summary.lod_lm {lodr}

Summarizing Linear Model Fits with Covariates Subject to a Limit of Detection

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

May RC, Ibrahim JG, Chu H (2011). “Maximum likelihood estimation in generalized linear models with multiple covariates subject to detection limits.” Statistics in medicine, 30(20), 2551–2561.

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)