deltaMethod: Estimate and Standard Error of a Nonlinear Function of... In car: Companion to Applied Regression

Description

`deltaMethod` is a generic function that uses the delta method to get a first-order approximate standard error for a nonlinear function of a vector of random variables with known or estimated covariance matrix.

Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33``` ```deltaMethod(object, ...) ## Default S3 method: deltaMethod(object, g., vcov., func=g., constants, level=0.95, rhs, ..., envir=parent.frame()) ## S3 method for class 'lm' deltaMethod(object, g., vcov.=vcov(object, complete=FALSE), parameterNames=names(coef(object)), ..., envir=parent.frame()) ## S3 method for class 'nls' deltaMethod(object, g., vcov.=vcov(object, complete=FALSE), ..., envir=parent.frame()) ## S3 method for class 'multinom' deltaMethod(object, g., vcov. = vcov(object, complete=FALSE), parameterNames = if (is.matrix(coef(object))) colnames(coef(object)) else names(coef(object)), ..., envir=parent.frame()) ## S3 method for class 'polr' deltaMethod(object, g., vcov.=vcov(object, complete=FALSE), ..., envir=parent.frame()) ## S3 method for class 'survreg' deltaMethod(object, g., vcov. = vcov(object, complete=FALSE), parameterNames = names(coef(object)), ..., envir=parent.frame()) ## S3 method for class 'coxph' deltaMethod(object, g., vcov. = vcov(object, complete=FALSE), parameterNames = names(coef(object)), ..., envir=parent.frame()) ## S3 method for class 'mer' deltaMethod(object, g., vcov. = vcov(object, complete=FALSE), parameterNames = names(fixef(object)), ..., envir=parent.frame()) ## S3 method for class 'merMod' deltaMethod(object, g., vcov. = vcov(object, complete=FALSE), parameterNames = names(fixef(object)), ..., envir=parent.frame()) ## S3 method for class 'lme' deltaMethod(object, g., vcov. = vcov(object, complete=FALSE), parameterNames = names(fixef(object)), ..., envir=parent.frame()) ## S3 method for class 'lmList' deltaMethod(object, g., ..., envir=parent.frame()) ```

Arguments

 `object` For the default method, `object` is either (1) a vector of `p` named elements, so `names(object)` returns a list of `p` character strings that are the names of the elements of `object`; or (2) a model object for which there are `coef` and `vcov` methods, and for which the named coefficient vector returned by `coef` is asymptotically normally distributed with asymptotic covariance matrix returned by `vcov`. For the other methods, `object` is a regression object for which `coef(object)` or `fixef(object)` returns a vector of parameter estimates. `g.` A quoted string that is the function of the parameter estimates to be evaluated; see the details below. `vcov.` The (estimated) covariance matrix of the coefficient estimates. For the default method, this argument is required. For all other methods, this argument must either provide the estimated covariance matrix or a function that when applied to `object` returns a covariance matrix. The default is to use the function `vcov`. `func` A quoted string used to annotate output. The default of `func = g.` is usually appropriate. `parameterNames` A character vector of length `p` that gives the names of the parameters in the same order as they appear in the vector of estimates. This argument will be useful if some of the names in the vector of estimates include special characters, like `I(x2^2)`, or `x1:x2` that will confuse the numerical differentiation function. See details below. `constants` This argument is a named vector whose elements are constants that are used in the `f` argument. It isn't generally necessary to specify this argument but it may be convenient to do so. `level` level for confidence interval, default `0.95`. `rhs` hypothesized value for the specified function of parameters; if absent no hypothesis test is performed. `...` Used to pass arguments to the generic method. `envir` Environment in which `g.` is evaluated; not normally specified by the user.

Details

Suppose x is a random vector of length p that is at least approximately normally distributed with mean β and estimated covariance matrix C. Then any function g(β) of β, is estimated by g(x), which is in large samples normally distributed with mean g(β) and estimated variance h'Ch, where h is the first derivative of g(β) with respect to β evaluated at x. This function returns both g(x) and its standard error, the square root of the estimated variance.

The default method requires that you provide x in the argument `object`, C in the argument `vcov.`, and a text expression in argument `g.` that when evaluated gives the function g. The call `names(object)` must return the names of the elements of `x` that are used in the expression `g.`.

Since the delta method is often applied to functions of regression parameter estimates, the argument `object` may be the name of a regression object from which the estimates and their estimated variance matrix can be extracted. In most regression models, estimates are returned by the `coef(object)` and the variance matrix from `vcov(object)`. You can provide an alternative function for computing the sample variance matrix, for example to use a sandwich estimator.

For mixed models using `lme4` or `nlme`, the coefficient estimates are returned by the `fixef` function, while for `multinom`, `lmList` and `nlsList` coefficient estimates are returned by `coef` as a matrix. Methods for these models are provided to get the correct estimates and variance matrix.

The argument `g.` must be a quoted character string that gives the function of interest. For example, if you set `m2 <- lm(Y ~ X1 + X2 + X1:X2)`, then `deltaMethod(m2,"X1/X2")` applies the delta method to the ratio of the coefficient estimates for `X1` and `X2`. The argument `g.` can consist of constants and names associated with the elements of the vector of coefficient estimates.

In some cases the names may include characters such as the colon `:` used in interactions, or mathematical symbols like `+` or `-` signs that would confuse the function that computes numerical derivatives, and for this case you can replace the names of the estimates with the `parameterNames` argument. For example, the ratio of the `X2` main effect to the interaction term could be computed using `deltaMethod(m2, "b1/b3", parameterNames=c("b0", "b1", "b2", "b3"))`. The name “`(Intercept)`” used for the intercept in linear and generalized linear models is an exception, and it will be correctly interpreted by `deltaMethod`. Another option is to use back-ticks to quote nonstandard R names, as in `deltaMethod(m2,"X1/`X1:X2`")`.

For `multinom` objects, the `coef` function returns a matrix of coefficients, with each row giving the estimates for comparisons of one category to the baseline. The `deltaMethod` function applies the delta method to each row of this matrix. Similarly, for `lmList` and `nlsList` objects, the delta method is computed for each element of the list of models fit.

For nonlinear regression objects produced by the `nls` function, the call `coef(object)` returns the estimated coefficient vectors with names corresponding to parameter names. For example, `m2 <- nls(y ~ theta/(1 + gamma * x), start = list(theta=2, gamma=3))` will have parameters named `c("theta", "gamma")`. In many other familiar regression models, such as those produced by `lm` and `glm`, the names of the coefficient estimates are the corresponding regressor names, not parameter names.

For mixed-effects models fit with `lmer` and `glmer` from the lme4 package or `lme` and `nlme` from the nlme package, only fixed-effect coefficients are considered.

For regression models for which methods are not provided, you can extract the named vector of coefficient estimates and and estimate of its covariance matrix and then apply the default `deltaMethod` function.

Note: Earlier versions of `deltaMethod` included an argument `parameterPrefix` that implemented the same functionality as the `parameterNames` argument, but which caused several problems that were not easily fixed without the change in syntax.

Value

An object of class `"deltaMethod"`, inheriting from `"data.frame"`, for which a `print` method is provided. The object contains columns named `Estimate` for the estimate, `SE` for its standard error, and columns for confidence limits and possibly a hypothesis test. The value of `g.` is given as a row label.

Author(s)

Sanford Weisberg, sandy@umn.edu, John Fox jfox@mcmaster.ca, and Pavel Krivitsky.

References

Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.

Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.

Weisberg, S. (2014) Applied Linear Regression, Fourth Edition, Wiley, Section 6.1.2.

First derivatives of `g.` are computed using symbolic differentiation by the function `D`.

Examples

 ```1 2 3 4 5 6 7 8``` ```m1 <- lm(time ~ t1 + t2, data = Transact) deltaMethod(m1, "b1/b2", parameterNames= paste("b", 0:2, sep="")) deltaMethod(m1, "t1/t2", rhs=1) # use names of preds. rather than coefs. deltaMethod(m1, "t1/t2", vcov=hccm) # use hccm function to est. vars. deltaMethod(m1, "1/(Intercept)") # The next example invokes the default method by extracting the # vector of estimates and covariance matrix explicitly deltaMethod(coef(m1), "t1/t2", vcov.=vcov(m1)) ```

Example output

```Loading required package: carData
Estimate        SE    2.5 %   97.5 %
b1/b2 2.684653 0.3189858 2.059452 3.309853
Estimate        SE    2.5 %   97.5 %
t1/t2 2.684653 0.3189858 2.059452 3.309853
Estimate        SE    2.5 %   97.5 %
t1/t2 2.684653 0.5583555 1.590296 3.779009
Estimate          SE        2.5 %     97.5 %
1/(Intercept) 0.006926674 0.008182503 -0.009110737 0.02296408
Estimate        SE    2.5 %   97.5 %
t1/t2 2.684653 0.3189858 2.059452 3.309853
```

car documentation built on Nov. 6, 2021, 9:06 a.m.