Simple regression of random normal on date fails, but identical data with small integers instead of dates works as expected.
# Example dataset with 100 observations at 2 second intervals.
set.seed(1)
df <- data.frame(x=as.POSIXct("2017-03-14 09:00:00") + seq(0, 199, 2),
y=rnorm(100))
#> head(df)
# x y
# 1 2017-03-14 09:00:00 -0.6264538
# 2 2017-03-14 09:00:02 0.1836433
# 3 2017-03-14 09:00:04 -0.8356286
# Simple regression model.
m <- lm(y ~ x, data=df)
The slope is missing due to singularities in the data. Calling the summary demonstrates this:
summary(m)
# Coefficients: (1 not defined because of singularities)
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 0.10889 0.08982 1.212 0.228
# x NA NA NA NA
Could this be because of the POSIXct class?
# Convert date variable to integer.
df$x2 <- as.integer(df$x)
lm(y ~ x2, data=df)
# Coefficients:
# (Intercept) x2
# 0.1089 NA
Nope, coefficient for x2 still missing.
What if we make the baseline of x2 zero?
# Subtract minimum of x.
df$x3 <- df$x2 - min(df$x2)
lm(y ~ x3, data=df)
# Coefficients:
# (Intercept) x3
# 0.1312147 -0.0002255
This works!
One more example to rule out that this is due to datetime variable.
# Subtract large constant from date (data is now from 1985).
df$x4 <- df$x - 1000000000
lm(y ~ x4, data=df)
# Coefficients:
# (Intercept) x4
# 1.104e+05 -2.255e-04
Not expected (why would an identical dataset with 30 years difference cause different behaviour?), but this works too.
Could be that .Machine$integer.max (2147483647 on my PC) has something to do with it, but I can't figure it out. It would be greatly appreciated if someone could explain what's going on here.