# FitLm: Fast estimates of linear regression In metR: Tools for Easier Analysis of Meteorological Fields

 FitLm R Documentation

## 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)

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 May 29, 2024, 5:40 a.m.