Error in linear model when values are 0 - r

Question

I have a data set that has names, value 1, and value 2. I need to run a regression and obtain the t-statistic for each of the names. I got help on StackOverflow in constructing the linear model. I noticed that sometimes I get data that's 0's. It's OK and I want the model to keep running and not bomb. However, when the 0's are in there, the linear model bombs.
v1<-rnorm(1:50)
v2<-rnorm(1:50)
data<-data.frame(v1,v2)
data[1:50,"nm"]<-"A"
data[50:100,"nm"]<-"B"
data[50:100,"v1"]<-0
data[50:100,"v2"]<-0
data<-data[c("nm","v1","v2")]
## run regression and generate universe
plyrFunc <- function(x){
mod <- lm(v1~v2, data = x)
return(summary(mod)$coefficients[2,3])
}
lm <- ddply(data, .(nm), plyrFunc)
As you can see, for name B, since everything is 0, the model bombs. I cannot just remove all 0's because often times the values are indeed 0.
I don't know how to edit the above code so that it keeps going.
Can anyone let me know? Thank you!

Answer 1

The model actually works fine, it is a subsetting of summary(mod)$coefficients that throws you an error because it contains only one row in the all-zeroes case:
> summary(lm(v1~v2,data[data$nm=="A",]))$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1462766 0.1591779 -0.9189503 0.3628138
v2 -0.1315238 0.1465024 -0.8977590 0.3738900
> summary(lm(v1~v2,data[data$nm=="B",]))$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0 0 NaN NaN
Thus, you need to modify your function to take this case into account:
plyrFunc <- function(x){
mod <- lm(v1~v2, data = x)
res <- summary(mod)$coefficients
if (nrow(res)>1) res[2,3] else NA
}
library(plyr)
result <- ddply(data, .(nm), plyrFunc)
Output for your sample data set:
nm V1
1 A -0.1825896
2 B NA