Function fleslie() returns age-based (Leslie) MPMs corresponding to
the patches and occasions given, including the associated component
transition and fecundity matrices, data frames detailing the characteristics
of the exact ages corresponding to rows and columns in estimated matrices,
and a data frame characterizing the patch and occasion combinations
corresponding to these matrices.
fleslie(
year = "all",
patch = NULL,
prebreeding = TRUE,
data = NULL,
modelsuite = NULL,
surv_model = NULL,
fec_model = NULL,
paramnames = NULL,
supplement = NULL,
start_age = NA,
last_age = NA,
fecage_min = NA,
fecage_max = NA,
continue = TRUE,
inda = NULL,
indb = NULL,
indc = NULL,
surv_dev = 0,
fec_dev = 0,
density = NA,
fecmod = 1,
random.inda = FALSE,
random.indb = FALSE,
random.indc = FALSE,
negfec = FALSE,
reduce = FALSE,
simple = FALSE,
err_check = FALSE,
exp_tol = 700,
theta_tol = 1e+08,
sparse_output = FALSE
)If all inputs are properly formatted, then this function will return
an object of class lefkoMat, which is a list that holds the matrix
projection model and all of its metadata. Its structure has the following
elements:
A list of full projection matrices in order of sorted patches and
occasions. All matrices output in R's matrix class, or in
the dgCMatrix class from the Matrix package if sparse.
A list of survival transition matrices sorted as in A. All
matrices output in R's matrix class, or in the dgCMatrix class
from the Matrix package if sparse.
A list of fecundity matrices sorted as in A. All matrices
output in R's matrix class, or in the dgCMatrix class from the
Matrix package if sparse.
Set to NA for Leslie MPMs.
Set to NA for Leslie MPMs.
A data frame detailing the characteristics of associated ages, in the form of a modified stageframe including reproduction status.
A data frame giving the patch and year of each matrix in order.
In fleslie(), only one population may be analyzed at once.
A vector showing the numbers of individuals and rows in the vertical dataset used as input.
A short vector describing the number of non-zero elements in
U and F matrices, and the number of annual matrices.
This is the qc portion of the modelsuite input.
An optional element only added if err_check = TRUE.
This is a list of vital rate probability matrices, with 7 columns in the
order of survival, observation probability, reproduction probability, primary
size transition probability, secondary size transition probability, tertiary
size transition probability, and probability of juvenile transition to
maturity.
A variable corresponding to observation occasion, or a set
of such values, given in values associated with the year term used in linear
model development. Defaults to "all", in which case matrices will be
estimated for all occasions.
A variable designating which patches or subpopulations will have
matrices estimated. Defaults to "all", but can also be set to specific
patch names or a vector thereof.
A logical value indicating whether the life history model
is a pre-breeding model. Defaults to TRUE.
The historical vertical demographic data frame used to estimate
vital rates (class hfvdata). The original data frame is generally
required in order to initialize occasions and patches properly, and to assess
the range of ages observed in the population. Not required if option
modelsuite is set to a vrm_input object.
One of two optional lists. THe first is an optional
lefkoMod object holding the vital rate models. Alternatively,
an object of class vrm_input may be provided. If given, then
surv_model, fec_model, and paramnames are not required.
No models should include size or reproductive status in any occasion, nor
should they include any variable for occasion t-1. Note that the
modelsuite must have been created from a modelsearch() run in which
vitalrates = c("surv", "fec") and the suite option was set to
either age or cons.
A linear model predicting survival probability. This can be
a model of class glm or glmer, and requires a predicted
binomial variable under a logit link. Ignored if modelsuite is
provided. This model must have been developed in a modeling exercise testing
only the impacts of occasion t.
A linear model predicting fecundity. This can be a model of
class glm, glmer, glmmTMB, zeroinfl,
vglm, lm, or lmer. Ignored if modelsuite is
provided. This model must have been developed in a modeling exercise testing
only the impacts of occasion t.
A data frame with three columns, the first describing all
terms used in linear modeling, the second (must be called mainparams)
giving the general model terms that will be used in matrix creation, and the
third showing the equivalent terms used in modeling (must be named
modelparams). Function create_pm() can be used to
create a skeleton paramnames object, which can then be edited. Only
required if modelsuite is not supplied.
An optional data frame of class lefkoSD that
provides supplemental data that should be incorporated into the MPM. Three
kinds of data may be integrated this way: transitions to be estimated via the
use of proxy transitions, transition overwrites from the literature or
supplemental studies, and transition multipliers for survival and fecundity.
This data frame should be produced using the supplemental()
function.
The age from which to start the matrix. Defaults to
NA, in which case age 1 is used if prebreeding = TRUE,
and age 0 is used if prebreeding = FALSE.
The final age to use in the matrix. Defaults to NA, in
which case the highest age in the dataset is used.
The minimum age at which reproduction is possible. Defaults
to NA, which is interpreted to mean that fecundity should be assessed
starting in the minimum age observed in the dataset.
The maximum age at which reproduction is possible. Defaults
to NA, which is interpreted to mean that fecundity should be assessed
until the final observed age.
A logical value designating whether to allow continued
survival of individuals past the final age noted in the stageframe, using the
demographic characteristics of the final age. Defaults to TRUE.
Can be a single value to use for individual covariate a
in all matrices, or a vector of such values corresponding to each occasion in
the dataset. Defaults to NULL.
Can be a single value to use for individual covariate b
in all matrices, or a vector of such values corresponding to each occasion in
the dataset. Defaults to NULL.
Can be a single value to use for individual covariate c
in all matrices, or a vector of such values corresponding to each occasion in
the dataset. Defaults to NULL.
A numeric value to be added to the y-intercept in the linear
model for survival probability. Defaults to 0.
A numeric value to be added to the y-intercept in the linear
model for fecundity. Defaults to 0.
A numeric value indicating density value to use to propagate
matrices. Only needed if density is an explanatory term used in linear
models. Defaults to NA.
A scalar multiplier of fecundity. Defaults to 1.0.
A logical value denoting whether to treat individual
covariate a as a random, categorical variable. Otherwise is treated as
a fixed, numeric variable. Defaults to FALSE.
A logical value denoting whether to treat individual
covariate b as a random, categorical variable. Otherwise is treated as
a fixed, numeric variable. Defaults to FALSE.
A logical value denoting whether to treat individual
covariate c as a random, categorical variable. Otherwise is treated as
a fixed, numeric variable. Defaults to FALSE.
A logical value denoting whether fecundity values estimated to
be negative should be reset to 0. Defaults to FALSE.
A logical value denoting whether to remove ages associated
solely with 0 transitions. These are only removed in cases where the
associated row and column sums in ALL matrices estimated equal 0. Defaults to
FALSE, and should generally not be used in age-based MPMs.
A logical value indicating whether to produce A,
U, and F matrices, or only the latter two. Defaults to
FALSE, in which case all three are output.
A logical value indicating whether to append extra
information used in matrix calculation within the output list. Defaults to
FALSE.
A numeric value used to indicate a maximum value to set
exponents to in the core kernel to prevent numerical overflow. Defaults to
700.
A numeric value used to indicate a maximum value to theta as
used in the negative binomial probability density kernel. Defaults to
100000000, but can be reset to other values during error checking.
A logical value indicating whether to output matrices
in sparse format. Defaults to FALSE, in which case all matrices are
output in standard matrix format.
Unlike rlefko2(), rlefko3(),
arlefko2(), and rleslie(), this function does not
currently distinguish populations.
This function will yield incorrect estimates if the models utilized incorporate state in occasion t-1, or any size or reproductive status terms.
Users may at times wish to estimate MPMs using a dataset incorporating
multiple patches or subpopulations, but without discriminating between those
patches or subpopulations. Should the aim of analysis be a general MPM that
does not distinguish these patches or subpopulations, the
modelsearch() run should not include patch terms.
Input options including multiple variable names must be entered in the order of variables in occasion t+1 and t. Rearranging the order will lead to erroneous calculations, and may lead to fatal errors.
Care should be taken to match the random status of year and patch to the states of those variables within the modelsuite. If they do not match, then they will be treated as zeroes in vital rate estimation.
Individual covariates are treated as categorical only if they are set as
random terms. Fixed categorical individual covariates are currently not
allowed. However, such terms may be supplied if the modelsuite option
is set to a vrm_input object. In that case, the user should also set
the logical random switch for the individual covariate to be used to
TRUE (e.g., random.inda = TRUE).
mpm_create()
flefko3()
flefko2()
aflefko2()
arlefko2()
rlefko3()
rlefko2()
rleslie()
# \donttest{
data(lathyrus)
lathvert_base <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,
patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,
sizeacol = "Volume88", repstracol = "FCODE88", fecacol = "Intactseed88",
deadacol = "Dead1988", censorcol = "Missing1988", censorkeep = NA,
censor = TRUE, NRasRep = TRUE, NOasObs = TRUE)
lathvert_base$feca3 <- round(lathvert_base$feca3)
lathvert_base$feca2 <- round(lathvert_base$feca2)
lathvert_base$feca1 <- round(lathvert_base$feca1)
lathvert_age <- subset(lathvert_base, firstseen > 1988)
lathmodels2_age <- modelsearch(lathvert_age, historical = FALSE,
approach = "mixed", suite = "cons", bestfit = "AICc&k", age = "obsage",
vitalrates = c("surv", "fec"), fecdist = "poisson", indiv = "individ",
year = "year2", show.model.tables = TRUE, quiet = "partial")
lathmat2fleslie <- fleslie(year = "all", data = lathvert_age,
modelsuite = lathmodels2_age, fecage_min = 1)
# }
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