Function modelsearch() runs exhaustive model building and selection
for each vital rate needed to estimate a function-based MPM or IPM. It
returns best-fit models for each vital rate, model table showing all models
tested, and model quality control data. The final output can be used as input
in other functions within this package.
modelsearch(
data,
stageframe = NULL,
historical = TRUE,
approach = "mixed",
suite = "size",
bestfit = "AICc&k",
vitalrates = c("surv", "size", "fec"),
surv = c("alive3", "alive2", "alive1"),
obs = c("obsstatus3", "obsstatus2", "obsstatus1"),
size = c("sizea3", "sizea2", "sizea1"),
sizeb = c(NA, NA, NA),
sizec = c(NA, NA, NA),
repst = c("repstatus3", "repstatus2", "repstatus1"),
fec = c("feca3", "feca2", "feca1"),
stage = c("stage3", "stage2", "stage1"),
matstat = c("matstatus3", "matstatus2", "matstatus1"),
indiv = "individ",
patch = NA,
year = "year2",
density = NA,
test.density = FALSE,
sizedist = "gaussian",
sizebdist = NA,
sizecdist = NA,
fecdist = "gaussian",
size.zero = FALSE,
sizeb.zero = FALSE,
sizec.zero = FALSE,
size.trunc = FALSE,
sizeb.trunc = FALSE,
sizec.trunc = FALSE,
fec.zero = FALSE,
fec.trunc = FALSE,
patch.as.random = TRUE,
year.as.random = TRUE,
juvestimate = NA,
juvsize = FALSE,
jsize.zero = FALSE,
jsizeb.zero = FALSE,
jsizec.zero = FALSE,
jsize.trunc = FALSE,
jsizeb.trunc = FALSE,
jsizec.trunc = FALSE,
fectime = 2,
censor = NA,
age = NA,
test.age = FALSE,
indcova = NA,
indcovb = NA,
indcovc = NA,
random.indcova = FALSE,
random.indcovb = FALSE,
random.indcovc = FALSE,
test.indcova = FALSE,
test.indcovb = FALSE,
test.indcovc = FALSE,
test.group = FALSE,
show.model.tables = TRUE,
global.only = FALSE,
accuracy = TRUE,
quiet = FALSE
)This function yields an object of class lefkoMod, which is a
list in which the first 14 elements are the best-fit models for survival,
observation status, primary size, secondary size, tertiary size,
reproductive status, fecundity, juvenile survival, juvenile observation,
juvenile primary size, juvenile secondary size, juvenile tertiary size,
juvenile transition to reproduction, and juvenile transition to maturity,
respectively. This is followed by 14 elements corresponding to the model
tables for each of these vital rates, in order, followed by a data frame
showing the order and names of variables used in modeling, followed by a
single character element denoting the criterion used for model selection, and
ending on a data frame with quality control data:
Best-fit model of the binomial probability of survival
from occasion t to occasion t+1. Defaults to 1.
Best-fit model of the binomial probability of
observation in occasion t+1 given survival to that occasion. Defaults
to 1.
Best-fit model of the primary size metric on occasion
t+1 given survival to and observation in that occasion. Defaults to
1.
Best-fit model of the secondary size metric on occasion
t+1 given survival to and observation in that occasion. Defaults to
1.
Best-fit model of the tertiary size metric on occasion
t+1 given survival to and observation in that occasion. Defaults to
1.
Best-fit model of the binomial probability of
reproduction in occasion t+1, given survival to and observation in
that occasion. Defaults to 1.
Best-fit model of fecundity in occasion t+1
given survival to, and observation and reproduction in that occasion.
Defaults to 1.
Best-fit model of the binomial probability of
survival from occasion t to occasion t+1 of an immature
individual. Defaults to 1.
Best-fit model of the binomial probability of
observation in occasion t+1 given survival to that occasion of an
immature individual. Defaults to 1.
Best-fit model of the primary size metric on occasion
t+1 given survival to and observation in that occasion of an immature
individual. Defaults to 1.
Best-fit model of the secondary size metric on
occasion t+1 given survival to and observation in that occasion of an
immature individual. Defaults to 1.
Best-fit model of the tertiary size metric on occasion
t+1 given survival to and observation in that occasion of an immature
individual. Defaults to 1.
Best-fit model of the binomial probability of
reproduction in occasion t+1, given survival to and observation in
that occasion of an individual that was immature in occasion t. This
model is technically not a model of reproduction probability for individuals
that are immature, rather reproduction probability here is given for
individuals that are mature in occasion t+1 but immature in occasion
t. Defaults to 1.
Best-fit model of the binomial probability of
becoming mature in occasion t+1, given survival to that occasion of an
individual that was immature in occasion t. Defaults to 1.
Full dredge model table of survival probability.
Full dredge model table of observation probability.
Full dredge model table of the primary size variable.
Full dredge model table of the secondary size variable.
Full dredge model table of the tertiary size variable.
Full dredge model table of reproduction probability.
Full dredge model table of fecundity.
Full dredge model table of immature survival probability.
Full dredge model table of immature observation probability.
Full dredge model table of primary size in immature individuals.
Full dredge model table of secondary size in immature individuals.
Full dredge model table of tertiary size in immature individuals.
Full dredge model table of immature reproduction probability.
Full dredge model table of the probability of an immature individual transitioning to maturity.
A data frame showing the names of variables from the input data frame used in modeling, their associated standardized names in linear models, and a brief comment describing each variable.
Character variable denoting the criterion used to determine the best-fit model.
Data frame with five variables: 1) Name of vital rate, 2) number of individuals used to model that vital rate, 3) number of individual transitions used to model that vital rate, 4) parameter distribution used to model the vital rats, and 5) accuracy of model, given as detailed in Notes section.
The vertical dataset to be used for analysis. This dataset should
be of class hfvdata, but can also be a data frame formatted similarly
to the output format provided by functions verticalize3() or
historicalize3(), as long as all needed variables are properly
designated.
The stageframe characterizing the life history model used.
Optional unless test.group = TRUE, in which case it is required.
Defaults to NULL.
A logical variable denoting whether to assess the effects
of state in occasion t-1, in addition to state in occasion t.
Defaults to TRUE.
The statistical approach to be taken for model building. The
default is "mixed", which uses the mixed model approach utilized in
packages lme4 and glmmTMB. Other options include "glm",
which uses generalized linear modeling assuming that all factors are fixed.
Either a single string value or a vector of 14 strings for each
vital rate model. Describes the global model for each vital rate estimation,
and has the following possible values: full, includes main effects and
all two-way interactions of size and reproductive status; main,
includes main effects only of size and reproductive status; size,
includes only size (also interactions between size in historical model);
rep, includes only reproductive status (also interactions between
status in historical model); age, all vital rates estimated with age
and y-intercepts only; cons, all vital rates estimated only as
y-intercepts. If approach = "glm" and year.as.random = FALSE,
then year is also included as a fixed effect, and, in the case of
full, included in two-way interactions. Order of models in the
string vector if more than 1 value is used is: 1) survival, 2) observation,
3) primary size, 4) secondary size, 5) tertiary size, 6) reproductive status,
7) fecundity, 8) juvenile survival, 9) juvenile observation, 10) juvenile
primary size, 11) juvenile secondary size, 12) juvenile tertiary size, 13)
juvenile reproductive status, and 14) juvenile maturity status. Defaults to
size.
A variable indicating the model selection criterion for the
choice of best-fit model. The default is AICc&k, which chooses the
best-fit model as the model with the lowest AICc or, if not the same model,
then the model that has the lowest degrees of freedom among models with
\(\Delta AICc <= 2.0\). Alternatively, AICc may be chosen, in which
case the best-fit model is simply the model with the lowest AICc value.
A vector describing which vital rates will be estimated via
linear modeling, with the following options: surv, survival
probability; obs, observation probability; size, overall size;
repst, probability of reproducing; and fec, amount of
reproduction (overall fecundity). May also be set to
vitalrates = "leslie", which is equivalent to setting
c("surv", "fec") for a Leslie MPM. This choice also determines how
internal data subsetting for vital rate model estimation will work. Defaults
to c("surv", "size", "fec").
A vector indicating the variable names coding for status as alive
or dead in occasions t+1, t, and t-1, respectively.
Defaults to c("alive3", "alive2", "alive1").
A vector indicating the variable names coding for observation
status in occasions t+1, t, and t-1, respectively.
Defaults to c("obsstatus3", "obsstatus2", "obsstatus1").
A vector indicating the variable names coding for the primary
size variable on occasions t+1, t, and t-1,
respectively. Defaults to c("sizea3", "sizea2", "sizea1").
A vector indicating the variable names coding for the secondary
size variable on occasions t+1, t, and t-1,
respectively. Defaults to c(NA, NA, NA), in which case sizeb is
not used.
A vector indicating the variable names coding for the tertiary
size variable on occasions t+1, t, and t-1,
respectively. Defaults to c(NA, NA, NA), in which case sizec is
not used.
A vector indicating the variable names coding for reproductive
status in occasions t+1, t, and t-1, respectively.
Defaults to c("repstatus3", "repstatus2", "repstatus1").
A vector indicating the variable names coding for fecundity in
occasions t+1, t, and t-1, respectively. Defaults to
c("feca3", "feca2", "feca1").
A vector indicating the variable names coding for stage in
occasions t+1, t, and t-1. Defaults to
c("stage3", "stage2", "stage1").
A vector indicating the variable names coding for maturity
status in occasions t+1, t, and t-1. Defaults to
c("matstatus3", "matstatus2", "matstatus1").
A text value indicating the variable name coding individual
identity. Defaults to "individ".
A text value indicating the variable name coding for patch,
where patches are defined as permanent subgroups within the study population.
Defaults to NA.
A text value indicating the variable coding for observation
occasion t. Defaults to year2.
A text value indicating the name of the variable coding for
spatial density, should the user wish to test spatial density as a fixed
factor affecting vital rates. Defaults to NA.
Either a logical value indicating whether to include
density as a fixed categorical variable in linear models, or a logical
vector of such values for 14 models, in order: 1) survival, 2) observation,
3) primary size, 4) secondary size, 5) tertiary size, 6) reproductive
status, 7) fecundity, 8) juvenile survival, 9) juvenile observation,
10) juvenile primary size, 11) juvenile secondary size, 12) juvenile
tertiary size, 13) juvenile reproductive status, and 14) juvenile maturity
status. Defaults to FALSE.
The probability distribution used to model primary size.
Options include "gaussian" for the Normal distribution (default),
"poisson" for the Poisson distribution, "negbin" for the
negative binomial distribution (quadratic parameterization), and
"gamma" for the Gamma distribution.
The probability distribution used to model secondary size.
Options include "gaussian" for the Normal distribution,
"poisson" for the Poisson distribution, "negbin" for the
negative binomial distribution (quadratic parameterization), and
"gamma" for the Gamma distribution. Defaults to NA.
The probability distribution used to model tertiary size.
Options include "gaussian" for the Normal distribution,
"poisson" for the Poisson distribution, "negbin" for the
negative binomial distribution (quadratic parameterization), and
"gamma" for the Gamma distribution. Defaults to NA.
The probability distribution used to model fecundity. Options
include "gaussian" for the Normal distribution (default),
"poisson" for the Poisson distribution, "negbin" for the
negative binomial distribution (quadratic parameterization), and
"gamma" for the Gamma distribution.
A logical variable indicating whether the primary size
distribution should be zero-inflated. Only applies to Poisson and negative
binomial distributions. Defaults to FALSE.
A logical variable indicating whether the secondary size
distribution should be zero-inflated. Only applies to Poisson and negative
binomial distributions. Defaults to FALSE.
A logical variable indicating whether the tertiary size
distribution should be zero-inflated. Only applies to Poisson and negative
binomial distributions. Defaults to FALSE.
A logical variable indicating whether the primary size
distribution should be zero-truncated. Only applies to Poisson and negative
binomial distributions. Defaults to FALSE. Cannot be TRUE if
size.zero = TRUE.
A logical variable indicating whether the secondary size
distribution should be zero-truncated. Only applies to Poisson and negative
binomial distributions. Defaults to FALSE. Cannot be TRUE if
sizeb.zero = TRUE.
A logical variable indicating whether the tertiary size
distribution should be zero-truncated. Only applies to Poisson and negative
binomial distributions. Defaults to FALSE. Cannot be TRUE if
sizec.zero = TRUE.
A logical variable indicating whether the fecundity
distribution should be zero-inflated. Only applies to Poisson and negative
binomial distributions. Defaults to FALSE.
A logical variable indicating whether the fecundity
distribution should be zero-truncated. Only applies to the Poisson and
negative binomial distributions. Defaults to FALSE. Cannot be
TRUE if fec.zero = TRUE.
If set to TRUE and approach = "mixed",
then patch is included as a random factor. If set to FALSE and
approach = "glm", then patch is included as a fixed factor. All
other combinations of logical value and approach lead to patch
not being included in modeling. Defaults to TRUE.
If set to TRUE and approach = "mixed",
then year is included as a random factor. If set to FALSE, then
year is included as a fixed factor. All other combinations of logical
value and approach lead to year not being included in modeling.
Defaults to TRUE.
An optional variable denoting the stage name of the
juvenile stage in the vertical dataset. If not NA, and stage is
also given (see below), then vital rates listed in vitalrates other
than fec will also be estimated from the juvenile stage to all adult
stages. Defaults to NA, in which case juvenile vital rates are not
estimated.
A logical variable denoting whether size should be used as a
term in models involving transition from the juvenile stage. Defaults to
FALSE, and is only used if juvestimate does not equal
NA.
A logical variable indicating whether the primary size
distribution of juveniles should be zero-inflated. Only applies to Poisson
and negative binomial distributions. Defaults to FALSE.
A logical variable indicating whether the secondary size
distribution of juveniles should be zero-inflated. Only applies to Poisson
and negative binomial distributions. Defaults to FALSE.
A logical variable indicating whether the tertiary size
distribution of juveniles should be zero-inflated. Only applies to Poisson
and negative binomial distributions. Defaults to FALSE.
A logical variable indicating whether the primary size
distribution in juveniles should be zero-truncated. Defaults to FALSE.
Cannot be TRUE if jsize.zero = TRUE.
A logical variable indicating whether the secondary size
distribution in juveniles should be zero-truncated. Defaults to FALSE.
Cannot be TRUE if jsizeb.zero = TRUE.
A logical variable indicating whether the tertiary size
distribution in juveniles should be zero-truncated. Defaults to FALSE.
Cannot be TRUE if jsizec.zero = TRUE.
A variable indicating which year of fecundity to use as the
response term in fecundity models. Options include 2, which refers to
occasion t, and 3, which refers to occasion t+1.
Defaults to 2.
A vector denoting the names of censoring variables in the
dataset, in order from occasion t+1, followed by occasion t,
and lastly followed by occasion t-1. Defaults to NA.
Designates the name of the variable corresponding to age in time
t in the vertical dataset. Defaults to NA, in which case age
is not included in linear models. Should only be used if building Leslie or
age x stage matrices.
Either a logical value indicating whether to include
age as a fixed categorical variable in linear models, or a logical
vector of such values for 14 models, in order: 1) survival, 2) observation,
3) primary size, 4) secondary size, 5) tertiary size, 6) reproductive
status, 7) fecundity, 8) juvenile survival, 9) juvenile observation,
10) juvenile primary size, 11) juvenile secondary size, 12) juvenile
tertiary size, 13) juvenile reproductive status, and 14) juvenile maturity
status. Defaults to FALSE.
Vector designating the names in occasions t+1,
t, and t-1 of an individual covariate. Defaults to NA.
Vector designating the names in occasions t+1,
t, and t-1 of a second individual covariate. Defaults to
NA.
Vector designating the names in occasions t+1,
t, and t-1 of a third individual covariate. Defaults to
NA.
A logical value indicating whether indcova
should be treated as a random categorical factor, rather than as a fixed
factor. Defaults to FALSE.
A logical value indicating whether indcovb
should be treated as a random categorical factor, rather than as a fixed
factor. Defaults to FALSE.
A logical value indicating whether indcovc
should be treated as a random categorical factor, rather than as a fixed
factor. Defaults to FALSE.
Either a logical value indicating whether to include the
indcova variable as a fixed categorical variable in linear models, or
a logical vector of such values for 14 models, in order: 1) survival,
2) observation, 3) primary size, 4) secondary size, 5) tertiary size,
6) reproductive status, 7) fecundity, 8) juvenile survival, 9) juvenile
observation, 10) juvenile primary size, 11) juvenile secondary size,
12) juvenile tertiary size, 13) juvenile reproductive status, and
14) juvenile maturity status. Defaults to FALSE.
Either a logical value indicating whether to include the
indcovb variable as a fixed categorical variable in linear models, or
a logical vector of such values for 14 models, in order: 1) survival,
2) observation, 3) primary size, 4) secondary size, 5) tertiary size,
6) reproductive status, 7) fecundity, 8) juvenile survival, 9) juvenile
observation, 10) juvenile primary size, 11) juvenile secondary size,
12) juvenile tertiary size, 13) juvenile reproductive status, and
14) juvenile maturity status. Defaults to FALSE.
Either a logical value indicating whether to include the
indcovc variable as a fixed categorical variable in linear models, or
a logical vector of such values for 14 models, in order: 1) survival,
2) observation, 3) primary size, 4) secondary size, 5) tertiary size,
6) reproductive status, 7) fecundity, 8) juvenile survival, 9) juvenile
observation, 10) juvenile primary size, 11) juvenile secondary size,
12) juvenile tertiary size, 13) juvenile reproductive status, and
14) juvenile maturity status. Defaults to FALSE.
Either a logical value indicating whether to include the
group variable from the input stageframe as a fixed categorical
variable in linear models, or a logical vector of such values for 14 models,
in order: 1) survival, 2) observation, 3) primary size, 4) secondary size,
5) tertiary size, 6) reproductive status, 7) fecundity, 8) juvenile survival,
9) juvenile observation, 10) juvenile primary size, 11) juvenile secondary
size, 12) juvenile tertiary size, 13) juvenile reproductive status, and
14) juvenile maturity status. Defaults to FALSE.
If set to TRUE, then includes full modeling tables
in the output. Defaults to TRUE.
If set to TRUE, then only global models will be built and
evaluated. Defaults to FALSE.
A logical value indicating whether to test accuracy of
models. See Notes section for details on how accuracy is assessed.
Defaults to TRUE.
May be a logical value, or any one of the strings "yes",
"no", or "partial". If set to TRUE or "yes", then
model building and selection will proceed with most warnings and diagnostic
messages silenced. If set to FALSE or "no", then all warnings
and diagnostic messages will be displayed. If set to "partial", then
only messages related to transitions between different vital rate models will
be displayed. Defaults to FALSE.
When modelsearch() is called, it first trims the dataset down to just
the variables that will be used, and just data for complete cases in those
variables. It then builds global models for all vital rates and runs them. If
a global model fails, then the function proceeds by dropping any two-way
interactions and trying again. If this fails, then the function will continue
to attempt dropping terms, first patch, then year, then individual
covariates, then combinatons of the above, and finally individual identity.
If these attempts fail and the approach used is mixed, then the
function will try running a glm version of the original failed model, and use
that as a global model if it runs properly. Finally, if all attempts fail,
then the function returns a 1 to allow model building assuming a
constant rate or probability.
Setting suite = "cons" prevents the inclusion of size and reproductive
status as fixed, independent factors in modeling. However, it does not
prevent any other terms from being included. Density, age, individual
covariates, individual identity, patch, and year may all be included.
The mechanics governing model building are fairly robust to errors and
exceptions. The function attempts to build global models, and simplifies
models automatically should model building fail. Model building proceeds
through the functions lm() (GLM with Gaussian response),
glm() (GLM with Poisson, Gamma, or binomial response),
glm.nb() (GLM with negative binomial response),
zeroinfl() (GLM with zero-inflated Poisson or negative
binomial response), vglm() (GLM with zero-truncated
Poisson or negative binomial response), lmer() (mixed
model with Gaussian response), glmer() (mixed model with
binomial, Poisson, or Gamma response), and glmmTMB()
(mixed model with negative binomial, or zero-truncated or zero-inflated
Poisson or negative binomial response). See documentation related to these
functions for further information. Any response term that is invariable in
the dataset will lead to a best-fit model for that response represented by a
single constant value.
Exhaustive model building and selection proceeds via the
dredge() function in package MuMIn. This function
is verbose, so that any errors and warnings developed during model building,
model analysis, and model selection can be found and dealt with.
Interpretations of errors during global model analysis may be found in
documentation for the functions and packages mentioned. Package MuMIn
is used for model dredging (see dredge()), and errors and
warnings during dredging can be interpreted using the documentation for that
package. Errors occurring during dredging lead to the adoption of the global
model as the best-fit, and the user should view all logged errors and
warnings to determine the best way to proceed. The quiet = TRUE and
quiet = "partial" options can be used to silence dredge warnings, but
users should note that automated model selection can be viewed as a black
box, and so care should be taken to ensure that the models run make
biological sense, and that model quality is prioritized.
Exhaustive model selection through dredging works best with larger datasets
and fewer tested parameters. Setting suite = "full" may initiate a
dredge that takes a dramatically long time, particularly if the model is
historical, individual covariates are used, or a zero-inflated distribution
is assumed. In such cases, the number of models built and tested will run at
least in the millions. Small datasets will also increase the error associated
with these tests, leading to adoption of simpler models overall. Note also
that zero-inflated models are processed as two models, and so include twice
the assumed number of parameters. If suite = "full", then this
function will switch to a main effects global model for the zero-inflated
parameter models if the total number of parameters to test rises above the
limits imposed by the dredge() function in package
MuMIn.
Accuracy of vital rate models is calculated differently depending on vital
rate and assumed distribution. For all vital rates assuming a binomial
distribution, including survival, observation status, reproductive status,
and juvenile version of these, accuracy is calculated as the percent of
predicted responses equal to actual responses. In all other models, accuracy
is actually assessed as a simple R-squared in which the observed response
values per data subset are compared to the predicted response values
according to each best-fit model. Note that some situations in which factor
variables are used may result in failure to assess accuracy. In these cases,
function modelsearch() simply yields NA values.
Care must be taken to build models that test the impacts of state in occasion
t-1 for historical models, and that do not test these impacts for
ahistorical models. Ahistorical matrix modeling particularly will yield
biased transition estimates if historical terms from models are ignored. This
can be dealt with at the start of modeling by setting
historical = FALSE for the ahistorical case, and
historical = TRUE for the historical case.
This function handles generalized linear models (GLMs) under zero-inflated
distributions using the zeroinfl() function, and zero-
truncated distributions using the vglm() function. Model
dredging may fail with these functions, leading to the global model being
accepted as the best-fit model. However, model dredges of mixed models work
for all distributions. We encourage the use of mixed models in all cases.
The negative binomial and truncated negative binomial distributions use the quadratic structure emphasized in Hardin and Hilbe (2018, 4th Edition of Generalized Linear Models and Extensions). The truncated negative binomial distribution may fail to predict size probabilities correctly when dispersion is near that expected of the Poisson distribution. To prevent this problem, we have integrated a cap on the overdispersion parameter. However, when using this distribution, please check the matrix column sums to make sure that they do not predict survival greater than 1.0. If they do, then please use either the negative binomial distribution or the zero-truncated Poisson distribution.
If density dependence is explored through function modelsearch(),
then the interpretation of density is not the full population size but rather
the spatial density term included in the dataset.
Users building vital rate models for Leslie matrices must set
vitalrates = c("surv", "fec") or vitalrates = "leslie" rather
than the default, because only survival and fecundity should be estimated in
these cases. Also, the suite setting can be set to either age
or cons, as the results will be exactly the same.
Users wishing to test age, density, group, or individual covariates, must
include test.age = TRUE, test.density = TRUE,
test.group = TRUE, or test.indcova = TRUE (or
test.indcovb = TRUE or test.indcovc = TRUE, whichever is most
appropriate), respectively, in addition to stipulating the name of the
variable within the dataset. The default for these options is always
FALSE.
# \donttest{
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)
lathmodelsln3 <- modelsearch(lathvertln, historical = TRUE,
approach = "mixed", suite = "main",
vitalrates = c("surv", "obs", "size", "repst", "fec"), juvestimate = "Sdl",
bestfit = "AICc&k", sizedist = "gaussian", fecdist = "poisson",
indiv = "individ", patch = "patchid", year = "year2",year.as.random = TRUE,
patch.as.random = TRUE, show.model.tables = TRUE, quiet = "partial")
# Here we use supplemental() to provide overwrite and reproductive info
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)
lathmat3ln <- flefko3(year = "all", patch = "all", stageframe = lathframeln,
modelsuite = lathmodelsln3, data = lathvertln, supplement = lathsupp3,
reduce = FALSE)
# }
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