[R] Re:logistic regression

[Vito Ricci]

Hi,

I don't know if a pseudo squared R for glm exists in
any R package, but I find some interesting functions
in S mailing list:

http://www.math.yorku.ca/Who/Faculty/Monette/S-news/0422.html

Here are some functions for calculating (pseudo-)R^2,
which may be of use to some.

Rsquared <- function(object)
{
# object is an lm, glm or gam object
# Rsquared() is implemented only for glm or gam
objects with
# one of the three link/variance combinations:
# (1) log and mu (which is canonical for Poisson),
# (2) logit and mu(1-mu) (which is canonical for
binomial), or
# (3) identity and constant (which is canonical for
gaussian).
# However these link/variance pairs may have been
passed to quasi()
# to allow for overdispersion.
UseMethod("Rsquared")
}

Rsquared.lm <-
function(o)
{
R2 <- summary.lm(o)$r.squared
names(R2) <- "Rsquared"
R2
}

Rsquared.glm <-
function(o)
{ def <- family(o)$family[-1] # character vector of
link and variance
typ <- matrix(c("Logit: log(mu/(1 - mu))", "Log:
log(mu)", "Identity: mu",
"Binomial: mu(1-mu)", "Identity: mu", "Constant: 1" ),
2, 3, byrow=T) #
# typ is matrix of supported link and variance
combinations
ml <- match(def[1],typ[1,], nomatch=-1)
mv <- pmatch(def[2],typ[2,], nomatch=-1)
if ( (ml != mv) || (ml <1 ) )
stop("Implemented only for canonical links for
gaussian, binomial or poisson
(with optional provision for overdispersion using
quasi)")
if (ml == 3)
return(Rsquared.lm(o)) # gaussian family
# Remainder of code is for binomial and poisson
families (perhaps with provision for overdispersion)
n <- length(o$residuals)
# number of observations
R2 <- (1 - exp((o$deviance - o$null.deviance)/n))/(1 -
exp( - o$null.deviance/n))
names(R2) <- "pseudo.Rsquared"
R2
}

http://www.math.yorku.ca/Who/Faculty/Monette/S-news/0408.html

Here are some functions for calculating (pseudo-)R^2,
which may be of use to some.

Rsquared <- function(o){
# o is an lm, glm or gam object
UseMethod("Rsquared")
}

Rsquared.lm _ function(o) {
R2 <- summary(o)$r.squared
names(R2) <- 'Rsquared'
R2
}

Rsquared.glm <- function(o) {
n <- length(o$residuals) # number of observations
R2 <- ( 1 - exp( (o$deviance - o$null.deviance)/n ) )
/ ( 1 - exp( -o$null.deviance/n ) )
names(R2) <- 'pseudo.Rsquared'
R2
}


I don't know if S code is completely running in R
environment.

See: 
http://www.econ.ucdavis.edu/faculty/cameron/research/je97preprint.pdf

Hoping I helped you!
you wrote:

Hi,

I'm using glm function to do logistic regression and
now I want to know if it exists a kind of R-squared
with this function in order to check the model.

Thank you.

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