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In addition to establishing a three-factor measurement model for the total sample, we fitted the same model to the subsample of female and male teachers.
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To obtain quartiles of total urine arsenic adjusted for specific gravity (a proxy for urine dilution of the spot urine sample), we fit linear regressions of measured urine arsenic on specific gravity and took quartiles of the residuals.
To account for repeated sampling, we fitted generalized estimating equation (GEE) models with test fish ID and dominant pair ID as subject effects and repeats per test fish and repeats per dominant pair as within-subject effects.
To avoid reduction in sample size, we fitted these particular measurements with a dummy ringer before the mixed linear model analyses.
Here, pseudo-absences were allowed to fall within the true distribution of the species, but did not overlap with the 130 presence sample points 3.3 – We fitted a BIOCLIM model using the 130 presence records selected in Step 2 and the same three linear predictors used to generate the virtual species.
To more flexibly estimate the relationship of amplification success to sample age, we fit a binomial general additive linear model (GAM) to the success data with age as a covariate.
Both Q and K were detected in the samples, so we fit both Q and K into the mixed model to control population structure and relatedness.
For a sample of 1397 trips, we fitted a multinomial logit regression model.
To the scores of the twin 1 and 2 samples on these subtests, we fitted a one-factor model representing the general intelligence factor.
Conservation of these pseudoexons depends on their length and presence or absence of internal stop-codon and we fitted the sample size to achieve coverage of more than 100 pseudoexons per each 12-nucleotides interval of exon lengths.
In order to verify findings obtained in the case control GWAS in the whole sample (related and unrelated individuals) we fitted a logistic regression model via Generalized Estimation Equation (GEE) implemented in the GWAF package in R (http://cran.r-project.org/) to test association between the affection status and imputed SNPs under additive genetic model.
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