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We then generated a random intercept for each family and built a linear predictor for each individual using this random intercept and beta coefficients for the effect of familial longevity corresponding to 1, 5, and 10percentt differences between GRSs.
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To do this, we built a linear mixed-effects model using ln-transformed growth response of seedling species x as the response variable, sapling species identity y as a fixed predictor, and spatial block as a random predictor.
To examine the effect of DNA methylation on recombination while controlling for other factors, we built a linear model where recombination rates were response variables, and several sequence features (G+C content, number of CpGs, proportion of repeats) and sperm DNA methylation level were predictor variables.
A linear predictor based on a fractional ARIMA process is introduced.
The simplest structure of a linear predictor is to assume the effects of the covariates to be linear (Sileshi [2008]).
Age was included as a linear predictor.
Crowding was included in the analysis as a linear predictor.
Assessment of a nonlinear trend was made by including fiscal year as both a linear predictor and a qualitative predictor.
Finally, we built a multivariate linear regression model with all predictor variables to disentangle the factors explaining geographical variation in absolute and relative pandemic death rates.
Finally, we built a multivariate linear regression model with all predictor variables to disentangle the factors explaining geographical variation in pandemic onset and peak timing across Chilean regions.
To evaluate the concomitant effect of REM sleep in upper and lower airway disease, we built a multivariable linear regression model with asthma and rhinitis as joint predictors for the maximal percentage of SaO2 desaturation during REM sleep (Table 4).
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