Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Marlies Vervloet"'
Publikováno v:
Journal of Statistical Software, Vol 65, Iss 1, Pp 1-14 (2015)
In this article, we present PCovR, an R package for performing principal covariates regression (PCovR; De Jong and Kiers 1992). PCovR was developed for analyzing regression data with many and/or highly collinear predictor variables. The method simult
Externí odkaz:
https://doaj.org/article/e44e855e32f040eb8362eb1722c6b7ca
Publikováno v:
Behavior Research Methods, 53(4), 1648-1668. Springer
Behavior Research Methods, 53. SPRINGER
Behavior Research Methods, 53. SPRINGER
Principal covariates regression (PCovR) allows one to deal with the interpretational and technical problems associated with running ordinary regression using many predictor variables. In PCovR, the predictor variables are reduced to a limited number
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b0a29c6b444f173f81ae331410c31ab
https://research.tilburguniversity.edu/en/publications/83d23451-86f7-4458-9f48-7bbdc902da0c
https://research.tilburguniversity.edu/en/publications/83d23451-86f7-4458-9f48-7bbdc902da0c
Autor:
Paul Verkempynck, Mariola Moeyaert, Maaike Ugille, Wim Van Den Noortgate, Marlies Vervloet, Mieke Heyvaert, Patrick Onghena
Publikováno v:
The Journal of Experimental Education. 85:175-196
This article reports on a Monte Carlo simulation study, evaluating two approaches for testing the intervention effect in replicated randomized AB designs: two-level hierarchical linear modeling (HLM) and using the additive method to combine randomiza
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0578e3c3c181d75855d7a11d77ec5039
https://doi.org/10.1080/00220973.2015.1123667
https://doi.org/10.1080/00220973.2015.1123667
Publikováno v:
Chemometrics & Intelligent Laboratory Systems, 151, 26-33. Elsevier Science BV
Dimension-reduction based regression methods reduce the predictors to a few components and predict the criterion using these components. When applying such methods, it is often not only important to achieve good prediction of the criterion, but also
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61b47211f384766601929f4e87b92ed4
https://doi.org/10.1016/j.chemolab.2015.12.004
https://doi.org/10.1016/j.chemolab.2015.12.004
Behavioral researchers often linearly regress a criterion on multiple predictors, aiming to gain insight into the relations between the criterion and predictors. Obtaining this insight from the ordinary least squares (OLS) regression solution may be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a0c51b0c5250ecb33c7eddefb475a44
https://lirias.kuleuven.be/handle/123456789/611166
https://lirias.kuleuven.be/handle/123456789/611166
Publikováno v:
Chemometrics and Intelligent Laboratory Systems. 123:36-43
Ordinary linear regression falls short when many predictors are available, especially when some of these are highly correlated with (a linear combination of) other predictors. One possible solution for this problem is Principal Covariates Regression
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d22f63930cd534c1a44fd89a763c6bb6
https://doi.org/10.1016/j.chemolab.2013.02.005
https://doi.org/10.1016/j.chemolab.2013.02.005
Publikováno v:
Journal of Statistical Software; Vol 65 (2015); 1-14
Journal of Statistical Software, 65(8), 1-14
Journal of Statistical Software, Vol 65, Iss 1, Pp 1-14 (2015)
Scopus-Elsevier
Journal of Statistical Software, 65(8), 1-14
Journal of Statistical Software, Vol 65, Iss 1, Pp 1-14 (2015)
Scopus-Elsevier
© 2015, American Statistical Association. All rights reserved. In this article, we present PCovR, an R package for performing principal covariates regression (PCovR; De Jong and Kiers 1992). PCovR was developed for analyzing regression data with man
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::824bd5cb639535fa70b94ec975401e44
https://doi.org/10.18637/jss.v065.i08
https://doi.org/10.18637/jss.v065.i08