Methods for output InformativeTesting()
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.
## 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")x |
object of class "InformativeTesting". |
digits |
the number of significant digits to use when printing. |
... |
Currently not used. |
type |
If |
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 |
breaks |
see |
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 |
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 |
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 |
Leonard Vanbrabant lgf.vanbrabant@gmail.com
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.