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We also found that models fit using non-linear regression provided equally good fits to the data compared to the traditional approach of log-transforming the data.

We used linear mixed-effects models fit using maximum likelihood to investigate the relationship between relative host use and host reproductive biology.

All statistical analyses were conducted using the computing environment R [ 77] with mixed-effects models fit using lme4[ 78].

Analyses were performed using the SAS® system version 8.02 for Unix (Cary, North Carolina) with predictive models fit using PROC GENMOD.

Furthermore, while interpreting the statistical significance of models fit using traditional methods is fairly straightforward, correction must be made for multiple testing.

The final models, fit using a training subset of the data, were cross-validated against both the overall dataset and a holdout subset of the data.

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We subsequently compared the explanatory power of texture-only models with models fitted using landscape composition metrics derived from the National Land Cover Dataset, as well as models fitted using both texture and composition metrics.

This paper considers the design problem for linear regression models fitted using both function and gradient data.

F-statistics of the selected linear mixed effects models, fitted using ML estimation.

Mapping models fitted using the Tobit model and censored least absolute deviation regression did not appear superior to GLS regression.

For models fitted using a binomial or Poisson distribution, we assessed these differences using the Wald Z test in lme4.

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