How do I calculate age specific R^2 in R -

i got demography dataset of swedish mortality mortality.org goes year 1751 2011 , age 0 110. i've used {demography} package fit lee-carter model data period 1900 2011 , age 0 100. need asses fit of estimates , intend use coefficient of determination so. problem now, need age-specific r^2's every age 0 100 , overall r^2 given when estimating model. in other words, need find r^2 via formular

r^2 (x) = 1 - ∑_x ( m(x,t) - predict{m(x,t)})^2 / ∑_x (m(x,t) - mean{m(x)})^2

for every x = 0,...,100. here m(x,t) mortality rate year t , age x, predict{m(x,t)} lee-carter prediction of m(x,t) , mean{m(x)} mean rate of mortality @ age x. code far:

#lee-carter analysis of swedish mortality  #packages used in mortality analysis library(demography) library(forecast) library(lifecontingencies)  #import of mortality data:  sweden <-hmd.mx(country="swe", username="username@email.domin",password="password", label="sweden")   #fit of lc model (in logarithmes)   swe.lcam <- lca(sweden, series="male", max.age=100, years = 1900:2011, interpolate = true) swe.lcaf <- lca(sweden, series="female", max.age=100, years = 1900:2011, interpolate = true)  #computation of r^2(x). fitted values in logarithms, need transformed exp().  m_hat.f=exp(swe.lcaf$fitted$y) 

i not know how proceed here.