R/mplus_ml.r

In zhanglj37/blcfa: Bayesian Covariance Lasso Prior Confirmatory Factor Analysis

mplus_ml<-function(filename,varnames,usevar,myModel, ms = 'na')
{
if(file.exists("ml.inp"))
{
 file.remove("ml.inp")
}



## The variable name part is split into multiple lines in order to be smaller than the 90 characters constrained by Mplus.
if (length(varnames)%%10 == 0)
{
	var_row_num<- length(varnames)%/%10
}else{
	var_row_num<- length(varnames)%/%10+1
}

for (i in 1:var_row_num)
{
	if (i == var_row_num)
	{
		tempnum = length(varnames)-10*(i-1)
		if (tempnum == 1)
		{
			assign(paste0("combine_names",i),varnames[(i-1)*10+1])		
		}else if (tempnum ==2)
		{
			assign(paste0("combine_names",i),paste(varnames[(i-1)*10+1],varnames[(i-1)*10+2]))		
		}else{
			assign(paste0("combine_names",i),paste(varnames[(i-1)*10+1],varnames[(i-1)*10+2]))
			for (j in 3:tempnum)
			{
				assign(paste0("combine_names",i),paste(get(paste0("combine_names",i)),varnames[(i-1)*10+j]))
			}	
		}			
	}else{
		assign(paste0("combine_names",i),paste(varnames[(i-1)*10+1],varnames[(i-1)*10+2]))
		for (j in 3:10)
		{
			assign(paste0("combine_names",i),paste(get(paste0("combine_names",i)),varnames[(i-1)*10+j]))
		}
	}
	
}

if (length(varnames)%%10 == 0)
{
	usevar_row_num<- length(usevar)%/%10
}else{
	usevar_row_num<- length(usevar)%/%10+1
}

for (i in 1:usevar_row_num)
{
	if (i == usevar_row_num)
	{
		tempnum = length(usevar)-10*(i-1)
		if (tempnum == 1)
		{
			assign(paste0("combine_usenames",i),usevar[(i-1)*10+1])		
		}else if (tempnum ==2)
		{
			assign(paste0("combine_usenames",i),paste(usevar[(i-1)*10+1],usevar[(i-1)*10+2]))		
		}else{
			assign(paste0("combine_usenames",i),paste(usevar[(i-1)*10+1],usevar[(i-1)*10+2]))
			for (j in 3:tempnum)
			{
				assign(paste0("combine_usenames",i),paste(get(paste0("combine_usenames",i)),usevar[(i-1)*10+j]))
			}	
		}			
	}else{
		assign(paste0("combine_usenames",i),paste(usevar[(i-1)*10+1],usevar[(i-1)*10+2]))
		for (j in 3:10)
		{
			assign(paste0("combine_usenames",i),paste(get(paste0("combine_usenames",i)),usevar[(i-1)*10+j]))
		}
	}
	
}

## Model part: replace # with !, replace + with spaces, replace =~ with BY & spaces
model1<-gsub("#","!",myModel)
model2<-gsub("\\+","",model1)
model3<-gsub("\\=~"," BY ",model2)
model4<-gsub("\n",";\n\t",model3)


## generate input file
cat(
	"TITLE: Bayesian Lasso CFA\n", #_fix211_
	file = paste("ml.inp", sep = ''), append = T)


cat(
	"DATA: FILE = ", filename, ";",
	file = paste("ml.inp", sep = ''), append = T)

if(ms != 'na')
{
cat(
	"\n LISTWISE = ON", filename, ";",
	file = paste("ml.inp", sep = ''), append = T)

}

cat(
	"\n",
	"VARIABLE:\n",
	"NAMES = ",
	file = paste("ml.inp", sep = ''), append = T)

if (var_row_num == 1)
{  
	cat(get(paste0("combine_names",var_row_num)),";\n",
		file = paste("ml.inp", sep = ''), append = T)
	
}else{
	for (i in 1:(var_row_num-1))
	{
		cat(get(paste0("combine_names",i)),"\n\t",
			file = paste("ml.inp", sep = ''), append = T)
	}
		cat(get(paste0("combine_names",var_row_num)),";\n",
			file = paste("ml.inp", sep = ''), append = T)
}	
	
cat(
	"USEV = ",
	file = paste("ml.inp", sep = ''), append = T)

if (usevar_row_num == 1)
{
	cat(get(paste0("combine_usenames",usevar_row_num)),";\n",
		file = paste("ml.inp", sep = ''), append = T)
}else{
	for (i in 1:(usevar_row_num-1))
	{
		cat(get(paste0("combine_usenames",i)),"\n\t",
			file = paste("ml.inp", sep = ''), append = T)
	}
		cat(get(paste0("combine_usenames",usevar_row_num)),";\n",
			file = paste("ml.inp", sep = ''), append = T)
} 
if (is.numeric(ms))
{
	cat(
		"MISSING = ALL(",
		ms,
		");\n\n\t",
		file = paste("ml.inp", sep = ''), append = T)
}
 
cat(
	"ANALYSIS:\n\t",
	file = paste("ml.inp", sep = ''), append = T)

nonnormal = normal_detect(filename,varnames,usevar)
if (nonnormal == 1)
{
	cat(
		"ESTIMATOR = MLM;\n\t",
		file = paste("ml.inp", sep = ''), append = T)
}else{
	cat(
		"ESTIMATOR = ML;\n\t",
		file = paste("ml.inp", sep = ''), append = T)
}
cat(
	"MODEL:\n\t",
	model4,
	"\n\n\t",
	file = paste("ml.inp", sep = ''), append = T)
 


cat(
	"\n",
	"OUTPUT: TECH1  STDY;\n",
	file = paste("ml.inp", sep = ''), append = T)
 
## run
runModels("ml.inp")

if(file.exists("Mplus Run Models.log"))
{
 file.remove("Mplus Run Models.log")
}

}