FitLm function

Fast estimates of linear regression

Computes a linear regression with stats::.lm.fit and returns the estimate and, optionally, standard error for each regressor.

FitLm(y, ..., intercept = TRUE, weights = NULL, se = FALSE, r2 = se)

ResidLm(y, ..., intercept = TRUE, weights = NULL)

Detrend(y, time = seq_along(y))

Arguments

• y: numeric vector of observations to model
• ...: numeric vectors of variables used in the modelling
• intercept: logical indicating whether to automatically add the intercept
• weights: numerical vector of weights (which doesn't need to be normalised)
• se: logical indicating whether to compute the standard error
• r2: logical indicating whether to compute r squared
• time: time vector to use for detrending. Only necessary in the case of irregularly sampled timeseries

Returns

FitLm returns a list with elements

• term: the name of the regressor
• estimate: estimate of the regression
• std.error: standard error
• df: degrees of freedom
• r.squared: Percent of variance explained by the model (repeated in each term)
• adj.r.squared: r.squared` adjusted based on the degrees of freedom)

ResidLm and Detrend returns a vector of the same length

If there's no complete cases in the regression, NAs are returned with no warning.

Examples

# Linear trend with "signficant" areas shaded with points
library(data.table)
library(ggplot2)
system.time({
  regr <- geopotential[, FitLm(gh, date, se = TRUE), by = .(lon, lat)]
})

ggplot(regr[term != "(Intercept)"], aes(lon, lat)) +
    geom_contour(aes(z = estimate, color = after_stat(level))) +
    stat_subset(aes(subset = abs(estimate) > 2*std.error), size = 0.05)

# Using stats::lm() is much slower and with no names.
## Not run:

system.time({
  regr <- geopotential[, coef(lm(gh ~ date))[2], by = .(lon, lat)]
})
## End(Not run)