A good place to look for changes. Often I'll add changes here but don't always get to it in the documentation for awhile. They are ordered from newest to oldest.
Version 2.0.9 (1 Dec 2011)
Patch was made to
make.mark.model to fix bug in
PIM creation for a multi-session model and there was just 1 session. Thanks to Erin Roche for helping to
identify this bug.
Patch was made to
make.mark.model to fix bug in
handling of mlogits for pi and Omega parameters with more than one group. These parameters were introduced
with the RDMSMisClass and other new models that were recently added.
Additional changes were made to
export.MARK to re-fix changes for robust and nest survival
model export to MARK.
mark.wrapper.parallel written by Eldar Rakhimberdiev provides a parallel processing
version of mark.wrapper. See the example in the help for the function. The parallel version is functionally the same
and can be used in place of
mark.wrapper to run sequentially or in parallel. It does not include the run argument
A bug was fixed in
get.real which caused an R error for a triangular PIM that had only a single entry.
Thanks to Amanda Goldberg for discovering and reporting this error.
Version 2.0.8 (7 Oct 2011)
setup.parameters were re-written to use data files
models.txt and parameters.txt to define models and parameters which should
make it easier to add new models. The latter function is now much simpler
Model RDOccupEG now allows sharing Epsilon and Gamma
parameters. Epsilon is the dominant parameter which gets the share=TRUE
RDOccupancy for an example. Thanks to Jake Ivan
for an example of what was needed.
To avoid confusion, the arguments
component.name were removed from the parameter
specification because these have not been required since v1.3 when full
support for individual covariates were included. Likewise the argument
was removed because it was only needed to support the component approach.
export.MARK was modified so that if all individual covariates
are output, it excludes factor covariates.
Many more models were added to those supported in RMark. Now 92 of the 137 models in MARK are supported. See MarkModels.pdf in the RMark directory of your R library to see which models are supported (in red). Most of the remaining unsupported models are versions with mis-identification error and they are not shown in MarkModels.pdf.
Previously RMark stored the input file in a temporary file Markxxx.tmp. Using a common filename caused problems when more than one model were spawned to different CPUs, so now it uses a random temporary file name. It also no longer uses common file markxxx.vcv. Thanks to Glenn Stauffer for testing these changes.
Sessions are now labelled using value of session time rather than numerically from 1 to largest session number in robust designs. Thanks to Tommy Garrison for this suggestion.
A bug was fixed in
get.real which caused incorrect assignments of fixed
parameter values in the unusual case where a fixed parameter had a non-zero design matrix row.
mark.wrapper was modified so it returns a list of the models that were
constructed if run==FALSE. Thanks to Eldar Rakhimberdiev for the suggestion and code.
Code was added to
extract.mark.output to extract deviance degrees of freedom. Thanks
again to Eldar for contributing this code.
A bug was fixed in
make.mark.model that prevented use of sin link on within session parameters
in a robust design model. Thanks to Tommy Garrison for reporting this bug.
A bug was fixed in
make.mark.model which prevented the use of time varying covariates
with shared parameters. Thanks to Andre Breton for reporting this bug.
simplify argument was removed from functions because I have not found a reason not to simplify and I have not been testing code with simplify=FALSE.
Version 2.0.7 (25 August 2011)
make.mark.model to fix bug in which mlogits were incorrectly
assigned in ORDMS model when both Psi and pent used mlogit links. Thanks to
Glenn Stauffer for identifying and tracking down this error.
export.MARK so Nest survival
models can be exported. Also changed default of argument ind.covariates to
"all" which will use all individual covariates in the data in the file sent
to MARK. Thanks to Jay Rotella for his help.
Made change to
mark to include a new argument
reverse, which if set to TRUE with model="Multistratum" will reverse the
timing of transition and survival. See
mstrata for an example
of a reverse multistratum model.
Made change to
make.design.data to allow for zero time intervals in
non-robust design model. This was needed to allow use of the reverse time
structure in multi-state models. In addition, for the reverse multistate
model the function now adds an occasion (occ) field to the design data
because the time field will be constant when with a 0 time interval. The
row names of the design matrix and real parameters was extended for this
model to include occasion (o) and occasion cohort (oc) to create unique
labels because cohort and time are not unique with 0 time intervals.
Version 2.0.6 (1 July 2011)
Change to MR resight examples so the output does not appear in notepad which was causing problem with check on CRAN submission
Version 2.0.5 (29 June 2011)
model.average.marklist were switched incorrectly
such that ... was the second argument. This has been fixed to the original
format where ... is at the end. This resulted in the value of any arguments
other than the first to be ignored unless specifically named e.g.,
parameter="Phi". Thanks to Rod Towell for reporting this error.
Additional changes were made to .First.lib to 1) examine any MarkPath setting, 2) look in C:/program files/mark or c:/program files (x86)/mark, 3) or to search the path. If MARK executable cannot be found in any of those ways then a warning is issued that the user needs to set MarkPath to the path specification for the location of the MARK executable. Thanks to Bryan Wright for help with tracking down a bug.
A bug in
add.design.data was fixed where the function gave incorrect
results in pim.type was anything other than "all". Thanks to Jeff Hostetler
for reporting this error.
The mark-resight models PoissonMR,
LogitNormalMR, and IELogitNormalMR were added. This required some changes
compute.design.data and the addition of an argument
process.data to provide mark-resight count
data that are not in the capture history format. The format for
counts is a named list of vectors (one group) or matrices (each group
is a row) where the names of the list elements are specific to the model.
Currently there is no checking to make sure these are named correctly. Some
of these models are very sensitive to starting values; thus the use of
initial values in the examples. Thanks to Brett McClintock for his help
incorporating these models.
Version 2.0.4 (1 June 2011)
Change made to .First.lib to check for availability of mark software that depends on operating system. It now provides a warning rather than stopping package attachment. This allows the user to set MarkPath to some location outside of default location Program Files or Program Files (x86) without changing path which requires administrator privilege on Windows.
Change made to
run.mark.model to use shQuote because link to
mark.exe was not working in some cases.
Version 2.0.3 (17 May 2011)
Now requires R 2.13 to use path.package function
Change made to .First.lib to check for availability of mark software that depends on operating system.
Version 2.0.2 (16 May 2011)
MarkPath no longer needs to be set if mark.exe is no longer in the default location (c:/Program Files/mark) but is specified in the path. The code now uses the R function Sys.which to find the correct location for mark.exe, if it is contained in the Path.
Code for crm models was removed from RMark and moved to a different R package called marked that is under-development. This removes the FORTRAN code and accompanying dll and some functions and help files that were extraneous to RMark capabilities. There is still some code in some functions for crm that could be removed at some point. None of this matters to those who use RMark for its original purpose as an interface to mark.exe.
Many superficial changes were made to code so it could be posted on CRAN. Three changes that may be noticed by users involved renaming deriv.inverse.link, summary.ch and merge.design.covariates to deriv_inverse.link, summary_ch, and merge_design.covariates. These names conflicted with the generic functions deriv, summary and merge. It is only the last 2 that you may have in your scripts and you will have to rename them. The deprecated function merge.occasion.data was removed. Sorry for any inconvenience.
The dependency on Hmisc for the examples was removed by replacing errbar with plotCI.
Version 2.0.1 (21 Feb 2011)
Made a change to
run.mark.model to handle output filenames that exceeded
Added an argument
cleanup. Like other
mark, it can also be used in
mark.wrapper as one of the ... arguments. Previously the mark
files have always been named "marknnn.*". By specifying
can now create sets of models with different prefixes. For example,
prefix="cu" would result in cu001.*(cu001.out, cu001.inp, etc),
cu002.* etc. This provides the ability to name files to do things like
naming them based on the species being analyzed. In general, there is no
need to work with these files directly because the filename.* is stored with
mark object and the various R functions use that link to provide
the information from the files. If you use prefixes other than "mark",
you'll need to call call
cleanup with each prefix to remove
unused files. See
run.mark.model for an example that shows
use of the prefix argument to split the dipper data into separate analyses
for each sex. Note that use of prefix was not mandatory here to separate
the analyses but it provided a useful example.
has been coded for argument
initial for assigning initial values to
beta parameters. Previously, the options were either a vector of the same
length as the new model to be run or a previously run model in a
object from which equivalent betas are extracted based on their names in the
design matrix. Now if the vector contains names for the elements they will
be matched with the new model like with the model option for initial. If
any betas in the new model are not matched, they are assigned 0 as their
initial value, so the length of the
initial vector no longer needs to
match the number of parameters in the new model as long as the elements are
named. The names can be retrieved either from the column names of the design
matrix or from
x is the name of
Using the feature above, I added a new
use.initial=TRUE, prior to running a model it looks for the first
model that has already been run (if any) for each parameter formula and
initial vector from that previous run. For example, if
you provided 5 models for p and 3 for Phi in a CJS model, as soon as the
first model for p is run, in the subsequent 2 models with different Phi
models, the initial values for p are assigned based on the run with the
first Phi model. At the outset this seemed like a good idea to speed up
execution times, but from the one set of examples I ran where several
parameters were at boundaries, the results were discouraging because the
models converged to a sub-optimal likelihood value than the runs using the
default initial values. I've left this option in but set its default value
A possibly more useful argument and feature was added to
mark.wrapper in the argument
initial. Previously, you
initial=model and it would use the estimates from that
model to assign initial values for any model in the set defined in
mark.wrapper. Now I've defined
initial as a specific
argument and it can be used as above but you can also use it to specify a
marklist of previously run models. When you do that, the code will
lookup each new model to be run in the set of models specified by
initial and if it finds one with the matching name then it will use
the estimates for any matching parameters as initial values in the same way
initial=model does. The model name is based on concatenating the
names of each of the parameter specification objects. To make this useful,
you'll want to adapt to an approach that I've started to use of naming the
objects something like p.1,p.2 etc rather than naming them something like
p.dot, p.time as done in many of the examples. I've found that using
numeric approach is much less typing and cumbersome rather than trying to
reflect the formula in the name. By default, the formula is shown in the
model selection results table, so it was a bit redundant. Now where I see
this being the most benefit. Individual covariate models tend to run rather
slowly. So one approach is to run the sequence of models (eg results stored
in initial_marklist), including the set of formulas with all of the
variables other than individual covariates. Then run another set with the
same numbering scheme, but adding the individual covariates to the formula
initial=initial_marklist That will work if each parameter
specification has the same name (eg., p.1=list(formula=~time) and then
p.1=list(formula=~time+an_indiv_covariate)). All of the initial values will
be assigned for the previous run except for any added parameters (eg.
an_indiv_covariate) which will start with a 0 initial value.
I added a
search.output.files to the set of utility
functions. This can be useful to search all of the output files in a
marklist for a specific string like "numerical convergence suspect"
or just "WARNING". The function returns the model numbers in the
marklist that contain that string in the output file.
changes were needed to
popan.derived to handle data that are
summarized (i.e. frequency of capture history >1). Thanks to Carl Schwarz
for reporting and finding the change needed.
A bug was fixed in
create.dm was fixed so it would return a matrix instead of a vector
with the formula ~1
Version 2.0.0 (14 Jan 2011)
The packages msm, Hmisc, nlme, plotrix are now explicitly required to install RMark. These were used in examples or for specialty functions, but to avoid problems these must be installed as well.
Added example for "CRDMS"
model that was created by Andrew Paul. See
Change was made for gamma link in v1.9.3 was only made to Pradel model and not Pradsen like it stated. Both now correctly use the logit link as the default for gamma. Thanks to Gina Barton for bringing this to my attention.
Change was made in
make.mark.model that prevented use of
groups with Nest survival models. Thanks to Jeff Warren for bringing this
to my attention.
Change was made in
because re-ordering of parameters caused issues with the CRDMS model because
subtract.stratum were not being set for
Version 1.9.9 (2 Nov 2010)
This version was built with R 2.12 and will not work with earlier versions of R. It contains both 32 and 64 bit versions and R will automatically ascertain which to use.
ordering for some models (RDHet, RDFullHet, RDHHet, RDHFHet, OccupHet,
RDOccupHetPE, RDOccupHetPG, RDOccupHetEG, MSOccupancy, ORDMS, and CRDMS) had
to be changed such that the models could be imported into the MARK
interface. This change can influence any code you have written for those
models if you specified parameter indices because the ordering of the
parameters were changed. For example, see change in example for
RDOccupancy to use indices=c(1) from c(10). Thanks to Gary
White for helping me work this out.
Modified code in
extract.mark.output to handle cases with more than 9999 real
parameters because MARK outputs **** when it exceeds 9999.
Version 1.9.8 (15 Sept 2010)
model="CRDMS" with parameters S,
Psi, N, p, and c for closed robust design multi-state models
popan.derived which produced incorrect abundance estimates
with unequal time intervals. Thanks to Andy Paul for finding this error and
testing for me.
export.MARK which failed for
robust design models. Thanks to Dave Hewitt and Gary White for discovering
and isolating the problem.
Version 1.9.7 (14 April 2010)
model="Brownie" with parameters S and f which is the Brownie et
al. parameterization of the recovery model. "Recovery only" (in RMark)
model="Recovery" which is also encounter type "dead" in MARK uses the
Seber parameterization with parameters S and r which is also used in the
models for live and dead encounter models.
model="MSLiveDead" with parameters S, r, Psi and p. It is the
multistate version of the Burnham model in which F=1.
compute.Sn to utility functions for computation of natural
survival from total survival when all harvest is reported. Patched
nat.surv which was incorrectly rejecting based on model type.
var.components.reml to provide an alternate
variance components estimation using REML or maximum likelihood. It allows a
random component that is not iid which is all that
var.components can do.
Replaced all T/F values with TRUE/FALSE to avoid conflicts with objects named TRUE or FALSE.
Version 1.9.6 (1 February 2010)
popan.derived function has led me down all sorts of paths. I
had to make one small change to this function to handle externally saved
models. However, various changes listed below were brought on by using this
function with a relatively large POPAN model.
Most importantly I
discovered an error in computations of real parameters with an mlogit link
in which some of the real parameters involved in the mlogit link were fixed.
This is NOT a problem if you simply used the real parameter values extracted
from MARK; however, if you were using either
(model.average uses this function) or
compute those real parameters (with an mlogit link) then they may be
incorrect. This would have been apparent because their value would have
changed relative to the original values extracted from the MARK output.
Correcting this error involved changes in
to correct the real parameter estimates and their standard errors. I've not
found it in the MARK documentation but deduced that if you use the mlogit
link and fix real parameters it uses those fixed real parameter values in
the calculation. A simple example will make it clear. Consider pent for 5
occasions where the first is computed by subtraction and you then have 4
real parameters. Let's assume that the 3rd and fourth parameters were fixed
to 0. Then the real parameters are calculated as follows:
pent3=exp(beta3)/(1+ exp(beta2)+exp(beta3)+exp(0)+exp(0)), pent4=0, pent5=0
and pent1=1-pent2-pent3-0-0 (in this case). Obviously you would not want to
fix any real parameters to be >1, <0 or to have the sum to be <0 or >1.
This structure also had implications on how the standard error was
In addition the coding was made more efficient in
covariate.predictions for the case where
data(index=somevector) is used without any data entries for covariate
values in the design matrix. While the task performed with that use of the
function could be done with
compute.real, it is useful to have
the capability in
covariate.predictions as well because it
will then model average over the listed set of parameters. The previous
approach to coding was inefficient and led to very large matrices that were
unnecessary and could cause failure with insufficient memory for large
Calculation of NGross was added to
popan.derived and logical arguments
were added to control what was computed in the call. In addition, argument
drop was added which is passed to
to control whether models are dropped when variance of betas are not all
var.components was modified to use qr matrix
inversion. The returned value for beta is now a dataframe that includes the
std errors which are extracted from the vcv matrix. Also, if the design
matrix only uses a portion of the vcv matrix, the appropriate rows and
columns are now extracted. Prior to this change, the standard errors would
have been unreliable if the design matrix didn't use the entire set of
thetas and vcv matrix.
Version 1.9.5 (4 December 2009)
covariate.predictions was fixed that would assign fixed
values incorrectly if the parameter indices were specified in anything but
ascending order. The error would have been obvious to anyone that may have
encountered it because estimated parameters would likely have been assigned
a fixed value. In most cases indices would be passed in order if they were
selected from the design data unless indices were chosen from more than one
parameter type. I discovered it using the
function I added in v1.9.4 because it requests indices for multiple
parameters in a single function call.
Version 1.9.4 (6 November 2009)
Note that this version was built with R 2.10 which no longer supports compiled help files (chtml). If you were using compiled help files to get help with RMark, you'll need to switch to regular html files by using options(help_type="html") in R. You can put this command in your RProfile.site file so it is set up that way each time you start R. The functionality is the same but it is not as pretty. You can find the index (what used to be in a window on the left) as a link at the bottom of each help page.
export.MARK so it will not allow
selection of a project name that would over-write an existing .inp file.
Added the function
popan.derived which for POPAN models
computes derived abundance estimates by group and occasion and sum of group
abundances for each occasion. For some reason RMark is unable to extract all
of the derived parameters from the MARK binary file for POPAN models. This
function provides the derived abundance estimates and their var-cov matrix
and adds the abundance estimate sum across groups for each occasion which is
not provided by MARK. Note that by default confidence intervals are based on
a normal distribution to match the output of MARK, but if you want
log-normal intervals use
functions were modified to use revised estimator for the unconditional
standard error (eq 6.12 of Burnham and Anderson (2002)) which is now the
default in MARK. To use eq 4.9 (the prior formula) set the argument
Fixed bug in
which in some cases failed when the list of var-cov matrices were specified.
model.average.marklist was changed to set
standard error to 0 if the variance is negative. The same is done in the
var-cov matrix for the variance and any corresponding covariances. The
results from RMark will now match the model average results from MARK for
this case. It is not entirely clear that this is the best approach when
ill-fitted models are included.
Changed code in
to handle case in which a model did not run.
Changed use of
extract.mark.output to accomodate change in R.2.10. The code
should work in earlier versions of R but if not update to R2.10.
Created a function
adjust.value and kept special case of
adjust.chat. For any field other than
chat it will adjust the
model$results. As an example, to adjust the effective
sample size (ESS) use
where value is replaced with the ESS you want to use. As part of the change
model.table was changed to recompute AICc.
Version 1.9.3 (24 September 2009)
Default link function for Gamma in the Pradel seniority model had been incorrectly set to log and has now been changed to logit to restrict it to be a probability.
A bug was fixed
which confidence intervals were being incorrectly scaled by c (chat
adjustment) instead of sqrt(c). The reported standard errors were correctly
using sqrt(c) and only the confidence intervals for the real predictions
were too large. Simply re-running the prediction computations for a model
will provide the correct results. There is no reason to re-run the models.
Also this in no way affects model selection.
A related bug was fixed
created invalid confidence intervals for real parameters if a probability
link other than logit was used and a single type of link was used for all
real parameters (e.g., sin).
Version 1.9.2 (10 August 2009)
Added the function
export.MARK which creates a .Rinp,
.inp and optionally renames one or more output files for import to MARK.
The July 2009 version of MARK now contains a File/RMARK Import menu item
which will automatically create the MARK project using the information in
these files. This prevents problems that have been encountered in creating
MARK projects with RMark output because the data/group structures are setup
exactly in MARK as they were in RMark. See
export.MARK for an
example and instructions.
Fixed a problem in
make.design.data which prevented use of
with unequal time intervals and more than one group.
At least one person has encountered a problem with a very large number of parameters in which RMark created the input file with PIMs written in exponential notation for the larger indices. MARK will not accept that format and it will fail. The solution to this is to set the R option scipen to a positive number. Start with options(scipen=1) and increase if necessary.
Version 1.9.1 (2 June 2009)
Fixed a problem
which was not using
begin.time to label the session values
a change in
export.chdata like the change in
make.mark.model to accomodate change with release of version
Made a change in
process.data so that
strata.labels can be specified for Multistrata designs like with
ORDMS so an unobserved strata can be included.
A warning was added to
the help file for
so it is clear that the MARK database must be created correctly and with the
.inp file created by
export.chdata from the processed data
that was used to create the models that are being exported. This is to
ensure that the group structure is setup such that the assumed model
structure for groups matches the model structure setup in the .inp file.
Version 1.9.0 (30 April 2009)
Fixed a bug in
summary.mark which occasionally produced erroneous results
Made a change in
make.mark.model to accomodate change with release of version
RMark now requires R version 2.8.1 or higher.
Version 1.8.9 (9 March 2009)
covariate.predictions to set NaN or Inf results in v-c
matrix to 0 to cope with poorly determined models. Also, for each function
the dropping of models is now restricted to cases in which there are
negative variances for the betas being used in the averaged parameter
estimates. Unused betas are ignored. For example, if
model.average is called with
parameter="Phi", then the
model will only be dropped if there is a negative variance for one of the
betas associated with "Phi".
tolerance value (
tol) in the call to
uniroot was reduced to
1e-15 which should provide better estimates of the process variance when it
is small. Previously a process variance less than 1e-5 would be treated as
Made changes to
make.mark.model to accomodate externally saved model objects
Version 1.8.8 (5 December 2008)
error was fixed in
make.time.factor which created incorrect
assignments when only some of the time dependent variables contained a "."
for occasions with no data.
which was not creating the design data in the same order as the PIM
construction for the newly added
section of code in
make.mark.model to handle
Fixed a bug in
process.data in which the initial ages were not correctly
assigned in some situations with multiple grouping variables. Note that it
is always a good idea to examine the design data after it is created to make
sure it is structured properly because it relates the data and model
structure via the grouping variables and the pre-defined variables (ie age,
time etc), While I've done a lot of testing, I have certainly not tried
every possible example and there is always the potential for an error to
occur in a circumstance that I've not encountered.
Version 1.8.7 (13 November 2008)
common.zero was added to
compute.design.data. It can be set to TRUE to make the
Time variable have a common time origin of
begin.time which is
useful for shared parameters like
c in closed capture
and similar models.
patched to work with the newer versions of MARK.EXE since 1 Oct 2008.
The model type
ORDMS for open robust design multi-state models was
added. An example data set will be added at a later date after further
testing has been completed.
Some patches were made to fix some aspects
of profile intervals and to fix adjustment by chat in
showall=F. The notation for profile
intervals is now included in the field
model is the name of a mark model. Previously an incomplete notation
was kept in
model$results$real$fixed but that field is now used
exclusively to denote fixed parameters. It is important to realize that
profile intervals computed by MARK are only found in
model$results$real$note and are not changed by a
adjustment unless the model is re-run. None of the intervals computed by
RMark and displayed by
summary.mark are profile
Version 1.8.6 (28 October 2008)
A bug in an
error message for
A new function
var.components was added to
provide variance components capability as in the MARK interface except that
shrinkage estimators are not computed currently.
values are now being reported correctly by
covariate.predictions. Also over-dispersion (c>1) was not
being included in the variances for parameters except those using the mlogit
Some utility functions were added including
model.average has been changed to a generic function.
Currently it supports 2 classes: 1) list, and 2) marklist. The latter was
model.average which has been renamed
model.average.marklist and the first argument has been renamed
x instead of
model.list to match the standard generic function
approach. The previous syntax
model.average(...) will work as long
as the usage does not name the first agument as in the example
model.average(model.list=dipper.results,...). The list formulation
model.average.list) was created to enable a generic model
averaging of estimates instead of just real parameter estimates from a
mark model. It could be used with any set of estimates, model
weights and estimates of precision.
A change is needed to
read.mark.binary to accomodate the change to mark.exe with the
version dated 1 Oct 2008. Some data types (notably Nest survival) may not
work with the new version of mark.exe. Working with Gary to make the patch.
If you need an older version of mark.exe contact me.
Version 1.8.5 (8 October 2008)
A bug in
process.data was fixed
that prevented use of a dataframe contained in a list while using the
Profile intervals on the real parameters can
now be obtained from MARK using the arguments
mark. The argument
profile.int can be set to
TRUE and a profile interval will be
constructed for all real parameters, or a vector of parameter indices can be
specified to restrict the profiling to certain parameters. The value
chat is passed to MARK for over-dispersion.
References to cjs, js etc have been removed from here because this code was removed 5/11/11.
Yet another fix to
gave incorrect results for the number recaptured at least once when
marray=F and the data contained non-unity values for
Version 1.8.4 (29 August 2008)
A generic function
coef.mark was added to extract the table of betas from the
model with the expression
model is a
mark model that has been run and contains output. The table includes
standard errors and confidence intervals.
brief=TRUE the real
parameters are not included in the summary.
References to cjs, js etc have been removed from here because this code was removed 5/11/11.
summary.ch was fixed. It would produce erroneous
results when the data contained a non-constant
freq field. Results
with the default of
freq=1 were fine.
adjust.chat and its help file were changed such that it was
clear that a
model.list argument was needed.
Version 1.8.3 (25 July 2008)
For robust design models, an error trap was added to
process.data to make sure that the capture history length
matches the specification for the
time.intervals. This error was
already trapped for non-robust models.
Fixed an error in
make.mark.model that prevented interaction model of
session/time-specific individual covariates in a robust design model.
Fixed an error in
process.data so that the field
is optional for nest survival data sets.
modified to add an argument
input which if set to
will have the MARK input file be displayed rather than the output file.
wait=FALSE was set in the system command which means the viewer
window will be opened and you can carry on with R. Before you had to close
the viewer window before proceeding with R.
RDOccupancy provided by Bret Collier was added for the Robust
Occupancy model which shows the use of session and time-varying individual
covariates in a robust design model.
Version 1.8.2 (26 June 2008)
summary.ch was modified to allow missing
cohorts (no captures/recaptures) for an occasion and to fix a bug in which
bygroup=FALSE did not work when groups were defined.
running out of memory, an argument
external has been added to
run.mark.model. As with all arguments of
external can also be set in
external can also be set in
run.models and it is passed to
The default is
external=FALSE but if it is set to
the mark model object is saved in an external file with an extension
.rda and the same base filename as its matching MARK output files.
The mark object in the workspace is a character string which is the name of
the file with the saved image (e.g., "mark001.rda"). If
mark.wrapper then the resulting
marklist contains a list entry for each mark model which is only the
filename and then the last entry is the
model.table. All of the
functions recognize the dual nature of the mark object (i.e., filename or
mark object) in the workspace. So even if the mark object only contains the
filename, functions like
summary.mark will work. However, if you have used
external=TRUE and you want to look at part of a mark object without
using one of the functions, then use the function
Whereas, before you may have typed
mymark$results, if you use
external=TRUE, you would replace the above with
restore were created to
store externally and
restore models from external storage into the R workspace. They work
marklist and are only needed to
store externally existing
marklist models or ones originally created with
external=FALSE or to
restore if you change your mind and decide to keep them in the R
Error in setup for robust design occupancy models with more than 2 primary sessions was fixed. The error resulted in mark.exe crashing.
The concept of time-varying individual covariates has been
expanded to include robust design models which have both primary (session)
and secondary (time) occasion-specific data. For a robust design, a
time-varying individual covariate can be either session-dependent or
session-time dependent. As an example, if there are 3 primary sessions and
each has 4 secondary occasions, then the individual covariates can be named
x1,x2,x3 to be primary session-dependent or named
x11,x12,x13,x14,x21,x22,x23,x24,x31,x32,x33,x34. The value of x can be any
name for the covariate. In the formula only the base name is used (e.g.,
~x) and RMark fills in the individual covariate names that it finds
that match either the session or session-time individual covariates.
Version 1.8.1 (19 May 2008)
summary.ch to provide summaries of the capture history data
(resighting matrices and m-arrays). It will not work with all types of
models at present. It will work with CJS and Jolly-type models.
model.table to be able to use
alternate names in the model table. It can use either the model name with
each mark object which uses a formula notation (the current approach) or it
can use the name of the R object containing the mark model
model.table for an example.
Also, the help file for
model.table was updated to reflect the code
changes implemented in version 1.7.3.
mark.wrapper was modified to output the number of columns and
column names of the design matrix for each model if
allows a check of each of the columns included in the model. By reviewing
these you can assess whether the model was constucted as you intended. If
there is any question you can either use
make.mark.model to examine the design matrix more thoroughly.
A bug in the new function
fixed in which
merge was sorting the design data which does obvious
bad things. Adding
sort=FALSE does not appear to mean that the data
frame is left in its original order. To prevent this, the dataframe is
forced to remain in its original order by adding a sequence field for
re-sorting after the merge.
Version 1.8.0 (8 May 2008)
Fixed a bug in
model.average that caused it to fail and issue
an error when any of the models included a time dependent covariate in the
parameter being averaged.
which is meant to replace
merge.occasion.data. This new function
allows covariates to be assigned by
group, or just
group. It also uses a simplified list of
arguments and works with individual design dataframes rather than the entire
ddl. It uses the R function
merge which can be used on its own
to merge design covariates into the design data. You can use
merge directly as this function only checks for some common
mistakes before it calls
merge and it handles reassignemnt of row
names in the case were design data have been deleted. An example, where you
might want to use
merge instead of this function would be situations
where the design data are not just group, time or group-time specific. For
example, if groups were specified by two different factor variables say
initial age and region and the design covariates were only region-specific.
It would be more efficient to use
merge directly rather than this
function which would require an entry for each group which would be each
pairing of inital age and region. If you use
merge and you
deleted design data prior to merging, save the row.names, merge and then
reassign the row.names.
run was added to
mark.wrapper. If set to FALSE, then it will run through each
set of models in
model.list and try to build each model but does not
attempt to run it. This is useful to check for and fix any errors in the
formula before setting off a large run. If you use
run=FALSE do not
include arguments that are meant to be passed to
Version 1.7.9 (7 April 2008)
make.design.data was fixed so that
remove.unused=T will work properly when different
values are specified for each group.
Version 1.7.8 (12 March 2008)
Changed the default link for N to log in the
setup.parameters for the
It was incorrectly set to logit which created incorrect estimates to be
model.average because MARK forces the log link for
N regardless of what is set in the input file.
Version 1.7.7 (6 March 2008)
Supressed warning message that occurred with code to
check the validity of the sin link in
Fixed a couple of bugs in
covariate.predictions that prevented
it from working for some cases after including code for the sin link.
release.gof to construct the RELEASE goodness
of fit test and extract the TEST2 and TEST3 final chi-square, df and
Version 1.7.6 (26 Feb 2008)
make.mark.model was modified to change the capitalization of
the link functions and to remove all spaces after "=" in the input file for
mark.exe. These differences were preventing the MARK interface from fully
importing the model. Although the model would be imported and could be run
inside the MARK interface, median c-hat would not run and would give an
error stating "Invalid Link" for any model imported from RMark. Now
transfering a model from RMark to the MARK interface is fully functional (I
hope). If you want to import an output file that was created with a prior
version of RMark without re-running it, use a text editor on the output file
and remove any spaces before and after an = sign. Then change the
capitalization of the links to "Logit", "MLogit", "Log", "LogLog",
The sin link is now supported if the formula for the parameter generates an identity matrix for the parameter. For example, if you use ~-1+time instead of ~time then the resulting design matrix will be an identity for time. Likewise, for interactions use ~-1+group:time instead of ~group*time. If you select the sin link and the resulting design matrix is not an identity for the parameter, an error will be given and the run will stop.
To match the output from MARK, the confidence intervals for real parameters using any 0-1 link including loglog,cloglog,logit and sin are now computed using the logit transformation. For previous versions this will only affect any results that were using loglog and cloglog. Previously, it was using the chosen link to compute the se and the interval endpoints. The latter is still used for the log and identity links which are not bounded in 0-1.
"Jolly" was added to the supported list of models. Parameters include
Phi,p,Lambda,N. It is not a particularly numerically stable model and often
will not converge. Use of options="SIMANNEAL" in call to
is recommended for better convergence. It will take much longer to converge
but is mroe reliable.
Version 1.7.5 (24 Jan 2008)
model.average was modified to ignore any models that did not
run and either had no attached output file or no results.
modified to extract and store the real.vcv matrix (var-cov matrix of the
simplified real parameters) in the mark object if realvcv=TRUE. The default
is realvcv=FALSE. This argument has been added to functions
An argument delete has also been added to
run.mark.model. The default value is
FALSE but if set to TRUE it deletes all output files created by MARK after
extracting the results. This is most useful for simulations that could
easily create thousands of output files and after extracting the results the
model objects are no longer needed. This is just a convenience to replace
the need to call
Version 1.7.4 (10 Jan 2008)
A bug in
make.mark.model was fixed. It was
preventing creation of individual (site) covariate models for parameters
with only a single parameter (single index) in certain circumstances like
Psi1 in the MSOccupancy model.
The fix to
in version 1.7.1 did not work when design data had been deleted. That has
Various functions with some operating specific calls
have been modified so they will work on either Windows or Linux. Thus, the
there is a single file for all source/help for both operating systems in
RMarkSource.zip. It can be downloaded to either Windows or Linux to build
the package. You need to build the package for Linux but not for Windows.
For Windows, you only need RMark.zip which contains the pre-built package
which only needs to be installed. Currently, with Linux the variable
MarkPath is ignored and mark.exe is assumed to be in the path. Also, for
Linux the default for MarkViewer is "pico" (an editor on some Linux
machines). This can be modified in
print.mark or by setting
MarkViewer to a different value. The one Linux specific function is
read.mark.binary.linux. The function
extract.mark.output calls either
read.mark.binary depending on the operating system. A Linux
version of mark.exe (32 or 64 bit) can be obtained from Evan.
Version 1.7.3 (4 Jan 2008)
In working with the occupancy models, it
became apparent that it would be useful to have a new function called
make.time.factor which creates time-varying dummy variables
from a time-varying factor variable. An example is given using observer
with the occupancy dataset
weta from the MacKenzie et al
Occupancy modelling book.
To match the results in the book, I added
use.lnl to function
model.table to construct a results table with AIC rather than
AICc and -2LnL values. The latter is more useful with a mix of models some
using individual covariates and others not.
A modification was made to
make.mark.model with the
MSOccupancy model to fix the
name of the added data for parameter
share=TRUE to be
p2. For an example which uses p2 to construct an additive model, see
Version 1.7.2 (20 Dec 2007)
In changing code for the occupancy models, a brace was misplaced which
prevented the nest survival models from working. This has been fixed. Also,
the example code for
modified to exclude the calls to process the input file. This enables use
of the function
example() to run the example code (e.g.
example(mallard)). From now on as I add examples they are being
included in my test set to avoid this type of problem in the future.
Version 1.7.1 (14 Dec 2007)
If you update with this version of RMark make sure to update MARK also, so you get the fixes for some of the occupancy models.
A minor bug was fixed in function
merge.occasion.data that created duplicate row names and prevented
the design data from being used in a model.
occupancy models were added. Models in the following list use the
designation from MARK:
Occupancy, OccupHet, RDOccupEG, RDOccupPE,
RDOccupPG, RDOccupHetEG, RDOccupHetPE, RDOccupHetPG,OccupRNPoisson,
means it uses the Pledger mixture and those with
RD are the robust
design models. The 2 letter designations for the RD models are shorthand for
the parameters that are estimated. For
EG, Psi, Epsilon, and Gamma
are estimated, for
PE gamma is dropped and for
PG, Epsilon is
dropped. For the latter 2 models, Psi can be estimated for each primary
occasion. The last 5 models include the Royle/Nichols count (RPoisson) and
presence (RNPoisson) models and the multi-state occupancy model. See
salamander for an example of
Donovan.7 for an example of
Donovan.8 for an example
RDSalamander for an
example of the robust design models and
an example of
were modified so that a list of parameters can be used to loop. This is
useful in the situation with shared parameters such as
p2 in the
closed models etc. See
NicholsMSOccupancy for an
example. It can also be useful if the model definitions are linked
conceptually (e.g., when one parameter is time dependent, the other should
also be time dependent).
The "." value in an encounter history is now acceptable to RMark and gets passed to MARK for interpretation as a missing value.
print.marklist was fixed to show the model table
properly after a c-hat adjustment was made. The change in the code in
version 1.6.5 to add parameter specific values to the model table had the
side-effect of dropping the model name if c-hat was adjusted.
Version 1.7.0 (7 Nov 2007)
deltamethod.special for computation of delta method variances
of some special functions was added. It uses the function
deltamethod from the package
msm. You need to install the
msm from CRAN to use it.
A more complete example
mallard) created by Jay Rotella was added for the nest
survival model. His script provides a nice tutorial for RMark and the
utility of R to provide a wide-open capability to calculate/plot etc with
the results. It also demonstrates the advantages of scripting in R to
document your analysis and enable it to be repeated. Before you use his
tutorial you need to install the package plotrix from CRAN. At a later date,
Jay has said he will add some additional examples to demonstrate use of the
deltamethod function to create variances for functions of the results
Various changes were made to help files. A more complete
cleanup was given to tie into
Version 1.6.9 (10 Oct 2007)
Nest survival model was added to list of MARK models supported by RMark. See
killdeer for an example. Note that the data structure for
nest models is completely different from the standard capture history so the
do not work with nest data structure.
Slight change was made to
change in R 2.6.0.
Version 1.6.8 (2 Oct 2007)
merge.occasion.data to enable group and time-specific design
covariates to be added to the design data.
Change was made to
setup.parameters to use a log-link for N in the closed-capture
models. MARK forces that link for N but the change was needed for
model.average which does the inverse-link computation. Note
that the reported N in
model.average is actually f0 (number
not seen). To get the correct values for N simply add M_t+1 (unique number
captured) to f0. That is the way MARK computes N. The std error and
confidence interval is on f0 such that the lower ci on N will never be less
An error was fixed in the output of
model.average. When you selected a specific parameter, it was
giving a UCL which was a copy from one of the models and not the UCL from
the model averaging. If you didn't specify
vcv=T it only showed the
errant UCL and if you did specify
vcv=T then it showed the correct
LCL and UCL but then added the errant UCL in a column. This occurred because
it was adding covariate data for the specific parameter and was shifted a
column because of a change in 1.6.1.
Version 1.6.7 (7 Aug 2007)
Changes were made in
acommodate changes in V2.5.1 of R. When upgrading versions of R problems may
occur if RMark was built with an earlier version of R. The version of R
that was used to build RMark is listed on the screen each time it is loaded
This is RMark 1.6.7 Built: R 2.5.1;
i386-pc-mingw32; 2007-08-07 09:00:33; windows
The help file for
import.chdata was expanded to clarify the differences between
convert.inp and the use of the
Version 1.6.6 (14 May 2007)
make.mark.model was fixed so that the real label indices were
properly written when
simplify=FALSE is used.
make.mark.model was also changed to remove the parameter
mlogit parameters that was added in v1.4.5. I
mistakenly assumed that the
mlogit parameters were setup such that
the normalization to sum to 1 was done will all the real parameters in the
set (i.e., all PSI for a single stratum). In fact, the
are only specified for the unique real parameters so if there is any
simplification and the sum of the probabilities is close to 1 (excluding
subtraction value) the values will not be properly constrained. For
example, with the
mstrata data if the problem was constrained
such that PSI from AtoB was equal to AtoC, it is still necessary to have
these as separate real parameters and constrain them with the design matrix.
As it turns out, with the
mstrata example it does not matter
because the problem is such that the sum of Psi for AtoB and AtoC is not
close to 1 (same for other strata) and any link will work. This change will
only be noticeable in situations in which the constraint matters (i.e., the
probability for the subtraction parameter is near 0). The change back to
mlogit parameters may increase execution times
because the design matrix size has been increased. Previous users of the
Multistrata design will see very little difference in there results if they
only used models containing stratum:tostratum because that will create an
all-different PIM within each
mlogit set. When I ran the
mstrata examples with this version and compared them to v1.6.5
the results were different but they were differences in the 5th or smaller
decimal point due to differences in numerical optimization.
Version 1.6.5 ( 3 May 2007)
modified to include parameter formula fields in the
dataframe of a
marklist. Previously only the
included which is a concatenation of the individual parameter formulas. The
additional fields allows extracting the model table results based on one of
the parameter formulas or to create a matrix of model AICc or other values
with rows as one parameter and colums as the other. See
model.table for an example.
process.data was modified such that factor variables used for
grouping retain the ordering of the factor levels in the data file.
Previously they would revert back to default ordering and the re-leveling
would also have to be repeated on the design data also.
brief was added to
mark to control amount of summary
Fixed a bug in
get.real that prevented
computation for models without the stored covariate values.
make.mark.model that prevents constructing models with
empty rows in the design matrix unless the parameter is fixed. For example,
if you were to try ~-1+Time for the dipper data, it will fail now because
there is no value for the intercept (Time=0).
mark.wrapper outputs the model name to the screen before
running the model which helps associate any error messages to the model if
Version 1.6.4 (7 March 2007)
export.model was created to copy the output files
into the naming convention needed to append them into a MARK .dbf/.fpt
database so they can be used with the MARK interface features. This is
useful to be able to use some of the features not contained in RMark such as
median c-hat and variance component estimation. To create a MARK database,
export.chdata to create a .inp file to pass the data
into MARK. Start MARK and use File/New to create a new database. Select
the appropriate Data Type (model in RMark) and fill in the appropriate
values for encounter occasions etc. For the Encounter Histories File Name,
select the file you created with
export.chdata. Once you have
created the database in the Program MARK interface, click on the Browse menu
item and then Output/Append and select the output file(s) (i.e those with a
Y.tmp) that you exported with
export.model. Note that this
will not work with output files run with versions of RMark prior to this one
because the MARK interface will give a parse error for the design matrix.
To get around that you can edit the output file and remove the spaces in the
line with the design matrix header. For example, it should look as follows
design matrix constraints=7 covariates=7 without spaces around the =
The minor change described above was made in the input file with spacing on the design matrix line to enable proper appending of the output into a MARK .dbf/.fpt database.
modified to delete all mark*.tmp files. Do not use
cleanup until you
have appended any exported models.
import.chdata to enable comment fields to be used as
row names in the data frame. A comment is indicated as in MARK with /*
comment */. They can be anywhere in the record but they must be unique and
they can not have a column header (field name).
create.model.list was modified such that it only includes
lists with a
formula element. This prevents collecting other objects
that are named similarly but are not model defintions.
Version 1.6.3 (5 March 2007)
A minor change in
find.covariates was made to accommodate use of the same
covariate in different formulas (e.g. Phi and p). Previous code worked
except any call to
get.real would fail. Previously a duplicate
of the covariate was entered in the data file to MARK. Now only a single
copy is passed.
default has been added to the model
mark). The argument sets the
default value for parameters represented by design data that have been
Checks were added in
make.mark.model to fail if
any of the individual covariates used are either factor variables or contain
NAs. Both could fail in the MARK.EXE run but the error message would be less
obvious. Factor variables can work as an individual covariate, if the
levels are numeric. But it was easier to exclude all factor variables from
being individual covariates. They can easily be converted to a continuous
version (e.g. Blackduck$BirdAge=as.numeric(Blackduck$BirdAge)-1). The code
Blackduck was changed to make BirdAge a continuous
rather than factor variable. Factor variables can still be used to define
groups and then used in the formula. They just can't be used as individual
covariates. This change was made because a factor variable was in the data
but not defined in
groups and when it was used in the formula it
would create a float error in MARK.EXE and that would be confusing and hard
to track down.
Version 1.6.2 (28 Feb 2007)
The fix in 1.6.1 to avoid the incorrect design matrix was not sufficiently general and created a parse error in R if you attempted to use any design data covariates that were created with a cut function to create factor variables by binning a variable. This has been corrected in this version.
read.mark.binary has been changed to skip over the v-c
matrix for the derived parameters if it is not found in the file. This was
causing an error with the PRADREC model type.
Version 1.6.1 (17 Jan 2007)
An important bug was fixed in
in which an incorrect design matrix would be created if you used two
individual covariates in the same formula whereby one of the covariate names
was contained within the other. For example, if you used ~mass+mass2 where
mass2=mass^2, it would actually create a design matrix with columns mass
product(mass,mass2) which would be the model mass+mass^3. This happened due
to the way the code identified columns where it needed to replace dummy
values with individual covariate names. Since mass was contained in mass2 it
added mass to the column as a product. The code now does exact matching so
the error can no longer occur.
indices was added to
model.average which enables restricting the model
averaging to a specific set of parameters as identified by the all-different
parameter indices. This is most useful in large models with many different
indices such that memory limitations are encountered in constructing the
variance-covariance matrix of the real parameters. For example, with a CJS
analysis of data with 18 groups and 26 years of data, the number of
parameter indices exceeds 22,000. Even by restricting the parameters to
either Phi or p with the
parameter argument there are still 11,000
which would attempt to create a matrix containing 11,000 x 11,000 elements
which can exceed the memory limit. In most cases, there are far fewer unique
parameters and this argument allows you to select which parameters to
Time-varying covariates are no longer needed for all times if
the formula is correctly written to exclude them in the resulting design
make.mark.model still reports missing time-varying
covariates but will continue to try and fit the model but if the missing
variables are used in the design matrix the model will fail. As an example
consider a time varying covariate x for recapture times 1990 to 1995. The
code expects to find variables x1990, x1991, x1992, x1993, x1994, x1995.
However, lets say that the values are only known for 1993-1995. If you
define a variable I'll call recap in the design data which has a value 1 for
1993-1995 and a value 0 for 1990-1992 then if you use the formula ~recap:x
the resulting design matrix will only use the known variables for 1993-1995
but you will still be warned that the other values (x1990 - x1992) are
A bug was fixed in
prevented it from obtaining more than the last mean covariate value from the
fill.covariates was modified such that only
a partial list of covariate values need to be specified with
the remainder are filled in with default values depending on argument
The output from
modified for real parameters when
se=T to include
all.diff.index to provide the indices of each real parameter in the
all-different PIM structure. They are useful to restrict
model.average to a
specific set of real parameters.
A new function
covariate.predictions was created to compute real parameter
values for multiple covariate values and their variance-covariance matrix.
It will also model average those values if a marklist is passed to the
function. Two examples from chapter 12 of Cooch and White are provided to
give examples of models with individual covariates and the use of this
The default value of
model.average has been changed to
Version 1.6.0 (27 Nov 2006)
A bug was fixed in
which prevented it from working with Multistrata models.
make.design.data which prevented use of argument
remove.unused=T with Multistrata models and also for any type of
model when there were no grouping variables.
Bugs were fixed in
process.data which gave incorrect ordering of intial ages if
the factor variable for the age group was numeric and more than two digits.
Also, the number of groups in the data was not correct if the number of loss
on capture records exceeded the number without loss on capture within a
Bugs were fixed in
setup.model that prevented use of the Barker model and that
reported an erroneous list of model names when an incorrect type of model
Version 1.5.9 (26 June 2006)
A bug was fixed
convert.inp which prevented the code from working with
groups and two or more covariates. Note that there are limitations to this
function which may require some minor editing of the file. The limitations
have been added to the help file (
Version 1.5.8 (22 June 2006)
options was added to
make.mark.model with a default NULL
value. It is simply a character string that is tacked onto the
Estimate statement for the MARK .inp file. It can be used to request
options such as NoStandDM (to not standardize the design matrix) or
SIMANNEAL (to request use of the simulated annealing optimization method) or
any existing or new options that can be set on the estimate proc.
model.table was fixed so it would accomodate the change
from v1.3 to a marklist in which the model.table was switched to the last
entry in the list.
A bug in
summary.mark was fixed so it
would properly display QAICc when chat > 1.
adjust.chat was modified such that it returns a marklist with
each model having a new chat value and the model.table is adjusted for the
new chat value.
modfied so it returns the mark model object rather than using eval to modify
the object in place. The latter does not work with models in a marklist and
calls made within functions.
Version 1.5.7 (8 June 2006)
data was added to function
enable model averaging parameters at specific covariate values rather than
the mean value of the observed data. An example is given in the help file.
parameter of function
has a default of NULL and if it is not specified then all of the real
parameters are model averaged rather than those for a particular type of
parameter (eg p or Psi).
A bug was fixed in function
compute.real that caused the function to fail for computations
Version 1.5.6 (6 June 2006)
print.summary.mark was modified so fixed parameters are noted.
show.fixed was added to
control whether fixed parameters are shown as NA (FALSE) or as the value at
which they were fixed. If
se=T the default is
show.fixed=F. The latter is most useful in displaying
values in PIM format (without std errors), so fixed values are displayed as
blanks instead of NA.
links was added to
convert.link.to.real and the default value for argument
model is now NULL. One or the other must be given. If the value for
links is given then they are used in place of the links specified in
model object. This provides for additional flexibility in
changing link values for computation (eg use of log with mlogit).
drop was added to
drop=TRUE (the default), then any model with one or more non-positive
(0 or negative) variances is not used in the model averaging.
in computation of the v-c matrix of mlogit link values in
compute.links.from.reals was fixed. This did not affect confidence
intervals for real parameters (eg Psi) in
model.average because it
uses the logit transformation for confidence intervals on real parameters
that use mlogit link (eg Psi).
get.real was unable to
extract a single parameter value(eg constant Phi model). This was fixed.
parm.indices was removed from the functions
the subsetting can be done easily with the complete results returned by the
functions. This changed the examples in
compute.real and subsequently
return a field
fixed when se=TRUE that denotes whether a real
parameter is a fixed parameter or an estimated parameter at a boundary which
is identified by having a standard error=0.
Version 1.5.5 (1 June 2006)
model has been deleted from the arguments in
TransitionMatrix. It was only being used to ascertain whether the model was
a Multistrata model. This is now determined more accurately by looking for
the presence of
tostratum in the argument
x which is a
dataframe created for
Psi from the function
The function also works with the estimates dataframe generated from
model.average. See help for
vcv was added to function
model.average. If the argument is TRUE (the default value)
then the var-cov matrix of the model averaged real parameters is computed
and returned and the confidence intervals for the model averaged parameters
are constructed. Models with non-positive variances for betas are reported
and dropped from model averaging and the weights are renormalized for the
A new function
compute.links.from.reals was added to the library to transform
real parameters to its link space. It has 2 functions both related to model
averaged estimates. Firstly, it is used to transform model averaged
estimates so the normal confidence interval can be constructed on the link
values and then back-transformed to real space. The second function is to
enable parametric bootstrapping in which the error distbution is assumed to
be multivariate normal for the link values. From a single model, the link
values are easily constructed from the betas and design matrix so this
function is not needed. But for model averaging there is no equivalent
because the real parameters are averaged over a variety of models with the
same real parameter structure but differing design structures. This
function allows for link values and their var-cov matrix to be created from
the model averaged real estimates.
Version 1.5.4 (30 May 2006)
mark an argument
retry was added to
enable the analysis to be re-run up to the number of times specified. An
analysis is only re-run if there are "singular" beta parameters which means
that they are either non-estimable (confounded) or they are at a boundary.
Beginning with this version,
extract.mark.output was modified
such that the singular parameters identified by MARK are extracted from the
output (if any) and the indices for the beta parameters are stored in the
model$results$singular. The default value for
retry is 0 which means it will not retry. When the model is re-run
the initial values are set to the values at the completion of the last run
except for the "singular" parameters which are set to 0. Using
will not help if the parameters are non-estimable. However, if the
parameters are at a boundary because the optimization "converged" to a
sub-optimal set of parameters, then setting
retry to 1 or a suitably
small value will often help it find the MLEs by moving away from the
boundary. If the parameters are estimable and setting
retry does not
work, then it may be better to set new initial parameters by either
specifying their values or using a model with similar parameters that did
A new function
rerun.mark was created to
simplify the process of refitting models with new starting values when the
models were initially created with
mark.wrapper which runs a
list of models by using all combinations of the formulas defined for the
various parameters in the model. Thus, individual calls to
not constructed by the user and re-running an analysis from the resulting
list would require constructing those calls. The argument
model.parameters is now stored in the model object and it is used by
this new function to avoid constructing calls to rerun the analysis. With
this new function you only need to specify the model list element to be
refitted, the processed dataframe, the design data and the model list
element (or different model) to be used for initial values. See
rerun.mark for an example.
rerun.mark a viable approach for all circumstances, the
modified such that models that fail to converge at the outset (i.e., does
not provide estimates in the output file) are stored in the model list
created by the former function and they are reported as models that did not
run and are skipped in the model.table by the latter function. This enables
a failed model to be reanalyzed with
rerun.mark using another
model that converged for starting values.
Version 1.5.3 (25 May 2006)
get.real a fix was made to
accommodate constant pims and a warning is given if the v-c matrix for the
betas has non-positive variances.
make.mark.model, the argument
initial can now be a
single value which is then assigned as the initial value for all betas. I
have found this useful for POPAN models. For some models I have run, the
models fail to converge in MARK with the default initial values it uses (I
believe it uses
initial=0). I have had better luck using
initial=1. By allowing the use of a single value you can use the
same generic starting value for each model without figuring out the number
of betas in each model. Also note that you can specify another model that
has already been run to use as initial values for a new model and it will
match parameter values.
A bad bug was fixed in
which was unfortunately deleting files containing "out", "inp", "res" or
"vcv" rather than those having these as extensions. This happened without
your knowledge if you chose ask=FALSE. Good thing I had a backup. Anyhow,
I have now restricted it to files that are named by RMark with markxxxx.inp
etc where xxxx is a numeric value. Thus if you assign your own basefile
name for output files you'll have to delete them manually. Better safe than
Version 1.5.2 (18 May 2006)
Two new functions were
added in this version.
convert.inp converts a MARK encounter
history input file to an RMark dataframe. This will be particularly useful
for those folks who have already been using MARK. Instead of converting and
importing their data with
import.chdata they can use the
convert.inp to import their .inp file directly. It can also
be used to directly import any of the example .inp files that accompany MARK
and the MARK electronic book
(http://www.phidot.org/software/mark/docs/book/). The second new
function is only useful for tutorials and for first time users trying to
understand the way RMark works. The function
the full PIM structure or the simplified PIM structure for a parameter in a
model. The user does not directly manipulate PIMS in RMark and they are
essentially transparent to the user but for those with MARK experience being
able to look at the PIMS may help with the transition.
Version 1.5.1 (11 May 2006)
get.link were added to compute link values rather than the
added to convert link estimates to real parameter estimates. Previously a
similar internal function was used within
compute.real but to provide
more flexibility it was put into a separate function.
beta was added to
get.real to enable it to be changed
in the computation of the real parameters rather than always using the
TransitionMatrix was added to create a transition matrix for
the Psi values. It is provided for all strata including the
subtract.stratum. Standard errors and confidence intervals can also
make.mark.model was modified to include
time.intervals as an element in the mark object.
Version 1.5.0 (9 May 2006)
If output file already exists user is given option
to create mark model from existing files. Only really useful if a bug
occurs (which occurred to me from 1.4.9 changes) and once fixed any models
already run can be brought into R by running the same model over and
specifying the existing base
are no longer prefixed with MRK to enable this change.
On occasion MARK will complete the analysis but fail to create the v-c matrix and v-c file. The code has been modified to skip over the file if it is missing and output a warning.
Two new functions have been added to ease handling
of marklist objects.
merge.mark merges an unspecified number
of marklist and mark model objects into a new marklist with an optional
remove.mark can be used to remove mark models
from a marklist. See
dipper for examples of each function.
Various changes were made to functions that compute real parameter
estimates, their standard errors, confidence intervals and
variance-covariance matrix. The functions that were changed include
For examples, see help for latter two functions.
Version 1.4.9 (3 May 2006)
make.mark.model was not working after model simplification was
added in v1.2. This was modified to select initial values from the model
based on names of design matrix columns rather than column contents which
have different numbers of rows depending on the simplification.
extract.mark.output was fixed to extract the correct -2LnL
from the output file in situations in which initial values were specified.
Version 1.4.8 (25 April 2006)
mark.wrapper with a default
FALSE. This overcomes the problem described above in 1.4.7.
Code was added to
collect.model.names to prevent it from
tripping up when files contain an asterisk which R uses for special names.
Use of T and F was properly changed to TRUE and FALSE in various functions to prevent errors when T or F are R objects.
Code for naming files was modified to avoid problems when more than 999 analyses were run in the same directory.
Bug in setting fixed parameters with argument
fixed=list(index=,value=) was corrected.
remove.intercept was added to parameter definition to force removal
of intercept in designs with nested factor interactions with additional
factor variables (e.g.,
Version 1.4.7 (10 April 2006)
An error was fixed in the
Psi simplification code. Note that with the fix in 1.4.2 to trap
errors, a side effect is that non-trapped errors that occur in the R code
will now fail without any error messages. If the error occurs in making the
model, then the model will not be run, but you will not receive a message
that the model failed. I may have to make the error trapping a
user-settable option to provide better error tracking.
Version 1.4.6 (7 April 2006)
Assurance code was added to test that the mlogits were properly assigned. An error message will be given if there has been any unforeseen problem created by the simplification. This eliminates any need for the user to check them as described under 1.4.5 above.
Version 1.4.5 (6 April 2006)
For multistrata models, the code for creating the mlogit links for Psi was not working properly if there was more than one group. This was fixed in this version.
Simplification of the PIMS has now been extended to include mlogit
parameters. That was not a trivial exercise and while I feel confident it
is correct, double check the assignment of mlogit links for complex models,
as I have not checked many examples at present. Within a stratum, the
corresponding elements for Psi for each of the tostratum (movement from
stratum to each of the other strata excluding the
should have the same mlogit(xxx) value such that it can properly compute the
subtract.stratum by subtraction.
Version 1.4.4 (4 April 2006)
By including the test on model failure, errors that would stop program were not being displayed. This has been fixed in this version.
An error was fixed in using time-varying covariates when some of the design data had been deleted.
Version 1.4.3 (30 March 2006)
Problem with pop up window has been fixed. It will no longer appear if the model does not converge but the model will show as having failed.
An error was fixed in extracting output from the MARK output file when for some circumstances the label for beta parameters included spaces. This now works properly.
Version 1.4.2 (14 March 2006)
Errors in the FORTRAN code were preventing completion of
large batch jobs. Now these errors are caught and models that fail are
reported and skipped over. Unfortunately, it does require user intervention
to close the popup window. Make sure you select Yes to close the window
especially if you use the default
invisible=FALSE such that the
window does not appear. If you select No, you will not able to close the
window and R will hang.
A new list element was added to
make.design.data for parameters such as
Psi to set the value of
tostratum that is computed by
subtraction. The default is to compute the probabilitity of remaining in the
stratum. The following is an example with strata A to D and setting A to be
computed by subtraction for each stratum:
Version 1.4.1 (11 March 2006)
A value "constant" was added for the
pim.type. Note that
pim.type is only used for
triangular PIMS. See
Some code changes
were made to
make.mark.model which lessen time to create the
MARK input file for large models.
add.design.data was modified to accomodate robust design and
deletion of design data; this was missed in v1.4 changes.
model.name argument in
make.mark.model was not working. This was fixed.
Version 1.4 (9 March 2006)
Robust design models added. See
robust for an example.
was modified so warning messages/errors do not occur if no models/files are
Parameters in the design matrix are now ordered in the same consistent arrangement. In prior versions they were arranged based on their order in the argument call.
right was added to
add.design.data and in
make.mark.model to control whether
bins are inclusive on the right (default). The
uses this argument in a call to
Version 1.3 (22 Feb 2006)
Time varying covariates can now be included in the
model formula. See
make.mark.model for details.
model types for Known (Known-fate) and Multistrata (CJS with different
strata) were added. See
Specific rows of the design data can now be removed for
parameters that should not be estimated. Default fixed values can be
assigned. The function
make.design.data now accepts an
remove.unused which can be used to automatically remove
unused design data for nested models. It's behavior is also determined by
the new argument
summary.mark now produces a summary object and
print.summary.mark prints the summary object. Changes were
made to output when
A new function
merge.occasion.data was created to add occasion specific covariates
to the design data.
create.model.list were created to automate running models from
a set of model specifications for each model parameter.
process.data can now be a vector to
enable a different beginning time for each group.
pim.type was added to parameter specification to enable using pims
with time structure for data sets with a single release cohort for CJS. See
Model lists created with
collect.models are now given the class "marklist" which is
now places the model.table at the end of the returned list such that each
model number in model.table is now the element number in the returned list.
Previously it was 1+ that number.
Input, output, v-c and residual
results files from MARK are now stored in the directory containing the
.Rdata workspace. They are numbered consecutively and the field
output contains the base filename. The function
cleanup was created to delete files that are no longer linked
Model averaged estimates
and standard errors of real parameters can be obtained with the function
Version 1.2 (4 Oct 2005)
By default the PIM structure is simplified to use the fewest number of unique parameters. This reduces the size of the design matrix and should reduce run times.
The above change was made in some versions still numbered 1.1, but it contained an error that caused the links command for MARK to be constructed incorrectly.
adjust argument has been added to
collect.models to enable control of number of parameters and
resulting AIC values.
adjust.chat can now also be a list of models created by
collect.models which allows operating on sets of models.
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