Check all indirect paths in a model and
return them as a list of arguments of x, y,
and m, to be used by indirect_effect().
Usage
all_indirect_paths(
fit = NULL,
exclude = NULL,
x = NULL,
y = NULL,
group = NULL
)
all_paths_to_df(all_paths)Arguments
- fit
A fit object. It can be the output of
lavaan::lavaan()or its wrapper such aslavaan::sem(), or a list of the output oflm()or the output oflm2list(). If it is a single model fitted bylm(), it will be automatically converted to a list bylm2list().- exclude
A character vector of variables to be excluded in the search, such as control variables.
- x
A character vector of variables that will be included as the
xvariables. If supplied, only paths that start from these variables will be included in the search. IfNULL, the default, then all variables that are one of the predictors in at least one regression equation will be included in the search.- y
A character vector of variables that will be included as the
yvariables. If supplied, only paths that start from these variables will be included in the search. IfNULL, the default, then all variables that are the outcome variables in at least one regression equation will be included in the search.- group
Either the group number as appeared in the
summary()orlavaan::parameterEstimates()output of a lavaan::lavaan object, or the group label as used in the lavaan::lavaan object. Used only when the number of groups is greater than one. Default isNULL. If not specified by the model has more than one group, than paths that appears in at least one group will be included in the output.- all_paths
An
all_paths-class object. For example, the output ofall_indirect_paths().
Value
all_indirect_paths() returns
a list of the class all_paths. Each argument is a
list of three character vectors,
x, the name of the predictor that starts a path, y,
the name of the outcome that ends a path, and m, a
character vector of one or more names of the mediators,
from x to y. This class has a print method.
all_paths_to_df() returns a data frame with three
columns, x, y, and m, which can be used by
functions such as indirect_effect().
Details
It makes use of igraph::all_simple_paths()
to identify paths in a model.
Multigroup Models
Since Version 0.1.14.2, support for
multigroup models has been added for models
fitted by lavaan. If a model has more
than one group and group is not
specified, than paths in all groups
will be returned. If group is
specified, than only paths in the
selected group will be returned.
Functions
all_indirect_paths(): Enumerate all indirect paths.all_paths_to_df(): Convert the output ofall_indirect_paths()to a data frame with three columns:x,y, andm.
Author
Shu Fai Cheung https://orcid.org/0000-0002-9871-9448
Examples
library(lavaan)
#> This is lavaan 0.6-19
#> lavaan is FREE software! Please report any bugs.
data(data_serial_parallel)
mod <-
"
m11 ~ x + c1 + c2
m12 ~ m11 + x + c1 + c2
m2 ~ x + c1 + c2
y ~ m12 + m2 + m11 + x + c1 + c2
"
fit <- sem(mod, data_serial_parallel,
fixed.x = FALSE)
# All indirect paths
out1 <- all_indirect_paths(fit)
out1
#> Call:
#> all_indirect_paths(fit = fit)
#> Path(s):
#> path
#> 1 m11 -> m12 -> y
#> 2 x -> m11 -> m12
#> 3 x -> m11 -> m12 -> y
#> 4 x -> m11 -> y
#> 5 x -> m12 -> y
#> 6 x -> m2 -> y
#> 7 c1 -> m11 -> m12
#> 8 c1 -> m11 -> m12 -> y
#> 9 c1 -> m11 -> y
#> 10 c1 -> m12 -> y
#> 11 c1 -> m2 -> y
#> 12 c2 -> m11 -> m12
#> 13 c2 -> m11 -> m12 -> y
#> 14 c2 -> m11 -> y
#> 15 c2 -> m12 -> y
#> 16 c2 -> m2 -> y
names(out1)
#> [1] "m11 -> m12 -> y" "x -> m11 -> m12" "x -> m11 -> m12 -> y"
#> [4] "x -> m11 -> y" "x -> m12 -> y" "x -> m2 -> y"
#> [7] "c1 -> m11 -> m12" "c1 -> m11 -> m12 -> y" "c1 -> m11 -> y"
#> [10] "c1 -> m12 -> y" "c1 -> m2 -> y" "c2 -> m11 -> m12"
#> [13] "c2 -> m11 -> m12 -> y" "c2 -> m11 -> y" "c2 -> m12 -> y"
#> [16] "c2 -> m2 -> y"
# Exclude c1 and c2 in the search
out2 <- all_indirect_paths(fit, exclude = c("c1", "c2"))
out2
#> Call:
#> all_indirect_paths(fit = fit, exclude = c("c1", "c2"))
#> Path(s):
#> path
#> 1 m11 -> m12 -> y
#> 2 x -> m11 -> m12
#> 3 x -> m11 -> m12 -> y
#> 4 x -> m11 -> y
#> 5 x -> m12 -> y
#> 6 x -> m2 -> y
names(out2)
#> [1] "m11 -> m12 -> y" "x -> m11 -> m12" "x -> m11 -> m12 -> y"
#> [4] "x -> m11 -> y" "x -> m12 -> y" "x -> m2 -> y"
# Exclude c1 and c2, and only consider paths start
# from x and end at y
out3 <- all_indirect_paths(fit, exclude = c("c1", "c2"),
x = "x",
y = "y")
out3
#> Call:
#> all_indirect_paths(fit = fit, exclude = c("c1", "c2"), x = "x",
#> y = "y")
#> Path(s):
#> path
#> 1 x -> m11 -> m12 -> y
#> 2 x -> m11 -> y
#> 3 x -> m12 -> y
#> 4 x -> m2 -> y
names(out3)
#> [1] "x -> m11 -> m12 -> y" "x -> m11 -> y" "x -> m12 -> y"
#> [4] "x -> m2 -> y"
# Multigroup models
data(data_med_complicated_mg)
mod <-
"
m11 ~ x1 + x2 + c1 + c2
m12 ~ m11 + c1 + c2
m2 ~ x1 + x2 + c1 + c2
y1 ~ m11 + m12 + x1 + x2 + c1 + c2
y2 ~ m2 + x1 + x2 + c1 + c2
"
fit <- sem(mod, data_med_complicated_mg, group = "group")
summary(fit)
#> lavaan 0.6-19 ended normally after 13 iterations
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 66
#>
#> Number of observations per group:
#> Group A 100
#> Group B 100
#>
#> Model Test User Model:
#>
#> Test statistic 16.359
#> Degrees of freedom 14
#> P-value (Chi-square) 0.292
#> Test statistic for each group:
#> Group A 7.443
#> Group B 8.917
#>
#> Parameter Estimates:
#>
#> Standard errors Standard
#> Information Expected
#> Information saturated (h1) model Structured
#>
#>
#> Group 1 [Group A]:
#>
#> Regressions:
#> Estimate Std.Err z-value P(>|z|)
#> m11 ~
#> x1 0.360 0.089 4.037 0.000
#> x2 0.222 0.103 2.157 0.031
#> c1 0.275 0.091 3.005 0.003
#> c2 0.114 0.092 1.240 0.215
#> m12 ~
#> m11 0.593 0.088 6.698 0.000
#> c1 0.030 0.091 0.327 0.743
#> c2 -0.178 0.089 -1.998 0.046
#> m2 ~
#> x1 0.005 0.102 0.045 0.964
#> x2 0.542 0.117 4.626 0.000
#> c1 0.082 0.104 0.791 0.429
#> c2 0.208 0.104 1.992 0.046
#> y1 ~
#> m11 0.372 0.119 3.116 0.002
#> m12 0.351 0.105 3.342 0.001
#> x1 -0.099 0.098 -1.011 0.312
#> x2 -0.067 0.107 -0.629 0.529
#> c1 -0.056 0.097 -0.572 0.567
#> c2 -0.149 0.096 -1.554 0.120
#> y2 ~
#> m2 0.395 0.083 4.771 0.000
#> x1 0.105 0.084 1.249 0.212
#> x2 0.178 0.107 1.666 0.096
#> c1 -0.076 0.087 -0.874 0.382
#> c2 0.080 0.088 0.912 0.362
#>
#> Covariances:
#> Estimate Std.Err z-value P(>|z|)
#> .y1 ~~
#> .y2 -0.031 0.084 -0.368 0.713
#>
#> Intercepts:
#> Estimate Std.Err z-value P(>|z|)
#> .m11 0.084 0.096 0.874 0.382
#> .m12 -0.042 0.093 -0.452 0.651
#> .m2 0.013 0.109 0.116 0.907
#> .y1 0.011 0.098 0.108 0.914
#> .y2 -0.108 0.090 -1.191 0.234
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .m11 0.868 0.123 7.071 0.000
#> .m12 0.820 0.116 7.071 0.000
#> .m2 1.126 0.159 7.071 0.000
#> .y1 0.904 0.128 7.071 0.000
#> .y2 0.774 0.109 7.071 0.000
#>
#>
#> Group 2 [Group B]:
#>
#> Regressions:
#> Estimate Std.Err z-value P(>|z|)
#> m11 ~
#> x1 0.104 0.107 0.970 0.332
#> x2 -0.012 0.106 -0.114 0.909
#> c1 0.364 0.103 3.526 0.000
#> c2 0.106 0.109 0.978 0.328
#> m12 ~
#> m11 0.346 0.096 3.606 0.000
#> c1 0.219 0.102 2.136 0.033
#> c2 -0.135 0.105 -1.285 0.199
#> m2 ~
#> x1 -0.057 0.104 -0.542 0.588
#> x2 0.307 0.103 2.965 0.003
#> c1 0.223 0.101 2.218 0.027
#> c2 0.181 0.106 1.700 0.089
#> y1 ~
#> m11 0.351 0.100 3.509 0.000
#> m12 0.056 0.098 0.568 0.570
#> x1 0.080 0.102 0.781 0.435
#> x2 0.016 0.100 0.157 0.875
#> c1 -0.294 0.106 -2.782 0.005
#> c2 0.061 0.104 0.582 0.561
#> y2 ~
#> m2 0.398 0.099 4.025 0.000
#> x1 0.023 0.104 0.224 0.823
#> x2 0.301 0.107 2.812 0.005
#> c1 0.110 0.102 1.076 0.282
#> c2 -0.008 0.107 -0.076 0.940
#>
#> Covariances:
#> Estimate Std.Err z-value P(>|z|)
#> .y1 ~~
#> .y2 -0.077 0.096 -0.805 0.421
#>
#> Intercepts:
#> Estimate Std.Err z-value P(>|z|)
#> .m11 0.112 0.104 1.075 0.282
#> .m12 0.149 0.100 1.478 0.139
#> .m2 0.112 0.101 1.107 0.268
#> .y1 0.057 0.100 0.575 0.565
#> .y2 0.198 0.101 1.959 0.050
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .m11 1.044 0.148 7.071 0.000
#> .m12 0.969 0.137 7.071 0.000
#> .m2 0.992 0.140 7.071 0.000
#> .y1 0.933 0.132 7.071 0.000
#> .y2 0.978 0.138 7.071 0.000
#>
all_indirect_paths(fit,
x = "x1",
y = "y1")
#> Call:
#> all_indirect_paths(fit = fit, x = "x1", y = "y1")
#> Path(s):
#> path
#> 1 Group A.x1 -> m11 -> m12 -> y1
#> 2 Group A.x1 -> m11 -> y1
#> 3 Group B.x1 -> m11 -> m12 -> y1
#> 4 Group B.x1 -> m11 -> y1
all_indirect_paths(fit,
x = "x1",
y = "y1",
group = 1)
#> Call:
#> all_indirect_paths(fit = fit, x = "x1", y = "y1", group = 1)
#> Path(s):
#> path
#> 1 Group A.x1 -> m11 -> m12 -> y1
#> 2 Group A.x1 -> m11 -> y1
all_indirect_paths(fit,
x = "x1",
y = "y1",
group = "Group B")
#> Call:
#> all_indirect_paths(fit = fit, x = "x1", y = "y1", group = "Group B")
#> Path(s):
#> path
#> 1 Group B.x1 -> m11 -> m12 -> y1
#> 2 Group B.x1 -> m11 -> y1