Cross-validation is a common method to validate a QSAR model. In cross-validation, some compounds are held out as a test set, while the remaining compounds form a training set. A model is built from the training set, and the test set compounds are predicted on that model. The agreement of the predicted and observed activity values of the test set (measured by, say, R2) is an estimate of the self-consistency of the model and is sometimes taken as an indication of the predictivity of the model. This estimate of predictivity can be optimistic or pessimistic compared to true prospective prediction, depending how compounds in the test set are selected. Here, we show that time-split selection gives an R2 that is more like that of true prospective prediction than the R2 from random selection (too optimistic) or from our analog of leave-class-out selection (too pessimistic). Time-split selection should be used in addition to random selection as a standard for cross-validation in QSAR model building.

Clemens, Michael A., and Tiongson, Erwin R., (2017) "Split Decisions: Household Finance When a Policy Discontinuity Allocates Overseas Work." Review of Economics and Statistics 99:3, 531-543.

Despite recent papers on problems associated with full-model and stepwise regression, their use is still common throughout ecological and environmental disciplines. Alternative approaches, including generating multiple models and comparing them post-hoc using techniques such as Akaike's Information Criterion (AIC), are becoming more popular. However, these are problematic when there are numerous independent variables and interpretation is often difficult when competing models contain many different variables and combinations of variables. Here, we detail a new approach, REVS (Regression with Empirical Variable Selection), which uses all-subsets regression to quantify empirical support for every independent variable. A series of models is created; the first containing the variable with most empirical support, the second containing the first variable and the next most-supported, and so on. The comparatively small number of resultant models (n = the number of predictor variables) means that post-hoc comparison is comparatively quick and easy. When tested on a real dataset – habitat and offspring quality in the great tit (Parus major) – the optimal REVS model explained more variance (higher R2), was more parsimonious (lower AIC), and had greater significance (lower P values), than full, stepwise or all-subsets models; it also had higher predictive accuracy based on split-sample validation. Testing REVS on ten further datasets suggested that this is typical, with R2 values being higher than full or stepwise models (mean improvement = 31% and 7%, respectively). Results are ecologically intuitive as even when there are several competing models, they share a set of “core” variables and differ only in presence/absence of one or two additional variables. We conclude that REVS is useful for analysing complex datasets, including those in ecology and environmental disciplines.

Validating a novel housing method for inbred mice: mixed-strain housing. To see if this housing method affected strain-typical mouse phenotypes, if variance in the data was affected, and how statistical power was increased through this split-plot design.

Dataset

This dataset was created by Devi Hemamalini R

Contents

Dataset Card for "programming-languages-keywords"

Structured version of https://github.com/e3b0c442/keywords Generated using: r = requests.get("https://raw.githubusercontent.com/e3b0c442/keywords/main/README.md") keywords = r.text.split("### ")[1:] keywords = [i for i in keywords if not i.startswith("Sources")] keywords = {i.split(" ")[0]:[j for j in re.findall("[a-zA-Z]*", i.split(" ",1)[1]) if j] for i in keywords} keywords =… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/programming-languages-keywords.

Nonsense mutations and split genes in R. peacockii relative to R. rickettsii Sheila Smith.

Abstract The main part of the code presented in this work represents an implementation of the split-operator method [J.A. Fleck, J.R. Morris, M.D. Feit, Appl. Phys. 10 (1976) 129-160; R. Heather, Comput. Phys. Comm. 63 (1991) 446] for calculating the time-evolution of Dirac wave functions. It allows to study the dynamics of electronic Dirac wave packets under the influence of any number of laser pulses and its interaction with any number of charged ion potentials. The initial wave function can be eith...

Title of program: Dirac++ or (abbreviated) d++ Catalogue Id: AEAS_v1_0

Nature of problem The relativistic time evolution of wave functions according to the Dirac equation is a challenging numerical task. Especially for an electron in the presence of high intensity laser beams and/or highly charged ions, this type of problem is of considerable interest to atomic physicists.

Versions of this program held in the CPC repository in Mendeley Data AEAS_v1_0; Dirac++ or (abbreviated) d++; 10.1016/j.cpc.2008.01.042

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

The Pearson correlation coefficients (r) of diversity measures based on heterozygosity and split system diversity applied on subsets of Atlantic salmon populations with size k = 2, 3, and 4.

Attachment regarding a request by Strata Solar for a Conditional Use Permit on Parcel No. 12233, located of US 64 W, Hickory Mountain Township, for a solar farm on approximately 42 acres. The parcel is split between R-1 zoning and unzoned. The R-1 zoning is the portion subject to this CUP request which is approximately 23.3 acres.

Spline smoothing is a widely used nonparametric method that allows data to speak for themselves. Due to its complexity and flexibility, fitting smoothing spline models is usually computationally intensive which may become prohibitive with large datasets. To overcome memory and CPU limitations, we propose four divide and recombine (D&R) approaches for fitting cubic splines with large datasets. We consider two approaches to divide the data: random and sequential. For each approach of division, we consider two approaches to recombine. These D&R approaches are implemented in parallel without communication. Extensive simulations show that these D&R approaches are scalable and have comparable performance as the method that uses the whole data. The sequential D&R approaches are spatially adaptive which lead to better performance than the method that uses the whole data when the underlying function is spatially inhomogeneous.

Total IES-R scores split for psychological impact level- PIL.

Vegetation variables in the case study dataset.