lm called from inside dlply throws "0 (non-NA) cases" error [r]

Question

I'm using dlply() with a custom function that averages slopes of lm() fits on data that contain some NA values, and I get the error "Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-NA) cases"

This error only happens when I call dlply with two key variables - separating by one variable works fine.

Annoyingly I can't reproduce the error with a simple dataset, so I've posted the problem dataset in my dropbox.

Here's the code, as minimized as possible while still producing an error:

masterData <- read.csv("http://dl.dropbox.com/u/48901983/SOquestionData.csv", na.strings="#N/A")

workingData <- data.frame(sample = masterData$sample,
                      substrate = masterData$substrate,
                      el1 = masterData$elapsedHr1,
                      F1 = masterData$r1 - masterData$rK)

#This function is trivial as written; in reality it takes the average of many slopes
meanSlope <- function(df) {
     lm1 <- lm(df$F1 ~ df$el1, na.action=na.omit) #changing to na.exclude doesn't help
     slope1 <- lm1$coefficients[2]
     meanSlope <- mean(c(slope1)) 
}

lsGOOD <- dlply(workingData, .(sample), meanSlope) #works fine

lsBAD <- dlply(workingData, .(sample, substrate), meanSlope) #throws error

Thanks in advance for any insight.

Answer 1

For several of your cross-classifications you have missing covariates:

 with(masterData, table(sample, substrate, r1mis = is.na(r1) ) )
#
snipped the nonmissing reports
, , r1mis = TRUE

      substrate
sample 1 2 3 4 5 6 7 8
    3  0 0 0 0 0 0 0 0
    4  0 0 0 0 0 0 0 0
    5  0 0 0 0 0 0 0 0
    6  0 0 0 0 0 0 0 0
    7  0 0 0 0 0 0 3 3
    8  0 0 0 0 0 0 0 3
    9  0 0 0 0 0 0 0 3
    10 0 0 0 0 0 0 0 3
    11 0 0 0 0 0 0 0 3
    12 0 0 0 0 0 0 0 3
    13 0 0 0 0 0 0 0 3
    14 0 0 0 0 0 0 0 3

This would let you skip over the subsets with insufficient data in this particular data:

meanSlope <- function(df) { if ( sum(!is.na(df$el1)) < 2 ) { return(NA) } else {
     lm1 <- lm(df$F1 ~ df$el1, na.action=na.omit) #changing to na.exclude doesn't help
     slope1 <- lm1$coefficients[2]
     meanSlope <- mean(c(slope1)) }
}

Although it depends on the missingness being in one particular covariate. A more robust solution would be to use try to capture errors and convert to NA's.

?try