Retain numerical precision in an R data frame?
- by David
When I create a dataframe from numeric vectors, R seems to truncate the value below the precision that I require in my analysis:
data.frame(x=0.99999996)
returns 1 (see update 1)
I am stuck when fitting spline(x,y) and two of the x values are set to 1 due to rounding while y changes. I could hack around this but I would prefer to use a standard solution if available.
example
Here is an example data set
d <- data.frame(x = c(0.668732936336141, 0.95351462456867,
0.994620622127435, 0.999602102672081, 0.999987126195509, 0.999999955814133,
0.999999999999966), y = c(38.3026509783688, 11.5895099585560,
10.0443344234229, 9.86152339768516, 9.84461434575695, 9.81648333804257,
9.83306725758297))
The following solution works, but I would prefer something that is less subjective:
plot(d$x, d$y, ylim=c(0,50))
lines(spline(d$x, d$y),col='grey') #bad fit
lines(spline(d[-c(4:6),]$x, d[-c(4:6),]$y),col='red') #reasonable fit
Update 1
Since posting this question, I realize that this will return 1 even though the data frame still contains the original value, e.g.
> dput(data.frame(x=0.99999999996))
returns
structure(list(x = 0.99999999996), .Names = "x", row.names = c(NA,
-1L), class = "data.frame")
Update 2
After using dput to post this example data set, and some pointers from Dirk, I can see that the problem is not in the truncation of the x values but the limits of the numerical errors in the model that I have used to calculate y. This justifies dropping a few of the equivalent data points (as in the example red line).
