methods.matreg.RdVarious extractor functions for objects of class "matreg".
# S3 method for class 'matreg'
coef(object, ...)
# S3 method for class 'matreg'
vcov(object, ...)
# S3 method for class 'matreg'
sigma(object, REML=TRUE, ...)
# S3 method for class 'matreg'
logLik(object, REML=FALSE, ...)
# S3 method for class 'matreg'
AIC(object, ..., k=2, correct=FALSE, REML=FALSE)
# S3 method for class 'matreg'
BIC(object, ..., REML=FALSE)
# S3 method for class 'matreg'
confint(object, parm, level, digits, ...)
# S3 method for class 'confint.matreg'
print(x, digits=x$digits, ...)an object of class "matreg".
logical whether the returned value should be based on ML or REML estimation.
numeric value to specify the penalty per parameter. The default (k=2) is the classical AIC. See AIC for more details.
logical to specify whether the regular (default) or corrected (i.e., AICc) should be extracted.
For confint():
this argument is here for compatibility with the generic function confint, but is (currently) ignored.
numeric value between 0 and 100 to specify the confidence interval level (see here for details). If unspecified, the default is to take the value from the object.
optional integer to specify the number of decimal places to which the results should be rounded. If unspecified, the default is to take the value from the object.
an object of class "confint.matreg".
other arguments.
The coef function extracts the estimated (standardized) regression coefficients from objects of class "matreg". The vcov function extracts the corresponding variance-covariance matrix (note: the se function can also be used to extract the standard errors). The confint function extracts the confidence intervals.
Under the ‘Regular \(R\) Matrix’ case (see matreg), the sigma function extracts the square root of the estimated error variance (by default, based on the unbiased estimate of the error variance). The logLik, AIC, and BIC functions extract the corresponding values (note: for compatibility with the behavior for lm objects, these values are based by default on ML estimation).
Depending on the function, either a vector, a matrix, or a scalar with the extracted value(s).
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03
matreg for the function to create matreg objects.
### fit a regression model with lm() to the 'mtcars' dataset
res <- lm(mpg ~ hp + wt + am, data=mtcars)
summary(res)
#>
#> Call:
#> lm(formula = mpg ~ hp + wt + am, data = mtcars)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -3.4221 -1.7924 -0.3788 1.2249 5.5317
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 34.002875 2.642659 12.867 2.82e-13 ***
#> hp -0.037479 0.009605 -3.902 0.000546 ***
#> wt -2.878575 0.904971 -3.181 0.003574 **
#> am 2.083710 1.376420 1.514 0.141268
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 2.538 on 28 degrees of freedom
#> Multiple R-squared: 0.8399, Adjusted R-squared: 0.8227
#> F-statistic: 48.96 on 3 and 28 DF, p-value: 2.908e-11
#>
coef(res)
#> (Intercept) hp wt am
#> 34.00287512 -0.03747873 -2.87857541 2.08371013
vcov(res)
#> (Intercept) hp wt am
#> (Intercept) 6.983648371 8.169829e-03 -2.110453741 -2.931655806
#> hp 0.008169829 9.226413e-05 -0.006091009 -0.005187758
#> wt -2.110453741 -6.091009e-03 0.818971675 0.908534063
#> am -2.931655806 -5.187758e-03 0.908534063 1.894532436
se(res)
#> (Intercept) hp wt am
#> 2.642659337 0.009605422 0.904970538 1.376420152
sigma(res)
#> [1] 2.537512
confint(res)
#> 2.5 % 97.5 %
#> (Intercept) 28.58963286 39.41611738
#> hp -0.05715454 -0.01780291
#> wt -4.73232353 -1.02482730
#> am -0.73575874 4.90317900
logLik(res)
#> 'log Lik.' -73.06742 (df=5)
AIC(res)
#> [1] 156.1348
BIC(res)
#> [1] 163.4635
### covariance matrix of the dataset
S <- cov(mtcars)
### fit the same regression model using matreg()
res <- matreg(y="mpg", x=c("hp","wt","am"), R=S, cov=TRUE,
means=colMeans(mtcars), n=nrow(mtcars))
summary(res)
#>
#> estimate se tval df pval ci.lb ci.ub
#> intrcpt 34.0029 2.6427 12.8669 28 <.0001 28.5896 39.4161 ***
#> hp -0.0375 0.0096 -3.9018 28 0.0005 -0.0572 -0.0178 ***
#> wt -2.8786 0.9050 -3.1808 28 0.0036 -4.7323 -1.0248 **
#> am 2.0837 1.3764 1.5139 28 0.1413 -0.7358 4.9032
#>
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 2.5375 on 28 degrees of freedom
#> Multiple R-squared: 0.8399, Adjusted R-squared: 0.8227
#> F-statistic: 48.9600 on 3 and 28 DF, p-value: <.0001
#>
coef(res)
#> intrcpt hp wt am
#> 34.00287512 -0.03747873 -2.87857541 2.08371013
vcov(res)
#> intrcpt hp wt am
#> intrcpt 6.983648371 8.169829e-03 -2.110453741 -2.931655806
#> hp 0.008169829 9.226413e-05 -0.006091009 -0.005187758
#> wt -2.110453741 -6.091009e-03 0.818971675 0.908534063
#> am -2.931655806 -5.187758e-03 0.908534063 1.894532436
se(res)
#> intrcpt hp wt am
#> 2.642659337 0.009605422 0.904970538 1.376420152
sigma(res)
#> [1] 2.537512
confint(res)
#>
#> estimate ci.lb ci.ub
#> intrcpt 34.0029 28.5896 39.4161
#> hp -0.0375 -0.0572 -0.0178
#> wt -2.8786 -4.7323 -1.0248
#> am 2.0837 -0.7358 4.9032
#>
logLik(res)
#> 'log Lik.' -73.06742 (df=5)
AIC(res)
#> [1] 156.1348
BIC(res)
#> [1] 163.4635