Do a nonlinear effects analysis
Source:R/postestimate_doNonlinearEffectsAnalysis.R
doNonlinearEffectsAnalysis.Rd
maturing
Usage
doNonlinearEffectsAnalysis(
.object = NULL,
.dependent = NULL,
.independent = NULL,
.moderator = NULL,
.n_steps = 100,
.values_moderator = c(-2, -1, 0, 1, 2),
.value_independent = 0,
.alpha = 0.05
)
Arguments
- .object
An R object of class cSEMResults resulting from a call to
csem()
.- .dependent
Character string. The name of the dependent variable.
- .independent
Character string. The name of the independent variable.
- .moderator
Character string. The name of the moderator variable.
- .n_steps
Integer. A value giving the number of steps (the spotlights, i.e., values of .moderator in surface analysis or floodlight analysis) between the minimum and maximum value of the moderator. Defaults to
100
.- .values_moderator
A numeric vector. The values of the moderator in a the simple effects analysis. Typically these are difference from the mean (=0) measured in standard deviations. Defaults to
c(-2, -1, 0, 1, 2)
.- .value_independent
Integer. Only required for floodlight analysis; The value of the independent variable in case that it appears as a higher-order term.
- .alpha
An integer or a numeric vector of significance levels. Defaults to
0.05
.
Value
A list of class cSEMNonlinearEffects
with a corresponding method
for plot()
. See: plot.cSEMNonlinearEffects()
.
Details
Calculate the expected value of the dependent variable conditional on the values of an independent variables and a moderator variable. All other variables in the model are assumed to be zero, i.e., they are fixed at their mean levels. Moreover, it produces the input for the floodlight analysis.
Examples
if (FALSE) { # \dontrun{
model_Int <- "
# Measurement models
INV =~ INV1 + INV2 + INV3 +INV4
SAT =~ SAT1 + SAT2 + SAT3
INT =~ INT1 + INT2
# Structrual model containing an interaction term.
INT ~ INV + SAT + INV.SAT
"
# Estimate model
out <- csem(.data = Switching, .model = model_Int,
# ADANCO settings
.PLS_weight_scheme_inner = 'factorial',
.tolerance = 1e-06,
.resample_method = 'bootstrap'
)
# Do nonlinear effects analysis
neffects <- doNonlinearEffectsAnalysis(out,
.dependent = 'INT',
.moderator = 'INV',
.independent = 'SAT')
# Get an overview
neffects
# Simple effects plot
plot(neffects, .plot_type = 'simpleeffects')
# Surface plot using plotly
plot(neffects, .plot_type = 'surface', .plot_package = 'plotly')
# Surface plot using persp
plot(neffects, .plot_type = 'surface', .plot_package = 'persp')
# Floodlight analysis
plot(neffects, .plot_type = 'floodlight')
} # }