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

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 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)

metR documentation built on Oct. 14, 2024, 5:09 p.m.