lodr (version 1.0)

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

Description

summary method for class "lod_lm"

Usage

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

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

Details

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

References

maylodr

See Also

The model fitting function lod_lm, summary.

Examples

# NOT RUN {
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)
# }