batch_project3: Project Multiple MPMs In Batches With Varying Starting...
In adapt3: Adaptive Dynamics and Community Matrix Model Projections
batch_project3 R Documentation
Project Multiple MPMs In Batches With Varying Starting Conditions
Description
Function batch_project3 runs function project3() across series
of differing beginning conditions. Currently used to run sensitivity
analyses.
Usage
batch_project3(
used_mpms = NULL,
givenrate = NULL,
offset = NULL,
multiplier = NULL,
all_elems = NULL,
mpms = NULL,
vrms = NULL,
stageframes = NULL,
supplements = NULL,
equivalence = NULL,
starts = NULL,
years = NULL,
patches = NULL,
tweights = NULL,
format = NULL,
entry_time = NULL,
sp_density = NULL,
ind_terms = NULL,
dev_terms = NULL,
fb_sparse = NULL,
firstage = NULL,
finalage = NULL,
fecage_min = NULL,
fecage_max = NULL,
cont = NULL,
fecmod = NULL,
density = NULL,
density_vr = NULL,
err_check = NULL,
benchmark = NULL,
stochastic = FALSE,
integeronly = FALSE,
substoch = 0L,
nreps = 1L,
times = 10000L,
prep_mats = 20L,
force_fb = FALSE,
exp_tol = 700,
theta_tol = 1e+08
)
Arguments
used_mpms
An integer vector detailing the MPMs entered in argument
mpms to run the batch projection on. Although all MPMs entered in
argument mpms will be projected, the alterations given in
givenrate, offset, or multiplier will only be performed
with those specified here. Defaults to "all", which is equivalent to
an integer vector holding the sequence of 1 through the number of MPMs
entered.
givenrate
A numeric vector giving values to replace targeted matrix
elements with. If used, then arguments offset and multiplier
cannot be used. Defaults to NULL.
offset
A numeric vector giving the values to offset matrix elements
by. If used, then arguments givenrate and multiplier cannot be
used. Defaults to c(0.005, 0.010, 0.015, 0.020, 0.025).
multiplier
A numeric vector giving values to multiply targeted matrix
elements by. If used, then arguments givenrate and offset
cannot be used. Defaults to NULL.
all_elems
A logical value indicating whether to use the alterations
specified in argument givenrate, offset, or multiplier
on all matrix elements. Defaults to FALSE.
mpms
An optional list of MPMs. Each MPM must be of class
lefkoMat.
vrms
An optional list of vrm_input objects, each corresponding
to a distinct MPM that will be created during projection. Each
vrm_input object requires its own stageframe, entered in the same
order via argument stageframes.
stageframes
An optional list of stageframes, corresponding in number
and order to the MPMs in argument vrms. Each stageframe must be of
class stageframe.
supplements
An optional list of data frames of class lefkoSD
that provide supplemental data that should be incorporated into MPMs. If
used, then should have the same number of elements as the number of MPMs
provided in the list for argument vrms, with MPMs that do not need
supplemental data entered as NULL elements in this list. See
supplemental() for details of how these data frames
are used in function-based MPM construction.
equivalence
An optional numeric vector, list of numeric vectors,
data frame of class adaptEq or class lefkoEq, or a list of such
data frames. If a numeric vector, then must have the same number of elements
as the number of MPMs, with each element giving the effect of an individual
of each MPM relative to a reference individual. If a list of vectors, then
the list should be composed of as many numeric vectors as MPMs, or as a two-
layered list of such vectors for each annual matrix per MPM, with each
vector giving the effect of each individual in each stage relative to a
reference individual. Data frames of class adaptEq, and lists of such
data frames, can be made with function equiv_input(). Numeric
entries used in these vectors can be thought of as Lotka-Volterra
competition terms, such as are used in multiple species competition models.
starts
An optional list of lefkoSV objects, which are data
frames providing the starting numbers of individuals of each stage. If
provided, then one is needed per MPM. If not provided, then all projections
start with a single individual of each stage per MPM.
years
An optional term corresponding either to a single integer vector
of time t values, if all MPMs will use the same time t or set
of time t's, or a list of such vectors with each vector corresponding
to each MPM in order. In the latter case, a vector composed of a single
NA value is interpreted to mean that all time t values in the
MPM should be utilized. If a vector shorter than times is supplied,
then this vector will be cycled.
patches
An optional string vector with length equal to the number of
MPMs, detailing the name of each patch to project for each MPM, in order.
Only a single pop-patch may be projected for each MPM given. A value of
NA can be supplied to indicate that the population-level matrices
should be projected (if argument mpms is used and a population-level
set of matrices exist), or that the first patch noted should be used.
Defaults to the population-level set or the first patch, depending on
whether the former exists.
tweights
An optional list composed of numeric vectors or matrices
denoting the probabilities of choosing each matrix in each MPM in a
stochastic projection. If an element of the list is a matrix, then a
first-order Markovian environment is assumed, in which the probability of
choosing a specific annual matrix depends on which annual matrix is
currently chosen. If an element of the list is a vector, then the choice of
annual matrix is assumed to be independent of the current matrix. Defaults
to equal weighting among matrices. If used, then one element per MPM is
required, with equal weighting assumed for any element set to NULL.
format
An optional integer vector indicating the kind of
function-based MPM to create for each vrm_input object entered in
argument vrms. Possible choices include: 1, Ehrlen-format
historical MPM; 2, deVries-format historical MPM; 3,
ahistorical MPM (default); 4, age-by-stage MPM; and 5, Leslie
(age-based) MPM.
entry_time
An optional integer vector giving the entry time for each
MPM into the projection. Defaults to a zero vector with the length of the
number of MPMs, as given either by argument mpms or vrms.
sp_density
An optional argument for use with vrm_input objects
that specifies the spatial density to be used in each time step. If used,
may either be a numeric vector giving a single spatial density for each
vrm_input object entered in argument vrms (in this case, the
value of spatial density given for each vrm_input object will be held
constant through the projection), or a list of as many numeric vectors as
vrm_input objects, with the length of each vector giving the spatial
density at each time step. If vectors are shorter than specified in
times, then these values will be cycled.
ind_terms
An optional argument providing values of individual or
environmental covariate values for vrm_input objects used in
function-based projection. Can be set either to a single data frame with 3
columns giving values for up to 3 covariates across time (rows give the time
order of these values), or a list of as many such data frames as
vrm_input objects. In the latter case, vrm_input objects that
do not use such covariates should have the associated element set to
NULL. Unused terms within each data frame must be set to 0
(use of NA will produce errors.) If the number of rows is less than
times, then these values will be cycled.
dev_terms
An optional list of data frames, one for each
vrm_input object. Each should include either 14 or 17 columns, and up
to times rows showing the values of the deviation terms to be added
to each linear vital rate. The column order should be: 1: survival,
2: observation, 3: primary size, 4: secondary size, 5: tertiary size,
6: reproduction, 7: fecundity, 8: juvenile survival, 9: juvenile
observation, 10: juvenile primary size, 11: juvenile secondary size,
12: juvenile tertiary size, 13: juvenile reproduction, and 14: juvenile
maturity transition. In addition, users may supply 3 more columns
designating additive deviations to: 15: individual covariate a,
16: individual covariate b, and 17: individual covariate c. Unused terms
must be set to 0 (use of NA will produce errors). Single or
small numbers of values per vital rate model are also allowed, and if the
number of rows is less than times, then the terms will be cycled.
fb_sparse
A logical vector indicating whether function-based MPMs
should be produced in sparse matrix format. Defaults to FALSE for
each MPM.
firstage
An optional integer vector used for function-based Leslie
and age-by-stage MPMs giving the starting ages in such MPMs. Use only if at
least one MPM is both function-based and has age structure. Typically,
the starting age in such MPMs should be set to 0 if post-breeding and
1 if pre-breeding. All other MPMs should be set to 0. Do not
use if no MPM has age structure. Defaults to 1 in Leslie and
age-by-stage MPMs.
finalage
An optional integer vector used for function-based Leslie
and age-by-stage MPMs giving the final ages in such MPMs. Use only if at
least one MPM is both function-based and has age structure. Do not use if no
MPM has age structure.
fecage_min
An optional integer vector used for function-based Leslie
MPMs giving the first age at which organisms can be reproductive in such
MPMs. Use only if at least one MPM is a function-based Leslie MPM. Defaults
to the values given in firstage.
fecage_max
An optional integer vector used for function-based Leslie
MPMs giving the final age at which organisms can be reproductive in such
MPMs. Use only if at least one MPM is a function-based Leslie MPM. Defaults
to the values given in finalage.
cont
An optional vector used for function-based Leslie and
age-by-stage MPMs stating whether the MPM should should include a stasis
transition within the final age. This should be used only when an organism
can maintain the demographic characteristics of the final described age
after reaching that age. Can be entered as a logical vector or an integer
vector. MPMs without age structure should be entered as 0 or
FALSE. Do not use if no MPM has age structure.
fecmod
An optional vector used for function-based MPMs giving scalar
multipliers for fecundity terms, when two fecundity variables are used for a
collective fecundity per individual. Each entry refers to each
vrm_input object in argument vrms, in the same order.
density
An optional list of data frames of class lefkoDens,
which provide details for density dependence in MPM elements and have been
created with function density_input(), or a two-
layered list of such data frames, with a data frame per annual matrix per
MPM.
density_vr
An optional list of data frames of class
lefkoDensVR, which provide details for density dependence in vital
rate models and have been created with function
link[lefko3]{density_vr}(). If used, then one such data frame per MPM
is required. MPMs to be run without vital describing density dependence
relationships in vital rates should be set to NULL. Can only be used
with function-based projections.
err_check
A logical value indicating whether to include an extra list
of output objects for error checking with each projection. Can also be set
to the text value "extreme", in which case all err_check
output plus a multiple level list with each MPM used in each time step will
be output.
benchmark
A logical value indicating whether to issue messages during
projection indicating progress. Defaults to FALSE.
stochastic
A logical value indicating whether the projection will be
run as a temporally stochastic projection. Defaults to FALSE.
integeronly
A logical value indicating whether to round the number of
individuals projected in each stage at each occasion in each MPM to the
nearest integer. Defaults to FALSE.
substoch
An integer value indicating whether to force survival-
transition matrices to be substochastic in density dependent and density
independent simulations. Defaults to 0, which does not enforce
substochasticity. Alternatively, 1 forces all survival-transition
elements to range from 0.0 to 1.0, and forces fecundity to be non-negative;
and 2 forces all column rows in the survival-transition matrices to
total no more than 1.0, in addition to the actions outlined for option
1. Both settings 1 and 2 change negative fecundity
elements to 0.0.
nreps
The number of replicate projections. Defaults to 1.
times
Number of occasions to iterate per replicate. Defaults to
10000.
prep_mats
An integer value for use when creating function-based MPM
projections. If using vrms input instead of mpms input, then
this argument determines how many matrices should be used as a limit to
develop matrices prior to running the projection. See Notes for
further details.
force_fb
A logical value indicating whether to force function-based
MPMs to be developed at each time step even if fewer than prep_mats.
Defaults to FALSE.
exp_tol
A numeric value used to indicate a maximum value to set
exponents to in the core kernel to prevent numerical overflow. Defaults to
700.
theta_tol
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.
Value
A list of class adaptProjBatch, which is composed of three
elements:
proj_out
A multi-layered list, with the top number of elements equal
to the number of used_mpms. Each element is a list, with the number
of elements equal to the number of alterations tried. Each of these elements
is, in turn, an adaptProj object.
ref
A list with the same structure as proj_out. This list is
composed of supplements (lefkoSD objects) giving the exact
alterations performed in the associated MCM.
control
An integer vector giving the identities of the MPM altered
in each top list.
Notes
This function is currently used to run sensitivity analyses, in which the
slope of the final population size is regressed against the the starting
offset values for each element. Each regression is run on the offset values
within each element.
The only arguments unique to this function are arguments used_mpms,
givenrate, offset, multiplier, and all_elems.
All others are passed to function project3.
Setting all_elems = TRUE will lead to very time-intensive analysis.
We encourage users to break down their analyses into smaller batches, in
order to make them more tractable.
Examples
library(lefko3)
data(cypdata)
sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)
stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",
"XLg")
repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)
obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)
matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)
immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)
propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)
binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)
cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,
repstatus = repvector, obsstatus = obsvector, matstatus = matvector,
propstatus = propvector, immstatus = immvector, indataset = indataset,
binhalfwidth = binvec)
sizevector <- c(0, 0, 3.0, 15)
stagevector <- c("P1", "D", "Sm", "Lg")
repvector <- c(0, 0, 1, 1)
obsvector <- c(0, 0, 1, 1)
matvector <- c(0, 1, 1, 1)
immvector <- c(1, 0, 0, 0)
indataset <- c(0, 1, 1, 1)
binvec <- c(0, 0.5, 2.5, 9.5)
cypframe_small_raw <- sf_create(sizes = sizevector, stagenames = stagevector,
repstatus = repvector, obsstatus = obsvector, matstatus = matvector,
immstatus = immvector, indataset = indataset, binhalfwidth = binvec)
cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,
patchidcol = "patch", individcol = "plantid", blocksize = 4,
sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",
repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",
stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,
NRasRep = TRUE)
cypraw_v2 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,
patchidcol = "patch", individcol = "plantid", blocksize = 4,
sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",
repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",
stageassign = cypframe_small_raw, stagesize = "sizeadded", NAas0 = TRUE,
NRasRep = TRUE)
cypraw_v3 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,
patchidcol = "patch", individcol = "plantid", blocksize = 4,
sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",
repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",
NAas0 = TRUE, NRasRep = TRUE)
cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",
"XSm", "Sm", "SD", "P1"),
stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",
"rep"),
eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),
eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),
givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),
multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 1500, 500),
type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),
stageframe = cypframe_raw, historical = FALSE)
cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,
year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),
size = c("size3added", "size2added"), supplement = cypsupp2r,
yearcol = "year2", patchcol = "patchid", indivcol = "individ")
cypmean <- lmean(cypmatrix2r)
cypsupp2r_small <- supplemental(stage3 = c("D", "Sm", "Lg", "P1"),
stage2 = c("P1", "P1", "P1", "rep"), eststage3 = c(NA, "Sm", "Lg", NA),
eststage2 = c(NA, "D", "D", NA), givenrate = c(0.05, NA, NA, NA),
offset = c(NA, NA, -0.1, NA), multiplier = c(NA, NA, NA, 0.5),
type =c(1, 1, 1, 3), stageframe = cypframe_small_raw, historical = FALSE)
cypmatrix2r_small <- rlefko2(data = cypraw_v2, stageframe = cypframe_small_raw,
year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),
size = c("size3added", "size2added"), supplement = cypsupp2r_small,
yearcol = "year2", patchcol = "patchid", indivcol = "individ")
cypmean_small <- lmean(cypmatrix2r_small)
cypmatrixL_small <- rleslie(data = cypraw_v3, start_age = 1, last_age = 4,
continue = TRUE, fecage_min = 3, year = "all", pop = NA, patch = "all",
yearcol = "year2", patchcol = "patchid", indivcol = "individ")
cyp_mpms1 <- list(cypmatrix2r, cypmatrix2r_small, cypmatrixL_small)
c2d_4 <- density_input(cypmean, stage3 = c("P1", "P1"), stage2= c("SD", "rep"),
style = 1, time_delay = 1, alpha = 1, beta = 0.0005, type = c(2, 2))
c2d_4a <- density_input(cypmean_small, stage3 = c("P1", "P1"), stage2= c("P1", "rep"),
style = 1, time_delay = 1, alpha = 1, beta = 0.0005, type = c(2, 2))
cypL_dv <- density_input(cypmatrixL_small, stage3 = c("Age1"), stage2 = c("rep"),
style = c(1), alpha = c(0.5), beta = c(1.0), type = c(2))
cyp_density <- list(c2d_4, c2d_4a, cypL_dv)
cyp_start1 <- start_input(cypmatrix2r, stage2 = c("SD", "P1", "D"),
value = c(100, 200, 4))
cyp_start2 <- start_input(cypmatrix2r_small, stage2 = c("P1", "D"),
value = c(10, 2000))
cypL_start_1 <- start_input(cypmatrixL_small, stage2 = c("Age1"),
value = c(200))
cyp_start <- list(cyp_start1, cyp_start2, cypL_start_1)
new_supplement_cyp2_small <- sup_skeleton(2)
new_supplement_cyp2_small$stage3 <- c("D", "Sm")
new_supplement_cyp2_small$stage2 <- c("Lg", "Lg")
new_supplement_cyp2_small$convtype <- c(1, 1)
used_supplements <- list(new_supplement_cyp2_small,
new_supplement_cyp2_small, NULL)
aaa1_prj_batch2 <- batch_project3(used_mpms = "all", all_elems = FALSE,
mpms = cyp_mpms1, entry_time = c(0, 5, 8), times = 15, nreps = 3,
supplement = used_supplements, integeronly = TRUE, density = cyp_density)
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| batch_project3 | R Documentation |
Project Multiple MPMs In Batches With Varying Starting Conditions
Description
Function batch_project3 runs function project3() across series
of differing beginning conditions. Currently used to run sensitivity
analyses.
Usage
batch_project3(
used_mpms = NULL,
givenrate = NULL,
offset = NULL,
multiplier = NULL,
all_elems = NULL,
mpms = NULL,
vrms = NULL,
stageframes = NULL,
supplements = NULL,
equivalence = NULL,
starts = NULL,
years = NULL,
patches = NULL,
tweights = NULL,
format = NULL,
entry_time = NULL,
sp_density = NULL,
ind_terms = NULL,
dev_terms = NULL,
fb_sparse = NULL,
firstage = NULL,
finalage = NULL,
fecage_min = NULL,
fecage_max = NULL,
cont = NULL,
fecmod = NULL,
density = NULL,
density_vr = NULL,
err_check = NULL,
benchmark = NULL,
stochastic = FALSE,
integeronly = FALSE,
substoch = 0L,
nreps = 1L,
times = 10000L,
prep_mats = 20L,
force_fb = FALSE,
exp_tol = 700,
theta_tol = 1e+08
)
Arguments
used_mpms |
An integer vector detailing the MPMs entered in argument
|
givenrate |
A numeric vector giving values to replace targeted matrix
elements with. If used, then arguments |
offset |
A numeric vector giving the values to offset matrix elements
by. If used, then arguments |
multiplier |
A numeric vector giving values to multiply targeted matrix
elements by. If used, then arguments |
all_elems |
A logical value indicating whether to use the alterations
specified in argument |
mpms |
An optional list of MPMs. Each MPM must be of class
|
vrms |
An optional list of |
stageframes |
An optional list of stageframes, corresponding in number
and order to the MPMs in argument |
supplements |
An optional list of data frames of class |
equivalence |
An optional numeric vector, list of numeric vectors,
data frame of class |
starts |
An optional list of |
years |
An optional term corresponding either to a single integer vector
of time |
patches |
An optional string vector with length equal to the number of
MPMs, detailing the name of each patch to project for each MPM, in order.
Only a single pop-patch may be projected for each MPM given. A value of
|
tweights |
An optional list composed of numeric vectors or matrices
denoting the probabilities of choosing each matrix in each MPM in a
stochastic projection. If an element of the list is a matrix, then a
first-order Markovian environment is assumed, in which the probability of
choosing a specific annual matrix depends on which annual matrix is
currently chosen. If an element of the list is a vector, then the choice of
annual matrix is assumed to be independent of the current matrix. Defaults
to equal weighting among matrices. If used, then one element per MPM is
required, with equal weighting assumed for any element set to |
format |
An optional integer vector indicating the kind of
function-based MPM to create for each |
entry_time |
An optional integer vector giving the entry time for each
MPM into the projection. Defaults to a zero vector with the length of the
number of MPMs, as given either by argument |
sp_density |
An optional argument for use with |
ind_terms |
An optional argument providing values of individual or
environmental covariate values for |
dev_terms |
An optional list of data frames, one for each
|
fb_sparse |
A logical vector indicating whether function-based MPMs
should be produced in sparse matrix format. Defaults to |
firstage |
An optional integer vector used for function-based Leslie
and age-by-stage MPMs giving the starting ages in such MPMs. Use only if at
least one MPM is both function-based and has age structure. Typically,
the starting age in such MPMs should be set to |
finalage |
An optional integer vector used for function-based Leslie and age-by-stage MPMs giving the final ages in such MPMs. Use only if at least one MPM is both function-based and has age structure. Do not use if no MPM has age structure. |
fecage_min |
An optional integer vector used for function-based Leslie
MPMs giving the first age at which organisms can be reproductive in such
MPMs. Use only if at least one MPM is a function-based Leslie MPM. Defaults
to the values given in |
fecage_max |
An optional integer vector used for function-based Leslie
MPMs giving the final age at which organisms can be reproductive in such
MPMs. Use only if at least one MPM is a function-based Leslie MPM. Defaults
to the values given in |
cont |
An optional vector used for function-based Leslie and
age-by-stage MPMs stating whether the MPM should should include a stasis
transition within the final age. This should be used only when an organism
can maintain the demographic characteristics of the final described age
after reaching that age. Can be entered as a logical vector or an integer
vector. MPMs without age structure should be entered as |
fecmod |
An optional vector used for function-based MPMs giving scalar
multipliers for fecundity terms, when two fecundity variables are used for a
collective fecundity per individual. Each entry refers to each
|
density |
An optional list of data frames of class |
density_vr |
An optional list of data frames of class
|
err_check |
A logical value indicating whether to include an extra list
of output objects for error checking with each projection. Can also be set
to the text value |
benchmark |
A logical value indicating whether to issue messages during
projection indicating progress. Defaults to |
stochastic |
A logical value indicating whether the projection will be
run as a temporally stochastic projection. Defaults to |
integeronly |
A logical value indicating whether to round the number of
individuals projected in each stage at each occasion in each MPM to the
nearest integer. Defaults to |
substoch |
An integer value indicating whether to force survival-
transition matrices to be substochastic in density dependent and density
independent simulations. Defaults to |
nreps |
The number of replicate projections. Defaults to |
times |
Number of occasions to iterate per replicate. Defaults to
|
prep_mats |
An integer value for use when creating function-based MPM
projections. If using |
force_fb |
A logical value indicating whether to force function-based
MPMs to be developed at each time step even if fewer than |
exp_tol |
A numeric value used to indicate a maximum value to set
exponents to in the core kernel to prevent numerical overflow. Defaults to
|
theta_tol |
A numeric value used to indicate a maximum value to theta as
used in the negative binomial probability density kernel. Defaults to
|
Value
A list of class adaptProjBatch, which is composed of three
elements:
proj_out |
A multi-layered list, with the top number of elements equal
to the number of |
ref |
A list with the same structure as |
control |
An integer vector giving the identities of the MPM altered in each top list. |
Notes
This function is currently used to run sensitivity analyses, in which the slope of the final population size is regressed against the the starting offset values for each element. Each regression is run on the offset values within each element.
The only arguments unique to this function are arguments used_mpms,
givenrate, offset, multiplier, and all_elems.
All others are passed to function project3.
Setting all_elems = TRUE will lead to very time-intensive analysis.
We encourage users to break down their analyses into smaller batches, in
order to make them more tractable.
Examples
library(lefko3)
data(cypdata)
sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)
stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",
"XLg")
repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)
obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)
matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)
immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)
propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)
binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)
cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,
repstatus = repvector, obsstatus = obsvector, matstatus = matvector,
propstatus = propvector, immstatus = immvector, indataset = indataset,
binhalfwidth = binvec)
sizevector <- c(0, 0, 3.0, 15)
stagevector <- c("P1", "D", "Sm", "Lg")
repvector <- c(0, 0, 1, 1)
obsvector <- c(0, 0, 1, 1)
matvector <- c(0, 1, 1, 1)
immvector <- c(1, 0, 0, 0)
indataset <- c(0, 1, 1, 1)
binvec <- c(0, 0.5, 2.5, 9.5)
cypframe_small_raw <- sf_create(sizes = sizevector, stagenames = stagevector,
repstatus = repvector, obsstatus = obsvector, matstatus = matvector,
immstatus = immvector, indataset = indataset, binhalfwidth = binvec)
cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,
patchidcol = "patch", individcol = "plantid", blocksize = 4,
sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",
repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",
stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,
NRasRep = TRUE)
cypraw_v2 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,
patchidcol = "patch", individcol = "plantid", blocksize = 4,
sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",
repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",
stageassign = cypframe_small_raw, stagesize = "sizeadded", NAas0 = TRUE,
NRasRep = TRUE)
cypraw_v3 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,
patchidcol = "patch", individcol = "plantid", blocksize = 4,
sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",
repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",
NAas0 = TRUE, NRasRep = TRUE)
cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",
"XSm", "Sm", "SD", "P1"),
stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",
"rep"),
eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),
eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),
givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),
multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 1500, 500),
type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),
stageframe = cypframe_raw, historical = FALSE)
cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,
year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),
size = c("size3added", "size2added"), supplement = cypsupp2r,
yearcol = "year2", patchcol = "patchid", indivcol = "individ")
cypmean <- lmean(cypmatrix2r)
cypsupp2r_small <- supplemental(stage3 = c("D", "Sm", "Lg", "P1"),
stage2 = c("P1", "P1", "P1", "rep"), eststage3 = c(NA, "Sm", "Lg", NA),
eststage2 = c(NA, "D", "D", NA), givenrate = c(0.05, NA, NA, NA),
offset = c(NA, NA, -0.1, NA), multiplier = c(NA, NA, NA, 0.5),
type =c(1, 1, 1, 3), stageframe = cypframe_small_raw, historical = FALSE)
cypmatrix2r_small <- rlefko2(data = cypraw_v2, stageframe = cypframe_small_raw,
year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),
size = c("size3added", "size2added"), supplement = cypsupp2r_small,
yearcol = "year2", patchcol = "patchid", indivcol = "individ")
cypmean_small <- lmean(cypmatrix2r_small)
cypmatrixL_small <- rleslie(data = cypraw_v3, start_age = 1, last_age = 4,
continue = TRUE, fecage_min = 3, year = "all", pop = NA, patch = "all",
yearcol = "year2", patchcol = "patchid", indivcol = "individ")
cyp_mpms1 <- list(cypmatrix2r, cypmatrix2r_small, cypmatrixL_small)
c2d_4 <- density_input(cypmean, stage3 = c("P1", "P1"), stage2= c("SD", "rep"),
style = 1, time_delay = 1, alpha = 1, beta = 0.0005, type = c(2, 2))
c2d_4a <- density_input(cypmean_small, stage3 = c("P1", "P1"), stage2= c("P1", "rep"),
style = 1, time_delay = 1, alpha = 1, beta = 0.0005, type = c(2, 2))
cypL_dv <- density_input(cypmatrixL_small, stage3 = c("Age1"), stage2 = c("rep"),
style = c(1), alpha = c(0.5), beta = c(1.0), type = c(2))
cyp_density <- list(c2d_4, c2d_4a, cypL_dv)
cyp_start1 <- start_input(cypmatrix2r, stage2 = c("SD", "P1", "D"),
value = c(100, 200, 4))
cyp_start2 <- start_input(cypmatrix2r_small, stage2 = c("P1", "D"),
value = c(10, 2000))
cypL_start_1 <- start_input(cypmatrixL_small, stage2 = c("Age1"),
value = c(200))
cyp_start <- list(cyp_start1, cyp_start2, cypL_start_1)
new_supplement_cyp2_small <- sup_skeleton(2)
new_supplement_cyp2_small$stage3 <- c("D", "Sm")
new_supplement_cyp2_small$stage2 <- c("Lg", "Lg")
new_supplement_cyp2_small$convtype <- c(1, 1)
used_supplements <- list(new_supplement_cyp2_small,
new_supplement_cyp2_small, NULL)
aaa1_prj_batch2 <- batch_project3(used_mpms = "all", all_elems = FALSE,
mpms = cyp_mpms1, entry_time = c(0, 5, 8), times = 15, nreps = 3,
supplement = used_supplements, integeronly = TRUE, density = cyp_density)
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