Error in -Inf : argument "e2" is missing, with no default

[KIG]

I am studying some software written by R programming language. The data frame of 'dep_var' has no "NA" nor "Inf/-Inf" in it.

for (dep in depvars){
  col <-  str_replace(paste("log.p.abst",substr(dep, 3, 11), sep = "."), 
                      ".votes", "")
  col2 <-  str_replace(paste("pred.p.abst",substr(dep, 3, 11), sep = "."), 
                       ".votes", "")
  dep_var <- data.frame(dep_var = log(data_filt2[, dep]/data_filt2$p.abstention))
  dep_var[dep_var == Inf,] <- max(dep_var[dep_var != Inf,])
  dep_var[dep_var == -Inf,] <- min(dep_var[dep_var != -Inf,])
  reg_data <- bind_cols(dep_var, data_filt2[, covars])
  reg_data <- reg_data[complete.cases(reg_data),]
  # run linear mixed effect model
  mlr_reg0 <- lme(dep_var ~ -1 + fpv2011_major + nbi + masculinidad +
                    extranjeros + analfabetismo + no_usa_pc + 
                    menor_15 + mayor_65 + desocupados + 
                    universitarios + per_propietario + per_urban, 
                  random = list(~1|CODIGO.PROVINCIA, 
                                ~1|CODIGO.DEPARTAMENTO), data = reg_data, 
                  control = lmeControl(opt = "optim"))
  # re-estimate excluding points with outlier residuals from the previous reg
  mlr_reg <- lme(dep_var ~ -1 + fpv2011_major + nbi + masculinidad +
                   extranjeros + analfabetismo + no_usa_pc + 
                   menor_15 + mayor_65 + desocupados + 
                   universitarios + per_propietario + per_urban, 
                 random = list(~1|CODIGO.PROVINCIA, 
                               ~1|CODIGO.DEPARTAMENTO),  
                 data = reg_data[-which(abs(residuals(mlr_reg0, 
                                                      type = "normalized"))>qnorm(0.975)),],
                 control = lmeControl(opt = "optim"))
  # calculate the standard deviation of the residuals
  sd_resid <- sd(residuals(mlr_reg))
  # predict outcome variable for each mesa + randomly generated noise
  pred_mlr <- predict(mlr_reg, newdata = reg_data)
  pred_mlr[is.na(pred_mlr)] <- mean(pred_mlr, na.rm = T)
  noise_pred <- rnorm(nrow(reg_data), 0, sd_resid)
  pred_mlr_noisy <- pred_mlr + noise_pred
  clean_table[[col]] <- pred_mlr_noisy
  clean_table[[col2]] <- exp(pred_mlr_noisy)
}

> dput(head(dep_var))
structure(list(dep_var = c(0.1221026968009, -0.350860974073313, 
-0.106609735058258, -0.266628663253948, -0.208401269577006, 0.1221026968009
)), row.names = c(NA, 6L), class = "data.frame")


> summary(dep_var)
    dep_var        
 Min.   :-3.44999  
 1st Qu.:-0.02381  
 Median : 0.37729  
 Mean   : 0.35284  
 3rd Qu.: 0.76214  
 Max.   : 5.09987  

> head(dep_var)
     dep_var
1  0.1221027
2 -0.3508610
3 -0.1066097
4 -0.2666287
5 -0.2084013
6  0.1221027

> tail(dep_var)
        dep_var
90007 0.5675210
90008 0.4788926
90009 0.4468503
90010 0.4587465
90011 0.6061358
90012 0.6350395

>   dep_var[dep_var == Inf,] <- max(dep_var[dep_var != Inf,])
# This is not problematic.

>  dep_var[dep_var == -Inf,] <- min(dep_var[dep_var != -Inf,])

Error in -Inf : argument "e2" is missing, with no default

However, these codes make errors, and I did not find resolution although I had endeavored trying to get a solution for that problem through stack overflow etc. in internet for 1 month.