math_indirect | R Documentation |
Mathematic operators for
'indirect'-class object, the output
of indirect_effect()
and
cond_indirect()
.
## S3 method for class 'indirect'
e1 + e2
## S3 method for class 'indirect'
e1 - e2
e1 |
An 'indirect'-class object. |
e2 |
An 'indirect'-class object. |
For now, only +
operator
and -
operator are supported. These
operators can be used to estimate and
test a function of effects between
the same pair of variables.
For example, they can be used to compute and test the total effects along different paths. They can also be used to compute and test the difference between the effects along two paths.
The operators will check whether an operation is valid. An operation is not valid if
the two paths do not start from the same variable,
the two paths do not end at the same variable,
moderators are involved but they are not set to the same values in both objects, and
bootstrap estimates stored in
boot_out
, if any, are not identical.
Monte Carlo simulated
estimates stored in
mc_out
, if any, are not identical.
If bootstrap estimates are stored and both objects used the same type of bootstrap confidence interval, that type will be used. Otherwise, percentile bootstrap confidence interval, the recommended method, will be used.
Since Version 0.1.14.2, support for
multigroup models has been added for models
fitted by lavaan
. Both bootstrapping
and Monte Carlo confidence intervals
are supported. These operators can
be used to compute and test the
difference of an indirect effect
between two groups. This can also
be used to compute and test the
difference between a function of
effects between groups, for example,
the total indirect effects between
two groups.
The operators are flexible and allow users to do many possible computations. Therefore, users need to make sure that the function of effects is meaningful.
An 'indirect'-class object
with a list of effects stored. See
indirect_effect()
on details for
this class.
indirect_effect()
and
cond_indirect()
library(lavaan)
dat <- modmed_x1m3w4y1
mod <-
"
m1 ~ a1 * x + d1 * w1 + e1 * x:w1
m2 ~ m1 + a2 * x
y ~ b1 * m1 + b2 * m2 + cp * x
"
fit <- sem(mod, dat,
meanstructure = TRUE, fixed.x = FALSE,
se = "none", baseline = FALSE)
est <- parameterEstimates(fit)
hi_w1 <- mean(dat$w1) + sd(dat$w1)
# Examples for cond_indirect():
# Conditional effect from x to m1 when w1 is 1 SD above mean
out1 <- cond_indirect(x = "x", y = "y", m = c("m1", "m2"),
wvalues = c(w1 = hi_w1), fit = fit)
out2 <- cond_indirect(x = "x", y = "y", m = c("m2"),
wvalues = c(w1 = hi_w1), fit = fit)
out3 <- cond_indirect(x = "x", y = "y",
wvalues = c(w1 = hi_w1), fit = fit)
out12 <- out1 + out2
out12
out123 <- out1 + out2 + out3
out123
coef(out1) + coef(out2) + coef(out3)
# Multigroup model with indirect effects
dat <- data_med_mg
mod <-
"
m ~ x + c1 + c2
y ~ m + x + c1 + c2
"
fit <- sem(mod, dat, meanstructure = TRUE, fixed.x = FALSE, se = "none", baseline = FALSE,
group = "group")
# If a model has more than one group,
# the argument 'group' must be set.
ind1 <- indirect_effect(x = "x",
y = "y",
m = "m",
fit = fit,
group = "Group A")
ind1
ind2 <- indirect_effect(x = "x",
y = "y",
m = "m",
fit = fit,
group = 2)
ind2
# Compute the difference in indirect effects between groups
ind2 - ind1
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