MSSURVIV is intended to be used in a situation where one is interested in not only survival and capture probabilities, but also transition probabilities (the probability of moving from one stratum to another). The strata may be defined as any discrete categories to which captured animals can be assigned at any given time. For example, strata may be based on such factors as capture location or individual variables such as breeding status or weight class. This situation may be thought of as a more general Jolly-Seber model where a matrix of survival/transition probabilities replaces a single survival rate and a vector of capture probabilities replaces a single capture probability. Output from MSSURVIV includes survival/movement probability estimates, capture probability estimates, goodness-of-fit tests, and likelihood-ratio tests. Estimates may be computed under the "Markovian" models or the "Non-Markovian/memory" models. The Markovian models assume that survival/transition probabilities of an animal depend on the stratum in which the animal is located at the beginning of the interval. The parameters under these models would be a matrix of survival/transition probabilities and a vector of capture probabilities for each time-period. Optionally, MSSURVIV may be instructed to decompose the combined survival/transition probabilities into separate survival and transition probabilities. Non-Markovian models assume that survival/transition probabilities for the interval (i, i+1) depend not only on stratum at time i, but also on stratum at i-1. These models produce much larger matrices of parameter estimates than Markovian models.
By default, three models are generated for either of these two model-sets. The three models are analogous to Models "D", "B", and "A" produced by program JOLLY for standard Jolly-Seber analysis (Pollock et. al., 1990). Model "D" computes estimates under the assumption that survival/transition probabilities and capture probabilities are constant over time. Model "B" assumes survival/transition probabilities are constant over time, but capture probabilities are time-dependent. Model "A" assumes both survival/transition and capture probabilities are time-dependent. If necessary, users may examine these models to generate statements for their own models.
The experimental situation to which this program applies is one in which animals are initially marked with a unique tag, and released. This process is repeated for each of the sampling periods. Information used to assign the animal to the proper stratum (eg. sex, weight, age, capture location, ...) is recorded for each capture of each animal. Using these data, the capture-history of each animal is generated consisting of codes indicating the status of the animal at each capture period. For example, if an animal were captured in stratum A in time-period 1, not captured in periods 2 and 3, and captured in stratum B in time-period 4, the capture history would be: "A 0 0 B". If the variable of interest is a continuous variable, cutpoints must be defined to break it into discrete strata (eg., weight -> weight-class) before the capture-history records can be generated.
Input to MSSURVIV consists of statements which define the capture data and statements defining the selected model structure. The format of the input file is the same as for program SURVIV except that no cell probabilities need be given. The statements defining the data consist of the number of animals captured and released in each time-period and stratum, and the number next recaptured in each subsequent time-period and stratum. Statements which set parameters equal to other parameters define model structure.
Although MSSURVIV eliminates the need for specifying cell probabilities, the job of summarizing capture-history records and defining model structure can be very complicated and can lead to errors. Program CNVMEMOV was created to automate this process. CNVMEMOV reads as input the capture-history records and produces all of the statements necessary to run program MSSURVIV under the "Markovian" or "memory" model sets described above.
Using CNVMEMOV
To run CNVMEMOV, type CNVMEMOV at the DOS prompt and respond to the program prompts. When the program is run, the following should appear on the screen:
Program CNVMEMOV - Converts "Capture-history" data into MSSURVIV input data Date compiled: 1/23/98 Programmer: James E. Hines Biological Research Div., USGS 11510 American Holly Dr. #201 Patuxent Wildlife Research Center Laurel, MD. 20708-4017 email: jim_hines%40usgs.gov Version 2.0 - added capability for >2 age-classes added capability for >1 group ** Note: The order of age-classes in old version was ADULT followed by YOUNG. In this version the order must be from youngest to oldest. ** Also: The old version used "GROUP" as a synonym of "STRATA". (That was stupid!) In this version "STRATA" are the transitional states and "GROUPS" are the separate classification of animals to/from which animals cannot move. Examples of STRATA might be location, breeding-status, weight-class. Examples of GROUP might be sex, species.
After this informatory text is printed, CNVMEMOV attempts to open an output file called CNVMEMOV.OUT. This is the file which will contain the summarized data to be input to program MSSURVIV. If this file already exists (from a previous run) the program will ask if it is OK to overwrite this file. If "y" or "Y" is typed, CNVMEMOV will proceed. Any other response will abort the program.
*** Output file (CNVMEMOV.OUT) exists!, overwrite(Y/N)? Y
The next prompt is for the name of the input file containing the capture-history records. Respond with any legal filename, including drive and subdirectory if not in the current directory.
Enter the name of the file containing the capture-history records FILE:C:\MSSRV\SAMPLE.DAT
If the input file does not exist, CNVMEMOV will print an error message and abort. If it does exist, the first five lines of the file will be displayed on the screen. This helps the user find the column numbers needed for later prompts.
FILE:C:\MSSRV\SAMPLE.DAT 1 2 3 4 5 6 7 ....5....0....5....0....5....0....5....0....5....0....5....0....5....0 A000 491 A00A 15 A00B 12 A00C 11 A0A0 37 ......................................................................
The next prompt is for the number of strata and time-periods. The program requires that you enter two numbers separated by a comma, e.g.,
Enter the number of strata, time-periods, age-classes and groups: STRATA,TIME-PERIODS,AGE-CLASSES,GROUPS(eg 3,7,1,1):3,4,1,1
In this example, there are 4 capture periods, 3 strata (designated A, B, and C), 1 age-class and 1 group. The next prompt is for the codes representing each stratum. These are the codes which represent each of the strata of capture in the capture-history records. Any character other than the ones in this list indicate that the animal was not captured in a time-period. Selected strata may be omitted from an analysis by not including them in the this list. Any single character may be used to represent each of the strata, e.g.,
Enter the codes representing each strata: (If upper case in input file, type upper case here.) STRATA CODES(no quotes or spaces- eg 123):ABC
CNVMEMOV will distinguish between lower and upper case characters for these codes, so it is imperative that these codes match the codes in the data file. If "abc" were entered instead of "ABC", none of the captures would be used.
The next prompt asks for the column numbers containing stratum codes for each time period. The first five lines of data have been listed previously to help locate the column numbers, e.g.,
If strata codes are in consecutive columns, enter the column number of the first strata code, Or enter 0 if they are not sequential FIRST COLUMN OF STRATA CODES:1
The next prompt asks for the column number containing the fate on last capture. If the data set contains a field indicating whether animals were released or not released (e.g., dead in trap), the column number of this field would be entered here. If there is no such field in the data, enter 0 and it will be assumed that all animals were released on last capture. Any non-blank character in this column indicates that the animals were not released on last capture.
Enter column number containing fate on last capture, or 0 if all captured animals were always released. Note: Any non-blank character in this column is interpreted as indicating that the animal was not released FATE COLUMN:0
If more than one group was specified in the earlier prompt, the next prompt would ask for the column number containing the group of the animals. If the data set contains a field indicating a group the animals belong to, (e.g. sex, size), the column number of this field would be entered here. As with the strata codes, upper/lower case is significant here.
Enter the column number containing the GROUP code, GROUP COLUMN: Enter groupe-codes (eg. MF for male, female) GROUP-CODES:
The next prompt is for the starting column of a frequency variable. If the capture-histories are summarized, the data will contain a frequency variable for each capture-history and the starting column is entered here. If the data are not summarized (i.e., one record per animal), enter 0 for the starting column, e.g.,
Enter starting column of cap-history frequency, or 0 if capture-histories are not summarized. STARTING COLUMN:6
If a number greater than zero is entered for the frequency starting column, CNVMEMOV will prompt for the ending column of the frequency variable.
The next prompt is for the selection of model sets to be run with MSSURVIV. One or both of the model sets may be chosen, e.g.,
Enter 1 for the "MEMORY-MOVEMENT model set 2 for the "MOVEMENT-ONLY model set 3 for both model sets 4 for the "MOVEMENT-ONLY model set w/ S-M parameterization MODEL SETS (1,2,3, or 4):4
In some cases, it is desirable to compute estimates in terms of separate survival rate and movement probabilities rather than with transition probabilities that include both survival and movement. If we assume survival from time i to i+1 does not depend on stratum in time-period i+1, then we can rewrite Φirs as Φirs = SirΨirs, where Ψirs is the conditional probability that an animal in stratum r at time i is in stratum s at time i+1, given that the animal is alive at i+1. The sum of the survival/transition probabilities is equal to the survival rate (i.e., ΣSΦirs = Sir, where Φirs is the probability that an animal alive in stratum r at period i is alive and in stratum s at period i+1, and Sir is the probability that an animal in stratum r at time i is alive at period i+1.) In a k-stratum experiment, we can compute a survival rate and k movement probabilities for each stratum and time-interval from the k transition probabilities, Φirs. This computation may be done from the output for the movement-only model set, however the computation of variances would be very difficult. MSSURVIV can be instructed to treat survival rate and movement probabilities as separate parameters instead of combined transition probabilities in order to produce the desired estimates and variances. I have called this the "S-M parameterization" for the estimation of separate survival and movement parameters.
If the "MOVEMENT-ONLY model set with S-M parameterization" is chosen, CNVMEMOV will prompt for a stratum number. This number controls which movement probability is replaced by the survival probability in the output parameters. For the S-M parameterization, the k×k survival/movement estimates for each time-period will be changed to N survival estimates and k×(k-1) movement estimates. Since the sum of the movement probabilities for any cohort must equal 1.0, one of the movement parameters is not needed (i.e., it is obtained as 1 minus the sum of the others). Any of the k movement parameters may be omitted, however, the optimization routine in MSSURVIV works better if the parameters are not close to 0.0 or 1.0. For this reason, the best movement parameter to omit is the one closest to 0.0 or 1.0.
For the S-M parameterization, one of the 3 movement parameters must be replaced by the survival parameter. Choose 0 to replace the diagonal parameter (probability of returning to a stratum), or the index of one of the 3 strata. Stratum (0 or 1-3)?3
By choosing 3, CNVMEMOV will produce the statements to cause MSSURVIV to use the "S-M parameterization" of the movement-only model set. Instead of a 3×3 matrix of survival/movement probability estimates, MSSURVIV will compute 3 survival rate estimates and 6 movement probabilities for each time period. The 9 parameters for time i are:
The next prompt is for a title to appear on the output. Any string of characters (not including quotes) is acceptable as long as the length is less than 256, e.g.,
Enter a title to appear on the MSSURVIV output TITLE(no quotes):MSSURVIV SAMPLE DATA (SAMPLE.DAT)
CNVMEMOV will output the number of records read and the total number of transitions. The total number of transitions can be used to determine if some of the strata need to be combined to help with convergence problems (See section on sparse data).
Here is the file created by CNVMEMOV which can be input to MSSURVIV:
PROC TITLE 'expected value data w/ 2 groups'; PROC MODEL NPAR=0036 ADDCELL NAGE=1 NYRS=04 STRATA=2 NGROUPS=2 PHIMISS=2; COHORT=1000;236:;102:;76:;79:;22:;42:; COHORT=1001;0:;338:;0:;156:;0:;64:; COHORT=1236;267:;114:;77:;80:; COHORT=1440;0:;444:;0:;183:; COHORT=1343;260:;111:; COHORT=1793;0:;495:; COHORT=1000;62:;41:;11:;18:;2:;7:; COHORT=1001;0:;105:;0:;29:;0:;8:; COHORT=1062;74:;50:;14:;23:; COHORT=1146;0:;135:;0:;40:; COHORT=1085;83:;56:; COHORT=1232;0:;158:; LABELS; S(001)=MOV(01,A,M) AA ; S(002)=SRV(01,A,M) A ; S(003)=MOV(01,A,M) BA ; S(004)=SRV(01,A,M) B ; S(005)=MOV(02,A,M) AA ; S(006)=SRV(02,A,M) A ; S(007)=MOV(02,A,M) BA ; S(008)=SRV(02,A,M) B ; S(009)=MOV(03,A,M) AA ; S(010)=SRV(03,A,M) A ; S(011)=MOV(03,A,M) BA ; S(012)=SRV(03,A,M) B ; S(013)=p(02,A,M) A ; S(014)=p(02,A,M) B ; S(015)=p(03,A,M) A ; S(016)=p(03,A,M) B ; S(017)=p(04,A,M) A ; S(018)=p(04,A,M) B ; S(019)=MOV(01,A,F) AA ; S(020)=SRV(01,A,F) A ; S(021)=MOV(01,A,F) BA ; S(022)=SRV(01,A,F) B ; S(023)=MOV(02,A,F) AA ; S(024)=SRV(02,A,F) A ; S(025)=MOV(02,A,F) BA ; S(026)=SRV(02,A,F) B ; S(027)=MOV(03,A,F) AA ; S(028)=SRV(03,A,F) A ; S(029)=MOV(03,A,F) BA ; S(030)=SRV(03,A,F) B ; S(031)=p(02,A,F) A ; S(032)=p(02,A,F) B ; S(033)=p(03,A,F) A ; S(034)=p(03,A,F) B ; S(035)=p(04,A,F) A ; S(036)=p(04,A,F) B ; PROC ESTIMATE NOVAR NSIG=6 MAXFN=64000 NAME=MODL_D; INITIAL; ALL= 0.500; CONSTRAINTS; S(005)=S(001) /* MOV(02,A,M) AA=MOV(01,A,M) AA */; S(006)=S(002) /* SRV(02,A,M) A =SRV(01,A,M) A */; S(007)=S(003) /* MOV(02,A,M) BA=MOV(01,A,M) BA */; S(008)=S(004) /* SRV(02,A,M) B =SRV(01,A,M) B */; S(009)=S(001) /* MOV(03,A,M) AA=MOV(01,A,M) AA */; S(010)=S(002) /* SRV(03,A,M) A =SRV(01,A,M) A */; S(011)=S(003) /* MOV(03,A,M) BA=MOV(01,A,M) BA */; S(012)=S(004) /* SRV(03,A,M) B =SRV(01,A,M) B */; S(015)=S(013) /* p(03,A,M) A =p(02,A,M) A */; S(016)=S(014) /* p(03,A,M) B =p(02,A,M) B */; S(017)=S(013) /* p(04,A,M) A =p(02,A,M) A */; S(018)=S(014) /* p(04,A,M) B =p(02,A,M) B */; S(023)=S(019) /* MOV(02,A,F) AA=MOV(01,A,F) AA */; S(024)=S(020) /* SRV(02,A,F) A =SRV(01,A,F) A */; S(025)=S(021) /* MOV(02,A,F) BA=MOV(01,A,F) BA */; S(026)=S(022) /* SRV(02,A,F) B =SRV(01,A,F) B */; S(027)=S(019) /* MOV(03,A,F) AA=MOV(01,A,F) AA */; S(028)=S(020) /* SRV(03,A,F) A =SRV(01,A,F) A */; S(029)=S(021) /* MOV(03,A,F) BA=MOV(01,A,F) BA */; S(030)=S(022) /* SRV(03,A,F) B =SRV(01,A,F) B */; S(033)=S(031) /* p(03,A,F) A =p(02,A,F) A */; S(034)=S(032) /* p(03,A,F) B =p(02,A,F) B */; S(035)=S(031) /* p(04,A,F) A =p(02,A,F) A */; S(036)=S(032) /* p(04,A,F) B =p(02,A,F) B */; PROC ESTIMATE NOVAR NSIG=6 MAXFN=64000 NAME=MODL_B; INITIAL; RETAIN=MODL_D; CONSTRAINTS; S(005)=S(001) /* MOV(02,A,M) AA=MOV(01,A,M) AA */; S(006)=S(002) /* SRV(02,A,M) A =SRV(01,A,M) A */; S(007)=S(003) /* MOV(02,A,M) BA=MOV(01,A,M) BA */; S(008)=S(004) /* SRV(02,A,M) B =SRV(01,A,M) B */; S(009)=S(001) /* MOV(03,A,M) AA=MOV(01,A,M) AA */; S(010)=S(002) /* SRV(03,A,M) A =SRV(01,A,M) A */; S(011)=S(003) /* MOV(03,A,M) BA=MOV(01,A,M) BA */; S(012)=S(004) /* SRV(03,A,M) B =SRV(01,A,M) B */; S(023)=S(019) /* MOV(02,A,F) AA=MOV(01,A,F) AA */; S(024)=S(020) /* SRV(02,A,F) A =SRV(01,A,F) A */; S(025)=S(021) /* MOV(02,A,F) BA=MOV(01,A,F) BA */; S(026)=S(022) /* SRV(02,A,F) B =SRV(01,A,F) B */; S(027)=S(019) /* MOV(03,A,F) AA=MOV(01,A,F) AA */; S(028)=S(020) /* SRV(03,A,F) A =SRV(01,A,F) A */; S(029)=S(021) /* MOV(03,A,F) BA=MOV(01,A,F) BA */; S(030)=S(022) /* SRV(03,A,F) B =SRV(01,A,F) B */; PROC ESTIMATE NOVAR NSIG=6 MAXFN=64000 NAME=MODL_A; INITIAL; RETAIN=MODL_B; CONSTRAINTS; S(017)=1.0 /* p(04,A,M) A */; S(018)=1.0 /* p(04,A,M) B */; S(035)=1.0 /* p(04,A,F) A */; S(036)=1.0 /* p(04,A,F) B */; PROC ESTIMATE NOVAR NSIG=6 MAXFN=64000 NAME=1GRP_A; INITIAL; RETAIN=MODL_A; CONSTRAINTS; S(019)=S(001) /* MOV(01,A,F) AA=MOV(01,A,M) AA */; S(020)=S(002) /* SRV(01,A,F) A =SRV(01,A,M) A */; S(021)=S(003) /* MOV(01,A,F) BA=MOV(01,A,M) BA */; S(022)=S(004) /* SRV(01,A,F) B =SRV(01,A,M) B */; S(023)=S(005) /* MOV(02,A,F) AA=MOV(02,A,M) AA */; S(024)=S(006) /* SRV(02,A,F) A =SRV(02,A,M) A */; S(025)=S(007) /* MOV(02,A,F) BA=MOV(02,A,M) BA */; S(026)=S(008) /* SRV(02,A,F) B =SRV(02,A,M) B */; S(027)=S(009) /* MOV(03,A,F) AA=MOV(03,A,M) AA */; S(028)=S(010) /* SRV(03,A,F) A =SRV(03,A,M) A */; S(029)=S(011) /* MOV(03,A,F) BA=MOV(03,A,M) BA */; S(030)=S(012) /* SRV(03,A,F) B =SRV(03,A,M) B */; S(031)=S(013) /* p(02,A,F) A =p(02,A,M) A */; S(032)=S(014) /* p(02,A,F) B =p(02,A,M) B */; S(033)=S(015) /* p(03,A,F) A =p(03,A,M) A */; S(034)=S(016) /* p(03,A,F) B =p(03,A,M) B */; S(017)=1.0 /* p(04,A,M) A */; S(035)=1.0 /* p(04,A,F) A */; S(018)=1.0 /* p(04,A,M) B */; S(036)=1.0 /* p(04,A,F) B */; PROC TEST; PROC STOP;
The output file, CNVMEMOV.OUT, contains a title statement, statements to define the input data, label definitions, statements to describe models, a test statement and a stop statement. The title statement is used to identify the data used in the analysis.
The model definition statements start with "PROC MODEL NPAR=0036 ADDCELL NAGE=1 NYRS=04 STRATA=2 NGROUPS=2 PHIMISS=2;" and end with "COHORT=1232;0:;158:; ". The "NPAR=36" indicates the maximum number of parameters (estimated or fixed) in any of the following models. In this example there are 4 (2×2) transition probability parameters for each of the 3 intervals (4 sample periods), giving 12 parameters per group. There are 2 capture probabilities for each period (not including the first) giving 6 parameters per group. So, the total number of paramters is 12x2+6x2=36. The NAGE, NYRS, STRATA, and NGROUPS keywords indicate the respective values for each, and PHIMISS indicates which of the transitions is to be computed by subtraction. In this case, PHIMISS=2 indicates that transitions to the 2nd strata are to be computed as 1 - the probability of moving to the first strata.
The rest of the model definition statements contains the summary data in a form which could be thought of as a generalized "Leslie Method-B Table" or "m-array" where each 2×2 matrix is treated as one element in the m-array. Each of the records starts with "COHORT=x" where x is the number of animals released in a stratum and year. The numbers that follow indicate the number of recaptures in succeeding periods and strata. Figure 1 shows the structure of the m-array for these data.
R1 M1,2 M1,3 M1,4 R2 M2,3 M2,4 R3 M3,4 Ri = (Ri1 Ri2) (Mi1,1 Mi1,2) Mi = (Mi2,1 Mi2,2)where Rir=number released in time i, stratum r, and Mi,jrs=number captured in time j in stratum s, last captured in time i in stratum r.
From the output file, the numbers of animals released in time period 1 are: 1000 from stratum 1 and 1001 from stratum 2. The matrix of recaptures in time period 2 of animals captured in time period 1 is:
[236 102] M1,2 = [0 338]Of the 676 animals captured in time period 2 which were also captured in time period 1, 236 animal started in stratum 1 and stayed in stratum 1, 102 animals started in 1 and moved to 2. (Row indicates stratum of previous capture. Column indicates stratum of capture.) 0 animals moved from 2 to 1 and 338 moved from 2 to 2.
The next 2 columns of rows 1-2 in the data are the matrix of transitions of animals captured in time period 3 that were last captured in time period 1 (not captured in time period 2). The last 2 columns in the first 2 rows form the matrix of transitions of animals captured in time period 4 which were last captured in time period 1 (not captured in time periods 2 or 3).
Following the data are the labels for each of the parameters. Internally, the parameters are called "S(1), S(2), ... S(NPAR). The labels relate these internal parameters to meaningful labels for these models. If the "Movement-only" model was chosen, the transition probabilities are labeled "PHI" followed by the time period in parenthesis and the transition. So, "PHI(01) AB" is the probability of survival from time period one to time period two and moving from stratum "A" to stratum "B". If the "Memory-movement" models were chosen, there will be three strata following the time period for each "PHI" since transition in time period i depends on stratum of capture in time period i-1 as well as in time period i. If the S-M parameterization is chosen, the survival parameter (SRV) will be followed by time period, age, and group in parenthesis, followed the stratum of capture in time period i. The movement parameters (MOV) will be followed by the time period, age, and group in parenthesis, then stratum in time period i and stratum in period i+1.
In all models, capture-probability parameters are labeled "p" and are followed by time period of capture, age, and group in parentheses, and stratum of capture.
After the label definitions come the model definitions for each model. Each model starts with a "PROC ESTIMATE" statement. Options on the "PROC ESTIMATE" statement include "NOVAR" which inhibits printing of the variance-covariance matrix, the number of significant digits, "NSIG" (i.e., number of digits following the decimal point which do not change at the end of the iterative process), the maximum number of function evaluations, "MAXFN", and the name of the model. If the variance-covariance matrix of parameter estimates is desired, delete the string "NOVAR" using a text editor.
CNVMEMOV produces the model definitions from most restrictive (model "D") to most general (model "A"). The reason for this is that the most restrictive model has the fewest estimable parameters and converges more easily. Final estimates from this model can then be input as starting values to more general models. MSSURVIV requires starting values for all models and CNVMEMOV sets all parameters to 1/(# of strata) for the first model to ensure that the estimates of transition probability are less than or equal to 1.0. (INITIAL; ALL=0.333;).
The statements following the "CONSTRAINTS" statement describe each model in terms of the most general model. In model "D", the survival and movement probabilities are assumed to be constant across time, so the survival and movement probabilities for time period two and three are set equal to the survival and movement probabilities for time period one. The capture probabilities are also assumed constant over time, so the capture probabilities for time periods 3 and 4 are set equal to the capture probabilities for time period 2. These equalities must be specified in terms of parameter number which can be obtained from the labels section. In the example, the first constraint is "S(005)=S(001);". A comment appears immediately after the constraint which indicates which parameters are constrained.
The sequence of statements starting with "PROC ESTIMATE ... NAME=MODL_B" cause MSSURVIV to produce estimates under the model with time-specific capture probabilities and constant survival and movement probabilities. The initial values are set equal to the final values obtained for model "D" (INITIAL; RETAIN=MODL_D;), and the constraints on the survival and movement probability parameters are the same as for model "D". Since capture probabilities may vary with time, there are no constraints on the capture probability parameters.
Statements for model "A" follow model "B". Model "A" assumes time-specific survival and movement probabilities and time-specific capture probabilities. Since there is no information on animals after the last time period, the last survival and movement probabilities and last capture probabilities cannot be separately estimated under model "A". For this reason, the last capture probability parameters have to be constrained to a constant. This causes the last survival/movement probability estimates to be the product of survival and movement and capture-probability for this model.
If more than one group is to be analyzed, CNVMEMOV produces one other model which is equivalent to model A except that the parameters are constrained equal across groups.
The "PROC TEST;" statement causes MSSURVIV to print tables of statistics used for comparison of the models. "PROC STOP;" causes MSSURVIV to stop execution even if more statements follow. MSSURVIV
MSSURVIV prompts for one line of input to specify the name of the input and output files and command line options. When the program is run, the following prompt appears:
Enter command line parameters [i=in_file] [l=out_file] [lines=n] [compile run] [noecho]:At this prompt, any or all of the items enclosed in brackets may be specified. If "i=in_file" is specified, the input will be read from the file "in_file". Usually, this is the file created by CNVMEMOV and is called CNVMEMOV.OUT unless it has been renamed. A full pathname may be used to indicate a different directory. If this item is omitted, MSSURVIV expects the input from the keyboard. (Cntl-Break will abort the program).
If "l=out_file" is specified, output from MSSURVIV will be directed to the file "out_file". The default output file is the CRT screen. To direct output directly to the printer, use "l=lpt1".
If "lines=n" is included, MSSURVIV will print a header and the title in the output file after every n lines. The default value for n is 9999.
The "noecho" option causes MSSURVIV to suppress printing of the input data. This option is useful when there are several runs of models on the same data and you would like to conserve paper, but at least one run should contain a listing of the data to check for "typos".
To run the sample data file with MSSURVIV, enter the following at the above prompt:
i=cnvmemov.out l=sample.out
The output produced by MSSURVIV contains a listing of the input data, estimates of the parameters under each model, a goodness-of-fit test for each model, an AIC statistic for each model, and between model tests. The following output was created using MSSURVIV on the sample data file listed previously:
MSSURVIV - Survival Rate Estimation with User Specified Cell Probabilities 28-Jan-98 11:20:03 Ver 2.0 01/01/98 Page 001 INPUT --- PROC TITLE 'SADF'; CPU time in seconds for last procedure was 0.00 INPUT --- PROC MODEL NPAR=0036 ADDCELL NAGE=1 NYRS=04 STRATA=2 INPUT --- NGROUPS=2 PHIMISS=2; INPUT --- COHORT=1000; 236:;102:;76:;79:;22:;42:; INPUT --- COHORT=1001; 0:;338:;0:;156:;0:;64:; INPUT --- COHORT=1236; 267:;114:;77:;80:; INPUT --- COHORT=1440; 0:;444:;0:;183:; INPUT --- COHORT=1343; 260:;111:; INPUT --- COHORT=1793; 0:;495:; INPUT --- COHORT=1000; 62:;41:;11:;18:;2:;7:; INPUT --- COHORT=1001; 0:;105:;0:;29:;0:;8:; INPUT --- COHORT=1062; 74:;50:;14:;23:; INPUT --- COHORT=1146; 0:;135:;0:;40:; INPUT --- COHORT=1085; 83:;56:; INPUT --- COHORT=1232; 0:;158:; INPUT --- PROC ESTIMATE NOVAR NSIG=6 MAXFN=64000 NAME=MODL_D; Number of parameters in model = 36 Number of parameters set equal = 24 Number of parameters fixed = 0 Number of parameters estimated = 12 Final function value 11038.617 (Error Return = 130) Number of significant digits 5 Number of function evaluations 834 * * WARNING * * Rounding errors became dominant before parameters estimated * * WARNING * * to NSIG digits for Model MODL_D * * WARNING * * Check to be sure the parameters are identifiable, * * WARNING * * but the problem may just be ill-conditioned. Cohort Cell Observed Expected Chi-square Note ------ ---- -------- -------- ---------- ------------- 1 1 236. 213.083 2.465 0 < P < 1 1 2 102. 91.077 1.310 0 < P < 1 1 3 76. 70.554 0.420 0 < P < 1 1 4 79. 73.626 0.392 0 < P < 1 1 5 22. 23.361 0.079 0 < P < 1 1 6 42. 45.126 0.217 0 < P < 1 1 7 443. 483.173 3.340 0 < P < 1 1 Cohort df= 6 8.223 P = 0.2222 2 2 338. 301.610 4.391 0 < P < 1 2 4 156. 143.954 1.008 0 < P < 1 2 6 64. 68.707 0.322 0 < P < 1 2 7 443. 486.730 3.929 0 < P < 1 2 Cohort df= 3 9.650 P = 0.0218 3 1 267. 263.370 0.050 0 < P < 1 3 2 114. 112.571 0.018 0 < P < 1 3 3 77. 87.205 1.194 0 < P < 1 3 4 80. 91.002 1.330 0 < P < 1 3 5 698. 681.852 0.382 0 < P < 1 3 Cohort df= 4 2.975 P = 0.5620 4 2 444. 433.884 0.236 0 < P < 1 4 4 183. 207.086 2.801 0 < P < 1 4 5 813. 799.030 0.244 0 < P < 1 4 Cohort df= 2 3.282 P = 0.1938 5 1 260. 286.170 2.393 0 < P < 1 5 2 111. 122.316 1.047 0 < P < 1 5 3 972. 934.514 1.504 0 < P < 1 5 Cohort df= 2 4.944 P = 0.0844 6 2 495. 540.246 3.789 0 < P < 1 6 3 1298. 1252.754 1.634 0 < P < 1 6 Cohort df= 1 5.424 P = 0.0199 7 1 62. 69.693 0.849 0 < P < 1 7 2 41. 46.448 0.639 0 < P < 1 7 3 11. 11.855 0.062 0 < P < 1 7 4 18. 20.221 0.244 0 < P < 1 7 5 2. 2.017 0.000 0 < P < 1 7 6 7. 6.708 0.013 0 < P < 1 7 7 859. 843.057 0.301 0 < P < 1 7 Cohort df= 6 2.108 P = 0.9095 8 2 105. 118.159 1.466 0 < P < 1 8 4 29. 31.341 0.175 0 < P < 1 8 6 8. 8.313 0.012 0 < P < 1 8 7 859. 843.186 0.297 0 < P < 1 8 Cohort df= 3 1.949 P = 0.5831 9 1 74. 74.014 0.000 0 < P < 1 9 2 50. 49.328 0.009 0 < P < 1 9 3 14. 12.590 0.158 0 < P < 1 9 4 23. 21.475 0.108 0 < P < 1 9 5 901. 904.592 0.014 0 < P < 1 9 Cohort df= 4 0.290 P = 0.9905 10 2 135. 135.275 0.001 0 < P < 1 10 4 40. 35.881 0.473 0 < P < 1 10 5 971. 974.843 0.015 0 < P < 1 10 Cohort df= 2 0.488 P = 0.7833 11 1 83. 75.617 0.721 0 < P < 1 11 2 56. 50.396 0.623 0 < P < 1 11 3 946. 958.987 0.176 0 < P < 1 11 Cohort df= 2 1.520 P = 0.4677 12 2 158. 145.427 1.087 0 < P < 1 12 3 1074. 1086.573 0.145 0 < P < 1 12 Cohort df= 1 1.233 P = 0.2669 ------------------------------------------------------------ @@ 2 130 0 36 42.4007 24 42.0848 -126.955 277.910 G Total (Degrees of freedom = 36) 42.401 Pr(Larger Chi-square) = 0.2144 With pooling, Degrees of freedom = 24 Pearson Chi-square = 42.085 Pr(Larger Chi-square) = 0.0126 Log-likelihood = -126.95523 Akaike Information Criterion = 277.91045 PARAMETER ESTIMATES FOR MODEL MODL_D 95% Confidence Interval I Parameter S(I) Standard Error Lower Upper --- -------------------- ------------ ------------ ------------ ------------ 1 1 MOV(01,A,M) AA 0.698096 0.148408E-01 0.669008 0.727184 2 2 SRV(01,A,M) A 0.779541 0.176877E-01 0.744873 0.814209 3 3 MOV(01,A,M) BA 0.223989E-15 0.331077E-09 -.648912E-09 0.648912E-09 4 4 SRV(01,A,M) B 0.778593 0.165709E-01 0.746114 0.811071 5 1 MOV(02,A,M) AA 0.698096 0.148408E-01 0.669008 0.727184 6 2 SRV(02,A,M) A 0.779541 0.176877E-01 0.744873 0.814209 7 3 MOV(02,A,M) BA 0.223989E-15 0.331077E-09 -.648912E-09 0.648912E-09 8 4 SRV(02,A,M) B 0.778593 0.165709E-01 0.746114 0.811071 9 1 MOV(03,A,M) AA 0.698096 0.148408E-01 0.669008 0.727184 10 2 SRV(03,A,M) A 0.779541 0.176877E-01 0.744873 0.814209 11 3 MOV(03,A,M) BA 0.223989E-15 0.331077E-09 -.648912E-09 0.648912E-09 12 4 SRV(03,A,M) B 0.778593 0.165709E-01 0.746114 0.811071 13 5 p(02,A,M) A 0.391556 0.196561E-01 0.353030 0.430082 14 6 p(02,A,M) B 0.386991 0.134749E-01 0.360580 0.413402 15 5 p(03,A,M) A 0.391556 0.196561E-01 0.353030 0.430082 16 6 p(03,A,M) B 0.386991 0.134749E-01 0.360580 0.413402 17 5 p(04,A,M) A 0.391556 0.196561E-01 0.353030 0.430082 18 6 p(04,A,M) B 0.386991 0.134749E-01 0.360580 0.413402 19 7 MOV(01,A,F) AA 0.613898 0.419646E-01 0.531647 0.696148 20 8 SRV(01,A,F) A 0.390622 0.272521E-01 0.337207 0.444036 21 9 MOV(01,A,F) BA 0.357247E-11 0.848155E-07 -.166235E-06 0.166242E-06 22 10 SRV(01,A,F) B 0.383289 0.248185E-01 0.334644 0.431933 23 7 MOV(02,A,F) AA 0.613898 0.419646E-01 0.531647 0.696148 24 8 SRV(02,A,F) A 0.390622 0.272521E-01 0.337207 0.444036 25 9 MOV(02,A,F) BA 0.357247E-11 0.848155E-07 -.166235E-06 0.166242E-06 26 10 SRV(02,A,F) B 0.383289 0.248185E-01 0.334644 0.431933 27 7 MOV(03,A,F) AA 0.613898 0.419646E-01 0.531647 0.696148 28 8 SRV(03,A,F) A 0.390622 0.272521E-01 0.337207 0.444036 29 9 MOV(03,A,F) BA 0.357247E-11 0.848155E-07 -.166235E-06 0.166242E-06 30 10 SRV(03,A,F) B 0.383289 0.248185E-01 0.334644 0.431933 31 11 p(02,A,F) A 0.290629 0.408893E-01 0.210486 0.370772 32 12 p(02,A,F) B 0.307970 0.265552E-01 0.255922 0.360018 33 11 p(03,A,F) A 0.290629 0.408893E-01 0.210486 0.370772 34 12 p(03,A,F) B 0.307970 0.265552E-01 0.255922 0.360018 35 11 p(04,A,F) A 0.290629 0.408893E-01 0.210486 0.370772 36 12 p(04,A,F) B 0.307970 0.265552E-01 0.255922 0.360018 CPU time in seconds for last procedure was 1.56 INPUT --- PROC ESTIMATE NOVAR NSIG=6 MAXFN=64000 NAME=MODL_B; Number of parameters in model = 36 Number of parameters set equal = 16 Number of parameters fixed = 0 Number of parameters estimated = 20 Final function value 11019.531 (Error Return = 130) Number of significant digits 3 Number of function evaluations 1147 * * WARNING * * Rounding errors became dominant before parameters estimated * * WARNING * * to NSIG digits for Model MODL_B * * WARNING * * Check to be sure the parameters are identifiable, * * WARNING * * but the problem may just be ill-conditioned. Cohort Cell Observed Expected Chi-square Note ------ ---- -------- -------- ---------- ------------- 1 1 236. 235.015 0.004 0 < P < 1 1 2 102. 101.021 0.009 0 < P < 1 1 3 76. 72.577 0.161 0 < P < 1 1 4 79. 75.912 0.126 0 < P < 1 1 5 22. 21.873 0.001 0 < P < 1 1 6 42. 41.662 0.003 0 < P < 1 1 7 443. 451.940 0.177 0 < P < 1 1 Cohort df= 6 0.481 P = 0.9981 2 2 338. 336.898 0.004 0 < P < 1 2 4 156. 149.009 0.328 0 < P < 1 2 6 64. 63.507 0.004 0 < P < 1 2 7 443. 451.586 0.163 0 < P < 1 2 Cohort df= 3 0.499 P = 0.9192 3 1 267. 270.924 0.057 0 < P < 1 3 2 114. 116.580 0.057 0 < P < 1 3 3 77. 81.651 0.265 0 < P < 1 3 4 80. 84.436 0.233 0 < P < 1 3 5 698. 682.409 0.356 0 < P < 1 3 Cohort df= 4 0.968 P = 0.9146 4 2 444. 452.505 0.160 0 < P < 1 4 4 183. 192.855 0.504 0 < P < 1 4 5 813. 794.640 0.424 0 < P < 1 4 Cohort df= 2 1.088 P = 0.5805 5 1 260. 255.726 0.071 0 < P < 1 5 2 111. 108.837 0.043 0 < P < 1 5 3 972. 978.437 0.042 0 < P < 1 5 Cohort df= 2 0.157 P = 0.9246 6 2 495. 484.098 0.246 0 < P < 1 6 3 1298. 1308.902 0.091 0 < P < 1 6 Cohort df= 1 0.336 P = 0.5620 7 1 62. 62.242 0.001 0 < P < 1 7 2 41. 41.537 0.007 0 < P < 1 7 3 11. 11.494 0.021 0 < P < 1 7 4 18. 19.590 0.129 0 < P < 1 7 5 2. 2.058 0.002 0 < P < 1 7 6 7. 6.882 0.002 0 < P < 1 7 7 859. 856.196 0.009 0 < P < 1 7 Cohort df= 6 0.171 P = 0.9999 8 2 105. 105.229 0.000 0 < P < 1 8 4 29. 30.287 0.055 0 < P < 1 8 6 8. 8.518 0.032 0 < P < 1 8 7 859. 856.965 0.005 0 < P < 1 8 Cohort df= 3 0.092 P = 0.9928 9 1 74. 73.260 0.007 0 < P < 1 9 2 50. 48.660 0.037 0 < P < 1 9 3 14. 13.116 0.060 0 < P < 1 9 4 23. 22.435 0.014 0 < P < 1 9 5 901. 904.530 0.014 0 < P < 1 9 Cohort df= 4 0.132 P = 0.9979 10 2 135. 132.891 0.033 0 < P < 1 10 4 40. 37.375 0.184 0 < P < 1 10 5 971. 975.734 0.023 0 < P < 1 10 Cohort df= 2 0.241 P = 0.8866 11 1 83. 83.808 0.008 0 < P < 1 11 2 56. 55.908 0.000 0 < P < 1 11 3 946. 945.284 0.001 0 < P < 1 11 Cohort df= 2 0.008 P = 0.9958 12 2 158. 160.665 0.044 0 < P < 1 12 3 1074. 1071.335 0.007 0 < P < 1 12 Cohort df= 1 0.051 P = 0.8216 ------------------------------------------------------------ @@ 3 130 0 28 4.22760 16 4.22315 -107.869 255.737 G Total (Degrees of freedom = 28) 4.228 Pr(Larger Chi-square) = 1.0000 With pooling, Degrees of freedom = 16 Pearson Chi-square = 4.223 Pr(Larger Chi-square) = 0.9985 Log-likelihood = -107.86867 Akaike Information Criterion = 255.73734 PARAMETER ESTIMATES FOR MODEL MODL_B 95% Confidence Interval I Parameter S(I) Standard Error Lower Upper --- -------------------- ------------ ------------ ------------ ------------ 1 1 MOV(01,A,M) AA 0.699495 0.156129E-01 0.668893 0.730096 2 2 SRV(01,A,M) A 0.809333 0.194461E-01 0.771219 0.847447 3 3 MOV(01,A,M) BA 0.153274E-12 0.875236E-08 -.171545E-07 0.171548E-07 4 4 SRV(01,A,M) B 0.810277 0.186997E-01 0.773626 0.846929 5 1 MOV(02,A,M) AA 0.699495 0.156129E-01 0.668893 0.730096 6 2 SRV(02,A,M) A 0.809333 0.194461E-01 0.771219 0.847447 7 3 MOV(02,A,M) BA 0.153274E-12 0.875236E-08 -.171545E-07 0.171548E-07 8 4 SRV(02,A,M) B 0.810277 0.186997E-01 0.773626 0.846929 9 1 MOV(03,A,M) AA 0.699495 0.156129E-01 0.668893 0.730096 10 2 SRV(03,A,M) A 0.809333 0.194461E-01 0.771219 0.847447 11 3 MOV(03,A,M) BA 0.153274E-12 0.875236E-08 -.171545E-07 0.171548E-07 12 4 SRV(03,A,M) B 0.810277 0.186997E-01 0.773626 0.846929 13 5 p(02,A,M) A 0.415130 0.255054E-01 0.365139 0.465121 14 6 p(02,A,M) B 0.415366 0.181679E-01 0.379757 0.450975 15 7 p(03,A,M) A 0.387183 0.243717E-01 0.339415 0.434952 16 8 p(03,A,M) B 0.387817 0.163238E-01 0.355823 0.419812 17 9 p(04,A,M) A 0.336347 0.247926E-01 0.287753 0.384940 18 10 p(04,A,M) B 0.333211 0.163182E-01 0.301228 0.365195 19 11 MOV(01,A,F) AA 0.612762 0.430408E-01 0.528403 0.697122 20 12 SRV(01,A,F) A 0.373504 0.269556E-01 0.320671 0.426337 21 13 MOV(01,A,F) BA 0.795825E-12 0.398934E-07 -.781903E-07 0.781919E-07 22 14 SRV(01,A,F) B 0.366048 0.250264E-01 0.316996 0.415099 23 11 MOV(02,A,F) AA 0.612762 0.430408E-01 0.528403 0.697122 24 12 SRV(02,A,F) A 0.373504 0.269556E-01 0.320671 0.426337 25 13 MOV(02,A,F) BA 0.795825E-12 0.398934E-07 -.781903E-07 0.781919E-07 26 14 SRV(02,A,F) B 0.366048 0.250264E-01 0.316996 0.415099 27 11 MOV(03,A,F) AA 0.612762 0.430408E-01 0.528403 0.697122 28 12 SRV(03,A,F) A 0.373504 0.269556E-01 0.320671 0.426337 29 13 MOV(03,A,F) BA 0.795825E-12 0.398934E-07 -.781903E-07 0.781919E-07 30 14 SRV(03,A,F) B 0.366048 0.250264E-01 0.316996 0.415099 31 15 p(02,A,F) A 0.271953 0.438024E-01 0.186101 0.357806 32 16 p(02,A,F) B 0.287187 0.286197E-01 0.231093 0.343282 33 17 p(03,A,F) A 0.301408 0.495588E-01 0.204273 0.398543 34 18 p(03,A,F) B 0.316791 0.315622E-01 0.254929 0.378653 35 19 p(04,A,F) A 0.337494 0.593484E-01 0.221171 0.453817 36 20 p(04,A,F) B 0.356265 0.389534E-01 0.279916 0.432613 CPU time in seconds for last procedure was 2.22 INPUT --- PROC ESTIMATE NOVAR NSIG=6 MAXFN=64000 NAME=MODL_A; Number of parameters in model = 36 Number of parameters set equal = 0 Number of parameters fixed = 4 Number of parameters estimated = 32 Final function value 11017.458 (Error Return = 130) Number of significant digits 5 Number of function evaluations 2182 * * WARNING * * Rounding errors became dominant before parameters estimated * * WARNING * * to NSIG digits for Model MODL_A * * WARNING * * Check to be sure the parameters are identifiable, * * WARNING * * but the problem may just be ill-conditioned. Cohort Cell Observed Expected Chi-square Note ------ ---- -------- -------- ---------- ------------- 1 1 236. 236.000 0.000 0 < P < 1 1 2 102. 101.823 0.000 0 < P < 1 1 3 76. 75.943 0.000 0 < P < 1 1 4 79. 79.281 0.001 0 < P < 1 1 5 22. 21.929 0.000 0 < P < 1 1 6 42. 42.024 0.000 0 < P < 1 1 7 443. 443.000 0.000 0 < P < 1 1 Cohort df= 6 0.002 P = 1.0000 2 2 338. 338.177 0.000 0 < P < 1 2 4 156. 155.691 0.001 0 < P < 1 2 6 64. 64.131 0.000 0 < P < 1 2 7 443. 443.000 0.000 0 < P < 1 2 Cohort df= 3 0.001 P = 1.0000 3 1 267. 267.057 0.000 0 < P < 1 3 2 114. 113.949 0.000 0 < P < 1 3 3 77. 77.116 0.000 0 < P < 1 3 4 80. 79.878 0.000 0 < P < 1 3 5 698. 698.000 0.000 0 < P < 1 3 Cohort df= 4 0.000 P = 1.0000 4 2 444. 444.079 0.000 0 < P < 1 4 4 183. 182.921 0.000 0 < P < 1 4 5 813. 813.000 0.000 0 < P < 1 4 Cohort df= 2 0.000 P = 1.0000 5 1 260. 259.955 0.000 0 < P < 1 5 2 111. 111.045 0.000 0 < P < 1 5 3 972. 972.000 0.000 0 < P < 1 5 Cohort df= 2 0.000 P = 1.0000 6 2 495. 495.000 0.000 0 < P < 1 6 3 1298. 1298.000 0.000 0 < P < 1 6 Cohort df= 1 0.000 P = 1.0000 7 1 62. 62.000 0.000 0 < P < 1 7 2 41. 40.877 0.000 0 < P < 1 7 3 11. 10.940 0.000 0 < P < 1 7 4 18. 18.418 0.010 0 < P < 1 7 5 2. 2.033 0.001 0 < P < 1 7 6 7. 6.731 0.011 0 < P < 1 7 7 859. 859.000 0.000 0 < P < 1 7 Cohort df= 6 0.021 P = 1.0000 8 2 105. 105.123 0.000 0 < P < 1 8 4 29. 28.555 0.007 0 < P < 1 8 6 8. 8.322 0.012 0 < P < 1 8 7 859. 859.000 0.000 0 < P < 1 8 Cohort df= 3 0.019 P = 0.9993 9 1 74. 74.060 0.000 0 < P < 1 9 2 50. 49.516 0.005 0 < P < 1 9 3 14. 13.764 0.004 0 < P < 1 9 4 23. 23.660 0.018 0 < P < 1 9 5 901. 901.000 0.000 0 < P < 1 9 Cohort df= 4 0.027 P = 0.9999 10 2 135. 135.510 0.002 0 < P < 1 10 4 40. 39.490 0.007 0 < P < 1 10 5 971. 971.000 0.000 0 < P < 1 10 Cohort df= 2 0.008 P = 0.9958 11 1 83. 83.203 0.000 0 < P < 1 11 2 56. 55.797 0.001 0 < P < 1 11 3 946. 946.000 0.000 0 < P < 1 11 Cohort df= 2 0.001 P = 0.9994 12 2 158. 158.000 0.000 0 < P < 1 12 3 1074. 1074.000 0.000 0 < P < 1 12 Cohort df= 1 0.000 P = 1.0000 ------------------------------------------------------------ @@ 4 130 0 16 0.811741E-01 4 0.810022E-01 -105.795 275.591 G Total (Degrees of freedom = 16) 0.081 Pr(Larger Chi-square) = 1.0000 With pooling, Degrees of freedom = 4 Pearson Chi-square = 0.081 Pr(Larger Chi-square) = 0.9992 Log-likelihood = -105.79546 Akaike Information Criterion = 275.59091 PARAMETER ESTIMATES FOR MODEL MODL_A 95% Confidence Interval I Parameter S(I) Standard Error Lower Upper --- -------------------- ------------ ------------ ------------ ------------ 1 1 MOV(01,A,M) AA 0.698291 0.272011E-01 0.644977 0.751605 2 2 SRV(01,A,M) A 0.841311 0.301219E-01 0.782272 0.900350 3 3 MOV(01,A,M) BA 0.791250E-13 0.152194E-07 -.298299E-07 0.298300E-07 4 4 SRV(01,A,M) B 0.842191 0.313906E-01 0.780665 0.903717 5 5 MOV(02,A,M) AA 0.700908 0.263021E-01 0.649355 0.752460 6 6 SRV(02,A,M) A 0.768141 0.344931E-01 0.700534 0.835747 7 7 MOV(02,A,M) BA 0.161038E-11 0.505247E-07 -.990267E-07 0.990300E-07 8 8 SRV(02,A,M) B 0.768514 0.332930E-01 0.703260 0.833769 9 9 MOV(03,A,M) AA 0.700688 0.224495E-01 0.656687 0.744689 10 10 SRV(03,A,M) A 0.276247 0.122013E-01 0.252333 0.300162 11 11 MOV(03,A,M) BA 0.267271E-11 0.639044E-07 -.125250E-06 0.125255E-06 12 12 SRV(03,A,M) B 0.276074 0.105577E-01 0.255381 0.296767 13 13 p(02,A,M) A 0.401716 0.288422E-01 0.345185 0.458247 14 14 p(02,A,M) B 0.401144 0.206752E-01 0.360620 0.441667 15 15 p(03,A,M) A 0.401314 0.289046E-01 0.344661 0.457967 16 16 p(03,A,M) B 0.401278 0.190077E-01 0.364023 0.438533 17 -33 p(04,A,M) A 1.00000 0.000000E+00 1.00000 1.00000 18 -34 p(04,A,M) B 1.00000 0.000000E+00 1.00000 1.00000 19 19 MOV(01,A,F) AA 0.618899 0.707288E-01 0.480270 0.757527 20 20 SRV(01,A,F) A 0.353662 0.409073E-01 0.273484 0.433841 21 21 MOV(01,A,F) BA 0.181829E-15 0.134831E-08 -.264270E-08 0.264270E-08 22 22 SRV(01,A,F) B 0.346269 0.414183E-01 0.265089 0.427449 23 23 MOV(02,A,F) AA 0.610099 0.647022E-01 0.483283 0.736916 24 24 SRV(02,A,F) A 0.391317 0.437016E-01 0.305662 0.476972 25 25 MOV(02,A,F) BA 0.883231E-15 0.231888E-08 -.454501E-08 0.454501E-08 26 26 SRV(02,A,F) B 0.386940 0.429353E-01 0.302787 0.471094 27 27 MOV(03,A,F) AA 0.598583 0.402479E-01 0.519697 0.677469 28 28 SRV(03,A,F) A 0.128111 0.101463E-01 0.108224 0.147997 29 29 MOV(03,A,F) BA 0.658437E-15 0.190194E-08 -.372781E-08 0.372781E-08 30 30 SRV(03,A,F) B 0.128247 0.952609E-02 0.109576 0.146918 31 31 p(02,A,F) A 0.283259 0.590986E-01 0.167425 0.399092 32 32 p(02,A,F) B 0.303285 0.399842E-01 0.224915 0.381654 33 17 p(03,A,F) A 0.292098 0.563421E-01 0.181667 0.402528 34 18 p(03,A,F) B 0.305592 0.351321E-01 0.236733 0.374451 35 -35 p(04,A,F) A 1.00000 0.000000E+00 1.00000 1.00000 36 -36 p(04,A,F) B 1.00000 0.000000E+00 1.00000 1.00000 CPU time in seconds for last procedure was 4.20 INPUT --- PROC ESTIMATE NOVAR NSIG=6 MAXFN=64000 NAME=1GRP_A; Number of parameters in model = 36 Number of parameters set equal = 16 Number of parameters fixed = 4 Number of parameters estimated = 16 Final function value 11767.052 (Error Return = 130) Number of significant digits 5 Number of function evaluations 946 * * WARNING * * Rounding errors became dominant before parameters estimated * * WARNING * * to NSIG digits for Model 1GRP_A * * WARNING * * Check to be sure the parameters are identifiable, * * WARNING * * but the problem may just be ill-conditioned. Cohort Cell Observed Expected Chi-square Note ------ ---- -------- -------- ---------- ------------- 1 1 236. 149.000 50.799 0 < P < 1 1 2 102. 70.658 13.902 0 < P < 1 1 3 76. 43.346 24.599 0 < P < 1 1 4 79. 49.802 17.119 0 < P < 1 1 5 22. 11.529 9.510 0 < P < 1 1 6 42. 24.665 12.183 0 < P < 1 1 7 443. 651.000 66.458 0 < P < 1 1 Cohort df= 6 194.570 P = 0.0000 2 2 338. 222.342 60.164 0 < P < 1 2 4 156. 92.258 44.041 0 < P < 1 2 6 64. 35.401 23.104 0 < P < 1 2 7 443. 651.000 66.458 0 < P < 1 2 Cohort df= 3 193.766 P = 0.0000 3 1 267. 183.576 37.912 0 < P < 1 3 2 114. 86.748 8.561 0 < P < 1 3 3 77. 48.826 16.257 0 < P < 1 3 4 80. 56.814 9.462 0 < P < 1 3 5 698. 860.037 30.529 0 < P < 1 3 Cohort df= 4 102.721 P = 0.0000 4 2 444. 322.746 45.554 0 < P < 1 4 4 183. 123.843 28.258 0 < P < 1 4 5 813. 993.411 32.764 0 < P < 1 4 Cohort df= 2 106.576 P = 0.0000 5 1 260. 190.367 25.470 0 < P < 1 5 2 111. 91.729 4.048 0 < P < 1 5 3 972. 1060.904 7.450 0 < P < 1 5 Cohort df= 2 36.969 P = 0.0000 6 2 495. 387.051 30.107 0 < P < 1 6 3 1298. 1405.949 8.288 0 < P < 1 6 Cohort df= 1 38.396 P = 0.0000 7 1 62. 149.000 50.799 0 < P < 1 7 2 41. 70.658 12.449 0 < P < 1 7 3 11. 43.346 24.137 0 < P < 1 7 4 18. 49.802 20.307 0 < P < 1 7 5 2. 11.529 7.876 0 < P < 1 7 6 7. 24.665 12.652 0 < P < 1 7 7 859. 651.000 66.458 0 < P < 1 7 Cohort df= 6 194.678 P = 0.0000 8 2 105. 222.342 61.927 0 < P < 1 8 4 29. 92.258 43.373 0 < P < 1 8 6 8. 35.401 21.209 0 < P < 1 8 7 859. 651.000 66.458 0 < P < 1 8 Cohort df= 3 192.967 P = 0.0000 9 1 74. 157.732 44.449 0 < P < 1 9 2 50. 74.536 8.077 0 < P < 1 9 3 14. 41.953 18.625 0 < P < 1 9 4 23. 48.816 13.652 0 < P < 1 9 5 901. 738.963 35.531 0 < P < 1 9 Cohort df= 4 120.334 P = 0.0000 10 2 135. 256.852 57.807 0 < P < 1 10 4 40. 98.559 34.793 0 < P < 1 10 5 971. 790.589 41.169 0 < P < 1 10 Cohort df= 2 133.769 P = 0.0000 11 1 83. 153.796 32.589 0 < P < 1 11 2 56. 74.107 4.424 0 < P < 1 11 3 946. 857.096 9.222 0 < P < 1 11 Cohort df= 2 46.235 P = 0.0000 12 2 158. 265.949 43.817 0 < P < 1 12 3 1074. 966.051 12.063 0 < P < 1 12 Cohort df= 1 55.879 P = 0.0000 ------------------------------------------------------------ @@ 5 130 0 32 1499.27 20 1416.86 -855.390 1742.78 G Total (Degrees of freedom = 32) 1499.270 Pr(Larger Chi-square) = 0.0000 With pooling, Degrees of freedom = 20 Pearson Chi-square = 1416.861 Pr(Larger Chi-square) = 0.0000 Log-likelihood = -855.38982 Akaike Information Criterion = 1742.7796 PARAMETER ESTIMATES FOR MODEL 1GRP_A 95% Confidence Interval I Parameter S(I) Standard Error Lower Upper --- -------------------- ------------ ------------ ------------ ------------ 1 1 MOV(01,A,M) AA 0.686338 0.259820E-01 0.635413 0.737262 2 2 SRV(01,A,M) A 0.642315 0.258643E-01 0.591621 0.693009 3 3 MOV(01,A,M) BA 0.424835E-17 0.995660E-10 -.195149E-09 0.195149E-09 4 4 SRV(01,A,M) B 0.633335 0.262895E-01 0.581808 0.684862 5 5 MOV(02,A,M) AA 0.686668 0.248164E-01 0.638027 0.735308 6 6 SRV(02,A,M) A 0.622153 0.280548E-01 0.567166 0.677141 7 7 MOV(02,A,M) BA 0.308311E-12 0.204126E-07 -.400083E-07 0.400089E-07 8 8 SRV(02,A,M) B 0.622532 0.269198E-01 0.569769 0.675295 9 9 MOV(03,A,M) AA 0.674830 0.196852E-01 0.636247 0.713413 10 10 SRV(03,A,M) A 0.210049 0.826678E-02 0.193847 0.226252 11 11 MOV(03,A,M) BA 0.133071E-10 0.118946E-06 -.233120E-06 0.233147E-06 12 12 SRV(03,A,M) B 0.215868 0.748042E-02 0.201206 0.230529 13 13 p(02,A,M) A 0.337987 0.248288E-01 0.289323 0.386652 14 14 p(02,A,M) B 0.350714 0.179123E-01 0.315606 0.385822 15 15 p(03,A,M) A 0.347658 0.249419E-01 0.298772 0.396544 16 16 p(03,A,M) B 0.360028 0.165166E-01 0.327656 0.392401 17 -33 p(04,A,M) A 1.00000 0.000000E+00 1.00000 1.00000 18 -34 p(04,A,M) B 1.00000 0.000000E+00 1.00000 1.00000 19 1 MOV(01,A,F) AA 0.686338 0.259820E-01 0.635413 0.737262 20 2 SRV(01,A,F) A 0.642315 0.258643E-01 0.591621 0.693009 21 3 MOV(01,A,F) BA 0.424835E-17 0.995660E-10 -.195149E-09 0.195149E-09 22 4 SRV(01,A,F) B 0.633335 0.262895E-01 0.581808 0.684862 23 5 MOV(02,A,F) AA 0.686668 0.248164E-01 0.638027 0.735308 24 6 SRV(02,A,F) A 0.622153 0.280548E-01 0.567166 0.677141 25 7 MOV(02,A,F) BA 0.308311E-12 0.204126E-07 -.400083E-07 0.400089E-07 26 8 SRV(02,A,F) B 0.622532 0.269198E-01 0.569769 0.675295 27 9 MOV(03,A,F) AA 0.674830 0.196852E-01 0.636247 0.713413 28 10 SRV(03,A,F) A 0.210049 0.826678E-02 0.193847 0.226252 29 11 MOV(03,A,F) BA 0.133071E-10 0.118946E-06 -.233120E-06 0.233147E-06 30 12 SRV(03,A,F) B 0.215868 0.748042E-02 0.201206 0.230529 31 13 p(02,A,F) A 0.337987 0.248288E-01 0.289323 0.386652 32 14 p(02,A,F) B 0.350714 0.179123E-01 0.315606 0.385822 33 15 p(03,A,F) A 0.347658 0.249419E-01 0.298772 0.396544 34 16 p(03,A,F) B 0.360028 0.165166E-01 0.327656 0.392401 35 -35 p(04,A,F) A 1.00000 0.000000E+00 1.00000 1.00000 36 -36 p(04,A,F) B 1.00000 0.000000E+00 1.00000 1.00000 CPU time in seconds for last procedure was 1.77 INPUT --- PROC TEST; Submodel Name Log-likelihood NDF Akaike Inf. Criter. G-O-F -------- ------------------- -------------- --- ------------------- ------ 3 MODL_B -107.869 28 255.73734 1.0000 1 ULTRA1/* -105.812 22 263.62421 1.0000 4 MODL_A -105.795 16 275.59091 1.0000 2 MODL_D -126.955 36 277.91045 0.0000 5 1GRP_A -855.390 32 1742.7796 0.0000 Likelihood Ratio Tests Between Models General Reduced Degrees Pr(Larger Submodel Submodel Chi-square Freedom Chi-square) ---------- ---------- ---------- ------- ----------- ULTRA1/* MODL_B 4.113 6 0.6614 MODL_A MODL_B 4.146 12 0.9806 MODL_B MODL_D 38.173 8 0.0000 MODL_B 1GRP_A 1495.042 4 0.0000 MODL_A ULTRA1/* 0.033 6 1.0000 ULTRA1/* MODL_D 42.286 14 0.0001 ULTRA1/* 1GRP_A 1499.155 10 0.0000 MODL_A MODL_D 42.320 20 0.0025 MODL_A 1GRP_A 1499.189 16 0.0000 1GRP_A MODL_D 0.000 4 1.0000 * * WARNING * * Sequence of models reinitialized to zero. CPU time in seconds for last procedure was 0.00 INPUT --- PROC STOP; CPU time in minutes for this job was 0.30 E X E C U T I O N S U C C E S S F U L
User-defined models
After running CNVMEMOV, other models may be added to the ones produced by default. To add new models, edit the CNVMEMOV output file, CNVMEMOV.OUT with a text editor. If a word-processor (eg. Word, Word Perfect, WordPro,...) is used, the file must be saved in ASCII or DOS text format.
Each model must begin with a ‘PROC ESTIMATE' statement. This is followed by ‘INITIAL', and ‘CONSTRAINTS' statements. The statements following the ‘CONSTRAINTS;' statement define how the parameters are related to each other. The best procedure for designing a model is to copy the statements from a previous model and modify the copy. For example, to build a model where survival/movement is time-specific and capture-probabilities are constant over time, you could copy the statements from model ‘D' and delete the statements which constrain survival/transition parameters.
The following rules must be followed when constraining parameters:
1) Parameters can be constrained to be less than, greater than, or equal to constant values. (Eg. S(10)=1; S(11)<0.5; S(12)>0.1;) 2) Parameters can not be constrained to be less than or greater than other parameters. (Eg. S(10)>S(11) - invalid) 3) Parameters can not be constrained to be equal to a previously constrained parameter. (Eg. S(5)=S(1); S(10)=S(5); - invalid S(5)=S(1); S(10)=S(1); - valid)
PROC ESTIMATE NOVAR NSIG=6 MAXFN=64000 NAME=MODL_C; INITIAL; ALL= 0.500; CONSTRAINTS; S(015)=S(013) /* p(03,A,M) A =p(02,A,M) A */; S(016)=S(014) /* p(03,A,M) B =p(02,A,M) B */; S(017)=S(013) /* p(04,A,M) A =p(02,A,M) A */; S(018)=S(014) /* p(04,A,M) B =p(02,A,M) B */; S(033)=S(031) /* p(03,A,F) A =p(02,A,F) A */; S(034)=S(032) /* p(03,A,F) B =p(02,A,F) B */; S(035)=S(031) /* p(04,A,F) A =p(02,A,F) A */; S(036)=S(032) /* p(04,A,F) B =p(02,A,F) B */;
External Covariates and additive models
To define models with external covariates a few new statements are needed. The list of parameters can be mapped to a new set of parameters using the ‘PIM' and ‘DESMAT' statements. The ‘PIM' (Parameter Information Matrix) statement is used to map the actual parameters to unique transformed parameters. The unique transformed parameters are multiplied by the design matrix to give the value for the actual parameter. For example, to model survival and transition probabilities and capture probabilities as constant over time (as in model D), the PIM would be:
PIM; 1 2 3 4 1 2 3 4 1 2 3 4 5 6 5 6 5 6 7 8 9 10 7 8 9 10 7 8 9 10 11 12 11 12 11 12;
These 2 statements cause parameters 1,5,9 {mov(*,a,m) AA} to be mapped to a new parameter 1. This parameter would be the probability of moving from strata A to A for the first group(m) for any of the time-periods. Similarly, parameters 2,6,10 would be mapped to a new parameter 2. Using these two statements gives the same results as using the constraints in model D.
Although this statement could be used to generate models, the intended purpose of the PIM statement is to help generate additive or covariate models. Additive models are models where one parameter is defined in terms of other parameter(s) plus fixed values. Covariate models are models where parameters are defined as a function of some external variable (eg. Rainfall, temperature). For example, if we wanted to define a model where survival for groups M and F are related to an external covariate (temperature), the PIM would be:
PIM; 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36;
In this example, each parameter is mapped to a transformed parameter. The transformed parameters are computed by multiplying the design matrix by the estimable parameters. The default design matrix is the identity matrix (zeros except on the diagonal; ones on the diagonal). This matrix would yield no transformation of the parameters. The transformation of parameters is achieved by matrix multiplication of the design matrix and the parameter vector. Here is an example where there are 4 parameters(p1-p4) , and the last two are equal to the first two plus a constant (t3)
[P1]= [1 0 0] [ ] , [T1] [P2]= [0 1 0] [T1] , [T2] [P3]= [1 0 1] [T2] , [T1+T3] [P4]= [0 1 1] [T3] , [T2+T3]In the previous example, the design matrix contains only ones and zeros. To specify that the parameters are to be a function of an external variable, the external variable can be inserted into a column of the design matrix. In the following example, only one parameter will be estimated, t1, and the actual parameters, p1-p4 will be computed using t1 and the covariates (.44 .48 .41 .52).
[P1]= [.44] , [.44T1] [P2]= [.48] [T1] , [.48T1] [P3]= [.41] , [.41T1] [P4]= [.52] , [.52T1]
Here is the input for generating a model where survival for the 2 groups are a function of an external variable and capture and movement probabilities vary over time and group:
PROC ESTIMATE NOVAR NSIG=6 MAXFN=64000 NAME=ULTRA1 /* SRV(M) = F(A1,X), SRV(F)=F(A2,X) */; LABELS; S(1)=MOV(01,A,M) AA ; S(2)=MOV(01,A,M) BA ; S(3)=MOV(02,A,M) AA ; S(4)=MOV(02,A,M) BA ; S(5)=MOV(03,A,M) AA ; S(6)=MOV(03,A,M) BA ; S(7)=p(02,A,M) A ; S(8)=p(02,A,M) B ; S(9)=p(03,A,M) A ; S(10)=p(03,A,M) B ; S(11)=p(04,A,M) A ; S(12)=p(04,A,M) B ; S(13)=MOV(01,A,F) AA ; S(14)=MOV(01,A,F) BA ; S(15)=MOV(02,A,F) AA ; S(16)=MOV(02,A,F) BA ; S(17)=MOV(03,A,F) AA ; S(18)=MOV(03,A,F) BA ; S(19)=p(02,A,F) A ; S(20)=p(02,A,F) B ; S(21)=p(03,A,F) A ; S(22)=p(03,A,F) B ; S(23)=p(04,A,F) A ; S(24)=p(04,A,F) B ; S(25)=A1 ; S(26)=A2 ; PIM; 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36; DESMAT 36 26; SUBMAT 1 1 TO 36 26; 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .21 0; 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .21 0; 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .15 0; 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .15 0; 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .10 0; 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .10 0; 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .21; 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .21; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .15; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .15; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .10; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .10; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0; INITIAL; ALL= 0.500;
First, notice the comment in the PROC ESTIMATE which is enclosed by /* and */. Since the parameters are transformed, new definitions of the transformed parameters would be helpful in reading the output (LABELS;). The PIM statement specifies that all 36 parameters are unique. The DESMAT statement declares the size of the matrix to be 36 rows by 26 columns. Each row of the design matrix corresponds to one of the 36 original parameters, and each column corresponds to one of the 26 transformed parameters.
To make defining the design matrix easier, you only need specify parts of the matrix which are different from the identity matrix. For clarity, the entire matrix is listed here, but it is possible to only specify a subset of the matrix using the SUBMAT statement. The 4 numbers of the SUBMAT statement specify the starting row and column followed by the ending row and column of values which follow.
The output produced by MSSURVIV for the previous model is:
INPUT --- PROC ESTIMATE NOVAR NSIG=6 MAXFN=64000 NAME=ULTRA1/* SRV(M) INPUT --- = F(A1,X), SRV(F)=F(A2,X) */; Number of parameters in model = 36 Number of parameters set equal = 0 Number of parameters fixed = 0 Number of parameters estimated = 26 Final function value 11017.474 (Error Return = 130) Number of significant digits 5 Number of function evaluations 2514 * * WARNING * * Rounding errors became dominant before parameters estimated * * WARNING * * to NSIG digits for Model ULTRA1/* * * WARNING * * Check to be sure the parameters are identifiable, * * WARNING * * but the problem may just be ill-conditioned. Cohort Cell Observed Expected Chi-square Note ------ ---- -------- -------- ---------- ------------- 1 1 236. 236.034 0.000 0 < P < 1 1 2 102. 101.866 0.000 0 < P < 1 1 3 76. 76.062 0.000 0 < P < 1 1 4 79. 79.362 0.002 0 < P < 1 1 5 22. 21.968 0.000 0 < P < 1 1 6 42. 42.052 0.000 0 < P < 1 1 7 443. 442.656 0.000 0 < P < 1 1 Cohort df= 6 0.002 P = 1.0000 2 2 338. 338.131 0.000 0 < P < 1 2 4 156. 155.669 0.001 0 < P < 1 2 6 64. 64.076 0.000 0 < P < 1 2 7 443. 443.124 0.000 0 < P < 1 2 Cohort df= 3 0.001 P = 1.0000 3 1 267. 266.950 0.000 0 < P < 1 3 2 114. 113.940 0.000 0 < P < 1 3 3 77. 77.098 0.000 0 < P < 1 3 4 80. 79.839 0.000 0 < P < 1 3 5 698. 698.173 0.000 0 < P < 1 3 Cohort df= 4 0.001 P = 1.0000 4 2 444. 443.987 0.000 0 < P < 1 4 4 183. 182.751 0.000 0 < P < 1 4 5 813. 813.262 0.000 0 < P < 1 4 Cohort df= 2 0.000 P = 0.9998 5 1 260. 259.938 0.000 0 < P < 1 5 2 111. 111.056 0.000 0 < P < 1 5 3 972. 972.006 0.000 0 < P < 1 5 Cohort df= 2 0.000 P = 1.0000 6 2 495. 495.210 0.000 0 < P < 1 6 3 1298. 1297.790 0.000 0 < P < 1 6 Cohort df= 1 0.000 P = 0.9911 7 1 62. 61.866 0.000 0 < P < 1 7 2 41. 40.652 0.003 0 < P < 1 7 3 11. 10.654 0.011 0 < P < 1 7 4 18. 18.216 0.003 0 < P < 1 7 5 2. 1.956 0.001 0 < P < 1 7 6 7. 6.662 0.017 0 < P < 1 7 7 859. 859.994 0.001 0 < P < 1 7 Cohort df= 5 0.035 P = 1.0000 8 2 105. 105.438 0.002 0 < P < 1 8 4 29. 28.856 0.001 0 < P < 1 8 6 8. 8.481 0.027 0 < P < 1 8 7 859. 858.225 0.001 0 < P < 1 8 Cohort df= 3 0.031 P = 0.9986 9 1 74. 74.262 0.001 0 < P < 1 9 2 50. 49.421 0.007 0 < P < 1 9 3 14. 13.630 0.010 0 < P < 1 9 4 23. 23.646 0.018 0 < P < 1 9 5 901. 901.040 0.000 0 < P < 1 9 Cohort df= 4 0.035 P = 0.9998 10 2 135. 135.649 0.003 0 < P < 1 10 4 40. 39.868 0.000 0 < P < 1 10 5 971. 970.483 0.000 0 < P < 1 10 Cohort df= 2 0.004 P = 0.9981 11 1 83. 83.399 0.002 0 < P < 1 11 2 56. 55.807 0.001 0 < P < 1 11 3 946. 945.794 0.000 0 < P < 1 11 Cohort df= 2 0.003 P = 0.9987 12 2 158. 157.567 0.001 0 < P < 1 12 3 1074. 1074.433 0.000 0 < P < 1 12 Cohort df= 1 0.001 P = 0.9705 ------------------------------------------------------------ @@ 1 130 0 22 0.114473 9 0.113113 -105.812 263.624 G Total (Degrees of freedom = 22) 0.114 Pr(Larger Chi-square) = 1.0000 With pooling, Degrees of freedom = 9 Pearson Chi-square = 0.113 Pr(Larger Chi-square) = 1.0000 Log-likelihood = -105.81211 Akaike Information Criterion = 263.62421 itrans,nyrs= 2 4 SURVIVAL/MOVEMENT & CAPTURE PROB. ESTIMATES 1 MOV(01,A,M) AA 0.698437 2 SRV(01,A,M) A 0.842177 3 MOV(01,A,M) BA 0.565833E-12 4 SRV(01,A,M) B 0.842177 5 MOV(02,A,M) AA 0.701015 6 SRV(02,A,M) A 0.767827 7 MOV(02,A,M) BA 0.103410E-10 8 SRV(02,A,M) B 0.767827 9 MOV(03,A,M) AA 0.700596 10 SRV(03,A,M) A 0.689415 11 MOV(03,A,M) BA 0.861494E-15 12 SRV(03,A,M) B 0.689415 13 p(02,A,M) A 0.401277 14 p(02,A,M) B 0.401096 15 p(03,A,M) A 0.401256 16 p(03,A,M) B 0.401554 17 p(04,A,M) A 0.400724 18 p(04,A,M) B 0.400616 19 MOV(01,A,F) AA 0.614067 20 SRV(01,A,F) A 0.348870 21 MOV(01,A,F) BA 0.548536E-45 22 SRV(01,A,F) B 0.348870 23 MOV(02,A,F) AA 0.606850 24 SRV(02,A,F) A 0.390379 25 MOV(02,A,F) BA 0.521048E-29 26 SRV(02,A,F) B 0.390379 27 MOV(03,A,F) AA 0.597835 28 SRV(03,A,F) A 0.426255 29 MOV(03,A,F) BA 0.422671E-11 30 SRV(03,A,F) B 0.426255 31 p(02,A,F) A 0.288786 32 p(02,A,F) B 0.301926 33 p(03,A,F) A 0.295174 34 p(03,A,F) B 0.303211 35 p(04,A,F) A 0.301634 36 p(04,A,F) B 0.300044 UNTRANSFORMED ESTIMATES FOR MODEL ULTRA1/* 95% Confidence Interval I Parameter S(I) Standard Error Lower Upper --- -------------------- ------------ ------------ ------------ ------------ 1 1 MOV(01,A,M) AA 0.839867 0.528164E-01 0.736346 0.943387 2 2 MOV(01,A,M) BA -20.0000 0.427416E-01 -20.0838 -19.9162 3 3 MOV(02,A,M) AA 0.852135 0.472473E-01 0.759530 0.944739 4 4 MOV(02,A,M) BA -20.0000 0.349412E-01 -20.0685 -19.9315 5 5 MOV(03,A,M) AA 0.850136 0.522983E-01 0.747631 0.952640 6 6 MOV(03,A,M) BA -20.0000 0.354779E-01 -20.0695 -19.9305 7 7 p(02,A,M) A -.400146 0.361840E-01 -.471066 -.329225 8 8 p(02,A,M) B -.400902 0.330581E-01 -.465696 -.336109 9 9 p(03,A,M) A -.400235 0.325735E-01 -.464080 -.336391 10 10 p(03,A,M) B -.398993 0.290952E-01 -.456019 -.341966 11 11 p(04,A,M) A -.402450 0.505944E-01 -.501615 -.303285 12 12 p(04,A,M) B -.402898 0.476067E-01 -.496207 -.309589 13 13 MOV(01,A,F) AA 0.464440 0.240815 -.755734E-02 0.936438 14 14 MOV(01,A,F) BA -20.0000 0.178446 -20.3498 -19.6502 15 15 MOV(02,A,F) AA 0.434089 0.193629 0.545757E-01 0.813603 16 16 MOV(02,A,F) BA -20.0000 0.132460 -20.2596 -19.7404 17 17 MOV(03,A,F) AA 0.396454 0.192605 0.189479E-01 0.773960 18 18 MOV(03,A,F) BA -20.0000 0.114655 -20.2247 -19.7753 19 19 p(02,A,F) A -.901288 0.113968 -1.12467 -.677910 20 20 p(02,A,F) B -.838141 0.973045E-01 -1.02886 -.647424 21 21 p(03,A,F) A -.870388 0.902454E-01 -1.04727 -.693507 22 22 p(03,A,F) B -.832054 0.737722E-01 -.976647 -.687460 23 23 p(04,A,F) A -.839531 0.818452E-01 -.999948 -.679114 24 24 p(04,A,F) B -.847088 0.587391E-01 -.962217 -.731959 25 25 A1 7.97388 1.13913 5.74118 10.2066 26 26 A2 -2.97148 0.408431 -3.77200 -2.17095 PARAMETER ESTIMATES FOR MODEL ULTRA1/* 95% Confidence Interval I Parameter S(I) Standard Error Lower Upper --- -------------------- ------------ ------------ ------------ ------------ 1 1 MOV(01,A,M) AA 0.698437 0.111245E-01 0.676633 0.720241 2 2 SRV(01,A,M) A 0.842177 0.317975E-01 0.779854 0.904500 3 3 MOV(01,A,M) BA 0.206115E-08 0.881853E-10 0.188831E-08 0.223400E-08 4 4 SRV(01,A,M) B 0.842177 0.317975E-01 0.779854 0.904500 5 5 MOV(02,A,M) AA 0.701015 0.990287E-02 0.681605 0.720424 6 6 SRV(02,A,M) A 0.767827 0.304617E-01 0.708122 0.827532 7 7 MOV(02,A,M) BA 0.206115E-08 0.720913E-10 0.191985E-08 0.220245E-08 8 8 SRV(02,A,M) B 0.767827 0.304617E-01 0.708122 0.827532 9 9 MOV(03,A,M) AA 0.700596 0.109703E-01 0.679094 0.722097 10 10 SRV(03,A,M) A 0.689415 0.243917E-01 0.641608 0.737223 11 11 MOV(03,A,M) BA 0.206115E-08 0.731985E-10 0.191768E-08 0.220462E-08 12 12 SRV(03,A,M) B 0.689415 0.243917E-01 0.641608 0.737223 13 13 p(02,A,M) A 0.401277 0.869338E-02 0.384238 0.418316 14 14 p(02,A,M) B 0.401096 0.794117E-02 0.385531 0.416660 15 15 p(03,A,M) A 0.401256 0.782581E-02 0.385917 0.416594 16 16 p(03,A,M) B 0.401554 0.699185E-02 0.387850 0.415258 17 17 p(04,A,M) A 0.400724 0.121500E-01 0.376910 0.424538 18 18 p(04,A,M) B 0.400616 0.114315E-01 0.378210 0.423022 19 19 MOV(01,A,F) AA 0.614067 0.570708E-01 0.502208 0.725926 20 20 SRV(01,A,F) A 0.348870 0.194838E-01 0.310682 0.387058 21 21 MOV(01,A,F) BA 0.206115E-08 0.368173E-09 0.133953E-08 0.278277E-08 22 22 SRV(01,A,F) B 0.348870 0.194838E-01 0.310682 0.387058 23 23 MOV(02,A,F) AA 0.606850 0.461969E-01 0.516304 0.697396 24 24 SRV(02,A,F) A 0.390379 0.145800E-01 0.361802 0.418955 25 25 MOV(02,A,F) BA 0.206115E-08 0.273294E-09 0.152550E-08 0.259681E-08 26 26 SRV(02,A,F) B 0.390379 0.145800E-01 0.361802 0.418955 27 27 MOV(03,A,F) AA 0.597835 0.463079E-01 0.507072 0.688599 28 28 SRV(03,A,F) A 0.426255 0.998868E-02 0.406677 0.445833 29 29 MOV(03,A,F) BA 0.206115E-08 0.236557E-09 0.159750E-08 0.252481E-08 30 30 SRV(03,A,F) B 0.426255 0.998868E-02 0.406677 0.445833 31 31 p(02,A,F) A 0.288786 0.234082E-01 0.242906 0.334666 32 32 p(02,A,F) B 0.301926 0.205089E-01 0.261729 0.342124 33 33 p(03,A,F) A 0.295174 0.187755E-01 0.258374 0.331974 34 34 p(03,A,F) B 0.303211 0.155864E-01 0.272662 0.333760 35 35 p(04,A,F) A 0.301634 0.172410E-01 0.267841 0.335426 36 36 p(04,A,F) B 0.300044 0.123364E-01 0.275865 0.324224 CPU time in seconds for last procedure was 8.38
Hardware Considerations
Two versions of MSSURVIV are available for PC's. A small version which will work on older PC's running DOS without Extended memory or a math coprocessor is set up to handle up to 25 cohorts, 20 classes and 50 parameters. See appendix A to determine the possible combinations of strata and sample periods for these limits.
A larger version, compiled with a "DOS extender", requires a 386/387 or 486 PC, 640Kb RAM, and at least 10Mb of free hard disk space (although MSSURVIV will run faster with more RAM). The maximum number of cohorts, classes and parameters for this version are: 64, 64, and 250, respectively.
The source code for MSSURVIV has been successfully compiled and run on a Prime minicomputer running PrimeOS, and two different unix workstations.
The program has also been compiled for Windows and OS/2. The limitations for the Windows version are the same as the DOS extender version.
Software Installation
To install MSSURVIV on a PC simply make a sub-directory to contain the programs and copy the files from the floppy disk. The disk contains the executable program file, so no compilation is necessary unless you wish to alter the dimensions. Here are the commands to install the MSSURVIV programs onto the hard disk of a PC:
c:> mkdir mssrv c:> cd mssrv c:> xcopy a:*.* /s
To install MSSURVIV on other computers, a FORTRAN compiler will be required. The files must first be transferred to disk on the computer, then compiled and linked into an executable program file. A "make" file is included which will create the executable program file from the source files if the target computer has the make utility (as most Unix systems do). If the target computer doesn't have a make utility, a "batch" file is included to compile all of the routines. Most likely, the make file or batch file will have to be edited to reflect the names of the compiler and linker on the target system.
Literature Cited
Brownie, C., J.E. Hines, J.D. Nichols, K.H. Pollock, and J.B. Hestbeck. 1993. Capture-recapture studies for multiple strata including non-Markovian transition probabilities. Biometrics 49:1173-1187.
Pollock, K.H., J.D. Nichols, C. Brownie, and J.E. Hines. 1990. Statistical inference for capture-recapture experiments. Wildlife Monographs 107. 97pp.
White, G.C. 1983. Numerical estimation of survival rates from band-recovery and biotelemetry data. The Journal of Wildlife Management 47:716-728. Appendix A: Maximum number of cohorts/classes/parameters for "Movement-only" and "Memory-Movement" models.
"Movement-only" models
SAMPLE STRATA PERIODS 1 2 3 4 5 6 3 2/ 2/ 4 4/ 4/ 12 6/ 6/ 24 8/ 8/ 40 10/10/ 60 12/ 12/ 84 4 3/ 3/ 6 6/ 6/ 18 9/ 9/ 36 12/12/ 60 15/15/ 90 18/ 18/126 5 4/ 4/ 8 8/ 8/ 24 12/12/ 48 16/16/ 80 20/20/120 24/ 24/168 6 5/ 5/10 10/10/ 30 15/15/ 60 20/20/100 25/25/150 30/ 30/210 7 6/ 6/12 12/12/ 36 18/18/ 72 24/24/120 30/30/180 36/ 36/252 8 7/ 7/14 14/14/ 42 21/21/ 84 28/28/140 35/35/210 42/ 42/294 9 8/ 8/16 16/16/ 48 24/24/ 96 32/32/160 40/40/240 48/ 48/336 10 9/ 9/18 18/18/ 54 27/27/108 36/36/180 45/45/270 54/ 54/378 11 10/10/20 20/20/ 60 30/30/120 40/40/200 50/50/300 60/ 60/420 12 11/11/22 22/22/ 66 33/33/132 44/44/220 55/55/330 66/ 66/462 13 12/12/24 24/24/ 72 36/36/144 48/48/240 60/60/360 72/ 72/504 14 13/13/26 26/26/ 78 39/39/156 52/52/260 65/65/390 78/ 78/546 15 14/14/28 28/28/ 84 42/42/168 56/56/280 70/70/420 84/ 84/588 16 15/15/30 30/30/ 90 45/45/180 60/60/300 75/75/450 90/ 90/630 17 16/16/32 32/32/ 96 48/48/192 64/64/320 80/80/480 96/ 96/672 18 17/17/34 34/34/102 51/51/204 68/68/340 85/85/510 102/102/714 19 18/18/36 36/36/108 54/54/216 72/72/360 90/90/540 108/108/756 20 19/19/38 38/38/114 57/57/228 76/76/380 95/95/570 114/114/798
"Memory-Movement" models
SAMPLE STRATA PERIODS 1 2 3 4 5 6 3 1/ 1/ 2 4/ 2/ 10 9/ 3/ 30 16/ 4/ 68 25/ 5/ 130 36/ 6/ 222 4 2/ 2/ 4 8/ 4/ 20 18/ 6/ 60 32/ 8/ 136 50/10/ 260 72/ 12/ 444 5 3/ 3/ 6 12/ 6/ 30 27/ 9/ 90 48/12/ 204 75/15/ 390 108/ 18/ 666 6 4/ 4/ 8 16/ 8/ 40 36/12/120 64/16/ 272 100/20/ 520 144/ 24/ 888 7 5/ 5/10 20/10/ 50 45/15/150 80/20/ 340 125/25/ 650 180/ 30/1,110 8 6/ 6/12 24/12/ 60 54/18/180 96/24/ 408 150/30/ 780 216/ 36/1,332 9 7/ 7/14 28/14/ 70 63/21/210 112/28/ 476 175/35/ 910 252/ 42/1,554 10 8/ 8/16 32/16/ 80 72/24/240 128/32/ 544 200/40/1,040 288/ 48/1,776 11 9/ 9/18 36/18/ 90 81/27/270 144/36/ 612 225/45/1,170 324/ 54/1,998 12 10/10/20 40/20/100 90/30/300 160/40/ 680 250/50/1,300 360/ 60/2,220 13 11/11/22 44/22/110 99/33/330 176/44/ 748 275/55/1,430 396/ 66/2,442 14 12/12/24 48/24/120 108/36/360 192/48/ 816 300/60/1,560 432/ 72/2,664 15 13/13/26 52/26/130 117/39/390 208/52/ 884 325/65/1,690 468/ 78/2,886 16 14/14/28 56/28/140 126/42/420 224/56/ 952 350/70/1,820 504/ 84/3,108 17 15/15/30 60/30/150 135/45/450 240/60/1,020 375/75/1,950 540/ 90/3,330 18 16/16/32 64/32/160 144/48/480 256/64/1,088 400/80/2,080 576/ 96/3,552 19 17/17/34 68/34/170 153/51/510 272/68/1,156 425/85/2,210 612/102/3,774 20 18/18/36 72/36/180 162/54/540 288/72/1,224 450/90/2,340 648/108/3,996