KGode (version 1.0.4)

diagnostic: The 'diagnostic' function

Description

This function is used to perform diagnostic procedure to compute the residual and make diagnostic plots.

Usage

diagnostic(infer_list, index, type, qq_plot)

Value

return list containing :

• residual - vector containing residual.

• interp - vector containing interpolation.

Arguments

infer_list

a list of inference results including ode objects and inference objects.

index

the index of the ode states which the user want to do the diagnostic analysis.

type

character containing the type of inference methods. User can choose 'rkg', 'third', or 'warp'.

qq_plot

boolean variable, enable or disable the plotting function.

Author

Mu Niu

Details

Arguments of the 'diagnostic' function are inference list , inference type, a list of interpolations for each of the ode state from gradient matching, and . It returns a vector of the median absolute standard deviations for each ode state.

Examples

# \dontshow{
  ##examples for checks: executable in < 5 sec together with the examples above not shown to users
  ### define ode
  toy_fun = function(t,x,par_ode){
       alpha=par_ode[1]
      as.matrix( c( -alpha*x[1]) )
   }

 toy_grlNODE= function(par,grad_ode,y_p,z_p) {
     alpha = par[1]
     dres= c(0)
      dres[1] = sum( 2*( z_p-grad_ode)*y_p*alpha ) #sum( -2*( z_p[1,2:lm]-dz1)*z1*alpha )
      dres
   }

  t_no = c(0.1,1,2,3,4,8)
  n_o = length(t_no)
  y_no =  matrix( c(exp(-t_no)),ncol=1  )
  ######################## create and initialise ode object #########################################
  init_par = rep(c(0.1))
 init_yode = t(y_no)
 init_t = t_no

 kkk = ode$new(1,fun=toy_fun,grfun=toy_grlNODE,t=init_t,ode_par= init_par, y_ode=init_yode )

 ##### standard gradient matching
 ktype='rbf'
 rkgres = rkg(kkk,(y_no),ktype)
 rkgdiag = diagnostic( rkgres,1,'rkg',qq_plot=FALSE )
# }
if (FALSE) {
require(mvtnorm)
set.seed(SEED);  SEED = 19537
FN_fun <- function(t, x, par_ode) {
a = par_ode[1]
b = par_ode[2]
c = par_ode[3]
as.matrix(c(c*(x[1]-x[1]^3/3 + x[2]),-1/c*(x[1]-a+b*x[2])))
}

solveOde = ode$new(sample=2,fun=FN_fun)
xinit = as.matrix(c(-1,-1))
tinterv = c(0,10)
solveOde$solve_ode(par_ode=c(0.2,0.2,3),xinit,tinterv)

n_o = max(dim(solveOde$y_ode))
noise = 0.01 
y_no = t(solveOde$y_ode)+rmvnorm(n_o,c(0,0),noise*diag(2))
t_no = solveOde$t

odem = ode$new(fun=FN_fun,grfun=NULL,t=t_no,ode_par=rep(c(0.1),3),y_ode=t(y_no))
ktype = 'rbf'
rkgres = rkg(odem,y_no,ktype)
rkgdiag = diagnostic( rkgres,1,'rkg',qq_plot=FALSE )
}