One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer? The lmerTest package extends the 'lmerMod' class of the lme4 package, by overloading the anova and summary functions by providing p values for tests for fixed effects. We have implemented the Satterthwaite's method for approximating degrees of freedom for the t and F tests. We have also implemented the construction of Type I - III ANOVA tables. Furthermore, one may also obtain the summary as well as the anova table using the Kenward-Roger approximation for denominator degrees of freedom (based on the KRmodcomp function from the pbkrtest package). Some other convenient mixed model analysis tools such as a step method, that performs backward elimination of nonsignificant effects - both random and fixed, calculation of population means and multiple comparison tests together with plot facilities are provided by the package as well.

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lmerTest Package: Tests in Linear Mixed Effects Models

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Modern Applied Statistics With S

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Extended-connectivity fingerprints (ECFPs) are a novel class of topological fingerprints for molecular characterization. Historically, topological fingerprints were developed for substructure and similarity searching. ECFPs were developed specifically for structure−activity modeling. ECFPs are circular fingerprints with a number of useful qualities: they can be very rapidly calculated; they are not predefined and can represent an essentially infinite number of different molecular features (including stereochemical information); their features represent the presence of particular substructures, allowing easier interpretation of analysis results; and the ECFP algorithm can be tailored to generate different types of circular fingerprints, optimized for different uses. While the use of ECFPs has been widely adopted and validated, a description of their implementation has not previously been presented in the literature.

Extended-Connectivity Fingerprints

The measurement of meaning

Multiplicative mixed models can be applied in a wide range of scientific disciplines, since they are relevant in every situation where an interaction between a fixed effect and a random effect is present. Until now, no R package has been published, which can fit this type of models. The lack of user-friendly open source tools to fit these models, is the main reason that the models are not used as often as they could or should be. In this paper we introduce the user-friendly R package $\mathsf{mumm}$ for fitting multiplicative mixed models in a time-efficient manner. To illustrate the interpretation of the multiplicative term, we provide four data analysis examples, where the model is fitted to data sets that stem from studies in sensometrics, agriculture and medicine. With these examples it is shown that the statistical inference can be improved by using a multiplicative mixed model, instead of a linear mixed model which is usually employed.

The Multiplicative Mixed Model with the mumm R package as a General and Easy Random Interaction Model Tool

Introduction to Statistics - eNotes

tablet formulations Applying statistical learning DTU Orbit (21/10/2019) Characterization of absorption enhancers for orally administered therapeutic peptides in tablet formulations Applying statistical learning To develop a successful an oral formulation of insulin for treatment of type-2 diabetes patients would be a great mile stone in terms of convenience. Besides protecting insulin from enzymatic cleavage in the small intestine, the formulation must overcome the intestinal epithelia barrier. Absorption enhancers are needed to ensure even a few percent of insulin are taken up. In thesis article 1, various methods to measure the effect of absorption enhancement and enzyme stability of insulin were applied. The major class of absorption enhancers is surfactant-like enhancers and is thought to promote absorption by mildly perturbing the epithelial membranes of the small intestine. The Caco-2 (Carcinoma Colon) cells can grow an artificial epithelial layer, and are used to test the potency of new absorption enhancers. This project was aimed to identify new absorption enhancers, that are both potent and sufficiently soluble. Quantitative structural activity relationship (QSAR) modeling is an empiric approach to learn relationships between molecular formulas and the biochemical properties using statistical models. A public data set testing the potency of absorption enhancers in Caco-2 was used to build a QSAR model to screen for new potent permeation enhancers. Thesis article 2 contains likely the first QSAR model to predict absorption enhancement. The model was verified by predicting molecules not tested before in Caco-2. The Caco-2 model overestimates the clinical effect of lipophilic permeation enhancers. In the Caco-2 model all reagents are pre-dissolved, and therefore the assay cannot predict critical solubility issues and bile salt interactions in the final tablet formulation. A QSAR solubility model was built to foresee and avoid slow tablet dissolution. Due to enzyme kinetics, slow tablet dissolution will allow most insulin to be deactivated by intestinal enzymes. The combined predictions of potency and solubility, will likely provide a more useful in-silico screening of potential permeation enhancers.

Characterization of absorption enhancers for orally administered therapeutic peptides in tablet formulations - Applying statistical learning

A future production of pork including meat from entire males is a realistic scenario for the major pig producing countries. To ensure that no boar tainted meat reaches the consumers, the only reliable preventive method is to detect and sort out the boar tainted carcasses at the slaughterhouse. One of the prerequisite for sorting at the slaughterhouse is reliable sorting limits based on consumer sensitivity. The aim of the work presented here was to describe the proportion of Danish consumers that are sensitive towards androstenon and the proportion highly sensitive towards skatole and further to investigate any age and gender effect on sensitivity. Consumers in the age interval of 15 - 99 years (n=1294) were tested using triangle tests with paper strips containing 20 ppm solution of androstenone and 0.15 ppm solution of skatole. The consumers were recruited in shopping malls, at schools and at companies in all major regions of Denmark. Sensitivity towards androstenone is age related. An increasing proportion of consumers are sensitive with increasing age up to 70 years, where after the sensitivity decreases. In total, between 19 and 38 % of the consumers are sensitive towards androstenone. More females than males are sensitive towards androstenone. Even though it is expected that most people can detect skatole, only 5-6% were classified as highly sensitive. This proportion was constant with age, and no difference between genders was seen.

Danish consumers' sensitivity towards the boar taint compounds androstenone and skatole

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SensMixed R package: Easy-to-use application with graphical user interface for analyzing sensory and consumer data within a mixed effects model framework

Per B. Brockhoff

Papers

The Multiplicative Mixed Model with the mumm R package as a General and Easy Random Interaction Model Tool

Introduction to Statistics - eNotes

Characterization of absorption enhancers for orally administered therapeutic peptides in tablet formulations - Applying statistical learning

Danish consumers' sensitivity towards the boar taint compounds androstenone and skatole

SensMixed R package: Easy-to-use application with graphical user interface for analyzing sensory and consumer data within a mixed effects model framework