lme: length of 'dimnames' [2] not equal to array extent

Question

I was able to successfully run the codes at the bottom without errors. However, I received the error

Error in matrix(unlist(value), nrow = nrow(data), dimnames = list(row.names(data),  : 
  length of 'dimnames' [2] not equal to array extent

after

1. only selected the variables used in the analysis from a bigger dataset

myVars<-c("Country", "familism_mean", "avoidance", "anxiety", "n1", "n2", "d1", "d2", "t", "m1","m2","GDP","HDI","self_expression","long_term_orientation", "power_distance","Residential_mobility", "age", "ses","gender")
data01<-data01[myVars]

2. or manually deleted the variables that are not used in the bigger dataset

Original codes

rm(list=ls())
data01 <- read.csv(file="c://Users//Xian//Dropbox//CCFSN_PSPB submission//Submission folder//Data.csv",header=TRUE)
names(data01)
data01$Z_avoidance = (data01$avoidance-mean(data01$avoidance, na.rm=T))/sd(data01$avoidance, na.rm=TRUE)
data01$Z_anxiety = (data01$anxiety-mean(data01$anxiety, na.rm=T))/sd(data01$anxiety, na.rm=TRUE)
data01$Z_familism= (data01$familism_mean-mean(data01$familism_mean, na.rm=T))/sd(data01$collectivism_mean, na.rm=TRUE)
data01$Z_long_term = (data01$long_term_orientation-mean(data01$long_term_orientation, na.rm=T))/sd(data01$long_term_orientation, na.rm=TRUE)
data01$Z_power_distance = (data01$power_distance-mean(data01$power_distance, na.rm=T))/sd(data01$power_distance, na.rm=TRUE)
data01$Z_self_expression = (data01$self_expression-mean(data01$self_expression, na.rm=T))/sd(data01$self_expression, na.rm=TRUE)
data01$Z_Residential_mobility = (data01$Residential_mobility-mean(data01$Residential_mobility, na.rm=T))/sd(data01$Residential_mobility, na.rm=TRUE)
data01$Z_GDP = (data01$GDP-mean(data01$GDP, na.rm=T))/sd(data01$GDP, na.rm=TRUE)
data01$Z_HDI = (data01$HDI-mean(data01$HDI, na.rm=T))/sd(data01$HDI, na.rm=TRUE)
data01$Z_age = (data01$age-mean(data01$age, na.rm=T))/sd(data01$age, na.rm=TRUE)
data01$Z_ses = (data01$ses-mean(data01$ses, na.rm=T))/sd(data01$ses, na.rm=TRUE)
library(RSA)
library(reshape) 
library(nlme) 
library(lme4)
library(lmerTest)
#==================familism * avoidance = n1==================

rsa.data <- rename(data01, c(Z_familism = "predictor1", #Replace "yourpredictor1" with the variable name of your first predictor
                             Z_avoidance = "predictor2", #Replace "yourpredictor2" with the variable name of your second predictor
                           n1      = "outcome"))   #Replace "youroutcome" with the variable name of your outcome
rsa.data <- within.data.frame(rsa.data, {
  centered.predictor1 <- predictor1 - 0    #Center predictor 1
  centered.predictor2 <- predictor2 - 0  #Center predictor 2
  squared.predictor1  <- centered.predictor1* centered.predictor1 #Create squared term
  squared.predictor2  <- centered.predictor2* centered.predictor2 #Create squared term
  interaction         <- centered.predictor1* centered.predictor2 #Create interaction term
})

mlm.model <- lme(outcome ~ centered.predictor1+centered.predictor2  + squared.predictor1 + interaction +squared.predictor2+Z_anxiety
                 +gender+Z_long_term+Z_power_distance+Z_self_expression+Z_Residential_mobility+Z_GDP+Z_HDI+Z_age+Z_ses,
                 data = rsa.data,
                 random = ~ 1|Country, # Replace "nesting.variable" with the name of your nesting variable 
                 na.action = "na.omit")
summary(mlm.model) #View Model
vcov(mlm.model) #View covariance of model

So I was confused. Why selecting variables can create this error? Any thoughts? Thank you in advance for your help!