FitLm: Fast estimates of linear regression

In metR: Tools for Easier Analysis of Meteorological Fields

Fast estimates of linear regression

Description

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

Usage

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

Details

The functions provide different ways to handle linear trends:

• ResidLm: Use this to compute anomalies. It subtracts the linear trend (including the intercept), effectively removing both the long-term trend and the mean. This corresponds to the standard "detrending and anomaly" step in climate analysis.

• Detrend: Use this to remove the slope (trend) while retaining the physical magnitude of the data. It subtracts the linear trend but adds the original mean back. Ideally suited for visualizing data without the distraction of long-term trends while keeping the values in their original level.

Value

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 returns a numeric vector of the same length as y. It represents the residuals (anomalies) of the linear model. The result is centered at approximately 0 (the trend is removed, and the mean is subtracted–Derived from the calculation of the least squares method).

Detrend returns a numeric vector of the same length as y. It represents the detrended data with the original mean preserved. Mathematically, it is residuals + mean(y).

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)

metR documentation built on Oct. 8, 2026, 9:17 a.m.