Glance accepts a model object and returns a tibble::tibble() with exactly one row of model summaries. The summaries are typically goodness of fit measures, p-values for hypothesis tests on residuals, or model convergence information.

Glance never returns information from the original call to the modelling function. This includes the name of the modelling function or any arguments passed to the modelling function.

Glance does not calculate summary measures. Rather, it farms out these computations to appropriate methods and gathers the results together. Sometimes a goodness of fit measure will be undefined. In these cases the measure will be reported as NA.

# S3 method for lmodel2
glance(x, ...)

Arguments

x

A lmodel2 object returned by lmodel2::lmodel2().

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

See also

glance(), lmodel2::lmodel2()

Other lmodel2 tidiers: tidy.lmodel2

Value

A tibble::tibble() with exactly one row and columns:

p.value

Needs custom info.

r.squared

R squared statistic, or the percent of variation explained by the model. Also known as the coefficient of determination.

theta

Angle between OLS lines `lm(y ~ x)` and `lm(x ~ y)`

H

H statistic for computing confidence interval of major axis slope

Examples

library(lmodel2) data(mod2ex2) Ex2.res <- lmodel2(Prey ~ Predators, data=mod2ex2, "relative", "relative", 99) Ex2.res
#> #> Model II regression #> #> Call: lmodel2(formula = Prey ~ Predators, data = mod2ex2, range.y = #> "relative", range.x = "relative", nperm = 99) #> #> n = 20 r = 0.8600787 r-square = 0.7397354 #> Parametric P-values: 2-tailed = 1.161748e-06 1-tailed = 5.808741e-07 #> Angle between the two OLS regression lines = 5.106227 degrees #> #> Permutation tests of OLS, MA, RMA slopes: 1-tailed, tail corresponding to sign #> A permutation test of r is equivalent to a permutation test of the OLS slope #> P-perm for SMA = NA because the SMA slope cannot be tested #> #> Regression results #> Method Intercept Slope Angle (degrees) P-perm (1-tailed) #> 1 OLS 20.02675 2.631527 69.19283 0.01 #> 2 MA 13.05968 3.465907 73.90584 0.01 #> 3 SMA 16.45205 3.059635 71.90073 NA #> 4 RMA 17.25651 2.963292 71.35239 0.01 #> #> Confidence intervals #> Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope #> 1 OLS 12.490993 27.56251 1.858578 3.404476 #> 2 MA 1.347422 19.76310 2.663101 4.868572 #> 3 SMA 9.195287 22.10353 2.382810 3.928708 #> 4 RMA 8.962997 23.84493 2.174260 3.956527 #> #> Eigenvalues: 269.8212 6.418234 #> #> H statistic used for computing C.I. of MA: 0.006120651 #>
tidy(Ex2.res)
#> # A tibble: 8 x 5 #> method term estimate conf.low conf.high #> <fct> <chr> <dbl> <dbl> <dbl> #> 1 MA Intercept 13.1 1.35 19.8 #> 2 MA Slope 3.47 2.66 4.87 #> 3 OLS Intercept 20.0 12.5 27.6 #> 4 OLS Slope 2.63 1.86 3.40 #> 5 RMA Intercept 17.3 8.96 23.8 #> 6 RMA Slope 2.96 2.17 3.96 #> 7 SMA Intercept 16.5 9.20 22.1 #> 8 SMA Slope 3.06 2.38 3.93
glance(Ex2.res)
#> # A tibble: 1 x 4 #> r.squared theta p.value H #> <dbl> <dbl> <dbl> <dbl> #> 1 0.740 5.11 0.00000116 0.00612
# this allows coefficient plots with ggplot2 library(ggplot2) ggplot(tidy(Ex2.res), aes(estimate, term, color = method)) + geom_point() + geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) + geom_errorbarh(aes(xmin = conf.low, xmax = conf.high))