Issues with conditional indirect effects when using a categorical moderator

[npudles]

Hi,

I have an issue with the manymome functions cond_indirect_effects() and mod_levels() when using a categorical moderator with two categories.

I get these warning and error messages :

Warning in seq_len(ncol(w_gp)): first element used of 'length.out' argument
Error in seq_len(ncol(w_gp)) : argument must be coercible to non-negative integer

I'm following the steps from Cheung & Cheung (2023) manymome:__ An R package for computing the indirect effects, conditional effects, and conditional indirect effects, standardized or unstandardized, and their bootstrap confidence intervals, in many (though not all) models.

I don't understand what's the issue. Is it a bug or is it an issue with my code/data ?

Mind that I don't have this issue when I'm using a continuous moderator with the same data.

Thanks in advance

library(lavaan)
#> This is lavaan 0.6-17
#> lavaan is FREE software! Please report any bugs.
library(manymome)
##Sample data
df <- data.frame(
     Intense = c(1.20302400035313,0.377413381825115,
                 -1.18646351469919,1.20057716684428,1.20138456799171,
                 -2.7774605624413,-1.94239715523117,0.402302832389528,-0.421912163704315,
                 0.331669138980736),
     CatPers = c("0.792961214431443","0.792961214431443",
                 "-1.25872969475283","-1.25872969475283","-1.25872969475283",
                 "0.792961214431443","0.792961214431443","-1.25872969475283",
                 "0.792961214431443","0.792961214431443"),
         Pos = c(-0.233043705579646,-0.376337459099043,
                 -0.530671521945183,1.34621539413837,-0.957223025488462,
                 0.90322432738519,-0.640637299527499,-0.158533233160109,
                 0.27334450686968,0.0162900160068483),
         Wfc = c(1.00645422416079,0.418767308104094,
                 0.543193979890933,0.150878583032073,0.671868144845523,
                 -0.203760346081739,0.522581840151886,0.123494795372463,0.20299495139938,
                 0.211670048655376)
)

##Dummy variable based on a categorical variable with two categories
corsci <- factor2var(df$CatPers, 
                     x_contrasts = "contr.treatment")
df[, "corsci"] <- corsci

##Interaction term
df$Intense_corsci <- df$Intense*df$corsci

##Structural model
mtest <- "
Pos ~ Intense + Intense_corsci + corsci
Wfc ~ Pos"

test.fit <- sem(mtest, df, std.lv = TRUE, fixed.x = FALSE, missing = "fiml")

##Conditional indirect effect
outest <- cond_indirect_effects(wlevels = "corsci",
                        x = "Intense",
                        y = "Wfc",
                        m = "Pos",
                        fit = test.fit)
#> Warning in seq_len(ncol(w_gp)): seul le premier élément de l'argument
#> 'length.out' est utilisé
#> Error in seq_len(ncol(w_gp)): l'argument doit être convertible automatiquement en un entier non négatif

^{Created on 2024-02-14 with reprex v2.1.0}

[gaborcsardi]

Hi, this is a read only mirror of CRAN, please see the package authors in the DESCRIPTION file. Look for Maintainer, BugReports and URL. Thanks!