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This was done simply by multiplying the parameter estimates (Beta) obtained from the GLM with the surface (S) under their respective predictor (S = BetaauditoryxSauditory + BetaactionxSaction). Brain activity across conditions were compared using this surface (for details please see [4]).
First, we perturb one parameter of by multiplying the parameter value by either 0.99 or 1.01 to generate a starting point, Θ 0), for GD.
Percentage signal change within the ROI was calculated by multiplying the parameter estimate by the peak-to-peak height of the EV model, and dividing by the mean signal across the whole acquisition period (Mumford 2007).
For a selection of the next set of the rate parameters, lognormal proposal distributions were used based on logarithm of random values selected from a normal distribution with mean 0 and standard deviation 0.05; this can be achieved either by adding a normal deviate to the log of the parameter, or by multiplying the parameter by the exponential of the normal deviate.
Hazard ratios for each quartile of serum C1M was determined by multiplying the parameter estimate of the log-transformed C1M value derived from the multivariate Cox proportional-hazard analysis, with the range between the log-transformed means of serum C1M levels in each quartile, followed by a back-transformation to the original scale using the exponential function.
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The indirect effect is found by multiplying the parameters along each of the (directed) paths from X to Y that include M and summing them; here, this is (α xβ m + γ xα lβ m).
The parameter size column in Table 1 approximates the memory size which is needed to store the parameter vector by multiplying the number of parameters by 8. Since the parameter vector is not sparse, we store it as a dense vector which include continuous real values.
Carrying this out in the analogous way by multiplying the 1-parameter groups (mathrm {e}^{-tX}) and (mathrm {e}^{ -tY}) on the right, one obtains the morphism defined by (Xmapsto R_X,).
The external costs of the emission can be calculated by multiplying the various parameters along the pathway.
The final estimate was obtained by multiplying the two parameters defined above; its 99.9% confidence interval (CI) was calculated using the following formula (which incorporates the calculated variance for the product of two independent variables): X1*X2±3.29*√[ X1+SE X1))*(X2+SE(X2))–(X1*X2)], where X1 and X2 are the two parameters described.
We also calculated the length of branches within a 10 μm unit of tissue volume by dividing the total branch length of an individual microglia by its volume (calculated by multiplying the tree area parameter by the depth of the microglia, as revealed by the tracing analysis).
More suggestions(17)
by doubling the parameter
by multiplying the factor
by multiplying the element
by applying the parameter
by calculating the parameter
by multiplying the data
by multiplying the metric
by multiplying the parametric
by multiplying the variables
by multiplying the value
by multiplying the delta
by multiplying the source
by multiplying the nitrogen
by multiplying the frequency
by multiplying the percentage
by multiplying the stroke
by multiplying the model
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