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For hypothesis testing we calculated the annual stem volume growth at the three levels individual tree, species, and total stand.
To control for multiple testing, we calculated the false discovery rate (FDR) according to Benjamini and Hochberg [25].
To correct for multiple testing, we calculated the FDR of each TF, also based on permutations.
According to correction for multiple testing, we calculated the q values with FDR (false discovery rate) method by QVALUE software with a significant threshold of 0.05.
To further consider the effect of multiple testing, we calculated the false discovery rate (FDR) for any significant results using the Simes procedure [ 12].
To determine the significance of expression differences, and adjust for multiple testing, we calculated the False Discovery Rate (FDR; [ 33] for each gene using the Bioconductor LIMMA package.
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To further assess the probability of a spurious association due to multiple testing, we calculate the false-positive report probability (FPRP) for a SNP from an estimated OR and 95% CIs using the methodology described by Wacholder et al [ 32].
For the numerical inversion test, we calculated the true values of these hyperparameters from the true slip s t.
After the trend test, we calculated the slope and standard error of the linear regression (y = fx + g) for the residual Sq amplitude that passed through the trend test.
As an accuracy test, we calculated the displacement discontinuity along an isolated crack acted on by harmonic waves using the present method and compared this with the corresponding results based on a reliable BIEM of Kawahara and Yamashita (1992).
As part of the t test, we calculated the mean values of D for each experiment, matrix type (size and noise ratio), and possible pairs of A and B algorithms.
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