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grep rough audit - static analysis tool
v2.8 written by @Wireghoul
=================================[justanotherhacker.com]===
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r-cran-psychtools-2.0.8/vignettes/overview.Rnw-992-
r-cran-psychtools-2.0.8/vignettes/overview.Rnw:993:There are three possible options for this condition: setting the general factor loadings between the two lower order factors to be ``equal" which will be the $\sqrt{r_{ab}}$ where $r_{ab}$ is the oblique correlation between the factors) or to ``first" or ``second" in which case the general factor is equated with either the first or second group factor. A message is issued suggesting that the model is not really well defined. This solution discussed in Zinbarg et al., 2007. To do this in omega, add the option=``first" or option=``second" to the call.
r-cran-psychtools-2.0.8/vignettes/overview.Rnw-994-
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r-cran-psychtools-2.0.8/vignettes/overview.Rnw-1472-\section{Set Correlation and Multiple Regression from the correlation matrix}
r-cran-psychtools-2.0.8/vignettes/overview.Rnw:1473:An important generalization of multiple regression and multiple correlation is \iemph{set correlation} developed by \cite{cohen:set} and discussed by \cite{cohen:03}. Set correlation is a multivariate generalization of multiple regression and estimates the amount of variance shared between two sets of variables. Set correlation also allows for examining the relationship between two sets when controlling for a third set. This is implemented in the \pfun{setCor} function. Set correlation is $$R^{2} = 1 - \prod_{i=1}^n(1-\lambda_{i})$$ where $\lambda_{i}$ is the ith eigen value of the eigen value decomposition of the matrix $$R = R_{xx}^{-1}R_{xy}R_{xx}^{-1}R_{xy}^{-1}.$$ Unfortunately, there are several cases where set correlation will give results that are much too high. This will happen if some variables from the first set are highly related to those in the second set, even though most are not. In this case, although the set correlation can be very high, the degree of relationship between the sets is not as high. In this case, an alternative statistic, based upon the average canonical correlation might be more appropriate.
r-cran-psychtools-2.0.8/vignettes/overview.Rnw-1474-
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r-cran-psychtools-2.0.8/vignettes/factor.Rnw-1359-
r-cran-psychtools-2.0.8/vignettes/factor.Rnw:1360:There are three possible options for this condition: setting the general factor loadings between the two lower order factors to be ``equal" which will be the $\sqrt{r_{ab}}$ where $r_{ab}$ is the oblique correlation between the factors) or to ``first" or ``second" in which case the general factor is equated with either the first or second group factor. A message is issued suggesting that the model is not really well defined. This solution discussed in Zinbarg et al., 2007. To do this in omega, add the option=``first" or option=``second" to the call.
r-cran-psychtools-2.0.8/vignettes/factor.Rnw-1361-
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r-cran-psychtools-2.0.8/vignettes/factor.Rnw-1728-\section{Set Correlation and Multiple Regression from the correlation matrix}
r-cran-psychtools-2.0.8/vignettes/factor.Rnw:1729:An important generalization of multiple regression and multiple correlation is \iemph{set correlation} developed by \cite{cohen:set} and discussed by \cite{cohen:03}. Set correlation is a multivariate generalization of multiple regression and estimates the amount of variance shared between two sets of variables. Set correlation also allows for examining the relationship between two sets when controlling for a third set. This is implemented in the \pfun{setCor} function. Set correlation is $$R^{2} = 1 - \prod_{i=1}^n(1-\lambda_{i})$$ where $\lambda_{i}$ is the ith eigen value of the eigen value decomposition of the matrix $$R = R_{xx}^{-1}R_{xy}R_{xx}^{-1}R_{xy}^{-1}.$$ Unfortunately, there are several cases where set correlation will give results that are much too high. This will happen if some variables from the first set are highly related to those in the second set, even though most are not. In this case, although the set correlation can be very high, the degree of relationship between the sets is not as high. In this case, an alternative statistic, based upon the average canonical correlation might be more appropriate.
r-cran-psychtools-2.0.8/vignettes/factor.Rnw-1730-
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r-cran-psychtools-2.0.8/inst/doc/overview.Rnw-992-
r-cran-psychtools-2.0.8/inst/doc/overview.Rnw:993:There are three possible options for this condition: setting the general factor loadings between the two lower order factors to be ``equal" which will be the $\sqrt{r_{ab}}$ where $r_{ab}$ is the oblique correlation between the factors) or to ``first" or ``second" in which case the general factor is equated with either the first or second group factor. A message is issued suggesting that the model is not really well defined. This solution discussed in Zinbarg et al., 2007. To do this in omega, add the option=``first" or option=``second" to the call.
r-cran-psychtools-2.0.8/inst/doc/overview.Rnw-994-
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r-cran-psychtools-2.0.8/inst/doc/overview.Rnw-1472-\section{Set Correlation and Multiple Regression from the correlation matrix}
r-cran-psychtools-2.0.8/inst/doc/overview.Rnw:1473:An important generalization of multiple regression and multiple correlation is \iemph{set correlation} developed by \cite{cohen:set} and discussed by \cite{cohen:03}. Set correlation is a multivariate generalization of multiple regression and estimates the amount of variance shared between two sets of variables. Set correlation also allows for examining the relationship between two sets when controlling for a third set. This is implemented in the \pfun{setCor} function. Set correlation is $$R^{2} = 1 - \prod_{i=1}^n(1-\lambda_{i})$$ where $\lambda_{i}$ is the ith eigen value of the eigen value decomposition of the matrix $$R = R_{xx}^{-1}R_{xy}R_{xx}^{-1}R_{xy}^{-1}.$$ Unfortunately, there are several cases where set correlation will give results that are much too high. This will happen if some variables from the first set are highly related to those in the second set, even though most are not. In this case, although the set correlation can be very high, the degree of relationship between the sets is not as high. In this case, an alternative statistic, based upon the average canonical correlation might be more appropriate.
r-cran-psychtools-2.0.8/inst/doc/overview.Rnw-1474-
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r-cran-psychtools-2.0.8/inst/doc/factor.Rnw-1359-
r-cran-psychtools-2.0.8/inst/doc/factor.Rnw:1360:There are three possible options for this condition: setting the general factor loadings between the two lower order factors to be ``equal" which will be the $\sqrt{r_{ab}}$ where $r_{ab}$ is the oblique correlation between the factors) or to ``first" or ``second" in which case the general factor is equated with either the first or second group factor. A message is issued suggesting that the model is not really well defined. This solution discussed in Zinbarg et al., 2007. To do this in omega, add the option=``first" or option=``second" to the call.
r-cran-psychtools-2.0.8/inst/doc/factor.Rnw-1361-
##############################################
r-cran-psychtools-2.0.8/inst/doc/factor.Rnw-1728-\section{Set Correlation and Multiple Regression from the correlation matrix}
r-cran-psychtools-2.0.8/inst/doc/factor.Rnw:1729:An important generalization of multiple regression and multiple correlation is \iemph{set correlation} developed by \cite{cohen:set} and discussed by \cite{cohen:03}. Set correlation is a multivariate generalization of multiple regression and estimates the amount of variance shared between two sets of variables. Set correlation also allows for examining the relationship between two sets when controlling for a third set. This is implemented in the \pfun{setCor} function. Set correlation is $$R^{2} = 1 - \prod_{i=1}^n(1-\lambda_{i})$$ where $\lambda_{i}$ is the ith eigen value of the eigen value decomposition of the matrix $$R = R_{xx}^{-1}R_{xy}R_{xx}^{-1}R_{xy}^{-1}.$$ Unfortunately, there are several cases where set correlation will give results that are much too high. This will happen if some variables from the first set are highly related to those in the second set, even though most are not. In this case, although the set correlation can be very high, the degree of relationship between the sets is not as high. In this case, an alternative statistic, based upon the average canonical correlation might be more appropriate.
r-cran-psychtools-2.0.8/inst/doc/factor.Rnw-1730-