| Type: | Package |
| Title: | Regularized Structural Equation Modeling |
| Version: | 1.9.5 |
| Maintainer: | Ross Jacobucci <rcjacobuc@gmail.com> |
| Description: | Uses both ridge and lasso penalties (and extensions) to penalize specific parameters in structural equation models. The package offers additional cost functions, cross validation, and other extensions beyond traditional structural equation models. Also contains a function to perform exploratory mediation (XMed). |
| URL: | https://github.com/Rjacobucci/regsem/ |
| BugReports: | https://github.com/Rjacobucci/regsem/issues/ |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| VignetteBuilder: | knitr |
| Depends: | lavaan, Rcpp, Rsolnp |
| Suggests: | snowfall, markdown, MASS, GA, caret, glmnet, ISLR, lbfgs, numDeriv, psych, knitr, nloptr, NlcOptim, optimx, semPlot, colorspace, plyr, matrixStats, stringr |
| LinkingTo: | Rcpp, RcppArmadillo |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | yes |
| Packaged: | 2023-06-01 17:13:30 UTC; rjacobuc |
| Author: | Ross Jacobucci [aut, cre], Kevin Grimm [ctb], Andreas Brandmaier [ctb], Sarfaraz Serang [ctb], Rogier Kievit [ctb], Florian Scharf [ctb], Xiaobei Li [ctb], Ai Ye [ctb] |
| Repository: | CRAN |
| Date/Publication: | 2023-06-02 09:00:02 UTC |
The main function that runs multiple penalty values.
Description
The main function that runs multiple penalty values.
Usage
cv_regsem(
model,
n.lambda = 40,
pars_pen = "regressions",
metric = ifelse(fit.ret2 == "train", "BIC", "chisq"),
mult.start = FALSE,
multi.iter = 10,
jump = 0.01,
lambda.start = 0,
alpha = 0.5,
gamma = 3.7,
type = "lasso",
random.alpha = 0.5,
fit.ret = c("rmsea", "BIC", "chisq"),
fit.ret2 = "train",
n.boot = 20,
data = NULL,
optMethod = "rsolnp",
gradFun = "ram",
hessFun = "none",
test.cov = NULL,
test.n.obs = NULL,
prerun = FALSE,
parallel = FALSE,
ncore = 2,
Start = "lavaan",
subOpt = "nlminb",
diff_par = NULL,
LB = -Inf,
UB = Inf,
par.lim = c(-Inf, Inf),
block = TRUE,
full = TRUE,
calc = "normal",
max.iter = 2000,
tol = 1e-05,
round = 3,
solver = FALSE,
quasi = FALSE,
solver.maxit = 5,
alpha.inc = FALSE,
step = 0.1,
momentum = FALSE,
step.ratio = FALSE,
line.search = FALSE,
nlminb.control = list(),
warm.start = FALSE,
missing = "listwise",
verbose = TRUE,
...
)
Arguments
model |
Lavaan output object. This is a model that was previously run with any of the lavaan main functions: cfa(), lavaan(), sem(), or growth(). It also can be from the efaUnrotate() function from the semTools package. Currently, the parts of the model which cannot be handled in regsem is the use of multiple group models, missing other than listwise, thresholds from categorical variable models, the use of additional estimators other than ML, most notably WLSMV for categorical variables. Note: the model does not have to actually run (use do.fit=FALSE), converge etc... regsem() uses the lavaan object as more of a parser and to get sample covariance matrix. |
n.lambda |
number of penalization values to test. |
pars_pen |
Parameter indicators to penalize. There are multiple ways to specify. The default is to penalize all regression parameters ("regressions"). Additionally, one can specify all loadings ("loadings"), or both c("regressions","loadings"). Next, parameter labels can be assigned in the lavaan syntax and passed to pars_pen. See the example.Finally, one can take the parameter numbers from the A or S matrices and pass these directly. See extractMatrices(lav.object)$A. |
metric |
Which fit index to use to choose a final model? Note that it chooses the best fit that also achieves convergence (conv=0). |
mult.start |
Logical. Whether to use multi_optim() (TRUE) or regsem() (FALSE). |
multi.iter |
maximum number of random starts for multi_optim |
jump |
Amount to increase penalization each iteration. |
lambda.start |
What value to start the penalty at |
alpha |
Mixture for elastic net. 1 = ridge, 0 = lasso |
gamma |
Additional penalty for MCP and SCAD |
type |
Penalty type. Options include "none", "lasso", "ridge", "enet" for the elastic net, "alasso" for the adaptive lasso and "diff_lasso". diff_lasso penalizes the discrepency between parameter estimates and some pre-specified values. The values to take the deviation from are specified in diff_par. Two methods for sparser results than lasso are the smooth clipped absolute deviation, "scad", and the minimum concave penalty, "mcp". Last option is "rlasso" which is the randomised lasso to be used for stability selection. |
random.alpha |
Alpha parameter for randomised lasso. Has to be between 0 and 1, with a default of 0.5. Note this is only used for "rlasso", which pairs with stability selection. |
fit.ret |
Fit indices to return. |
fit.ret2 |
Return fits using only dataset "train" or bootstrap "boot"? Have to do 2 sample CV manually. |
n.boot |
Number of bootstrap samples if fit.ret2="boot" |
data |
Optional dataframe. Only required for missing="fiml". |
optMethod |
Solver to use. Two main options for use: rsoolnp and coord_desc. Although slightly slower, rsolnp works much better for complex models. coord_desc uses gradient descent with soft thresholding for the type of of penalty. Rsolnp is a nonlinear solver that doesn't rely on gradient information. There is a similar type of solver also available for use, slsqp from the nloptr package. coord_desc can also be used with hessian information, either through the use of quasi=TRUE, or specifying a hess_fun. However, this option is not recommended at this time. |
gradFun |
Gradient function to use. Recommended to use "ram", which refers to the method specified in von Oertzen & Brick (2014). Only for use with optMethod="coord_desc". |
hessFun |
hessian function to use. Currently not recommended. |
test.cov |
Covariance matrix from test dataset. Necessary for CV=T |
test.n.obs |
Number of observations in test set. Used when CV=T |
prerun |
Logical. Use rsolnp to first optimize before passing to gradient descent? Only for use with coord_desc |
parallel |
Logical. whether to parallelize the processes running models for all values of lambda. |
ncore |
Number of cores to use when parallel=TRUE |
Start |
type of starting values to use. |
subOpt |
type of optimization to use in the optimx package. |
diff_par |
parameter values to deviate from. |
LB |
lower bound vector. |
UB |
upper bound vector |
par.lim |
Vector of minimum and maximum parameter estimates. Used to stop optimization and move to new starting values if violated. |
block |
Whether to use block coordinate descent |
full |
Whether to do full gradient descent or block |
calc |
Type of calc function to use with means or not. Not recommended for use. |
max.iter |
Number of iterations for coordinate descent |
tol |
Tolerance for coordinate descent |
round |
Number of digits to round results to |
solver |
Whether to use solver for coord_desc |
quasi |
Whether to use quasi-Newton |
solver.maxit |
Max iterations for solver in coord_desc |
alpha.inc |
Whether alpha should increase for coord_desc |
step |
Step size |
momentum |
Momentum for step sizes |
step.ratio |
Ratio of step size between A and S. Logical |
line.search |
Use line search for optimization. Default is no, use fixed step size |
nlminb.control |
list of control values to pass to nlminb |
warm.start |
Whether start values are based on previous iteration. This is not recommended. |
missing |
How to handle missing data. Current options are "listwise" and "fiml". |
verbose |
Print progress bar? |
... |
Any additional arguments to pass to regsem() or multi_optim(). |
Value
parameters Matrix of parameter estimates across the penalties
fits Fit metrics across penalties
final_pars Parameter estimates from the best fitting model according to metric
pars_pen Parameter indicators that were penalized.
df Degrees of freedom
metric The fit function used to choose a final model
call
Examples
library(regsem)
# put variables on same scale for regsem
HS <- data.frame(scale(HolzingerSwineford1939[,7:15]))
mod <- '
f =~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9
'
outt = cfa(mod, HS)
# increase to > 25
cv.out = cv_regsem(outt,type="lasso", pars_pen=c(1:2,6:8),
n.lambda=5,jump=0.01)
# check parameter numbers
extractMatrices(outt)["A"]
# equivalent to
mod <- '
f =~ 1*x1 + l1*x2 + l2*x3 + l3*x4 + l4*x5 + l5*x6 + l6*x7 + l7*x8 + l8*x9
'
outt = cfa(mod,HS)
# increase to > 25
cv.out = cv_regsem(outt, type="lasso", pars_pen=c("l1","l2","l6","l7","l8"),
n.lambda=5,jump=0.01)
summary(cv.out)
plot(cv.out, show.minimum="BIC")
mod <- '
f =~ x1 + x2 + x3 + x4 + x5 + x6
'
outt = cfa(mod, HS)
# can penalize all loadings
cv.out = cv_regsem(outt,type="lasso", pars_pen="loadings",
n.lambda=5,jump=0.01)
mod2 <- '
f =~ x4+x5+x3
#x1 ~ x7 + x8 + x9 + x2
x1 ~ f
x2 ~ f
'
outt2 = cfa(mod2, HS)
extractMatrices(outt2)$A
# if no pars_pen specification, defaults to all
# regressions
cv.out = cv_regsem(outt2,type="lasso",
n.lambda=15,jump=0.03)
# check
cv.out$pars_pen
Determine the initial range for stability selection
Description
This function perform regsem on bootstrap samples to determine the initial range for stability selection. Interquartile range of the bootstrap optimal regularization amounts are uesd as the final range.
Usage
det_range(data, model, times = 50, ...)
Arguments
data |
data frame |
model |
lavaan output object. |
times |
number of bootstrap samples used. |
... |
Any additional arguments to pass to regsem() or cv_regsem(). |
Value
result the lambda values and the upper bound and lower bound of the interquartile range.
Determine the initial range for stability selection, parallel version
Description
This function perform regsem on bootstrap samples to determine the initial range for stability selection. Interquartile range of the bootstrap optimal regularization amounts are uesd as the final range. Parallelization is used to achieve faster performance.
Usage
det_range_par(data, model, times = 50, ...)
Arguments
data |
data frame |
model |
lavaan output object. |
times |
number of bootstrap samples used. |
... |
Any additional arguments to pass to regsem() or cv_regsem(). |
Value
result the lambda values and the upper bound and lower bound of the interquartile range.
Generates an EFA model to be used by lavaan and regsem Function created by Florian Scharf for the paper Should regularization replace simple structure rotation in Exploratory Factor Analysis – Scharf & Nestler (in press at SEM)
Description
Generates an EFA model to be used by lavaan and regsem Function created by Florian Scharf for the paper Should regularization replace simple structure rotation in Exploratory Factor Analysis – Scharf & Nestler (in press at SEM)
Usage
efaModel(nFactors, variables)
Arguments
nFactors |
Number of latent factors to generate. |
variables |
Names of variables to be used as indicators |
Value
model Full EFA model parameters.
Examples
## Not run:
HS <- data.frame(scale(HolzingerSwineford1939[,7:15]))
# Note to find number of factors, recommended to use
# fa.parallel() from the psych package
# using the wrong number of factors can distort the results
mod = efaModel(3, colnames(HS))
semFit = sem(mod, data = HS, int.ov.free = FALSE, int.lv.free = FALSE,
std.lv = TRUE, std.ov = TRUE, auto.fix.single = FALSE, se = "none")
# note it requires smaller penalties than other applications
reg.out2 = cv_regsem(model = semFit, pars_pen = "loadings",
mult.start = TRUE, multi.iter = 10,
n.lambda = 100, type = "lasso", jump = 10^-5, lambda.start = 0.001)
reg.out2
plot(reg.out2) # note that the solution jumps around -- make sure best fit makes sense
## End(Not run)
This function extracts RAM matrices from a lavaan object.
Description
This function extracts RAM matrices from a lavaan object.
Usage
extractMatrices(model)
Arguments
model |
Lavaan model object. |
Value
The RAM matrices from model.
Examples
library(lavaan)
data(HolzingerSwineford1939)
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
mod <- cfa(HS.model, data=HolzingerSwineford1939)
mats = extractMatrices(mod)
Calculates the fit indices
Description
Calculates the fit indices
Usage
fit_indices(model, CV = FALSE, CovMat = NULL, data = NULL, n.obs = NULL)
Arguments
model |
regsem model object. |
CV |
cross-validation. Note that this requires splitting the dataset into a training and test set prior to running the model. The model should be run on the training set, with the test set held out and then passed to CovMat=. |
CovMat |
If CV=T then test covariance matrix must be supplied. Note That this should be done before running the lavaan model and should not overlap with the data or covariance matrix used to run the model. |
data |
supply the dataset? |
n.obs |
Number of observations in the test set for CV. |
Value
fits Full set of fit indices
Examples
## Not run:
fit_indices()
## End(Not run)
Multiple starts for Regularized Structural Equation Modeling
Description
Multiple starts for Regularized Structural Equation Modeling
Usage
multi_optim(
model,
max.try = 10,
lambda = 0,
alpha = 0.5,
gamma = 3.7,
random.alpha = 0.5,
LB = -Inf,
UB = Inf,
par.lim = c(-Inf, Inf),
block = TRUE,
full = TRUE,
type = "lasso",
optMethod = "rsolnp",
gradFun = "ram",
pars_pen = "regressions",
diff_par = NULL,
hessFun = "none",
tol = 1e-05,
round = 3,
solver = FALSE,
quasi = FALSE,
solver.maxit = 50000,
alpha.inc = FALSE,
line.search = FALSE,
prerun = FALSE,
step = 0.1,
momentum = FALSE,
step.ratio = FALSE,
verbose = FALSE,
warm.start = FALSE,
Start2 = NULL,
nlminb.control = NULL,
max.iter = 500
)
Arguments
model |
Lavaan output object. This is a model that was previously run with any of the lavaan main functions: cfa(), lavaan(), sem(), or growth(). It also can be from the efaUnrotate() function from the semTools package. Currently, the parts of the model which cannot be handled in regsem is the use of multiple group models, missing other than listwise, thresholds from categorical variable models, the use of additional estimators other than ML, most notably WLSMV for categorical variables. Note: the model does not have to actually run (use do.fit=FALSE), converge etc... regsem() uses the lavaan object as more of a parser and to get sample covariance matrix. |
max.try |
number of starts to try before convergence. |
lambda |
Penalty value. Note: higher values will result in additional convergence issues. |
alpha |
Mixture for elastic net. |
gamma |
Additional penalty for MCP and SCAD |
random.alpha |
Alpha parameter for randomised lasso. Has to be between 0 and 1, with a default of 0.5. Note this is only used for "rlasso", which pairs with stability selection. |
LB |
lower bound vector. Note: This is very important to specify when using regularization. It greatly increases the chances of converging. |
UB |
Upper bound vector |
par.lim |
Vector of minimum and maximum parameter estimates. Used to stop optimization and move to new starting values if violated. |
block |
Whether to use block coordinate descent |
full |
Whether to do full gradient descent or block |
type |
Penalty type. Options include "none", "lasso", "enet" for the elastic net, "alasso" for the adaptive lasso and "diff_lasso". If ridge penalties are desired, use type="enet" and alpha=1. diff_lasso penalizes the discrepency between parameter estimates and some pre-specified values. The values to take the deviation from are specified in diff_par. Two methods for sparser results than lasso are the smooth clipped absolute deviation, "scad", and the minimum concave penalty, "mcp". Last option is "rlasso" which is the randomised lasso to be used for stability selection. |
optMethod |
Solver to use. Two main options for use: rsoolnp and coord_desc. Although slightly slower, rsolnp works much better for complex models. coord_desc uses gradient descent with soft thresholding for the type of of penalty. Rsolnp is a nonlinear solver that doesn't rely on gradient information. There is a similar type of solver also available for use, slsqp from the nloptr package. coord_desc can also be used with hessian information, either through the use of quasi=TRUE, or specifying a hess_fun. However, this option is not recommended at this time. |
gradFun |
Gradient function to use. Recommended to use "ram", which refers to the method specified in von Oertzen & Brick (2014). Only for use with optMethod="coord_desc". |
pars_pen |
Parameter indicators to penalize. There are multiple ways to specify. The default is to penalize all regression parameters ("regressions"). Additionally, one can specify all loadings ("loadings"), or both c("regressions","loadings"). Next, parameter labels can be assigned in the lavaan syntax and passed to pars_pen. See the example.Finally, one can take the parameter numbers from the A or S matrices and pass these directly. See extractMatrices(lav.object)$A. |
diff_par |
Parameter values to deviate from. Only used when type="diff_lasso". |
hessFun |
Hessian function to use. Currently not recommended. |
tol |
Tolerance for coordinate descent |
round |
Number of digits to round results to |
solver |
Whether to use solver for coord_desc |
quasi |
Whether to use quasi-Newton. Currently not recommended. |
solver.maxit |
Max iterations for solver in coord_desc |
alpha.inc |
Whether alpha should increase for coord_desc |
line.search |
Use line search for optimization. Default is no, use fixed step size |
prerun |
Logical. Use rsolnp to first optimize before passing to gradient descent? Only for use with coord_desc. |
step |
Step size |
momentum |
Momentum for step sizes |
step.ratio |
Ratio of step size between A and S. Logical |
verbose |
Whether to print iteration number. |
warm.start |
Whether start values are based on previous iteration. This is not recommended. |
Start2 |
Provided starting values. Not required |
nlminb.control |
list of control values to pass to nlminb |
max.iter |
Number of iterations for coordinate descent |
Value
fit Full set of output from regsem()
Examples
## Not run:
# Note that this is not currently recommended. Use cv_regsem() instead
library(regsem)
# put variables on same scale for regsem
HS <- data.frame(scale(HolzingerSwineford1939[ ,7:15]))
mod <- '
f =~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9
'
outt = cfa(mod, HS, meanstructure=TRUE)
fit1 <- multi_optim(outt, max.try=40,
lambda=0.1, type="lasso")
# growth model
model <- ' i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4
s =~ 0*t1 + s1*t2 + s2*t3 + 3*t4 '
fit <- growth(model, data=Demo.growth)
summary(fit)
fitmeasures(fit)
fit3 <- multi_optim(fit, lambda=0.2, type="lasso")
summary(fit3)
## End(Not run)
Takes either a vector of parameter ids or a vector of named parameters and returns a vector of parameter ids
Description
Takes either a vector of parameter ids or a vector of named parameters and returns a vector of parameter ids
Usage
parse_parameters(x, model)
Arguments
x |
Parameter labels |
model |
Lavaan model |
Value
NULL if undefined input. Else vector of parameter ids
Penalized model syntax.
Description
This function create a lavaan model syntax with paths corresponding to paremeters penalized to 0 removed.
Usage
pen_mod(model, nm = NULL, pars_pen = NULL)
Arguments
model |
lavaan output object. |
nm |
names(regsemOutput$coefficients). |
pars_pen |
a vector of numbers corresponding to paths to be removed (same sequence as regsemOutput$coefficients). |
Value
new.mod new model in lavaan syntax.
Plot function for cv_regsem
Description
Plot function for cv_regsem
Usage
## S3 method for class 'cvregsem'
plot(
x,
...,
pars = NULL,
show.minimum = "BIC",
col = NULL,
type = "l",
lwd = 3,
h_line = 0,
lty = 1,
xlab = NULL,
ylab = NULL,
legend.x = NULL,
legend.y = NULL,
legend.cex = 1,
legend.bg = par("bg"),
grey.out = FALSE
)
Arguments
x |
An x from cv_regsem. |
... |
Other arguments. |
pars |
Which parameters to plot |
show.minimum |
What fit index to use |
col |
A specification for the default plotting color. |
type |
what type of plot should be drawn. Possible types are "p" for points, "l" for lines, or "b" for both |
lwd |
line width |
h_line |
Where to draw horizontal line |
lty |
line type |
xlab |
X axis label |
ylab |
Y axis label |
legend.x |
x-coordinate of legend. See ?legend |
legend.y |
y-coordinate of legend. See ?legend |
legend.cex |
cex of legend. See ?legend |
legend.bg |
legend background color. See ?legend |
grey.out |
Add grey to background |
Value
Plot of parameter estimates across penalties
Take RAM matrices, multiplies, and returns Implied Covariance matrix.
Description
Take RAM matrices, multiplies, and returns Implied Covariance matrix.
Usage
rcpp_RAMmult(par, A, S, S_fixed, A_fixed, A_est, S_est, Fmat, I)
Arguments
par |
parameter estimates. |
A |
A matrix with parameter labels. |
S |
S matrix with parameter labels. |
S_fixed |
S matrix with fixed indicators. |
A_fixed |
A matrix with fixed indicators. |
A_est |
A matrix with parameter estimates. |
S_est |
S matrix with parameter estimates. |
Fmat |
Fmat matrix. |
I |
Diagonal matrix of ones. |
Calculates the objective function values.
Description
Calculates the objective function values.
Usage
rcpp_fit_fun(
ImpCov,
SampCov,
type2,
lambda,
gamma,
pen_vec,
pen_diff,
e_alpha,
rlasso_pen,
pen_vec1,
pen_vec2,
dual_pen1,
dual_pen2
)
Arguments
ImpCov |
expected covariance matrix. |
SampCov |
Sample covariance matrix. |
type2 |
penalty type. |
lambda |
penalty value. |
gamma |
additional penalty for mcp and scad |
pen_vec |
vector of penalized parameters. |
pen_diff |
Vector of values to take deviation from. |
e_alpha |
Alpha for elastic net |
rlasso_pen |
Alpha for rlasso2 |
pen_vec1 |
vector of penalized parameters for lasso penalty. |
pen_vec2 |
vector of penalized parameters for ridge penalty. |
dual_pen1 |
vector of penalized parameters for lasso penalty. |
dual_pen2 |
vector of penalized parameters for ridge penalty. |
Calculates the gradient vector based on Von Oertzen and Brick, 2014
Description
Calculates the gradient vector based on Von Oertzen and Brick, 2014
Usage
rcpp_grad_ram(
par,
ImpCov,
SampCov,
Areg,
Sreg,
A,
S,
Fmat,
lambda,
type2,
pen_vec,
diff_par
)
Arguments
par |
vector with parameters. |
ImpCov |
expected covariance matrix. |
SampCov |
Sample covariance matrix. |
Areg |
A matrix with current parameter estimates. |
Sreg |
S matrix with current parameter estimates. |
A |
A matrix with parameter labels. |
S |
S matrix with parameter labels. |
Fmat |
Fmat matrix. |
lambda |
penalty value. |
type2 |
penalty type. |
pen_vec |
parameter indicators to be penalized. |
diff_par |
parameter values to take deviations from. |
Compute quasi Hessian
Description
Compute quasi Hessian
Usage
rcpp_quasi_calc(I, s, y, H)
Arguments
I |
identity matrix. |
s |
s vector. |
y |
y vector. |
H |
previous Hessian. |
Regularized Structural Equation Modeling. Tests a single penalty. For testing multiple penalties, see cv_regsem().
Description
Regularized Structural Equation Modeling. Tests a single penalty. For testing multiple penalties, see cv_regsem().
Usage
regsem(
model,
lambda = 0,
alpha = 0.5,
gamma = 3.7,
type = "lasso",
dual_pen = NULL,
random.alpha = 0.5,
data = NULL,
optMethod = "rsolnp",
estimator = "ML",
gradFun = "none",
hessFun = "none",
prerun = FALSE,
parallel = "no",
Start = "lavaan",
subOpt = "nlminb",
longMod = FALSE,
pars_pen = "regressions",
diff_par = NULL,
LB = -Inf,
UB = Inf,
par.lim = c(-Inf, Inf),
block = TRUE,
full = TRUE,
calc = "normal",
max.iter = 500,
tol = 1e-05,
round = 3,
solver = FALSE,
quasi = FALSE,
solver.maxit = 5,
alpha.inc = FALSE,
line.search = FALSE,
step = 0.1,
momentum = FALSE,
step.ratio = FALSE,
nlminb.control = list(),
missing = "listwise"
)
Arguments
model |
Lavaan output object. This is a model that was previously run with any of the lavaan main functions: cfa(), lavaan(), sem(), or growth(). It also can be from the efaUnrotate() function from the semTools package. Currently, the parts of the model which cannot be handled in regsem is the use of multiple group models, missing other than listwise, thresholds from categorical variable models, the use of additional estimators other than ML, most notably WLSMV for categorical variables. Note: the model does not have to actually run (use do.fit=FALSE), converge etc... regsem() uses the lavaan object as more of a parser and to get sample covariance matrix. |
lambda |
Penalty value. Note: higher values will result in additional
convergence issues. If using values > 0.1, it is recommended to use
mutli_optim() instead. See |
alpha |
Mixture for elastic net. 1 = ridge, 0 = lasso |
gamma |
Additional penalty for MCP and SCAD |
type |
Penalty type. Options include "none", "lasso", "enet" for the elastic net, "alasso" for the adaptive lasso and "diff_lasso". If ridge penalties are desired, use type="enet" and alpha=1. diff_lasso penalizes the discrepency between parameter estimates and some pre-specified values. The values to take the deviation from are specified in diff_par. Two methods for sparser results than lasso are the smooth clipped absolute deviation, "scad", and the minimum concave penalty, "mcp". Last option is "rlasso" which is the randomised lasso to be used for stability selection. |
dual_pen |
Two penalties to be used for type="dual", first is lasso, second ridge |
random.alpha |
Alpha parameter for randomised lasso. Has to be between 0 and 1, with a default of 0.5. Note this is only used for "rlasso", which pairs with stability selection. |
data |
Optional dataframe. Only required for missing="fiml" which is not currently working. |
optMethod |
Solver to use. Two main options for use: rsoolnp and coord_desc. Although slightly slower, rsolnp works much better for complex models. coord_desc uses gradient descent with soft thresholding for the type of of penalty. Rsolnp is a nonlinear solver that doesn't rely on gradient information. There is a similar type of solver also available for use, slsqp from the nloptr package. coord_desc can also be used with hessian information, either through the use of quasi=TRUE, or specifying a hess_fun. However, this option is not recommended at this time. |
estimator |
Whether to use maximum likelihood (ML) or unweighted least squares (ULS) as a base estimator. |
gradFun |
Gradient function to use. Recommended to use "ram", which refers to the method specified in von Oertzen & Brick (2014). Only for use with optMethod="coord_desc". |
hessFun |
Hessian function to use. Recommended to use "ram", which refers to the method specified in von Oertzen & Brick (2014). This is currently not recommended. |
prerun |
Logical. Use rsolnp to first optimize before passing to gradient descent? Only for use with coord_desc. |
parallel |
Logical. Whether to parallelize the processes? |
Start |
type of starting values to use. Only recommended to use "default". This sets factor loadings and variances to 0.5. Start = "lavaan" uses the parameter estimates from the lavaan model object. This is not recommended as it can increase the chances in getting stuck at the previous parameter estimates. |
subOpt |
Type of optimization to use in the optimx package. |
longMod |
If TRUE, the model is using longitudinal data? This changes the sample covariance used. |
pars_pen |
Parameter indicators to penalize. There are multiple ways to specify. The default is to penalize all regression parameters ("regressions"). Additionally, one can specify all loadings ("loadings"), or both c("regressions","loadings"). Next, parameter labels can be assigned in the lavaan syntax and passed to pars_pen. See the example.Finally, one can take the parameter numbers from the A or S matrices and pass these directly. See extractMatrices(lav.object)$A. |
diff_par |
Parameter values to deviate from. Only used when type="diff_lasso". |
LB |
lower bound vector. Note: This is very important to specify when using regularization. It greatly increases the chances of converging. |
UB |
Upper bound vector |
par.lim |
Vector of minimum and maximum parameter estimates. Used to stop optimization and move to new starting values if violated. |
block |
Whether to use block coordinate descent |
full |
Whether to do full gradient descent or block |
calc |
Type of calc function to use with means or not. Not recommended for use. |
max.iter |
Number of iterations for coordinate descent |
tol |
Tolerance for coordinate descent |
round |
Number of digits to round results to |
solver |
Whether to use solver for coord_desc |
quasi |
Whether to use quasi-Newton |
solver.maxit |
Max iterations for solver in coord_desc |
alpha.inc |
Whether alpha should increase for coord_desc |
line.search |
Use line search for optimization. Default is no, use fixed step size |
step |
Step size |
momentum |
Momentum for step sizes |
step.ratio |
Ratio of step size between A and S. Logical |
nlminb.control |
list of control values to pass to nlminb |
missing |
How to handle missing data. Current options are "listwise" and "fiml". "fiml" is not currently working well. |
Value
out List of return values from optimization program
convergence Convergence status. 0 = converged, 1 or 99 means the model did not converge.
par.ret Final parameter estimates
Imp_Cov Final implied covariance matrix
grad Final gradient.
KKT1 Were final gradient values close enough to 0.
KKT2 Was the final Hessian positive definite.
df Final degrees of freedom. Note that df changes with lasso penalties.
npar Final number of free parameters. Note that this can change with lasso penalties.
SampCov Sample covariance matrix.
fit Final F_ml fit. Note this is the final parameter estimates evaluated with the F_ml fit function.
coefficients Final parameter estimates
nvar Number of variables.
N sample size.
nfac Number of factors
baseline.chisq Baseline chi-square.
baseline.df Baseline degrees of freedom.
Examples
# Note that this is not currently recommended. Use cv_regsem() instead
library(lavaan)
# put variables on same scale for regsem
HS <- data.frame(scale(HolzingerSwineford1939[,7:15]))
mod <- '
f =~ 1*x1 + l1*x2 + l2*x3 + l3*x4 + l4*x5 + l5*x6 + l6*x7 + l7*x8 + l8*x9
'
# Recommended to specify meanstructure in lavaan
outt = cfa(mod, HS, meanstructure=TRUE)
fit1 <- regsem(outt, lambda=0.05, type="lasso",
pars_pen=c("l1", "l2", "l6", "l7", "l8"))
#equivalent to pars_pen=c(1:2, 6:8)
#summary(fit1)
Stability selection
Description
Stability selection
Usage
stabsel(
data,
model,
det.range = FALSE,
from,
to,
times = 50,
jump = 0.01,
detr.nlambda = 20,
n.lambda = 40,
n.boot = 100,
det.thr = FALSE,
p = 0.8,
p.from = 0.5,
p.to = 1,
p.jump = 0.05,
p.method = "aic",
type = "lasso",
pars_pen = "regressions",
...
)
Arguments
data |
data frame |
model |
lavaan syntax model. |
det.range |
Whether to determine the range of penalization values for stability selection through bootstrapping. Default is FALSE, from and to arguments are then needed. If set to TRUE, then jump, times and detr.nlambda arguments will be needed. |
from |
Minimum value of penalization values for stability selection. |
to |
Maximum value of penalization values for stability selection. |
times |
Number of bootstrapping sample used to determine the range. Default is 50. |
jump |
Amount to increase penalization each iteration. Default is 0.01 |
detr.nlambda |
Number of penalization values to test for determining range. |
n.lambda |
Number of penalization values to test for stability selection. |
n.boot |
Number of bootstrap samples needed for stability selection. |
det.thr |
Whether to determine the probability threshold value. Default is FALSE, p is then needed. If set to TRUE, p.from, p.to, p.method arguments will be needed. |
p |
Probability threshold: above which selection probability is the path kept in the modle. Default value is 0.8. |
p.from |
Lower bound of probability threshold to test. Default is 0.5. |
p.to |
Upper bound of probability threshold to test. Default is 1. |
p.jump |
Amount to increase threshold each iteration. Default is 0.05. |
p.method |
Which fit index to use to choose a final model? |
type |
Penalty type |
pars_pen |
Parameter indicators to penalize. |
... |
Any additional arguments to pass to regsem() or cv_regsem(). |
Examples
library(regsem)
# put variables on same scale for regsem
HS <- data.frame(scale(HolzingerSwineford1939[,7:15]))
mod <- '
f =~ 1*x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9
x1 ~~ r1*x2;x1 ~~ r2*x3;x1 ~~ r3*x4;x1 ~~ r4*x5
'
outt = cfa(mod, HS)
stabsel.out = stabsel(data=HS,model=mod,det.range=TRUE,detr.nlambda=20,n.lambda=5,
n.boot=10,p=0.9,type="alasso", p.method="aic",
pars_pen=c("r1","r2","r3","r4"))
stabsel.out$selection_results
Stability selection, parallelized version
Description
Stability selection, parallelized version
Usage
stabsel_par(
data,
model,
det.range = FALSE,
from,
to,
times = 50,
jump = 0.01,
detr.nlambda = 20,
n.lambda = 40,
n.boot = 100,
det.thr = FALSE,
p = 0.8,
p.from = 0.5,
p.to = 1,
p.jump = 0.05,
p.method = "aic",
type = "lasso",
pars_pen = "regressions",
...
)
Arguments
data |
data frame |
model |
lavaan syntax model. |
det.range |
Whether to determine the range of penalization values for stability selection through bootstrapping. Default is FALSE, from and to arguments are then needed. If set to TRUE, then jump, times and detr.nlambda arguments will be needed. |
from |
Minimum value of penalization values for stability selection. |
to |
Maximum value of penalization values for stability selection. |
times |
Number of bootstrapping sample used to determine the range. Default is 50. |
jump |
Amount to increase penalization each iteration. Default is 0.01 |
detr.nlambda |
Number of penalization values to test for determing range. |
n.lambda |
Number of penalization values to test for stability selection. |
n.boot |
Number of bootstrap samples needed for stability selection. |
det.thr |
Whether to determine the probability threshold value. Default is FALSE, p is then needed. If set to TRUE, p.from, p.to, p.method arguments will be needed. |
p |
Probability threshold: above which selection probability is the path kept in the modle. Default value is 0.8. |
p.from |
Lower bound of probability threshold to test. Default is 0.5. |
p.to |
Upper bound of probability threshold to test. Default is 1. |
p.jump |
Amount to increase threshold each iteration. Default is 0.05. |
p.method |
Which fit index to use to choose a final model? |
type |
Penalty type |
pars_pen |
Parameter indicators to penalize. |
... |
Any additional arguments to pass to regsem() or cv_regsem(). |
Tuning the probability threshold.
Description
This function tune the probability threshold parameter.
Usage
stabsel_thr(
stabsel = NULL,
data = NULL,
model = NULL,
est_model = NULL,
prob = NULL,
nm = NULL,
pars.pen = NULL,
from = 0.5,
to = 1,
jump = 0.01,
method = "aic"
)
Arguments
stabsel |
output object from stabsel function. If specified, data, model, est_model, prob, nm, and pars.pen parameters are not needed. |
data |
data frame |
model |
lavaan syntax model. |
est_model |
lavaan output object. |
prob |
matrix of selection probabilities. |
nm |
names(regsemOutput$coefficients). |
pars.pen |
a vector of numbers corresponding to paths to be removed (same sequence as regsemOutput$coefficients). |
from |
starting value of the threshold parameter. |
to |
end value of the threshold parameter. |
jump |
increment of the threshold parameter. |
method |
fit indices uesd to tune the parameter. |
Value
rtn results using the optimal threshold.
print information about cvregsem object
Description
print information about cvregsem object
Usage
## S3 method for class 'cvregsem'
summary(object, ...)
Arguments
object |
cv_regsem object |
... |
Additional arguments |
Value
Details regarding convergence and fit
Summary results from regsem.
Description
Summary results from regsem.
Usage
## S3 method for class 'regsem'
summary(object, ...)
Arguments
object |
An object from regsem. |
... |
Other arguments. |
Value
Details regarding convergence and fit
Function to performed exploratory mediation with continuous and categorical variables
Description
Function to performed exploratory mediation with continuous and categorical variables
Usage
xmed(
data,
iv,
mediators,
dv,
covariates = NULL,
type = "lasso",
nfolds = 10,
show.lambda = F,
epsilon = 0.001,
seed = NULL
)
Arguments
data |
Name of the dataset |
iv |
Name (or vector of names) of independent variable(s) |
mediators |
Name of mediators |
dv |
Name of dependent variable |
covariates |
Name of covariates to be included in model. |
type |
What type of penalty. Options include lasso, ridge, and enet. |
nfolds |
Number of cross-validation folds. |
show.lambda |
Displays lambda values in output |
epsilon |
Threshold for determining whether effect is 0 or not. |
seed |
Set seed to control CV results |
Value
Coefficients from best fitting model
Examples
# example
library(ISLR)
College1 = College[which(College$Private=="Yes"),]
Data = data.frame(scale(College1[c("Grad.Rate","Accept","Outstate","Room.Board","Books","Expend")]))
Data$Grad.Rate <- ifelse(Data$Grad.Rate > 0,1,0)
Data$Grad.Rate <- as.factor(Data$Grad.Rate)
#lavaan model with all mediators
model1 <-
' # direct effect (c_prime)
Grad.Rate ~ c_prime*Accept
# mediators
Outstate ~ a1*Accept
Room.Board ~ a2*Accept
Books ~ a3*Accept
Expend ~ a6*Accept
Grad.Rate ~ b1*Outstate + b2*Room.Board + b3*Books + b6*Expend
# indirect effects (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
a3b3 := a3*b3
a6b6 := a6*b6
# total effect (c)
c := c_prime + (a1*b1) + (a2*b2) + (a3*b3) + (a6*b6)
'
#p-value approach using delta method standard errors
fit.delta = sem(model1,data=Data,fixed.x=TRUE,ordered="Grad.Rate")
summary(fit.delta)
#xmed()
iv <- "Accept"
dv <- "Grad.Rate"
mediators <- c("Outstate","Room.Board","Books","Expend")
out <- xmed(Data,iv,mediators,dv)
out