IhsanKhaliq
commented
2 years ago Methods of comparing prediction with observations. It can be used now since Adam has done the data wrangling for the model (selected quadrats)
From: Paul Melloy @.> Reply to: IhsanKhaliq/valascotraceR @.> Date: Friday, 12 November 2021 at 7:32 am To: IhsanKhaliq/valascotraceR @.> Cc: Subscribed @.> Subject: [IhsanKhaliq/valascotraceR] Method for assessing optimum model parameters (Issue #6)
I was pondering last night a method for assessing the fit of the model simulation vs the observed.
Assuming we are using the proportion of infected quadrats, we could use the sum of squared residuals to assess the best fit to the mean observed proportion of infected quadrats to the simulated infected quadrats.
Using this as a metric might help automate some permutations on a HPC. To undertake permutations I think we need to make more model parameters available at the trace_asco level. see issue 120 in the ascotraceR repo
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adamhsparks
I was pondering last night a method for assessing the fit of the model simulation vs the observed.
Assuming we are using the proportion of infected quadrats, we could use the sum of squared residuals to assess the best fit to the mean observed proportion of infected quadrats to the simulated infected quadrats.
Using this as a metric might help automate some permutations on a HPC. To undertake permutations I think we need to make more model parameters available at the
trace_ascolevel. see issue 120 in the ascotraceR repo