| Title: | Piecewise Structural Equation Modeling |
|---|---|
| Description: | Implements piecewise structural equation modeling from a single list of structural equations, with new methods for non-linear, latent, and composite variables, standardized coefficients, query-based prediction and indirect effects. See <http://jslefche.github.io/piecewiseSEM/> for more. |
| Authors: | Jon Lefcheck [aut, cre], Jarrett Byrnes [aut], James Grace [aut] |
| Maintainer: | Jon Lefcheck <[email protected]> |
| License: | GPL-3 |
| Version: | 2.3.0.1 |
| Built: | 2024-09-15 05:17:32 UTC |
| Source: | https://github.com/jslefche/piecewisesem |
Piecewise structural equation modeling
Fitting and evaluation of piecewise structural equation models, complete
with goodness-of-fit tests, estimates of (standardized) path coefficients,
and evaluation of individual model fits (e.g., through R-squared values).
Compared with traditional variance-covariance based SEM, piecewise SEM
allows for fitting of models to different distributions through GLM and/or
hierarchical/nested random structures through (G)LMER. Supported model
classes include: lm, glm, gls, Sarlm, lme, glmmPQL, lmerMod, merModLmerTest,
glmerMod, glmmTMB, gam.
| Package: | piecewiseSEM |
| Type: | Package |
| Version: | 2.3.0.1 |
| Date: | 2024-06-11 |
| Depends: | R (>= 4.4.0), car, DiagrammeR, emmeans, igraph, lme4, multcomp, MuMIn, MASS, methods, nlme |
| License: | MIT |
The primary functions in the package are psem
which unites structural equations in a single model, and summary.psem can
be used on an object of class psem to provide various summary statistics for
evaluation and interpretation.
Jon Lefcheck <[email protected]>
Shipley, Bill. "A new inferential test for path models based on directed acyclic graphs." Structural Equation Modeling 7.2 (2000): 206-218.
Shipley, Bill. Cause and correlation in biology: a user's guide to path analysis, structural equations and causal inference. Cambridge University Press, 2002.
Shipley, Bill. "Confirmatory path analysis in a generalized multilevel context." Ecology 90.2 (2009): 363-368.
Shipley, Bill. "The AIC model selection method applied to path analytic models compared using a d-separation test." Ecology 94.3 (2013): 560-564.
Shipley, Bill, and Jacob C. Douma. "Generalized AIC and chi‐squared statistics for path models consistent with directed acyclic graphs." Ecology 101.3 (2020): e02960.
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.
Nakagawa, Shinichi, Paul CD Johnson, and Holger Schielzeth. "The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded." Journal of the Royal Society Interface 14.134 (2017): 20170213.
Useful links:
Specifies correlated errors among predictors
e1 %~~% e2e1 %~~% e2
e1 |
first variable involved in correlated error |
e2 |
second variable involved in correlated error |
For use in psem to identify correlated sets of variables.
Jon Lefcheck <[email protected]>, Jarrett Byrnes
# Generate example data dat <- data.frame(x1 = runif(50), x2 = runif(50), y1 = runif(50), y2 = runif(50)) # Create list of structural equations sem <- psem( lm(y1 ~ x1 + x2, dat), lm(y2 ~ y1 + x1, dat) ) # Look at correlated error between x1 and x2 # (exogenous) cerror(x1 %~~% x2, sem, dat) # Same as cor.test with(dat, cor.test(x1, x2)) # Look at correlatde error between x1 and y1 # (endogenous) cerror(y1 %~~% x1, sem, dat) # Not the same as cor.test # (accounts for influence of x1 and x2 on y1) with(dat, cor.test(y1, x1)) # Specify in psem sem <- update(sem, x1 %~~% y1) coefs(sem)# Generate example data dat <- data.frame(x1 = runif(50), x2 = runif(50), y1 = runif(50), y2 = runif(50)) # Create list of structural equations sem <- psem( lm(y1 ~ x1 + x2, dat), lm(y2 ~ y1 + x1, dat) ) # Look at correlated error between x1 and x2 # (exogenous) cerror(x1 %~~% x2, sem, dat) # Same as cor.test with(dat, cor.test(x1, x2)) # Look at correlatde error between x1 and y1 # (endogenous) cerror(y1 %~~% x1, sem, dat) # Not the same as cor.test # (accounts for influence of x1 and x2 on y1) with(dat, cor.test(y1, x1)) # Specify in psem sem <- update(sem, x1 %~~% y1) coefs(sem)
Information criterion values for SEM
AIC_psem( modelList, AIC.type = "loglik", Cstat = NULL, add.claims = NULL, basis.set = NULL, direction = NULL, interactions = FALSE, conserve = FALSE, conditional = FALSE, .progressBar = FALSE )AIC_psem( modelList, AIC.type = "loglik", Cstat = NULL, add.claims = NULL, basis.set = NULL, direction = NULL, interactions = FALSE, conserve = FALSE, conditional = FALSE, .progressBar = FALSE )
modelList |
a list of structural equations |
AIC.type |
whether the log-likelihood |
Cstat |
Fisher's C statistic obtained from |
add.claims |
an optional vector of additional independence claims (P-values) to be added to the basis set |
basis.set |
An optional list of independence claims. |
direction |
a vector of claims defining the specific directionality of any independence claim(s) |
interactions |
whether interactions should be included in independence claims. Default is FALSE |
conserve |
whether the most conservative P-value should be returned (See Details) Default is FALSE |
conditional |
whether the conditioning variables should be shown in the table. Default is FALSE |
.progressBar |
an optional progress bar. Default is FALSE |
a data.frame of AIC, AICc, d.f., and sample size
Jon Lefcheck <[email protected]>, Jim Grace
Shipley, Bill, and Jacob C. Douma. "Generalized AIC and chi‐squared statistics for path models consistent with directed acyclic graphs." Ecology 101.3 (2020): e02960.
Shipley, Bill. "The AIC model selection method applied to path analytic models compared using a d‐separation test." Ecology 94.3 (2013): 560-564.
Generic function for SEM AIC(c) score
## S3 method for class 'psem' AIC(object, ..., AIC.type = "loglik", aicc = FALSE)## S3 method for class 'psem' AIC(object, ..., AIC.type = "loglik", aicc = FALSE)
object |
a psem object |
... |
additional arguments to AIC |
AIC.type |
whether the log-likelihood |
aicc |
whether correction for small sample size should be applied. Default is |
mod <- psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), lm(firesev ~ age, data = keeley), data = keeley ) # Get log-likelihood based AIC AIC(mod) # Get d-sep based AIC AIC(mod, AIC.type = "dsep")mod <- psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), lm(firesev ~ age, data = keeley), data = keeley ) # Get log-likelihood based AIC AIC(mod) # Get d-sep based AIC AIC(mod, AIC.type = "dsep")
Compute analysis of variance table for one or more structural equation models.
## S3 method for class 'psem' anova(object, ..., digits = 3, anovafun = "Anova")## S3 method for class 'psem' anova(object, ..., digits = 3, anovafun = "Anova")
object |
a |
... |
additional objects of the same type |
digits |
number of digits to round results. Default is 3 |
anovafun |
The function used for ANOVA. Defaults to |
Additional models will be tested against the first model using a Chi-squared difference test.
an F, LRT, or other table for a single model, or a list of comparisons between multiple models
Jon Lefcheck <[email protected]>, Jarrett Byrnes <[email protected]>
data(keeley) mod1 <- psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), lm(firesev ~ age, data = keeley), data = keeley ) # get type II Anova anova(mod1) # conduct LRT mod2 <- psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), age ~ 1, data = keeley ) anova(mod1, mod2)data(keeley) mod1 <- psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), lm(firesev ~ age, data = keeley), data = keeley ) # get type II Anova anova(mod1) # conduct LRT mod2 <- psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), age ~ 1, data = keeley ) anova(mod1, mod2)
Convert list to psem object
as.psem(object, Class = "psem")as.psem(object, Class = "psem")
object |
any |
Class |
the name of the class to which |
Acquires the set of independence claims–or the 'basis set'–for use in evaluating the goodness-of-fit for piecewise structural equation models.
basisSet(modelList, direction = NULL, interactions = FALSE)basisSet(modelList, direction = NULL, interactions = FALSE)
modelList |
A list of structural equations |
direction |
a vector of claims defining the specific directionality of any independence claim(s) |
interactions |
whether interactions should be included in independence claims. Default is FALSE |
This function returns a list of independence claims. Each claim is a vector of the predictor of interest, followed by the response, and, if present, any conditioning variables.
Relationships among exogenous variables are omitted from the basis set because the directionality is unclear–e.g., does temperature cause latitude or does latitude cause temperature?–and the assumptions of the variables are not specified in the list of structural equations, so evaluating the relationship becomes challenging without further input from the user. This creates a circular scenario whereby the user specifies relationships among exogenous variables, raising the issue of whether they should be included as directed paths if they can be assigned directional relationships.
Paths can be omitted from the basis set by specifying them as correlated
errors using %~~% or by assigning a directionality using
the argument direction, e.g. direction = c("X <- Y").
This can be done if post hoc examination of the d-sep tests reveals
nonsensical independence claims (e.g., arthropod abundance predicting
photosynthetically-active radiation) that the user may wish to
exclude from evaluation.
A list of independence claims.
Jon Lefcheck <[email protected]>
Shipley, Bill. "A new inferential test for path models based on directed acyclic graphs." Structural Equation Modeling 7.2 (2000): 206-218.
From: https://stackoverflow.com/a/31678079
cbind_fill(...)cbind_fill(...)
... |
data.frames to be bound, separated by commas @keywords internal |
Calculates partial correlations and partial significance tests.
cerror(formula., modelList, data = NULL)cerror(formula., modelList, data = NULL)
formula. |
A formula specifying the two correlated variables using |
modelList |
A list of structural equations. |
data |
A |
If the variables are exogenous, then the correlated error is the raw bivariate correlation.
If the variables are endogenous, then the correlated error is the partial correlation, accounting for the influence of any predictors.
The significance of the correlated error is conducted using cor.test
if the variables are exogenous. Otherwise, a t-statistic is constructed and
compared to a t-distribution with N - k - 2 degrees of freedom (where N is
the total number of replicates, and k is the total number of variables
informing the relationship) to derive a P-value.
Returns a data.frame containing the (partial) correlation and
associated significance test.
Jon Lefcheck <[email protected]>
# Generate example data dat <- data.frame(x1 = runif(50), x2 = runif(50), y1 = runif(50), y2 = runif(50)) # Create list of structural equations sem <- psem( lm(y1 ~ x1 + x2, dat), lm(y2 ~ y1 + x1, dat) ) # Look at correlated error between x1 and x2 # (exogenous) cerror(x1 %~~% x2, sem, dat) # Same as cor.test with(dat, cor.test(x1, x2)) # Look at correlatde error between x1 and y1 # (endogenous) cerror(y1 %~~% x1, sem, dat) # Not the same as cor.test # (accounts for influence of x1 and x2 on y1) with(dat, cor.test(y1, x1)) # Specify in psem sem <- update(sem, x1 %~~% y1) coefs(sem)# Generate example data dat <- data.frame(x1 = runif(50), x2 = runif(50), y1 = runif(50), y2 = runif(50)) # Create list of structural equations sem <- psem( lm(y1 ~ x1 + x2, dat), lm(y2 ~ y1 + x1, dat) ) # Look at correlated error between x1 and x2 # (exogenous) cerror(x1 %~~% x2, sem, dat) # Same as cor.test with(dat, cor.test(x1, x2)) # Look at correlatde error between x1 and y1 # (endogenous) cerror(y1 %~~% x1, sem, dat) # Not the same as cor.test # (accounts for influence of x1 and x2 on y1) with(dat, cor.test(y1, x1)) # Specify in psem sem <- update(sem, x1 %~~% y1) coefs(sem)
Extracts (standardized) path coefficients from a psem object.
coefs( modelList, standardize = "scale", standardize.type = "latent.linear", test.statistic = "F", test.type = "II", intercepts = FALSE )coefs( modelList, standardize = "scale", standardize.type = "latent.linear", test.statistic = "F", 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 non-Gaussian responses:
|
test.statistic |
the type of test statistic generated by |
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. |
P-values for models constructed using lme4 are obtained
using the Kenward-Roger approximation of the denominator degrees of freedom
as implemented in the Anova function.
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 pre-selected 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 non-Gaussian responses, standardized coefficients are obtained in one of two ways:
latent.linear Referred to in Grace et al. 2019 as the standard form of
the latent-theoretic (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 square-root of the variance of the predictions
(on the linear or 'link' scale) plus the distribution-specific theoretical variance in the
case of binomial responses (for logit links: pi^2/3, for probit links: 1).
Menard.OE Referred to in Grace et al. 2019 as the standard form of
the observed-empirical (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 square-root 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 n-1 coefficients are reported for n levels, the output instead
reports model-estimated 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 multcomp::cld function.
For non-linear variables (i.e., smoothing functions from mgcv::gam), there are
no linear estimates reported.
Returns a data.frame of coefficients, their standard errors,
degrees of freedom, and significance tests.
Jon Lefcheck <[email protected]>, Jim Grace
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.
mod <- psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), lm(firesev ~ age, data = keeley), data = keeley ) coefs(mod)mod <- psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), lm(firesev ~ age, data = keeley), data = keeley ) coefs(mod)
Evaluation of conditional independence claims to be used in determining the goodness-of-fit for piecewise structural equation models.
dSep( modelList, basis.set = NULL, direction = NULL, interactions = FALSE, conserve = FALSE, conditioning = FALSE, .progressBar = TRUE )dSep( modelList, basis.set = NULL, direction = NULL, interactions = FALSE, conserve = FALSE, conditioning = FALSE, .progressBar = TRUE )
modelList |
A list of structural equations created using |
basis.set |
An optional list of independence claims. |
direction |
A |
interactions |
whether interactions should be included in independence claims. Default is FALSE |
conserve |
Whether the most conservative P-value should be returned; for use in special cases (see Details). Default is FALSE. |
conditioning |
Whether the conditioning variables should be shown in the summary table. Default is FALSE. |
.progressBar |
An optional progress bar. Default is TRUE. |
In cases involving non-normally distributed responses in the independence
claims that are modeled using generalized linear models, the significance of
the independence claim is not reversible (e.g., the P-value of Y ~ X is not
the same as X ~ Y). This is due to the transformation of the response via
the link function. In extreme cases, this can bias the goodness-of-fit
tests. summary.psem will issue a warning when this case is present
and provide guidance for solutions.
One solution is to specify the directionality of the relationship using the
direction argument, e.g. direction = c("X <- Y"). Another is
to run both tests (Y ~ X, X ~ Y) and return the most conservative (i.e.,
lowest) P-value, which can be toggled using the conserve = TRUE
argument.
Returns a data.frame of independence claims and their
significance values.
Jon Lefcheck <[email protected]>, Jarrett Byrnes
Shipley, Bill. "A new inferential test for path models based on directed acyclic graphs." Structural Equation Modeling 7.2 (2000): 206-218.
Evaluate model classes and stop if unsupported model class
evaluateClasses(x)evaluateClasses(x)
x |
a list of structural equations or a model object |
Summarize tests of directed separation using Fisher's C statistic
fisherC( dTable, add.claims = NULL, basis.set = NULL, direction = NULL, interactions = FALSE, conserve = FALSE, conditional = FALSE, .progressBar = FALSE )fisherC( dTable, add.claims = NULL, basis.set = NULL, direction = NULL, interactions = FALSE, conserve = FALSE, conditional = FALSE, .progressBar = FALSE )
dTable |
a |
add.claims |
an optional vector of additional independence claims (i.e., P-values) to be added to the basis set |
basis.set |
An optional list of independence claims. |
direction |
a vector of claims defining the specific directionality of any independence claim(s) |
interactions |
whether interactions should be included in independence claims. Default is FALSE |
conserve |
whether the most conservative P-value should be returned. Default is FALSE |
conditional |
whether the conditioning variables should be shown in the table. Default is FALSE |
.progressBar |
an optional progress bar. Default is FALSE |
a data.frame corresponding to the C statistic, d.f., and P-value
Generate adjacency matrix from list of structural equations
getDAG(modelList)getDAG(modelList)
modelList |
A list of structural equations |
Takes a [psem] object, pulls out the DAG, and then sorts the psem object into the order of the DAG (from exogenous to terminal endogenous variable) for use by other functions. Note: removes correlated errors.
getSortedPsem(object, keepdata = TRUE)getSortedPsem(object, keepdata = TRUE)
object |
A fit [psem] object |
keepdata |
Defaults to TRUE. Should the data with the psem be included in the returned object? |
A new [psem] object, without the data.
Data set from Grace & Keeley (2006)
keeleykeeley
A data.frame with 90 observations of 8 variables.
Distance to coast
Elevation from sea level
Abiotic favorability
Age of stand before fire
Plot heterogeneity
Severity of fire
Cover of plants
Plant species richness
Derivation of log-likelihoods to be used in determining the goodness-of-fit for piecewise structural equation models.
LLchisq( modelList, basis.set = NULL, direction = NULL, interactions = FALSE, conserve = FALSE )LLchisq( modelList, basis.set = NULL, direction = NULL, interactions = FALSE, conserve = FALSE )
modelList |
A list of structural equations created using |
basis.set |
An optional list of independence claims. |
direction |
A |
interactions |
whether interactions should be included in basis set. Default is FALSE |
conserve |
Whether the most conservative log-likelihood should be returned; for use in special cases (see Details). Default is FALSE. |
Here, a list of saturated models is first derived from the list of structured equations using the basis set. Then, the differences in summed log-likelihoods are computed and used to calculate the Chi-squared statistic.
a data.frame corresponding to the Chi-squared statistic, d.f., and P-value
Jon Lefcheck <[email protected]>
Shipley, Bill, and Jacob C. Douma. "Generalized AIC and chi‐squared statistics for path models consistent with directed acyclic graphs." Ecology 101.3 (2020): e02960.
mod <- psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), lm(firesev ~ age, data = keeley), data = keeley ) LLchisq(mod)mod <- psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), lm(firesev ~ age, data = keeley), data = keeley ) LLchisq(mod)
Data set from Grace & Jutila (1999)
meadowsmeadows
A data.frame with 354 observations of 4 variables.
Whether meadows were exposed to grazing: 0 = no, 1 = yes
Plant biomass in g m[-2]
Elevation of the plot above mean sea level
Plant species richness per m[2]
Multigroup Analysis for Piecewise SEM
multigroup( modelList, group, standardize = "scale", standardize.type = "latent.linear", test.type = "III" )multigroup( modelList, group, standardize = "scale", standardize.type = "latent.linear", test.type = "III" )
modelList |
a list of structural equations |
group |
the name of the grouping variable in quotes |
standardize |
The type of standardization: |
standardize.type |
The type of standardized for non-Gaussian responses:
|
test.type |
what kind of ANOVA should be reported. Default is type III |
Jon Lefcheck <[email protected]>
data(meadows) jutila <- psem( lm(rich ~ elev + mass, data = meadows), lm(mass ~ elev, data = meadows) ) jutila.multigroup <- multigroup(jutila, group = "grazed") jutila.multigroupdata(meadows) jutila <- psem( lm(rich ~ elev + mass, data = meadows), lm(mass ~ elev, data = meadows) ) jutila.multigroup <- multigroup(jutila, group = "grazed") jutila.multigroup
Extracts partial residuals from a model or psem object for a given
x and y.
partialResid(formula., modelList, data = NULL)partialResid(formula., modelList, data = NULL)
formula. |
A formula where the |
modelList |
A list of structural equations. |
data |
A |
This function computes the partial residuals of y ~ x + Z in a
two-step procedure to remove the variation explained by Z: (1) remove
x from the equation and model y ~ Z, and (2) replace y
with x and model x ~ Z.
Returns a data.frame of residuals of y ~ Z called
yresids, of x ~ Z called xresids.
Jon Lefcheck <[email protected]>
# Generate data dat <- data.frame(y = rnorm(100), x1 = rnorm(100), x2 = rnorm(100)) # Build model model <- lm(y ~ x1 + x2, dat) # Compute partial residuals of y ~ x1 yresid <- resid(lm(y ~ x2, dat)) xresid <- resid(lm(x1 ~ x2, dat)) plot(xresid, yresid) # Use partialResid presid <- partialResid(y ~ x1, model) with(presid, plot(xresid, yresid)) # identical plot!# Generate data dat <- data.frame(y = rnorm(100), x1 = rnorm(100), x2 = rnorm(100)) # Build model model <- lm(y ~ x1 + x2, dat) # Compute partial residuals of y ~ x1 yresid <- resid(lm(y ~ x2, dat)) xresid <- resid(lm(x1 ~ x2, dat)) plot(xresid, yresid) # Use partialResid presid <- partialResid(y ~ x1, model) with(presid, plot(xresid, yresid)) # identical plot!
plot.psem uses [DiagrammeR] to generate path diagrams of 'piecewiseSEM“ fits within R.
## S3 method for class 'psem' plot( x, return = FALSE, node_attrs = data.frame(shape = "rectangle", color = "black", fillcolor = "white"), edge_attrs = data.frame(style = "solid", color = "black"), ns_dashed = T, alpha = 0.05, show = "std", digits = 3, add_edge_label_spaces = TRUE, ... )## S3 method for class 'psem' plot( x, return = FALSE, node_attrs = data.frame(shape = "rectangle", color = "black", fillcolor = "white"), edge_attrs = data.frame(style = "solid", color = "black"), ns_dashed = T, alpha = 0.05, show = "std", digits = 3, add_edge_label_spaces = TRUE, ... )
x |
a [psem()] object |
return |
whether to return the output from [DiagrammeR::create_graph()] for modification and later plotting |
node_attrs |
List of node attributes to override defaults of rectangular nodes with black outline and white fill. See [here](http://visualizers.co/diagrammer/articles/node-edge-data-frames.html) and [here](http://visualizers.co/diagrammer/articles/graphviz-mermaid.html) for a more complete rundown of options. |
edge_attrs |
List of edge attributes to override defaults of solid black arrows. See [here](http://visualizers.co/diagrammer/articles/node-edge-data-frames.html) and [here](http://visualizers.co/diagrammer/articles/graphviz-mermaid.html) for a more complete rundown of options. |
ns_dashed |
If TRUE, paths that are not different from 0 will be dashed rather than solid, unless the whole is overridden in 'edge_attrs' |
alpha |
The alpha level for assessing whether a path is different from 0 |
show |
What types of path coefficients are shown? Default '"std"' is standardized coefficients. For undstandardized, use '"unstd"' |
digits |
How many significant digits should be shown? |
add_edge_label_spaces |
Should spaces by added on either side of edge labels? Default is 'TRUE' as otherwise paths too often overlap edges. |
... |
Other arguments to [DiagrammeR::render_graph()] |
Returns an object of class [DiagrammeR::dgr_graph]
Jarrett Byrnes <[email protected]>
data(keeley) mod <- psem( lm(rich ~ cover, data=keeley), lm(cover ~ firesev, data=keeley), lm(firesev ~ age, data=keeley), data = keeley ) plot(mod) ### More customized plot plot(mod, node_attrs = list( shape = "rectangle", color = "black", fillcolor = "orange", x = 3, y=1:4))data(keeley) mod <- psem( lm(rich ~ cover, data=keeley), lm(cover ~ firesev, data=keeley), lm(firesev ~ age, data=keeley), data = keeley ) plot(mod) ### More customized plot plot(mod, node_attrs = list( shape = "rectangle", color = "black", fillcolor = "orange", x = 3, y=1:4))
Print anova
## S3 method for class 'anova.psem' print(x, ...)## S3 method for class 'anova.psem' print(x, ...)
x |
an object of class anova.psem |
... |
further arguments passed to or from other methods |
Print basis set
## S3 method for class 'basisSet' print(x, ...)## S3 method for class 'basisSet' print(x, ...)
x |
a basis set |
... |
further arguments passed to or from other methods |
Print multigroup
## S3 method for class 'multigroup.psem' print(x, ...)## S3 method for class 'multigroup.psem' print(x, ...)
x |
an object to print |
... |
additional arguments to print |
Print psem
## S3 method for class 'psem' print(x, ...)## S3 method for class 'psem' print(x, ...)
x |
an object of class psem |
... |
further arguments passed to or from other methods |
Print summary
## S3 method for class 'summary.psem' print(x, ...)## S3 method for class 'summary.psem' print(x, ...)
x |
an object of class summary.psem |
... |
further arguments passed to or from other methods |
psem is used to unite a list of structural equations into a single
structural equation model.
psem(...)psem(...)
... |
A list of structural equations |
psem takes a list of structural equations, which can be model objects
of classes: lm, glm, gls, pgls, Sarlm, lme, glmmPQL, lmerMod,
lmerModLmerTest, glmerMod, glmmTMB, gam.
It also takes objects of class formula, formula.cerror, corresponding
to additional variables to be included in the tests of directed separation
(X ~ 1) or correlated errors (X1 %~~% X2).
The function optionally accepts data objects of classes: matrix,
data.frame, SpatialPointsDataFrame, comparative.data, or these are derived
internally from the structural equations.
Returns an object of class psem
Jon Lefcheck <[email protected]>
mod <- psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), lm(firesev ~ age, data = keeley), data = keeley ) summary(mod)mod <- psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), lm(firesev ~ age, data = keeley), data = keeley ) summary(mod)
Residual values from fit models
## S3 method for class 'psem' residuals(object, ...)## S3 method for class 'psem' residuals(object, ...)
object |
a |
... |
additional arguments to residuals |
a data.frame of residuals for endogenous variables as columns
Returns (pseudo)-R^2 values for all linear, generalized linear, and generalized linear mixed effects models.
rsquared(modelList, method = NULL)rsquared(modelList, method = NULL)
modelList |
a regression, or a list of structural equations. |
method |
The method used to compute the R2 value (See Details) |
For mixed models, marginal R2 considers only the variance by the fixed effects, and the conditional R2 by both the fixed and random effects.
For generalized additive models fit to gaussian distribution, the function returns ths adjusted-R2. For all other distributions, it returns the proportion of deviance explained.
For GLMs (glm), supported methods include:
mcfadden 1 - ratio of likelihoods of full vs. null models
coxsnell McFadden's R2 but raised to 2/N. Upper limit is < 1
nagelkerke Adjusts Cox-Snell R2 so that upper limit = 1. The
DEFAULT method
For GLMERs fit to Poisson, Gamma, and negative binomial distributions
(glmer, glmmPQL, glmer.nb), supported methods include
delta Approximates the observation variance based on
second-order Taylor series expansion. Can be used with many families and
link functions
lognormal Observation variance is the variance of the log-normal
distribution
trigamma Provides most accurate estimate of the observation
variance but is limited to only the log link. The DEFAULT method
For GLMERs fit to the binomial distribution (glmer,
glmmPQL), supported methods include:
theoretical Assumes observation variance is pi^2/3
delta Approximates the observation variance as above. The
DEFAULT method
Returns a data.frame with the response, its family and link,
the method used to estimate R2, and the R2 value itself. Mixed models also
return marginal and conditional R2 values.
Jon Lefcheck <[email protected]>
Nakagawa, Shinichi, Paul CD Johnson, and Holger Schielzeth. "The coefficient of determination R 2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded." Journal of the Royal Society Interface 14.134 (2017): 20170213.
## Not run: # Create data dat <- data.frame( ynorm = rnorm(100), ypois = rpois(100, 100), x1 = rnorm(100), random = letters[1:5] ) # Get R2 for linear model rsquared(lm(ynorm ~ x1, dat)) # Get R2 for generalized linear model rsquared(glm(ypois ~ x1, "poisson", dat)) rsquared(glm(ypois ~ x1, "poisson", dat), method = "mcfadden") # McFadden R2 # Get R2 for generalized least-squares model rsquared(gls(ynorm ~ x1, dat)) # Get R2 for linear mixed effects model (nlme) rsquared(nlme::lme(ynorm ~ x1, random = ~ 1 | random, dat)) # Get R2 for linear mixed effects model (lme4) rsquared(lme4::lmer(ynorm ~ x1 + (1 | random), dat)) # Get R2 for generalized linear mixed effects model (lme4) rsquared(lme4::glmer(ypois ~ x1 + (1 | random), family = poisson, dat)) rsquared(lme4::glmer(ypois ~ x1 + (1 | random), family = poisson, dat), method = "delta") # Get R2 for generalized linear mixed effects model (glmmPQL) rsquared(MASS::glmmPQL(ypois ~ x1, random = ~ 1 | random, family = poisson, dat)) # Get R2 for generalized additive models (gam) rsquared(mgcv::gam(ynorm ~ x1, dat)) ## End(Not run)## Not run: # Create data dat <- data.frame( ynorm = rnorm(100), ypois = rpois(100, 100), x1 = rnorm(100), random = letters[1:5] ) # Get R2 for linear model rsquared(lm(ynorm ~ x1, dat)) # Get R2 for generalized linear model rsquared(glm(ypois ~ x1, "poisson", dat)) rsquared(glm(ypois ~ x1, "poisson", dat), method = "mcfadden") # McFadden R2 # Get R2 for generalized least-squares model rsquared(gls(ynorm ~ x1, dat)) # Get R2 for linear mixed effects model (nlme) rsquared(nlme::lme(ynorm ~ x1, random = ~ 1 | random, dat)) # Get R2 for linear mixed effects model (lme4) rsquared(lme4::lmer(ynorm ~ x1 + (1 | random), dat)) # Get R2 for generalized linear mixed effects model (lme4) rsquared(lme4::glmer(ypois ~ x1 + (1 | random), family = poisson, dat)) rsquared(lme4::glmer(ypois ~ x1 + (1 | random), family = poisson, dat), method = "delta") # Get R2 for generalized linear mixed effects model (glmmPQL) rsquared(MASS::glmmPQL(ypois ~ x1, random = ~ 1 | random, family = poisson, dat)) # Get R2 for generalized additive models (gam) rsquared(mgcv::gam(ynorm ~ x1, dat)) ## End(Not run)
Data set from Shipley (2006)
shipleyshipley
A data.frame with 1900 observations of 9 variables.
Site of observation
Individual tree of observation
Latitude
Year of observation
Julian date of first bud burst
Cumulative degree days until first bud burst
Increase in stem diameter
Proportional survival
Alive (1) or dead (0)
Returns information necessary to interpret piecewise structural equation models, including tests of directed separation, path coefficients, information criterion values, and R-squared values of individual models.
## S3 method for class 'psem' summary( object, ..., basis.set = NULL, direction = NULL, interactions = FALSE, conserve = FALSE, conditioning = FALSE, add.claims = NULL, standardize = "scale", standardize.type = "latent.linear", test.statistic = "F", test.type = "II", intercepts = FALSE, AIC.type = "loglik", .progressBar = TRUE )## S3 method for class 'psem' summary( object, ..., basis.set = NULL, direction = NULL, interactions = FALSE, conserve = FALSE, conditioning = FALSE, add.claims = NULL, standardize = "scale", standardize.type = "latent.linear", test.statistic = "F", test.type = "II", intercepts = FALSE, AIC.type = "loglik", .progressBar = TRUE )
object |
a list of structural equations |
... |
additional arguments to summary |
basis.set |
an optional basis set |
direction |
a vector of claims defining the specific directionality of any independence claim(s) |
interactions |
whether interactions should be included in independence claims. Default is FALSE |
conserve |
whether the most conservative P-value should be returned (See Details) Default is FALSE |
conditioning |
whether all conditioning variables should be shown in the table Default is FALSE |
add.claims |
an optional vector of additional independence claims (P-values) to be added to the basis set |
standardize |
whether standardized path coefficients should be reported Default is "scale" |
standardize.type |
the type of standardized for non-Gaussian responses:
|
test.statistic |
the type of test statistic generated by |
test.type |
the type of test ("II" or "III") for significance of categorical
variables (from |
intercepts |
whether intercepts should be included in the coefficient table Default is FALSE |
AIC.type |
whether the log-likelihood |
.progressBar |
an optional progress bar. Default is TRUE |
The forthcoming argument groups splits the analysis based on an optional grouping
factor, conducts separate d-sep tests, and reports goodness-of-fit and path
coefficients for each submodel. The procedure is approximately similar to a
multigroup analysis in traditional variance-covariance SEM. Coming in version 2.1.
In cases involving non-normally distributed responses in the independence
claims that are modeled using generalized linear models, the significance of
the independence claim is not reversible (e.g., the P-value of Y ~ X is not
the same as X ~ Y). This is due to the transformation of the response via
the link function. In extreme cases, this can bias the goodness-of-fit
tests. summary.psem will issue a warning when this case is present
and provide guidance for solutions. One solution is to specify the
directionality of the relationship using the direction argument, e.g.
direction = c("X <- Y"). Another is to run both tests (Y ~ X, X ~ Y)
and return the most conservative (i.e., lowest) P-value, which can be
toggled using the conserve = TRUE argument.
In some cases, additional claims that were excluded from the basis set can
be added back in using the argument add.claims. These could be, for
instance, independence claims among exogenous variables. See Details in
basisSet.
Standardized path coefficients are scaled by standard deviations.
The function summary.psem returns a list of summary
statistics:
dTable |
A summary table of the tests of directed
separation, from |
CStat |
Fisher's C statistic, degrees of freedom, and significance value based on a Chi-square test. |
AIC |
Information criterion (Akaike, corrected Akaike) as well as degrees of freedom and sample size. |
coefficients |
A
summary table of the path coefficients, from |
R2 |
(Pseudo)-R2 values, from |
Jon Lefcheck <[email protected]>
Shipley, Bill. "A new inferential test for path models based on directed acyclic graphs." Structural Equation Modeling 7.2 (2000): 206-218.
Shipley, Bill. Cause and correlation in biology: a user's guide to path analysis, structural equations and causal inference. Cambridge University Press, 2002.
Shipley, Bill. "Confirmatory path analysis in a generalized multilevel context." Ecology 90.2 (2009): 363-368.
Shipley, Bill. "The AIC model selection method applied to path analytic models compared using a d-separation test." Ecology 94.3 (2013): 560-564.
The model fitting function psem.
Update psem model object with additional values.
## S3 method for class 'psem' update(object, ...)## S3 method for class 'psem' update(object, ...)
object |
a psem object |
... |
additional arguments to update |
mod <- psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), lm(firesev ~ age, data = keeley), data = keeley ) update(mod, firesev ~ age + cover)mod <- psem( lm(rich ~ cover, data = keeley), lm(cover ~ firesev, data = keeley), lm(firesev ~ age, data = keeley), data = keeley ) update(mod, firesev ~ age + cover)