fitting a gam with constraints on parameters to deal with separation in parametric terms

Question

I am trying to fit a gam using mgcv which has a mix of smooth and parametric terms. The model is for some count data on fish catches. I am modelling variation in location and time, but also differences among individual operators. I am interested in the differences in operators specifically, and so am treating them as a fixed effect in my model. While there are solutions for my problem outlined below in a glm context, I need to work with a gam as I have a 2 dimensional surface that is a key feature of the model.

My challenge is that some levels of the fixed effect for vessels always have responses of 0, leading them to have coefficient estimates that move to negative infinity, due to the log link. This leads to convergence failures.

I am looking for ways to solve this issue. One obvious solution is to move to a Bayesian approach, and use weakly informative priors on the fixed effects terms. I have implemented this in brms, using the same structure. This works, but the complexity of the models means that the fitting time is much longer and I have a number of data sets to analyze.

I have found a few related questions: Dealing with quasi-complete separation in General Additive Model?
How to deal with perfect separation in logistic regression?
but the first which dealt with gams wasn’t resolved, and the second didn’t appear to have any viable solutions that I haven’t tried.

I would like to find a way to address this using gams in mgcv. It seems like there should be two possibilities, using penalties of some sort on the fixed effect term or imposing a constraint on the fixed effect term. After a lot of reading, I have not been able to find a good example of a penalty approach, and I am struggling to operationalize the pcls example from the mgcv help.

The help for pcls in mgcv is https://rdrr.io/cran/mgcv/man/pcls.html and the first example is the closest to what we are trying to do.

I have included an example dataset to illustrate the problem, any suggestions are greatly appreciated.

#set up data
Lat <- runif(100,1,20)
Lon <- runif(100,1,20)
Year <- runif(100,1,10)
Vessel <- rep((1:5)/10,each = 20)
Lambda <- exp(0.1*Lat - 0.01*Lat^2 + 0.1*Lon + 0.01*Lon^2 + 0.05*Year + Vessel)
Vessel <- as.factor(Vessel)
Catch <- apply(matrix(Lambda),1,rpois,n=1)
dt <- data.frame(Lat,Lon,Year,Vessel,Catch)
#now fit gam
M <- gam(Catch ~ s(Lat,Lon) + s(Year) + Vessel, family = tw, data = dt)
#create separation, all observations zero for one vessel
dt$Catch[dt$Vessel == levels(dt$Vessel)[2]] <- 0
#now fit gam again with separation
M <- gam(Catch ~ s(Lat,Lon) + s(Year) + Vessel, family = tw, data = dt)

Answer 1

I think you'd be better off putting a ridge penalty on the Vessel parametric term using the paraPen argument to gam() rather than trying to squish the whole model in pcls().

library("mgcv")
#set up data
set.seed(1) # repeatable
Lat <- runif(100,1,20)
Lon <- runif(100,1,20)
Year <- runif(100,1,10)
Vessel <- rep((1:5)/10,each = 20)
Lambda <- exp(0.1*Lat - 0.01*Lat^2 + 0.1*Lon + 0.01*Lon^2 + 0.05*Year + Vessel)
Vessel <- as.factor(Vessel)
Catch <- apply(matrix(Lambda),1,rpois,n=1)
dt <- data.frame(Lat,Lon,Year,Vessel,Catch)
create separation, all observations zero for one vessel
dt$Catch[dt$Vessel == levels(dt$Vessel)[2]] <- 0
now fit gam with separation problem
m1 <- gam(Catch ~ s(Lat,Lon) + s(Year) + Vessel, family = tw, data = dt)

With the paraPen argument we can set whatever penalty you want on the individual parametric terms. A ridge penalty is an identity penalty matrix, so we can do

pp <- list(Vessel = list(rank = 4, diag(4)))
m2 <- gam(Catch ~ s(Lat,Lon) + s(Year) + Vessel, family = tw, data = dt,
          paraPen = pp, method = "REML")

We need a rank 4 penalty because the Vessel term will become 4 columns in the model matrix with the default contrasts

> contrasts(dt$Vessel)                                                        
    0.2 0.3 0.4 0.5
0.1   0   0   0   0
0.2   1   0   0   0
0.3   0   1   0   0
0.4   0   0   1   0
0.5   0   0   0   1

so we need an identity matrix that is 4x4.

Now the coefficient is reasonable and the Wald-like test indicates the importance of this term (though note Simon's warning in ?summary.gam about the default p values for paraPen terms, so we might use freq = TRUE in the summary call):

summary(m2, freq = TRUE)

> summary(m2, freq = TRUE)
Family: Tweedie(p=1.01) 
Link function: log
Formula:
Catch ~ s(Lat, Lon) + s(Year) + Vessel
Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)

(Intercept)  2.61557    0.06388  40.947  < 2e-16 ***
Vessel0.2   -7.88552    1.41870  -5.558 3.66e-07 ***
Vessel0.3    0.16667    0.05882   2.833  0.00586 ** 
Vessel0.4    0.26836    0.05867   4.574 1.77e-05 ***
Vessel0.5    0.36089    0.06286   5.741 1.72e-07 ***

Signif. codes:  0 ‘*’ 0.001 ‘’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
              edf Ref.df      F p-value

s(Lat,Lon) 15.385 19.139 259.80  <2e-16 ***
s(Year)     1.712  2.125  24.76  <2e-16 ***

Signif. codes:  0 ‘*’ 0.001 ‘’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) =  0.996   Deviance explained = 99.4%
-REML = 242.41  Scale est. = 0.99247   n = 100

However this is just a common-or-garden random intercept so we can do this much more easily using the random effect basis

m3 <- gam(Catch ~ s(Lat,Lon) + s(Year) + s(Vessel, bs = "re"),
          family = tw, data = dt, method = "REML")
summary(m3)

> summary(m3)
Family: Tweedie(p=1.01) 
Link function: log
Formula:
Catch ~ s(Lat, Lon) + s(Year) + s(Vessel, bs = "re")
Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)    1.192      1.870   0.637    0.526
Approximate significance of smooth terms:
              edf Ref.df      F p-value

s(Lat,Lon) 15.383 19.138 259.81  <2e-16 ***
s(Year)     1.716  2.131  24.73  <2e-16 ***
s(Vessel)   3.890  4.000  16.58  <2e-16 ***

Signif. codes:  0 ‘*’ 0.001 ‘’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) =  0.996   Deviance explained = 99.4%
-REML = 242.86  Scale est. = 0.99247   n = 100

We can extract the random effects relatively easily:

library("gratia")
smooth_coefs(m3, "s(Vessel)")

> smooth_coefs(m3, "s(Vessel)")                                               
s(Vessel).1 s(Vessel).2 s(Vessel).3 s(Vessel).4 s(Vessel).5 
   1.424014   -6.489148    1.589729    1.691450    1.783955 

remembering those are about the overall model constant term (the (Intercept) bit). And they match (approximately) the estimates from the paraPen model:

> sc <- smooth_coefs(m3, "s(Vessel)")
> sc[1] - sc[-1]                                                              
s(Vessel).2 s(Vessel).3 s(Vessel).4 s(Vessel).5 
  7.9131616  -0.1657155  -0.2674363  -0.3599419

Answer 2

The suggestion from Simon works a treat. Just for completeness I thought I would also provide the answer for how to make the constrained approach to the problem work.

Lat <- runif(100,1,20)
Lon <- runif(100,1,20)
Year <- runif(100,1,10)
Vessel <- rep((1:5)/10,each = 20)
Lambda <- exp(0.1*Lat - 0.01*Lat^2 + 0.1*Lon + 0.01*Lon^2 + 0.05*Year + Vessel)
Vessel <- as.factor(Vessel)
Catch <- apply(matrix(Lambda),1,rpois,n=1)
dt <- data.frame(Lat,Lon,Year,Vessel,Catch)
#create separation, all observations zero for one vessel
dt$Catch[dt$Vessel == levels(dt$Vessel)[2]] <- 0
#now fit gam again with separation
M <- gam(Catch ~ s(Lat,Lon) + s(Year) + Vessel, family = tw, data = dt)

Now we want to impose constraints on the parametric terms for the vessel effect

#get length of fixed effects coefficients and all coefficients
coefs <- coef(M)
NG <- nlevels(dt$Vessel) - 1  
NC <- length(coefs)

Set up a model but don't fit it, so we can build the necessary inputs

mf <- Catch ~ s(Lat,Lon) + s(Year) + Vessel
mgam <- gam(mf, family=poisson, data=dt, fit=FALSE)

inequality constraints: all vessel coefficients must be > 0. We want zeros in all positions not occupied by terms we want to constrain.

mgam$Ain <- cbind(matrix(0, NG, 1),
                  diag(NG),
                  matrix(0, NG, NC - (1 + NG)))
 #this should match the no columns in design matrix wide, and the number of constrained parameters in rows
mgam$bin <- rep(-100, NG)
mgam$sp <- M$sp
mgam$p <- coefs
mgam$p[2:(NG+1)] <- 1 # make initial parameters values satisfy constraints
mgam$C <- matrix(0, 0, 0)
#fit with pcls and save the output
p <- pcls(mgam)