Hi there,
I am stuck trying to solve what should be a fairly easy problem.
I have a data frame that essentially consists of (ID, time as seqMonth,
variable, value) and i want to find the regression coefficient of value vs
time for each combination of ID and Variable.
I have tried several approaches and none of them seems to work as i
expected.
For example, i have tried:

theSplit<-split(theTestLineal, list(as.factor(theTestLineal $ids),
as.factor(theTestLineal $variable)))

I can then use 
lm(value~seqMonth,data=zongSplit[[1]])
...
lm(value~seqMonth,data=zongSplit[[4]])

that works well, (it fails for some combinations of ID and variable where
there is one datapoint)

however when i try to use an lapply:
lapply(zongSplit,function(x)lm(value~seqMonth,data=x,na.action=na.exclude))

it fails, with error message:
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
 0 (non-NA) cases

I have tried to change the na.action with no success (na.pass, na.fail,
na.exclude... all give the same error message)


I have also tried to follow the approach suggested by Charles Sharpsteen
(http://www.mail-archive.com/r-help@r-project.org/msg74759.html) with
similar results.
The code is as follows:
theModels <- by( theTestLineal, list( theTestLineal$ids,
zongTestLineal$variable), function( dataSlice ){
linMod <- lm( value ~ seqMonth, data = dataSlice )

# Slope and intercept may be recovered from the output of the coef()
function:
intercept <- coef( linMod )[1]
slope <- coef( linMod )[2]

# The R-Squared value is returned by the summary() function:
rsq <- summary( linMod )[[ 'r.squared' ]]

# The summary function also provides statistics for the F-distribution,
# extract them, reformat as a list, rename and feed to pf() using do.call()
# in order to get the p-value:
fStats <- as.list( summary( linMod )[[ 'fstatistic' ]] )
names( fStats ) <- c( 'q', 'df1', 'df2' )
fStats[[ 'lower.tail' ]] <- FALSE

pVal <- do.call( pf, fStats )

return(data.frame( slope, intercept, rsq, pVal ))
})

Any help will be appreciated!

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