SLRMss {SLRMss} | R Documentation |
Symmetric Linear Regression Models for small samples
Description
Computes Wald, Likelihood Ratio, Score, or Gradient statistics for symmetric linear regression models. Also computes modified versions of the Likelihood Ratio, Score, and Gradient tests for small sample sizes.
Usage
SLRMss(formula, family, xi, statistic, testingbeta, data)
Arguments
formula |
An object of class |
family |
A description of the error distribution to be used in the model. There are four supported families, Normal, t-Student, Power Exponential and Logistic ("Normal", "Student", "Powerexp" and "Logistic", respectively) |
xi |
An extra parameter of some specified error distribution. For t-Student is a positive value and for Power Exponential is a real number between -1 and 1/3. |
statistic |
The statistic which will be used. It includes "Wald", "LR", "Score" or "Gradient". |
testingbeta |
A vector containing the names of the variables to be testing. |
data |
An optional data frame containing the variables in the model. |
Value
A list with the following components
beta.coefficients |
A matrix with the estimated position parameters under alternative hypothesis. |
phi |
A numeric value with the estimated precision paramater under alternative hypothesis. |
beta.coefficients.h0 |
A matrix with the estimated position parameters under null hypothesis. |
phi.h0 |
A numeric value with the estimated precision paramater under null hypothesis. |
y.fitted |
A vector with the fitted values of the model. |
null.hypothesis |
The description of the null hypothesis. |
statistics |
A matrix with the selected statistics and theirs p-values. The corrected statistic is marked with an asterisk. |
statistic.distribution |
The name of the statistics' distribution used to test null hypothesis. It always return "Chi-Squared". |
df |
The degrees of freedom of the statistics' distribution. It's the length of the testingbeta vector. |
residuals |
The difference among the real y values and the fitted y. |
std.residuals |
The residuals divided by the precision parameter |
AICc |
The corrected Akaike Information Criterion for small samples. |
BIC |
The Bayesian Information Criterion. |
References
Medeiros, F. M. C and Ferrari, S. L. P. (2017). Small-sample testing inference in symmetric and log-symmetric linear regression models, Statistica Neerlandica. doi:10.1111/stan.12107.
Examples
data(orange)
fit1 <- SLRMss(emulsion ~ arabicgum + xanthangum + orangeoil, family="Student",
xi=3, testingbeta="xanthangum", statistic="LR", data=orange)
print(fit1)
data(cheese)
fit2 <- SLRMss(cohe ~ fat + xangum + sodcase, family="Normal",
testingbeta=c("xangum","sodcase"), statistic="Gradient", data=cheese)
print(fit2)