Fix logit link and add const to global objects

The inverse of the logit link should have a negative sign in the
exponent.  This is present in other examples but not in these two.

It might be a good idea to contrast the MCMC results, at least in the
documentation, with those from fitting a GLM.  Right now it is kind of
clumsy to fit a GLM with a given model matrix but that could be
changed.

With the data from `example/binomial.jl` and the definition of the coefficients
```jl
julia> m = model(ex, vars=zeros(nbeta), gradient=true)
MCMCLikelihoodModel(##ll#319,# function,nothing,nothing,##ll#340,nothing,nothing,[:vars=>PDims(1,(10,))],10,[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0])

julia> res = run(m * RWM(0.05), steps=1000:10000)
10 parameters, 9001 samples (per parameter), 4.1 sec.

julia> res = run(m * RWM(0.05), steps=1000:10000)
10 parameters, 9001 samples (per parameter), 3.3 sec.

julia> head(res.samples)
6x10 DataFrame:
           vars.1    vars.2     vars.3    vars.4   vars.5  vars.6    vars.7   vars.8    vars.9   vars.10
[1,]     -0.21506 -0.443009 -0.0923568 -0.765506 -1.50518 1.45962 -0.166601 -1.21317 -0.510049 -0.470377
[2,]    -0.180667 -0.460005 -0.0881983 -0.730407 -1.50652 1.42352 -0.257553 -1.22122 -0.427078 -0.489092
[3,]    -0.172506 -0.516425 -0.0515133 -0.704989 -1.46919 1.46088 -0.209047 -1.23566 -0.464969 -0.438487
[4,]    -0.172506 -0.516425 -0.0515133 -0.704989 -1.46919 1.46088 -0.209047 -1.23566 -0.464969 -0.438487
[5,]    -0.172506 -0.516425 -0.0515133 -0.704989 -1.46919 1.46088 -0.209047 -1.23566 -0.464969 -0.438487
[6,]    -0.172506 -0.516425 -0.0515133 -0.704989 -1.46919 1.46088 -0.209047 -1.23566 -0.464969 -0.438487

julia> [colwise(mean, res.samples) beta0 ] # mean sample vs original coefs
10x2 Array{Any,2}:
 [-0.21482366575183443]   -0.0717078
 [-0.4725536125628145]    -0.250912
 [-0.020901613339271534]  -0.100819
 [-0.5815990199432688]    -0.512176
 [-1.405229813825649]     -1.19319
 [1.3544778459403048]      1.22552
 [-0.2580605077816288]    -0.246421
 [-1.0614956381864762]    -1.00204
 [-0.423581899678947]     -0.59563
 [-0.5593022590885337]    -0.681701

julia> res = run(m * HMC(2, 0.1), steps=1000:10000)
10 parameters, 9001 samples (per parameter), 10.0 sec.

julia> [colwise(mean, res.samples) beta0 ] # mean sample vs original coefs
10x2 Array{Any,2}:
 [-0.2046847981894676]    -0.0717078
 [-0.462927351516865]     -0.250912
 [-0.024959659831684532]  -0.100819
 [-0.5805002542280536]    -0.512176
 [-1.413034972454002]     -1.19319
 [1.358553312357889]       1.22552
 [-0.2584049999023584]    -0.246421
 [-1.058018576520552]     -1.00204
 [-0.42451432264431094]   -0.59563
 [-0.5546427132866298]    -0.681701

julia> res = run(m * NUTS(), steps=1000:10000)
10 parameters, 9001 samples (per parameter), 17.4 sec.

julia> [colwise(mean, res.samples) beta0 ] # mean sample vs original coefs
10x2 Array{Any,2}:
 [-0.20488317740629458]   -0.0717078
 [-0.465418897468125]     -0.250912
 [-0.024554388672484415]  -0.100819
 [-0.5822459997386241]    -0.512176
 [-1.4147503896216957]    -1.19319
 [1.3579861510776314]      1.22552
 [-0.2573139709672702]    -0.246421
 [-1.0575141534744146]    -1.00204
 [-0.42458208644443585]   -0.59563
 [-0.5552305354105672]    -0.681701

julia> fr = DataFrame(y = Y,x1=X[:,2],x2=X[:,3],x3=X[:,4],x4=X[:,5],x5=X[:,6],x6=X[:,7],x7=X[:,8],x8=X[:,9],x9=X[:,10]);

julia> using GLM

julia> glm(:(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9), fr, Binomial())

Formula: y ~ :(+(x1,x2,x3,x4,x5,x6,x7,x8,x9))

Coefficients:

10x4 DataFrame:
           Estimate Std.Error   z value    Pr(>|z|)
[1,]      -0.206247 0.0878745  -2.34707   0.0189218
[2,]      -0.467542 0.0924892   -5.0551  4.30171e-7
[3,]     -0.0246491 0.0844057 -0.292031    0.770263
[4,]       -0.58779 0.0903566  -6.50522 7.75793e-11
[5,]        -1.4265  0.117335  -12.1575 5.23478e-34
[6,]        1.37016  0.112457   12.1839 3.78862e-34
[7,]      -0.259542 0.0864448   -3.0024  0.00267858
[8,]       -1.06695  0.105179  -10.1441 3.51962e-24
[9,]      -0.430233 0.0900796  -4.77614  1.78689e-6
[10,]      -0.56062 0.0927221  -6.04624  1.48267e-9
```