How can I load a library in R to call it from Excel with bert-toolkit?

Question

Bert-toolkit is a very nice package to call R functions from Excel. See: https://bert-toolkit.com/

I have used bert-toolkit to call a fitted neuralnet (avNNnet fitted with Caret) within a wrapper function in R from Excel VBA. This runs perfect. This is the code to load the model within the wrapper function in bert-toolkit:

    load("D:/my_model_avNNet.rda")

    neuraln <- function(x1,x2,x3){
    xx <- data.frame(x1,x2,x3)
    z <- predict(my_model_avNNET, xx)
    z
}

Currently I tried to do this with a fitted GAM (fitted with package mgcv). Although I do not succeed. If I call the fitted GAM from Excel VBA it gives error 2015. If I call the fitted GAM from a cell it gives #VALUE! At the same time the correct outcome of the calculation is shown in the bert-console!

This is the code to load the model in the wrapperfunction in bert-toolkit:

library(mgcv)
load("D:/gam_y_model.rda")

testfunction <- function(k1,k2){
  z <- predict(gam_y, data.frame(x = k1, x2 = k2))
  print (z)
}

The difference between the avNNnet-model (Caret) and the GAM-model (mgcv) is that the avNNnet-model does NOT need the Caret library to be loaded to generate a prediction, while the GAM-model DOES need the mgcv library to be loaded.

It seems to be not sufficient to load the mgvc-library in the script with the GAM-model which loads the GAM-model in a wrapper function in bert-toolkit, as I did in the code above. Although the correct outcome of the model is shown in the bert-console. It does not generate the correct outcome in Excel. I wonder how this is possible and can be solved. It seems to me that maybe there are two instances of R running in bert-toolkit.

How can I load the the mgcv-library in such a way that it can be used by the GAM-model within the function called from Excel? This is some example code to fit the GAM with mgcv and save to model (after running this code the model can uploaded in bert-toolkit with the code above) :

library(mgcv)

# construct some sample data:
x <- seq(0, pi * 2, 0.1)
x2 <- seq(0, pi * 20, 1)
sin_x <- sin(x)
tan_x2 <- tan(x2)
y <- sin_x + rnorm(n = length(x), mean = 0, sd = sd(sin_x / 2))
Sample_data <- data.frame(y,x,x2)

# fit gam:
gam_y <- gam(y ~ s(x) + s(x2), method = "REML")

# Make predictions with the fitted model:
  x_new <- seq(0, max(x), length.out = 100)
  x2_new <- seq(0, max(x2), length.out = 100)
y_pred <- predict(gam_y, data.frame(x = x_new, x2 = x2_new))

# save model, to load it later in bert-toolkit:
setwd("D:/")
save(gam_y, file = "gam_y_model.rda")

Answer 1

One of R's signatures is method dispatching where users call the same named method such as predict but internally a different variant is run such as predict.lm, predict.glm, or predict.gam depending on the model object passed into it. Therefore, calling predict on an avNNet model is not the same predict on a gam model. Similarly, just as the function changes due to the input, so does the output change.

According to MSDN documents regarding the Excel #Value! error exposed as Error 2015:

#VALUE is Excel's way of saying, "There's something wrong with the way your formula is typed. Or, there's something wrong with the cells you are referencing."

Fundamentally, without seeing actual results, Excel may not be able to interpret or translate into Excel range or VBA type the result R returns from gam model especially as you describe R raises no error.

For example, per docs, the return value of the standard predict.lm is:

predict.lm produces a vector of predictions or a matrix of predictions...

However, per docs, the return value of predict.gam is a bit more nuanced:

If type=="lpmatrix" then a matrix is returned which will give a vector of linear predictor values (minus any offest) at the supplied covariate values, when applied to the model coefficient vector. Otherwise, if se.fit is TRUE then a 2 item list is returned with items (both arrays) fit and se.fit containing predictions and associated standard error estimates, otherwise an array of predictions is returned. The dimensions of the returned arrays depends on whether type is "terms" or not: if it is then the array is 2 dimensional with each term in the linear predictor separate, otherwise the array is 1 dimensional and contains the linear predictor/predicted values (or corresponding s.e.s). The linear predictor returned termwise will not include the offset or the intercept.

Altogether, consider adjusting parameters of your predict call to render a numeric vector for easy Excel interpretation and not a matrix/array or some other higher dimension R type that Excel cannot render:

testfunction <- function(k1,k2){
  z <- mgcv::predict.gam(gam_y, data.frame(x = k1, x2 = k2), type=="response")
  return(z)
}

testfunction <- function(k1,k2){
  z <- mgcv::predict.gam(gam_y, data.frame(x = k1, x2 = k2), type=="lpmatrix")
  return(z)
}

testfunction <- function(k1,k2){
  z <- mgcv::predict.gam(gam_y, data.frame(x = k1, x2 = k2), type=="linked")
  return(z$fit)   # NOTICE fit ELEMENT USED
}
...

Further diagnostics:

• Check returned object of predict.glm with str(obj) and class(obj)/ typeof(obj) to see dimensions and underlying elements and compare with predict in caret;
• Check if high precision of decimal numbers is the case such as Excel's limits of 15 decimal points;
• Check amount of data returned (exceeds Excel's sheet row limit of 2²⁰ or cell limit of 32,767 characters?).