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modsem_pi() is a function for estimating interaction effects between latent variables, in structural equation models (SEMs), using product indicators. Methods for estimating interaction effects in SEMs can basically be split into two frameworks: 1. Product Indicator based approaches ("dblcent", "rca", "uca", "ca", "pind"), and 2. Distributionally based approaches ("lms", "qml"). modsem_pi() is essentially a fancy wrapper for lavaan::sem() which generates the necessary syntax and variables for the estimation of models with latent product indicators. Use default_settings_pi() to get the default settings for the different methods.

Usage

modsem_pi(
  model.syntax = NULL,
  data = NULL,
  method = "dblcent",
  match = NULL,
  standardize.data = FALSE,
  center.data = FALSE,
  first.loading.fixed = TRUE,
  center.before = NULL,
  center.after = NULL,
  residuals.prods = NULL,
  residual.cov.syntax = NULL,
  constrained.prod.mean = NULL,
  constrained.loadings = NULL,
  constrained.var = NULL,
  constrained.res.cov.method = NULL,
  auto.scale = "none",
  auto.center = "none",
  estimator = "ML",
  group = NULL,
  run = TRUE,
  na.rm = FALSE,
  suppress.warnings.lavaan = FALSE,
  suppress.warnings.match = FALSE,
  ...
)

Arguments

model.syntax

lavaan syntax

data

dataframe

method

method to use: "rca" = residual centering approach (passed to lavaan), "uca" = unconstrained approach (passed to lavaan), "dblcent" = double centering approach (passed to lavaan), "pind" = prod ind approach, with no constraints or centering (passed to lavaan), "custom" = use parameters specified in the function call (passed to lavaan)

match

should the product indicators be created by using the match-strategy

standardize.data

should data be scaled before fitting model

center.data

should data be centered before fitting model

first.loading.fixed

Should the first factor loading in the latent product be fixed to one?

center.before

should indicators in products be centered before computing products (overwritten by method, if method != NULL)

center.after

should indicator products be centered after they have been computed?

residuals.prods

should indicator products be centered using residuals (overwritten by method, if method != NULL)

residual.cov.syntax

should syntax for residual covariances be produced (overwritten by method, if method != NULL)

constrained.prod.mean

should syntax for product mean be produced (overwritten by method, if method != NULL)

constrained.loadings

should syntax for constrained loadings be produced (overwritten by method, if method != NULL)

constrained.var

should syntax for constrained variances be produced (overwritten by method, if method != NULL)

constrained.res.cov.method

method for constraining residual covariances

auto.scale

methods which should be scaled automatically (usually not useful)

auto.center

methods which should be centered automatically (usually not useful)

estimator

estimator to use in lavaan

group

group variable for multigroup analysis

run

should the model be run via lavaan, if FALSE only modified syntax and data is returned

na.rm

should missing values be removed (case-wise)? Defaults to FALSE. If TRUE, missing values are removed case-wise. If FALSE they are not removed.

suppress.warnings.lavaan

should warnings from lavaan be suppressed?

suppress.warnings.match

should warnings from match be suppressed?

...

arguments passed to other functions, e.g., lavaan

Value

modsem object

Examples

library(modsem)
# For more examples, check README and/or GitHub.
# One interaction
m1 <- '
  # Outer Model
  X =~ x1 + x2 +x3
  Y =~ y1 + y2 + y3
  Z =~ z1 + z2 + z3

  # Inner model
  Y ~ X + Z + X:Z
'

# Double centering approach
est1 <- modsem_pi(m1, oneInt)
summary(est1)
#> modsem (version 1.0.4, approach = dblcent):
#> lavaan 0.6-19 ended normally after 161 iterations
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                        60
#> 
#>   Number of observations                          2000
#> 
#> Model Test User Model:
#>                                                       
#>   Test statistic                               122.924
#>   Degrees of freedom                               111
#>   P-value (Chi-square)                           0.207
#> 
#> Parameter Estimates:
#> 
#>   Standard errors                             Standard
#>   Information                                 Expected
#>   Information saturated (h1) model          Structured
#> 
#> Latent Variables:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>   X =~                                                
#>     x1                1.000                           
#>     x2                0.804    0.013   63.612    0.000
#>     x3                0.916    0.014   67.144    0.000
#>   Y =~                                                
#>     y1                1.000                           
#>     y2                0.798    0.007  107.428    0.000
#>     y3                0.899    0.008  112.453    0.000
#>   Z =~                                                
#>     z1                1.000                           
#>     z2                0.812    0.013   64.763    0.000
#>     z3                0.882    0.013   67.014    0.000
#>   XZ =~                                               
#>     x1z1              1.000                           
#>     x2z1              0.805    0.013   60.636    0.000
#>     x3z1              0.877    0.014   62.680    0.000
#>     x1z2              0.793    0.013   59.343    0.000
#>     x2z2              0.646    0.015   43.672    0.000
#>     x3z2              0.706    0.016   44.292    0.000
#>     x1z3              0.887    0.014   63.700    0.000
#>     x2z3              0.716    0.016   45.645    0.000
#>     x3z3              0.781    0.017   45.339    0.000
#> 
#> Regressions:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>   Y ~                                                 
#>     X                 0.675    0.027   25.379    0.000
#>     Z                 0.561    0.026   21.606    0.000
#>     XZ                0.702    0.027   26.360    0.000
#> 
#> Covariances:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>  .x1z1 ~~                                             
#>    .x2z2              0.000                           
#>    .x2z3              0.000                           
#>    .x3z2              0.000                           
#>    .x3z3              0.000                           
#>  .x2z1 ~~                                             
#>    .x1z2              0.000                           
#>  .x1z2 ~~                                             
#>    .x2z3              0.000                           
#>  .x3z1 ~~                                             
#>    .x1z2              0.000                           
#>  .x1z2 ~~                                             
#>    .x3z3              0.000                           
#>  .x2z1 ~~                                             
#>    .x1z3              0.000                           
#>  .x2z2 ~~                                             
#>    .x1z3              0.000                           
#>  .x3z1 ~~                                             
#>    .x1z3              0.000                           
#>  .x3z2 ~~                                             
#>    .x1z3              0.000                           
#>  .x2z1 ~~                                             
#>    .x3z2              0.000                           
#>    .x3z3              0.000                           
#>  .x3z1 ~~                                             
#>    .x2z2              0.000                           
#>  .x2z2 ~~                                             
#>    .x3z3              0.000                           
#>  .x3z1 ~~                                             
#>    .x2z3              0.000                           
#>  .x3z2 ~~                                             
#>    .x2z3              0.000                           
#>  .x1z1 ~~                                             
#>    .x1z2              0.115    0.008   14.802    0.000
#>    .x1z3              0.114    0.008   13.947    0.000
#>    .x2z1              0.125    0.008   16.095    0.000
#>    .x3z1              0.140    0.009   16.135    0.000
#>  .x1z2 ~~                                             
#>    .x1z3              0.103    0.007   14.675    0.000
#>    .x2z2              0.128    0.006   20.850    0.000
#>    .x3z2              0.146    0.007   21.243    0.000
#>  .x1z3 ~~                                             
#>    .x2z3              0.116    0.007   17.818    0.000
#>    .x3z3              0.135    0.007   18.335    0.000
#>  .x2z1 ~~                                             
#>    .x2z2              0.135    0.006   20.905    0.000
#>    .x2z3              0.145    0.007   21.145    0.000
#>    .x3z1              0.114    0.007   16.058    0.000
#>  .x2z2 ~~                                             
#>    .x2z3              0.117    0.006   20.419    0.000
#>    .x3z2              0.116    0.006   20.586    0.000
#>  .x2z3 ~~                                             
#>    .x3z3              0.109    0.006   18.059    0.000
#>  .x3z1 ~~                                             
#>    .x3z2              0.138    0.007   19.331    0.000
#>    .x3z3              0.158    0.008   20.269    0.000
#>  .x3z2 ~~                                             
#>    .x3z3              0.131    0.007   19.958    0.000
#>   X ~~                                                
#>     Z                 0.201    0.024    8.271    0.000
#>     XZ                0.016    0.025    0.628    0.530
#>   Z ~~                                                
#>     XZ                0.062    0.025    2.449    0.014
#> 
#> Variances:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>    .x1                0.160    0.009   17.871    0.000
#>    .x2                0.162    0.007   22.969    0.000
#>    .x3                0.163    0.008   20.161    0.000
#>    .y1                0.159    0.009   17.896    0.000
#>    .y2                0.154    0.007   22.640    0.000
#>    .y3                0.164    0.008   20.698    0.000
#>    .z1                0.168    0.009   18.143    0.000
#>    .z2                0.158    0.007   22.264    0.000
#>    .z3                0.158    0.008   20.389    0.000
#>    .x1z1              0.311    0.014   22.227    0.000
#>    .x2z1              0.292    0.011   27.287    0.000
#>    .x3z1              0.327    0.012   26.275    0.000
#>    .x1z2              0.290    0.011   26.910    0.000
#>    .x2z2              0.239    0.008   29.770    0.000
#>    .x3z2              0.270    0.009   29.117    0.000
#>    .x1z3              0.272    0.012   23.586    0.000
#>    .x2z3              0.245    0.009   27.979    0.000
#>    .x3z3              0.297    0.011   28.154    0.000
#>     X                 0.981    0.036   26.895    0.000
#>    .Y                 0.990    0.038   25.926    0.000
#>     Z                 1.016    0.038   26.856    0.000
#>     XZ                1.045    0.044   24.004    0.000
#> 

if (FALSE) { # \dontrun{
# The Constrained Approach
est1Constrained <- modsem_pi(m1, oneInt, method = "ca")
summary(est1Constrained)
} # }

# Theory Of Planned Behavior
tpb <- '
# Outer Model (Based on Hagger et al., 2007)
  ATT =~ att1 + att2 + att3 + att4 + att5
  SN =~ sn1 + sn2
  PBC =~ pbc1 + pbc2 + pbc3
  INT =~ int1 + int2 + int3
  BEH =~ b1 + b2

# Inner Model (Based on Steinmetz et al., 2011)
  # Covariances
  ATT ~~ SN + PBC
  PBC ~~ SN
  # Causal Relationships
  INT ~ ATT + SN + PBC
  BEH ~ INT + PBC
  BEH ~ INT:PBC
'

# Double centering approach
estTpb <- modsem_pi(tpb, data = TPB)
summary(estTpb)
#> modsem (version 1.0.4, approach = dblcent):
#> lavaan 0.6-19 ended normally after 171 iterations
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                        78
#> 
#>   Number of observations                          2000
#> 
#> Model Test User Model:
#>                                                       
#>   Test statistic                               207.615
#>   Degrees of freedom                               222
#>   P-value (Chi-square)                           0.747
#> 
#> Parameter Estimates:
#> 
#>   Standard errors                             Standard
#>   Information                                 Expected
#>   Information saturated (h1) model          Structured
#> 
#> Latent Variables:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>   ATT =~                                              
#>     att1              1.000                           
#>     att2              0.878    0.012   71.509    0.000
#>     att3              0.789    0.012   66.368    0.000
#>     att4              0.695    0.011   61.017    0.000
#>     att5              0.887    0.013   70.884    0.000
#>   SN =~                                               
#>     sn1               1.000                           
#>     sn2               0.889    0.017   52.553    0.000
#>   PBC =~                                              
#>     pbc1              1.000                           
#>     pbc2              0.912    0.013   69.500    0.000
#>     pbc3              0.801    0.012   65.830    0.000
#>   INT =~                                              
#>     int1              1.000                           
#>     int2              0.914    0.016   58.982    0.000
#>     int3              0.808    0.015   55.547    0.000
#>   BEH =~                                              
#>     b1                1.000                           
#>     b2                0.960    0.030   31.561    0.000
#>   INTPBC =~                                           
#>     int1pbc1          1.000                           
#>     int2pbc1          0.931    0.015   63.809    0.000
#>     int3pbc1          0.774    0.013   60.107    0.000
#>     int1pbc2          0.893    0.013   68.173    0.000
#>     int2pbc2          0.826    0.017   48.845    0.000
#>     int3pbc2          0.690    0.015   45.300    0.000
#>     int1pbc3          0.799    0.012   67.008    0.000
#>     int2pbc3          0.738    0.015   47.809    0.000
#>     int3pbc3          0.622    0.014   45.465    0.000
#> 
#> Regressions:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>   INT ~                                               
#>     ATT               0.213    0.026    8.170    0.000
#>     SN                0.177    0.028    6.416    0.000
#>     PBC               0.217    0.030    7.340    0.000
#>   BEH ~                                               
#>     INT               0.191    0.024    7.817    0.000
#>     PBC               0.230    0.022   10.507    0.000
#>     INTPBC            0.204    0.018   11.425    0.000
#> 
#> Covariances:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>   ATT ~~                                              
#>     SN                0.629    0.029   21.977    0.000
#>     PBC               0.678    0.029   23.721    0.000
#>   SN ~~                                               
#>     PBC               0.678    0.029   23.338    0.000
#>  .int1pbc1 ~~                                         
#>    .int2pbc2          0.000                           
#>    .int2pbc3          0.000                           
#>    .int3pbc2          0.000                           
#>    .int3pbc3          0.000                           
#>  .int2pbc1 ~~                                         
#>    .int1pbc2          0.000                           
#>  .int1pbc2 ~~                                         
#>    .int2pbc3          0.000                           
#>  .int3pbc1 ~~                                         
#>    .int1pbc2          0.000                           
#>  .int1pbc2 ~~                                         
#>    .int3pbc3          0.000                           
#>  .int2pbc1 ~~                                         
#>    .int1pbc3          0.000                           
#>  .int2pbc2 ~~                                         
#>    .int1pbc3          0.000                           
#>  .int3pbc1 ~~                                         
#>    .int1pbc3          0.000                           
#>  .int3pbc2 ~~                                         
#>    .int1pbc3          0.000                           
#>  .int2pbc1 ~~                                         
#>    .int3pbc2          0.000                           
#>    .int3pbc3          0.000                           
#>  .int3pbc1 ~~                                         
#>    .int2pbc2          0.000                           
#>  .int2pbc2 ~~                                         
#>    .int3pbc3          0.000                           
#>  .int3pbc1 ~~                                         
#>    .int2pbc3          0.000                           
#>  .int3pbc2 ~~                                         
#>    .int2pbc3          0.000                           
#>  .int1pbc1 ~~                                         
#>    .int1pbc2          0.126    0.009   14.768    0.000
#>    .int1pbc3          0.102    0.007   13.794    0.000
#>    .int2pbc1          0.104    0.007   14.608    0.000
#>    .int3pbc1          0.091    0.006   14.109    0.000
#>  .int1pbc2 ~~                                         
#>    .int1pbc3          0.095    0.007   13.852    0.000
#>    .int2pbc2          0.128    0.007   19.320    0.000
#>    .int3pbc2          0.119    0.006   19.402    0.000
#>  .int1pbc3 ~~                                         
#>    .int2pbc3          0.110    0.006   19.911    0.000
#>    .int3pbc3          0.097    0.005   19.415    0.000
#>  .int2pbc1 ~~                                         
#>    .int2pbc2          0.152    0.008   18.665    0.000
#>    .int2pbc3          0.138    0.007   18.779    0.000
#>    .int3pbc1          0.082    0.006   13.951    0.000
#>  .int2pbc2 ~~                                         
#>    .int2pbc3          0.121    0.007   18.361    0.000
#>    .int3pbc2          0.104    0.005   19.047    0.000
#>  .int2pbc3 ~~                                         
#>    .int3pbc3          0.087    0.005   19.180    0.000
#>  .int3pbc1 ~~                                         
#>    .int3pbc2          0.139    0.007   21.210    0.000
#>    .int3pbc3          0.123    0.006   21.059    0.000
#>  .int3pbc2 ~~                                         
#>    .int3pbc3          0.114    0.005   21.021    0.000
#>   ATT ~~                                              
#>     INTPBC            0.086    0.024    3.519    0.000
#>   SN ~~                                               
#>     INTPBC            0.055    0.025    2.230    0.026
#>   PBC ~~                                              
#>     INTPBC            0.087    0.024    3.609    0.000
#> 
#> Variances:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>    .att1              0.167    0.007   23.528    0.000
#>    .att2              0.150    0.006   24.693    0.000
#>    .att3              0.160    0.006   26.378    0.000
#>    .att4              0.163    0.006   27.649    0.000
#>    .att5              0.159    0.006   24.930    0.000
#>    .sn1               0.178    0.015   12.110    0.000
#>    .sn2               0.156    0.012   13.221    0.000
#>    .pbc1              0.145    0.008   18.440    0.000
#>    .pbc2              0.160    0.007   21.547    0.000
#>    .pbc3              0.154    0.007   23.716    0.000
#>    .int1              0.158    0.009   18.152    0.000
#>    .int2              0.160    0.008   20.345    0.000
#>    .int3              0.167    0.007   23.414    0.000
#>    .b1                0.186    0.018   10.058    0.000
#>    .b2                0.135    0.017    8.080    0.000
#>    .int1pbc1          0.266    0.013   20.971    0.000
#>    .int2pbc1          0.292    0.012   24.421    0.000
#>    .int3pbc1          0.251    0.010   26.305    0.000
#>    .int1pbc2          0.290    0.012   24.929    0.000
#>    .int2pbc2          0.269    0.010   26.701    0.000
#>    .int3pbc2          0.253    0.009   29.445    0.000
#>    .int1pbc3          0.223    0.009   24.431    0.000
#>    .int2pbc3          0.234    0.008   27.633    0.000
#>    .int3pbc3          0.203    0.007   29.288    0.000
#>     ATT               0.998    0.037   27.138    0.000
#>     SN                0.987    0.039   25.394    0.000
#>     PBC               0.962    0.035   27.260    0.000
#>    .INT               0.490    0.020   24.638    0.000
#>    .BEH               0.455    0.023   20.068    0.000
#>     INTPBC            1.020    0.041   24.612    0.000
#> 

if (FALSE) { # \dontrun{
# The Constrained Approach
estTpbConstrained <- modsem_pi(tpb, data = TPB, method = "ca")
summary(estTpbConstrained)
} # }