lm_window performs a linear regression with the Theil-Sen estimator on a subset of data.
Arguments
- m
linear model (
lm) object- x
vector of independent variable (e.g. time).
- y
vector of dependent variable (concentration of organisms).
- i0
index of first value used for a window.
- h
with of the window (number of data).
Value
linear model object of class lm (lm_window)
resp. vector with parameters of the fit (lm_parms).
References
Hall, B. G., H. Acar and M. Barlow 2013. Growth Rates Made Easy. Mol. Biol. Evol. 31: 232-238 doi:10.1093/molbev/mst197
Examples
# Create random growth dataset
rnd.dataset <- rdm.data(d = 35, mu = 0.8, A = 5, label = "Test1")
# Extract time and growth data for single sample
time <- rnd.dataset$time[1,]
data <- as.numeric(rnd.dataset$data[1,-(1:3)]) # Remove identifier columns
data.log <- log(data/data[1])
# Perform linear fit on 8th window of size 8
linreg <- lm_window(time, data.log, 8, h=8)
summary(linreg)
#>
#> Call:
#> theil_sen_regression(formula = y ~ x)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.009851 -0.004634 0.000000 0.005977 0.045185
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.09267 0.03371 -2.749 0.033326 *
#> x 0.07727 0.01255 6.159 0.000841 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.02033 on 6 degrees of freedom
#> Multiple R-squared: 0.8634, Adjusted R-squared: 0.8407
#> F-statistic: 37.93 on 1 and 6 DF, p-value: 0.0008405
#>
lm_parms(linreg)
#> a b b.se r2 b.rsd
#> -0.09267294 0.07727160 0.01254623 0.86342745 0.16236528