solymos, P., Kemencei, Z. Pall-Gergely, B., Farkas, R., Vilisics, F., Nagy, A., Kisfali, M. & Hornung, E., 2016. Public data from the dolina project. Version 1.0. Zenodo, <doi:10.5281/zenodo.53080>
data(dolina) str(dolina) ## species richness by microhabitat and method Richness <- rowSums(dolina$xtab > 0) boxplot(Richness ~ mhab + method, dolina$samp, ylab="Species richness", main="Dolina data set", col=rep(c("#2C7BB6", "#D7191C"), each=4))data(dolina) str(dolina) ## species richness by microhabitat and method Richness <- rowSums(dolina$xtab > 0) boxplot(Richness ~ mhab + method, dolina$samp, ylab="Species richness", main="Dolina data set", col=rep(c("#2C7BB6", "#D7191C"), each=4))
Lorenz curve based thresholds and partitions.
lorenz(x, n = rep(1, length(x)), na.last = TRUE) ## S3 method for class 'lorenz' quantile(x, probs = seq(0, 1, 0.25), type = c("L", "p"), ...) iquantile(x, ...) ## S3 method for class 'lorenz' iquantile(x, values, type = c("L", "p"),...) ## S3 method for class 'lorenz' plot(x, type = c("L", "x"), tangent = NA, h = NA, v = NA, ...) ## S3 method for class 'summary.lorenz' print(x, digits, ...) ## S3 method for class 'lorenz' summary(object, ...)lorenz(x, n = rep(1, length(x)), na.last = TRUE) ## S3 method for class 'lorenz' quantile(x, probs = seq(0, 1, 0.25), type = c("L", "p"), ...) iquantile(x, ...) ## S3 method for class 'lorenz' iquantile(x, values, type = c("L", "p"),...) ## S3 method for class 'lorenz' plot(x, type = c("L", "x"), tangent = NA, h = NA, v = NA, ...) ## S3 method for class 'summary.lorenz' print(x, digits, ...) ## S3 method for class 'lorenz' summary(object, ...)
x |
a vector of nonnegative numbers for |
n |
a vector of frequencies, must be same length as |
na.last |
logical, for controlling the treatment of |
probs |
numeric vector of probabilities with values in [0,1],
as in |
values |
numeric vector of values for which the corresponding population quantiles are to be returned. |
type |
character. For the |
tangent |
color value for the Lorenz-curve tangent when plotted. The default
|
h |
color value for the horizontal line for the Lorenz-curve tangent
when plotted. The default
|
v |
color value for the vertical line for the Lorenz-curve tangent
when plotted. The default
|
digits |
numeric, number of significant digits in output. |
object |
object to summarize. |
... |
other arguments passed to the underlying functions. |
The Lorenz curve is a continuous piecewise linear function
representing the distribution of abundance (income, or wealth).
Cumulative portion of the population: (), vs.
cumulative portion of abundance:
.
where are indexed in non-decreasing order ().
By convention, p_0 = L_0 = 0.
n can represent unequal frequencies.
The following charactersitics of the Lorenz curve are calculated:
"t": index where tangent (slope 1) touches the curve;
"x[t]", "p[t]", and "L[t]" are values corresponding to
index t, x_t is the unmodified input.
"S": Lorenz asymmetry coefficient (),
indicates symmetry.
"G": Gini coefficient, 0 is perfect equality,
values close to 1 indicate high inequality.
"J": Youden index is the (largest) distance between the anti-diagonal
and the curve, distance is largest at the tangent point
().
lorenz returns an object of class lorenz. It is a matrix with m+1 rows
(m = length(x)) and 3 columns (p, L, x).
The quantile method finds values of x_i corresponding to
quantiles L_i or p_i (depending on the type argument).
The iquantile (inverse quantile) method
finds quantiles of L_i or p_i corresponding to values of x_i.
The plot method draws a Lorenz curve.
Because the object is a matrix, lines
and points will work for adding multiple lines.
The summary method returns characteristics of the Lorenz curve.
Peter Solymos <[email protected]>
Damgaard, C., & Weiner, J. (2000): Describing inequality in plant size or fecundity. Ecology 81:1139–1142. <doi:10.2307/177185>
Schisterman, E. F., Perkins, N. J., Liu, A., & Bondell, H. (2005): Optimal cut-point and its corresponding Youden index to discriminate individuals using pooled blood samples. Epidemiology 16:73–81. <doi:10.1097/01.ede.0000147512.81966.ba>
Youden, W. J. (1950): Index for rating diagnostic tests. Cancer 3:32–5. <doi:10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3>
set.seed(1) x <- c(rexp(100, 10), rexp(200, 1)) l <- lorenz(x) head(l) tail(l) summary(l) summary(unclass(l)) (q <- c(0.05, 0.5, 0.95)) (p_i <- quantile(l, probs=q, type="p")) iquantile(l, values=p_i, type="p") (p_i <- quantile(l, probs=q, type="L")) iquantile(l, values=p_i, type="L") op <- par(mfrow=c(2,1)) plot(l, lwd=2, tangent=2, h=3, v=4) abline(0, 1, lty=2, col="grey") abline(1, -1, lty=2, col="grey") plot(l, type="x", lwd=2, h=3, v=4) par(op) ## Lorenz-tangent approach to binarize a multi-level problem n <- 100 g <- as.factor(sort(sample(LETTERS[1:4], n, replace=TRUE, prob=4:1))) x <- rpois(n, exp(as.integer(g))) mu <- aggregate(x, list(g), mean) (l <- lorenz(mu$x, table(g))) (s <- summary(l)) plot(l) abline(0, 1, lty=2) lines(rep(s["p[t]"], 2), c(s["p[t]"], s["L[t]"]), col=2)set.seed(1) x <- c(rexp(100, 10), rexp(200, 1)) l <- lorenz(x) head(l) tail(l) summary(l) summary(unclass(l)) (q <- c(0.05, 0.5, 0.95)) (p_i <- quantile(l, probs=q, type="p")) iquantile(l, values=p_i, type="p") (p_i <- quantile(l, probs=q, type="L")) iquantile(l, values=p_i, type="L") op <- par(mfrow=c(2,1)) plot(l, lwd=2, tangent=2, h=3, v=4) abline(0, 1, lty=2, col="grey") abline(1, -1, lty=2, col="grey") plot(l, type="x", lwd=2, h=3, v=4) par(op) ## Lorenz-tangent approach to binarize a multi-level problem n <- 100 g <- as.factor(sort(sample(LETTERS[1:4], n, replace=TRUE, prob=4:1))) x <- rpois(n, exp(as.integer(g))) mu <- aggregate(x, list(g), mean) (l <- lorenz(mu$x, table(g))) (s <- summary(l)) plot(l) abline(0, 1, lty=2) lines(rep(s["p[t]"], 2), c(s["p[t]"], s["L[t]"]), col=2)
The functions fits the multi-level response model for each species, possibly controlling for modifying/confounding variables.
multicut1(Y, X, Z, dist = "gaussian", sset=NULL, ...) multicut(...) ## Default S3 method: multicut(Y, X, strata, dist = "gaussian", sset=NULL, cl = NULL, ...) ## S3 method for class 'formula' multicut(formula, data, strata, dist = "gaussian", sset=NULL, cl = NULL, ...) ## S3 method for class 'multicut' bestmodel(object, which = NULL, ...) ## S3 method for class 'multicut' bestpart(object, ...) ## S3 method for class 'multicut' strata(object, ...) ## S3 method for class 'multicut' getMLE(object, which, vcov=FALSE, ...) ## S3 method for class 'multicut' subset(x, subset=NULL, ...) ## S3 method for class 'multicut' fitted(object, ...) ## S3 method for class 'multicut' predict(object, gnew=NULL, xnew=NULL, ...) ## S3 method for class 'multicut' plot(x, which = NULL, cut, sort, las, ylab = "Relative abundance", xlab = "Strata", show_I = TRUE, show_S = TRUE, hr = TRUE, tick = TRUE, theme, mar = c(5, 4, 4, 4) + 0.1, bty = "o", lower = 0, upper = 1, pos = 0, horizontal=TRUE, ...) ## S3 method for class 'multicut1' plot(x, ylab = "Relative abundance", xlab = "Strata", ...) lcplot(x, ...) ## S3 method for class 'multicut1' lcplot(x, ylab="Cumulative abundance", xlab="Strata", bty = "o", theme, ...) ## S3 method for class 'multicut1' print(x, digits, ...) ## S3 method for class 'multicut' print(x, digits, ...) ## S3 method for class 'summary.multicut' print(x, cut, sort, digits, ...) ## S3 method for class 'multicut' summary(object, ...) ## S3 method for class 'multicut' as.data.frame(x, row.names = NULL, optional = FALSE, cut, sort, ...) ## S3 method for class 'summary.multicut' as.data.frame(x, row.names = NULL, optional = FALSE, cut, sort, ...)multicut1(Y, X, Z, dist = "gaussian", sset=NULL, ...) multicut(...) ## Default S3 method: multicut(Y, X, strata, dist = "gaussian", sset=NULL, cl = NULL, ...) ## S3 method for class 'formula' multicut(formula, data, strata, dist = "gaussian", sset=NULL, cl = NULL, ...) ## S3 method for class 'multicut' bestmodel(object, which = NULL, ...) ## S3 method for class 'multicut' bestpart(object, ...) ## S3 method for class 'multicut' strata(object, ...) ## S3 method for class 'multicut' getMLE(object, which, vcov=FALSE, ...) ## S3 method for class 'multicut' subset(x, subset=NULL, ...) ## S3 method for class 'multicut' fitted(object, ...) ## S3 method for class 'multicut' predict(object, gnew=NULL, xnew=NULL, ...) ## S3 method for class 'multicut' plot(x, which = NULL, cut, sort, las, ylab = "Relative abundance", xlab = "Strata", show_I = TRUE, show_S = TRUE, hr = TRUE, tick = TRUE, theme, mar = c(5, 4, 4, 4) + 0.1, bty = "o", lower = 0, upper = 1, pos = 0, horizontal=TRUE, ...) ## S3 method for class 'multicut1' plot(x, ylab = "Relative abundance", xlab = "Strata", ...) lcplot(x, ...) ## S3 method for class 'multicut1' lcplot(x, ylab="Cumulative abundance", xlab="Strata", bty = "o", theme, ...) ## S3 method for class 'multicut1' print(x, digits, ...) ## S3 method for class 'multicut' print(x, digits, ...) ## S3 method for class 'summary.multicut' print(x, cut, sort, digits, ...) ## S3 method for class 'multicut' summary(object, ...) ## S3 method for class 'multicut' as.data.frame(x, row.names = NULL, optional = FALSE, cut, sort, ...) ## S3 method for class 'summary.multicut' as.data.frame(x, row.names = NULL, optional = FALSE, cut, sort, ...)
formula |
two sided model formula, response species data (matrix,
or possible a vector for single species case) in the left-hand side,
model terms for modifying effects in the right-hand side
(its structure depending on the underlying functions).
For example, in the most basic Gaussian case it can be
|
data |
an optional data frame, list or environment containing the variables
in the model. If not found in data, the variables are taken from
|
strata, Z
|
a factor, unique values define strata (must have at least 2 unique levels, empty levels are dropped). |
dist |
character or function, a distribution to fit.
If character, it can follow one of these patterns: |
sset |
an optional vector specifying a subset of observations (rows)
to be used in the fitting process. |
cl |
a cluster object, or an integer for multiple cores in parallel computations (integer value for forking is ignored on Windows). |
Y |
numeric vector of observations for |
X |
numeric, design matrix for possible confounding/modifier variables. Can be missing, in which case an intercept-only model is assumed. |
x, object
|
object to plot, print, summarize. |
cut |
log likelihood ratio value to be used as a cut-off for showing species whose log likelihood ratio is not less than the cut-off. |
sort |
logical value indicating if species/partitions
should be meaningfully sorted, the default is |
show_I |
logical, if indicator potential (I) should be shown. |
show_S |
logical, if number of indicator species should be shown. |
hr, tick
|
logical, if horizontal rules ( |
theme |
color theme as defined by |
mar |
numeric, graphical parameters for plot margin |
ylab, xlab, las
|
graphical arguments, see |
bty |
Character, determines the type of box which is drawn around plots,
see |
lower, upper
|
numeric (between 0 and 1), |
pos |
numeric, position of rectangles in the plot relative to the baseline. Value must be in the [-1, 1] range (below vs. above baseline). |
horizontal |
logical, plot orientation: species as rows ( |
digits |
numeric, number of significant digits in output. |
which |
numeric or character (can be a vector) defining
a subset of species from the fitted object,
or |
row.names |
|
optional |
logical. If |
subset |
logical, numeric, or character index indicating species to keep,
missing values are not accepted. The default |
vcov |
logical, if variance-covariance matrix is to be returned. |
gnew, xnew
|
new values for |
... |
other arguments passed to the underlying functions. |
multicut1 returns an object of class 'multicut1'.
multicut returns an object of class 'multicut', that is a list
with the following components:
"call"the function call.
"species"a list of species specific multicut1 objects.
"X"modifying variables as model matrix.
"Y"response, single species vector or matrix.
"strata"defines the stratification.
"nobs"sample size.
"sset"subset, if specified.
"dist"distribution.
"failed"IDs for failed species models dropped from results list.
The strata method extracts the strata argument
as factor.
The print and summary methods are called for their side effects
showing expected values, and log likelihood ratio (logLR).
Optimal binary partitions are determined as part of the
summary based on Lorenz-tangent based thresholding,
which requires nonnegative expected values.
Indicator potential (I) is based on largest the
contrast (difference) between the minimum and maximum
estimates on the linear predictor (link) scale.
The subset method subsets the species in the multicut object.
The plot method presents the estimates by species and strata.
The lcplot method plots the Lorenz curve for a single
species 'multicut1' object.
bestpart returns a matrix with the best supported
partitions for each species (samples and rows, species as columns).
Binary partitions are based on Lorenz-tangent based optimal threshold
(see lorenz).
lorenz requires nonnegative fitted values
which is not guaranteed under dist = "gaussian" with identity
link, see fix_fitted ocoptions setting
for how to resolve this (choosing a different link function,
distribution, or centering modified variables is advised).
bestmodel returns the best supported model for further
manipulation (e.g. prediction). Note: custom distribution
functions are designed to return only point estimates,
thus the best model cannot be returned. In this case,
use the best partition returned by bestpart to refit the model.
getMLE returns a named list corresponding to the best supported
model. The list has the following elements:
coef is the Maximum Likelihood Estimate (MLE),
vcov is the variance-covariance matrix for the MLE or NULL,
dist is the distribution inherited from input object.
fitted returns expected values on the predictor scale
for the observations as a matrix (number of observations by number of species).
predict returns fitted values when both gnew
and xnew are NULL, or corresponding point predictions
(expected values) on the predictor scale.
The coercion methods as.data.frame return a data frame.
The use of the multicut1 function is generally discouraged:
some of the internal checks are not guaranteed to
flag issues when the formula-to-model-matrix translation is side-stepped
(this is what is happening when the modifier variables are supplied
as X argument in multicut1).
Use the multicut function with a single species instead.
Peter Solymos <[email protected]>
lorenz Examples for how multi-level partitions are binarized
using the Lorenz-tangent approach.
opticut for optimal binary response model,
optilevels for finding the optimal number of factor levels.
beta2i for indicator potential (I) calculations in summaries.
bestmodel, bestpart, and uncertainty
for manipulating fitted objects.
ocoptions on how to set some of the global options
related to the presentation of the results in the package
and how errors encountered during model fitting are handled.
## --- Gaussian ## simple example from Legendre 2013 ## Indicator Species: Computation, in ## Encyclopedia of Biodiversity, Volume 4 ## https://dx.doi.org/10.1016/B978-0-12-384719-5.00430-5 gr <- as.factor(paste0("X", rep(1:5, each=5))) spp <- cbind(Species1=rep(c(4,6,5,3,2), each=5), Species2=c(rep(c(8,4,6), each=5), 4,4,2, rep(0,7)), Species3=rep(c(18,2,0,0,0), each=5)) rownames(spp) <- gr ## must add some noise to avoid perfect fit spp[6, "Species1"] <- 7 spp[1, "Species3"] <- 17 spp ## negative expected values are not good oco <- ocoptions(fix_fitted=TRUE) summary(ocall <- multicut(spp ~ 1, strata=gr, dist="gaussian")) summary(multicut(spp, strata=gr, dist="gaussian")) # alternative ocoptions(oco) # reset options ## --- Binomial ## simulated binary data set.seed(1234) n <- 200 x0 <- sample(1:4, n, TRUE) x1 <- ifelse(x0 <= 2, 1, 0) x2 <- rnorm(n, 0.5, 1) p1 <- plogis(-0.5 + 2*x1 + -0.8*x2) Y1 <- rbinom(n, 1, p1) p2 <- plogis(-0.1 + 2*ifelse(x0==4,1,0) + -0.8*x2) Y2 <- rbinom(n, 1, p2) p3 <- plogis(-0.1 + -0.8*x2) Y3 <- rbinom(n, 1, p3) Y <- cbind(SPP1=Y1, SPP2=Y2, SPP3=Y3) X <- model.matrix(~x2) (m0 <- multicut1(Y1, X, as.factor(x0), dist="binomial")) lcplot(m0) summary(m1 <- multicut(Y ~ x2, strata=x0, dist="poisson")) plot(m1) ## subset results summary(subset(m1, 1:2)) ## best partition head(bestpart(m1)) ## best model mods <- bestmodel(m1) mods ## explore further confint(mods[[1]]) ## MLE and variance-covariance matrix (species 1) getMLE(m1, which = 1, vcov=TRUE) ## fitted values head(fitted(m1)) ## prediction for new data head(predict(m1, gnew=x0, xnew=data.frame(x2=x2))) ## Not run: ## --- Zero-inflated Negative Binomial ## dolina example data(dolina) ## stratum as ordinal dolina$samp$stratum <- as.integer(dolina$samp$stratum) ## filter species to speed up things a bit Y <- dolina$xtab[,colSums(dolina$xtab > 0) >= 20] ## opticut results, note the cloglog link function dol <- multicut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="zinb:cloglog") summary(dol) ## vertical plot orientation plot(dol, horizontal=FALSE, pos=1, upper=0.8) ## parallel library(parallel) cl <- makeCluster(2) multicut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="zip",cl=cl) stopCluster(cl) ## --- Customizing distributions ## we may want to expand the Zero-inflation component in a ZIP model ## see how the return value needs to be structured fun <- function(Y, X, linkinv, zi_term, ...) { X <- as.matrix(X) mod <- pscl::zeroinfl(Y ~ X-1 | zi_term, dist = "poisson", ...) list(coef=coef(mod), logLik=logLik(mod), linkinv=mod$linkinv) } Xdol <- model.matrix(~ stratum + lmoist + method, data=dolina$samp) ## this fits the null model (i.e. no partitions added) fun(Y[,"amin"], Xdol, zi_term=dolina$samp$method) ## now we can use dist=fun multicut1(Y[,"amin"], Xdol, Z=dolina$samp$mhab, dist=fun, zi_term=dolina$samp$method) dol2 <- multicut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist=fun, zi_term=dolina$samp$method) summary(dol2) ## End(Not run)## --- Gaussian ## simple example from Legendre 2013 ## Indicator Species: Computation, in ## Encyclopedia of Biodiversity, Volume 4 ## https://dx.doi.org/10.1016/B978-0-12-384719-5.00430-5 gr <- as.factor(paste0("X", rep(1:5, each=5))) spp <- cbind(Species1=rep(c(4,6,5,3,2), each=5), Species2=c(rep(c(8,4,6), each=5), 4,4,2, rep(0,7)), Species3=rep(c(18,2,0,0,0), each=5)) rownames(spp) <- gr ## must add some noise to avoid perfect fit spp[6, "Species1"] <- 7 spp[1, "Species3"] <- 17 spp ## negative expected values are not good oco <- ocoptions(fix_fitted=TRUE) summary(ocall <- multicut(spp ~ 1, strata=gr, dist="gaussian")) summary(multicut(spp, strata=gr, dist="gaussian")) # alternative ocoptions(oco) # reset options ## --- Binomial ## simulated binary data set.seed(1234) n <- 200 x0 <- sample(1:4, n, TRUE) x1 <- ifelse(x0 <= 2, 1, 0) x2 <- rnorm(n, 0.5, 1) p1 <- plogis(-0.5 + 2*x1 + -0.8*x2) Y1 <- rbinom(n, 1, p1) p2 <- plogis(-0.1 + 2*ifelse(x0==4,1,0) + -0.8*x2) Y2 <- rbinom(n, 1, p2) p3 <- plogis(-0.1 + -0.8*x2) Y3 <- rbinom(n, 1, p3) Y <- cbind(SPP1=Y1, SPP2=Y2, SPP3=Y3) X <- model.matrix(~x2) (m0 <- multicut1(Y1, X, as.factor(x0), dist="binomial")) lcplot(m0) summary(m1 <- multicut(Y ~ x2, strata=x0, dist="poisson")) plot(m1) ## subset results summary(subset(m1, 1:2)) ## best partition head(bestpart(m1)) ## best model mods <- bestmodel(m1) mods ## explore further confint(mods[[1]]) ## MLE and variance-covariance matrix (species 1) getMLE(m1, which = 1, vcov=TRUE) ## fitted values head(fitted(m1)) ## prediction for new data head(predict(m1, gnew=x0, xnew=data.frame(x2=x2))) ## Not run: ## --- Zero-inflated Negative Binomial ## dolina example data(dolina) ## stratum as ordinal dolina$samp$stratum <- as.integer(dolina$samp$stratum) ## filter species to speed up things a bit Y <- dolina$xtab[,colSums(dolina$xtab > 0) >= 20] ## opticut results, note the cloglog link function dol <- multicut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="zinb:cloglog") summary(dol) ## vertical plot orientation plot(dol, horizontal=FALSE, pos=1, upper=0.8) ## parallel library(parallel) cl <- makeCluster(2) multicut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="zip",cl=cl) stopCluster(cl) ## --- Customizing distributions ## we may want to expand the Zero-inflation component in a ZIP model ## see how the return value needs to be structured fun <- function(Y, X, linkinv, zi_term, ...) { X <- as.matrix(X) mod <- pscl::zeroinfl(Y ~ X-1 | zi_term, dist = "poisson", ...) list(coef=coef(mod), logLik=logLik(mod), linkinv=mod$linkinv) } Xdol <- model.matrix(~ stratum + lmoist + method, data=dolina$samp) ## this fits the null model (i.e. no partitions added) fun(Y[,"amin"], Xdol, zi_term=dolina$samp$method) ## now we can use dist=fun multicut1(Y[,"amin"], Xdol, Z=dolina$samp$mhab, dist=fun, zi_term=dolina$samp$method) dol2 <- multicut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist=fun, zi_term=dolina$samp$method) summary(dol2) ## End(Not run)
A convenient way of setting color palettes for the opticut package.
occolors(theme) col2gray(col, method="BT.709")occolors(theme) col2gray(col, method="BT.709")
theme |
character value, character vector, or a function used to interpolate the colors.
The built-in values are |
col |
vector of color specification as described on the help page
for the |
method |
character, the method used for grayscale conversion. See Details. |
Grayscale conversion methods in col2gray calculate gray levels based on
red (R), green (G), and blue (B) color channels as follows:
"BT.709"0.2126 * R + 0.7152 * G + 0.0722 * B, luminosity correction following the ITU-R BT.709 recommendation;
"BT.601"0.299 * R + 0.587 * G + 0.114 * B, luminosity correction following the ITU-R BT.601 recommendation;
"desaturate"(max(R, G, B) + min(R, G, B)) / 2, also called lightness;
"average"(R + G + B) / 3;
"maximum"max(R, G, B);
"minimum"min(R, G, B);
"red"R;
"green"G;
"blue"B.
occolors returns a function, see colorRampPalette.
col2gray returns a vector of gray colors based on
the conversion method and gray.
Peter Solymos <[email protected]>
Hexadecimal values for the built-in palettes are taken from https://colorbrewer2.org/.
Converting color to grayscale: https://en.wikipedia.org/wiki/Grayscale
colorRampPalette for a general description of palettes.
ocoptions for setting the color theme option in the
opticut package.
## using palettes plot(1:100, rep(2, 100), pch = 15, ylim = c(0, 21), axes = FALSE, ann = FALSE, col = occolors()(100)) # default 'bg' text(50, 1, "theme = 'br'") points(1:100, rep(5, 100), pch = 15, col=occolors("gr")(100)) text(50, 4, "theme = 'gr'") points(1:100, rep(8, 100), pch = 15, col=occolors("bw")(100)) text(50, 7, "theme = 'bw'") points(1:100, rep(11, 100), pch = 15, col=occolors(terrain.colors)(100)) text(50, 10, "theme = terrain.colors") points(1:100, rep(14, 100), pch = 15, col=occolors(c("purple", "pink", "orange"))(100)) text(50, 13, "theme = c('purple', 'pink', 'orange')") points(1:100, rep(17, 100), pch = 15, col=occolors(c("#a6611a", "#ffffbf", "#018571"))(100)) text(50, 16, "theme = c('#a6611a', '#ffffbf', '#018571')") points(1:100, rep(20, 100), pch = 15, col=occolors(c("#7b3294", "#ffffbf", "#008837"))(100)) text(50, 19, "theme = c('#7b3294', '#ffffbf', '#008837')") ## grayscale conversions n <- 25 col <- occolors("br")(n) method <- c("BT.709", "BT.601", "desaturate", "average", "maximum", "minimum", "red", "green", "blue") plot(0, type="n", ann=FALSE, axes=FALSE, xlim=c(0, n), ylim=c(3*length(method), 0)) for (j in 1:length(method)) { for (i in 1:n) { polygon(c(i-1, i, i, i-1), c(0, 0, 1, 1)+((j-1)*3), col=col[i], border=col[i]) polygon(c(i-1, i, i, i-1), c(1, 1, 2, 2)+((j-1)*3), col=col2gray(col[i], method=method[j]), border=col2gray(col[i], method=method[j])) text(n/2, 1+((j-1)*3), method[j]) } }## using palettes plot(1:100, rep(2, 100), pch = 15, ylim = c(0, 21), axes = FALSE, ann = FALSE, col = occolors()(100)) # default 'bg' text(50, 1, "theme = 'br'") points(1:100, rep(5, 100), pch = 15, col=occolors("gr")(100)) text(50, 4, "theme = 'gr'") points(1:100, rep(8, 100), pch = 15, col=occolors("bw")(100)) text(50, 7, "theme = 'bw'") points(1:100, rep(11, 100), pch = 15, col=occolors(terrain.colors)(100)) text(50, 10, "theme = terrain.colors") points(1:100, rep(14, 100), pch = 15, col=occolors(c("purple", "pink", "orange"))(100)) text(50, 13, "theme = c('purple', 'pink', 'orange')") points(1:100, rep(17, 100), pch = 15, col=occolors(c("#a6611a", "#ffffbf", "#018571"))(100)) text(50, 16, "theme = c('#a6611a', '#ffffbf', '#018571')") points(1:100, rep(20, 100), pch = 15, col=occolors(c("#7b3294", "#ffffbf", "#008837"))(100)) text(50, 19, "theme = c('#7b3294', '#ffffbf', '#008837')") ## grayscale conversions n <- 25 col <- occolors("br")(n) method <- c("BT.709", "BT.601", "desaturate", "average", "maximum", "minimum", "red", "green", "blue") plot(0, type="n", ann=FALSE, axes=FALSE, xlim=c(0, n), ylim=c(3*length(method), 0)) for (j in 1:length(method)) { for (i in 1:n) { polygon(c(i-1, i, i, i-1), c(0, 0, 1, 1)+((j-1)*3), col=col[i], border=col[i]) polygon(c(i-1, i, i, i-1), c(1, 1, 2, 2)+((j-1)*3), col=col2gray(col[i], method=method[j]), border=col2gray(col[i], method=method[j])) text(n/2, 1+((j-1)*3), method[j]) } }
A convenient way of handling options related to the opticut package.
ocoptions(...)ocoptions(...)
... |
arguments in |
When parameters are set by ocoptions, their former values are
returned in an invisible named list. Such a list can be passed as an
argument to ocoptions to restore the parameter values.
Tags are the following:
collapse |
character value to be used when merging factor levels,
the default is |
cut |
log likelihood ratio value, model/species with lower
values are excluded from summaries and plots,
the default is |
sort |
logical value indicating if species/partitions
should be meaningfully sorted, the default is |
theme |
the color theme to be used based on |
check_comb |
check the design matrices for complementary partitions
using |
try_error |
if |
scale |
the scaling factor used to calculate indicator potential (I) based on the estimated contrast (x): I = abs(tanh(x*scale)), the default is 0.5. |
fix_fitted |
|
robust_loglik |
if ill-defined models resulting in perfect fit
(infinite log likelihood, or |
Peter Solymos <[email protected]>
## simple example from Legendre 2013 ## Indicator Species: Computation, in ## Encyclopedia of Biodiversity, Volume 4 ## https://dx.doi.org/10.1016/B978-0-12-384719-5.00430-5 gr <- as.factor(paste0("X", rep(1:5, each=5))) spp <- cbind(Species1=rep(c(4,6,5,3,2), each=5), Species2=c(rep(c(8,4,6), each=5), 4,4,2, rep(0,7)), Species3=rep(c(18,2,0,0,0), each=5)) rownames(spp) <- gr ## must add some noise to avoid perfect fit spp[6, "Species1"] <- 7 spp[1, "Species3"] <- 17 spp ## current settings print(unlist(ocoptions())) # these give identical answers unlist(getOption("ocoptions")) summary(ocall <- opticut(spp ~ 1, strata=gr, dist="gaussian", comb="all")) ## resetting pboptions and checking new settings ocop <- ocoptions(collapse="&", sort=FALSE) unlist(getOption("ocoptions")) ## running again with new settings summary(ocall <- opticut(spp ~ 1, strata=gr, dist="gaussian", comb="all")) ## resetting original ocoptions(ocop) unlist(getOption("ocoptions"))## simple example from Legendre 2013 ## Indicator Species: Computation, in ## Encyclopedia of Biodiversity, Volume 4 ## https://dx.doi.org/10.1016/B978-0-12-384719-5.00430-5 gr <- as.factor(paste0("X", rep(1:5, each=5))) spp <- cbind(Species1=rep(c(4,6,5,3,2), each=5), Species2=c(rep(c(8,4,6), each=5), 4,4,2, rep(0,7)), Species3=rep(c(18,2,0,0,0), each=5)) rownames(spp) <- gr ## must add some noise to avoid perfect fit spp[6, "Species1"] <- 7 spp[1, "Species3"] <- 17 spp ## current settings print(unlist(ocoptions())) # these give identical answers unlist(getOption("ocoptions")) summary(ocall <- opticut(spp ~ 1, strata=gr, dist="gaussian", comb="all")) ## resetting pboptions and checking new settings ocop <- ocoptions(collapse="&", sort=FALSE) unlist(getOption("ocoptions")) ## running again with new settings summary(ocall <- opticut(spp ~ 1, strata=gr, dist="gaussian", comb="all")) ## resetting original ocoptions(ocop) unlist(getOption("ocoptions"))
The functions fits the multi-level response model for each species by finding the best binary partition based on model selection. Possibly controlling for modifying/confounding variables. The general algorithm is described in Kemencei et al. 2014.
opticut1(Y, X, Z, dist = "gaussian", sset=NULL, ...) opticut(...) ## Default S3 method: opticut(Y, X, strata, dist = "gaussian", comb = c("rank", "all"), sset=NULL, cl = NULL, ...) ## S3 method for class 'formula' opticut(formula, data, strata, dist = "gaussian", comb = c("rank", "all"), sset=NULL, cl = NULL, ...) fix_levels(x, sep = "_") strata(object, ...) ## S3 method for class 'opticut' strata(object, ...) ## S3 method for class 'opticut' bestmodel(object, which = NULL, ...) ## S3 method for class 'opticut' bestpart(object, pos_only = FALSE, ...) ## S3 method for class 'opticut' getMLE(object, which, vcov=FALSE, ...) ## S3 method for class 'opticut' subset(x, subset=NULL, ...) ## S3 method for class 'opticut' fitted(object, ...) ## S3 method for class 'opticut' predict(object, gnew=NULL, xnew=NULL, ...) wplot(x, ...) ## S3 method for class 'opticut1' wplot(x, cut, ylim = c(-1, 1), las=1, ylab = "Model weight * Association", xlab = "Partitions", theme, mar = c(5, 4, 4, 4) + 0.1, bty = "o", ...) ## S3 method for class 'opticut' wplot(x, which = NULL, cut, sort, las = 1, ylab = "Model weight * Association", xlab = "Partitions", theme, mar = c(5, 4, 4, 4) + 0.1, bty = "o", ...) ## S3 method for class 'opticut' plot(x, which = NULL, cut, sort, las, ylab = "Relative abundance", xlab = "Strata", show_I = TRUE, show_S = TRUE, hr = TRUE, tick = TRUE, theme, mar = c(5, 4, 4, 4) + 0.1, bty = "o", lower = 0, upper = 1, pos = 0, horizontal=TRUE, ...) ## S3 method for class 'opticut1' print(x, cut, sort, digits, ...) ## S3 method for class 'opticut' print(x, digits, ...) ## S3 method for class 'summary.opticut' print(x, cut, sort, digits, ...) ## S3 method for class 'opticut' summary(object, ...) ## S3 method for class 'opticut' as.data.frame(x, row.names = NULL, optional = FALSE, cut, sort, ...) ## S3 method for class 'summary.opticut' as.data.frame(x, row.names = NULL, optional = FALSE, cut, sort, ...)opticut1(Y, X, Z, dist = "gaussian", sset=NULL, ...) opticut(...) ## Default S3 method: opticut(Y, X, strata, dist = "gaussian", comb = c("rank", "all"), sset=NULL, cl = NULL, ...) ## S3 method for class 'formula' opticut(formula, data, strata, dist = "gaussian", comb = c("rank", "all"), sset=NULL, cl = NULL, ...) fix_levels(x, sep = "_") strata(object, ...) ## S3 method for class 'opticut' strata(object, ...) ## S3 method for class 'opticut' bestmodel(object, which = NULL, ...) ## S3 method for class 'opticut' bestpart(object, pos_only = FALSE, ...) ## S3 method for class 'opticut' getMLE(object, which, vcov=FALSE, ...) ## S3 method for class 'opticut' subset(x, subset=NULL, ...) ## S3 method for class 'opticut' fitted(object, ...) ## S3 method for class 'opticut' predict(object, gnew=NULL, xnew=NULL, ...) wplot(x, ...) ## S3 method for class 'opticut1' wplot(x, cut, ylim = c(-1, 1), las=1, ylab = "Model weight * Association", xlab = "Partitions", theme, mar = c(5, 4, 4, 4) + 0.1, bty = "o", ...) ## S3 method for class 'opticut' wplot(x, which = NULL, cut, sort, las = 1, ylab = "Model weight * Association", xlab = "Partitions", theme, mar = c(5, 4, 4, 4) + 0.1, bty = "o", ...) ## S3 method for class 'opticut' plot(x, which = NULL, cut, sort, las, ylab = "Relative abundance", xlab = "Strata", show_I = TRUE, show_S = TRUE, hr = TRUE, tick = TRUE, theme, mar = c(5, 4, 4, 4) + 0.1, bty = "o", lower = 0, upper = 1, pos = 0, horizontal=TRUE, ...) ## S3 method for class 'opticut1' print(x, cut, sort, digits, ...) ## S3 method for class 'opticut' print(x, digits, ...) ## S3 method for class 'summary.opticut' print(x, cut, sort, digits, ...) ## S3 method for class 'opticut' summary(object, ...) ## S3 method for class 'opticut' as.data.frame(x, row.names = NULL, optional = FALSE, cut, sort, ...) ## S3 method for class 'summary.opticut' as.data.frame(x, row.names = NULL, optional = FALSE, cut, sort, ...)
formula |
two sided model formula, response species data (matrix,
or possible a vector for single species case) in the left-hand side,
model terms for modifying effects in the right-hand side
(its structure depending on the underlying functions).
For example, in the most basic Gaussian case it can be
|
data |
an optional data frame, list or environment containing the variables
in the model. If not found in data, the variables are taken from
|
strata |
vector (usually a factor), unique values define partitions (must have at least 2 unique levels, empty levels are dropped). It can also be a matrix with rows as observations and binary partitions as columns. |
dist |
character or function, a distribution to fit.
If character, it can follow one of these patterns: |
comb |
character, how to define the binary partitions.
|
sset |
an optional vector specifying a subset of observations (rows)
to be used in the fitting process. |
cl |
a cluster object, or an integer for multiple cores in parallel computations (integer value for forking is ignored on Windows). |
Y |
numeric vector of observations for |
X |
numeric, design matrix. Can be missing, in which case an intercept-only model is assumed. |
Z |
factor (must have at least 2 unique levels,
this triggers |
x, object
|
object to plot, print, summarize. For |
cut |
log likelihood ratio value to be used as a cut-off for showing species whose log likelihood ratio is not less than the cut-off. |
sort |
logical value indicating if species/partitions
should be meaningfully sorted, the default is |
show_I |
logical, if indicator potential (I) should be shown. |
show_S |
logical, if number of indicator species should be shown. |
hr, tick
|
logical, if horizontal rules ( |
theme |
color theme as defined by |
mar |
numeric, graphical parameters for plot margin |
ylab, xlab, las, ylim
|
graphical arguments, see |
bty |
Character, determines the type of box which is drawn around plots,
see |
lower, upper
|
numeric (between 0 and 1), |
pos |
numeric, position of rectangles in the plot relative to the baseline. Value must be in the [-1, 1] range (below vs. above baseline). |
horizontal |
logical, plot orientation: species as rows ( |
digits |
numeric, number of significant digits in output. |
which |
numeric or character (can be a vector) defining
a subset of species from the fitted object,
or |
sep |
a character string to separate the sub-strings in factor levels. |
row.names |
|
optional |
logical. If |
pos_only |
logical, best partition normally returns the original variable without
recognizing the direction of the association.
|
subset |
logical, numeric, or character index indicating species to keep,
missing values are not accepted. The default |
vcov |
logical, if variance-covariance matrix is to be returned. |
gnew, xnew
|
new values for |
... |
other arguments passed to the underlying functions. |
Currently available distributions:
"gaussian"real valued continuous observations, e.g. biomass,
uses lm of the stats package.
Identity link is assumed. Centering modified variables is generally advised
to avoid negative expected values when the response is nonnegative.
"poisson"Poisson count data,
uses glm of the stats package.
Exponential (log) link is assumed.
"binomial"presence-absence (detection-nondetection) type data,
uses glm of the stats package.
Logistic (logit) link is assumed.
"negbin"overdispersed Negative Binomial count data,
uses glm.nb of the MASS package.
Exponential (log) link is assumed.
"beta"continuous response in the unit interval (0-1),
e.g. percent cover,
uses betareg of the betareg package.
Logistic (logit) link for the mean model is assumed.
"zip"zero-inflated Poisson counts,
indicative properties are tested as part of the abundance model,
uses zeroinfl of the pscl package.
Exponential (log) link is used for count based analysis,
the second part of the dist argument following the colon
is used as link function for the zero component (logistic link assumed).
"zinb"zero-inflated Negative Binomial counts,
indicative properties are tested as part of the abundance model,
uses zeroinfl of the pscl package.
The zero-inflation component refers to the probability of 0.
Exponential (log) link is used for count based analysis,
the second part of the dist argument following the colon
is used as link function for the zero component (logistic link assumed).
"zip2"zero-inflated Poisson counts,
indicative properties are tested as part of the zero-model,
uses zeroinfl of the pscl package.
The zero-inflation component refers to the probability of 1
to be consistent with other methods regarding positive and negative effects.
Logistic (logit) link is assumed for zero-nonzero based analysis,
only symmetric link functions (logit, probit) allowed.
Exponential (log) link is used for the count data part which cannot be changed.
"zinb2"zero-inflated Negative Binomial counts,
indicative properties are tested as part of the zero-model,
uses zeroinfl of the pscl package.
The zero-inflation component refers to the probability of 1
to be consistent with other methods regarding positive and negative effects.
Logistic (logit) link is assumed for zero-nonzero based analysis,
only symmetric link functions (logit, probit) allowed.
Exponential (log) link is used for the count data part which cannot be changed.
"rsf"presence-only data using resource selection
functions (RSF) as explained in rsf
in the ResourceSelection package, assuming global availability (m = 0).
The "rsf" works only for single species using opticut1
because 'presence-only' type data cannot be kept in a single
matrix-like object for multiple species.
Intercept only model (i.e. no modifier variables in right-hand-side
of the formula) is accepted for "rsf".
Exponential (log) link is assumed.
"rspf"presence-only data using resource selection
probability functions (RSPF)
as explained in rspf
in the ResourceSelection package, assuming global availability (m = 0).
The "rspf" works only for single species using opticut1
because 'presence-only' type data cannot be kept in a single
matrix-like object for multiple species.
Intercept only model is not accepted for "rspf", need to have
at least one continuous modifier variable for identifiability
(see Solymos & Lele 2016).
Logistic (logit) link is assumed.
Custom distributions can be defined, see Examples. Note: not all downstream algorithms and methods work with custom distributions.
fix_levels is a utility function for replacing characters in
factor levels that are identical to the value of the
getOption("ocoptions")$collapse value.
This case can lead to an error when specifying the strata argument,
and the fix_levels can help.
opticut1 returns an object of class opticut1, that is a modified
data frame with additional attributes.
opticut returns an object of class opticut, that is a list
with the following components:
"call"the function call.
"species"a list of species specific opticut1 objects.
"X"modifying variables as model matrix.
"Y"response, single species vector or matrix.
"strata"defines the partitions.
"nobs"sample size.
"sset"subset, if specified.
"nsplit"number of binary splits considered.
"dist"distribution.
"comb"combination type.
"failed"IDs for failed species models dropped from results list.
"collapse"character used for combining partition labels.
fix_levels returns a factor with modified levels.
The strata method extracts the strata argument
as factor. The method finds unique row combinations
when custom matrix is supplied for strata.
The print and summary methods are called for their side effects.
The summary shows the following information:
best supported split, strength and sign of association,
indicator potential (I), expected values (mu0, mu1),
log likelihood ratio (logLR), and model weights(w).
The subset method subsets the species in the opticut object.
The plot method presents the contrasts by species and strata.
The wplot (weight plot) shows model weights for partitions.
bestpart returns a matrix with the best supported
partitions for each species (samples and rows, species as columns).
bestmodel returns the best supported model for further
manipulation (e.g. prediction). Note: custom distribution
functions are designed to return only point estimates,
thus the best model cannot be returned. In this case,
use the best partition returned by bestpart to refit the model.
getMLE returns a named list corresponding to the best supported
model. The list has the following elements:
coef is the Maximum Likelihood Estimate (MLE),
vcov is the variance-covariance matrix for the MLE or NULL,
dist is the distribution inherited from input object.
fitted returns expected values on the predictor scale
for the observations as a matrix (number of observations by number of species).
predict returns fitted values when both gnew
and xnew are NULL, or corresponding point predictions
(expected values) on the predictor scale
(available for comb = "rank" models only).
The coercion methods as.data.frame return a data frame.
The use of the opticut1 function is generally discouraged:
some of the internal checks are not guaranteed to
flag issues when the formula-to-model-matrix translation is side-stepped
(this is what is happening when the modifier variables are supplied
as X argument in opticut1).
Use the opticut with a single species instead.
Peter Solymos <[email protected]> and Ermias T. Azeria
Kemencei, Z., Farkas, R., Pall-Gergely, B., Vilisics, F., Nagy, A., Hornung, E. & Solymos, P. (2014): Microhabitat associations of land snails in forested dolinas: implications for coarse filter conservation. Community Ecology 15:180–186. <doi:10.1556/ComEc.15.2014.2.6>
Solymos, P. & Lele, S. R. (2016): Revisiting resource selection probability functions and single-visit methods: clarification and extensions. Methods in Ecology and Evolution 7:196–205. <doi:10.1111/2041-210X.12432>
allComb, and rankComb
for partitioning algorithms.
beta2i for indicator potential (I) calculations in summaries.
bestmodel, bestpart, and uncertainty
for manipulating fitted objects.
ocoptions on how to set some of the global options
related to the presentation of the results in the package
and how errors encountered during model fitting are handled.
multicut for multinomial-response model,
optilevels for finding the optimal number of factor levels.
## --- Gaussian ## simple example from Legendre 2013 ## Indicator Species: Computation, in ## Encyclopedia of Biodiversity, Volume 4 ## https://dx.doi.org/10.1016/B978-0-12-384719-5.00430-5 gr <- as.factor(paste0("X", rep(1:5, each=5))) spp <- cbind(Species1=rep(c(4,6,5,3,2), each=5), Species2=c(rep(c(8,4,6), each=5), 4,4,2, rep(0,7)), Species3=rep(c(18,2,0,0,0), each=5)) rownames(spp) <- gr ## must add some noise to avoid perfect fit spp[6, "Species1"] <- 7 spp[1, "Species3"] <- 17 spp ## all partitions summary(ocall <- opticut(spp ~ 1, strata=gr, dist="gaussian", comb="all")) summary(opticut(spp, strata=gr, dist="gaussian", comb="all")) # alternative ## rank based partitions summary(ocrank <- opticut(spp ~ 1, strata=gr, dist="gaussian", comb="rank")) summary(opticut(spp, strata=gr, dist="gaussian", comb="rank")) # alternative ## --- Binomial ## simulated binary data set.seed(1234) n <- 200 x0 <- sample(1:4, n, TRUE) x1 <- ifelse(x0 <= 2, 1, 0) x2 <- rnorm(n, 0.5, 1) p1 <- plogis(-0.5 + 2*x1 + -0.8*x2) Y1 <- rbinom(n, 1, p1) p2 <- plogis(-0.1 + 2*ifelse(x0==4,1,0) + -0.8*x2) Y2 <- rbinom(n, 1, p2) p3 <- plogis(-0.1 + -0.8*x2) Y3 <- rbinom(n, 1, p3) Y <- cbind(SPP1=Y1, SPP2=Y2, SPP3=Y3) X <- model.matrix(~x2) ## all partitions, single species Z <- allComb(x0) opticut1(Y1, X, Z, dist="binomial") ## rank based partitions, single species opticut1(Y1, X, as.factor(x0), dist="binomial") ## all partitions, multiple species (m1 <- opticut(Y ~ x2, strata=x0, dist="poisson", comb="all")) summary(m1) ## show all species summary(m1, cut=0) ## plot best partitions and indicator values plot(m1) ## model weights for all species wplot(m1) ## different ways of plotting weights for single species wplot(m1$species[[1]]) wplot(m1, which = 1) ## rank based partitions, multiple species summary(m2 <- opticut(Y ~ x2, strata=x0, dist="poisson", comb="rank")) ## subset results summary(subset(m2, 1:2)) ## best partition head(bestpart(m2)) ## best model mods <- bestmodel(m2) mods ## explore further confint(mods[[1]]) ## MLE and variance-covariance matrix (species 1) getMLE(m2, which=1, vcov=TRUE) ## fitted values head(fitted(m2)) ## prediction for new data head(predict(m2, gnew=x0, xnew=data.frame(x2=x2))) ## Not run: ## --- Zero-inflated Negative Binomial ## dolina example data(dolina) ## stratum as ordinal dolina$samp$stratum <- as.integer(dolina$samp$stratum) ## filter species to speed up things a bit Y <- dolina$xtab[,colSums(dolina$xtab > 0) >= 20] ## opticut results, note the cloglog link function dol <- opticut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="zinb:cloglog") summary(dol) ## vertical plot orientation plot(dol, horizontal=FALSE, pos=1, upper=0.8) ## parallel computing comparisons library(parallel) cl <- makeCluster(2) ## sequential, all combinations (2^(K-1) - 1) system.time(opticut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="zinb", comb="all", cl=NULL)) ## sequential, rank based combinations (K - 1) system.time(opticut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="zinb", comb="rank", cl=NULL)) ## parallel, all combinations (2^(K-1) - 1) system.time(opticut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="zinb", comb="all", cl=cl)) ## parallel, rank based combinations (K - 1) system.time(opticut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="zinb", comb="rank", cl=cl)) stopCluster(cl) ## --- Customizing distributions ## we may want to expand the Zero-inflation component in a ZIP model ## see how the return value needs to be structured fun <- function(Y, X, linkinv, zi_term, ...) { X <- as.matrix(X) mod <- pscl::zeroinfl(Y ~ X-1 | zi_term, dist = "poisson", ...) list(coef=coef(mod), logLik=logLik(mod), linkinv=mod$linkinv) } Xdol <- model.matrix(~ stratum + lmoist + method, data=dolina$samp) ## this fits the null model (i.e. no partitions added) fun(Y[,"amin"], Xdol, zi_term=dolina$samp$method) ## now we can use dist=fun opticut1(Y[,"amin"], Xdol, Z=dolina$samp$mhab, dist=fun, zi_term=dolina$samp$method) dol2 <- opticut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist=fun, zi_term=dolina$samp$method) summary(dol2) ## End(Not run) ## current collapse value getOption("ocoptions")$collapse ## factor levels sometimes need to be manipulated ## before feeding it to opticut fix_levels(as.factor(c("A b", "C d")), sep=":") fix_levels(as.factor(c("A b", "C d")), sep="")## --- Gaussian ## simple example from Legendre 2013 ## Indicator Species: Computation, in ## Encyclopedia of Biodiversity, Volume 4 ## https://dx.doi.org/10.1016/B978-0-12-384719-5.00430-5 gr <- as.factor(paste0("X", rep(1:5, each=5))) spp <- cbind(Species1=rep(c(4,6,5,3,2), each=5), Species2=c(rep(c(8,4,6), each=5), 4,4,2, rep(0,7)), Species3=rep(c(18,2,0,0,0), each=5)) rownames(spp) <- gr ## must add some noise to avoid perfect fit spp[6, "Species1"] <- 7 spp[1, "Species3"] <- 17 spp ## all partitions summary(ocall <- opticut(spp ~ 1, strata=gr, dist="gaussian", comb="all")) summary(opticut(spp, strata=gr, dist="gaussian", comb="all")) # alternative ## rank based partitions summary(ocrank <- opticut(spp ~ 1, strata=gr, dist="gaussian", comb="rank")) summary(opticut(spp, strata=gr, dist="gaussian", comb="rank")) # alternative ## --- Binomial ## simulated binary data set.seed(1234) n <- 200 x0 <- sample(1:4, n, TRUE) x1 <- ifelse(x0 <= 2, 1, 0) x2 <- rnorm(n, 0.5, 1) p1 <- plogis(-0.5 + 2*x1 + -0.8*x2) Y1 <- rbinom(n, 1, p1) p2 <- plogis(-0.1 + 2*ifelse(x0==4,1,0) + -0.8*x2) Y2 <- rbinom(n, 1, p2) p3 <- plogis(-0.1 + -0.8*x2) Y3 <- rbinom(n, 1, p3) Y <- cbind(SPP1=Y1, SPP2=Y2, SPP3=Y3) X <- model.matrix(~x2) ## all partitions, single species Z <- allComb(x0) opticut1(Y1, X, Z, dist="binomial") ## rank based partitions, single species opticut1(Y1, X, as.factor(x0), dist="binomial") ## all partitions, multiple species (m1 <- opticut(Y ~ x2, strata=x0, dist="poisson", comb="all")) summary(m1) ## show all species summary(m1, cut=0) ## plot best partitions and indicator values plot(m1) ## model weights for all species wplot(m1) ## different ways of plotting weights for single species wplot(m1$species[[1]]) wplot(m1, which = 1) ## rank based partitions, multiple species summary(m2 <- opticut(Y ~ x2, strata=x0, dist="poisson", comb="rank")) ## subset results summary(subset(m2, 1:2)) ## best partition head(bestpart(m2)) ## best model mods <- bestmodel(m2) mods ## explore further confint(mods[[1]]) ## MLE and variance-covariance matrix (species 1) getMLE(m2, which=1, vcov=TRUE) ## fitted values head(fitted(m2)) ## prediction for new data head(predict(m2, gnew=x0, xnew=data.frame(x2=x2))) ## Not run: ## --- Zero-inflated Negative Binomial ## dolina example data(dolina) ## stratum as ordinal dolina$samp$stratum <- as.integer(dolina$samp$stratum) ## filter species to speed up things a bit Y <- dolina$xtab[,colSums(dolina$xtab > 0) >= 20] ## opticut results, note the cloglog link function dol <- opticut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="zinb:cloglog") summary(dol) ## vertical plot orientation plot(dol, horizontal=FALSE, pos=1, upper=0.8) ## parallel computing comparisons library(parallel) cl <- makeCluster(2) ## sequential, all combinations (2^(K-1) - 1) system.time(opticut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="zinb", comb="all", cl=NULL)) ## sequential, rank based combinations (K - 1) system.time(opticut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="zinb", comb="rank", cl=NULL)) ## parallel, all combinations (2^(K-1) - 1) system.time(opticut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="zinb", comb="all", cl=cl)) ## parallel, rank based combinations (K - 1) system.time(opticut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="zinb", comb="rank", cl=cl)) stopCluster(cl) ## --- Customizing distributions ## we may want to expand the Zero-inflation component in a ZIP model ## see how the return value needs to be structured fun <- function(Y, X, linkinv, zi_term, ...) { X <- as.matrix(X) mod <- pscl::zeroinfl(Y ~ X-1 | zi_term, dist = "poisson", ...) list(coef=coef(mod), logLik=logLik(mod), linkinv=mod$linkinv) } Xdol <- model.matrix(~ stratum + lmoist + method, data=dolina$samp) ## this fits the null model (i.e. no partitions added) fun(Y[,"amin"], Xdol, zi_term=dolina$samp$method) ## now we can use dist=fun opticut1(Y[,"amin"], Xdol, Z=dolina$samp$mhab, dist=fun, zi_term=dolina$samp$method) dol2 <- opticut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist=fun, zi_term=dolina$samp$method) summary(dol2) ## End(Not run) ## current collapse value getOption("ocoptions")$collapse ## factor levels sometimes need to be manipulated ## before feeding it to opticut fix_levels(as.factor(c("A b", "C d")), sep=":") fix_levels(as.factor(c("A b", "C d")), sep="")
Finds the optimal number of factor levels given the data and a model using a likelihood-based agglomerative algorithm.
optilevels(y, x, z = NULL, alpha = 0, dist = "gaussian", ...) ## S3 method for class 'optilevels' bestmodel(object, ...)optilevels(y, x, z = NULL, alpha = 0, dist = "gaussian", ...) ## S3 method for class 'optilevels' bestmodel(object, ...)
y |
vector of observations. |
x |
a factor or a matrix of proportions (i.e. the values 0 and 1 should have
consistent meaning across the columns, often through a unit sum constraint).
It is the user's responsibility to ensure that values supplied
for |
z |
a design matrix with predictor variables besides the one(s) defined
via the argument |
alpha |
numeric [0-1], weighting factor for calculating information criteria for model selection (i.e. IC = (1-alpha)*AIC + alpha*BIC, also referred to as CAIC: consistent AIC). |
dist |
character, distribution argument passed to underlying functions,
see listed on the help page of |
object |
fitted object. |
... |
other arguments passed to the underlying functions, see |
An object of class 'optilevels' that is a list with the following elements:
"delta"delta IC values along the selection path considering best models.
"ic"IC values along the selection path considering best models.
"coef"matrix of coefficients (linear predictor scale)
corresponding to argument x
along the selection path considering best models.
"zcoef"matrix of coefficients (linear predictor scale)
corresponding to argument z when not NULL
along the selection path considering best models, or NULL.
"rank"matrix ranks based on the coefficients
along the selection path considering best models.
Ranking uses the default ties.method = "average" in rank.
"deltalist"delta IC values along the selection path considering all competing models.
"iclist"IC values along the selection path considering all competing models.
"coeflist"matrix of coefficients (linear predictor scale)
corresponding to argument x
along the selection path considering all competing models.
"zcoeflist"matrix of coefficients (linear predictor scale)
corresponding to argument z when not NULL
along the selection path considering all competing models, or NULL.
"ranklist"matrix ranks based on the coefficients along the selection path considering all competing models.
"levels"list of (merged) factor levels along the selection path considering best models.
"Y"vector of observations (argument y).
"X"design matrix component corresponding to argument x.
"Z"design matrix component corresponding to argument z.
"alpha"weighting argument.
"dist"distribution argument.
"factor"logical, indicating if argument x is a factor
(TRUE) or a matrix (FALSE).
bestmodel returns the best supported model for further
manipulation (e.g. prediction).
Peter Solymos <[email protected]>
opticut and multicut
for fitting best binary and multi-level response models.
## --- Factor levels with Gaussian distribution ## simple example from Legendre 2013 ## Indicator Species: Computation, in ## Encyclopedia of Biodiversity, Volume 4 ## https://dx.doi.org/10.1016/B978-0-12-384719-5.00430-5 gr <- as.factor(paste0("X", rep(1:5, each=5))) spp <- cbind(Species1=rep(c(4,6,5,3,2), each=5), Species2=c(rep(c(8,4,6), each=5), 4,4,2, rep(0,7)), Species3=rep(c(18,2,0,0,0), each=5)) rownames(spp) <- gr ## must add some noise to avoid perfect fit spp[6, "Species1"] <- 7 spp[1, "Species3"] <- 17 spp ol <- optilevels(spp[,"Species3"], gr) ol[c("delta", "coef", "rank", "levels")] ## get the final factor level gr1 <- gr levels(gr1) <- ol$level[[length(ol$level)]] table(gr, gr1) ## compare the models o0 <- lm(spp[,"Species3"] ~ gr - 1) o1 <- lm(spp[,"Species3"] ~ gr1 - 1) data.frame(AIC(o0, o1), delta=AIC(o0, o1)$AIC - AIC(o0)) ol$delta # should be identical ## --- Proportions with Poisson distribution ## simulation set.seed(123) n <- 500 # number of observations k <- 5 # number of habitat types b <- c(-1, -0.2, -0.2, 0.5, 1) names(b) <- LETTERS[1:k] x <- replicate(k, exp(rnorm(n))) x <- x / rowSums(x) # proportions X <- model.matrix(~.-1, data=data.frame(x)) lam <- exp(drop(crossprod(t(X), b))) y <- rpois(n, lam) z <- optilevels(y, x, dist="poisson") ## best model refit bestmodel(z) ## estimates plogis(z$coef) plogis(b) ## optimal classification z$rank ## get the final matrix x1 <- mefa4::groupSums(x, 2, z$levels[[length(z$levels)]]) head(x) head(x1) ## compare the models m0 <- glm(y ~ x - 1, family="poisson") m1 <- glm(y ~ x1 - 1, family="poisson") data.frame(AIC(m0, m1), delta=AIC(m0, m1)$AIC - AIC(m0)) z$delta # should be identical ## Not run: ## dolina example with factor data(dolina) dolina$samp$stratum <- as.integer(dolina$samp$stratum) y <- dolina$xtab[dolina$samp$method == "Q", "ppyg"] x <- dolina$samp$mhab[dolina$samp$method == "Q"] z <- scale(model.matrix(~ stratum + lmoist - 1, dolina$samp[dolina$samp$method == "Q",])) ## without additional covariates dol1 <- optilevels(y, x, z=NULL, dist="poisson") dol1$rank summary(bestmodel(dol1)) ## with additional covariates dol2 <- optilevels(y, x, z, dist="poisson") dol2$rank summary(bestmodel(dol2)) ## compare the two models AIC(bestmodel(dol1), bestmodel(dol2)) ## End(Not run)## --- Factor levels with Gaussian distribution ## simple example from Legendre 2013 ## Indicator Species: Computation, in ## Encyclopedia of Biodiversity, Volume 4 ## https://dx.doi.org/10.1016/B978-0-12-384719-5.00430-5 gr <- as.factor(paste0("X", rep(1:5, each=5))) spp <- cbind(Species1=rep(c(4,6,5,3,2), each=5), Species2=c(rep(c(8,4,6), each=5), 4,4,2, rep(0,7)), Species3=rep(c(18,2,0,0,0), each=5)) rownames(spp) <- gr ## must add some noise to avoid perfect fit spp[6, "Species1"] <- 7 spp[1, "Species3"] <- 17 spp ol <- optilevels(spp[,"Species3"], gr) ol[c("delta", "coef", "rank", "levels")] ## get the final factor level gr1 <- gr levels(gr1) <- ol$level[[length(ol$level)]] table(gr, gr1) ## compare the models o0 <- lm(spp[,"Species3"] ~ gr - 1) o1 <- lm(spp[,"Species3"] ~ gr1 - 1) data.frame(AIC(o0, o1), delta=AIC(o0, o1)$AIC - AIC(o0)) ol$delta # should be identical ## --- Proportions with Poisson distribution ## simulation set.seed(123) n <- 500 # number of observations k <- 5 # number of habitat types b <- c(-1, -0.2, -0.2, 0.5, 1) names(b) <- LETTERS[1:k] x <- replicate(k, exp(rnorm(n))) x <- x / rowSums(x) # proportions X <- model.matrix(~.-1, data=data.frame(x)) lam <- exp(drop(crossprod(t(X), b))) y <- rpois(n, lam) z <- optilevels(y, x, dist="poisson") ## best model refit bestmodel(z) ## estimates plogis(z$coef) plogis(b) ## optimal classification z$rank ## get the final matrix x1 <- mefa4::groupSums(x, 2, z$levels[[length(z$levels)]]) head(x) head(x1) ## compare the models m0 <- glm(y ~ x - 1, family="poisson") m1 <- glm(y ~ x1 - 1, family="poisson") data.frame(AIC(m0, m1), delta=AIC(m0, m1)$AIC - AIC(m0)) z$delta # should be identical ## Not run: ## dolina example with factor data(dolina) dolina$samp$stratum <- as.integer(dolina$samp$stratum) y <- dolina$xtab[dolina$samp$method == "Q", "ppyg"] x <- dolina$samp$mhab[dolina$samp$method == "Q"] z <- scale(model.matrix(~ stratum + lmoist - 1, dolina$samp[dolina$samp$method == "Q",])) ## without additional covariates dol1 <- optilevels(y, x, z=NULL, dist="poisson") dol1$rank summary(bestmodel(dol1)) ## with additional covariates dol2 <- optilevels(y, x, z, dist="poisson") dol2$rank summary(bestmodel(dol2)) ## compare the two models AIC(bestmodel(dol1), bestmodel(dol2)) ## End(Not run)
Blindly fitting a model to all possible partitions is wasteful use of resources. Instead, one can rank the K levels (strata) based on expected response values to explore only K-1 binary partitions along the gradient defined by the ranks of the expected values.
oComb(x, collapse) rankComb(Y, X, Z, dist = "gaussian", collapse, ...)oComb(x, collapse) rankComb(Y, X, Z, dist = "gaussian", collapse, ...)
Y |
numeric, vector of observations. |
X |
numeric, design matrix. |
Z |
factor, must have at least 2 unique levels. |
dist |
character, distribution argument passed to underlying functions,
see listed on the help page of |
x |
and a numeric vector. |
collapse |
character, what to paste between levels.
Defaults to |
... |
other arguments passed to the underlying functions, see |
oComb returns the 'contrast' matrix based on the rank vector as input.
Ranked from lowest to highest expected value among the partitions.
The function rankComb fits the model with multiple (K > 2) factor levels
to find out the ranking, and returns a binary classification matrix
as returned by oComb corresponding to the ranking.
Peter Solymos <[email protected]>
allComb for alternative partitioning algorithm.
opticut for the user interface.
## simulate some data set.seed(1234) n <- 200 x0 <- sample(1:4, n, TRUE) x1 <- ifelse(x0 %in% 1:2, 1, 0) x2 <- rnorm(n, 0.5, 1) lam <- exp(0.5 + 0.5*x1 + -0.2*x2) Y <- rpois(n, lam) ## binary partitions head(rc <- rankComb(Y, model.matrix(~x2), as.factor(x0), dist="poisson")) attr(rc, "est") # expected values in factor levels aggregate(exp(0.5 + 0.5*x1), list(x0=x0), mean) # true values ## simple example oComb(1:4, "+") ## using estimates oComb(attr(rc, "est"))## simulate some data set.seed(1234) n <- 200 x0 <- sample(1:4, n, TRUE) x1 <- ifelse(x0 %in% 1:2, 1, 0) x2 <- rnorm(n, 0.5, 1) lam <- exp(0.5 + 0.5*x1 + -0.2*x2) Y <- rpois(n, lam) ## binary partitions head(rc <- rankComb(Y, model.matrix(~x2), as.factor(x0), dist="poisson")) attr(rc, "est") # expected values in factor levels aggregate(exp(0.5 + 0.5*x1), list(x0=x0), mean) # true values ## simple example oComb(1:4, "+") ## using estimates oComb(attr(rc, "est"))
Quantifying uncertainty for fitted objects.
uncertainty(object, ...) ## S3 method for class 'opticut' uncertainty(object, which = NULL, type = c("asymp", "boot", "multi"), B = 99, cl = NULL, ...) ## S3 method for class 'multicut' uncertainty(object, which = NULL, type = c("asymp", "boot"), B = 99, cl = NULL, ...) check_strata(x, mat) ## S3 method for class 'uncertainty' strata(object, ...) ## S3 method for class 'uncertainty' subset(x, subset=NULL, ...) ## S3 method for class 'uncertainty' bestpart(object, ...) ## S3 method for class 'uncertainty1' bestpart(object, ...) ## S3 method for class 'uncertainty1' print(x, ...) ## S3 method for class 'uncertainty' print(x, ...) ## S3 method for class 'summary.uncertainty' print(x, sort, digits, ...) ## S3 method for class 'uncertainty' summary(object, level = 0.95, ...) ## S3 method for class 'uncertainty' as.data.frame(x, row.names = NULL, optional = FALSE, sort, ...) ## S3 method for class 'summary.uncertainty' as.data.frame(x, row.names = NULL, optional = FALSE, sort, ...) ## S3 method for class 'uncertainty1' bsmooth(object, ...) ## S3 method for class 'uncertainty' bsmooth(object, ...)uncertainty(object, ...) ## S3 method for class 'opticut' uncertainty(object, which = NULL, type = c("asymp", "boot", "multi"), B = 99, cl = NULL, ...) ## S3 method for class 'multicut' uncertainty(object, which = NULL, type = c("asymp", "boot"), B = 99, cl = NULL, ...) check_strata(x, mat) ## S3 method for class 'uncertainty' strata(object, ...) ## S3 method for class 'uncertainty' subset(x, subset=NULL, ...) ## S3 method for class 'uncertainty' bestpart(object, ...) ## S3 method for class 'uncertainty1' bestpart(object, ...) ## S3 method for class 'uncertainty1' print(x, ...) ## S3 method for class 'uncertainty' print(x, ...) ## S3 method for class 'summary.uncertainty' print(x, sort, digits, ...) ## S3 method for class 'uncertainty' summary(object, level = 0.95, ...) ## S3 method for class 'uncertainty' as.data.frame(x, row.names = NULL, optional = FALSE, sort, ...) ## S3 method for class 'summary.uncertainty' as.data.frame(x, row.names = NULL, optional = FALSE, sort, ...) ## S3 method for class 'uncertainty1' bsmooth(object, ...) ## S3 method for class 'uncertainty' bsmooth(object, ...)
object |
fitted model object
(which should not contain extra arguments as part of |
which |
numeric or character (can be a vector) defining
a subset of species from the fitted object,
or or |
type |
character, describing the type of uncertainty calculation. See Details. |
B |
numeric, number of iterations. For |
cl |
a cluster object, or an integer for multiple cores in parallel computations (integer value for forking is ignored on Windows). |
x |
an object to be printed. |
level |
the confidence level required. |
sort |
logical value indicating if species
should be meaningfully sorted, the default is |
digits |
numeric, number of significant digits in output. |
mat |
a matrix with resampling indices (rows as samples, columns as iterations). |
row.names |
|
optional |
logical. If |
subset |
logical, numeric, or character index indicating species to keep, missing values are not accepted. |
... |
other arguments passed to the underlying functions. |
Uncertainty is calculated for
indicator potential I, and expected values
(mu0, and mu1 for opticut, and mu_* for multicut objects).
"asymp": asymptotic distribution is based on best supported model
(this option is unavailable for custom distribution functions because
it requires the Hessian matrix).
This type is available for both opticut and multicut objects.
"boot": non-parametric bootstrap distribution
based on best partition found for the input object.
This type is available for both opticut and multicut objects.
"multi": non-parametric bootstrap distribution
based on best partition found for the bootstrap data (i.e.
the model ranking is re-evaluated each time).
"multi" works only if comb = "rank" in the
opticut call.
This type is not available for multicut objects.
uncertainty returns an object of class 'uncertainty'.
The uncertainty element of the object is a list with species specific
output as elements (object class 'uncertainty1').
Each 'uncertainty1' output is a data frame with columns:
best partition, indicator potential I,
and expected values
(mu0, and mu1 for opticut, and mu_* for multicut objects).
check_strata returns a logical vector checking if
all original strata from the input object are represented
by resampling indices. Number of strata are attached as attributes
for further diagnostics.
The summary method prints the name of the best supported split,
selection frequency (R, reliability), indicator values (I, based on
the distribution of values within the best supported split with highest
reliability) and confidence interval for I (based on level).
The subset method subsets the species in the uncertainty object.
bestpart finds the selection frequencies for
strata as best partitions (number of strata x number of species).
The coercion method as.data.frame returns a data frame.
The bsmooth method returns bootstrap smoothed results
for each strata (not available for multicut based uncertainty objects,
check uncertainty results instead).
Resampling methods can lead to complete exclusion of certain strata
when sample size is small. Try revising the stratification of the
input object, or provide custom resampling indices via the B
argument using stratified (block) bootstrap, jackknife (leave-one-out),
or similar techniques. Finding a suitable random seed
via set.seed or dropping unsuitable iterations
can also resolve the issue.
Peter Solymos <[email protected]>
opticut and multicut for the
user interface of the input objects.
set.seed(2345) n <- 50 x0 <- sample(1:4, n, TRUE) x1 <- ifelse(x0 %in% 1:2, 1, 0) x2 <- rnorm(n, 0.5, 1) x3 <- ifelse(x0 %in% 2:4, 1, 0) lam1 <- exp(0.5 + 1*x1 + -0.2*x2) Y1 <- rpois(n, lam1) lam2 <- exp(1 + 0.5*x3) Y2 <- rpois(n, lam2) Y3 <- rpois(n, exp(0)) Y <- cbind(Spp1=Y1, Spp2=Y2, Spp3=Y3) oc <- opticut(Y ~ x2, strata=x0, dist="poisson", comb="rank") ## asymptotic confidence intervals (uc1 <- uncertainty(oc, type="asymp", B=999)) summary(uc1) ## bootstrap-based confidence intervals (uc2 <- uncertainty(oc, type="boot", B=19)) summary(uc2) ## use user-supplied indices ## multi-model bootstrap based uncertainties B <- replicate(25, sample.int(n, replace=TRUE)) check_strata(oc, B) # check representation (uc3 <- uncertainty(oc, type="multi", B=B)) summary(uc3) ## best partitions: ## selection frequencies for strata and species bestpart(uc3) heatmap(bestpart(uc3), scale="none", col=occolors()(25)) ## bootstrap smoothed predictions per strata bsmooth(uc3) heatmap(bestpart(uc3), scale="none", col=occolors()(25)) ## individual species results uc3$uncertainty bestpart(uc3$uncertainty[[1]]) bsmooth(uc3$uncertainty[[1]]) ## Not run: ## block bootstrap block_fun <- function() unlist(lapply(unique(x0), function(z) if (sum(x0==z) < 2) which(x0==z) else sample(which(x0==z), sum(x0==z), replace=TRUE))) B <- replicate(25, block_fun()) check_strata(oc, B) # check representation summary(uncertainty(oc, type="multi", B=B)) ## jackknife B <- sapply(1:n, function(i) which((1:n) != i)) check_strata(oc, B) # check representation summary(uncertainty(oc, type="multi", B=B)) ## multicut based uncertainty mc <- multicut(Y ~ x2, strata=x0, dist="poisson") ## asymptotic confidence intervals (muc1 <- uncertainty(mc, type="asymp", B=999)) summary(muc1) bestpart(muc1) ## bootstrap-based confidence intervals (muc2 <- uncertainty(mc, type="boot", B=19)) summary(muc2) bestpart(muc2) ## dolina example data(dolina) ## stratum as ordinal dolina$samp$stratum <- as.integer(dolina$samp$stratum) ## filter species to speed up things a bit Y <- ifelse(dolina$xtab[,colSums(dolina$xtab > 0) >= 20] > 0, 1, 0) ## opticut results, note the cloglog link function dol <- opticut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="binomial:cloglog") ## parallel computing for uncertainty library(parallel) cl <- makeCluster(2) ucdol <- uncertainty(dol, type="multi", B=25, cl=cl) stopCluster(cl) bestpart(ucdol) heatmap(t(bestpart(ucdol)), scale="none", col=occolors()(25), distfun=function(x) dist(x, "manhattan")) ## See how indicator value changes with different partitions ## (and why it is the wrong metric to use in this calse) with(ucdol$uncertainty[["pvic"]], boxplot(I ~ best, col="gold", ylab="Indicator value")) ## What we should calculate is the bootstrap smoothed mean of the ## expected value and its confidence intervals bs <- bsmooth(ucdol$uncertainty[["pvic"]]) boxplot(t(bs), ylab="Expected value") cbind(Mean=rowMeans(bs), t(apply(bs, 1, quantile, probs=c(0.025, 0.975)))) ## A more interesting simulated example for bootstrap smoothing ## and comparing opticut vs. multicut set.seed(1) n <- 2000 x <- sort(runif(n, -8, 8)) p <- plogis(0.5 + -0.1 * x + -0.2 * x^2) y <- rbinom(n, 1, p) d <- diff(range(x))/10 br <- seq(min(x), max(x), by=d) g <- cut(x, br, include.lowest=TRUE) levels(g) <- LETTERS[1:nlevels(g)] o <- opticut(y ~ 1, strata=g, dist="binomial") m <- multicut(y ~ 1, strata=g, dist="binomial") library(parallel) cl <- makeCluster(2) uo <- uncertainty(o, type="multi", B=99, cl=cl) um <- uncertainty(m, type="boot", B=99, cl=cl) stopCluster(cl) ## bootstrap average for opticut bs <- bsmooth(uo$uncertainty[[1]]) stat <- cbind(Mean=rowMeans(bs), t(apply(bs, 1, quantile, probs=c(0.025, 0.975)))) ## bootstrap average for multicut bsm <- as.matrix(um$uncertainty[[1]][,-(1:2)]) statm <- cbind(Mean=colMeans(bsm), t(apply(bsm, 2, quantile, probs=c(0.025, 0.975)))) op <- par(mfrow=c(2,1)) plot(p ~ x, type="l", ylim=c(0,1), main="Binary partitions (opticut)") abline(v=br, col="grey", lty=3) lines(br[-1]-0.5*d, stat[,1], col=4) lines(br[-1]-0.5*d, stat[,2], col=4, lty=2) lines(br[-1]-0.5*d, stat[,3], col=4, lty=2) lines(br[-1]-0.5*d, bs[,1], col=2) legend("topright", bty="n", lty=c(1,1,2,1), col=c(1,4,4,2), legend=c("True response","bsmooth","0.95 CI","Best partition")) plot(p ~ x, type="l", ylim=c(0,1), main="Multi-level model (multicut)") abline(v=br, col="grey", lty=3) lines(br[-1]-0.5*d, statm[,1], col=4) lines(br[-1]-0.5*d, statm[,2], col=4, lty=2) lines(br[-1]-0.5*d, statm[,3], col=4, lty=2) legend("topright", bty="n", lty=c(1,1,2), col=c(1,4,4), legend=c("True response","bsmooth","0.95 CI")) par(op) ## End(Not run)set.seed(2345) n <- 50 x0 <- sample(1:4, n, TRUE) x1 <- ifelse(x0 %in% 1:2, 1, 0) x2 <- rnorm(n, 0.5, 1) x3 <- ifelse(x0 %in% 2:4, 1, 0) lam1 <- exp(0.5 + 1*x1 + -0.2*x2) Y1 <- rpois(n, lam1) lam2 <- exp(1 + 0.5*x3) Y2 <- rpois(n, lam2) Y3 <- rpois(n, exp(0)) Y <- cbind(Spp1=Y1, Spp2=Y2, Spp3=Y3) oc <- opticut(Y ~ x2, strata=x0, dist="poisson", comb="rank") ## asymptotic confidence intervals (uc1 <- uncertainty(oc, type="asymp", B=999)) summary(uc1) ## bootstrap-based confidence intervals (uc2 <- uncertainty(oc, type="boot", B=19)) summary(uc2) ## use user-supplied indices ## multi-model bootstrap based uncertainties B <- replicate(25, sample.int(n, replace=TRUE)) check_strata(oc, B) # check representation (uc3 <- uncertainty(oc, type="multi", B=B)) summary(uc3) ## best partitions: ## selection frequencies for strata and species bestpart(uc3) heatmap(bestpart(uc3), scale="none", col=occolors()(25)) ## bootstrap smoothed predictions per strata bsmooth(uc3) heatmap(bestpart(uc3), scale="none", col=occolors()(25)) ## individual species results uc3$uncertainty bestpart(uc3$uncertainty[[1]]) bsmooth(uc3$uncertainty[[1]]) ## Not run: ## block bootstrap block_fun <- function() unlist(lapply(unique(x0), function(z) if (sum(x0==z) < 2) which(x0==z) else sample(which(x0==z), sum(x0==z), replace=TRUE))) B <- replicate(25, block_fun()) check_strata(oc, B) # check representation summary(uncertainty(oc, type="multi", B=B)) ## jackknife B <- sapply(1:n, function(i) which((1:n) != i)) check_strata(oc, B) # check representation summary(uncertainty(oc, type="multi", B=B)) ## multicut based uncertainty mc <- multicut(Y ~ x2, strata=x0, dist="poisson") ## asymptotic confidence intervals (muc1 <- uncertainty(mc, type="asymp", B=999)) summary(muc1) bestpart(muc1) ## bootstrap-based confidence intervals (muc2 <- uncertainty(mc, type="boot", B=19)) summary(muc2) bestpart(muc2) ## dolina example data(dolina) ## stratum as ordinal dolina$samp$stratum <- as.integer(dolina$samp$stratum) ## filter species to speed up things a bit Y <- ifelse(dolina$xtab[,colSums(dolina$xtab > 0) >= 20] > 0, 1, 0) ## opticut results, note the cloglog link function dol <- opticut(Y ~ stratum + lmoist + method, data=dolina$samp, strata=dolina$samp$mhab, dist="binomial:cloglog") ## parallel computing for uncertainty library(parallel) cl <- makeCluster(2) ucdol <- uncertainty(dol, type="multi", B=25, cl=cl) stopCluster(cl) bestpart(ucdol) heatmap(t(bestpart(ucdol)), scale="none", col=occolors()(25), distfun=function(x) dist(x, "manhattan")) ## See how indicator value changes with different partitions ## (and why it is the wrong metric to use in this calse) with(ucdol$uncertainty[["pvic"]], boxplot(I ~ best, col="gold", ylab="Indicator value")) ## What we should calculate is the bootstrap smoothed mean of the ## expected value and its confidence intervals bs <- bsmooth(ucdol$uncertainty[["pvic"]]) boxplot(t(bs), ylab="Expected value") cbind(Mean=rowMeans(bs), t(apply(bs, 1, quantile, probs=c(0.025, 0.975)))) ## A more interesting simulated example for bootstrap smoothing ## and comparing opticut vs. multicut set.seed(1) n <- 2000 x <- sort(runif(n, -8, 8)) p <- plogis(0.5 + -0.1 * x + -0.2 * x^2) y <- rbinom(n, 1, p) d <- diff(range(x))/10 br <- seq(min(x), max(x), by=d) g <- cut(x, br, include.lowest=TRUE) levels(g) <- LETTERS[1:nlevels(g)] o <- opticut(y ~ 1, strata=g, dist="binomial") m <- multicut(y ~ 1, strata=g, dist="binomial") library(parallel) cl <- makeCluster(2) uo <- uncertainty(o, type="multi", B=99, cl=cl) um <- uncertainty(m, type="boot", B=99, cl=cl) stopCluster(cl) ## bootstrap average for opticut bs <- bsmooth(uo$uncertainty[[1]]) stat <- cbind(Mean=rowMeans(bs), t(apply(bs, 1, quantile, probs=c(0.025, 0.975)))) ## bootstrap average for multicut bsm <- as.matrix(um$uncertainty[[1]][,-(1:2)]) statm <- cbind(Mean=colMeans(bsm), t(apply(bsm, 2, quantile, probs=c(0.025, 0.975)))) op <- par(mfrow=c(2,1)) plot(p ~ x, type="l", ylim=c(0,1), main="Binary partitions (opticut)") abline(v=br, col="grey", lty=3) lines(br[-1]-0.5*d, stat[,1], col=4) lines(br[-1]-0.5*d, stat[,2], col=4, lty=2) lines(br[-1]-0.5*d, stat[,3], col=4, lty=2) lines(br[-1]-0.5*d, bs[,1], col=2) legend("topright", bty="n", lty=c(1,1,2,1), col=c(1,4,4,2), legend=c("True response","bsmooth","0.95 CI","Best partition")) plot(p ~ x, type="l", ylim=c(0,1), main="Multi-level model (multicut)") abline(v=br, col="grey", lty=3) lines(br[-1]-0.5*d, statm[,1], col=4) lines(br[-1]-0.5*d, statm[,2], col=4, lty=2) lines(br[-1]-0.5*d, statm[,3], col=4, lty=2) legend("topright", bty="n", lty=c(1,1,2), col=c(1,4,4), legend=c("True response","bsmooth","0.95 CI")) par(op) ## End(Not run)
Five species of warblers were studied to determine the factors controlling the species abundances and competition (MacArthur 1958).
data("warblers")data("warblers")
A list with 3 elements: xtab is a list of sample x species matrices
(sec_prc: percentages of total number of seconds of observations,
num_prc: percentages of total number of observations in seconds,
sec_cnt: counts based on percentages and totals of seconds,
num_cnt: counts based on percentages and totals),
samp is a data frame with sample level attributes such as height
(6 is the base, 1 is the top of trees, most trees were 50–60 feet tall)
and depth of branches in the canopy
(B: bare or lichen- covered base, M: middle zone of old needles,
T: terminal zone of new, less than 1.5 years old, needles or buds) ,
taxa is a data frame with scientific and common names for the species.
MacArthur 1958.
MacArthur, R. H., 1958. Population ecology of some warblers of northeastern coniferous forests. Ecology 39:599–619. <doi:10.2307/1931600>
data(warblers) str(warblers) warbh <- mefa4::groupSums(warblers$xtab$num_cnt, 1, warblers$samp$height) warbd <- mefa4::groupSums(warblers$xtab$num_cnt, 1, warblers$samp$depth) op <- par(mfrow=c(1,2)) matplot(rownames(warbh), warbh, type="b", xlab="height") matplot(warbd, type="b", axes=FALSE, xlab="depth") box() axis(2) axis(1, 1:3, rownames(warbd)) par(op)data(warblers) str(warblers) warbh <- mefa4::groupSums(warblers$xtab$num_cnt, 1, warblers$samp$height) warbd <- mefa4::groupSums(warblers$xtab$num_cnt, 1, warblers$samp$depth) op <- par(mfrow=c(1,2)) matplot(rownames(warbh), warbh, type="b", xlab="height") matplot(warbd, type="b", axes=FALSE, xlab="depth") box() axis(2) axis(1, 1:3, rownames(warbd)) par(op)