# stepwise_replacement: implementation of the decomposition algorithm of stepwise... In DemoDecomp: Decompose Demographic Functions

## Description

This implements the algorithm described in Andreev et al (2002), with defaults set to approximate their recommendations for replacement ordering and result averaging.

## Usage

 ```1 2``` ```stepwise_replacement(func, pars1, pars2, symmetrical = TRUE, direction = "up", ...) ```

## Arguments

 `func` A function specified by the user. This must be able to take the vectors `pars1` or `pars2` as its argument, and to return the value of the function, `y`, when evaluated for these rates. It may also have additional arguments, not to be decomposed. `pars1` vector of covariates to be passed on as arguments to `func()`. Covariates can be in any order, as long as `func()` knows what to do with them. `pars1` is for time 1 (or population 1). `pars2` is the same as `pars1` but for time/population 2. `symmetrical` logical. default `TRUE` as recommended by authors. Shall we average the results of replacing 1 with 2 and 2 with 1? `direction` character. One of `"up"`, `"down"`, or `"both"`. Default `"up"`, as recommended by authors. `...` optional parameters to pass on to `func()`.

## Details

The `symmetrical` argument toggles whether or not we replace pars1 with pars2 (`FALSE`), or take the arithmetic average or replacement in both directions. `direction` refers to whether we go from the bottom up or top down, or take the arithmetic average of these when replacing vector elements. Although the total difference will always sum correctly, the calculated contribution from individual components can vary greatly depending on the order in general. Defaults are set to symmetrically replace from the bottom up, per the authors' suggestion.

## Value

a matrix of the variable effects that is organized in the same way as `pars1` and `pars2`.

## References

\insertRef

horiuchi2008decompositionDemoDecomp \insertRefandreev2012excelDemoDecomp

## Examples

 ``` 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``` ```data(Mxc1) data(Mxc2) # we'll want to pass in these dimensions dims <- dim(Mxc1) # we need parameters in vec form Mxc1v <- c(Mxc1) Mxc2v <- c(Mxc2) B <- stepwise_replacement(func = Mxc2e0abrvec, pars1 = Mxc1v, pars2 = Mxc2v, dims = dims, # authors' recommendations: symmetrical = TRUE, direction = "up") dim(B) <- dims # the output, B, is also a single vector. Each element corresponds # to the effect of changes in that particular covariate toward the # overall change in the function value. sum(B) should equal the # original difference (check1 <- Mxc2e0abr(Mxc2) - Mxc2e0abr(Mxc1)) (check2 <- sum(B)) # This package does not supply default plotting functions, but one # strategy might be the following: ## Not run: Age <- c(0, 1, seq(5, 85, by = 5)) matplot(Age, B, type = 'l', xlab = "Age", ylab = "Contrib to diff in e(0)", col = 1:6) legend("bottomleft",lty=1:5,col=1:6, legend = c("Neoplasms","Circulatory","Respiratory", "Digestive","Acc/viol","Other")) ## End(Not run) ```

DemoDecomp documentation built on May 2, 2019, 6:36 a.m.