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WinSTAT
Published in Paul W. Ross, The Handbook of Software for Engineers and Scientists, 2018
The partial correlation is a measure of the linear dependency between two variables, where the influence of a third variable is “partialled out.” If one suspects that the correlation results of two variables are being clouded because they are both related to a third variable, this command can be used to find out. As with Pearson correlation, the variables must be measured at the interval level. An example from one of the sample files may illustrate the principle.
Advance Methods
Published in Atsushi Kawaguchi, Multivariate Analysis for Neuroimaging Data, 2021
In this output, the non-significant relationship at the 5% level was observed with p=0.70576. Because the x2 and x3 were generated from the regression model with the explanatory variable x1 and the noise, the crude correlation between x2 and x3 would be observed, however, it would be reduced by adjusting x1, which is its cause. The partial correlation can also represent the relationship between variables adjusting the remaining variables.
Modern Predictive Analytics and Big Data Systems Engineering
Published in Anna M. Doro-on, Handbook of Systems Engineering and Risk Management in Control Systems, Communication, Space Technology, Missile, Security and Defense Operations, 2023
Partial correlation quantifies the level of a linear relationship between two random variables in conjunction with calibrating or controlling the outcome of one or more covariates. In this distinct case, we can examine the correlation coefficients between the three variables x, y, and z. Suppose x,y, and z are centered, and considering the regressions y and z versus x:
Assessing remotely sensed and reanalysis products in characterizing surface soil moisture in the Mongolian Plateau
Published in International Journal of Digital Earth, 2021
Min Luo, Chula Sa, Fanhao Meng, Yongchao Duan, Tie Liu, Yuhai Bao
Partial correlation analysis is commonly used to analyse the relationship between two variables after eliminating the impacts from other factors (Wang 2016). The degree of correlation between variables X and Y without the influences from variable Z can be defined as follows: where is the partial correlation coefficient between X and Y after removing the effects from Z. RXY, RXZ, and RYZ are the correlation coefficients between X and Y, X and Z, and Y and Z, respectively. In our study, the partial correlation analysis approach was applied to calculate the relationship between SM and precipitation as well as temperature.
A partial correlation network indicates links between wellbeing, loneliness, FOMO and problematic internet use in university students
Published in Behaviour & Information Technology, 2022
Oonagh O’Brien, Alexander Sumich, Thom Baguley, Daria J. Kuss
The regularised partial correlation networks were generated using Extended Bayesian Information Criterion (EBIC) and graphical lasso (Barber and Drton 2010). Nodes in the graph represent the psychometric tests and the edges represent a statistical relationship that is estimated using partial correlation techniques. These techniques have been shown to perform well in retrieving the network structure between variables that are interdependent (Friedman, Hastie, and Tibshirani 2008). The observed variables (nodes) may influence one another and edges represent a partial correlation among two nodes when all other variables under consideration are controlled for (Epskamp and Fried 2018). The strength of the partial correlation is directly related to the strength of the regression coefficient. Unlike what can be seen from a multiple regression analysis of a single dependent variable, the partial correlation network also highlights which other variables have an impact. By linking separate multiple regression models, partial correlation networks allow for mapping out linear prediction and multicollinearity among all variables (Epskamp et al. 2018). In order to prevent overinterpretation, spurious connections or edges that represent very small partial correlations are limited. Minimising EBIC has been effective in identifying the relationships between nodes in the network (Barber and Drton 2010, 2015; Van Borkulo et al. 2014). In this research, the hyperparameter is set at 0.5 as a more parsimonious model is preferred and expected to highlight the more important relationships (Barber and Drton 2010). When observed variables are continuous, but not normally distributed, the variables can be transformed to have a marginal normal distribution. A nonparanormal transformation was applied to the data (Liu, Lafferty, and Wasserman 2009).
Cycling Skill Inventory: Assessment of motor–tactical skills and safety motives
Published in Traffic Injury Prevention, 2019
J. C. F. de Winter, N. Kovácsová, M. P. Hagenzieker
In addition to orthogonal rotation, another approach to account for method bias is the use of partial correlations (Lindell and Whitney 2001). A partial correlation describes the association between 2 variables with the effect of a third variable removed. For the obliquely rotated components, correlations between safety motives and the 5 criterion variables were calculated while partialling out the motor–tactical skills. Similarly, correlations between motor–tactical skills and criterion variables were calculated while partialling out the safety motives score.