confint.mblm: Confidence Intervals for 'mblm' Model

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Description

Computes confidence intervals for one or more parameters in a fitted model of 'mblm' class.

Usage

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## S3 method for class 'mblm'
confint(object, parm, level = 0.95, ...)

Arguments

object

a fitted model object

parm

a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. Not yet implemented for 'mblm'

level

the confidence level required

...

additional arguments

Details

This function computes confidence intervals for slope and intercept in linear model based on single median or repeated medians. The confidence intervals are computed in simpliest way, as confidence interval for the median of all slopes or intercepts found during fitting.

Value

A matrix (or vector) with columns giving lower and upper confidence limits for each parameter.

Note

The recommended method of calculating confidence intervals, given by Sen and based on Kendall's tau, not Wilcoxon test, is not implemented at this time and is considered to be implemented in next version of this package.

Author(s)

Lukasz Komsta

References

Sen, P.K. (1968). Estimates of Regression Coefficient Based on Kendall's tau. J. Am. Stat. Ass. 63, 324, 1379-1389.

See Also

mblm, summary.mblm

Examples

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set.seed(1234)
x <- 1:100+rnorm(100)
y <- x+rnorm(100)
y[100] <- 200
fit <- mblm(y~x)
fit
summary(fit)
confint(fit)

Example output

Call:
mblm(formula = y ~ x)

Coefficients:
(Intercept)            x  
    -0.1124       1.0024  


Call:
mblm(formula = y ~ x)

Residuals:
   Min     1Q Median     3Q    Max 
-2.961 -0.568  0.064  0.617 97.750 

Coefficients:
            Estimate     MAD V value Pr(>|V|)    
(Intercept)  -0.1124  0.6203    2049    0.102    
x             1.0024  0.0128    5050   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 9.926 on 98 degrees of freedom

                 0.025      0.975
(Intercept) -0.2742497 0.02631869
x            1.0009335 1.00682383

mblm documentation built on May 2, 2019, 2:13 p.m.

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