The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
In the previous vignette, we discussed the model setup process
in-depth. But how do we get our estimates once we’ve run our model? In
this vignette, we discuss extracting estimates from our model object
with the get_estimates() function, and how to
age-standardize those estimates with age_standardize().
get_estimates() functionIn the RSTr introductory vignette, we generated age-standardized
estimates for lambda based on our example Michigan dataset.
To extract rates from an RSTr object, we can simply run
get_estimates():
# For computational reasons, full model fitting is not run during CRAN checks.
# When building on CRAN, this vignette loads a pre-fitted example model included with the package.
# The pkgdown website shows the full model-fitting workflow.
example_dir <- system.file("extdata", package = "RSTr")
mod_mst <- load_model("mstcar_example", example_dir)estimates <- get_estimates(mod_mst, rates_per = 1e5)
head(estimates)
#> county group year medians ci_lower ci_upper rel_prec events population
#> 1 26001 35-44 1979 41.09875 31.76009 47.11024 2.677417 1 964
#> 2 26003 35-44 1979 61.79864 50.74198 80.73919 2.060146 1 1011
#> 3 26005 35-44 1979 23.44843 18.67969 28.12923 2.481436 0 9110
#> 4 26007 35-44 1979 38.04293 26.32669 48.99415 1.678306 0 3650
#> 5 26009 35-44 1979 36.87313 31.41068 44.70636 2.773316 0 1763
#> 6 26011 35-44 1979 36.02715 32.00151 47.83611 2.275216 0 1470age_standardization() functionIn many cases, we will want to age-standardize our estimates based on
some (or all) age groups in our dataset. In our Michigan dataset, we
have six ten-year age groups over which we can standardize; let’s
age-standardize from ages 35-64. For RSTr objects,
age_standardize() takes in four arguments:
RSTr_obj: The RSTr model object created
with *car();
std_pop: A vector of standard
populations associated with the age groups of interest. Since our
Michigan data is from 1979-1988, we can use 1980 standard populations
from NIH.
It is recommended that you use the standard population that is most
closely associated with your dataset;
new_name: The name of your new standard population
group; and
groups: A vector of names matching each
group of interest. To age-standardize by all groups in a dataset, leave
this argument blank.
Once we have our std_pop vector, we can age-standardize
our estimates:
std_pop <- c(113154, 100640, 95799)
mod_mst <- age_standardize(mod_mst, std_pop, new_name = "35-64", groups = c("35-44", "45-54", "55-64"))
mod_mst
#> RSTr object:
#>
#> Model name: mstcar_example
#> Model type: MSTCAR
#> Data likelihood: binomial
#> Estimate Credible Interval: 95%
#> Number of geographic units: 83
#> Number of samples: 3000
#> Estimates age-standardized: Yes
#> Age-standardized groups: 35-64
#> Estimates suppressed: NoNotice now that the mod_mst object indicates we have
age-standardized our estimates and the names of our age-standardized
group. We can also add on to our list of age-standardized estimates by
simply specifying a different group:
std_pop <- c(68775, 34116, 9888)
mod_mst <- age_standardize(mod_mst, std_pop, new_name = "65up", groups = c("65-74", "75-84", "85+"))
mod_mst
#> RSTr object:
#>
#> Model name: mstcar_example
#> Model type: MSTCAR
#> Data likelihood: binomial
#> Estimate Credible Interval: 95%
#> Number of geographic units: 83
#> Number of samples: 3000
#> Estimates age-standardized: Yes
#> Age-standardized groups: 35-64 65up
#> Estimates suppressed: NoIf we want to generate estimates for all groups, i.e. 35 and
up, we can omit the groups argument and expand
std_pop to include all of our populations:
std_pop <- c(113154, 100640, 95799, 68775, 34116, 9888)
mod_mst <- age_standardize(mod_mst, std_pop, new_name = "35up")
mod_mst
#> RSTr object:
#>
#> Model name: mstcar_example
#> Model type: MSTCAR
#> Data likelihood: binomial
#> Estimate Credible Interval: 95%
#> Number of geographic units: 83
#> Number of samples: 3000
#> Estimates age-standardized: Yes
#> Age-standardized groups: 35-64 65up 35up
#> Estimates suppressed: No
mst_estimates_as <- get_estimates(mod_mst)
head(mst_estimates_as)
#> county group year medians ci_lower ci_upper rel_prec events population
#> 1 26001 35-64 1979 202.9310 166.2747 242.0648 2.677538 7 3353
#> 2 26003 35-64 1979 256.0684 214.3235 348.2249 1.912365 12 3105
#> 3 26005 35-64 1979 126.2409 112.8141 147.4087 3.649160 27 23926
#> 4 26007 35-64 1979 174.0603 154.6135 209.3011 3.182813 15 10000
#> 5 26009 35-64 1979 184.0658 163.5568 205.2774 4.411868 11 5152
#> 6 26011 35-64 1979 207.9429 185.7425 231.7124 4.523455 8 4517Now, get_estimates(mod_mst) shows the age-standardized
estimates as opposed to our non-standardized estimates. Should you want
to see the non-standardized estimates instead, you can set the argument
standardized = FALSE.
In this vignette, we explored the get_estimates()
function and investigated age-standardization with the
age_standardize() function. Age-standardization is one of
the most important features of the RSTr package; using just a few
arguments, we can easily generate estimates across our population
groups.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.