I can't reproduce your issue (see reprex below), please provide a proper REPRoducible EXample (reprex) that actually shows your problem.
library(tidyLPA)
library(dplyr)
# Sample data on a copy/paste friendly format
msps1 <- data.frame(
RISK1_PHMS1 = c(8, 8, 9, 5, 8, 5, 8, 9, 8, 11),
RISK1_PHMS4 = c(5, 4, 9, 6, 9, 5, 3, 3, 10, 3),
RISK1_PHMS6 = c(8, 11, 8, 6, 11, 7, 11, 11, 6, 10),
RISK1_PHMS8 = c(9, 6, 9, 3, 6, 8, 4, 4, 11, 6),
RISK1_PHMS18 = c(8, 8, 8, 7, 8, 7, 11, 9, 10, 8),
RISK1_PHMS20 = c(8, 10, 9, 6, 8, 7, 4, 9, 10, 6),
RISK_PHMS_SEVERITY2 = c(6, 5, 8, 7, 9, 5, 1, 5, 3, 6)
)
msps1 %>%
select (RISK1_PHMS1, RISK1_PHMS4, RISK1_PHMS6, RISK1_PHMS8, RISK1_PHMS18, RISK1_PHMS20,
RISK_PHMS_SEVERITY2) %>%
estimate_profiles(1:5, variances = "varying", covariances="varying")
#> tidyLPA analysis using mclust:
#>
#> Model Classes AIC BIC Entropy prob_min prob_max n_min n_max BLRT_p
#> 6 1 310.59 321.18 1.00 1.00 1.00 1.00 1.00
#> 6 2
#> 6 3
#> 6 4
#> 6 5
Created on 2021-03-18 by the reprex package (v1.0.0)