Bootstrap_of__observTableFile00plant year stage reprstr recruitment
2 1987 D 0
4 1987 J 0
5 1987 D 0
31 1987 V 0
36 1987 G 5
37 1987 J 0
41 1987 J 0
43 1987 S 0
46 1987 V 0
2 1988 V 0
4 1988 D 0
5 1988 V 0
9 1988 J 0 RJ
14 1988 J 0 RJ2012-10-12 12:05:46.851 UTCTable containing demographic data of individuals in two year
The input data (a .txt-file) has to have the format of a table containing the demographic data on a series of individuals in two years. Each individual has a table row for each year and is identified by a number (plant column in example). For the year specified in the column 'year', each individual has a certain life stage ('stage'). The stage codes can be chosen by the user, not longer than 5 characters. For all individuals, the number of offspring is specified in a chosen way (in the example, 'repstr' gives the number of flowers/fruits for each plant. In the example, it can be seen that only generative adults (stage=G) had flowers. Individuals without offspring have to be indicated by filling in '0' in this column, which cannot have empty cells. The column 'recruitment' specifies those individuals that are new to the population by means of a code. In the example, 'RS' is a new seedling, and 'RJ' a new juvenile plant. As can be seen in the example, no code is needed in this column for individuals that were already present.2012-10-12 12:05:58.914 UTCSpeciesName00Gentiana pneumonanthe2012-10-19 13:29:46.648 UTCIn this input port SpeciesName comes the title of the bar plot that will be generated with the analysis. As an example, it can be the name of the species or the name of the place where the research has been conducted, between others.2012-11-05 15:01:23.953 UTCBootstrap_Iterations00100002012-10-18 08:38:58.81 UTCNumber of iterations for calculation of bootstrap distributions
2012-10-18 08:39:43.774 UTCConfidence_Interval_CI95% Confidence interval of Lambda
In statistics, a confidence interval (CI) is a type of interval estimate of a population parameter and is used to indicate the reliability of an estimate. It is an observed interval (i.e. it is calculated from the observations), in principle different from sample to sample, that frequently includes the parameter of interest if the experiment is repeated. How frequently the observed interval contains the parameter is determined by the confidence level or confidence coefficient.
2012-10-12 12:24:22.164 UTC 2.5% 97.5%
0.954955 1.468701 2012-10-05 14:22:04.620 UTChistogramHistogram plotting the frecuencies of the lambda values and the 95% confidence intervals resulting from the bootstrap analysis
2012-10-12 11:51:41.883 UTCDisplayConfidenceIntervalinput1output00net.sf.taverna.t2.activitiesrshell-activity1.4net.sf.taverna.t2.activities.rshell.RshellActivityinput1falseoutput00falselocalhost6311falsefalseinputR_EXPoutputTEXT_FILEnet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeBootstrap_of__observcensus_data1startYear0stages1recruitedStages1plotTitle0bootstrapIterations0confidence_interval11histogram00net.sf.taverna.t2.activitiesdataflow-activity1.4net.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeCensusData_ReadFromFilecensus_data_file0census_data11net.sf.taverna.t2.activitiesrshell-activity1.4net.sf.taverna.t2.activities.rshell.RshellActivitycensus_data_file0falsecensus_data11falselocalhost6311falsefalsecensus_data_fileTEXT_FILEcensus_dataR_EXPnet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeSelectYearcensus_data1yearColumnName0initialYear00net.sf.taverna.t2.activitiesdataflow-activity1.4net.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokeyearvalue00net.sf.taverna.t2.activitiesstringconstant-activity1.4net.sf.taverna.t2.activities.stringconstant.StringConstantActivityyearnet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeSortAndCategoriseStacensus_data1stageColumnHeader0matrixStages11recruitedStages11net.sf.taverna.t2.activitiesdataflow-activity1.4net.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokestagevalue00net.sf.taverna.t2.activitiesstringconstant-activity1.4net.sf.taverna.t2.activities.stringconstant.StringConstantActivitystagenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeDisplayConfidenceIntervalinputBootstrap_of__observcensus_dataBootstrap_of__observstartYearBootstrap_of__observstagesBootstrap_of__observrecruitedStagesBootstrap_of__observplotTitleBootstrap_of__observbootstrapIterationsCensusData_ReadFromFilecensus_data_fileSelectYearcensus_dataSelectYearyearColumnNameSortAndCategoriseStacensus_dataSortAndCategoriseStastageColumnHeaderConfidence_Interval_CIhistogramb8fe02f6-e147-4416-aeb4-6c4e545895122012-06-27 08:19:06.56 UTC8ab40f79-92fd-40d5-811f-77b34d941a142012-10-19 13:24:50.273 UTC671f03a1-3f9b-49b6-955a-f1f0eda1bc0f2012-10-19 04:09:44.131 UTCe98a3578-498e-4d02-9059-57c0cdf09e832012-06-27 05:56:22.181 UTCaa848d79-a9f7-497e-ba06-ee6feddc1da32012-06-27 06:00:45.433 UTCThis workflow calculates bootstrap distributions of population growth rates (λ), stage vectors, and projection matrix elements by randomly sampling with replacement from a stage-fate data frame of observed transitions. The goal of a demographic analysis is very often to estimate lambda, because lambda is estimated from imperfect data, such estimation are uncertain. Therefore, when the results have policy implications it is important to quantify that uncertainty. Confidence interval is one of the traditional tools to doing so (see outputs: Bootstrap analysis).
A detailed description of resampling methods to estimate confidence intervals for demographic estimates is described Caswell (2001, Chapter 12)
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Literature
Caswell, H. 2001. Matrix population models: Construction, analysis and interpretation, 2nd Edition. Sinauer Associates, Sunderland, Massachusetts.
=======================================================
This workflow has been created by the Biodiversity Virtual e-Laboratory (BioVeL http://www.biovel.eu/) project. BioVeL is funded by the EU’s Seventh Framework Program, grant no. 283359.
This workflow was created based on Package ‘popbio’ in R.
Stubben, C & B. Milligan. 2007. Estimating and Analysing Demographic Models Using the popbio Package in R. Journal of Statistical Software 22 (11): 1-23
Stubben, C., B. Milligan, P. Nantel. 2011. Package ‘popbio’. Construction and analysis of matrix population models. Version 2.3.1
2012-11-02 16:06:49.640 UTC619e109f-43f3-48b3-ac8d-8deb22b7f0142012-10-18 15:02:38.208 UTCMaria Paula Balcázar-Vargas, Jonathan Giddy and G. Oostermeijer
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UTCParseYears_FromCensucensus_data11yearColumnName00censusYearsParseYearsyear_column_name0census_data1census_years11net.sf.taverna.t2.activitiesrshell-activity1.4net.sf.taverna.t2.activities.rshell.RshellActivitycensus_data1falseyear_column_name0falsecensus_years11falselocalhost6311falsefalsecensus_dataR_EXPyear_column_nameSTRINGcensus_yearsSTRING_LISTnet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeParseYearsyear_column_nameParseYearscensus_datacensusYears1e1e8835-e729-4aba-90b7-ba7c653a1f2d2012-07-13 07:52:51.988 UTC23762bae-1b51-4606-9a3a-a2a8b04649a12012-07-11 13:15:24.271 UTC8af0f726-006f-4a83-9eec-9cdc97dcb9d02012-07-06 06:48:37.151 UTCfea78a2d-fde4-46ab-9a54-741b27dc5a902012-07-11 12:32:16.405 UTCcdf75737-30bb-4c7a-9693-2aa928c8569c2012-10-17 11:46:13.979 UTC4b236f5e-9a91-4db9-924c-6674c32311332012-07-13 07:50:51.370 UTC450ea8ae-73da-42ed-95c9-02cf53fa556f2012-10-17 11:43:58.240 UTCParseYears_FromCensusData2012-10-17 11:40:35.234 UTC0e09a93a-32f2-49b8-8a35-7499aa48b3a22012-07-11 12:30:56.575 UTC81324627-fa47-4a0d-8ffe-64698a7ee09d2012-07-13 06:24:44.255 UTC5787d50e-f7cc-47e6-b10a-c49b57e36e272012-07-11 16:05:30.475 UTC8a5082d0-529b-4205-b3e2-8e0cd88122392012-07-06 06:56:06.356 UTC2d4260a9-618f-45c7-9cee-2234c370a5142012-07-11 13:14:26.564 UTCBootstrap_of__observcensus_data11Table containing demographic data of individuals in two year
The input data (a .txt-file) has to have the format of a table containing the demographic data on a series of individuals in two years. Each individual has a table row for each year and is identified by a number (plant column in example). For the year specified in the column 'year', each individual has a certain life stage ('stage'). The stage codes can be chosen by the user, not longer than 5 characters. For all individuals, the number of offspring is specified in a chosen way (in the example, 'repstr' gives the number of flowers/fruits for each plant. In the example, it can be seen that only generative adults (stage=G) had flowers. Individuals without offspring have to be indicated by filling in '0' in this column, which cannot have empty cells. The column 'recruitment' specifies those individuals that are new to the population by means of a code. In the example, 'RS' is a new seedling, and 'RJ' a new juvenile plant. As can be seen in the example, no code is needed in this column for individuals that were already present.2012-10-12 12:05:58.914 UTCplant year stage reprstr recruitment
2 1987 D 0
4 1987 J 0
5 1987 D 0
31 1987 V 0
36 1987 G 5
37 1987 J 0
41 1987 J 0
43 1987 S 0
46 1987 V 0
2 1988 V 0
4 1988 D 0
5 1988 V 0
9 1988 J 0 RJ
14 1988 J 0 RJ2012-10-12 12:05:46.851 UTCstages11S
J
V
G
D 2012-10-12 11:26:02.851 UTCStage input port:
Here come the names of the stages or categories of the input matrix. It is very important that the stages names are not longer than 8 characters. The name of the stages must be added one by one.
The respective name stages must be filled one by one. First press add value, fill a stage name (not longer than 8 characters) and press enter, then press add value and fill once again the next stage name, repeat the action until you have fill all the stages names.
In the following example, the matrix has 5 stages or categories:
S J V G D
S 0.0000 0.0000 0.0000 7.6660 0.0000
J 0.0579 0.0100 0.0000 8.5238 0.0000
V 0.4637 0.8300 0.9009 0.2857 0.8604
G 0.0000 0.0400 0.0090 0.6190 0.1162
D 0.0000 0.0300 0.0180 0.0000 0.0232
The stages of this matrix are called:
1) Seedlings S
2) Juveniles J
3) Vegetative V
4) Reproductive individuals G
5) Dormant plants D
2012-10-12 11:25:20.445 UTCrecruitedStages11startYear00plotTitle00bootstrapIterations00projection_matrixProjection matrix Output port:
Creates a stage matrix from the input data.2012-10-12 11:33:07.101 UTC S J V G D
S 0.0000 0.0000 0.0000 7.6660 0.0000
J 0.0579 0.0100 0.0000 8.5238 0.0000
V 0.4637 0.8300 0.9009 0.2857 0.8604
G 0.0000 0.0400 0.0090 0.6190 0.1162
D 0.0000 0.0300 0.0180 0.0000 0.02322012-10-12 11:32:28.195 UTCconfidence_interval 2.5% 97.5%
0.954955 1.468701 2012-10-05 14:22:04.620 UTC95% Confidence interval of Lambda
In statistics, a confidence interval (CI) is a type of interval estimate of a population parameter and is used to indicate the reliability of an estimate. It is an observed interval (i.e. it is calculated from the observations), in principle different from sample to sample, that frequently includes the parameter of interest if the experiment is repeated. How frequently the observed interval contains the parameter is determined by the confidence level or confidence coefficient.
2012-10-12 12:24:22.164 UTChistogramHistogram plotting the frecuencies of the lambda values and the 95% confidence intervals resulting from the bootstrap analysis
2012-10-12 11:51:41.883 UTCtrans1logPreparecensus_data1recruited_stages1stages1start_year0trans111log00net.sf.taverna.t2.activitiesrshell-activity1.4net.sf.taverna.t2.activities.rshell.RshellActivitycensus_data1falsestages1falserecruited_stages1falsestart_year0falsetrans111trans11log00falselocalhost6311falsefalsecensus_dataR_EXPstagesSTRING_LISTrecruited_stagesSTRING_LISTstart_yearINTEGERtrans1R_EXPtransR_EXPlogTEXT_FILEnet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeAnalysisstages1trans11projection_matrix11net.sf.taverna.t2.activitiesrshell-activity1.4net.sf.taverna.t2.activities.rshell.RshellActivitytrans11falsestages1falseprojection_matrix11falselocalhost6311falsefalsetrans1R_EXPstagesSTRING_LISTprojection_matrixR_EXPnet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokePlotHistogramplot_title0a1ci1histogram00net.sf.taverna.t2.activitiesrshell-activity1.4net.sf.taverna.t2.activities.rshell.RshellActivitya1falseci1falseplot_title0falseci11histogram00falselocalhost6311falsefalseaR_EXPciR_EXPplot_titleSTRINGciR_EXPhistogramPNG_FILEnet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeConfidenceIntervaltrans11bootstrap_iterations0ci11a11net.sf.taverna.t2.activitiesrshell-activity1.4net.sf.taverna.t2.activities.rshell.RshellActivitytrans11falsebootstrap_iterations0falsea11ci11falselocalhost6311falsefalsetrans1R_EXPbootstrap_iterationsINTEGERaR_EXPciR_EXPnet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeDisplayTrans1input1output00net.sf.taverna.t2.activitiesrshell-activity1.4net.sf.taverna.t2.activities.rshell.RshellActivityinput1falseoutput00falselocalhost6311falsefalseinputR_EXPoutputTEXT_FILEnet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokePreparecensus_dataPreparerecruited_stagesPreparestagesPreparestart_yearAnalysisstagesAnalysistrans1PlotHistogramplot_titlePlotHistogramaPlotHistogramciConfidenceIntervaltrans1ConfidenceIntervalbootstrap_iterationsDisplayTrans1inputprojection_matrixconfidence_intervalhistogramtrans1logThis Workflow was created by:
Maria Paula Balcázar-Vargas, Jonathan Giddy and G. Oostermeijer
This workflow has been created by the Biodiversity Virtual e-Laboratory (BioVeL http://www.biovel.eu/) project. BioVeL is funded by the EU’s Seventh Framework Program, grant no. 283359.
This workflow was created based on Package ‘popbio’ in R.
Stubben, C & B. Milligan. 2007. Estimating and Analysing Demographic Models Using the popbio Package in R. Journal of Statistical Software 22 (11): 1-23
Stubben, C., B. Milligan, P. Nantel. 2011. Package ‘popbio’. Construction and analysis of matrix population models. Version 2.3.12012-10-12 11:04:44.914 UTCee769dbe-15e6-4f27-bf6f-feb4292a5a472012-10-19 04:38:45.215 UTCbd29b59d-e91a-48ae-8996-df31dd66ba7c2012-06-27 07:39:46.142 UTCfa80c538-bf1e-4c79-8c8a-b1a2b38e60562012-10-12 11:51:39.383 UTC4280c8bb-56c9-4795-b78a-106cdbf36e1a2012-10-18 06:07:44.858 UTC75353ac2-b7b7-4842-bcd4-309605dac4482012-10-12 12:01:54.789 UTC245ec9d7-9e92-478a-8214-d4f3f8a11e1e2012-10-18 05:43:24.204 UTC294ceddd-75cb-4181-b87c-3939b2ee08f42012-10-18 12:50:20.12 UTCfd4211f0-3d79-4602-8aaf-dc75784ede5a2012-06-27 08:17:14.444 UTCd646dd81-0fc6-46b9-891d-205f2e5a04a62012-10-12 11:51:58.883 UTCBootstrap of observed census transitions.2012-10-05 14:10:39.479 UTCaa848d79-a9f7-497e-ba06-ee6feddc1da32012-06-27 06:00:45.433 UTCThe goal of a demographic analysis is very often to estimate lambda, because lambda is estimated from imperfect data, such estimation are uncertain. Therefore, when the results have policy implications it is important to quantify that uncertainty. Confidence intervals is one of the traditional tools to doing so (Caswell 2001).
This workflow calculate bootstrap distributions of population growth rates (lambda), stage vectors, and projection matrix elements by randomly sampling with replacement from a stage-fate data frame of observed transitions.
A detailed description of resampling methods to estimate confidence intervals for demographic estimates is described Caswell (2001, Chapter 12).
2012-10-12 12:10:40.351 UTC1f58efab-3e46-4669-aadd-23ff3fdd62f42012-10-05 14:23:50.167 UTCe0a9cd7d-9df2-484d-bfb5-293e3485e6922012-10-18 05:47:47.58 UTCbe9d3a0d-f115-4b04-9737-99f2a1dbed3b2012-10-12 12:24:31.305 UTCea499285-044d-4fcf-b361-82e705bce5352012-10-19 03:58:49.660 UTC142a1764-0032-4f03-be33-a00089d40ab42012-06-27 05:53:46.91 UTC0a45c68e-bb69-40fc-b695-3d6f8ebda19b2012-10-18 08:38:21.536 UTCb8fe02f6-e147-4416-aeb4-6c4e545895122012-06-27 08:19:06.56 UTC1da10601-a701-4542-ac84-b210462a58122012-06-27 06:02:05.237 UTCb6490574-28b7-488e-837e-31cb2c2fa5d92012-10-12 11:33:07.305 UTCa01daa2d-e2f8-4615-81cb-606b7c3243fb2012-06-27 05:57:46.34 UTC96776de8-09fd-4460-8184-862440d9756e2012-10-05 14:13:16.854 UTCb60b6baa-0ced-481b-8ad5-2a9413a4fa2c2012-10-12 12:10:41.867 UTC2ca6a24a-223b-435c-94df-72f7e34d19c02012-10-17 13:53:27.500 UTCb72b3f2b-ccd6-4520-9d21-c5fe7b69b11a2012-06-27 07:51:21.217 UTC105112aa-5a6e-4ace-9437-6421b756a88b2012-10-19 04:44:24.480 UTCe98a3578-498e-4d02-9059-57c0cdf09e832012-06-27 05:56:22.181 UTCaa6cba9d-a378-40a3-aeb7-c42870cdb7762012-10-19 04:02:43.872 UTC8b334097-9faa-4020-b1b2-6e37585c8b302012-06-27 07:32:37.375 UTCc444c1e9-3edc-4516-bae8-7863671fae6b2012-10-05 14:07:31.729 UTC32904aa5-ff7a-4ad8-84a5-199a181f790f2012-10-18 12:36:09.164 UTCb52aa862-b3ab-4602-a90f-14cf4a02b16f2012-10-12 11:28:21.258 UTC7f76e2ec-0550-44c2-ac29-00ab2441c50a2012-06-27 05:33:43.841 UTC023bd080-a31d-46d1-a2b6-6f70249bf3192012-10-18 15:02:35.315 UTC208fcd5f-7e4f-4c74-8014-864e118dac6d2012-10-12 11:26:23.664 UTCdc4a1937-f538-413c-ab37-4601c9f40c672012-10-18 14:55:56.19 UTC096ec245-ec16-40b5-bad6-7da6a1d58ce02012-10-19 03:55:10.475 UTC3d8f7330-9060-42ea-94bc-f7c2878ce81a2012-10-12 12:06:43.914 UTCSelectYearcensus_data11yearColumnName00initialYearSelectYear_Interactionyears1year00net.sf.taverna.t2.activitiesinteraction-activity1.0.4net.sf.taverna.t2.activities.interaction.InteractionActivityyears1text/plainjava.lang.Stringfalseyear00http://biovel.googlecode.com/svn/tags/popmod-20121018/select_year.htmlLocallyPresentedHtmlfalsenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeParseYears_FromCensuyearColumnName0census_data1censusYears11net.sf.taverna.t2.activitiesdataflow-activity1.4net.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize1net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry1.0100050000net.sf.taverna.t2.coreworkflowmodel-impl1.4net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeSelectYear_InteractionyearsParseYears_FromCensuyearColumnNameParseYears_FromCensucensus_datainitialYear0556bfe7-45af-4fad-ba6a-50b93e7550f82012-10-17 11:47:27.572 UTC001a52e9-fb0b-4c0f-97fc-33bcc37490292012-10-17 12:15:53.73 UTC1066a8b8-63d1-4f5b-aebf-43172f877d3c2012-09-28 08:44:39.139 UTCb033a199-d1e5-4225-b27b-4b828c693d522012-07-11 16:06:33.247 UTC6f93190c-3845-41bb-ba74-775b9596ae5c2012-07-11 16:05:56.5 UTCddf40f01-1f62-4477-a4a4-6467bcc05f252012-07-13 07:52:57.834 UTCSelectYear2012-10-17 11:47:50.307 UTC3c1aa296-4541-43a3-8b8a-6448e435e6002012-07-13 07:51:05.500 UTC7e051801-8211-4f67-bebe-d7fd628d68c62012-07-13 06:24:48.987 UTC7172c953-a063-4d58-b19d-6912223595e72012-10-17 11:47:53.180 UTCbe02d73d-ace1-46be-9b88-98048dac2d452012-07-11 16:03:25.310 UTCca84a3a5-d487-4aed-acb8-69a077a1e2c82012-07-13 06:25:21.195 UTC