View source: R/matrixcreation.R
flefko3  R Documentation 
Function flefko3()
returns functionbased historical MPMs
corresponding to the patches and occasions given, including the associated
component transition and fecundity matrices, data frames detailing the
characteristics of the ahistorical stages used and historical stage pairs
created, and a data frame characterizing the patch and occasion combinations
corresponding to these matrices.
flefko3(
year = "all",
patch = "all",
stageframe,
supplement = NULL,
repmatrix = NULL,
overwrite = NULL,
data = NULL,
modelsuite = NULL,
surv_model = NULL,
obs_model = NULL,
size_model = NULL,
sizeb_model = NULL,
sizec_model = NULL,
repst_model = NULL,
fec_model = NULL,
jsurv_model = NULL,
jobs_model = NULL,
jsize_model = NULL,
jsizeb_model = NULL,
jsizec_model = NULL,
jrepst_model = NULL,
jmatst_model = NULL,
paramnames = NULL,
inda = NULL,
indb = NULL,
indc = NULL,
surv_dev = 0,
obs_dev = 0,
size_dev = 0,
sizeb_dev = 0,
sizec_dev = 0,
repst_dev = 0,
fec_dev = 0,
jsurv_dev = 0,
jobs_dev = 0,
jsize_dev = 0,
jsizeb_dev = 0,
jsizec_dev = 0,
jrepst_dev = 0,
jmatst_dev = 0,
density = NA,
fecmod = 1,
random.inda = FALSE,
random.indb = FALSE,
random.indc = FALSE,
negfec = FALSE,
format = "ehrlen",
ipm_method = "CDF",
reduce = FALSE,
simple = FALSE,
err_check = FALSE,
exp_tol = 700,
theta_tol = 1e+08,
sparse_output = FALSE
)
year 
A variable corresponding to the observation occasion, or a set
of such values, given in values associated with the year term used in linear
model development. Defaults to 
patch 
A variable designating which patches or subpopulations will have
matrices estimated. Defaults to 
stageframe 
An object of class 
supplement 
An optional data frame of class 
repmatrix 
An optional reproduction matrix. This matrix is composed
mostly of 
overwrite 
An optional data frame developed with the

data 
The historical vertical demographic data frame used to estimate
vital rates (class 
modelsuite 
One of two kinds of lists. The first is a 
surv_model 
A linear model predicting survival probability. This can
be a model of class 
obs_model 
A linear model predicting sprouting or observation
probability. This can be a model of class 
size_model 
A linear model predicting primary size. This can be a model
of class 
sizeb_model 
A linear model predicting secondary size. This can be a
model of class 
sizec_model 
A linear model predicting tertiary size. This can be a
model of class 
repst_model 
A linear model predicting reproduction probability. This
can be a model of class 
fec_model 
A linear model predicting fecundity. This can be a model of
class 
jsurv_model 
A linear model predicting juvenile survival probability.
This can be a model of class 
jobs_model 
A linear model predicting juvenile sprouting or observation
probability. This can be a model of class 
jsize_model 
A linear model predicting juvenile primary size. This
can be a model of class 
jsizeb_model 
A linear model predicting juvenile secondary size. This
can be a model of class 
jsizec_model 
A linear model predicting juvenile tertiary size. This
can be a model of class 
jrepst_model 
A linear model predicting reproduction probability of a
mature individual that was immature in time t. This can be a model
of class 
jmatst_model 
A linear model predicting maturity probability of an
individual that was immature in time t. This can be a model of class

paramnames 
A data frame with three columns, the first describing all
terms used in linear modeling, the second (must be called 
inda 
Can be a single value to use for individual covariate 
indb 
Can be a single value to use for individual covariate 
indc 
Can be a single value to use for individual covariate 
surv_dev 
A numeric value to be added to the yintercept in the linear
model for survival probability. Defaults to 
obs_dev 
A numeric value to be added to the yintercept in the linear
model for observation probability. Defaults to 
size_dev 
A numeric value to be added to the yintercept in the linear
model for primary size. Defaults to 
sizeb_dev 
A numeric value to be added to the yintercept in the linear
model for secondary size. Defaults to 
sizec_dev 
A numeric value to be added to the yintercept in the linear
model for tertiary size. Defaults to 
repst_dev 
A numeric value to be added to the yintercept in the linear
model for probability of reproduction. Defaults to 
fec_dev 
A numeric value to be added to the yintercept in the linear
model for fecundity. Defaults to 
jsurv_dev 
A numeric value to be added to the yintercept in the linear
model for juvenile survival probability. Defaults to 
jobs_dev 
A numeric value to be added to the yintercept in the linear
model for juvenile observation probability. Defaults to 
jsize_dev 
A numeric value to be added to the yintercept in the linear
model for juvenile primary size. Defaults to 
jsizeb_dev 
A numeric value to be added to the yintercept in the
linear model for juvenile secondary size. Defaults to 
jsizec_dev 
A numeric value to be added to the yintercept in the
linear model for juvenile tertiary size. Defaults to 
jrepst_dev 
A numeric value to be added to the yintercept in the
linear model for juvenile reproduction probability. Defaults to 
jmatst_dev 
A numeric value to be added to the yintercept in the
linear model for juvenile maturity probability. Defaults to 
density 
A numeric value indicating density value to use to propagate
matrices. Only needed if density is an explanatory term used in one or more
vital rate models. Defaults to 
fecmod 
A scalar multiplier of fecundity. Defaults to 
random.inda 
A logical value denoting whether to treat individual
covariate 
random.indb 
A logical value denoting whether to treat individual
covariate 
random.indc 
A logical value denoting whether to treat individual
covariate 
negfec 
A logical value denoting whether fecundity values estimated to
be negative should be reset to 
format 
A string indicating whether to estimate matrices in

ipm_method 
A string indicating what method to use to estimate size
transition probabilities, if size is treated as continuous. Options include:

reduce 
A logical value denoting whether to remove historical stages
associated solely with 
simple 
A logical value indicating whether to produce 
err_check 
A logical value indicating whether to append extra
information used in matrix calculation within the output list. Defaults to

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

sparse_output 
A logical value indicating whether to output matrices
in sparse format. Defaults to 
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. The structure has the following
elements:
A 
A list of full projection matrices in order of sorted patches and
occasion times. All matrices output in R's 
U 
A list of survival transition matrices sorted as in 
F 
A list of fecundity matrices sorted as in 
hstages 
A data frame matrix showing the pairing of ahistorical stages used to create historical stage pairs. 
agestages 
A data frame showing agestage pairs. In this function, it
is set to 
ahstages 
A data frame detailing the characteristics of associated ahistorical stages, in the form of a modified stageframe that includes status as an entry stage through reproduction. 
labels 
A data frame giving the population, patch, and year of each
matrix in order. In 
dataqc 
A vector showing the numbers of individuals and rows in the vertical dataset used as input. 
matrixqc 
A short vector describing the number of nonzero elements in

modelqc 
This is the 
prob_out 
An optional element only added if 
allstages 
An optional element only added if 
Unlike rlefko2()
, rlefko3()
,
arlefko2()
, and rleslie()
, this function does not
currently distinguish populations. Users wishing to use the same vital rate
models across populations should label them as patches (though we do not
advise this approach, as populations should typically be treated as
statistically independent).
The default behavior of this function is to estimate fecundity with regards
to transitions specified via associated fecundity multipliers in either
supplement
or repmatrix
. If both of these fields are left
empty, then fecundity will be estimated at full for all transitions leading
from reproductive stages to immature and propagule stages. However, if a
supplement
is provided and a repmatrix
is not, or if
repmatrix
is set to 0
, then only fecundity transitions noted in
the supplement
will be set to nonzero values. To use the default
behavior of setting all reproductive stages to reproduce at full fecundity
into immature and propagule stages, but also incorporate given or proxy
survival transitions, input those given and proxy transitions through the
overwrite
option.
If used, the reproduction matrix (field repmatrix
) may be supplied as
either historical or ahistorical. If provided as ahistorical, then
flefko3()
will assume that all historical transitions involving stages
noted for occasions t and t+1 should be set to the respective
fecundity multipliers noted.
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, t, and t1. Rearranging the order will lead to erroneous calculations, and will may lead to fatal errors.
The ipm_method
function gives the option of using two different means
of estimating the probability of size transition. The midpoint method
("midpoint"
) refers to the method in which the probability is
estimated by first estimating the probability associated with transition from
the exact size at the midpoint of the size class using the corresponding
probability density function, and then multiplying that value by the bin
width of the size class. Doak et al. 2021 (Ecological Monographs) noted that
this method can produce biased results, with total size transitions
associated with a specific size not totaling to 1.0 and even specific size
transition probabilities capable of being estimated at values greater than
1.0. The alternative and default method, "CDF"
, uses the corresponding
cumulative density function to estimate the probability of size transition as
the cumulative probability of size transition at the greater limit of the
size class minus the cumulative probability of size transition at the lower
limit of the size class. This latter method avoids this bias. Note, however,
that both methods are exact and unbiased for negative binomial and Poisson
distributions.
Under the Gaussian and gamma size distributions, the number of estimated
parameters may differ between the two ipm_method
settings. Because
the midpoint method has a tendency to incorporate upward bias in the
estimation of size transition probabilities, it is more likely to yield non
zero values when the true probability is extremely close to 0. This will
result in the summary.lefkoMat
function yielding higher numbers of
estimated parameters than the ipm_method = "CDF"
yields in some cases.
Using the err_check
option will produce a matrix of 7 columns, each
characterizing a different vital rate. The product of each row yields an
element in the associated U
matrix. The number and order of elements
in each column of this matrix matches the associated matrix in column vector
format. Use of this option is generally for the purposes of debugging code.
'
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()
flefko2()
aflefko2()
arlefko2()
fleslie()
rlefko3()
rlefko2()
rleslie()
# Lathyrus example
data(lathyrus)
sizevector < c(0, 4.6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8,
9)
stagevector < c("Sd", "Sdl", "Dorm", "Sz1nr", "Sz2nr", "Sz3nr", "Sz4nr",
"Sz5nr", "Sz6nr", "Sz7nr", "Sz8nr", "Sz9nr", "Sz1r", "Sz2r", "Sz3r",
"Sz4r", "Sz5r", "Sz6r", "Sz7r", "Sz8r", "Sz9r")
repvector < c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1)
obsvector < c(0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
matvector < c(0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
immvector < c(1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
propvector < c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0)
indataset < c(0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
binvec < c(0, 4.6, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5)
lathframeln < sf_create(sizes = sizevector, stagenames = stagevector,
repstatus = repvector, obsstatus = obsvector, matstatus = matvector,
immstatus = immvector, indataset = indataset, binhalfwidth = binvec,
propstatus = propvector)
lathvertln < verticalize3(lathyrus, noyears = 4, firstyear = 1988,
patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,
juvcol = "Seedling1988", sizeacol = "lnVol88", repstracol = "Intactseed88",
fecacol = "Intactseed88", deadacol = "Dead1988",
nonobsacol = "Dormant1988", stageassign = lathframeln, stagesize = "sizea",
censorcol = "Missing1988", censorkeep = NA, NAas0 = TRUE, censor = TRUE)
lathvertln$feca2 < round(lathvertln$feca2)
lathvertln$feca1 < round(lathvertln$feca1)
lathvertln$feca3 < round(lathvertln$feca3)
lathsupp3 < supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "mat", "Sd", "Sdl"),
stage2 = c("Sd", "Sd", "Sd", "Sd", "Sdl", "rep", "rep"),
stage1 = c("Sd", "rep", "Sd", "rep", "Sd", "mat", "mat"),
eststage3 = c(NA, NA, NA, NA, "mat", NA, NA),
eststage2 = c(NA, NA, NA, NA, "Sdl", NA, NA),
eststage1 = c(NA, NA, NA, NA, "Sdl", NA, NA),
givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),
multiplier = c(NA, NA, NA, NA, NA, 0.345, 0.054),
type = c(1, 1, 1, 1, 1, 3, 3), type_t12 = c(1, 2, 1, 2, 1, 1, 1),
stageframe = lathframeln, historical = TRUE)
lathvertln_adults < subset(lathvertln, stage2index > 2)
surv_model < glm(alive3 ~ sizea2 + sizea1 + as.factor(patchid) +
as.factor(year2), data = lathvertln_adults, family = "binomial")
obs_data < subset(lathvertln_adults, alive3 == 1)
obs_model < glm(obsstatus3 ~ as.factor(patchid), data = obs_data,
family = "binomial")
size_data < subset(obs_data, obsstatus3 == 1)
siz_model < lm(sizea3 ~ sizea2 + sizea1 + repstatus1 + as.factor(patchid) +
as.factor(year2), data = size_data)
reps_model < glm(repstatus3 ~ sizea2 + sizea1 + as.factor(patchid) +
as.factor(year2), data = size_data, family = "binomial")
fec_data < subset(lathvertln_adults, repstatus2 == 1)
fec_model < glm(feca2 ~ sizea2 + sizea1 + repstatus1 + as.factor(patchid),
data = fec_data, family = "poisson")
lathvertln_juvs < subset(lathvertln, stage2index < 3)
jsurv_model < glm(alive3 ~ as.factor(patchid), data = lathvertln_juvs,
family = "binomial")
jobs_data < subset(lathvertln_juvs, alive3 == 1)
jobs_model < glm(obsstatus3 ~ 1, family = "binomial", data = jobs_data)
jsize_data < subset(jobs_data, obsstatus3 == 1)
jsiz_model < lm(sizea3 ~ as.factor(year2), data = jsize_data)
jrepst_model < 0
jmatst_model < 1
mod_params < create_pm(name_terms = TRUE)
mod_params$modelparams[3] < "patchid"
mod_params$modelparams[4] < "alive3"
mod_params$modelparams[5] < "obsstatus3"
mod_params$modelparams[6] < "sizea3"
mod_params$modelparams[9] < "repstatus3"
mod_params$modelparams[11] < "feca2"
mod_params$modelparams[12] < "sizea2"
mod_params$modelparams[13] < "sizea1"
mod_params$modelparams[18] < "repstatus2"
mod_params$modelparams[19] < "repstatus1"
lathmat3ln < flefko3(year = "all", patch = "all", data = lathvertln,
stageframe = lathframeln, supplement = lathsupp3, paramnames = mod_params,
surv_model = surv_model, obs_model = obs_model, size_model = siz_model,
repst_model = reps_model, fec_model = fec_model, jsurv_model = jsurv_model,
jobs_model = jobs_model, jsize_model = jsiz_model,
jrepst_model = jrepst_model, jmatst_model = jmatst_model, reduce = FALSE)
# Cypripedium example using three size metrics for classification
data(cypdata)
sizevector_f < c(0, 0, 0, 0, 0, 0, seq(1, 12, by = 1), seq(0, 9, by = 1),
seq(0, 8, by = 1), seq(0, 7, by = 1), seq(0, 6, by = 1), seq(0, 5, by = 1),
seq(0, 4, by = 1), seq(0, 3, by = 1), 0, 1, 2, 0, 1, 0,
0, 0, 1, 0)
sizebvector_f < c(0, 0, 0, 0, 0, 0, rep(0, 12), rep(1, 10), rep(2, 9),
rep(3, 8), rep(4, 7), rep(5, 6), rep(6, 5), rep(7, 4), rep(8, 3), 9, 9, 10,
0, 1, 1, 2)
sizecvector_f < c(0, 0, 0, 0, 0, 0, rep(0, 12), rep(0, 10), rep(0, 9),
rep(0, 8), rep(0, 7), rep(0, 6), rep(0, 5), rep(0, 4), 0, 0, 0, 0, 0, 0,
1, 1, 1, 1)
stagevector_f < c("DS", "P1", "P2", "P3", "Sdl", "Dorm", "V1 I0 D0",
"V2 I0 D0", "V3 I0 D0", "V4 I0 D0", "V5 I0 D0", "V6 I0 D0", "V7 I0 D0",
"V8 I0 D0", "V9 I0 D0", "V10 I0 D0", "V11 I0 D0", "V12 I0 D0", "V0 I1 D0",
"V1 I1 D0", "V2 I1 D0", "V3 I1 D0", "V4 I1 D0", "V5 I1 D0", "V6 I1 D0",
"V7 I1 D0", "V8 I1 D0", "V9 I1 D0", "V0 I2 D0", "V1 I2 D0", "V2 I2 D0",
"V3 I2 D0", "V4 I2 D0", "V5 I2 D0", "V6 I2 D0", "V7 I2 D0", "V8 I2 D0",
"V0 I3 D0", "V1 I3 D0", "V2 I3 D0", "V3 I3 D0", "V4 I3 D0", "V5 I3 D0",
"V6 I3 D0", "V7 I3 D0", "V0 I4 D0", "V1 I4 D0", "V2 I4 D0", "V3 I4 D0",
"V4 I4 D0", "V5 I4 D0", "V6 I4 D0", "V0 I5 D0", "V1 I5 D0", "V2 I5 D0",
"V3 I5 D0", "V4 I5 D0", "V5 I5 D0", "V0 I6 D0", "V1 I6 D0", "V2 I6 D0",
"V3 I6 D0", "V4 I6 D0", "V0 I7 D0", "V1 I7 D0", "V2 I7 D0", "V3 I7 D0",
"V0 I8 D0", "V1 I8 D0", "V2 I8 D0", "V0 I9 D0", "V1 I9 D0", "V0 I10 D0",
"V0 I0 D1", "V0 I1 D1", "V1 I1 D1", "V0 I2 D1")
repvector_f < c(0, 0, 0, 0, 0, rep(0, 13), rep(1, 59))
obsvector_f < c(0, 0, 0, 0, 0, 0, rep(1, 71))
matvector_f < c(0, 0, 0, 0, 0, rep(1, 72))
immvector_f < c(0, 1, 1, 1, 1, rep(0, 72))
propvector_f < c(1, rep(0, 76))
indataset_f < c(0, 0, 0, 0, 0, rep(1, 72))
binvec_f < c(0, 0, 0, 0, 0, rep(0.5, 72))
binbvec_f < c(0, 0, 0, 0, 0, rep(0.5, 72))
bincvec_f < c(0, 0, 0, 0, 0, rep(0.5, 72))
vertframe_f < sf_create(sizes = sizevector_f, sizesb = sizebvector_f,
sizesc = sizecvector_f, stagenames = stagevector_f, repstatus = repvector_f,
obsstatus = obsvector_f, propstatus = propvector_f, immstatus = immvector_f,
matstatus = matvector_f, indataset = indataset_f, binhalfwidth = binvec_f,
binhalfwidthb = binbvec_f, binhalfwidthc = bincvec_f)
vert_data_f < verticalize3(cypdata, noyears = 6, firstyear = 2004,
individcol = "plantid", blocksize = 4, sizeacol = "Veg.04",
sizebcol = "Inf.04", sizeccol = "Inf2.04", repstracol = "Inf.04",
repstrbcol = "Inf2.04", fecacol = "Pod.04", censorcol = "censor",
censorkeep = 1, censorRepeat = FALSE, stageassign = vertframe_f,
stagesize = "sizeabc", NAas0 = TRUE, censor = FALSE)
vertsupp3f < supplemental(stage3 = c("DS", "P1", "DS", "P1", "P2", "P2", "P3",
"Sdl", "Sdl", "Sdl", "Dorm", "V1 I0 D0", "V2 I0 D0", "V3 I0 D0", "Dorm",
"V1 I0 D0", "V2 I0 D0", "V3 I0 D0", "mat", "mat", "mat", "mat", "DS", "P1"),
stage2 = c("DS", "DS", "DS", "DS", "P1", "P1", "P2", "P3", "Sdl", "Sdl", "Sdl",
"Sdl", "Sdl", "Sdl", "Sdl", "Sdl", "Sdl", "Sdl", "Dorm", "V1 I0 D0",
"V2 I0 D0", "V3 I0 D0", "rep", "rep"),
stage1 = c("DS", "DS", "rep", "rep", "DS", "rep", "P1", "P2", "P3", "Sdl",
"Sdl", "Sdl", "Sdl", "Sdl", "P3", "P3", "P3", "P3", "Sdl", "Sdl", "Sdl",
"Sdl", "mat", "mat"),
eststage3 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Dorm", "V1 I0 D0",
"V2 I0 D0", "V3 I0 D0", "Dorm", "V1 I0 D0", "V2 I0 D0", "V3 I0 D0", "mat",
"mat", "mat", "mat", NA, NA),
eststage2 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "V1 I0 D0", "V1 I0 D0",
"V1 I0 D0", "V1 I0 D0", "V1 I0 D0", "V1 I0 D0", "V1 I0 D0", "V1 I0 D0",
"Dorm", "V1 I0 D0", "V2 I0 D0", "V3 I0 D0", NA, NA),
eststage1 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "V1 I0 D0", "V1 I0 D0",
"V1 I0 D0", "V1 I0 D0", "V1 I0 D0", "V1 I0 D0", "V1 I0 D0", "V1 I0 D0",
"V1 I0 D0", "V1 I0 D0", "V1 I0 D0", "V1 I0 D0", NA, NA),
givenrate = c(0.10, 0.20, 0.10, 0.20, 0.20, 0.20, 0.20, 0.25, 0.40, 0.40, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA),
multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 0.5 * 5000, 0.5 * 5000),
type =c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
3, 3),
type_t12 = c(1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1),
stageframe = vertframe_f, historical = TRUE)
surv_model < glm(alive3 ~ sizea2 + sizeb2, data = vert_data_f,
family = "binomial")
obs_data < subset(vert_data_f, alive3 == 1)
obs_model < glm(obsstatus3 ~ sizeb2 + sizec1 + as.factor(year2),
data = obs_data, family = "binomial")
size_data < subset(obs_data, obsstatus3 == 1)
siz_model < MASS::glm.nb(sizea3 ~ sizea2 + sizea1 + sizeb1, data = size_data)
sizb_model < glm(sizeb3 ~ sizea2 + sizeb2 + sizec1 + repstatus2 + repstatus1 +
as.factor(year2), data = size_data, family = "poisson")
sizc_model < glm(sizec3 ~ sizea1 + repstatus2, data = size_data,
family = "poisson")
reps_model < glm(repstatus3 ~ sizea2 + sizeb2 + repstatus2 + as.factor(year2),
data = size_data, family = "binomial")
fec_data < subset(vert_data_f, repstatus2 == 1)
fec_model < glm(feca2 ~ sizeb2 + as.factor(year2), data = fec_data,
family = "poisson")
mod_params < create_pm(name_terms = TRUE)
mod_params$modelparams[3] < "patchid"
mod_params$modelparams[4] < "alive3"
mod_params$modelparams[5] < "obsstatus3"
mod_params$modelparams[6] < "sizea3"
mod_params$modelparams[9] < "repstatus3"
mod_params$modelparams[11] < "feca2"
mod_params$modelparams[12] < "sizea2"
mod_params$modelparams[13] < "sizea1"
mod_params$modelparams[18] < "repstatus2"
mod_params$modelparams[19] < "repstatus1"
vert_mats_f3 < flefko3(stageframe = vertframe_f, supplement = vertsupp3f,
data = vert_data_f, surv_model = surv_model, obs_model = obs_model,
size_model = siz_model, sizeb_model = sizb_model, sizec_model = sizc_model,
repst_model = reps_model, fec_model = fec_model, paramnames = mod_params,
sparse_output = TRUE)
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