Package crimcv. January 25, Index 6. Fits finite mixtures of Zero-inflated Poisson models
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1 Version Package crimcv January 25, 2018 Title Group-Based Modelling of Longitudinal Data Author Jason D. Nielsen Maintainer Jason D. Nielsen Depends R (>= ), splines A finite mixture of Zero-Inflated Poisson (ZIP) models for analyzing criminal trajectories. License GPL (>= 2) Date NeedsCompilation yes Repository CRAN Date/Publication :16:46 UTC R topics documented: crimcv TO TO1adj TO1Risk TO TO2adj TO2Risk Index 6 crimcv Fits finite mixtures of Zero-inflated Poisson models This software fits finite mixtures of ZIP models to longitudinal count data. 1
2 2 crimcv crimcv(dat,ng,dpolyp=3,dpolyl=3,model="zipt",rcv=false,init=20,risk=null) Arguments Dat ng Value A matrix of the number of criminal offenses. Each row contains the number of criminal offenses committed by a particular individual for all time intervals observed (columns). A negative number is interpreted as a missing value. The number of groups to use in the analysis. dpolyp The degree of the polynomial in the log-linear component. Defaults to 3. dpolyl model rcv init Risk A list of class "dmzip" or "dmzipt". Author(s) Jason D. Nielsen References The degree of the polynomial in the logistic component (ZIP model only). Defaults to 3. Either "ZIP" for the full ZIP or "ZIPt" for the ZIP(tau) sub-group model. Defaults to "ZIPt". Set to TRUE to compute the CVE. Defaults to FALSE. This controls how rigorously the initialization procedure searches for good starting values of the parameters. Larger values increase the odds of finding the true global solution but can dramatically increase the time required to fit the model. The default is set to 20 and from experimentation this seems to be a reasonable value for ng<=5. Note: As the number of groups (ng) gets larger finding the optimal global maximum of the likelihood becomes progressively more challenging. A matrix of the same dimension as Dat with the time-at-risk correction. Defaults to 1 for all elements (i.e. at risk for the full time period). J.D. Nielsen, J.S. Rosenthal, Y. Sun, D.M. Day, I. Bevc, and T. Duchesne (2011). Group-based Criminal Trajectory Analysis using Cross-Validation Criteria. A draft of the manuscript is available at Examples # Loads crimcv into the interpreter library(crimcv) # Load the "divide-and-round" TO1 dataset data(to1adj) # Fit a 2 component ZIP(tau) model of degree 2. Here the CVE is not
3 TO1 3 # calculated and only ~1/4 of the data is used so that the code will # run quickly enough to satisfy CRAN's package policies. To compute # CVE run as: # out1<-crimcv(to1adj,2,dpolyp=2,rcv=true) subto1adj<-to1adj[1:100,] out1<-crimcv(subto1adj,2,dpolyp=2,init=5) # Plot the component trajectories plot(out1) # Print out some useful output summary(out1) TO1 Adjudicated Toronto Youth Data (Sample 1) Number of criminal unique court contacts for 378 individuals in Toronto, Ontario, Canada. data(to1) A 378 by 31 matrix where each row contains the number of unique court contacts per year for an individual from the age of 8 to 38. TO1adj Adjusted Adjudicated Toronto Youth Data (Sample 1) Number of unique court contacts for 378 individuals in Toronto, Ontario, Canada adjusted for timeat-risk by the "divide and round" approach. data(to1adj) A 378 by 31 matrix where each row contains the number of unique court contacts per year corrected for time-at-risk for an individual from the age of 8 to 38.
4 4 TO2 TO1Risk Time-at-risk for the Adjudicated Toronto Youth Data (Sample 1) Time-at-risk per year for 378 individuals in the Toronto, Ontario, Canada. data(to1risk) A 378 by 31 matrix where each row contains the time-at-risk per year for an individual from the age of 8 to 38. TO2 Adjudicated Toronto Youth Data (Sample 2) Number of unique court contacts for 386 individuals in Toronto, Ontario, Canada. data(to2) A 386 by 30 matrix where each row contains the number of unique court contacts per year for an individual from the age of 9 to 38.
5 TO2adj 5 TO2adj Adjusted Adjudicated Toronto Youth Data (Sample 2) Number of unique court contacts for 386 individuals in Toronto, Ontario, Canada adjusted for timeat-risk by the "divide and round" approach. data(to2adj) A 386 by 30 matrix where each row contains the number of unique court contacts per year corrected for time-at-risk for an individual from the age of 9 to 38. TO2Risk Time-at-risk for the Adjudicated Toronto Youth Data (Sample 2) Time-at-risk for 386 individuals in the Toronto, Ontario, Canada. data(to2risk) A 386 by 30 matrix where each row contains the number of criminal offenses per year for an individual from the age of 9 to 38.
6 Index Topic Finite mixture model crimcv, 1 Topic Zero-inflated Poisson model crimcv, 1 Topic datasets TO1, 3 TO1adj, 3 TO1Risk, 4 TO2, 4 TO2adj, 5 TO2Risk, 5 crimcv, 1 TO1, 3 TO1adj, 3 TO1Risk, 4 TO2, 4 TO2adj, 5 TO2Risk, 5 6
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