std_beta

Fitted linear (mixed) model of class <code><a rd-options="" href="/link/lm?package=sjstats&version=0.2.0" data-mini-rdoc="sjstats::lm">lm</a></code> or
<code><a rd-options="lme4" href="/link/merMod?package=sjstats&version=0.2.0&to=lme4" data-mini-rdoc="lme4::merMod">merMod</a></code> (lme4 package).

Logical, if <code>TRUE</code>, a data frame with confidence
intervals will be returned, when <code>fit</code> is of class <code>lm</code>.
If <code>fit</code> is a <code>lmerMod</code> object (lme4 package),
always returns standard error instead of confidence intervals
(hence, this parameter will be ignored when <code>fit</code> is a
<code>lmerMod</code> object).

include.ci

If <code>fit</code> is of class <code>lm</code>, normal standardized coefficients
are computed by default. Use <code>type = "std2"</code> to follow
<a href="http://www.stat.columbia.edu/~gelman/research/published/standardizing7.pdf">Gelman's (2008)</a>
suggestion, rescaling the estimates by deviding them by two standard
deviations, so resulting coefficients are directly comparable for
untransformed binary predictors.

type

Returns the standardized beta coefficients and confidence intervals
 of a fitted linear (mixed) models, i.e. <code>fit</code> must either
 be of class <code>lm</code> or <code><a rd-options="lme4" href="/link/merMod?package=sjstats&version=0.2.0&to=lme4" data-mini-rdoc="lme4::merMod">merMod</a></code>.

Collection of convenient functions for common statistical computations,
which are not directly provided by R's base or stats packages.
This package aims at providing, first, shortcuts for statistical
measures, which otherwise could only be calculated with additional
effort (like standard errors or root mean squared errors). Second,
these shortcut functions are generic (if appropriate), and can be
applied not only to vectors, but also to other objects as well
(e.g., the Coefficient of Variation can be computed for vectors,
linear models, or linear mixed models; the r2()-function returns
the r-squared value for 'lm', 'glm', 'merMod' or 'lme' objects).
The focus of most functions lies on summary statistics or fit
measures for regression models, including generalized linear
models and mixed effects models. However, some of the functions
also deal with other statistical measures, like Cronbach's Alpha,
Cramer's V, Phi etc.

Daniel Lüdecke

sjstats

Collection of Convenient Functions for Common Statistical
Computations

std_beta function

Fitted linear (mixed) model of class <code><a rd-options='' href='lm'>lm</a></code> or
<code><a rd-options='lme4' href='merMod'>merMod</a></code> (lme4 package).

Logical, if <code>TRUE</code>, a data frame with confidence
intervals will be returned, when <code>fit</code> is of class <code>lm</code>.
If <code>fit</code> is a <code>lmerMod</code> object (lme4 package),
always returns standard error instead of confidence intervals
(hence, this parameter will be ignored when <code>fit</code> is a
<code>lmerMod</code> object).

If <code>fit</code> is of class <code>lm</code>, normal standardized coefficients
are computed by default. Use <code>type = "std2"</code> to follow
<a href = 'http://www.stat.columbia.edu/~gelman/research/published/standardizing7.pdf'>Gelman's (2008)</a>
suggestion, rescaling the estimates by deviding them by two standard
deviations, so resulting coefficients are directly comparable for
untransformed binary predictors.

Returns the standardized beta coefficients and confidence intervals
 of a fitted linear (mixed) models, i.e. <code>fit</code> must either
 be of class <code>lm</code> or <code><a rd-options='lme4' href='merMod'>merMod</a></code>.

std_beta: Standardized Beta coefficients and CI of lm and mixed models

Description

Usage

Arguments

Value

Details

References

Examples