Non-finite finite difference error when using optim with L-BFGS-B method in R

Question

I'm trying to obtain confidence intervals for estimates of five parameters (1 pi, a proportion with bounds between 0 and 1, 4 betas which are exponential rates of decline) using my log likelihood function. My data consists of time intervals after vaccination, immune statuses classified as ones and zeros as well as the past vaccination history and ownership status of individual dogs. My data is as below

data4mle <- 
  structure(
    list(
      Interval_18 = c(35, 123, 13, 140, 8, 115, 16, 
                      131, 132, 8, 13, 146, 44, 15, 138, 3, 143, 149, 12, 153, 17, 
                      154, 14, 8, 123, 16, 17, 154, 8, 135, 10, 133, 18, 8, 123, 10, 
                      133, 16, 163, 10, 150, 10, 3, 8, 8, 139, 22, 9, 122, 1, 130, 
                      16, 140, 16, 140, 20, 142, 42, 122, 16, 8, 126, 8, 8, 139, 20, 
                      122, 14, 8, 122, 146, 6, 20, 9, 6, 13, 13, 115, 12, 131, 12, 
                      12, 23, 116, 6, 102, 13, 137, 6, 107, 12, 14, 115, 18, 123, 155, 
                      158, 163, 35, 155, 16, 163, 16, 140, 44, 128, 9, 21, 36, 46, 
                      153, 27, 147, 47, 131, 8, 146, 12, 17, 9, 16, 155, 9, 10, 148, 
                      32, 148, 25, 15, 138, 46, 158, 121, 8, 131, 8, 8, 118, 31, 154, 
                      32, 144, 15, 8, 16, 8, 101, 15, 144, 12, 20, 10, 15, 142, 13, 
                      69, 28, 168, 15, 12, 24, 13, 142, 12, 115, 13, 131, 166, 13, 
                      118, 12, 32, 6, 36, 164, 16, 135, 20, 46, 19, 137, 11, 150, 6, 
                      107, 10, 148, 13, 118, 19, 164, 10, 16, 127, 19, 161, 156, 14, 
                      16, 145, 1, 143, 16, 5, 144, 158, 33, 19, 6, 6, 115, 16, 138, 
                      32, 13, 18, 13, 11, 158, 10, 151, 16, 16, 69, 17, 166, 26, 158, 
                      11, 11, 26, 16, 173, 16, 32, 155, 12, 16, 149, 16, 149, 16, 163, 
                      153, 57, 13, 128, 12, 113, 19, 144, 21, 35, 30, 30, 157, 108, 
                      14, 16, 33, 23, 149, 14, 14, 8, 18, 164, 115, 19, 11, 151, 19, 
                      144, 21, 13, 142, 54, 150, 16, 163, 22, 13, 11, 155, 155, 16, 
                      150, 16, 16, 57, 19, 17, 135, 12, 11, 12, 145, 11, 11, 170, 18, 
                      164, 10, 16, 12, 11, 155, 27, 11, 24, 16, 135, 47, 12, 17, 149, 
                      11, 12, 18, 137, 10, 131, 11, 151, 12, 131, 11, 158, 57, 12, 
                      5, 27, 23, 16, 24, 149, 29, 150, 12, 16, 157, 16, 10, 152, 16, 
                      10, 149, 23, 11, 30, 16, 141, 27, 147, 11, 151, 16, 157, 71, 
                      172, 32, 151, 11, 151, 10, 131, 27, 147, 22, 23, 20, 139, 10, 
                      131, 12, 145, 16, 149, 10, 135, 145, 13, 135, 16, 149, 30, 150, 
                      16, 163, 141, 51, 131, 16, 25, 16, 140, 32, 144, 41, 129, 14, 
                      8, 123, 16, 149, 101, 14, 94, 10, 139, 8, 10, 130, 96, 8, 24, 
                      144, 8, 8, 129, 20, 129, 10, 131, 8, 9, 131, 8, 127, 10, 130, 
                      22, 142, 8, 127, 13, 127, 10, 131, 10, 131, 24, 166, 8, 129, 
                      10, 131, 9, 20, 146, 8, 127, 10, 134, 10, 131, 10, 132, 10, 15, 
                      15, 20, 142, 8, 127, 11, 9, 11, 170, 10, 131, 20, 129, 144, 10, 
                      130, 10, 130, 8, 127, 10, 131, 8, 8, 130, 10, 14, 128, 10, 148, 
                      13, 127, 10, 131, 8, 127, 9, 125, 14, 129, 13, 13, 129, 27, 147, 
                      9, 131, 8, 130, 14, 128, 1, 105, 1, 105, 9, 13, 137, 8, 10, 131, 
                      17, 148, 13, 127, 8, 10, 8, 10, 10, 122, 116, 14, 128, 14, 128, 
                      11, 159, 10, 131, 20, 122), 
      Immune_status = c(1, 1, 0, 0, 1, 
                        0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 
                        1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 
                        1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 
                        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 
                        0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
                        0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 
                        1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 
                        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 
                        1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 
                        1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 
                        0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 
                        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 
                        1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 
                        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 
                        0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 
                        1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 
                        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 
                        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 
                        1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 
                        0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 
                        1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 
                        0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 
                        1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 
                        1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 
                        1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 
                        0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1), 
      Owned = c("No", "No", "No", 
                "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "Yes", 
                "Yes", "No", "No", "No", "No", "No", "Yes", "Yes", "No", "Yes", 
                "Yes", "No", "Yes", "Yes", "Yes", "Yes", "No", "No", "No", "Yes", 
                "Yes", "No", "No", "Yes", "Yes", "No", "No", "No", "No", "Yes", 
                "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", "No", 
                "No", "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", 
                "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
                "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
                "No", "No", "No", "No", "No", "No", "No", "Yes", "Yes", "No", 
                "No", "No", "No", "No", "No", "Yes", "No", "No", "No", "No", 
                "No", "No", "No", "No", "No", "Yes", "Yes", "No", "No", "No", 
                "No", "No", "No", "No", "No", "No", "No", "Yes", "Yes", "Yes", 
                "Yes", "No", "Yes", "Yes", "No", "No", "No", "No", "No", "No", 
                "Yes", "Yes", "No", "No", "Yes", "Yes", "No", "No", "No", "No", 
                "No", "Yes", "Yes", "No", "No", "No", "No", "No", "Yes", "No", 
                "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", "No", 
                "No", "No", "Yes", "Yes", "No", "No", "No", "No", "No", "No", 
                "No", "No", "No", "No", "No", "No", "No", "No", "No", "Yes", 
                "Yes", "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", 
                "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
                "No", "Yes", "No", "No", "No", "No", "No", "No", "No", "No", 
                "No", "No", "Yes", "Yes", "No", "No", "No", "No", "No", "No", 
                "No", "No", "No", "No", "No", "No", "Yes", "No", "No", "No", 
                "No", "No", "No", "No", "No", "No", "Yes", "No", "No", "No", 
                "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
                "No", "No", "No", "No", "No", "No", "Yes", "No", "No", "No", 
                "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
                "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
                "No", "No", "No", "Yes", "No", "Yes", "Yes", "No", "Yes", "Yes", 
                "No", "No", "No", "Yes", "No", "No", "No", "No", "Yes", "No", 
                "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "Yes", 
                "Yes", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
                "No", "No", "No", "No", "No", "Yes", "Yes", "No", "Yes", "Yes", 
                "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
                "Yes", "Yes", "No", "No", "No", "No", "No", "No", "Yes", "Yes", 
                "No", "No", "No", "No", "No", "No", "No", "Yes", "No", "No", 
                "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
                "No", "No", "Yes", "Yes", "Yes", "Yes", "No", "No", "No", "Yes", 
                "No", "No", "No", "Yes", "Yes", "No", "No", "No", "Yes", "Yes", 
                "No", "No", "No", "No", "No", "Yes", "Yes", "Yes", "Yes", "Yes", 
                "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", 
                "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", 
                "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", 
                "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", 
                "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", 
                "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", 
                "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", 
                "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", 
                "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", 
                "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", 
                "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", 
                "No", "No", "No", "Yes", "No", "No", "Yes", "Yes", "Yes", "Yes", 
                "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", 
                "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", 
                "No", "No"), 
      Not_vacc_not_revacc_before_R3 = c("No", "No", "No", 
                                        "No", "No", "No", "No", "No", "Yes", "No", "No", "No", "No", 
                                        "No", "No", "Yes", "Yes", "No", "Yes", "Yes", "No", "No", "Yes", 
                                        "No", "No", "No", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "No", 
                                        "No", "No", "No", "No", "No", "No", "No", "No", "No", "Yes", 
                                        "No", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "No", 
                                        "Yes", "Yes", "Yes", "Yes", "No", "No", "No", "No", "Yes", "No", 
                                        "No", "No", "No", "No", "No", "Yes", "Yes", "No", "Yes", "Yes", 
                                        "No", "Yes", "No", "No", "No", "Yes", "Yes", "No", "No", "Yes", 
                                        "Yes", "No", "No", "Yes", "Yes", "No", "No", "No", "No", "No", 
                                        "No", "No", "No", "Yes", "Yes", "No", "No", "No", "Yes", "No", 
                                        "No", "No", "No", "No", "No", "Yes", "Yes", "Yes", "No", "No", 
                                        "Yes", "Yes", "No", "Yes", "No", "No", "No", "Yes", "Yes", "Yes", 
                                        "No", "No", "No", "No", "Yes", "No", "No", "Yes", "Yes", "No", 
                                        "No", "No", "No", "No", "No", "No", "No", "No", "No", "Yes", 
                                        "No", "No", "No", "No", "No", "No", "No", "No", "No", "Yes", 
                                        "Yes", "No", "No", "Yes", "Yes", "No", "No", "No", "Yes", "Yes", 
                                        "No", "No", "Yes", "Yes", "No", "No", "No", "No", "Yes", "No", 
                                        "Yes", "Yes", "No", "No", "Yes", "Yes", "No", "No", "No", "No", 
                                        "No", "No", "Yes", "Yes", "No", "No", "Yes", "Yes", "No", "No", 
                                        "No", "No", "No", "Yes", "Yes", "No", "No", "Yes", "Yes", "No", 
                                        "No", "No", "Yes", "No", "No", "No", "No", "No", "No", "No", 
                                        "Yes", "No", "No", "No", "Yes", "Yes", "No", "No", "No", "No", 
                                        "No", "No", "No", "Yes", "Yes", "Yes", "No", "No", "Yes", "Yes", 
                                        "Yes", "Yes", "Yes", "No", "Yes", "Yes", "No", "No", "No", "No", 
                                        "No", "Yes", "No", "No", "No", "No", "No", "No", "Yes", "No", 
                                        "No", "Yes", "Yes", "No", "No", "No", "Yes", "No", "No", "No", 
                                        "No", "No", "Yes", "Yes", "No", "No", "No", "No", "No", "No", 
                                        "No", "No", "No", "Yes", "Yes", "Yes", "Yes", "No", "No", "Yes", 
                                        "Yes", "No", "Yes", "Yes", "Yes", "Yes", "No", "No", "Yes", "No", 
                                        "No", "Yes", "No", "No", "Yes", "Yes", "Yes", "No", "No", "Yes", 
                                        "Yes", "No", "No", "No", "No", "Yes", "Yes", "No", "No", "No", 
                                        "No", "No", "No", "No", "No", "Yes", "Yes", "No", "No", "No", 
                                        "No", "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No", 
                                        "No", "Yes", "Yes", "No", "No", "No", "Yes", "Yes", "Yes", "No", 
                                        "No", "No", "No", "No", "No", "No", "Yes", "Yes", "Yes", "No", 
                                        "No", "No", "No", "No", "No", "Yes", "Yes", "No", "No", "No", 
                                        "No", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "No", "No", "Yes", 
                                        "Yes", "No", "No", "Yes", "Yes", "Yes", "Yes", "No", "Yes", "Yes", 
                                        "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", "No", 
                                        "Yes", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
                                        "No", "No", "No", "No", "No", "Yes", "Yes", "No", "Yes", "Yes", 
                                        "No", "No", "No", "No", "Yes", "No", "No", "Yes", "Yes", "Yes", 
                                        "Yes", "Yes", "No", "No", "No", "No", "Yes", "Yes", "Yes", "Yes", 
                                        "Yes", "Yes", "No", "No", "Yes", "Yes", "Yes", "Yes", "Yes", 
                                        "Yes", "No", "No", "Yes", "Yes", "No", "No", "No", "No", "No", 
                                        "Yes", "No", "No", "No", "Yes", "Yes", "No", "No", "No", "No", 
                                        "No", "No", "No", "No", "Yes", "No", "No", "No", "No", "Yes", 
                                        "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", "No", 
                                        "Yes", "Yes", "Yes", "Yes", "No", "No", "Yes", "Yes", "No", "No", 
                                        "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
                                        "No", "No", "No", "No", "No", "No", "Yes", "Yes", "No", "No", 
                                        "No", "No", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "No", 
                                        "No", "No", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes", "Yes", 
                                        "Yes", "No", "No", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes")), 
    row.names = c(NA,-542L), 
    class = c("tbl_df", "tbl", "data.frame"))

And my log-likelihood function


nll.18.4betas <- function(v) {
  #function(pi,beta) {
  pi <- v[1]; beta_o_v <- v[2]; beta_o_nv <- v[3]; beta_no_v <- v[4]; beta_no_nv <- v[5]
  
  data <- data4mle %>% 
    filter(Interval_18 > 0 & Interval_18 <= 180 & !is.na(Immune_status))
  
  #Owned, vaccinated/revaccinated
  xi_o_v <- data %>% 
    filter(Owned == "Yes" & Not_vacc_not_revacc_before_R3 == "Yes") %>% 
    .$Immune_status
  ti_o_v <- data %>% 
    filter(Owned == "Yes" & Not_vacc_not_revacc_before_R3 == "Yes") %>% 
    .$Interval_18
  
  #Owned, not vaccinated/revaccinated
  xi_o_nv <- data %>% 
    filter(Owned == "Yes" & Not_vacc_not_revacc_before_R3 == "No") %>% 
    .$Immune_status
  ti_o_nv <- data %>% 
    filter(Owned == "Yes" & Not_vacc_not_revacc_before_R3 == "No") %>% 
    .$Interval_18
  
  #Not owned, vaccinated/revaccinated
  xi_no_v <- data %>% 
    filter(Owned == "No" & Not_vacc_not_revacc_before_R3 == "Yes") %>% 
    .$Immune_status
  ti_no_v <- data %>% 
    filter(Owned == "No" & Not_vacc_not_revacc_before_R3 == "Yes") %>% 
    .$Interval_18
  
  ##Not owned, not vaccinated/revaccinated
  xi_no_nv <- data %>% 
    filter(Owned == "No" & Not_vacc_not_revacc_before_R3 == "No") %>% 
    .$Immune_status
  ti_no_nv <- data %>% 
    filter(Owned == "No" & Not_vacc_not_revacc_before_R3 == "No") %>% 
    .$Interval_18
  
  #calculate neg log likelihood
  #Owned, vaccinated/revaccinated
  -sum(xi_o_v*(log(pi)-beta_o_v*ti_o_v) + (1-xi_o_v)*log(1 - (pi*exp(-beta_o_v*ti_o_v)))) + 
    #Owned, not vaccinated/revaccinated
    -sum(xi_o_nv*(log(pi)-beta_o_nv*ti_o_nv) + (1-xi_o_nv)*log(1 - (pi*exp(-beta_o_nv*ti_o_nv)))) + 
    #Not owned, vaccinated/revaccinated
    -sum(xi_no_v*(log(pi)-beta_no_v*ti_no_v) + (1-xi_no_v)*log(1 - (pi*exp(-beta_no_v*ti_no_v)))) +
    #Not owned, not vaccinated/revaccinated
    -sum(xi_no_nv*(log(pi)-beta_no_nv*ti_no_nv) + (1-xi_no_nv)*log(1 - (pi*exp(-beta_no_nv*ti_no_nv))))
  
}

trial <- optim(fn = nll.18.4betas, par = c(0.1,  0.003,0.003,0.003, 0.003), 
               lower = c(0.1, rep(1e-3, 4)),
               upper = c(0.99,rep(1e-1, 4)), 
               method = "L-BFGS-B", hessian = T)

I keep getting the non-finite finite difference error when trying to estimate a hessian, even though the optimisation method itself runs fine and generates estimates of the five parameters (i.e. when hessian = FALSE). I need to use L-BFGS-B, because the first parameter pi is a proportion that necessarily has to be bound between 0 and 1.

I looked at suggestions in this question (non-finite finite-difference value, many data become inf and NA after exponential), but from what I understand, I would need to use the Nelder-Mead method to use the numDeriv package.

Similarly, the response to this question (Optim: non-finite finite-difference value in L-BFGS-B) doesn't seem to apply, and I'm not sure if what's discussed here relates directly to my issue (optim in r :non finite finite difference error).

I'm not sure what's happening here, especially because when I run the optimisation for a nested model with six parameters (2 pis and 4 betas), 'optim' is able to generate a hessian and thus I can compute confidence intervals.

Not sure if this provides enough information for someone to help, but would appreciate any advice. Thanks!

I tried to run your code, but I get many errors. I suspect something is missing. — PaulS, Nov 03 '21 at 18:54
Sorry about that @PaulSmith, I've edited my post to dput the whole data. Hope that works now — SR1614, Nov 04 '21 at 07:54

score 0 · Answer 1 · answered Nov 04 '21 at 21:53

The following code seems to produce a valid solution, according to your constraints:

set.seed(100)
trial <- optim(fn = nll.18.4betas, 
               par = runif(5), 
               method = "Nelder-Mead", hessian = T,
               control=list(maxit=5000))

# RESULT

$par
[1] 0.999362670 0.005248378 0.011744520 0.002088864 0.007242104

$value
[1] 258.8472

$counts
function gradient 
    1034       NA 

$convergence
[1] 0

$message
NULL

$hessian
            [,1]       [,2]       [,3]      [,4]       [,5]
[1,]    9811.427  -4834.189  -9284.515 -106446.3  -43436.32
[2,]   -4834.189 677710.202      0.000       0.0       0.00
[3,]   -9284.515      0.000 354806.186       0.0       0.00
[4,] -106446.252      0.000      0.000 9402389.7       0.00
[5,]  -43436.320      0.000      0.000       0.0 1259387.74

Non-finite finite difference error when using optim with L-BFGS-B method in R

1 Answers1