summary.lod_lm: Summarizing Linear Model Fits with Covariates Subject to a...

In lodr: Linear Model Fitting with LOD Covariates

Description

summary method for class "lod_lm"

Usage

## S3 method for class 'lod_lm'
summary(object, ...)

## S3 method for class 'summary.lod_lm'
print(x, ...)

Arguments

object	An object of class "lod_lm", usually, a result of a call to lod_lm
x	An object of class "summary.lod_lm", usually, a result of a call to summary.lod_lm
...	further arguments passed to or from other methods.

Details

print.summary.lod_lm prints a table containing the coefficient estimates, standard errors, etc. from the lod_lm fit.

Value

The function summary.lod_lm returns a list of summary statistics of the fitted linear model given in object, using the components (list elements) "call" and "terms" from its argument, plus

residuals	residuals computed by lod_lm
coefficients	a p x 4 matrix for the estimated coefficient, its standard error, t-statistic and corresponding (two-sided) p-value.
sigma	the square root of the estimated variance of the random error.
df	degrees of freedom, a vector (p, n-p), where p is the number of regression coefficients and n is the sample size of the data used in the model fitting

Author(s)

Kevin Donovan, [e-mail].

Maintainer: Kevin Donovan <[e-mail]>

References

\insertRef

maylodr

See Also

The model fitting function lod_lm, summary.

Examples

library(lodr)
## Using example dataset provided in lodr package: lod_data_ex
## 3 covariates: x1, x2, x3 with x2 and x3 subject to a lower limit of
## detection of 0

## nSamples set to 100 for computational speed/illustration purposes only.  
## At least 250 is recommended.  Same for boots=0; results in NAs returned for standard errors

fit <- lod_lm(data=lod_data_ex, frmla=y~x1+x2+x3, lod=c(0,0),
                  var_LOD=c("x2", "x3"), nSamples=100, boots=0)
 summary(fit)

lodr documentation built on April 14, 2020, 5:42 p.m.