May, 1998<\/p>\n
- \n
- Because the identity design matrix is the default, I made the program “smarter” so that an actual matrix does not have to be passed between the interface and the numerical analysis program, and not stored in the output. \u00a0 Instead, the key work “identity” is added to the design matrix statement. \u00a0 No design matrix is printed in the output.\u00a0 More importantly, no design matrix pops up on the screen when you retrieve a model that had the “identity” key word on the design matrix statement.<\/li>\n
- I have modified the calculation of the threshold value for declaring a parameter not estimable.\u00a0 The old threshold value was 0.3E-6.\u00a0 The new value varies depending on the dimension of the variance-covariance matrix.\u00a0 Thus, some models may give a different number of parameters estimated than previously.\u00a0 If you find an error, let me know so that I can continue to refine this process.<\/li>\n<\/ol>\n
June, 1998<\/p>\n
- \n
- Seven new models were added to the list of data types:<\/li>\n<\/ol>\n
Huggins (1991) method where the closed captures likelihood is conditioned on only the animals captured has been added.\u00a0 This model allows individual covariates to be used for estimating the initial (p<\/em>) and recapture (c<\/em>) probabilities of the closed capture model.\u00a0 Estimates of population size (N<\/em>) are generated as derived parameters, because N<\/em> does not actually occur in the likelihood with Huggins’ model as it does in the closed captures models of Program CAPTURE. \u00a0 Estimates of N<\/em> are asymptotically equivalent to estimates of N<\/em> from the closed captures models of Program CAPTURE, but are not quite as efficient because some information is lost by conditioning the likelihood.<\/p>\n
Huggins (1991) method was also added to the robust design model, so that individual covariates can also be used with all the paramters.<\/p>\n
The Jolly-Seber model has been added.\u00a0 The first parameterization is from Ken Burnham, and uses the first population size N<\/em>1 and the rate of population change, lambda1 through lambda(k<\/em>-1) for k<\/em> encounter occasions.\u00a0\u00a0 This model does not converge readily, so I suggest you use one of the Jolly-Seber models described below.<\/p>\n
Pradel’s (1996) model that estimates seniority from the encounter histories has been added.<\/p>\n
Pradel’s (1996) model that estimates both seniority and apparent survival from the encounter histories has been added.\u00a0 This model is another parameterization of the Jolly-Seber model.<\/p>\n
Pradel’s (1996) model that estimates apparent survival and rate of population change (lambda) has been added.\u00a0 Again, this model is another parameterization of the Jolly-Seber model.<\/p>\n
Pradel’s (1996) model was reparameterized to include apparent survival and fecundity (number of new individuals in the population at time i<\/em>+1 per individual at time i<\/em>).\u00a0 Again, this model is another parameterization of the Jolly-Seber model.<\/p>\n
- \n
- The Graphics Server graphics package was added as the default graphics package.\u00a0 The reason for this second graphics package was to provide better graphics when multiple sets of parameters were displayed.\u00a0 Both packages are available via from the Results Browser Window with the Output | Specific Model Output | Interactive Graphics and the Output | Specific Model Output | Interactive Graphics GS menu choices.<\/li>\n<\/ol>\n
May, 1999<\/p>\n
- \n
- The variance components code was changed to use the full precision of estimates for the variance-covariance matrix.\u00a0 You will have to recompute parameter estimates of existing models to use the new code.<\/li>\n
- The older graphics package was eliminated.\u00a0 This change cut the size of the program distribution files.<\/li>\n
- An option to re-compute all the models in a Results Database was appended to the Run menu of the Results Browser.<\/li>\n<\/ol>\n
June, 1999<\/p>\n
- \n
- PIM matrices can now be copied to the clipboard, and vice versa the clipboard can be pasted into a PIM.\u00a0 This feature also means that you can create PIMs in a spreadsheet, and then paste the values into a PIM Window (or also on the PIM chart).<\/li>\n
- An “All Different Matrix” option has been added to the PIM menus to allow you to create PIMs with every value different.<\/li>\n
- The Full menu choice has been changed when you request a Design Matrix. Now, the Full menu choice produces a design matrix with an intercept, group effects (if needed), time effects, and group*time effects (if needed).\u00a0 The limitation of the Full option is that the PIMs must have the number of parameters equal to a full g*t model.\u00a0 However, you can create age structure across PIMs, and still use the Full menu choice as long as the number of parameters is equal to a full g*t model. \u00a0 The columns of the design matrix are initially labeled, although these labels disappear if you manipulate the matrix, e.g., add or delete columns.\u00a0 I’m thinking about how to keep the labels around.<\/li>\n<\/ol>\n
The old version of the Full menu choice has now been renamed to be a the Identity menu choice.<\/p>\n
- \n
- The list of Pre-Defined Models has now been expanded to include both PIM and Design Matrix coding of the standard models.\u00a0 In addition, the Design Matrix coding list includes g+t models.\u00a0 All the Design Matrix models are constructed based on a full g*t set of PIMs.\u00a0 Hence, you still can’t use the Pre-Defined Models option to construct age-structured models.<\/li>\n
- A menu choice has been added to the Output menu to list the m(ij<\/em>) arrays of live recaptures and dead recoveries of the encounter history matrix in a NotePad window.\u00a0 This option is useful for identifying anomalies in the data. In the past, you could only run RELEASE for the CJS model to look for anomalies.<\/li>\n
- Variance components outputs are further developed, with additional options specified by check boxes for output other than the main numerical output and the graph of the estimates.<\/li>\n
- The problem with a semicolon in the encounter histories file between comment delimiters, e.g.,<\/li>\n<\/ol>\n
\/* stuff; stuff *\/,<\/p>\n
has been fixed.<\/p>\n
- \n
- Problem behavior with the individual covariate standardization method has been uncovered.\u00a0 When you specify a model with a common a common intercept but 2 or more slopes for the individual covariate, e.g.,<\/li>\n<\/ol>\n
1 weight 0<\/p>\n
1 0 weight<\/p>\n
and specify to standardize the individual covariate, you will get a different value of the deviance than from the model run with unstandardized individual covariates.\u00a0 This behavior is because the centering effect of the standardization method affects the intercept differently depending on the value of the slope parameter. The effect is caused by the nonlinearity of the logit link function.\u00a0 You get the same effect if you standardize variables in a logistic regression, and run them with a common intercept. \u00a0 The result is that the estimates are not scale independent, but depend on how much centering is performed by subtracting the mean value.<\/p>\n
- \n
- I think I have gotten rid of the problem that caused the message “Unique ID > 8000.\u00a0 Tell Gary”.\u00a0 If you still get this message with the new version, let me know.<\/li>\n<\/ol>\n
July, 1999<\/p>\n
- \n
- The ability to open one of the last 4 files previously used has been added to the File menu.<\/li>\n
- Real parameter estimates are now labeled as “Estimates” instead of “S(I)” in the output.<\/li>\n
- An option was added to make the default to tell you when a model is retrieved.\u00a0 With the File | Preferences menu choice, you can change this option back to not making you respond to this message.<\/li>\n
- The option to list the Akaike Weights in the Results Browser has now been made the default.\u00a0 You can turn this option off with the File | Preferences menu choice.<\/li>\n<\/ol>\n
December, 1999<\/p>\n
- \n
- A problem with losses on capture in estimating p<\/em> and c<\/em> of the robust design and Huggins robust design models has been fixed.\u00a0 Previously, S<\/em>, gamma”, and gamma’ were estimated correctly with losses on capture, but not p<\/em> and c<\/em>, and hence N<\/em>.<\/li>\n
- Options for export of parameter estimates (both beta and real), and SE’s and confidence intervals to Excel spreadsheets or the clipboard were added.<\/li>\n
- Options for export of variance-covariance matrices to dBase files or the clipboard were added.\u00a0 These changes forced a re-organization of the menu choices under Output | Specific Model Output to keep the menu from becoming too clumsy. \u00a0\u00a0 Also, the model averaging variance-covariance matrix can now be exported to a dBase file.<\/li>\n
- Extensive testing of the Jolly-Seber model demonstrates that it works correctly, but is just difficult to achieve convergence with for sparse data.\u00a0 With simulations and large sample sizes, it works fine.<\/li>\n
- More work was done on the simulation procedure.\u00a0 However, I still have not removed the double-click trap to keep the faint of heart out of the simulation procedures.\u00a0 I now believe that all models are simulated correctly except the robust design,\u00a0 Huggins robust design, and Pradel model with recruitment but no survival. \u00a0 Currently, you are not allowed to simulate these models.\u00a0 All the rest should work correctly, including the Jolly-Seber model.\u00a0 The problem with simulating the robust design models is how to specify the poulation size so that it is not less than what is consistent with the S<\/em>, gamma”, and gamma’ parameters for the previous interval and preceding N<\/em>.\u00a0 This problem involves fixing the input interface, not the actual numerical simulation procedure.<\/li>\n<\/ol>\n
March, 2000 (updated to Version 1.7)<\/p>\n
- \n
- A “robust design” extension of the Barker model is now included in MARK.<\/li>\n
- A VPA – Virtual population model – is now included.<\/li>\n
- The ability to submit simulations to Program RELEASE is included in the Simulation menu.<\/li>\n
- The ability to submit simulations to Program CAPTURE is included in the Simulation menu.<\/li>\n
- The design matrix now displays different colors for cells with zero, one, and other values. This feature is handy for spotting the pattern in complex matrices. You can change the colors displayed with the File | Preferences menu choice.<\/li>\n
- The ability to run more than one data type within the same Results Browser window has been implemented.\u00a0 For example, you can now run both the dead recoveries {S, r} models and the Brownie et al. {S, f} models, and have the results appear in the same Results Browser window.\u00a0 To change the data type for models, select the PIM | Change Data Type menu choice from the Results Browser window.\u00a0 This convenience also introduces some problems, such as model averaging (not all the models have the same PIMs), so that the model averaging procedure only averages across the models with the same data type.\u00a0 Another example of where problems occur is when the likelihoods are very different between the data types, e.g., the closed captures models and Huggins’ models.\u00a0 The AIC values are not on the same scale for these different data types.\u00a0 However, this feature is very useful to compare the 2 sets of estimates from the same Results Brower window.\u00a0 With this change, I updated the version number of MARK to 1.7.<\/li>\n
- An option to use multi-threading was added to the File | Preferences menu choice.\u00a0 This option allows you to be building a new model while numerical estimation is running for a previous model in background.<\/li>\n
- An option was added to the Run Window that specifies to use the mean values of individual covariates to compute the real parameter estimates reported in the output.\u00a0 Note that when individual covariates are standardized, the mean value for a standardized covariate is zero.<\/li>\n<\/ol>\n
June, 2000 (updated to Version 1.8)<\/p>\n
- \n
- The “robust design” extension of the Barker model was modified to include the permanent emigration (F) parameter, and the notation for the temporary emigration parameters was changed to be gamma” (old F) and gamma’ (old F’) to be consistent with Lindberg et al. (In Press).<\/li>\n
- Calls to Programs ESTIMATE and BROWNIE were added to the Test menu choice in the Results Browser window. The programs are useful in assessing goodness-of-fit for dead recovery data.<\/li>\n
- The capability to “undelete” a model that was deleted from the Results Browser window was added. This menu choice is available under the Delete menu of the Results Browser window. You are presented with a list of all the models you have deleted during this execution of MARK, and allowed to select the models you want to undelete. However, once you end MARK, the deleted models permanently disappear.<\/li>\n
- A “Partial Zero” menu choice was added to the Design Matrix window to allow the user to “clear” or zero out a portion of a column. This option is useful when you copy a column, and only want to retain a portion of the values in the column.<\/li>\n
- The variance components output was modified to include a naive estimator of the process variance. Additionally, the estimate of the process variance and associated confidence intervals are now reported with negative values, if necessary. However, the estimate of the process standard deviation is reported as zero for negative process variances (and similarly for the lower confidence bound).<\/li>\n
- A new column has been added to the Results Browser — Model Likelihood.\u00a0 The values in this column are the Akaike weight of the model in the row divided by the Akaike weight of the minimum AICc model.\u00a0 This value is the model likelihood, the strength of evidence for the model.\u00a0 The best model has a value of 1, with all other models <1.<\/li>\n
- The Variance Components module now has a check box to run the Random Effects model that corresponds to the variance components analysis you have requested.\u00a0 Specifically, a Random Effects model can be viewed as intermediate in its number of parameters between the time-specific model (t) and the dot (.) model where all values are the same.\u00a0 The approach developed by Ken Burnham is to fix the parameter values that were used to estimate the process variance to their S-tilde values from the variance components\u00a0 analysis.\u00a0 The number of parameters that this set now represents is estimated from the trace of the G matrix.\u00a0 Adding trace(G) to the number of parameters estimated from the numerical analysis run with fixed values from the S-tilde gives the number of parameters estimated for the random effects model.\u00a0 Because trace(G) is not an integer, the number of parameters for the random effects model appears as a non-integer.\u00a0 To accommodate these non-integers, existing Results Files will be updated automatically the first time they are accessed.\u00a0 You should see a message stating so.\u00a0 To be safe, you may want to back up your MARK files before opening them with this new version.<\/li>\n
- Although the background colors used in the design matrix to represent the pattern of the matrix are very helpful, especially at first, the cost is a lot of time watching design matrices being created on the screen.\u00a0 Therefore, I put an option in the File | Preferences dialog to turn off the color in design matrices if you desire, which definitely increases speed for large matrices.<\/li>\n
- I fixed a bug with the probability of surviving the study (Kaplan-Meier estimate) for the Known Fate data type when time intervals were unequal.\u00a0 Previous versions assumed all time intervals were equal to 1.\u00a0 Now, the length of the time interval is used to compute the product of survivals and its standard error and confidence intervals.<\/li>\n
- Labels for the real parameter estimates are now provided in the output.\u00a0 Labels for real parameter estimates are taken from the “Parm” column that is added to the design matrix — giving you some idea about what the parameter type is for each estimate.\u00a0 Note however, that some parameter types can be set to others, e.g., initial capture probability (p<\/em>) and recapture probability (c<\/em>) in the closed capture models.\u00a0 Hence, be careful to not be mislead.\u00a0 You do not need a design matrix to obtain these labels — the process is automatic.\u00a0 Labels are generated from the parameter type for the first occurence of the parameter index in the PIMs.\u00a0 If you want to obtain these labels for existing model output, you need to re-run the model, i.e., from the Results Browser, Run | ReRun Models menu choices.\u00a0 The real parameter estimate labels are limited to 20 characters.<\/li>\n
- Labels for the beta parameter estimates and columns of the design matrix are now available.\u00a0 When in the design matrix, if you right-click the matrix, the menu choice “Label” allows you to enter 20 characters of information to include in the column heading of the column with the highlighted cell.\u00a0 If you start with the Design | Full menu choice, the headings in the design matrix will be automatically labeled.\u00a0 Likewise, the pre-defined design matrix models also have the columns automatically labeled.\u00a0 When the model is run, this information is passed to the numerical estimation process, and these labels will appear beside the beta parameter estimates.\u00a0 When models are retrieved, the design matrix column headings also are retrieved from the output.<\/li>\n
- Columns in the design matrix can be moved by dragging the column heading to a new position.\u00a0 The new ordering can be preserved by right-clicking the design matrix and selecting “Reorder Columns” from the pop-up menu.\u00a0 Basically reordering columns is equivalent to renumbering the beta parameters.<\/li>\n<\/ol>\n
August, 2000<\/p>\n
- \n
- A bug introduced in June concerning parsing of real parameter estimates from the output file when derived parameter estimates were present was fixed.\u00a0 This bug was detected because of incorrect output in the model averaging procedure when derived parameter estimates were present.<\/li>\n<\/ol>\n
September, 2000<\/p>\n
- \n
- The computer time required to process individual covariates has been dramatically reduced, at least 10 times.<\/li>\n
- A bug with the naive variance component estimator has been fixed — previously, 2 consecutive runs of the variance components module would give incorrect estimates for the naive estimator for the second and additional runs.<\/li>\n<\/ol>\n
January, 2001<\/p>\n
- \n
- The variance components estimator has been modified to show estimates of sigma^2 < 0, then to set the estimate of sigma to zero, and re-compute the beta-hats and their standard errors with sigma = 0.<\/li>\n
- Likelihood ratio tests when c <\/em>> 0 have been modified to incorporate the quasi-likelihood parameter.\u00a0 Likewise, the deviance is now displayed in the results browser is corrected for c<\/em>.<\/li>\n
- Design matrix window now has an option to set the font size, so that large matrices can be miniaturized and the colors used to determine if the pattern is correct.<\/li>\n
- Derived parameters are now handled separately from the real parameter estimates.\u00a0 Eventually, derived parameters will be a fully-supported third parameter type, and you will be able to graph them and perform variance components.<\/li>\n<\/ol>\n
May, 2001 (updated to Version 2.0 — Greater Prairie Chicken photo)<\/p>\n
- \n
- Two additional data types for closed captures have been added. Two heterogeneity models for closed captures have been created based on mixture distributions. The simple version of this model only has a constant capture probability for all occasions and for recaptures. The full heterogeneity model has a set of p<\/em> and c<\/em> parameters for each mixture. Encounter histories for these 2 models are identical to the closed captures data type. These models required a fairly major change to the PIM structure to allow for large rectangular PIMs.<\/li>\n
- A model for nest survival estimation was added. This model is different from the known fate data type because the exact time of failure of the nest is not known. Input for nest survival data can be in the form of a nest survival data type, where the time of first finding the nest, next to last time the nest was observed, and the last time the nest was observed are entered, followed by the fate of the nest (0=successful, 1=unsuccessful). Individual covariates may also be used. More details on the structure of this model and the encounter histories file can be found in the help file.<\/li>\n
- A bug that caused incorrect standard errors for the derived parameter estimates of N<\/em> was fixed in the Huggins robust design model.<\/li>\n
- Some really large problems (e.g., very large PIMs or a large number of PIMs) will not work correctly with the existing model averaging parameter selection interface. Therefore, I added another interface that will work. To use this new interface, click the “Use non-interactive model averaging parameter specification window” choice from the File | Preferences menu choice. With this interface, you have to pick the PIM and then specify the parameter within the PIM to model average. Not as visual, but more effective for large problems.<\/li>\n<\/ol>\n
June, 2001<\/p>\n
- \n
- A bug in computing the variance of the derived parameter estimate N-hat for the Huggins data type was fixed.\u00a0 Standard errors of N-hat from the Huggins models now correspond closely with standard errors from the closed captures data types.<\/li>\n
- The scaling of the gamma” and gamma’ for the length of the interval was removed.\u00a0 These parameters should be viewed as the probability of being available for capture conditional their previous status, and hence are not a function of the length of the time interval.<\/li>\n
- When setting up a new analysis, you can now paste in time interval lengths from the clipboard.\u00a0 Thus, if you have a complicated set of time intervals, enter them into a spreadsheet, and then paste the values into MARK.<\/li>\n
- A “Save As” capability was added to the File menu choice to save a copy of the current Results File.<\/li>\n
- The dialog to select a set of “Pre-defined Models” has been re-written to allow the user to pick models for each parameter, rather than select from a list of all possible models.\u00a0 This change allows pre-defined models for data types that previously would generate too long of potential models, allows the user to more easily select just models desired, and makes it harder to “Select All” models.\u00a0 In other words, rather than shamelessly data dredging with a few mouse clicks, you will now have to work at it!<\/li>\n
- The ability to summarize derived parameters from simulations was added.<\/li>\n
- Several bugs that caused incorrect estimates for the nest survival data type were fixed.<\/li>\n<\/ol>\n
July, 2001<\/p>\n
- \n
- The nest success data type was further modified to produce warnings when invalid data are entered for a nest record.\u00a0 The help file under the ‘Nest Survival’ topic was updated to present examples of these invalid cases.<\/li>\n<\/ol>\n
September, 2001<\/p>\n
- \n
- The multi-strata model with live and dead encounters is now working correctly after another bug was fixed on 3 September, 2001.\u00a0 The help file has also been updated to describe this model.<\/li>\n<\/ol>\n
November, 2001<\/p>\n
- \n
- The occupancy estimation model of MacKenzie et al. (2002) has been implemented.\u00a0 The help file has also been updated to describe this model.<\/li>\n<\/ol>\n
January, 2002<\/p>\n
- \n
- Minor fixes to several bugs that have surfaced, including aborts when no pre-selected models are selected, incorrect label on beta labeling window, and specification of default preferences when the program is installed.<\/li>\n<\/ol>\n
March, 2002<\/p>\n
- \n
- The Run Window has been modified to allow a user to specify values of individual covariates to be used in computing the real parameter estimates.\u00a0 With these changes, 3 options are available for specifying values of the individual covariates: values from the first encounter history in the encounter histories file, mean values, and values the user specifies.<\/li>\n
- Paste buttons were added to the simulation input screens for beta parameter estimates and numbers of releases, and initial input screens for group labels, individual covariate names, and strata names and labels.\u00a0 Now, the user can generate a set of input values for any of these screens in a spreadsheet, and then paste the values into MARK.<\/li>\n
- The Run Window has been modified to allow a user to specify different link functions for parameters in a model.\u00a0 However, this procedure is tricky, and can lead to numerical problems if not done intelligently.\u00a0 Default initial parameter values are more difficult to set, so numerical convergence becomes more of an issue.\u00a0 The user will have to take more responsibility in specifying initial parameter values if multiple link functions are used in one model.<\/li>\n<\/ol>\n
September, 2002 — Version 3.0<\/p>\n
- \n
- The robust-design occupancy estimation model of MacKenzie et al. (submitted) has been implemented.<\/li>\n
- An updated Graphics Server graphics package has replaced the older version.\u00a0 The interface to the graphics package now includes legends for each set of parameters plotted.<\/li>\n
- Some minor bugs with labeling columns in the design matrix, PIM Charts off the top of the screen, and some other little annoyances that have been pointed out have been fixed.<\/li>\n
- The capability to construct interaction terms between individual covariates has been implemented with the product function in the design matrix.\u00a0 In addition, an add function has also been implemented.\u00a0 See the revised help file for details of how these functions operate within the design matrix.<\/li>\n
- The Pearson chi-square statistic is now computed routinely in the full output, and can be saved in the simulation and bootstrap files.\u00a0 The Pearson chi-square statistic is sometimes better behaved than the deviance statistic, and may provide an improvement in estimating the over-dispersion parameter, c<\/em>.<\/li>\n<\/ol>\n
November, 2002 — Version 3.1<\/p>\n
- \n
- An entry in the design matrix menu now allows you to see the list of individual covariates available for the current data set, and to insert 1 or more of these individual covariate names in the current highlighted cell in the design matrix.<\/li>\n
- Added the capability to retrieve one or more columns from a previous model’s design matrix and insert them into the current design matrix.\u00a0 Note that the number of rows in the design matrix has to be the same for both design matrices.<\/li>\n<\/ol>\n
December, 2002<\/p>\n
- \n
- An option was added to the Run Window to allow estimation of profile likelihood confidence intervals for real parameters.\u00a0 However, numerical problems can cause the estimates to be wrong, so use this option carefully.\u00a0 The help file explains the procedure, and shows an example of incorrect profile likelihood confidence intervals.<\/li>\n<\/ol>\n
March, 2003<\/p>\n
- \n
- The multinomial logit (MLogit) and cumulative logit (CLogit) link functions were added as options under the Parm.-Specific link function option of the Run Window.\u00a0 The MLogit link is particularly useful for the multi-strata model to constrain the transitions from a strata to sum to <=1.\u00a0 Another application is the probability of entry parameter of the POPAN model (see below).\u00a0 See the revised link functions help file write-up for details.<\/li>\n
- The POPAN model was revised, and is now working correctly for the real parameter values.\u00a0 This is the Jolly-Seber model from Schwarz and Arnason (1996), where recruitment to the population is modeled as the probability of entry (pent in MARK, beta in the original paper) from a super population.<\/li>\n
- Problems with convergence of the robust design occupancy model were corrected, plus 2 additional parameterizations of this model were added.\u00a0 See the help file for details.<\/li>\n
- An additional parameterization of the Jolly-Seber model developed by Link and Barker (submitted to Biometrika) was added.\u00a0 To use this parameterization, start with one of the other Jolly-Seber data types (e.g., Burnham’s Jolly-Seber data type, one of the 3 Pradel data types, or the POPAN data type), and use the PIM | Change Data Type menu choice to obtain the Link-Barker parameterization.\u00a0 Details are in the help file.<\/li>\n<\/ol>\n
June, 2003<\/p>\n
- \n
- Fixed a bug in the Huggins closed-capture heterogeneity models so that N<\/em> is correctly estimated.<\/li>\n<\/ol>\n
August, 2003 — Version 3.2<\/p>\n
- \n
- There are 6 different data types for estimation under the closed capture models: Mt<\/em> (p<\/em> and c<\/em>), Mh<\/em> (pi and p<\/em>) and full Mh (pi, p<\/em>, and c<\/em>); with each of these data types available with population size (N<\/em>) in the likelihood, or a Huggins version where the likelihood does not contain N<\/em>.\u00a0 Now, when the Closed Captures data type is selected, you are given a choice of which of these models of capture probabilities to use for your initial model.\u00a0 However, you can select different data types with the Change Data Type menu choice that is available under the PIM menu.\u00a0 Note that the likelihood for the Huggins models is not comparable to the likelihood for the data types that include N<\/em> in the likelihood, so that AIC model selection should only be done within the 2 categories, not between them.<\/li>\n
- These same 6 closed models are now available for the robust design model, as well as the robust design version of Barker’s model.\u00a0 When you select either the robust design, or the Barker robust design, you are asked to select one of the 6 closed models.\u00a0 However, you can change the closed model with the Change Data Type menu choice.<\/li>\n
- The robust design multi-strata model has been added to MARK.\u00a0 The open model of Kendall and Bjorkland (2001 Biometrics 57:1113-1122) has been added, along with a closed version that can operate with any of the 6 closed models described above.\u00a0 When you select the closed robust design multi-strata data type, you are asked to select one of the 6 models, but you can change between them with the Change Data Type menu choice.<\/li>\n
- A new version of the installation program was used that has caused some difficulty, but seems to be working correctly.<\/li>\n<\/ol>\n
April, 2004<\/p>\n
- \n
- The derived parameters from the Huggins models (population size) can now be used in model averaging and variance components.\u00a0 Huggins models can be used with closed capture data, robust-design data, and robust-design multi-strata data.\u00a0 For robust-design multi-strata data, a population estimate is generated for each strata at each primary occasion.\u00a0 To allow derived parameters to be used in the model averaging and variance components analyses, the variance-covariance matrix of the derived parameters must be computed, and so this matrix is also available in either a notepad window or else an Excel spreadsheet.<\/li>\n
- For all of the multi-strata data types, including the robust-design multi-strata models, the transition probability (psi) obtained by subtraction can be selected with the Change PIM definitions menu choice.\u00a0 Previously, the default value of psi was the probability of remaining in the strata (e.g., psi A to A).\u00a0 Now, the user can select the transition probability to obtain by subtraction.\u00a0 This change allows fixing the probability of remaining in the strata to zero, a model that was not possible in the previous versions of MARK.\u00a0 One problem with this change is that care must be taken to only model average parameter values for psi PIMs that share the same definition.\u00a0 More details on this capability can be found in the MARK help file.<\/li>\n
- A new method of estimating the overdispersion parameter, c<\/em>, has been implemented with the Median chat menu choice.\u00a0 The approach is to simulate data for a range of c<\/em> values, and perform a logistic regression to estimate the value of c<\/em> for which the probability of obtaining a deviance c-<\/em>hat greater than the observed deviance c-<\/em>hat is 0.5.\u00a0\u00a0 More details on this procedure can be found in the MARK help file.<\/li>\n<\/ol>\n
May, 2004 — Version 4.0 — Female Lark Bunting<\/p>\n
- \n
- Numerous small irritations were fixed with this version (even though I’m sure you have many more that you would like to see addressed).<\/li>\n
- The capability to run multiple multiple models simultaneously has been re-instituted into the program, no matter what operating system is being used.\u00a0 Thus, you can start the numerical optimization of a model, and then begin preparing and then start of the optimization of additional models while the first one continues to execute.<\/li>\n
- Markov Chain Monte Carlo (MCMC) estimation for all data types is now implemented, mainly to provide increased capability for estimation of variance components.\u00a0 However, the code is for this estimation procedure is aimed primarily at modeling the beta parameters for models constructed with the logit link function, although inputs can be provided to accommodate other link functions for specific data types, such as the log link for population estimates (N<\/em>).\u00a0 The one or more hyperdistributions being modeled are thus generally on the logit scale.\u00a0 However, considerably flexibility has been provided to model the hyperdistributions, including a design matrix on the means and a full variance-covariance matrix to estimate process covariances (actually correlations).\u00a0 MCMC estimation is obtained by checking the MCMC check box in the Run Window.\u00a0 Typically the parameter estimates from the ML procedure would be used as the initial parameter values for the MCMC procedure to ensure that the Markov Chain is started at reasonable values.\u00a0 Output is displayed in a NotePad Window, consisting of the mean, standard deviation, mode, and median of the posterior distribution, plus the 2.5, 5, 10, 20, 80, 90, 95, and 97.5 percentiles.\u00a0 Note that no output is stored in the Results Browser.\u00a0 In addition, a binary file of the sample from the posterior distribution is provided, along with example SAS code in the help file for reading and summarizing this sample.\u00a0 Full details on the MCMC estimation procedure are provided in the MARK help file.<\/li>\n<\/ol>\n
June, 2004<\/p>\n
- \n
- Bug with the parameter labels in the reparameterizations of the multi-strata data types was fixed.\u00a0 This fix will not correct models that have already been run and are retrieved.\u00a0 Rather, the model will have to be reconstructed to fix the incorrect labels.<\/li>\n
- MCMC algorithm was modified to make the step size for the jumping distribution be adaptive.\u00a0 After 100 steps, the percent of jumps is evaluated and a new value computed to try to obtain 40% of the steps making a new jump.<\/li>\n<\/ol>\n
July, 2004<\/p>\n
- \n
- Bug with the multi-strata model with unequal time intervals was fixed.\u00a0 This bug was introduced during the update of April, 2004, when the modification to allow the user to specify the psi obtain by subtraction was implemented.<\/li>\n
- All 6 of the closed captures models now work with the median c<\/em>-hat estimation procedure, and can be simulated.\u00a0 Note that for the closed capture models with N<\/em> in the likelihood, the value of N<\/em> is specified as the actual value, and the link function specified for the true model is ignored for the N<\/em> parameters.<\/li>\n<\/ol>\n
July, 2004 — Version 4.1 — Female Lark Bunting<\/p>\n
- \n
- A new Visual Objects compiler was used to create the interface on July 14, 2004.\u00a0 The new compiler uses a different set of DLL files.\u00a0 Therefore, uninstall MARK and then install this new version.\u00a0 The old executable files will not work with the new DLL files, and vice versa.\u00a0 This new version seems to be more stable.\u00a0 As usual, please let me know of any bugs you encounter.<\/li>\n
- The simulation capability was added for the robust design models.\u00a0 Unfortunately, specification of the inputs for these models is tricky, in that for the second and following primary sessions, you do not actually specify the population size, but rather the number of new recruits.\u00a0 For the closed population models where N<\/em> is in the likelihood, you must specify the size of the initial population and the numbers of new recruits as actual values, regardless of the link function for the rest of the parameters.\u00a0 For the Huggins closed captures models, a separate tab window requests the initial population size, and the numbers of new recruits.<\/li>\n
- AICc values were incorrectly computed for models with deviance equal zero when a new value of c<\/em>-hat was applied.\u00a0 This bug is now fixed, but will not correct the AICc values in results files that have already been affected by this problem.\u00a0 To check to see if the AICc value is correct, recompute the model’s estimates and verify that the old and new values of AICc are the same.\u00a0 If not, then you will have to recompute the estimates for all the models to get back the correct AICc values.<\/li>\n<\/ol>\n
August, 2004<\/p>\n
- \n
- The ability to simulate multiple Markov chains was implemented in the MCMC estimation algorithm.<\/li>\n
- The “gibbsit” algorithm to estimate samples sizes for Markov chains in the MCMC estimation algorithm was implemented.<\/li>\n<\/ol>\n
December, 2004<\/p>\n
- \n
- A bug with viewing the selection of the parameter to constrain in the multistrata models was fixed.<\/li>\n
- The default value for the maximum number displayed in the PIMs was set to 9999.\u00a0 However, memory limits on your machine likely will still limit the size of MARK models.<\/li>\n<\/ol>\n
February, 2005<\/p>\n
- \n
- A bug with fixed parameters in simulating median c-hat values was fixed.<\/li>\n<\/ol>\n
April, 2005 — Version 4.2 — Female Lark Bunting<\/p>\n
- \n
- The models for estimation of closed populations when there is mis-identification of animals developed by Paul Lukacs, Colorado State University, have been added to MARK.\u00a0 These models were developed for use with DNA marking, when there is a “small” probability of the genotype of the animal being incorrectly determined because of low quantity or poor quality DNA.\u00a0 The existing 6 closed captures models in MARK were all extended to include the alpha parameter (probability that the animal was correctly identified, with 1 – alpha the probability that the animal was incorrectly identified).\u00a0 Thus there are 12 closed captures models now in MARK.\u00a0 Because the population size is no longer included in the likelihood with the mis-identification models, all of the closed captures models now also report the population estimate as a derived parameter, so that model averaging and variance components analyses can be conducted across these different data types.\u00a0 That is, model averaging is appropriate for the closed captures models with and without the mis-identification parameter is appropriate.\u00a0 However, as before, the Huggins models and the models that include the population size (N<\/em>) or more technically, the number of animals never encountered (f<\/em>0<\/sub>), do not have comparable likelihood values, so you should not try to perform model selection when both of these models are in the same results browser window.<\/li>\n
- The 6 robust design data types were extended to incorporate the mis-identification parameter, so that there are now 12 different robust design data types.<\/li>\n
- The 6 multi-strata robust design data types were extended to incorporate the mis-identification parameter, so that there are now 12 different multi-strata robust design data types.\u00a0 Note that the Barker robust design data types have NOT been extended to incorporate the mis-identification parameter because the most likely place in these models where the mis-identification would take place is the resightings during an interval (coded as a “2”), and the theory has not been worked out to handle mis-identification with this type of encounter.<\/li>\n
- A problem with retrieving the muti-strata models when the psi parameters had been redefined was fixed.\u00a0 Previously, the redefinitions of the psi parameters was lost when a model was retrieved, so that the default of obtaining the probability of staying in the current stratum was obtained by subtraction was always the case.\u00a0 Now, the redefinitions are maintained when the model is retrieved from the results browser.<\/li>\n
- A problem with retrieving models with design matrices that used the POWER function has now been fixed.<\/li>\n<\/ol>\n
August, 2005<\/p>\n
- \n
- An additional derived parameter (gross births, B<\/em>*) was added to 2 existing derived parameters (net births, B<\/em>; population size, N<\/em>) for the POPAN data type.\u00a0 The capability to do model averaging and variance components for these 3 derived parameters was added.<\/li>\n
- The estimation of the derived parameters for the known fate model (probability of surviving the entire interval) was cleaned up so that model averaging and variance components can be performed with this derived parameter.<\/li>\n<\/ol>\n
November, 2005<\/p>\n
- \n
- Modifications to the MCMC estimation code have been made: 1) the order of the beta parameters is randomized on each Markov chain iteration; 2) -2log likelihood is now stored and reported in the output, including the MCMC.BIN file, 3) the deviance information criterion (DIC) is reported in the output; 4) a “tuning” phase has been included prior to the burn-in phase to adjust the standard deviation of the proposal distribution; 5) the standard deviation of the proposal distribution is no longer changed during the iterations where the parameter values are sampled; and 5) values of the SAS macro variables to process the MCMC.BIN file are printed at the bottom of the MCMC output, and this output block can be copied and pasted into the SAS code to process the MCMC.BIN file.<\/li>\n
- A problem with reading encounter history file records longer than 256 characters was fixed.\u00a0 Encounter history records can now be up to 2048 characters.<\/li>\n<\/ol>\n
December, 2005<\/p>\n
- \n
- The open robust-design multistrata data type has been modified to now show the phi parameter as an upper-triangular matrix.\u00a0 This modification means that age models can be constructed, i.e., models that recognize the time since the animal first entered the primary session.\u00a0 Bill Kendall has developed this model based on the original Kendall and Bjorkland (2001) paper: Kendall, W.L. and R. Bjorkland. 2001. Using open robust design models to estimate temporary emigration from capture-recapture data.\u00a0 Biometrics 57(4): 1113-1122.\u00a0 Unfortunately, with this change in the PIM structure, you can no longer recall the previous models without getting errors.\u00a0 If you are using this data type currently, you may not want to update your version of MARK until you finish your current analysis.\u00a0 Additional derived parameters will be added to this data type in the near future to provide estimates of residency time and population size.<\/li>\n
- A labeling bug was fixed with the variance components module when a user-specified design matrix was used.<\/li>\n
- A bug when simulating models with fixed parameters was fixed.<\/li>\n<\/ol>\n
February, 2006, Version 4.3<\/p>\n
- \n
- A message asking the user if they really want to exit out of MARK has been added because some users are accidentally clicking the red exit box in the upper right corner of the MARK application.\u00a0 If you feel this message is not needed, you can turn it off in the File | Preferences menu.\u00a0\u00a0 Personally, I recommend you look before you click!<\/li>\n
- The design matrix is now scaled internally so that you do not have to use the “Standardize Individual Covariates” to scale covariates to obtain numerical convergence.\u00a0 Hence, the only reason to use the standardize option is to have the mean of the covariates equal to zero.<\/li>\n
- The revised variance formula is now the default for model averaging, i.e., this box is checked when you start the model averaging dialog.\u00a0 If you want the original equation, uncheck this box.<\/li>\n
- The absolute value link function has been added to the list of possible link functions.\u00a0 The absolute value link is handy for closed captures models when the estimated population size is close to M(t+1) because the parameter is counted as being estimated.\u00a0 In contrast, the default log link will not count such parameters as being estimated because the beta value is approaching negative infinity.\u00a0 The absolute value link function works particularly well with the Coulombe closed captures example distributed with MARK.\u00a0 To access the absolute value link function for population estimation, you must use the “Parm. Specific” link option.<\/li>\n<\/ol>\n
March, 2006<\/p>\n
- \n
- Robust-design Pradel models were added for the closed model situations with no mis-identification for a total of 18 new models.\u00a0 These models do not include the gamma” and gamma’ parameters (temporary emigration), so the Pradel robust design models assume no temporary emigration.\u00a0 Also turns out that the mis-identification models do not work well with Pradel models, so these 18 additional models are on hold.\u00a0 The File | New dialog page now only shows one entry for Pradel models, but when you click on this entry, a list of the possible models comes up.\u00a0 Then, if you select a robust design model, you are asked to select from the possible closed models.<\/li>\n
- An option has been added to the File | Preferences dialog to specify the location of the editor you want to use to view MARK text files.\u00a0 The default is NotePad, but you can change this default to WordPad or another editor of your choice.<\/li>\n<\/ol>\n
April, 2006<\/p>\n
- \n
- Simulation capability for the robust-design Pradel models for the 6 types of closed models times the 3 types of Pradel models (gamma, lambda, f) has been added so that power analyses can be conducted to design surveys.<\/li>\n<\/ol>\n
July, 2006<\/p>\n
- \n
- An option was added under the Adjustments menu of the Results Browser to specify the effective sample size for computing AICc and QAICc.\u00a0 The reason for this adjustment is the different types of parameters in the robust design capture-recapture and also robust design occupancy data types.\u00a0 Parameters related to primary occasions can be considered to have different sample sizes than parameters related to secondary occasions within primary occasions.\u00a0 The effective sample size is now stored in the data file so that all models in the results browser will have used the same effective sample size to compute AICc or QAICc.\u00a0 The default value is still the value computed by the MARK numerical routine.<\/li>\n
- Effective sample size calculations for the robust design occupancy data types were changed to be the sum of the number of sites sampled in each primary occasion, rather than just the number of sites sampled in the first primary occasion.\u00a0 The effective sample size for the regular occupancy data type is still the number of sites visited.<\/li>\n
- A bug with the specification of the Burnham Jolly-Seber data type was fixed.\u00a0 However, this model is still not recommended for general use.\u00a0 Rather, use the Pradel, Link-Barker, or POPAN data types.\u00a0 The Burnham Jolly-Seber data type often does not converge to the maximum likelihood estimates, whereas the other models usually do.\u00a0 Note, however, that the Pradel lambda (\u03bb) data type does not enforce the constraint that lambda(i) >= phi(i), so you can get nonsensical answers from this parameterization.\u00a0 Hence, I generally recommend use of the Pradel recruitment (f<\/em>) data type.<\/li>\n<\/ol>\n
September, 2006<\/p>\n
- \n
- The capability to increment or decrement the font size in the design matrix and the results browser was implemented.\u00a0 Buttons were added to the task bar to make this task quick to implement.<\/li>\n
- Calculation of profile likelihood confidence intervals was modified to include c-hat for data sets where overdispersion (c-hat > 1) is specified.\u00a0 When c-hat > 1, the profile intervals are only available in the full output window because the user can change the value of c-hat and the profile intervals would not be automatically changed.\u00a0 More details are provided in the help file in the Profile Likelihood Confidence Intervals entry.<\/li>\n<\/ol>\n
January, 2007<\/p>\n
- \n
- Mixture models were added to the occupancy data type and the robust design occupancy data type.\u00a0 You can access these models from the “Change Data Type” choice under the PIM menu when you open up the basic data types.<\/li>\n<\/ol>\n
April, 2007<\/p>\n
- \n
- I’ve discovered a problem with the Huggins-Pledger mixture models, where the conditioning on the never-seen category was done independently for each mixture rather than jointly.\u00a0 The new code corrects this problem, but is going to change estimates of pi and the derived population estimates.\u00a0 In most cases, the estimates of the p<\/em>‘s are the same under both the old and new parameterizations, with changes only in estimates of pi and the derived N<\/em>.\u00a0 But, I have also found datasets where the new parameterization does not converge to reasonable values, e.g., the either of the Carothers taxicab datasets distributed with the program.<\/li>\n<\/ol>\n
May, 2007<\/p>\n
- \n
- Four additional functions were added to the list of design matrix functions.\u00a0 The max and min functions return the maximum or minimum of the 2 arguments.\u00a0 As an example with the individual covariate Length, max(5,Length) entered into the design matrix cell will return a value of 5 or the value of Length if >5.\u00a0 The log and exp functions only use a single argument, and return the natural logarithm or the exponential of the argument.<\/li>\n
- The Reorder Columns command to reorder the columns of the design matrix has been added to the Appearance menu, but also remains in the popup menu that you get by right-clicking the design matrix.<\/li>\n
- Options to use the alternate optimization method (simulated annealing) for estimation have been added to the simulation and the median c-hat procedures.<\/li>\n
- The capability to bootstrap data (encounter histories) has been added to the simulation menu.\u00a0 To make this useful, you need an individual covariate that “blocks” sets of encounter histories.\u00a0 Details on the use of this procedure are in the help file under “Bootstrap Data”.<\/li>\n<\/ol>\n
June, 2007<\/p>\n
- \n
- The help file was updated to include R code for diagnostic analysis of the MCMC.BIN file, with the MARK output including parameter specification for the R code.<\/li>\n
- Cormack-Jolly-Seber (Live Captures) encounter histories will now allow dots (“.”) in the encounter history to identify occasions in the encounter history where no survey was conducted.<\/li>\n
- Huggins closed captures models will also allow dots (“.”) in the encounter history to identify occasions where no survey was conducted.\u00a0 However, dots do not work with the regular closed-captures models with N<\/em> in the likelihood, because there is no way to estimate a probability of the all zero encounter history when some of the\u00a0 animals never captured may not have actually been surveyed.<\/li>\n<\/ol>\n
August, 2007<\/p>\n
- \n
- The Royle and Nichols (2003) single-species occupancy model with the Poisson assumption has been added.\u00a0 The real parameters arer<\/em> and lambda, and psi and\u00a0 E(p<\/em>) are computed as derived parameters.\u00a0 To allow model averaging, psi has been added as a derived parameter for the other single-species occupancy models.\u00a0 The negative binomial version of the Royle and Nichols model has also been added to MARK, where the\u00a0 r<\/em> and lambda parameters are the same as for the Poisson model.\u00a0 The third real parameter is the amount of variance (VarAdd) that is increased over the mean density.\u00a0 Thus, if you run the negative binomial model and fix VarAdd to equal zero, you get back the same estimates as you would from the Poisson model.\u00a0 My main purpose in adding these data types is to account for heterogeneity across the sites, rather than produce an estimate of density.\u00a0 I’m quite skeptical about the usefulness of the density estimate in most scenarios.<\/li>\n<\/ol>\n
September, 2007<\/p>\n
- \n
- A bug was fixed with almost all of the robust design models that caused an array bounds error when the last primary session had the most secondary occasions.<\/li>\n
- A bug was fixed in the Pradel f<\/em> parameterizations for datasets with time intervals between occasions unequal to\u00a0 1.\u00a0 The f<\/em> parameterization now produces estimates consistent with the gamma and lambda parameterizations for unequal time intervals.<\/li>\n<\/ol>\n
October, 2007<\/p>\n
- \n
- Previously, I had build in a kluge in MARK to try to detect whether you were running Excel 2003 or Excel 2007 by checking the path to your Excel executable.\u00a0 However, it turns out that Excel 2007 can be run in a compatibility mode that makes it look like Excel 2003 to MARK.\u00a0 Therefore, I set an option in the File | Preferences window for the user to select Excel 2007 as your spreadsheet.\u00a0 If writing to an Excel file does not work on your system, try specifying the Excel 2007 option.<\/li>\n
- The mark-resight models that Brett McClintock<\/a> has developed for his Ph.D. work have been implemented.\u00a0 Details on running these models in MARK are contained in a PDF file here<\/a>.<\/li>\n
- The multiple-state occupancy model of Nichols et al. (2007) has been implemented.\u00a0 In addition, the other occupancy models (basic, Pledger heterogeneity, Royle-Nichols) have been modified to treat the “2” in encounter histories as a “1”, so that the multiple-state occupancy model is available from the “Change Data Type” menu choice in the PIM menu.<\/li>\n
- The Poisson and Negative Binomial Count models of Royle (2004) have been implemented.\u00a0 The data for these two data types requires that counts be entered in the encounter histories using the 2 characters of the LD pair to enter integers from 00 to 99.\u00a0 Therefore, these two data types are not compatible with the other occupancy models, but you still select these models from the Occupancy button on the new analyses page.\u00a0 Two examples are provided through the help file.\u00a0 Royle, J. A. 2004. N<\/em>-mixture models for estimating population size from spatially replicated counts. Biometrics 60:108-115.<\/li>\n<\/ol>\n
December, 2007<\/p>\n
- \n
- When\u00a0 the Visual Objects compiler was upgraded to version 2.7, users with “large” data sets encountered difficulties with re-opening a MARK DBF file that contained model results.\u00a0 I’ve fought with this bug ever since, and I think I figured out the solution (i.e., what was changed when the new compiler was used).\u00a0 So, try the new version posted on the web and see if you can now open your DBF files with it, and not have to use the pre-2.7 version of MARK.<\/li>\n
- Version 5.0 of MARK is now available as a test version.\u00a0 This version has been compiled with a new Visual Objects compiler, version 2.8.\u00a0 See the section above for details.<\/li>\n<\/ol>\n
February, 2008<\/p>\n
- \n
- Version 5.0 of MARK is now the production version.\u00a0 You should uninstall your old versions, or else install this new version in a different subdirectory.<\/li>\n<\/ol>\n
April, 2008 Version 5.1<\/p>\n
- \n
- Design matrix functions (add, product, power, min, max, log, exp, lt gt, le, ge, eq) can now be called recursively.\u00a0 So, an entry in the design matrix such as<\/li>\n<\/ol>\n
log(add(age,30))<\/p>\n
will now work.\u00a0 The price of this recursion is that the “COLx” capability to refer to a prior column in the design matrix had to be removed.\u00a0 See the help file on design matrix functions for full details.<\/p>\n
- \n
- The ability to enter missing encounter history data with a dot (‘.’) has been extended to the robust design and multi-state closed robust design data types.\u00a0 This capability was already available for the Cormack-Jolly-Seber (see # 139 above) and the multi-state data types.<\/li>\n
- The Pradel seniority (\u03b3) and Pradel recruitment (f<\/em>) data types now produce the full variance-covariance matrix of the derived lambda parameters.\u00a0 Therefore, variance components on lambda can now be conducted with these lambda estimates.<\/li>\n
- Four additional variables have been added to the Results File: BIC (Bayesian Information Criterion), -2log(Likelihood), a Time Stamp, and a memo field to contain notes about the model.\u00a0\u00a0 To see these variables in the Results Browser window, you have to go to File | Preferences and click on the appropriate checkboxes.\u00a0 If you select BIC, then AICc will not be displayed, and the model weights and model likelihood values will reflect BIC values, instead of AICc values.\u00a0 The Results Browser can be ordered (sorted) by BIC.\u00a0 I should have included -2log(Likelihood) in the Results File from the beginning, but at the time, Deviance seemed adequate.\u00a0 However, with the inclusion of many data types in MARK where the computation of Deviance is not clear, -2log(Likelihood) seems necessary.\u00a0 The Time Stamp is included so that you can see when the model was run.\u00a0 The format is YY:MM:DD:HH:MM:SS, i.e., last 2 digits of year, month, day, hour, minute, and seconds.\u00a0 The Results Browser can be ordered (sorted) on the Time Stamp.\u00a0 Model Notes can be included to describe some details about the model.\u00a0 An icon is on the tool bar, or a menu choice is available under the Adjustments menu.\u00a0 The conversion of old files to the new format that includes these four additional variables should be seamless, but just to be safe, you may want to back up your old Results File (both the DBF and FPT files) before opening the Results File with the new version.\u00a0 The MARK Help File has been updated to describe these new capabilities.<\/li>\n
- The capability to add notes about the entire analysis is now included.\u00a0 The File Notes menu choice is under the Output menu choice in the Results Browser, or available with an icon on the tool bar.<\/li>\n
- A bug was fixed so that the mean value of an individual covariate is now correctly computed when the encounter history frequency is >1.<\/li>\n
- The capability to plot the real parameter estimate as a function of individual covariate values has been implemented.\u00a0 An icon on the Toolbar and a menu entry in the Results Browser under Output | Specific Model Output | Individual Covariate Plot provide access to this capability.\u00a0 A plot of the function and 95% confidence intervals are provided, with the capability to set other individual covariates to specified values.\u00a0 The values can also be downloaded to Excel to produce publication quality plots, or more complex plots.\u00a0 See the help file “Individual Covariate Plot” for details.<\/li>\n<\/ol>\n
May, 2008<\/p>\n
- \n
- The Poisson log-normal mark-resight model was modified to produce an estimate of the unmarked population size as a real parameter (U<\/em>), and the estimate of the entire population size (N<\/em>) as a derived parameter.\u00a0 This change fixed a bug that caused the estimate of N<\/em> to be too small for cases where the number of marked animals was unknown.\u00a0 In addition, the mean resighting rate for this model was added as a second derived parameter.<\/li>\n
- Mark-resight models were modified to correctly estimate population size when individual covariates were used in the model.<\/li>\n<\/ol>\n
June, 2008<\/p>\n
- \n
- The capability to “lasso” PIM boxes in the PIM Chart has been added.\u00a0 Hold down the Shift key, then hold down the left mouse button and drag the cursor to “lasso” multiple PIM boxes.\u00a0 A dashed-line rectangle will enclose the lassoed boxes.\u00a0 The lower left corner of the box (the “origin”) must be in the lasso to be collected.\u00a0 Then, release the left mouse button and then the Shift key, and the collected boxes will be colored green.\u00a0 This collection can now be moved to the desired location.<\/li>\n
- A bug in the PIM Chart was fixed that allowed users to generate negative values in the PIM by dragging a box to the left of parameter 1.<\/li>\n
- The width of design matrix columns are now set wide enough to display the labels at the top of the columns.<\/li>\n
- The median c-hat and parametric bootstrap procedures were modified to use the real parameter values with an identity link to specify the model to be simulated.\u00a0 This change means that individual covariates cannot be used, but they couldn’t anyway.\u00a0 The reason for the change was to eliminate the need to handle link functions in the specification of the model to be simulated.<\/li>\n
- Columns in the design matrix are automatically reordered when a model is run if the user has changed the order.<\/li>\n
- A short-hand version of constant PIMs (PIMs with all values the same) was implemented to reduce the size of the input and output files, and to speed up the processing of the output file.<\/li>\n<\/ol>\n
July, 2008<\/p>\n
- \n
- A short-hand version of time PIMs (PIMs with a time structure, as described in the help file) was implemented to reduce the size of the input and output files, and to speed up the processing of the output file.<\/li>\n
- The help file was converted to the html (*.chm) format, so that now MARK is more compatible with Windows Vista.<\/li>\n<\/ol>\n
October, 2008<\/p>\n
- \n
- A choice under the Run menu in the Results Browser has been added to allow the user to specify columns from a design matrix as variables, and to then run all possible combinations of these variables.\u00a0 Details are provided in the help file.<\/li>\n<\/ol>\n
November, 2008<\/p>\n
- \n
- An immigration-emigration version of the logit-normal mark-resight data type has been implemented.<\/li>\n<\/ol>\n
February, 2009<\/p>\n
- \n
- A bug in the MCMC output summary was fixed.\u00a0 Beta estimates for columns with a scaling factor not equal to 1 (see item 121 above, February 2006) reported incorrect values of the mean and standard deviation of the posterior distribution.\u00a0 The median and mode were correct for these beta estates, as were the values written to the binary output file.<\/li>\n<\/ol>\n
March, 2009<\/p>\n
- \n
- A bug was fixed for the Huggins models with dots in the encounter histories.\u00a0 This bug affected all of the Huggins models, including the robust design and robust design multi-state models.\u00a0 So, if you have been using the Huggins model with dots in the encounter histories, re-run your models.<\/li>\n<\/ol>\n
April, 2009<\/p>\n
- \n
- The Huggins data types were modified with respect to how losses on capture and dots in the encounter history were handled.\u00a0 Previously, losses on capture were just added onto the estimate obtained by ignoring these individuals.\u00a0 The new code actually incorporates the losses on capture and individuals with dots in their encounter history into the calculation of p<\/em> and c<\/em>, then also into the calculation of N<\/em>.<\/li>\n
- The mis-identification models were modified to compute the confidence interval on N<\/em> from a lognormal distribution.<\/em><\/li>\n
- The individual covariate plot was fixed to properly handle values of the over-dispersion parameter c<\/em> > 1.<\/li>\n<\/ol>\n
June, 2009<\/p>\n
- \n
- The Kendall robust design data types were modified to make the gamma parameters a function of the time interval. This was done to make them consistent with the Barker robust design data types. So, if you have been running the Kendall robust design data type with unequal time intervals between primary sessions, your results will now differ from your previous runs.\u00a0 Note that an alternative to the Kendall robust design data type is to use the robust design multi-state data types with an unobserved state.\u00a0 For multi-state data types, the psi parameters are not a function of the time interval between primary sessions because the transitions are assumed to take place at the end of the interval.\u00a0 Further, I discovered that the gamma parameters in the Barker robust design data types are actually complements of the gamma parameters of the Kendall robust design. I thought this would be easy to correct, but it turns out to not be so. Hence, for the present, recognize that the estimates from the Barker robust design data type correspond to the F<\/em> and F<\/em>‘ of the Barker live-dead data type.<\/li>\n
- The Lukacs et al. (2004) model was implemented to estimate survival of young from marked adults. Counts of young are entered as pairs of digits in the encounter history — see the help file for details. Lukacs, P. M., V. J. Dreitz, F. L. Knopf, and K. P. Burnham. 2004. Estimating survival probabilities of unmarked dependent young when detection is imperfect. Condor 106:926-931.<\/li>\n
- A bug was fixed that caused incorrect effective sample sizes when a new results file was created, and only saved structures were put into the file.<\/li>\n
- Bugs were fixed with running CAPTURE and RELEASE from MARK when there were blanks in the subdirectory or file names.<\/li>\n<\/ol>\n
July, 2009<\/p>\n
- \n
- Simulation capability was added for the Lukacs et al. (2004) model of young survival from marked adults.<\/li>\n
- The Subsets of Models capability was modified to allowthe user to modify variable names, and to specify the maximum number of variables to be included in models in addition to variables specified to be always in the models. For example, a maximum variables per model value of 1 with 3 variables and anintercept that is specified to always be included would result in 4 models beingrun.<\/li>\n<\/ol>\n
August, 2009<\/p>\n
- \n
- Appropriate confidence limits were added to the median c-hat calculation.\u00a0 Two-sided confidence limits are picked off the logistic regression line for 2.5% and 97.5%, and a one-sided bound is reported for 95%.\u00a0 The SE reported represents the sampling variation from the Monte Carlo sampling process, whereas these confidence bounds represent the uncertainty from the data.\u00a0 To generate “good” estimates of the confidence bounds, you may have to prescribe more simulations for these tail areas.\u00a0 Because the lower bound on c<\/em> is 1, the lower bound is not particularly useful.<\/li>\n
- MARK has the capability to import data\/output from RMark.\u00a0 A new function export.MARK is now included in RMark that writes out a .Rinp file with the necessary parameters to define the model to MARK, the .inp file (encounter histories file) with the data and optionally any output files that should be imported into MARK.\u00a0 The .DBF and .FPT files (Results File) will be created in the same subdirectory as the .Rinp file is located.\u00a0 The RMark import capability should prevent the problems where folks have unknowingly changed the group structure or other aspects of the problem in creating the MARK project to import the RMark results.\u00a0 This caused some folks to get discrepancies.\u00a0 With this export\/import facility the data\/model structures will match between MARK and RMark.\u00a0 Also, it will import a whole set of model results rather than importing them one at a time with MARK.\u00a0 Note that you do need to delete the .tmp files manually after importing them into MARK.\u00a0 The RMark Import menu choice is under the File menu.<\/li>\n<\/ol>\n
September, 2009<\/p>\n
- \n
- Effective sample size calculation was changed for the logit-normal and immigration-emigration logit-normal mark-resight estimators.<\/li>\n<\/ol>\n
October, 2009<\/p>\n
- \n
- The multi-site occupancy model was installed, along with the ability to simulate this data type.\u00a0 Details are provided in the help file under the “Occupancy Estimation Multi-site” and in the article: Nichols, J. D., L. L. Bailey, A. F. O’Connell, N. W. Talancy, E. H. C. Grant, A. T. Gilbert, E. M. Annand, T. P. Husband, and J. E. Hines. 2008. Multi-scale occupancy estimation and modelling using multiple detection methods. Journal of Applied Ecology 45:1321-1329.<\/li>\n<\/ol>\n
January, 2010, Version 6.0<\/p>\n
- \n
- Version 6.0 is now the production version. In this version, the graphics package has been replaced with simpler code to reduce problems with installation. A 64-bit version of the mark.exe file is available upon request for those of you with VERY large jobs that require addditional memory.<\/li>\n
- The multiple state occupancy model with imperfect detecion single season model (Nichols, J. D., J. E. Hines, D. I. MacKenzie, M. E. Seamans, and R. J. Gutierrez. 2007. Occupancy estimation and modeling with multile states and state uncertainty. Ecology 88:1395-1400.) and the robust design moddel (MacKenzie, D. J., J. D. Nichols, M. E. Seamans, and R. J. Gutierrez. 2009. Modeling species occurrence dynamics with multiple states and imperfect detection. Ecology 90:823-835.) are now implemented. Both the general parameterization and the conditional binomial parameterization were implemented for the robust design data type. Details on how to use these data types are in the MARK help file under Occupancy Estimation Multiple States Robust Design.<\/li>\n<\/ol>\n
April, 2010<\/p>\n
- \n
- An estimator of density from trapping grids that uses radio-tracking data has been added. The estimator is a modification of the Huggins closed population estimator. See the help file (“Density Estimation”) for more details.<\/li>\n<\/ol>\n
May, 2010<\/p>\n
- \n
- The capability to include a Pledger mixture model to account for unmodeled heterogeneity has been included for the Cormack-Jolly-Seber data type, the Pradel data types, and the Link-Barker data type. For the Pradel (1996) and Link-Barker (2005) data types, the mixture can only be used with the detection probabilities (p<\/em>), but the implementation for the CJS data type follows the original Pledger et al. (2003) paper. You obtain access to these new data types through the PIM | Change Data Type menu choices from one of your existing CJS or Pradel MARK files.<\/li>\n<\/ol>\n
June, 2010<\/p>\n
- \n
- Numerical integration via the Gauss-Hermite quadrature (GHQ) can be efficiently used to approximate the capture\u2013recapture model likelihood with individual random effects. This technique has been employed for the Cormack-Jolly-Seber data type (Gimenez, O., and R. Choquet. 2010. Individual heterogeneity in studies on marked animals using numerical integration: capture-recapture mixed models. Ecology 91:951-957.), and the Link-Barker data type. For each basic parameter, an additional parameter is addded to specify the standard deviation (sigma) of the normal distribution You obtain access to these new data types through the PIM | Change Data Type menu choices from one of your existing CJS or Pradel MARK files. The mark-resight models had already incorporated this technique to model individual heterogeneity.<\/li>\n
- An additional 2-species occupancy model (Richmond et al. 2010) has been added to supplement the McKenzie et al. (2006) model. The McKenzie parametrization does not handle covariates well, whereas the new conditional occupancy model does successfully incorporate covariates, and is nmerically more stable.<\/li>\n<\/ol>\n
<\/p>\n
![](\"https:\/\/sites.warnercnr.colostate.edu\/gwhite\/wp-content\/uploads\/sites\/73\/2017\/05\/anarule.gif\")
<\/p>\n","protected":false},"excerpt":{"rendered":"April, 1998 Making the PIM Chart interactive, so that you can edit the PIM’s by dragging blocks of parameters, or by right-clicking on a block and changing the PIM. \u00a0 This feature makes for rapid editing, and also allows editing …<\/p>\n
- The capability to include a Pledger mixture model to account for unmodeled heterogeneity has been included for the Cormack-Jolly-Seber data type, the Pradel data types, and the Link-Barker data type. For the Pradel (1996) and Link-Barker (2005) data types, the mixture can only be used with the detection probabilities (p<\/em>), but the implementation for the CJS data type follows the original Pledger et al. (2003) paper. You obtain access to these new data types through the PIM | Change Data Type menu choices from one of your existing CJS or Pradel MARK files.<\/li>\n<\/ol>\n
- An estimator of density from trapping grids that uses radio-tracking data has been added. The estimator is a modification of the Huggins closed population estimator. See the help file (“Density Estimation”) for more details.<\/li>\n<\/ol>\n
- The multi-site occupancy model was installed, along with the ability to simulate this data type.\u00a0 Details are provided in the help file under the “Occupancy Estimation Multi-site” and in the article: Nichols, J. D., L. L. Bailey, A. F. O’Connell, N. W. Talancy, E. H. C. Grant, A. T. Gilbert, E. M. Annand, T. P. Husband, and J. E. Hines. 2008. Multi-scale occupancy estimation and modelling using multiple detection methods. Journal of Applied Ecology 45:1321-1329.<\/li>\n<\/ol>\n
- MARK has the capability to import data\/output from RMark.\u00a0 A new function export.MARK is now included in RMark that writes out a .Rinp file with the necessary parameters to define the model to MARK, the .inp file (encounter histories file) with the data and optionally any output files that should be imported into MARK.\u00a0 The .DBF and .FPT files (Results File) will be created in the same subdirectory as the .Rinp file is located.\u00a0 The RMark import capability should prevent the problems where folks have unknowingly changed the group structure or other aspects of the problem in creating the MARK project to import the RMark results.\u00a0 This caused some folks to get discrepancies.\u00a0 With this export\/import facility the data\/model structures will match between MARK and RMark.\u00a0 Also, it will import a whole set of model results rather than importing them one at a time with MARK.\u00a0 Note that you do need to delete the .tmp files manually after importing them into MARK.\u00a0 The RMark Import menu choice is under the File menu.<\/li>\n<\/ol>\n
- Appropriate confidence limits were added to the median c-hat calculation.\u00a0 Two-sided confidence limits are picked off the logistic regression line for 2.5% and 97.5%, and a one-sided bound is reported for 95%.\u00a0 The SE reported represents the sampling variation from the Monte Carlo sampling process, whereas these confidence bounds represent the uncertainty from the data.\u00a0 To generate “good” estimates of the confidence bounds, you may have to prescribe more simulations for these tail areas.\u00a0 Because the lower bound on c<\/em> is 1, the lower bound is not particularly useful.<\/li>\n
- The mis-identification models were modified to compute the confidence interval on N<\/em> from a lognormal distribution.<\/em><\/li>\n
- The Huggins data types were modified with respect to how losses on capture and dots in the encounter history were handled.\u00a0 Previously, losses on capture were just added onto the estimate obtained by ignoring these individuals.\u00a0 The new code actually incorporates the losses on capture and individuals with dots in their encounter history into the calculation of p<\/em> and c<\/em>, then also into the calculation of N<\/em>.<\/li>\n
- A bug was fixed for the Huggins models with dots in the encounter histories.\u00a0 This bug affected all of the Huggins models, including the robust design and robust design multi-state models.\u00a0 So, if you have been using the Huggins model with dots in the encounter histories, re-run your models.<\/li>\n<\/ol>\n
- A bug in the MCMC output summary was fixed.\u00a0 Beta estimates for columns with a scaling factor not equal to 1 (see item 121 above, February 2006) reported incorrect values of the mean and standard deviation of the posterior distribution.\u00a0 The median and mode were correct for these beta estates, as were the values written to the binary output file.<\/li>\n<\/ol>\n
- An immigration-emigration version of the logit-normal mark-resight data type has been implemented.<\/li>\n<\/ol>\n
- A choice under the Run menu in the Results Browser has been added to allow the user to specify columns from a design matrix as variables, and to then run all possible combinations of these variables.\u00a0 Details are provided in the help file.<\/li>\n<\/ol>\n
- Mark-resight models were modified to correctly estimate population size when individual covariates were used in the model.<\/li>\n<\/ol>\n
- Design matrix functions (add, product, power, min, max, log, exp, lt gt, le, ge, eq) can now be called recursively.\u00a0 So, an entry in the design matrix such as<\/li>\n<\/ol>\n
- Version 5.0 of MARK is now the production version.\u00a0 You should uninstall your old versions, or else install this new version in a different subdirectory.<\/li>\n<\/ol>\n
- The Royle and Nichols (2003) single-species occupancy model with the Poisson assumption has been added.\u00a0 The real parameters arer<\/em> and lambda, and psi and\u00a0 E(p<\/em>) are computed as derived parameters.\u00a0 To allow model averaging, psi has been added as a derived parameter for the other single-species occupancy models.\u00a0 The negative binomial version of the Royle and Nichols model has also been added to MARK, where the\u00a0 r<\/em> and lambda parameters are the same as for the Poisson model.\u00a0 The third real parameter is the amount of variance (VarAdd) that is increased over the mean density.\u00a0 Thus, if you run the negative binomial model and fix VarAdd to equal zero, you get back the same estimates as you would from the Poisson model.\u00a0 My main purpose in adding these data types is to account for heterogeneity across the sites, rather than produce an estimate of density.\u00a0 I’m quite skeptical about the usefulness of the density estimate in most scenarios.<\/li>\n<\/ol>\n
- I’ve discovered a problem with the Huggins-Pledger mixture models, where the conditioning on the never-seen category was done independently for each mixture rather than jointly.\u00a0 The new code corrects this problem, but is going to change estimates of pi and the derived population estimates.\u00a0 In most cases, the estimates of the p<\/em>‘s are the same under both the old and new parameterizations, with changes only in estimates of pi and the derived N<\/em>.\u00a0 But, I have also found datasets where the new parameterization does not converge to reasonable values, e.g., the either of the Carothers taxicab datasets distributed with the program.<\/li>\n<\/ol>\n
- Mixture models were added to the occupancy data type and the robust design occupancy data type.\u00a0 You can access these models from the “Change Data Type” choice under the PIM menu when you open up the basic data types.<\/li>\n<\/ol>\n
- Simulation capability for the robust-design Pradel models for the 6 types of closed models times the 3 types of Pradel models (gamma, lambda, f) has been added so that power analyses can be conducted to design surveys.<\/li>\n<\/ol>\n
- The estimation of the derived parameters for the known fate model (probability of surviving the entire interval) was cleaned up so that model averaging and variance components can be performed with this derived parameter.<\/li>\n<\/ol>\n
- The models for estimation of closed populations when there is mis-identification of animals developed by Paul Lukacs, Colorado State University, have been added to MARK.\u00a0 These models were developed for use with DNA marking, when there is a “small” probability of the genotype of the animal being incorrectly determined because of low quantity or poor quality DNA.\u00a0 The existing 6 closed captures models in MARK were all extended to include the alpha parameter (probability that the animal was correctly identified, with 1 – alpha the probability that the animal was incorrectly identified).\u00a0 Thus there are 12 closed captures models now in MARK.\u00a0 Because the population size is no longer included in the likelihood with the mis-identification models, all of the closed captures models now also report the population estimate as a derived parameter, so that model averaging and variance components analyses can be conducted across these different data types.\u00a0 That is, model averaging is appropriate for the closed captures models with and without the mis-identification parameter is appropriate.\u00a0 However, as before, the Huggins models and the models that include the population size (N<\/em>) or more technically, the number of animals never encountered (f<\/em>0<\/sub>), do not have comparable likelihood values, so you should not try to perform model selection when both of these models are in the same results browser window.<\/li>\n
- A bug with fixed parameters in simulating median c-hat values was fixed.<\/li>\n<\/ol>\n
- AICc values were incorrectly computed for models with deviance equal zero when a new value of c<\/em>-hat was applied.\u00a0 This bug is now fixed, but will not correct the AICc values in results files that have already been affected by this problem.\u00a0 To check to see if the AICc value is correct, recompute the model’s estimates and verify that the old and new values of AICc are the same.\u00a0 If not, then you will have to recompute the estimates for all the models to get back the correct AICc values.<\/li>\n<\/ol>\n
- There are 6 different data types for estimation under the closed capture models: Mt<\/em> (p<\/em> and c<\/em>), Mh<\/em> (pi and p<\/em>) and full Mh (pi, p<\/em>, and c<\/em>); with each of these data types available with population size (N<\/em>) in the likelihood, or a Huggins version where the likelihood does not contain N<\/em>.\u00a0 Now, when the Closed Captures data type is selected, you are given a choice of which of these models of capture probabilities to use for your initial model.\u00a0 However, you can select different data types with the Change Data Type menu choice that is available under the PIM menu.\u00a0 Note that the likelihood for the Huggins models is not comparable to the likelihood for the data types that include N<\/em> in the likelihood, so that AIC model selection should only be done within the 2 categories, not between them.<\/li>\n
- Fixed a bug in the Huggins closed-capture heterogeneity models so that N<\/em> is correctly estimated.<\/li>\n<\/ol>\n
- An option was added to the Run Window to allow estimation of profile likelihood confidence intervals for real parameters.\u00a0 However, numerical problems can cause the estimates to be wrong, so use this option carefully.\u00a0 The help file explains the procedure, and shows an example of incorrect profile likelihood confidence intervals.<\/li>\n<\/ol>\n
- Minor fixes to several bugs that have surfaced, including aborts when no pre-selected models are selected, incorrect label on beta labeling window, and specification of default preferences when the program is installed.<\/li>\n<\/ol>\n
- The occupancy estimation model of MacKenzie et al. (2002) has been implemented.\u00a0 The help file has also been updated to describe this model.<\/li>\n<\/ol>\n
- The multi-strata model with live and dead encounters is now working correctly after another bug was fixed on 3 September, 2001.\u00a0 The help file has also been updated to describe this model.<\/li>\n<\/ol>\n
- The nest success data type was further modified to produce warnings when invalid data are entered for a nest record.\u00a0 The help file under the ‘Nest Survival’ topic was updated to present examples of these invalid cases.<\/li>\n<\/ol>\n
- A problem with losses on capture in estimating p<\/em> and c<\/em> of the robust design and Huggins robust design models has been fixed.\u00a0 Previously, S<\/em>, gamma”, and gamma’ were estimated correctly with losses on capture, but not p<\/em> and c<\/em>, and hence N<\/em>.<\/li>\n
- I think I have gotten rid of the problem that caused the message “Unique ID > 8000.\u00a0 Tell Gary”.\u00a0 If you still get this message with the new version, let me know.<\/li>\n<\/ol>\n
- The Graphics Server graphics package was added as the default graphics package.\u00a0 The reason for this second graphics package was to provide better graphics when multiple sets of parameters were displayed.\u00a0 Both packages are available via from the Results Browser Window with the Output | Specific Model Output | Interactive Graphics and the Output | Specific Model Output | Interactive Graphics GS menu choices.<\/li>\n<\/ol>\n
- Seven new models were added to the list of data types:<\/li>\n<\/ol>\n