Timeline for Sum of predicted values to the power of 10
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| Mar 26, 2018 at 18:21 | comment | added | Stephan Kolassa | There may be. One would need to do the integration. This thread would be helpful. I don't really have the time at the moment, perhaps someone would like to chime in? Or you could post a follow-up question on calculating the expectation for a base 10 lognormal. Then again, since the difference is only a multiplicative factor, I personally would not go to the trouble (though I understand that some fields like the interpretability of base 10). | |
| Mar 26, 2018 at 17:00 | comment | added | Rasmus Ø. Pedersen | I meant that i prefer to use the $log_{10}$ values in the model instead of $log_e$, is there a way to correct the prediction then? | |
| Mar 26, 2018 at 16:35 | comment | added | Stephan Kolassa | Yes, if you wish to predict the expectation, then you need to correct with the variance. (Note that if you are working with quantiles, you don't need the variance - the exponential of the median on the log scale will come out correctly as the median on the original scale.) If you are plotting with log axes, then the data to be plotted are on the log scale, so no correction necessary. This earlier thread may be helpful. | |
| Mar 26, 2018 at 16:32 | comment | added | Rasmus Ø. Pedersen | Thank you. Does this mean that individual point estimates should also be corrected for the residual variance, if you are looking at a specific species? Further: the axes of the plot makes more intuitive sense by using base 10 for the log, is there a way to correct for residual variance here as well? | |
| Mar 26, 2018 at 16:25 | history | answered | Stephan Kolassa | CC BY-SA 3.0 |