glht2 {lavaSearch2} | R Documentation |
General Linear Hypothesis Testing With Small Sample Correction
Description
Test linear hypotheses on coefficients from a latent variable models with small sample corrections.
Usage
glht2(object, ...)
## S3 method for class 'lvmfit'
glht2(
object,
linfct,
rhs = NULL,
robust = FALSE,
cluster = NULL,
ssc = lava.options()$ssc,
df = lava.options()$df,
...
)
## S3 method for class 'lvmfit2'
glht2(object, linfct, rhs = NULL, robust = FALSE, cluster = NULL, ...)
## S3 method for class 'mmm'
glht2(object, linfct, rhs = 0, robust = FALSE, cluster = NULL, ...)
## S3 method for class 'lvmfit2'
glht(model, linfct, rhs = NULL, robust = FALSE, cluster = NULL, ...)
Arguments
object , model |
a |
... |
[logical] arguments passed to lower level methods. |
linfct |
[matrix or vector of character] the linear hypotheses to be tested. Same as the argument |
rhs |
[vector] the right hand side of the linear hypotheses to be tested. |
robust |
[logical] should robust standard error be used? Otherwise rescale the influence function with the standard error obtained from the information matrix. |
cluster |
[integer vector] the grouping variable relative to which the observations are iid. |
ssc |
[character] method used to correct the small sample bias of the variance coefficients: no correction ( |
df |
[character] method used to estimate the degree of freedoms of the Wald statistic: Satterthwaite |
Details
Whenever the argument linfct is not a matrix, it is passed to the function createContrast
to generate the contrast matrix and, if not specified, rhs.
Since only one degree of freedom can be specify in a glht object and it must be an integer, the degree of freedom of the denominator of an F test simultaneously testing all hypotheses is retained, after rounding.
Argument rhs and null are equivalent.
This redondance enable compatibility between lava::compare
, compare2
, multcomp::glht
, and glht2
.
Value
A glht
object.
See Also
createContrast
to create contrast matrices.
estimate2
to pre-compute quantities for the small sample correction.
Examples
library(multcomp)
## Simulate data
mSim <- lvm(c(Y1,Y2,Y3)~ beta * eta, Z1 ~ E, Z2 ~ E, Age[40:5]~1)
latent(mSim) <- "eta"
set.seed(10)
n <- 1e2
df.data <- lava::sim(mSim, n, latent = FALSE, p = c(beta = 1))
#### Inference on a single model ####
e.lvm <- estimate(lvm(Y1~E), data = df.data)
summary(glht2(e.lvm, linfct = c("Y1~E + Y1","Y1")))
#### Inference on separate models ####
## fit separate models
lvmX <- estimate(lvm(Z1 ~ E), data = df.data)
lvmY <- estimate(lvm(Z2 ~ E + Age), data = df.data)
lvmZ <- estimate(lvm(c(Y1,Y2,Y3) ~ eta, eta ~ E),
data = df.data)
#### create mmm object ####
e.mmm <- mmm(X = lvmX, Y = lvmY, Z = lvmZ)
#### create contrast matrix ####
resC <- createContrast(e.mmm, linfct = "E")
#### adjust for multiple comparisons ####
e.glht2 <- glht2(e.mmm, linfct = c(X="E"), df = FALSE)
summary(e.glht2)