Package index • lava

All functions

By(): Apply a Function to a Data Frame Split by Factors

Col(): Generate a transparent RGB color

Combine(): Report estimates across different models

Expand(): Create a Data Frame from All Combinations of Factors

Graph() `Graph<-`(): Extract graph

Grep(): Finds elements in vector or column-names in data.frame/matrix

IC(): Extract i.i.d. decomposition (influence function) from model object

Missing(): Missing value generator

Model() `Model<-`(): Extract model

NA2x(): Convert to/from NA

NR(): Newton-Raphson method

PD(): Dose response calculation for binomial regression models

Print(): Generic print method

Range.lvm(): Define range constraints of parameters

addvar(): Add variable to (model) object

backdoor(): Backdoor criterion

baptize(): Label elements of object

binomial.rd(): Define constant risk difference or relative risk association for binary exposure

blockdiag(): Combine matrices to block diagonal structure

bmd: Longitudinal Bone Mineral Density Data (Wide format)

bmidata: Data

bootstrap(): Generic bootstrap method

bootstrap(<lvm>) bootstrap(<lvmfit>): Calculate bootstrap estimates of a lvm object

brisa: Simulated data

calcium: Longitudinal Bone Mineral Density Data

cancel(): Generic cancel method

children(): Extract children or parent elements of object

click(<default>) idplot(): Identify points on plot

closed_testing(): Closed testing procedure

colorbar(): Add color-bar to plot

commutation(): Finds the unique commutation matrix

compare(): Statistical tests

complik(): Composite Likelihood for probit latent variable models

confband(): Add Confidence limits bar to plot

confint(<lvmfit>): Calculate confidence limits for parameters

confpred(): Conformal prediction

`constrain<-`(<default>) `constrain<-`(<multigroup>) constraints(): Add non-linear constraints to latent variable model

contr(): Create contrast matrix

correlation(): Generic method for extracting correlation coefficients of model object

`covariance<-`(<lvm>): Add covariance structure to Latent Variable Model

csplit(): Split data into folds

curly(): Adds curly brackets to plot

deprdiag: 50 patients from Monash Medical Centre, Melbourne

devcoords(): Returns device-coordinates and plot-region

diagtest(): Calculate diagnostic tests for 2x2 table

dsep(<lvm>): Check d-separation criterion

equivalence(): Identify candidates of equivalent models

estimate(<array>): Estimate parameters and influence function.

estimate(<default>): Estimation of functional of parameters

estimate(<lvm>): Estimation of parameters in a Latent Variable Model (lvm)

eventTime(): Add an observed event time outcome to a latent variable model.

fplot(): fplot

getMplus(): Read Mplus output

getSAS(): Read SAS output

gof() moments() logLik(<lvmfit>) score(<lvmfit>) information(<lvmfit>): Extract model summaries and GOF statistics for model object

hubble: Hubble data

hubble2: Hubble data

iid(): Extract i.i.d. decomposition from model object

images(): Organize several image calls (for visualizing categorical data)

indoorenv: Data

`intercept<-`(<lvm>): Fix mean parameters in 'lvm'-object

startvalues startvalues0 startvalues1 startvalues2 startvalues3 starter.multigroup addattr modelPar modelVar matrices pars pars.lvm pars.lvmfit pars.glm score.glm procdata.lvmfit mat.lvm reorderdata graph2lvm igraph.lvm subgraph finalize index.lvm index.lvmfit index reindex index<- rmvn0 dmvn0 logit expit tigol randomslope randomslope<- lisrel variances offdiags describecoef parlabels rsq stdcoef CoefMat CoefMat.multigroupfit deriv updatelvm checkmultigroup profci estimate.MAR missingModel Inverse Identical gaussian_logLik.lvm addhook gethook multigroup Weights fixsome parfix parfix<- merge IV parameter Specials procformula getoutcome decomp.specials na.pass0: For internal use

intervention(<lvm>): Define intervention

ksmooth2(): Plot/estimate surface

`labels<-`(<default>) `edgelabels<-`(<lvm>) `nodecolor<-`(<default>): Define labels of graph

lava.options(): Set global options for lava

lvm(): Initialize new latent variable model

makemissing(): Create random missing data

measurement.error(): Two-stage (non-linear) measurement error

missingdata: Missing data example

mixture(): Estimate mixture latent variable model.

modelsearch(): Model searching

multinomial(): Estimate probabilities in contingency table

mvnmix(): Estimate mixture latent variable model

nldata: Example data (nonlinear model)

nsem: Example SEM data (nonlinear)

`%++%`: Concatenation operator

`%ni%`: Matching operator (x not in y) oposed to the %in%-operator (x in y)

`ordinal<-`(): Define variables as ordinal

ordreg(): Univariate cumulative link regression models

parpos(): Generic method for finding indeces of model parameters

partialcor(): Calculate partial correlations

path(<lvm>) effects(<lvmfit>): Extract pathways in model graph

pcor(): Polychoric correlation

pdfconvert(): Convert pdf to raster format

plot(<estimate>): Plot method for 'estimate' objects

plot(<lvm>): Plot path diagram

plot(<sim>): Plot method for simulation 'sim' objects

plotConf(): Plot regression lines

predict(<lvm>): Prediction in structural equation models

predictlvm(): Predict function for latent variable models

rbind(<Surv>): Appending Surv objects

regression(<lvm>) `regression<-`(<lvm>): Add regression association to latent variable model

revdiag() offdiag() `revdiag<-`() `offdiag<-`(): Create/extract 'reverse'-diagonal matrix or off-diagonal elements

`rmvar<-`(): Remove variables from (model) object.

rotate2(): Performs a rotation in the plane

scheffe(): Calculate simultaneous confidence limits by Scheffe's method

semdata: Example SEM data

serotonin: Serotonin data

sim(<default>): Monte Carlo simulation

sim(<lvm>): Simulate model

spaghetti(): Spaghetti plot

stack(<estimate>): Stack estimating equations

subset(<lvm>): Extract subset of latent variable model

summary(<sim>): Summary method for 'sim' objects

timedep(): Time-dependent parameters

toformula(): Converts strings to formula

tr(): Trace operator

trim(): Trim string of (leading/trailing/all) white spaces

twindata: Twin menarche data

twostage(): Two-stage estimator

twostage(<lvmfit>): Two-stage estimator (non-linear SEM)

twostageCV(): Cross-validated two-stage estimator

vars() endogenous() exogenous() manifest() latent() `exogenous<-`(<lvm>) `latent<-`(<lvm>): Extract variable names from latent variable model

vec(): vec operator

wait(): Wait for user input (keyboard or mouse)

wkm(): Weighted K-means

wrapvec(): Wrap vector

zibreg(): Regression model for binomial data with unkown group of immortals