Returns the standardized beta coefficients, std. error and confidence intervals of a fitted linear (mixed) models.
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Fitted linear (mixed) model of class
Currently not used.
Numeric, the level of the confidence intervals.
“Standardized coefficients refer to how many standard deviations a dependent variable will change, per standard deviation increase in the predictor variable. Standardization of the coefficient is usually done to answer the question of which of the independent variables have a greater effect on the dependent variable in a multiple regression analysis, when the variables are measured in different units of measurement (for example, income measured in dollars and family size measured in number of individuals).” (Source: Wikipedia)
tibble with term names, standardized beta coefficients,
standard error and confidence intervals of
gls-objects, standardized beta coefficients may be wrong
for categorical variables (
factors), because the
gls objects returns the original data of the categorical vector,
and not the 'dummy' coded vectors as for other classes. See, as example:
head(model.matrix(lm(neg_c_7 ~ as.factor(e42dep), data = efc, na.action = na.omit)))
head(model.matrix(nlme::gls(neg_c_7 ~ as.factor(e42dep), data = efc, na.action = na.omit))).
In such cases, use
to_dummy to create dummies from
Wikipedia: Standardized coefficient
Gelman A. 2008. Scaling regression inputs by dividing by two standard deviations. Statistics in Medicine 27: 2865–2873. http://www.stat.columbia.edu/~gelman/research/published/standardizing7.pdf
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# fit linear model fit <- lm(Ozone ~ Wind + Temp + Solar.R, data = airquality) # print std. beta coefficients std_beta(fit) # print std. beta coefficients and ci, using # 2 sd and center binary predictors std_beta(fit, type = "std2") # std. beta for mixed models library(lme4) fit1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) std_beta(fit)
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