Use Julia's standard debug loggin mechanism instead of the custom verbose flag.

docs/src/index.md

@@ -256,3 +256,17 @@ of a ```LinPred``` type use a default step factor of 1.  The value of
 ```linpred``` is the argument to the ```updatemu``` method for
 ```ModResp``` types.  The ```updatemu``` method returns the updated
 deviance.
+
+## Debugging failed fits
+In the rare cases when a fit of a generalized linear model fails, it can be useful
+to enable more output from the fitting steps. This can be done through
+the Julia logging mechanism by setting `ENV[JULIA_DEBUG] = GLM`. Enabling debug output
+will result in ouput like the following
+```julia
+┌ Debug: Iteration: 1, deviance: 5.129147109764238, diff.dev.:0.05057195315968688
+└ @ GLM ~/.julia/dev/GLM/src/glmfit.jl:418
+┌ Debug: Iteration: 2, deviance: 5.129141077001254, diff.dev.:6.032762984276019e-6
+└ @ GLM ~/.julia/dev/GLM/src/glmfit.jl:418
+┌ Debug: Iteration: 3, deviance: 5.129141077001143, diff.dev.:1.1102230246251565e-13
+└ @ GLM ~/.julia/dev/GLM/src/glmfit.jl:418
+```

src/glmfit.jl

@@ -353,7 +353,7 @@
 
 dof(obj::GeneralizedLinearModel) = linpred_rank(obj) + dispersion_parameter(obj.rr.d)
 
-function _fit!(m::AbstractGLM, verbose::Bool, maxiter::Integer, minstepfac::Real,
+function _fit!(m::AbstractGLM, maxiter::Integer, minstepfac::Real,
                atol::Real, rtol::Real, start)
 
     # Return early if model has the fit flag set
@@ -415,7 +415,7 @@
         p.beta0 .+= p.delbeta .* f
 
         # Test for convergence
-        verbose && println("Iteration: $i, deviance: $dev, diff.dev.:$(devold - dev)")
+        @debug "Iteration: $i, deviance: $dev, diff.dev.:$(devold - dev)"
         if devold - dev < max(rtol*devold, atol)
             cvg = true
             break
@@ -429,7 +429,6 @@
 end
 
 function StatsBase.fit!(m::AbstractGLM;
-                        verbose::Bool=false,
                         maxiter::Integer=30,
                         minstepfac::Real=0.001,
                         atol::Real=1e-6,
@@ -461,14 +460,13 @@
     m.atol = atol
     m.rtol = rtol
 
-    _fit!(m, verbose, maxiter, minstepfac, atol, rtol, start)
+    _fit!(m, maxiter, minstepfac, atol, rtol, start)
 end
 
 function StatsBase.fit!(m::AbstractGLM,
                         y;
                         wts=nothing,
                         offset=nothing,
-                        verbose::Bool=false,
                         maxiter::Integer=30,
                         minstepfac::Real=0.001,
                         atol::Real=1e-6,
@@ -509,7 +507,7 @@
     m.atol = atol
     m.rtol = rtol
     if dofit
-        _fit!(m, verbose, maxiter, minstepfac, atol, rtol, start)
+        _fit!(m, maxiter, minstepfac, atol, rtol, start)
     else
         m
     end
@@ -531,7 +529,6 @@
     $COMMON_FIT_KWARGS_DOCS
     - `offset::Vector=similar(y,0)`: offset added to `Xβ` to form `eta`.  Can be of
       length 0
-    - `verbose::Bool=false`: Display convergence information for each iteration
     - `maxiter::Integer=30`: Maximum number of iterations allowed to achieve convergence
     - `atol::Real=1e-6`: Convergence is achieved when the relative change in
       deviance is less than `max(rtol*dev, atol)`.

src/negbinfit.jl

@@ -65,7 +65,6 @@ In both cases, `link` may specify the link function
 - `maxiter::Integer=30`: See `maxiter` for [`glm`](@ref)
 - `atol::Real=1.0e-6`: See `atol` for [`glm`](@ref)
 - `rtol::Real=1.0e-6`: See `rtol` for [`glm`](@ref)
-- `verbose::Bool=false`: See `verbose` for [`glm`](@ref)
 """
 function negbin(F,
                 D,
@@ -78,7 +77,6 @@ function negbin(F,
                 minstepfac::Real=0.001,
                 atol::Real=1e-6,
                 rtol::Real=1.e-6,
-                verbose::Bool=false,
                 kwargs...)
     if haskey(kwargs, :maxIter)
         Base.depwarn("'maxIter' argument is deprecated, use 'maxiter' instead", :negbin)
@@ -109,11 +107,11 @@ function negbin(F,
     if isinf(initialθ)
         regmodel = glm(F, D, Poisson(), args...;
                        wts=wts, dropcollinear=dropcollinear, method=method, maxiter=maxiter,
-                       atol=atol, rtol=rtol, verbose=verbose, kwargs...)
+                       atol=atol, rtol=rtol, kwargs...)
     else
         regmodel = glm(F, D, NegativeBinomial(initialθ), args...;
                        wts=wts, dropcollinear=dropcollinear, method=method, maxiter=maxiter,
-                       atol=atol, rtol=rtol, verbose=verbose, kwargs...)
+                       atol=atol, rtol=rtol, kwargs...)
     end
 
     μ = regmodel.rr.mu
@@ -136,10 +134,10 @@ function negbin(F,
             converged = true
             break
         end
-        verbose && println("[ Alternating iteration ", i, ", θ = ", θ, " ]")
+        @debug "[ Alternating iteration $i, θ = $θ ]"
         regmodel = glm(F, D, NegativeBinomial(θ), args...;
                        dropcollinear=dropcollinear, method=method, maxiter=maxiter,
-                       atol=atol, rtol=rtol, verbose=verbose, kwargs...)
+                       atol=atol, rtol=rtol, kwargs...)
         μ = regmodel.rr.mu
         prevθ = θ
         θ = mle_for_θ(y, μ, wts; maxiter=maxiter, tol=rtol)