Title: | Resource Selection (Probability) Functions for Use-Availability Data |
---|---|
Description: | Resource Selection (Probability) Functions for use-availability wildlife data based on weighted distributions as described in Lele and Keim (2006) <doi:10.1890/0012-9658(2006)87%5B3021:WDAEOR%5D2.0.CO;2>, Lele (2009) <doi:10.2193/2007-535>, and Solymos & Lele (2016) <doi:10.1111/2041-210X.12432>. |
Authors: | Subhash R. Lele [aut], Jonah L. Keim [aut], Peter Solymos [aut, cre] |
Maintainer: | Peter Solymos <[email protected]> |
License: | GPL-2 |
Version: | 0.3-6 |
Built: | 2024-10-18 05:00:42 UTC |
Source: | https://github.com/psolymos/ResourceSelection |
Resource Selection (Probability) Functions for use-availability wildlife data based on weighted distributions as described in Lele and Keim (2006), Lele (2009), and Solymos & Lele (2016).
rsf
: Resource Selection Functions (RSF)
rspf
: Resource Selection Probability Functions (RSPF)
hoslem.test
: Hosmer-Lemeshow Goodness of Fit (GOF) Test
Visual summaries: kdepairs
for 2D scatterplots and
mep
for marginal effect plots based on fitted model objects.
Subhash R. Lele, Jonah L. Keim, Peter Solymos
Maintainer: Peter Solymos <[email protected]>
Lele, S.R. (2009) A new method for estimation of resource selection probability function. Journal of Wildlife Management 73, 122–127. <doi:10.2193/2007-535>
Lele, S. R. & Keim, J. L. (2006) Weighted distributions and estimation of resource selection probability function. Ecology 87, 3021–3028. <doi:10.1890/0012-9658(2006)87
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>
rsf
, rspf
, kdepairs
, mep
,
hoslem.test
Consistent AIC
CAIC(object, ..., alpha) ## Default S3 method: CAIC(object, ..., alpha) CAICtable(object, ..., alpha)
CAIC(object, ..., alpha) ## Default S3 method: CAIC(object, ..., alpha) CAICtable(object, ..., alpha)
object |
A fitted model object. |
... |
More fitted model objects. |
alpha |
Weight factor between 0 and 1 (see Details). Default value is 0.5. |
CAIC = alpha * AIC + (1 - alpha) * BIC
Atomic vector if only one input object provided,
a data frame similar to what is returned by
AIC
and BIC
if there are more than one input objects.
CAICtable
returns a data frame with
delta CAIC (dCAIC = CAIC - min(CAIC)) and CAIC
weights (wCAIC = exp(-0.5 dCAIC_i) / sum(exp(-0.5 dCAIC_i)))
where i = 1,...,m are candidate models.
Subhash Lele and Peter Solymos
Bozdogan, H. 1987. Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika, 52, 345-370.
Taper, M. 2004. Model identification from many candidates. In: Taper, M. and Lele, S. R. (eds), The Nature of Scientific Evidence: Statistical, Philosophical, and Empirical Considerations. The University of Chicago Press, Chicago, IL, 567 pp.
## compare some random models y <- rnorm(10) a <- lm(y ~ runif(10)) b <- lm(y ~ runif(10)) 0.5*(AIC(a) + BIC(a)) CAIC(a) AIC(a) CAIC(a, alpha=1) BIC(a) CAIC(a, alpha=0) CAIC(a, b) CAIC(a, b, alpha=0.2) CAICtable(a, b, alpha=1) ## you can use global option ## useful when inside of xv or bootstrap ## no need for extra argument getOption("CAIC_alpha") op <- options(CAIC_alpha = 0.2) getOption("CAIC_alpha") CAIC(a,b) options(op) getOption("CAIC_alpha")
## compare some random models y <- rnorm(10) a <- lm(y ~ runif(10)) b <- lm(y ~ runif(10)) 0.5*(AIC(a) + BIC(a)) CAIC(a) AIC(a) CAIC(a, alpha=1) BIC(a) CAIC(a, alpha=0) CAIC(a, b) CAIC(a, b, alpha=0.2) CAICtable(a, b, alpha=1) ## you can use global option ## useful when inside of xv or bootstrap ## no need for extra argument getOption("CAIC_alpha") op <- options(CAIC_alpha = 0.2) getOption("CAIC_alpha") CAIC(a,b) options(op) getOption("CAIC_alpha")
GPS collar data of mountain goats (Oreamnos americanus) from Lele and Keim (2006).
data(goats)
data(goats)
A data frame with 19014 observations on the following 8 variables.
STATUS
a numeric vector, 1: used, 0: available
ID
a numeric vector, individuals
ELEVATION
a numeric vector (m)
SLOPE
a numeric vector (degrees, steep)
ET
a numeric vector, access to escape terrain (distance from steep slopes, m)
ASPECT
a numeric vector (degrees)
HLI
a numeric vector, heat load index (0-1)
TASP
a numeric vector, transformed aspect
Mountain goat telemetry data were collected in the Coast Mountains of northwest British Columbia, Canada, as described in Lele and Keim (2006).
Ecological Archives E087-181-S1, http://www.esapubs.org/archive/ecol/E087/181/
Lele, S. R. & Keim, J. L. (2006) Weighted distributions and estimation of resource selection probability functions. Ecology 87, 3021–3028.
data(goats) str(goats) summary(goats) ## Not run: goats$exp.HLI <- exp(goats$HLI) goats$sin.SLOPE <- sin(pi * goats$SLOPE / 180) goats$ELEVATION <- scale(goats$ELEVATION) goats$ET <- scale(goats$ET) goats$TASP <- scale(goats$TASP) m1 <- rspf(STATUS ~ TASP + sin.SLOPE + ELEVATION, goats, m=0, B = 99) m2 <- rspf(STATUS ~ TASP + ELEVATION, goats, m=0, B = 99) summary(m1) summary(m2) AIC(m1, m2) plot(m1) ## End(Not run)
data(goats) str(goats) summary(goats) ## Not run: goats$exp.HLI <- exp(goats$HLI) goats$sin.SLOPE <- sin(pi * goats$SLOPE / 180) goats$ELEVATION <- scale(goats$ELEVATION) goats$ET <- scale(goats$ET) goats$TASP <- scale(goats$TASP) m1 <- rspf(STATUS ~ TASP + sin.SLOPE + ELEVATION, goats, m=0, B = 99) m2 <- rspf(STATUS ~ TASP + ELEVATION, goats, m=0, B = 99) summary(m1) summary(m2) AIC(m1, m2) plot(m1) ## End(Not run)
Hosmer-Lemeshow Goodness of Fit (GOF) Test.
hoslem.test(x, y, g = 10)
hoslem.test(x, y, g = 10)
x |
a numeric vector of observations, binary (0/1). |
y |
expected values. |
g |
number of bins to use to calculate quantiles.
Needs to be at least 2. The degrees of freedom of the test is |
The Hosmer-Lemeshow test is a statistical test for goodness of fit for logistic regression models.
A list with class "htest"
containing the following components:
statistic |
the value of the chi-squared test statistic,
( |
parameter |
the degrees of freedom of the approximate
chi-squared distribution of the test statistic ( |
p.value |
the p-value for the test. |
method |
a character string indicating the type of test performed. |
data.name |
a character string giving the name(s) of the data. |
observed |
the observed frequencies in a |
expected |
the expected frequencies in a |
Peter Solymos by adapting code pieces from R help mailing list
Hosmer D W, Lemeshow S 2000. Applied Logistic Regression. New York, USA: John Wiley and Sons.
set.seed(123) n <- 500 x <- rnorm(n) y <- rbinom(n, 1, plogis(0.1 + 0.5*x)) m <- glm(y ~ x, family=binomial) hoslem.test(m$y, fitted(m))
set.seed(123) n <- 500 x <- rnorm(n) y <- rbinom(n, 1, plogis(0.1 + 0.5*x)) m <- glm(y ~ x, family=binomial) hoslem.test(m$y, fitted(m))
Scatterplot matrix with 2D kernel density.
kdepairs(x, ...) ## Default S3 method: kdepairs(x, n=25, density=TRUE, contour=TRUE, ...) ## S3 method for class 'rsf' kdepairs(x, n=25, density=TRUE, contour=TRUE, ...)
kdepairs(x, ...) ## Default S3 method: kdepairs(x, n=25, density=TRUE, contour=TRUE, ...) ## S3 method for class 'rsf' kdepairs(x, n=25, density=TRUE, contour=TRUE, ...)
x |
a matrix or data frame (or a fitted model object of class |
n |
number of bins to be used in kernel density estimation. |
density |
logical, if shades corresponding to densities should be plotted. |
contour |
logical, if contour on top of shades should be plotted. |
... |
other possible arguments passed to |
Produces a scatterplot matrix with histograms in diagonal, 2D kernel
density estimates and contours in the lower half and bivariate scatterplots
with lowess smooth curves and Pearson correlation values
in the upper half as a side effect.
Returns NULL
invisibly.
Peter Solymos
kdepairs(iris[1:4])
kdepairs(iris[1:4])
Make a used-available data frame from a presence-absence type data.
makeUsedAvail(x, ...) ## Default S3 method: makeUsedAvail(x, y, ...) ## S3 method for class 'formula' makeUsedAvail(formula, data = parent.frame(), ...)
makeUsedAvail(x, ...) ## Default S3 method: makeUsedAvail(x, y, ...) ## S3 method for class 'formula' makeUsedAvail(formula, data = parent.frame(), ...)
x |
a matrix or data frame. |
y |
a vector with 0/1 entries, 1s are taken as used observations. |
formula |
two sided model formula of the form |
data |
data. |
... |
other arguments. |
The function returns a data frame, where used and available portions of
the input data are bound on top of each other, the first column
refers to y
, where used (1) and available (0) locations are
indicated different from the input values.
All locations in the input data are treated as available (0),
while only nonzero observations in y
are treated as used (1).
Peter Solymos
(x <- data.frame(species=rep(1:0,each=4), var1=1:8, var2=11:18)) makeUsedAvail(species ~ var1 + var2, x)
(x <- data.frame(species=rep(1:0,each=4), var1=1:8, var2=11:18)) makeUsedAvail(species ~ var1 + var2, x)
Scatterplot of marginal effects based on fitted model objects.
mep(object, ...) ## Default S3 method: mep(object, which=NULL, link=NULL, level=0.95, unique=10, n=25, minbucket=5, digits=4, col.points, col.lines=c(2, 2), pch=19, lty=c(1, 2), lwd=c(2,2), ask, subset=NULL, ylab, ...)
mep(object, ...) ## Default S3 method: mep(object, which=NULL, link=NULL, level=0.95, unique=10, n=25, minbucket=5, digits=4, col.points, col.lines=c(2, 2), pch=19, lty=c(1, 2), lwd=c(2,2), ask, subset=NULL, ylab, ...)
object |
a fitted model object. |
which |
numeric, logical, or character. Indices for the variables in the model frame if only one or a subset is desired. |
link |
character accepted by |
level |
numeric [0, 1], the confidence level required. |
unique , digits
|
numeric, the number of unique points above which bins are used.
If the number of unique values is less than or equal to this number,
unique values are used without binning. Unique values are subject to
rounding to |
n , minbucket
|
number of bins ( |
col.points , pch
|
color and type of points to be plotted. |
col.lines , lty , lwd
|
color, type, and width of quantile lines to be plotted. The 1st value correspond to the median, the 2nd value to the upper and lower quantiles, respectively. |
ask |
logical. If |
subset |
an optional vector specifying a subset of the data to be used for plotting. |
ylab |
character or expression, optional y axis label. |
... |
other possible arguments passed to graphical functions. |
The input object must have a fitted
and model.frame
method, and possibly a well identifiable family/link component
(family(object)$link
).
In the absence of family/link information, the range of the fitted
value will be used to guess the scaling (identity, log, or logit)
unless directly supplied via the link
argument.
Fitted values (f(x) = f(x_1,...,x_i,...,x_p); i = 1,...,p) are plotted against x_i.
The visual display is determined by the type of x_i (un-ordered factor, ordered
factor, unique numeric values, binned numeric values).
For each unique vale or bin, the median and confidence intervals
(quantiles corresponding to level
) of f(x) are calculated.
Binned values are smoothed by lowess
unless n
< 3.
Jitter is added to factor and unique value types.
Jitter is calculated based on kernel density
.
The model frame includes the response variable as well. Plotting f(x) as a function of the observations might be a useful visualization too to indicate goodness of fit or the lack of it.
The produces one or several marginal plots as a side effect. Returns a list of quantiles of fitted values corresponding to binned/unique values of variables in the input object.
Peter Solymos and Subhash Lele
Avgar, T., Lele, S. R., Keim, J. L. & Boyce, M. S. (2017) Relative Selection Strength: Quantifying effect size in habitat- and step-selection inference. Ecology and Evolution 7, 5322–5330.
kdepairs
for 2D kernel density estimates and contours.
fitted
for fitted values and
model.frame
for model frames.
density
and lowess
for smoothing.
data(goats) goats$ELEVATION <- goats$ELEVATION/1000 goats$TASPc <- cut(goats$TASP, 3, ordered_result=FALSE) goats$SLOPEc <- cut(goats$SLOPE, 3, ordered_result=TRUE) fit <- rspf(STATUS ~ TASPc + SLOPEc + ELEVATION + I(ELEVATION^2), goats, m=0, B=0) op <- par(mfrow=c(2,2)) mep(fit, which=1:4)#, subset=sample.int(nrow(goats), 10^4)) par(op)
data(goats) goats$ELEVATION <- goats$ELEVATION/1000 goats$TASPc <- cut(goats$TASP, 3, ordered_result=FALSE) goats$SLOPEc <- cut(goats$SLOPE, 3, ordered_result=TRUE) fit <- rspf(STATUS ~ TASPc + SLOPEc + ELEVATION + I(ELEVATION^2), goats, m=0, B=0) op <- par(mfrow=c(2,2)) mep(fit, which=1:4)#, subset=sample.int(nrow(goats), 10^4)) par(op)
Resource Selection (Probability) Functions for use-availability wildlife data as described in Lele and Keim (2006) and Lele (2009).
rsf(formula, data, m, B = 99, inits, method = "Nelder-Mead", control, model = TRUE, x = FALSE, ...) rspf(formula, data, m, B = 99, link = "logit", inits, method = "Nelder-Mead", control, model = TRUE, x = FALSE, ...) rsf.fit(X, Y, m, link = "logit", B = 99, inits, method = "Nelder-Mead", control, ...) rsf.null(Y, m, inits, ...)
rsf(formula, data, m, B = 99, inits, method = "Nelder-Mead", control, model = TRUE, x = FALSE, ...) rspf(formula, data, m, B = 99, link = "logit", inits, method = "Nelder-Mead", control, model = TRUE, x = FALSE, ...) rsf.fit(X, Y, m, link = "logit", B = 99, inits, method = "Nelder-Mead", control, ...) rsf.null(Y, m, inits, ...)
formula |
two sided model formula of the form |
m |
argument describing the matching of use and available points, see Details. |
data |
data. |
B |
number of bootstrap iterations to make. |
link |
character, type of link function to be used. |
inits |
initial values, optional. |
method |
method to be used in |
control |
control options for |
model |
a logical value indicating whether model frame should be included as a component of the returned value |
x |
logical values indicating whether the model matrix used in the fitting process should be returned as components of the returned value. |
Y |
vector of observations. |
X |
covariate matrix. |
... |
other arguments passed to the functions. |
The rsf
function fits the Exponential Resource Selection Function
(RSF) model to presence only data.
The rspf
function fits the Resource Selection Probability Function
(RSPF) model to presence only data Link function "logit"
,
"cloglog"
, and "probit"
can be specified via the
link
argument.
The rsf.fit
is the workhorse behind the two functions.
link="log"
leads to Exponential RSF.
The rsf.null
function fits the 'no selection' version
of the Exponential Resource Selection Function (RSF) model to presence only data.
LHS of the formula
data must be binary, ones indicating used locations,
while zeros indicating available location.
All available points are used for each use points if m=0
(global availability). If m
is a single value, e.g. m=5
,
it is assumed that available data points are grouped in batches of 5,
e.g. with IDs c(1,2)
for used point locations and
c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2)
for available locations
(local availability, matched use-available design).
Similarly, a vector of matching IDs can also be provided, e.g.
c(1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2)
by combining the above two.
This potentially could allow for unbalanced matching
(e.g. c(1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2)
)
and for easier subsetting of the data,
but comes with an increased computing time.
Note, the response in the LHS of the formula
should be coded as c(1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
for all of the above examples. When m
is defined as
a mapping vector or the value is 0, the order of course does not matter.
However, ordering matters when m
is constant because that
implies a certain structure.
For model description and estimation details, see Lele and Keim (2006), Lele (2009), and Solymos and Lele (2016).
A list with class "rsf"
, "rsf.null"
, or "rspf"
containing the following components:
call |
the matched call. |
y |
vector from LHS of the formula. |
coefficients |
a named vector of coefficients. |
std.error |
a named vector of standard errors for the coefficients. |
loglik |
the maximized pseudo log-likelihood according to Lele 2009. |
results |
|
link |
character, value of the link function used. |
control |
control parameters for |
inits |
initial values used in optimization. |
m |
value of the |
np |
number of active parameters. |
fitted.values |
vector of fitted values. These are relative selection values for RSF models, and probability of selection for RSPF models. |
nobs |
number of used locations. |
bootstrap |
component to store bootstrap results if |
converged |
logical, indicating convergence of the optimization. |
formula |
the formula supplied. |
terms |
the |
levels |
a record of the levels of the factors used in fitting. |
contrasts |
the contrasts used. |
model |
if requested, the model frame. |
x |
if requested, the model matrix. |
Subhash R. Lele, Jonah L. Keim, Peter Solymos
Lele, S.R. (2009) A new method for estimation of resource selection probability function. Journal of Wildlife Management 73, 122–127.
Lele, S. R. & Keim, J. L. (2006) Weighted distributions and estimation of resource selection probability functions. Ecology 87, 3021–3028.
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.
## --- Simulated data example --- ## settings n.used <- 1000 m <- 10 n <- n.used * m set.seed(1234) x <- data.frame(x1=rnorm(n), x2=runif(n)) cfs <- c(1.5,-1,0.5) ## fitting Exponential RSF model dat1 <- simulateUsedAvail(x, cfs, n.used, m, link="log") m1 <- rsf(status ~ .-status, dat1, m=0, B=0) summary(m1) ## fitting Logistic RSPF model dat2 <- simulateUsedAvail(x, cfs, n.used, m, link="logit") m2 <- rspf(status ~ .-status, dat2, m=0, B=0) summary(m2) ## --- Real data analysis from Lele & Keim 2006 --- ## Not run: goats$exp.HLI <- exp(goats$HLI) goats$sin.SLOPE <- sin(pi * goats$SLOPE / 180) goats$ELEVATION <- scale(goats$ELEVATION) goats$ET <- scale(goats$ET) goats$TASP <- scale(goats$TASP) ## Fit two RSPF models: ## global availability (m=0) and bootstrap (B=99) m1 <- rspf(STATUS ~ TASP + sin.SLOPE + ELEVATION, goats, m=0, B = 99) m2 <- rspf(STATUS ~ TASP + ELEVATION, goats, m=0, B = 99) ## Inspect the summaries summary(m1) summary(m2) ## Compare models: looks like m1 is better supported CAIC(m1, m2) ## Visualize the relationships plot(m1) mep(m1) # marginal effects similar to plot but with CIs kdepairs(m1) # 2D kernel density estimates plot(m2) kdepairs(m2) mep(m2) ## fit and compare to null RSF model (not available for RSPF) m3 <- rsf(STATUS ~ TASP + ELEVATION, goats, m=0, B = 0) m4 <- rsf.null(Y=goats$STATUS, m=0) CAIC(m3, m4) ## End(Not run)
## --- Simulated data example --- ## settings n.used <- 1000 m <- 10 n <- n.used * m set.seed(1234) x <- data.frame(x1=rnorm(n), x2=runif(n)) cfs <- c(1.5,-1,0.5) ## fitting Exponential RSF model dat1 <- simulateUsedAvail(x, cfs, n.used, m, link="log") m1 <- rsf(status ~ .-status, dat1, m=0, B=0) summary(m1) ## fitting Logistic RSPF model dat2 <- simulateUsedAvail(x, cfs, n.used, m, link="logit") m2 <- rspf(status ~ .-status, dat2, m=0, B=0) summary(m2) ## --- Real data analysis from Lele & Keim 2006 --- ## Not run: goats$exp.HLI <- exp(goats$HLI) goats$sin.SLOPE <- sin(pi * goats$SLOPE / 180) goats$ELEVATION <- scale(goats$ELEVATION) goats$ET <- scale(goats$ET) goats$TASP <- scale(goats$TASP) ## Fit two RSPF models: ## global availability (m=0) and bootstrap (B=99) m1 <- rspf(STATUS ~ TASP + sin.SLOPE + ELEVATION, goats, m=0, B = 99) m2 <- rspf(STATUS ~ TASP + ELEVATION, goats, m=0, B = 99) ## Inspect the summaries summary(m1) summary(m2) ## Compare models: looks like m1 is better supported CAIC(m1, m2) ## Visualize the relationships plot(m1) mep(m1) # marginal effects similar to plot but with CIs kdepairs(m1) # 2D kernel density estimates plot(m2) kdepairs(m2) mep(m2) ## fit and compare to null RSF model (not available for RSPF) m3 <- rsf(STATUS ~ TASP + ELEVATION, goats, m=0, B = 0) m4 <- rsf.null(Y=goats$STATUS, m=0) CAIC(m3, m4) ## End(Not run)
Simulates used-available data.
simulateUsedAvail(data, parms, n.used, m, link="logit")
simulateUsedAvail(data, parms, n.used, m, link="logit")
data |
a matrix or data frame. |
parms |
coefficients corresponding to the columns of the design matrix
derived as |
n.used , m
|
number of used points ( |
link |
character, the type of link function to be used. |
A used-available data frame.
Subhash Lele, Peter Solymos
n.used <- 1000 m <- 10 n <- n.used * m set.seed(1234) x <- data.frame(x1=rnorm(n), x2=runif(n)) cfs <- c(1.5,-1,0.5) dat1 <- simulateUsedAvail(x, cfs, n.used, m, link="log") str(dat1) dat2 <- simulateUsedAvail(x, cfs, n.used, m, link="logit") str(dat2)
n.used <- 1000 m <- 10 n <- n.used * m set.seed(1234) x <- data.frame(x1=rnorm(n), x2=runif(n)) cfs <- c(1.5,-1,0.5) dat1 <- simulateUsedAvail(x, cfs, n.used, m, link="log") str(dat1) dat2 <- simulateUsedAvail(x, cfs, n.used, m, link="logit") str(dat2)
Calculates weighted relative suitability index.
sindex(y, x) wrsi(y, x)
sindex(y, x) wrsi(y, x)
y |
matrix of observations for |
x |
a matrix of proportions (i.e. the values 0 and 1 should have consistent meaning across the columns, often through a unit sum constraint). |
wrsi
returns a data frame (class 'wrsi') with the following columns:
WRSI
weighted relative suitability index, range (0- Inf).
zWRSI
log of WRSI
(z-transformed), range (-Inf, Inf).
rWRSI
inverse Fisher z-transformed zWRSI
, range (-1, 1).
Pused
and Pavail
total proportion of used (y > 0
)
and available of each feature (column) in x
.
Pw
weighted proportions from y
.
u
and a
used and available totals
for each feature (column) in x
.
sindex
returns a data frame (class 'sindex') with
one column for each species, and one row for each feature (column) in x
.
Cell values are inverse Fisher z-transformed (zWRSI
) indices.
Peter Solymos <[email protected]>
## --- habitat composition matrix set.seed(1234) n <- 1000 # sample size k <- 5 # habitat classes s <- runif(n, 1, 5) p <- plogis(rnorm(n*k, 0, rep(s, k))) p <- p*t(replicate(n, sample(c(10,4,2,1,1)))) x <- p / rowSums(p) summary(x) summary(rowSums(x)) ## --- observations ## expected abundance in each habitat class lam <- c(0.8, 0.6, 0.5, 0.4, 0.1)*1 ## sample x habitat level abundances yy <- t(sapply(seq_len(n), function(i) { ## intercept and modifier combined rpois(k, (x[i,]*lam)) })) ## total: sum over habitat classes ## this is what we observe y <- rowSums(yy) colSums(yy) table(y) ## --- wrsi calculations (w <- wrsi(y, x)) op <- par(mfrow=c(1,2)) ## habitat level observations are unknown plot(lam, colSums(yy) / sum(yy), type="b") ## this is approximated by the wrsi plot(lam, w$rWRSI, type="b") abline(h=0, lty=2) par(op) ## --- sindex calculations for multiple species y2 <- cbind(Spp1=y, Spp2=rev(y), Spp3=sample(y)) (w2 <- sindex(y2, x)) heatmap(t(as.matrix(w2)), scale="none")
## --- habitat composition matrix set.seed(1234) n <- 1000 # sample size k <- 5 # habitat classes s <- runif(n, 1, 5) p <- plogis(rnorm(n*k, 0, rep(s, k))) p <- p*t(replicate(n, sample(c(10,4,2,1,1)))) x <- p / rowSums(p) summary(x) summary(rowSums(x)) ## --- observations ## expected abundance in each habitat class lam <- c(0.8, 0.6, 0.5, 0.4, 0.1)*1 ## sample x habitat level abundances yy <- t(sapply(seq_len(n), function(i) { ## intercept and modifier combined rpois(k, (x[i,]*lam)) })) ## total: sum over habitat classes ## this is what we observe y <- rowSums(yy) colSums(yy) table(y) ## --- wrsi calculations (w <- wrsi(y, x)) op <- par(mfrow=c(1,2)) ## habitat level observations are unknown plot(lam, colSums(yy) / sum(yy), type="b") ## this is approximated by the wrsi plot(lam, w$rWRSI, type="b") abline(h=0, lty=2) par(op) ## --- sindex calculations for multiple species y2 <- cbind(Spp1=y, Spp2=rev(y), Spp3=sample(y)) (w2 <- sindex(y2, x)) heatmap(t(as.matrix(w2)), scale="none")