Making a loop for lme() in r

Question

I am trying to use lme function from nlme package inside a for loop. I have tried (almost) everything now, but without any luck. Without the loop my lme function are working

Answer 1

Solution: My loop is working now with this simple code:

lipid <-as.data.frame(cer_data[,c(7:9)]) dim(lipid) for (i in 1:length(lipid)) { variable <- lipid[,i] lme_cer <- lme(variable ~ factor(Remission) + Time + Age + BMI + SEX, random = ~1 | Lacal.Patient.ID, method = "REML", data = cer_data) print(summary(lme_cer)$tTable) }

Thank you all for the amazing help!

Answer 2

A blind answer, assuming that your dependent variables are organized in columns and not in rows (as I think they are).

The main difference between my approach and your approach is that I loop over the names of the lipids rather than their position in the data set. This allows me (a) to construct a temporary data set in a less error-prone way, and (b) to construct a temporary formula for the fixed-effects part of your model.

The lme function is then applied to the temporary data set with the temporary formula, and the result is saved in a list for easier access.

# names of lipids
lipid.names <- colnames(cer_data)[1:881]
no.lipids <- length(lipid.names)

# create a named list to hold the fitted models
fitlist <- as.list(1:no.lipids)
names(fitlist) <- lipid.names

# loop over lipid names
for(i in lipid.names){ 

  # print status
  print(paste("Running entity:", i, "which is", which(lipid.names==i), "out of", no.lipids))

  # create temporary data matrix and model formula
  tmp <- cer_data[, c(i,"Remission","Time","Age","BMI","SEX","Local.Patient.ID")]
  fml <- as.formula( paste( i, "~", paste(c("Remission","Time","Age","BMI","SEX"), collapse="+") ) )

  # assign fit to list by name
  fitlist[[i]] <- lme(fml, random=~1|Lacal.Patient.ID, method="REML", data=tmp)

}

In my opinion it's easiest to work with temporary objects that exactly contain what is needed at that iteration of the loop.

Note that I cannot check this solution for errors because you haven't supplied a reproducible example: Here's how.

Answer 3

Without knowing your data, conceptually it should be sth like that

df <- data.frame(lipid = rep(c(LETTERS[1:4]), each = 4), x1 = c(rnorm(16, 10, 1)), x2 = c(rnorm(16, 20, 5) ))
    df

for (i in levels(df$lipid)){
  print(paste("MODEL", i, sep = ""))
  df1 = subset(df, lipid == i)
  model <- lm(x1~x2, data = df1 )
  print(summary(model)$coef)
}