lm2list {manymome}

Join 'lm()' Output to Form an 'lm_list'-Class Object

Description

The resulting model can be used by indirect_effect(), cond_indirect_effects(), or cond_indirect() as a path method, as if fitted by lavaan::sem().

Usage

lm2list(...)

Arguments

...

The lm() outputs to be grouped in a list.

Details

If a path model with mediation and/or moderation is fitted by a set of regression models using lm(), this function can combine them to an object of the class lm_list that represents a path model, as one fitted by structural equation model functions such as lavaan::sem(). This class of object can be used by some functions, such as indirect_effect(), cond_indirect_effects(), and cond_indirect() as if they were the output of fitting a path model, with the regression coefficients treated as path coefficients.

The regression outputs to be combined need to meet the following requirements:

• All models must be connected to at least one another model. That is, a regression model must either have (a) at least on predictor that is the outcome variable of another model, or (b) its outcome variable the predictor of another model.

• All models must be fitted to the same sample. This implies that the sample size must be the same in all analysis.

Value

It returns an lm_list-class object that forms a path model represented by a set of regression models. This class has a summary method that shows the summary of each regression model stored (see summary.lm_list()), and a print method that prints the models stored (see print.lm_list()).

See Also

summary.lm_list() and print.lm_list() for related methods, indirect_effect() and cond_indirect_effects() which accept lm_list-class objects as input.

Examples

data(data_serial_parallel)
lm_m11 <- lm(m11 ~ x + c1 + c2, data_serial_parallel)
lm_m12 <- lm(m12 ~ m11 + x + c1 + c2, data_serial_parallel)
lm_m2 <- lm(m2 ~ x + c1 + c2, data_serial_parallel)
lm_y <- lm(y ~ m11 + m12 + m2 + x + c1 + c2, data_serial_parallel)
# Join them to form a lm_list-class object
lm_serial_parallel <- lm2list(lm_m11, lm_m12, lm_m2, lm_y)
lm_serial_parallel
summary(lm_serial_parallel)

# Compute indirect effect from x to y through m11 and m12
outm11m12 <- cond_indirect(x = "x", y = "y",
                           m = c("m11", "m12"),
                           fit = lm_serial_parallel)
outm11m12
# Compute indirect effect from x to y
# through m11 and m12 with bootstrapping CI
# R should be at least 2000 or even 5000 in read study.
# In real research, parallel and progress can be omitted.
# They are est to TRUE by default.
outm11m12 <- cond_indirect(x = "x", y = "y",
                           m = c("m11", "m12"),
                           fit = lm_serial_parallel,
                           boot_ci = TRUE,
                           R = 100,
                           seed = 1234,
                           parallel = FALSE,
                           progress = FALSE)
outm11m12