Object Not Found Error When Passing Model Formula to Another Function

Object not found error when passing model formula to another function

When you created your formula, lm.cars, in was assigned its own environment. This environment stays with the formula unless you explicitly change it. So when you extract the formula with the formula function, the original environment of the model is included.

I don't know if I'm using the correct terminology here, but I think you need to explicitly change the environment for the formula inside your function:

cv.step <- function(linmod,k=10,direction="both"){
  response <- linmod$y
  dmatrix <- linmod$x
  n <- length(response)
  datas <- linmod$model
  .env <- environment() ## identify the environment of cv.step

  ## extract the formula in the environment of cv.step
  form <- as.formula(linmod$call, env = .env) 

  ## The rest of your function follows

Using formula to predict inside of R function generates `object not found` error

For some reason the lme function expects a literal formula to be in the call. It does not expect to see a variable there. It uses nonstandard evaluation to try to separate the response from the fixed effect terms. In this case, it really doesn't have to do with the environment of the formula.

The easiest way around this would be to inject the formulas into the call with do.call. This should work

make_prediction_nlm <- function(data_in,y_var,x_var,treatment_var ,id_var,new_data){
  
  formula_used_nlm <- as.formula(paste(y_var, paste(x_var, treatment_var, sep = " * "), sep = " ~ "))
  random_used <-  as.formula(paste("~1|",id_var,sep = ""))
  
  lme_model <- do.call("lme", list(fixed = formula_used_nlm,
                   random = random_used,
                   data = quote(data_in)))
  
  predict(lme_model, newdata = new_data)
}

This only really affects the predict function when you pass newdata= because it goes back to see what the original call was.

R - object formula not found inside a function

You can just wrap y ~ x + I(x^2) in quotation marks ("y ~ x + I(x^2)").

Object [x] not found' When Testing a Custom Formula

I am not sure how the pot_revenue_calc function need to behave but here are some pointers which might help you to calrify what needs to be done.

Case 1 :

When weekly_rate != 0 and mins > gp , pot_revenue is first referred at this line

pot_revenue <- weekly_rate*num_of_weeks(mins, num_days)

Case 2:

When mins <= gp (irrespective of the value of weekly_rate), pot_revenue is first referred at this line

 if (mins <= gp) { 
    pot_revenue <- 0
 }

Case 3 :

When weekly_rate == 0 and mins > gp , pot_revenue is first referred at this line

pot_revenue <- pot_revenue + (number_of_full_days(mins, num_days, weekly_rate, rollover = FALSE)*dmax)

You calculate pot_revenue using the value of pot_revenue which does not exist. This case needs to be fixed. Maybe remove the if condition from case 2 and initialize to 0 by default.

Apart from that you also don't need if (rollover == TRUE), you can do if(rollover).

object '...' not found in R Functions with lm -- (Error in eval(predvars, data, env) : object '...' not found)

One of the coolest ways of doing this is using the new recipes package to generate the formula for us and then manipulating a tibble to produce or result

library(tidyverse)
library(recipes)
#> 
#> Attaching package: 'recipes'
#> The following object is masked from 'package:stringr':
#> 
#>     fixed
#> The following object is masked from 'package:stats':
#> 
#>     step
library(moderndive)

score_model_Fxn <- function(df,x, y){
  formula_1 <- df %>% 
    recipe() %>%
    update_role({{x}},new_role = "outcome") %>% 
    update_role({{y}},new_role = "predictor") %>% 
    formula()

  Reg_Table <- mtcars %>%
    summarise(score_mod = list(lm(formula_1,data = .))) %>%
    rowwise() %>% 
    mutate(Reg_Table = list(get_regression_table(score_mod))) %>% 
    pull(Reg_Table)

  print(paste('The regression table is', Reg_Table))
  Reg_Table
}

k <- mtcars %>%
  score_model_Fxn(x = cyl,y = gear)
#> [1] "The regression table is list(term = c(\"intercept\", \"gear\"), estimate = c(10.585, -1.193), std_error = c(1.445, 0.385), statistic = c(7.324, -3.101), p_value = c(0, 0.004), lower_ci = c(7.633, -1.978), upper_ci = c(13.537, -0.407))"

k
#> [[1]]
#> # A tibble: 2 x 7
#>   term      estimate std_error statistic p_value lower_ci upper_ci
#>   <chr>        <dbl>     <dbl>     <dbl>   <dbl>    <dbl>    <dbl>
#> 1 intercept    10.6      1.44       7.32   0         7.63   13.5  
#> 2 gear         -1.19     0.385     -3.10   0.004    -1.98   -0.407

^{Created on 2020-06-09 by the reprex package (v0.3.0)}

object not found within function: nlmer edition

It seems nlmer has an odd way of parsing the formula you pass to the function. You apparently cannot pass a variable that's not defined in the global environment. This seems to be because the nlformula calls as.formula on the unevaluated symbol that's pass as in as the formula. This means that lexical scoping is used to resolve the symbols so it's searching for model_formula in the stats namespace and then the global environment and not in the scope defined by your function.

A work-around would be to evaluate that parameter and then pass along that value via do.call(), for example

run_nonlin<- function(model_formula, data){
    Model<- ~b0+b2*exp(-b3*time)
    ModelGradient<-deriv(Model,namevec=c("b0","b2","b3"),
                       function.arg=c("time","b0","b2","b3"))

    out <- do.call('nlmer', list(as.formula(model_formula), 
        data=quote(data),
        start = c(b0=3,b2=1,b3=4),
        control=nlmerControl(optimizer="bobyqa",
        optCtrl=list(maxfun=200000))))

    return(out)
}

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