Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 2289387

Parallel line analysis: multifunctional software for the biomedical sciences.

P R Swank·M L Lewis·K L Damron·D R Morrison
View original source ↗Export metadata ↓

Abstract

An easy to use, interactive FORTRAN program for analyzing the results of parallel line assays is described. The program is menu driven and consists of five major components: data entry, data editing, manual analysis, manual plotting, and automatic analysis and plotting. Data can be entered from the terminal or from previously created data files. The data editing portion of the program is used to inspect and modify data and to statistically identify outliers. The manual analysis component is used to test the assumptions necessary for parallel line assays using analysis of covariance techniques and to determine potency ratios with confidence limits. The manual plotting component provides a graphic display of the data on the terminal screen or on a standard line printer. The automatic portion runs through multiple analyses without operator input. Data may be saved in a special file to expedite input at a future time.

Explore related subjects

Data Interpretation, StatisticalDose-Response Relationship, DrugMathematical ComputingProgramming LanguagesSoftwareSoftware DesignUser-Computer InterfaceNASA Center JSCNASA Discipline Cell BiologyJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

P R Swank, M L Lewis, K L Damron, D R Morrison. 1990. Parallel line analysis: multifunctional software for the biomedical sciences.. https://doi.org/10.1016/0169-2607(90)90066-i

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2001

A comparison of non-linear non-parametric models for epilepsy data.

EEG spike and wave (SW) activity has been described through a non-parametric stochastic model estimated by the Nadaraya-Watson (NW) method. In this paper the performance of the NW, the local linear polynomial regression and support vector machines (SVM) methods were compared. The noise-free realizations obtained by the NW and SVM methods reproduced SW better than as reported in previous works. The tuning parameters had to be estimated manually. Adding dynamical noise, only the NW method was capable of generating SW similar to training data. The standard deviation of the dynamical noise was estimated by means of the correlation dimension.

Data Interpretation, Statistical↗
PubMed2001

BJ: an S-Plus program to fit linear regression models to censored data using the Buckley-James method.

Most researchers are familiar with ordinary multiple regression models, most commonly fitted using the method of least squares. The method of Buckley and James (J. Buckley, I. James, Linear regression with censored data, Biometrika 66 (1979) 429-436.) is an extension of least squares for fitting multiple regression models when the response variable is right-censored as in the analysis of survival time data. The Buckley-James method has been shown to have good statistical properties under usual regularity conditions (T.L. Lai, Z. Ying, Large sample theory of a modified Buckley-James estimator for regression analysis with censored data, Ann. Stat. 19 (1991) 1370-1402.). Nevertheless, even after 20 years of its existence, it is almost never used in practice. We believe that this is mainly due to lack of software and we describe here an S-Plus program that through its inclusion in a public domain function library fully exploits the power of the S-Plus programming environment. This environment provides multiple facilities for model specification, diagnostics, statistical inference, and graphical depiction of the model fit.

Data Interpretation, Statistical↗