`R/postestimate_doNonlinearEffectsAnalysis.R`

`doNonlinearEffectsAnalysis.Rd`

```
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 |

A list of class `cSEMNonlinearEffects`

with a corresponding method
for `plot()`

. See: `plot.cSEMNonlinearEffects()`

.

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.

```
if (FALSE) {
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')
}
```