To make the plots even more informative, I’ve superimposed quantiles – here deciles computed using the Harrell-Davis quantile estimator. Essentially, the function creates violin plots in which the constituent points are visible. Here we create scatterplots shaped by local density using the geom_quasirandom() function from the ggbeeswarm package. Scatterplots and kernel density plots can be combined by using beeswarm plots. Relative to scatterplots, I find that kernel density plots make the comparisons between groups much easier. With smaller sample sizes the evaluation of these graphs could be much more challenging. The main reason is probably that we need to estimate local densities of points in different regions and compare them between groups.įor the purpose of this exercise, each group (g1 and g2) is composed of 1,000 observations, so the differences in shapes are quite striking. The 1D scatterplots give us a good idea of how the groups differ but they’re not the easiest to read. Other approaches not covered here include explicit mathematical models of decision making and fitting functions to model the shape of the distributions (Balota & Yap, 2011).įor our current example, I made up data for 2 independent groups with four patterns of differences:įor our first visualisation, we use geom_jitter() from ggplot2. So unless the distributions are at least illustrated, this information is lost (which is typically the case when distributions are summarised using a single value like the mean). Reaction time distributions are also a rich source of information to constrain cognitive theories and models. As an example, we consider reaction time data, which are typically positively skewed and can differ in different ways. Otherwise the method we have described can be followed with a well-structured creatinine recovery fest to identify and quantify assay interferences.In this post I’m going to show you a few simple steps to illustrate continuous distributions. So, especially when applied to peritoneal dialysis fluid measurements, if a creatinine assay reference method is not available, the correction formula can be applied directly as given. Every laboratory can reduce the error of the Jaffé kinetic assay by calculating their own correction formula in relation to the method and instrument used, because Jaffé kinetic assay gives different results with different kinetic windows. Applying formulas to biological samples there was a drop in accuracy, possibly explained by the presence of numerous unidentified substances in peritoneal dialysis biological samples that can amplify scatter. The best model in biological samples was: Corrected CR = K 1JafféCr + K 2Glucose (all values in mg/dl) where K 1 = 0.973 and K 2 = -0.00035 (Rsq = 0.987, F ratio = 10,945, p = 0.00001). All the computed formulas gave satisfactory corrections but different accuracy levels. Considering creatinine as well as glucose concentration interference, we obtained correction formulas from multiple regression application. The added pure powdered creatinine and enzymatic method were considered as references after proven accuracy. Overestimation of creatinine measurement using the Jaffé kinetic method in peritoneal dialysis solutions, due to glucose interference, has been quantified and corrected through the elaboration of linear formulas obtained from 110 recovery and 301 biological tests.
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