r - Error in GLM fitting -

i have working glm , have variables "surface" , "price", these numeric. add them log variant model.

in order did follows;

data$logprice<-log(data$price) 

then added model follows;

model <- glm(variablea ~ logprice + variableb +variablec , binomial) 

and when added log following error;

error in glm.fit(x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,  :  

na/nan/inf in 'x'

hope can me explain error, or guide me in how fix it. in advance!

you didn't provide data or runnable code, it's impossible caused error in case. however, have pretty idea.

i can show in general not case:

data(iris)  iris$logprice  <- log(iris$sepal.length) iris$variablea <- ifelse(iris$species=="setosa",1,0)  model <- glm(variablea ~ logprice, binomial, data = iris) summary(model) 

call: glm(formula = variablea ~ logprice, family = binomial, data = iris)  deviance residuals:       min        1q    median        3q       max   -2.28282  -0.29561  -0.06431   0.29645   2.13240    coefficients:             estimate std. error z value pr(>|z|)     (intercept)   46.767      7.978   5.862 4.58e-09 *** logprice     -27.836      4.729  -5.887 3.94e-09 *** --- signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  (dispersion parameter binomial family taken 1)      null deviance: 190.954  on 149  degrees of freedom residual deviance:  72.421  on 148  degrees of freedom aic: 76.421  number of fisher scoring iterations: 7 

however, let's have value 0 cannot survive log transformation without being infinite:

iris$sepal.length[1] <- 0 iris$logprice  <- log(iris$sepal.length) iris$variablea <- ifelse(iris$species=="setosa",1,0)  model <- glm(variablea ~ logprice, binomial, data = iris) 

error in glm.fit(x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,  :    na/nan/inf in 'x' 

why? because:

> log(0) 

[1] -inf 

one solution (which kind of hack) add tiny bit of jitter, or replace 0 infinitesimally small value. however, if makes statistical , research sense beyond scope of answer.

if have na values can drop or impute those.