Plotting Individual Covariate Functions

announcements (new versions, changes, bugs, installation problems...) related to program MARK

Plotting Individual Covariate Functions

Postby gwhite » Wed Apr 30, 2008 1:37 pm

The capability to plot the real parameter estimate as a function of individual covariate values has been implemented. An icon on the Toolbar and a menu entry in the Results Browser under Output | Specific Model Output | Individual Covariate Plot provide access to this capability. A plot of the function and 95% confidence intervals are provided, with the capability to set other individual covariates to specified values. The values can also be downloaded to Excel to produce publication quality plots, or more complex plots. See the help file "Individual Covariate Plot" for details.

Gary
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Re: Plotting Individual Covariate Functions

Postby cooch » Fri May 02, 2008 9:58 am

gwhite wrote:The capability to plot the real parameter estimate as a function of individual covariate values has been implemented. An icon on the Toolbar and a menu entry in the Results Browser under Output | Specific Model Output | Individual Covariate Plot provide access to this capability. A plot of the function and 95% confidence intervals are provided, with the capability to set other individual covariates to specified values. The values can also be downloaded to Excel to produce publication quality plots, or more complex plots. See the help file "Individual Covariate Plot" for details.

Gary



Reminder to folks that this nifty new capability (which also exists in RMark) plots the function for a given model - it does *not* plot a model averaged function, if such a function can be generated...
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RMark does model-averaged covariate plots

Postby dhewitt » Fri May 02, 2008 10:11 am

There is a function in RMark

Code: Select all
covariate.predictions


that will generate model-averaged predicted values (and CIs) for a parameter across a specified range of covariate values. You can ask it to use just a particular model, but you can also have it compute model-averaged predictions across all models in a set.

See Ch 16, pg 77 of the Appendix for RMark in GIM.
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