lavaSearch2 (version 2.0.3)

createContrast: Create Contrast matrix

Description

Returns a contrast matrix corresponding an object. The contrast matrix will contains the hypotheses in rows and the model coefficients in columns.

Usage

createContrast(object, ...)
# S3 method for character
createContrast(object, ...)
# S3 method for lvmfit
createContrast(object, linfct, ...)
# S3 method for lvmfit2
createContrast(object, linfct, ...)
# S3 method for list
createContrast(object, linfct = NULL, ...)
# S3 method for mmm
createContrast(object, linfct = NULL, ...)

Value

A list containing

• contrast [matrix] a contrast matrix corresponding to the left hand side of the linear hypotheses.

• null [vector] the right hand side of the linear hypotheses.

• Q [integer] the rank of the contrast matrix.

• ls.contrast [list, optional] the contrast matrix corresponding to each submodel. Only present when the argument object is a list of models.

Arguments

object

a lvmfit object or a list of a lvmfit objects.

...

Argument to be passed to .createContrast:

• diff.first [logical] should the contrasts between the first and any of the other coefficients define the null hypotheses.

• add.rowname [logical] add rownames to the contrast matrix and names to the right-hand side.

• rowname.rhs [logical] when naming the hypotheses, add the right-hand side (i.e. "X1-X2=0" instead of "X1-X2").

• sep [character vector of length2] character surrounding the left part of the row names.

linfct

[vector of characters] expression defining the linear hypotheses to be tested. Can also be a regular expression (of length 1) that is used to identify the coefficients to be tested using grep. See the examples section.

Details

One can initialize an empty contrast matrix setting the argumentlinfct to character(0).

Examples

## Simulate data
mSim <- lvm(X ~ Age + Treatment,
            Y ~ Gender + Treatment,
            c(Z1,Z2,Z3) ~ eta, eta ~ treatment,
            Age[40:5]~1)
latent(mSim) <- ~eta
categorical(mSim, labels = c("placebo","SSRI")) <- ~Treatment
categorical(mSim, labels = c("male","female")) <- ~Gender
n <- 1e2
set.seed(10)
df.data <- lava::sim(mSim,n)

## Estimate separate models
lmX <- lava::estimate(lvm(X ~ -1 + Age + Treatment), data = df.data)
lmY <- lava::estimate(lvm(Y ~ -1 + Gender + Treatment), data = df.data)
lvmZ <- lava::estimate(lvm(c(Z1,Z2,Z3) ~ -1 + 1*eta, eta ~ -1 + Treatment), 
                 data = df.data)

## Contrast matrix for a given model
createContrast(lmX, linfct = "X~Age")
createContrast(lmX, linfct = c("X~Age=0","X~Age+5*X~TreatmentSSRI=0"))
createContrast(lmX, linfct = c("X~Age=0","X~Age+5*X~TreatmentSSRI=0"), sep = NULL)
createContrast(lmX, linfct = character(0))

## Contrast matrix for the join model
ls.lvm <- list(X = lmX, Y = lmY, Z = lvmZ)
createContrast(ls.lvm, linfct = "TreatmentSSRI=0")
createContrast(ls.lvm, linfct = "TreatmentSSRI=0", rowname.rhs = FALSE)
createContrast(ls.lvm, linfct = character(0))

## Contrast for multigroup models
m <- lava::lvm(Y~Age+Treatment)
e <- lava::estimate(list(m,m), data = split(df.data, df.data$Gender))
print(coef(e))
createContrast(e, linfct = "Y~TreatmentSSRI@1 - Y~TreatmentSSRI@2 = 0")
createContrast(e, linfct = "Y~TreatmentSSRI@2 - Y~TreatmentSSRI@1 = 0")