WGTTEST: Stata module to test the impact of sampling weights in regression analysis

wgttest performs a test proposed by DuMouchel and Duncan (1983) to evaluate whether the weighted and unweighted estimates of a regression model are significantly different.

Linear transient analysis.

Includes bibliographies.

Modeling and interpreting interactive hypotheses in regression analysis /

Includes bibliographical references (p. 147-150) and index.

Regional adjustments in grain production : a linear programming analysis /

"U.S. Dept. of Agriculture, in cooperation with Iowa Agriculture and Home Economics Experiment Station Center for agricultural and economic adjustment."

Regional adjustments in grain production : a linear programming analysis : mathematical models, proofs, methods of data collection and estimation, and supplementary tables /

Cover title.

Aggregate economic effects of alternative land retirement programs : a linear programming analysis /

Includes bibliographical references (p. 53-54).

Regional location of production of major field crops at alternative demand and price levels, 1975; a linear programing analysis,

Issued in cooperation with Iowa State University of Science and Technology, Agriculture and Home Economics Experiment Station, and the Center for Agricultural and Economic Development.

Interregional adjustments in crop and livestock production; a linear programming analysis,

Prepared in cooperation with the Center for Agricultural and Economic Development. Iowa Agriculture and Home Economics Experiment Station, Iowa State University.

Better prediction of protein contact number using a support vector regression analysis of amino acid sequence

Background: Protein tertiary structure can be partly characterized via each amino acid's contact number measuring how residues are spatially arranged. The contact number of a residue in a folded protein is a measure of its exposure to the local environment, and is defined as the number of C-beta atoms in other residues within a sphere around the C-beta atom of the residue of interest. Contact number is partly conserved between protein folds and thus is useful for protein fold and structure prediction. In turn, each residue's contact number can be partially predicted from primary amino acid sequence, assisting tertiary fold analysis from sequence data. In this study, we provide a more accurate contact number prediction method from protein primary sequence. Results: We predict contact number from protein sequence using a novel support vector regression algorithm. Using protein local sequences with multiple sequence alignments (PSI-BLAST profiles), we demonstrate a correlation coefficient between predicted and observed contact numbers of 0.70, which outperforms previously achieved accuracies. Including additional information about sequence weight and amino acid composition further improves prediction accuracies significantly with the correlation coefficient reaching 0.73. If residues are classified as being either contacted or non-contacted, the prediction accuracies are all greater than 77%, regardless of the choice of classification thresholds. Conclusion: The successful application of support vector regression to the prediction of protein contact number reported here, together with previous applications of this approach to the prediction of protein accessible surface area and B-factor profile, suggests that a support vector regression approach may be very useful for determining the structure-function relation between primary sequence and higher order consecutive protein structural and functional properties.