purrr::accumulate() two variables not just one

[dougfir]

I have a model that as a predictor has the previous prediction. e.g. target ~ lag(target prediction)

Using purrr::accumulate I'm able to write a custom function to predict. Example of some silly data and a silly model that illustrates:

 ### A model that uses a lag prediction as a predictor using purrr::accumulate() ###
my_diamonds <- diamonds %>% 
  group_by(cut) %>% 
  mutate(cumprice = cumsum(price)) %>% # cumulative within groups
  mutate(lag_cumprice = lag(cumprice)) %>% 
  mutate(InitialValue = min(cumprice)) %>% 
  filter(!is.na(lag_cumprice)) %>% 
  select(cut, cumprice, lag_cumprice, x, InitialValue)

silly_model <- glm(formula = cumprice ~ x + lag_cumprice, family = 'poisson', data = my_diamonds)

This model uses the previous prediction as input to the next prediction. I'm able to write a custom function to mutate a prediction:

# when predicting won't have lag_cumprice, instead the result of the previous pediction should be an input to the model:
accPrice <- function(mod, acc, cur) {
  
  db=cur_data_all() # grouped data segment
  x = db$x[cur] # cur is the current row in the data, use it to get 'this' iterations value of x
  
  total_exponent <- mod$coefficients['(Intercept)'] + 
    (mod$coefficients['x'] * x) +
    (mod$coefficients['lag_cumprice'] * acc) # acc is the accumulated prediction for cumprice
}

# now predict
my_diamonds <- my_diamonds %>% 
  mutate(predicted = accumulate(.x = row_number()[-1], .init = InitialValue %>% unique, .f = accPrice, mod = silly_model))

So far so good. In this example I used the previous prediction acc as an input.

But, I created a variation model that now uses two lagged variables as predictors:

### now a model with lag on two variables not just one ###
my_diamonds2 <- diamonds %>% 
  group_by(cut) %>% 
  mutate(cumprice = cumsum(price)) %>% # cumulative within groups
  mutate(lag_cumprice = lag(cumprice)) %>% 
  mutate(InitialValue = min(cumprice)) %>% 
  mutate(rn = row_number()) %>% 
  mutate(cumrn = cumsum(rn)) %>% 
  mutate(lag_cumrn = lag(cumrn)) %>% 
  filter(!is.na(lag_cumprice)) %>% 
  select(cut, cumprice, lag_cumprice, lag_cumrn, x, InitialValue)

silly_model2 <- glm(formula = cumprice ~ x + lag_cumprice + lag_cumrn, family = 'poisson', data = my_diamonds2)

### Stuck after here ###

How can I modify the function accPrice() above to accumulate 2 variables, both lag_cumprice and lag_cumrn as opposed to just lag_cumprice as before?