| Type: | Package |
| Title: | Linear Specification Testing |
| Version: | 1.0.0 |
| Maintainer: | Federico Garza <fede.garza.ramirez@gmail.com> |
| Description: | Tests whether the linear hypothesis of a model is correct specified using Dominguez-Lobato test. Also Ramsey's RESET (Regression Equation Specification Error Test) test is implemented and Wald tests can be carried out. Although RESET test is widely used to test the linear hypothesis of a model, Dominguez and Lobato (2019) proposed a novel approach that generalizes well known specification tests such as Ramsey's. This test relies on wild-bootstrap; this package implements this approach to be usable with any function that fits linear models and is compatible with the update() function such as 'stats'::lm(), 'lfe'::felm() and 'forecast'::Arima(), for ARMA (autoregressive–moving-average) models. Also the package can handle custom statistics such as Cramer von Mises and Kolmogorov Smirnov, described by the authors, and custom distributions such as Mammen (discrete and continuous) and Rademacher. Manuel A. Dominguez & Ignacio N. Lobato (2019) <doi:10.1080/07474938.2019.1687116>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| Depends: | R (> 3.2) |
| LazyData: | true |
| Imports: | Matrix, sandwich, dplyr, ggplot2, viridis, tidyr, readr, parallel, forecast |
| Suggests: | testthat |
| RoxygenNote: | 7.0.2 |
| URL: | https://github.com/FedericoGarza/lineartestr |
| BugReports: | https://github.com/FedericoGarza/lineartestr/issues |
| NeedsCompilation: | no |
| Packaged: | 2020-06-08 01:30:01 UTC; federicogarza2 |
| Author: | Federico Garza 
[aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2020-06-12 09:10:20 UTC |
Tests the specification of a linear model using wild-bootstrap.
Description
Tests the specification of a linear model using wild-bootstrap.
Usage
dominguez_lobato_test(
model,
distribution = "rnorm",
statistic = "cvm_value",
times = 300,
quantiles = c(0.9, 0.95, 0.99),
verbose = FALSE,
n_cores = 1
)
Arguments
model |
An existing fit from a model function such as 'lm', 'lfe' and others compatible with 'update'. |
distribution |
Type of noise added to residuals, ej 'rnorm' or 'rrademacher'. |
statistic |
Type of statistic to be used, can be one of 'cvm_value' or 'kmv_value'. |
times |
Number of bootstrap samples. |
quantiles |
Vector of quantiles to calculate pvalues. |
verbose |
TRUE to print each bootstrap iteration. |
n_cores |
Number of cores to be used. |
Value
A list with dataframe results and the ordered values of each bootstrap iteration.
References
Manuel A. Dominguez and Ignacio N. Lobato (2019). Specification Testing with Estimated Variables. Econometric Reviews.
Examples
x <- 1:10 + rnorm(10)
y <- 1:10
model <- lm(y~x)
dl_test <- dominguez_lobato_test(model)
dl_test <- dominguez_lobato_test(model, distribution = "rmammen_point", statistic = "kmv_value")
dl_test <- dominguez_lobato_test(model, times = 100)
Plots the Dominguez-Lobato test.
Description
Plots the Dominguez-Lobato test.
Usage
plot_dl_test(x)
Arguments
x |
An object of class 'dl_test'. |
Value
Plot of type ggplot.
Examples
x <- 1:10
y <- 1:10
model <- lm(y~x-1)
dl_test <- dominguez_lobato_test(model)
plot_dl_test(dl_test)
Plot the reset test.
Description
Plot the reset test.
Usage
plot_reset_test(x)
Arguments
x |
An object of class 'reset_test'. |
Value
Plot of type ggplot.
Examples
x <- 1:10 + rnorm(10)
y <- 1:10
model <- lm(y~x-1)
r_test <- reset_test(model)
plot_reset_test(r_test)
Calculates the accumulated distribution of residuals at each residual point.
Description
Calculates the accumulated distribution of residuals at each residual point.
Usage
presiduals(fitted_values, resids)
Arguments
fitted_values |
Vector of fitted values. |
resids |
Residuals vector of each fitted value. |
Value
Vector of size length(resids).
Examples
y_hat <- c(4, 8, 7)
resids <- c(1, 5, 3)
presiduals(y_hat, resids)
Reset test. Tests the specification of a linear model adding and testing powers of fitted values.
Description
Reset test. Tests the specification of a linear model adding and testing powers of fitted values.
Usage
reset_test(
model,
robust = FALSE,
vcov = NULL,
max_power = 3,
quantiles = c(0.9, 0.95, 0.99)
)
Arguments
model |
An existing fit from a model function such as 'lm', 'lfe' and others compatible with 'update'. |
robust |
Use robust 'varcov' matrix. |
vcov |
Particular variance and covariances matrix. |
max_power |
Max power of fitted values to add. |
quantiles |
Vector of quantiles to calculate pvalues. |
Value
A 'tibble' with the Wald value, the corresponding pvalue, and the quantiles of the distribution.
Examples
x <- 1:10 + rnorm(10)
y <- 1:10
model <- lm(y~x)
r_test <- reset_test(model)
r_test <- reset_test(model, robust = TRUE)
r_test <- reset_test(model, quantiles = c(.97))
r_test <- reset_test(model, max_power = 4)
r_test <- reset_test(model, robust = TRUE, max_power = 4)
Random deviates of Mammen continuous distribution.
Description
Random deviates of Mammen continuous distribution.
Usage
rmammen_cont(n)
Arguments
n |
Number of observations. |
Value
Random deviates of size n.
Examples
rmammen_cont(10)
Random deviates of Mammen distribution.
Description
Random deviates of Mammen distribution.
Usage
rmammen_point(n)
Arguments
n |
Number of observations. |
Value
Random deviates of size n.
Examples
rmammen_point(10)
Random deviates of Rademacher distribution.
Description
Random deviates of Rademacher distribution.
Usage
rrademacher(n)
Arguments
n |
Number of observations. |
Value
Random deviates of size n.
Examples
rrademacher(10)
Calculates the Cramer von Mises value or Kolmogorov value given a linear model compatible with 'fitted.values' and 'residuals' functions.
Description
Calculates the Cramer von Mises value or Kolmogorov value given a linear model compatible with 'fitted.values' and 'residuals' functions.
Usage
statistic_value(model, value = "cvm_value")
Arguments
model |
An existing fit from a linear model function. |
value |
Type of value to compute, can be 'cvm_value' or 'kmv_value'. |
Value
The statistic value of the model.
Examples
x <- 1:10
y <- 2*x + rnorm(10)
model <- lm(y~x-1)
statistic_value(model)
statistic_value(model, value = "cvm_value")
statistic_value(model, value = "kmv_value")
Constructs a new model with noised residuals: y_new = y_fitted + residuals*noise
Description
Constructs a new model with noised residuals: y_new = y_fitted + residuals*noise
Usage
updated_model(model, fitting_data, distribution = "rnorm")
Arguments
model |
An existing fit from a model function such as 'lm', 'lfe', 'Arima' and others compatible with 'update'. |
fitting_data |
Data used to adjust a linear model. |
distribution |
Type of noise added to residuals, ej "rnorm" or "rrademacher". |
Value
Constructed linear model.
Examples
x <- 1:100
y <- 2*x + rnorm(100)
model <- lm(y~x-1)
fitting_data <- model.frame(model)
updated_model(model, fitting_data)
updated_model(model, fitting_data, distribution = "rnorm")
updated_model(model, fitting_data, distribution = "rmammen_point")
updated_model(model, fitting_data, distribution = "rmammen_cont")
updated_model(model, fitting_data, distribution = "rrademacher")
x_arma <- rnorm(100)
arma_model <- forecast::Arima(x_arma, c(1, 0, 1))
fitting_data_arma <- model.frame(arma_model)
updated_model(arma_model, fitting_data_arma)
Wald test. Tests restrictions*coefficients = value.
Description
Wald test. Tests restrictions*coefficients = value.
Usage
wald_test(
model,
restrictions,
value,
robust = FALSE,
vcov = NULL,
quantiles = c(0.9, 0.95, 0.99)
)
Arguments
model |
Model compatible with 'fitted' and 'residuals' functions. |
restrictions |
Matrix of size (number of restrictions) times length(coefficients), for free restrictions use zeros. |
value |
Values of restrictions. |
robust |
Use robust 'varcov' matrix. |
vcov |
Particular variance and covariances matrix. |
quantiles |
Vector of quantiles to calculate pvalues. |
Value
A 'tibbble' with the Wald value, the corresponding pvalue and the quantiles of the distribution.
Examples
x <- 1:10
z <- x**2
y <- 1:10
model <- lm(y~x+z)
restrictions <- diag(3)
value <- as.matrix(c(0, 0, 0))
w_test <- wald_test(model, restrictions, value)
w_test <- wald_test(model, restrictions, value, robust = TRUE)
w_test <- wald_test(model, restrictions, value, quantiles = c(.97))