Multigroup Analysis for Piecewise SEM

multigroup(modelList, group, standardize = "scale",
  standardize.type = "latent.linear", test.type = "III")

Arguments

modelList

a list of structural equations

group

the name of the grouping variable in quotes

standardize

The type of standardization: none, scale, range. Default is scale.

standardize.type

The type of standardized for non-Gaussian responses: latent.linear, Menard.OE. Default is latent.linear.

test.type

what kind of ANOVA should be reported. Default is type III

Examples

data(meadows) jutila <- psem( lm(rich ~ elev + mass, data = meadows), lm(mass ~ elev, data = meadows) ) jutila.multigroup <- multigroup(jutila, group = "grazed") jutila.multigroup
#> #> Structural Equation Model of jutila #> #> Groups = grazed [ 1, 0 ] #> #> --- #> #> Global goodness-of-fit: #> #> Fisher's C = 0 with P-value = 1 and on 0 degrees of freedom #> #> --- #> #> Model-wide Interactions: #> #> Response Predictor Sum Sq Df F value Pr(>F) #> rich elev:grazed 12.74536 1 0.9290087 0.335789924 #> rich mass:grazed 126.28919 1 9.2052107 0.002594746 ** #> mass elev:grazed 287418.50772 1 7.8200891 0.005452214 ** #> #> No paths constrained to the global model (P > 0.05) #> #> --- #> #> Group [1] coefficients: #> #> Response Predictor Estimate Std.Error DF Crit.Value P.Value Std.Estimate #> rich elev 0.0730 0.0102 162 7.1656 0.0000 0.4962 *** #> rich mass -0.0007 0.0017 162 -0.4198 0.6752 -0.0291 #> mass elev -1.2028 0.4728 163 -2.5438 0.0119 -0.1954 * #> #> Group [0] coefficients: #> #> Response Predictor Estimate Std.Error DF Crit.Value P.Value Std.Estimate #> rich elev 0.0875 0.0110 186 7.9247 0 0.4710 *** #> rich mass -0.0072 0.0013 186 -5.4216 0 -0.3222 *** #> mass elev -3.2735 0.5571 187 -5.8764 0 -0.3948 *** #> #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 c = constrained