Extracts (standardized) path coefficients from a psem
object.
coefs(modelList, standardize = "scale", standardize.type = "latent.linear", test.type = "II", intercepts = FALSE)
modelList  A list of structural equations, or a model. 

standardize  The type of standardization: 
standardize.type  The type of standardized for nonGaussian responses:

test.type  the type of test for significance of categorical variables
from 
intercepts  Whether intercepts should be included in the coefficients table. Default is FALSE. 
Returns a data.frame
of coefficients, their standard errors,
degrees of freedom, and significance tests.
Pvalues for models constructed using lme4
are obtained
using the KenwardRoger approximation of the denominator degrees of freedom
as implemented in the pbkrtest
package.
Different forms of standardization can be implemented using the standardize
argument:
none
No standardized coefficients are reported.
scale
Raw coefficients are scaled by the ratio of the standard deviation
of x divided by the standard deviation of y. See below for cases pertaining to GLM.
range
Raw coefficients are scaled by a preselected range of x
divided by a preselected range of y. The default argument is range
which takes the
two extremes of the data, otherwise the user must supply must a named list
where
the names are the variables to be standardized, and each entry contains a vector of
length == 2 to the ranges to be used in standardization.
For binary response models (i.e., binomial responses), standardized coefficients are obtained in one of two ways:
latent.linear
Referred to in Grace et al. (in review) as the standard form of
the latenttheoretic (LT) approach. In this method, there is assumed to be a continuous
latent propensity, y*, that underlies the observed binary responses. The standard
deviation of y* is computed as the squareroot of the variance of the predictions
(on the linear or 'link' scale) plus the distributionspecific assumed variance
(for logit links: pi^2/3, for probit links: 1).
Menard.OE
Referred to in Grace et al. (in review) as the standard form of
the observedempirical (OE) approach. In this method, error variance is based on the
differences between predicted scores and the observed binary data. The standard
deviation used for standardization is computed as the squareroot of the variance of
the predictions (on the linear scale) plus the correlation between the observed and
predicted (on the original or 'response' scale) values of y.
For categorical predictors: significance is determined using ANOVA (or analysis of
deviance). Because n1 coefficients are reported for n levels, the output instead
reports modelestimated means in the Estimate
column. This is done so all
n paths in the corresponding path diagram have assignable values.
The means are generated using function emmeans
in the emmeans
package.
Pairwise contrasts are further conducted among all levels using the default
correction for multiple testing. The results of those comparisons are given in the
significance codes (e.g., "a", "b", "ab") as reported in the emmeans::cld
function.
Grace, J.B., Johnson, D.A., Lefcheck, J.S., and Byrnes, J.E. "Standardized Coefficients in Regression and Structural Models with Binary Outcomes." Ecosphere 9(6): e02283.
KRmodcomp
, Anova
, emmeans
, CLD
mod < psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), lm(firesev ~ age, data = keeley), data = keeley ) coefs(mod)#> Response Predictor Estimate Std.Error DF Crit.Value P.Value Std.Estimate #> 1 rich cover 15.6727 4.7931 88 3.2698 0.0015 0.3291 ** #> 2 cover firesev 0.0839 0.0184 88 4.5594 0.0000 0.4371 *** #> 3 firesev age 0.0597 0.0125 88 4.7781 0.0000 0.4539 ***