worrisome warnings

[jpritikin]

lavaan 0.6-19 (cran), blavaan 0.5-6.1311 (via github)

Are these warnings harmless?

Warning messages:
1: lavaan->lavParTable():  
   using a single label per parameter in a multiple group setting implies imposing 
   equality constraints across all the groups; If this is not intended, either remove 
   the label(s), or use a vector of labels (one for each group); See the Multiple 
   groups section in the man page of model.syntax. 
2: In TH[[i]][exidx] <- fit$theta[fit$th.idx] :
  number of items to replace is not a multiple of replacement length
3: lavaan->lav_samplestats_step2():  
   correlation between variables sleep and anxiety is (nearly) 1.0 
4: In TH[[i]][exidx] <- fit$theta[fit$th.idx] :
  number of items to replace is not a multiple of replacement length

Code:

library(rstan)
options(mc.cores = parallel::detectCores()/2)
library(blavaan)

future::plan("multicore")

NoYesLevels <- c('No','Not sure','Yes')
EuphoricLevels <- c('Euphoric', 'Mildly euphoric', 'No change', 'Mild discomfort', 'Severe discomfort')
ImpactLevels <- c('Helped','Slightly helped','No impact','Slightly hindered','Hindered')
AbsItemNames <- c('boredom','frustration','anxiety', 'abort','bodyAcute','bodyAfter','sleep')

ImportStudyItems <- function(df) {
  if (any(is.na(match(AbsItemNames, colnames(df))))) stop("Items are mising")

  df[['boredom']] <- ordered(df[['boredom']], levels=NoYesLevels)
  df[['frustration']] <- ordered(df[['frustration']], levels=NoYesLevels)
  df[['anxiety']] <- ordered(df[['anxiety']], levels=NoYesLevels)
  df[['abort']] <- ordered(df[['abort']], levels=NoYesLevels)
  df[['bodyAcute']] <- ordered(df[['bodyAcute']], levels=EuphoricLevels)
  df[['bodyAfter']] <- ordered(df[['bodyAfter']], levels=EuphoricLevels)
  df[['sleep']] <- ordered(df[['sleep']], levels=ImpactLevels)
  df
}

psiData <- ImportStudyItems(read.csv("psi.csv"))
pmData <- ImportStudyItems(read.csv("pm.csv"))
combinedData <- rbind(cbind(psiData, group="psi"),
                      cbind(pmData, group="pm"))
combinedData$group <- factor(combinedData$group, levels=c('psi','pm'))

spec <- '
phenom =~ boredomL*boredom + frustrationL*frustration + anxietyL*anxiety + abortL*abort + bodyAcuteL*bodyAcute + bodyAfterL*bodyAfter + sleepL*sleep

phenom ~ c(0,NA)*1
boredom ~ boredomM*1
frustration ~ frustrationM*1
anxiety ~ anxietyM*1
abort ~ abortM*1
bodyAcute ~ bodyAcuteM*1
bodyAfter ~ bodyAfterM*1
sleep ~ sleepM*1

phenom ~~ 1*phenom
boredom ~~ 1*boredom
frustration ~~ 1*frustration
anxiety ~~ 1*anxiety
abort ~~ 1*abort
bodyAcute ~~ 1*bodyAcute
bodyAfter ~~ 1*bodyAfter
sleep ~~ 1*sleep

boredom | -.431*t1 + .431*t2
frustration | -.431*t1 + .431*t2
anxiety | -.431*t1 + .431*t2
abort | -.431*t1 + .431*t2
bodyAcute | -.842*t1 + -.253*t2 + .253*t3 + .842*t4
bodyAfter | -.842*t1 + -.253*t2 + .253*t3 + .842*t4
sleep | -.842*t1 + -.253*t2 + .253*t3 + .842*t4
'

fit <- blavaan(spec, data=combinedData,
               n.chains = 4,
               ordered = AbsItemNames,
               dp = dpriors(nu="normal(0,1)", alpha="normal(0,1)", lambda="normal(0,1)"),
               group = "group",
               std.lv=TRUE, allow.empty.cell = TRUE, test = "none")
summary(fit)

Data: pm.csv psi.csv

[ecmerkle]

I think the first warning is related to lines like this:

boredom ~ boredomM*1

you have two groups, but only one parameter label "boredomM". That implicitly fixes the parameter to be equal across groups. If you didn't want that, you need something like

boredom ~ c("boredom1", "boredom2") * 1

I believe the other warnings are related to lavaan setting up a model with empty cells and etc. I suspect they are harmless but will need to look.

[jpritikin]

you have two groups, but only one parameter label "boredomM". That implicitly fixes the parameter to be equal across groups.

Fixing these parameters equal across groups is desired and intentional. I guess that warning is fine.