How to calculate estimated log-sum coefficient in the context of nested logit model in R?

On page 13 of this document, it indicated the estimated log-sum coefficient. How can I obtain the result with R?

The R output I think is relevant for this answer:

##
## Call:
## mlogit(formula = depvar ~ ich + och + icca + occa + inc.room +
## inc.cooling + int.cooling | 0, data = HC, nests = list(cooling = c("gcc",
## "ecc", "erc", "hpc"), other = c("gc", "ec", "er")), un.nest.el = TRUE)
##
## Frequencies of alternatives:
## ec ecc er erc gc gcc hpc
## 0.004 0.016 0.032 0.004 0.096 0.744 0.104
##
## bfgs method
## 11 iterations, 0h:0m:0s
## g'(-H)^-1g = 7.26E-06
## successive function values within tolerance limits
##
## Coefficients :
## Estimate Std. Error z-value Pr(>|z|)
## ich -0.554878 0.144205 -3.8478 0.0001192 ***
## och -0.857886 0.255313 -3.3601 0.0007791 ***
## icca -0.225079 0.144423 -1.5585 0.1191212
## occa -1.089458 1.219821 -0.8931 0.3717882
## inc.room -0.378971 0.099631 -3.8038 0.0001425 ***
## inc.cooling 0.249575 0.059213 4.2149 2.499e-05 ***
## int.cooling -6.000415 5.562423 -1.0787 0.2807030
## iv 0.585922 0.179708 3.2604 0.0011125 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-Likelihood: -178.12