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In this paper, we incorporate the parameter model complexity into the supersaturated design analysis process, by assuming generalized linear models for a Bernoulli response, for analyzing main effects designs and discovering simultaneously the effects that are significant.
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For Y i ~ Poisson(μ i ), where μ i = λ i E i (i = 1,..., n), we assume the generalized linear model [ 47].
In the final model, each joinpoint indicates a statistically significant change in trend, and an estimated annual percentage change (EAPC) is computed for each of those trends by means of generalized linear models assuming a Poisson distribution.
To test this hypothesis for the SNP dataset, we conducted regression by implementing a generalized linear model assuming a negative binomial distribution of the number of nucleotide changes per gene in five differential expression categories: strong down-regulation, moderate down-regulation, no change, moderate up-regulation, and strong up-regulation.
We assessed the relative predictive value of the time-lagged media data streams by fitting generalized linear models to mortality counts, assuming a Poisson distribution.
Data on earthworm mortality and change in body mass during the experiment were analyzed by a generalized linear model with binomial error distribution and linear model, respectively in both cases with the type of microplastic as predictor variable.
This enables the book to handle a broader range of data types than those analyzed by traditional generalized linear models.
The genotype-phenotype correlation was performed by a generalized linear regression model.
Five response variables were analyzed by using generalized linear modeling.
Relative rates were estimated by using generalized linear models with binomial family and log link.
*Estimated by a generalized linear model for the binomial family with a log link.
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