multreg: A multiple regression analysis using individual data In rpsychi: Statistics for psychiatric research

Description

multreg conducts a multiple regression analysis using individual data.

Usage

 1 multreg(formula, data, sig.level = 0.05, digits = 3)

Arguments

 formula two-sided formula; the left-hand-side of which gives one dependent variable containing a numeric variable, and the right-hand-side of several independent variables containing a numeric variable data a data frame contains the variables in the fomrmula sig.level a numeric contains the significance level (default 0.05) digits the specified number of decimal places (default 3)

Details

This function conducts a multiple regression analysis using individual data. The dependent variable and independent variables should be a numeric vector. In this function, you cannot specify any interaction nor any curvilinear effect. Statistical power is calculated using the following specifications:

(a) small (R^{2} = 0.02), medium (R^{2} = 0.13), and large (R^{2} = 0.26) population effect sizes, according to the interpretive guideline for effect sizes by Cohen (1992)

(b) sample size specified by data

(c) significance level specified by sig.level

(d) numbers of independent variable specified by formula

Value

 samp.stat returns the means and unbiased standard deviations corr.partial.corr returns a product-moment correlation matrix (lower triangle) and a partial correlation matrix given all remaining variables (upper triangle) corr.confidence returns lower and upper confidence limits (lower and upper triangles, respectively) omnibus.es returns a coefficient of determination and its' confidence interval raw.estimates returns partial regression coefficients, their confidence intervals, and standard errors standardized.estimates returns standardized partial regression coefficients, their confidence intervals, and standard errors power returns statistical power for detecting small (R^{2} = 0.02), medium (R^{2} = 0.13), and large (R^{2} = 0.26) population effect sizes

Author(s)

Yasuyuki Okumura
Department of Social Psychiatry,
National Institute of Mental Health,
National Center of Neurology and Psychiatry
yokumura@blue.zero.jp

References

Cohen J (1992) A power primer. Psychological Bulletin, 112, 155-159.

Cohen J, Cohen P, Aiken LS (2003) Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed). Mahwah, NJ: Erlbaum.

Smithson M (2001) Correct confidence intervals for various regression effect sizes and parameters: The importance of noncentral distributions in computing intervals, 61, 605-632.