View source: R/postestimate_doNonlinearEffectsAnalysis.R
doNonlinearEffectsAnalysis | R Documentation |
maturing
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 )
.object |
An R object of class cSEMResults resulting from a call to |
.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 |
.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 |
.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 |
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.
A list of class cSEMNonlinearEffects
with a corresponding method
for plot()
. See: plot.cSEMNonlinearEffects()
.
csem()
, cSEMResults, plot.cSEMNonlinearEffects()
## Not run: 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') ## End(Not run)
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