Error when fitting CBFM-gam with tensor interaction (ti)

[chrishaak]

When fitting a model with a ti term, I get this error:

"Error in update_Xcoefsspp_cmpfn(j = j) : task 1 failed - "subscript out of bounds""

Please see attached script (adapted from example in the docs) for illustration:

CBFM_ti_error_example.R.txt

thanks!!!

[fhui28]

#SCRIPT TO ILLUSTRATE ERROR FITTING CBFM-GAM WITH TENSOR INTERACTION
#ADAPTED FROM CBFM EXAMPLE IN DOCUMENTATION
library(CBFM)
library(mvtnorm)
library(tidyverse)
library(RandomFields)
#library(autoFRK)

##------------------------------
## **Example 1a: Fitting a CBFM to spatial multivariate presence-absence data**
## simulated from a spatial latent variable model
## Please note the data generation process (thus) differs from CBFM.
##------------------------------
set.seed(2021)
num_sites <- 1000 # 500 (units) sites for training set + 500 sites for testing.
num_spp <- 50 # Number of species
num_X <- 4 # Number of regression slopes

spp_slopes <- matrix(runif(num_spp * num_X, -1, 1), nrow = num_spp)
spp_intercepts <- runif(num_spp, -2, 0)

# Simulate spatial coordinates and environmental covariate components
# We will use this information in later examples as well
xy <- data.frame(x = runif(num_sites, 0, 5), y = runif(num_sites, 0, 5))
X <- mvtnorm::rmvnorm(num_sites, mean = rep(0,4)) 
colnames(X) <- c("temp", "depth", "chla", "O2")
dat <- data.frame(xy, X)
mm <- model.matrix(~ temp + depth + chla + O2 - 1, data = dat) %>% 
  scale %>% 
  as.matrix

# Simulate latent variable component
# We will use this information in later examples as well
true_lvs <- RandomFields::RFsimulate(model = RMexp(var=1, scale=2), 
                       x = xy$x, y = xy$y, n = 2)@data %>% 
  as.matrix
spp_loadings <- matrix(runif(num_spp * 2, -1, 1), nrow = num_spp) 
set.seed(NULL)

# Simulate spatial multivariate abundance data (presence-absence)
# We will use this information in later examples as well
eta <- tcrossprod(cbind(1,mm), cbind(spp_intercepts,spp_slopes)) + 
  tcrossprod(true_lvs, spp_loadings)
simy <- matrix(rbinom(num_sites * num_spp, size = 1, 
                      prob = plogis(eta)), nrow = num_sites)

# Form training and test sets
dat_train <- dat[1:500,]
dat_test <- dat[501:1000,]
simy_train <- simy[1:500,]
simy_test <- simy[501:1000,]
rm(X, mm, spp_loadings, true_lvs, xy, simy, dat)

# Set up spatial basis functions for CBFM -- Most practitioners will start here! 
# This is the same set up as Example 1a
num_basisfunctions <- 25 # Number of spatial basis functions to use
# Training set basis functions
train_basisfunctions <- autoFRK::mrts(dat_train[,c("x","y")], num_basisfunctions) %>% 
  as.matrix %>%
  {.[,-(1)]} # Remove the first intercept column
# Testing set basis functions
test_basisfunctions <- autoFRK::mrts(dat_train[,c("x","y")], num_basisfunctions) %>% 
  predict(newx = dat_test[,c("x","y")]) %>% 
  as.matrix %>%
  {.[,-c(1)]} 

# Fit CBFM with tensor interaction term
tic <- proc.time()
useformula <- ~ s(temp) + s(depth) + s(chla) + s(O2) +ti(temp, depth)
fitcbfm_gam <- CBFM(y = simy_train, formula_X = useformula, 
                    data = dat_train, B_space = train_basisfunctions, family = binomial(), control = list(trace = 1))
toc <- proc.time()
toc-tic

#error!

# Fit CBFM 
#tic <- proc.time()
#useformula <- ~ s(temp) + s(depth) + s(chla) + s(O2)
#fitcbfm_gam <- CBFM(y = simy_train, formula_X = useformula, 
#                    data = dat_train, B_space = train_basisfunctions, family = binomial(), control = list(trace = 1))
#toc <- proc.time()
#toc-tic

[fhui28]

Thanks Chris.

This should now be corrected in the latest push to github. The problem was due to me not accounting for the fact that te and ti type smooths have multiple smoothing parameters and penalty matrices.