Package index • lava

Working with Influence Functions

Functions for working with Influence Functions

IC(<default>): Extract influence function from model object
c(<estimate>): Concatenate estimate objects
c(<summary.estimate>): Concatenate summary.estimate objects
estimate(<array>): Estimate parameters and influence function.
estimate(<default>): Influence function based inference
estimate(<glm>): Estimate method for GLM objects
estimate(<list>): Estimate method for lists
iid(): Extract i.i.d. decomposition from model object
merge(<estimate>): Merge estimate objects
multinomial(): Estimate probabilities in contingency table
plot(<estimate>): Plot method for 'estimate' objects
stack(<estimate>): Stack estimating equations
summary(<estimate>): Summary of estimate objects
var_ic(): Variance based on influence function

Simulation functions

plot(<sim>): Plot method for simulation 'sim' objects
sim(<default>): Monte Carlo simulation
sim(<lvm>): Simulate model
summary(<sim>): Summary method for 'sim' objects

Statistical Inference and model tools

backdoor(): Backdoor criterion
bootstrap(): Generic bootstrap method
closed_testing(): Closed testing procedure
confpred(): Conformal prediction
diagtest(): Calculate diagnostic tests for 2x2 table
dsep(<lvm>): Check d-separation criterion
equivalence(): Identify candidates of equivalent models
gof() moments() logLik(<lvmfit>) score(<lvmfit>) information(<lvmfit>): Extract model summaries and GOF statistics for model object
information(): Extract information matrix
mixture(): Estimate mixture latent variable model.
mvnmix(): Estimate mixture latent variable model
ordreg(): Univariate cumulative link regression models
contr(): Create contrast matrix
partialcor(): Calculate partial correlations
pcor(): Polychoric correlation
plot(<lvm>): Plot path diagram
predict(<lvm>): Prediction in structural equation models
predict_glm(): Predict from a GLM with modified coefficients
predictlvm(): Predict function for latent variable models
scheffe(): Calculate simultaneous confidence limits by Scheffe's method
score(): Extract score function
wkm(): Weighted K-means
zibreg(): Regression model for binomial data with unkown group of immortals

Data sets

bmd: Longitudinal Bone Mineral Density Data (Wide format)
bmidata: Data
brisa: Simulated data
calcium: Longitudinal Bone Mineral Density Data
deprdiag: 50 patients from Monash Medical Centre, Melbourne
hubble: Hubble data
hubble2: Hubble data
indoorenv: Data
missingdata: Missing data example
nldata: Example data (nonlinear model)
nsem: Example SEM data (nonlinear)
semdata: Example SEM data
serotonin: Serotonin data
twindata: Twin menarche data

Graphics functions

Col(): Generate a transparent RGB color
colorbar(): Add color-bar to plot
click(<default>) idplot(): Identify points on plot
confband(): Add Confidence limits bar to plot
curly(): Adds curly brackets to plot
devcoords(): Returns device-coordinates and plot-region
fplot(): fplot
images(): Organize several image calls (for visualizing categorical data)
ksmooth2(): Plot/estimate surface
plotConf(): Plot regression lines
spaghetti(): Spaghetti plot

Matrix functions and linear algebra utilities

Inverse(): Generalized matrix inverse
blockdiag(): Combine matrices to block diagonal structure
revdiag() offdiag() `revdiag<-`() `offdiag<-`(): Create/extract 'reverse'-diagonal matrix or off-diagonal elements
rotate2(): Performs a rotation in the plane
tr(): Trace operator
vec(): vec operator

Utility functions

By(): Apply a Function to a Data Frame Split by Factors
Combine(): Report estimates across different models
Expand(): Create a Data Frame from All Combinations of Factors
Grep(): Finds elements in vector or column-names in data.frame/matrix
NA2x(): Convert to/from NA
NR(): Newton-Raphson method
PD(): Dose response calculation for binomial regression models
Print(): Generic print method
click(<default>) idplot(): Identify points on plot
commutation(): Finds the unique commutation matrix
csplit(): Split data into folds
getSAS(): Read SAS output
lava.options(): Set global options for lava
na.pass0(): Handle Missing Values in Objects
pdfconvert(): Convert pdf to raster format
rbind(<Surv>): Appending Surv objects
toformula(): Converts strings to formula
trim(): Trim string of (leading/trailing/all) white spaces
wait(): Wait for user input (keyboard or mouse)
wrapvec(): Wrap vector
`%++%`: Concatenation operator
`%ni%`: Matching operator (x not in y) oposed to the %in%-operator (x in y)

Latent variable model building

Graph() `Graph<-`(): Extract graph
Missing(): Missing value generator
Model() `Model<-`(): Extract model
Range.lvm(): Define range constraints of parameters
addvar(): Add variable to (model) object
children(): Extract children or parent elements of object
baptize(): Label elements of object
binomial.rd(): Define constant risk difference or relative risk association for binary exposure
cancel(): Generic cancel method
sim(<lvm>): Simulate model
`constrain<-`(<default>) `constrain<-`(<multigroup>) constraints(): Add non-linear constraints to latent variable model
`covariance<-`(<lvm>): Add covariance structure to Latent Variable Model
startvalues startvalues0 startvalues1 startvalues2 starter.multigroup modelPar modelVar matrices pars pars.lvm regfix pars.lvmfit pars.glm score.glm procdata.lvmfit mat.lvm reorderdata graph2lvm igraph.lvm subgraph finalize randomslope randomslope<- lisrel variances offdiags describecoef parlabels rsq stdcoef CoefMat CoefMat.multigroupfit deriv updatelvm checkmultigroup profci estimate.MAR missingModel Identical gaussian_logLik.lvm addhook gethook multigroup Weights fixsome IV parameter Specials procformula getoutcome decomp.specials rmvn0 dmvn0 logit expit tigol: For internal use
`labels<-`(<default>) `edgelabels<-`(<lvm>) `nodecolor<-`(<default>): Define labels of graph
vars() endogenous() exogenous() manifest() latent() `exogenous<-`(<lvm>) `latent<-`(<lvm>): Extract variable names from latent variable model
eventTime(): Add an observed event time outcome to a latent variable model.
`intercept<-`(<lvm>): Fix mean parameters in 'lvm'-object
intervention(<lvm>): Define intervention
`rmvar<-`(): Remove variables from (model) object.
lvm(): Initialize new latent variable model
makemissing(): Create random missing data
subset(<lvm>): Extract subset of latent variable model
`ordinal<-`(): Define variables as ordinal
parpos(): Generic method for finding indeces of model parameters
path(<lvm>) effects(<lvmfit>): Extract pathways in model graph
regression(<lvm>) `regression<-`(<lvm>): Add regression association to latent variable model
index(<lvm>) `index<-`(<lvm>): Extract the parameter indicies of a lvm object
index() `index<-`(): Generic method for extract index of an object
timedep(): Time-dependent parameters

Latent variable model estimation

bootstrap(<lvm>) bootstrap(<lvmfit>): Calculate bootstrap estimates of a lvm object
compare(): Statistical tests
complik(): Composite Likelihood for probit latent variable models
confint(<lvmfit>): Calculate confidence limits for parameters
correlation(): Generic method for extracting correlation coefficients of model object
estimate(<formula>): Estimate method for formulas
estimate(<lvm>): Estimation of parameters in a Latent Variable Model (lvm)
measurement.error(): Two-stage (non-linear) measurement error
modelsearch(): Model searching
twostage(): Two-stage estimator
twostage(<lvmfit>): Two-stage estimator (non-linear SEM)
twostageCV(): Cross-validated two-stage estimator