Extend growth model

[jimianelli]

Extend growth model in GMACS (As per Andre Punt) to allow the annual expected growth increments to be parameters, and for the parameter that determines the variance in the growth increment to be estimated.

[smartell]

You mean a nonparametric growth transition matrix?

On Thu, Oct 4, 2018 at 7:34 AM Jim Ianelli notifications@github.com wrote:

Extend growth model in GMACS (As per Andre Punt) to allow the annual expected growth increments to be parameters, and for the parameter that determines the variance in the growth increment to be estimated.

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[wStockhausen]

Presumably like Tanner, with parametric distribution but potentially time-varying parameters.

On Thu, Oct 4, 2018 at 9:24 AM Steve Martell notifications@github.com wrote:

You mean a nonparametric growth transition matrix?

On Thu, Oct 4, 2018 at 7:34 AM Jim Ianelli notifications@github.com wrote:

Extend growth model in GMACS (As per Andre Punt) to allow the annual expected growth increments to be parameters, and for the parameter that determines the variance in the growth increment to be estimated.

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[quantifish]

Something like this?:

matrix get_growth_increment(int n, real tau, vector midpoint, vector gpars) { real invpi; real mininc; vector[2] gitmp; real gi; real pred_sd; real sd_total; matrix[2,n] growth_increment; real pen; real k; real Gconst; real gshape; real gcv; real gmin;

k = gpars[1]; Gconst = gpars[2]; gshape = gpars[3]; gcv = gpars[4]; gmin = gpars[5];

pen = 0.0; mininc = 1e-6; invpi = 1.0 / 3.14159265;

for (l in 1:n) { gi = - midpoint[l] + pow((pow(midpoint[l], gshape) exp(-k tau)) + Gconst (1.0 - exp(-k tau)), 1.0 / gshape); gitmp = posfun(gi, mininc, pen); gi = gitmp[1]; pred_sd = (gi gcv - gmin tau) (invpi atan(1e6 (gi gcv - gmin tau)) + 0.5) + gmin tau; sd_total = sqrt(square(pred_sd) / tau); growth_increment[1,l] = gi + midpoint[l]; growth_increment[2,l] = sd_total; } return growth_increment; }

matrix get_growth_matrix(int n, real tau, vector midpoint, vector gpars) { matrix[2,n] increment; // growth increment real gi; real sdi; real asum; real dsum; matrix[n,n] G;

increment = get_growth_increment(n, tau, midpoint, gpars); for (l in 1:n) { gi = increment[1,l]; sdi = increment[2,l]; asum = 0.0; for (ll in 1:(n-1)) { dsum = normal_cdf(midpoint[ll] + 1.0, gi, sdi); G[l,ll] = dsum - asum; asum = dsum; } G[l,n] = 1.0 - asum; } return G'; }