plot.InformativeTesting: Methods for output InformativeTesting()

In lavaan: Latent Variable Analysis

Methods for output InformativeTesting()

Description

The print function shows the results of hypothesis tests Type A and Type B. The plot function plots the distributions of bootstrapped LRT values and plug-in p-values.

Usage

    
## S3 method for class 'InformativeTesting'
print(x, digits = max(3, getOption("digits") - 3), ...)
  
## S3 method for class 'InformativeTesting'
plot(x, ..., type = c("lr","ppv"), 
   main = "main", xlab = "xlabel", ylab = "Frequency", freq = TRUE, 
   breaks = 15, cex.main = 1, cex.lab = 1, cex.axis = 1, 
   col = "grey", border = par("fg"), vline = TRUE, 
   vline.col = c("red", "blue"), lty = c(1,2), lwd = 1, 
   legend = TRUE, bty = "o", cex.legend = 1, loc.legend = "topright")

Arguments

x	object of class "InformativeTesting".
digits	the number of significant digits to use when printing.
...	Currently not used.
type	If "lr", a distribution of the first-level bootstrapped LR values is plotted. If "ppv" a distribution of the bootstrapped plug-in p-values is plotted.
main	The main title(s) for the plot(s).
xlab	A label for the x axis, default depends on input type.
ylab	A label for the y axis.
freq	Logical; if TRUE, the histogram graphic is a representation of frequencies, the counts component of the result; if FALSE, probability densities, component density, are plotted (so that the histogram has a total area of one). The default is set to TRUE.
breaks	see hist
cex.main	The magnification to be used for main titles relative to the current setting of cex.
cex.lab	The magnification to be used for x and y labels relative to the current setting of cex.
cex.axis	The magnification to be used for axis annotation relative to the current setting of cex.
col	A colour to be used to fill the bars. The default of NULL yields unfilled bars.
border	Color for rectangle border(s). The default means par("fg").
vline	Logical; if TRUE a vertical line is drawn at the observed LRT value. If double.bootstrap = "FDB" a vertical line is drawn at the 1-p* quantile of the second-level LRT values, where p* is the first-level bootstrapped p-value
vline.col	Color(s) for the vline.LRT.
lty	The line type. Line types can either be specified as an integer (0=blank, 1=solid (default), 2=dashed, 3=dotted, 4=dotdash, 5=longdash, 6=twodash) or as one of the character strings "blank", "solid", "dashed", "dotted", "dotdash", "longdash", or "twodash", where "blank" uses 'invisible lines' (i.e., does not draw them).
lwd	The line width, a positive number, defaulting to 1.
legend	Logical; if TRUE a legend is added to the plot.
bty	A character string which determined the type of box which is drawn about plots. If bty is one of "o" (the default), "l", "7", "c", "u", or "]" the resulting box resembles the corresponding upper case letter. A value of "n" suppresses the box.
cex.legend	A numerical value giving the amount by which the legend text and symbols should be magnified relative to the default. This starts as 1 when a device is opened, and is reset when the layout is changed.
loc.legend	The location of the legend, specified by a single keyword from the list "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center".

Author(s)

Leonard Vanbrabant [e-mail]

Examples

## Not run: 
#########################
### real data example ###
#########################
# Multiple group path model for facial burns example.

# model syntax with starting values.
  burns.model <- 'Selfesteem ~ Age + c(m1, f1)*TBSA + HADS +
                             start(-.10, -.20)*TBSA  
                 HADS ~ Age + c(m2, f2)*TBSA + RUM +
                        start(.10, .20)*TBSA '
 
 
# constraints syntax
 burns.constraints <- 'f2 > 0  ; m1 < 0
                       m2 > 0  ; f1 < 0
                       f2 > m2 ; f1 < m1'
 
# we only generate 2 bootstrap samples in this example; in practice
# you may wish to use a much higher number. 
# the double bootstrap was switched off; in practice you probably 
# want to set it to "standard".
example1 <- InformativeTesting(model = burns.model, data = FacialBurns,
                               R = 2, constraints = burns.constraints,
                               double.bootstrap = "no", group = "Sex")
example1
plot(example1)

##########################
### artificial example ###
##########################
# Simple ANOVA model with 3 groups (N = 20 per group)
set.seed(1234)
Y <- cbind(c(rnorm(20,0,1), rnorm(20,0.5,1), rnorm(20,1,1)))
grp <- c(rep("1", 20), rep("2", 20), rep("3", 20))
Data <- data.frame(Y, grp)

#create model matrix
fit.lm <- lm(Y ~ grp, data = Data)
mfit <- fit.lm$model
mm <- model.matrix(mfit)

Y <- model.response(mfit)
X <- data.frame(mm[,2:3])
names(X) <- c("d1", "d2")
Data.new <- data.frame(Y, X)

# model
model <- 'Y ~ 1 + a1*d1 + a2*d2'

# fit without constraints
fit <- sem(model, data = Data.new)

# constraints syntax: mu1 < mu2 < mu3
constraints <- ' a1 > 0
                 a1 < a2 '

# we only generate 10 bootstrap samples in this example; in practice
# you may wish to use a much higher number, say > 1000. The double 
# bootstrap is not necessary in case of an univariate ANOVA model.
example2 <- InformativeTesting(model = model, data = Data.new, 
                               start = parTable(fit),
                               R = 10L, double.bootstrap = "no",
                               constraints = constraints)
example2
# plot(example2)

## End(Not run)

lavaan documentation built on Sept. 27, 2024, 9:07 a.m.