Calculates a regression tree estimator for a finite population mean/proportion or total based on sample data collected from a complex sampling design and auxiliary population data.
Usage
gregTree(
y,
xsample,
xpop,
pi = NULL,
pi2 = NULL,
var_est = FALSE,
var_method = "LinHB",
B = 1000,
pval = 0.05,
perm_reps = 500,
bin_size = NULL,
fpc = TRUE,
messages = TRUE,
parallel = "multicore",
ncpus = 2
)Arguments
- y
A numeric vector of the sampled response variable.
- xsample
A data frame of the auxiliary data in the sample.
- xpop
A data frame of population level auxiliary information. It must contain the same names as xsample. If datatype = "raw", must contain unit level data. If datatype = "totals" or "means", then contains one row of aggregated, population totals or means for the auxiliary data. Default is "raw".
- pi
A numeric vector of inclusion probabilities for each sampled unit in y. If NULL, then simple random sampling without replacement is assumed.
- pi2
A square matrix of the joint inclusion probabilities. Needed for the "LinHT" variance estimator.
- var_est
A logical indicating whether or not to compute a variance estimator. Default is FALSE.
- var_method
The method to use when computing the variance estimator. Options are a Taylor linearized technique: "LinHB"= Hajek-Berger estimator, "LinHH" = Hansen-Hurwitz estimator, "LinHTSRS" = Horvitz-Thompson estimator under simple random sampling without replacement, and "LinHT" = Horvitz-Thompson estimator or a resampling technique: "bootstrapSRS" = bootstrap variance estimator under simple random sampling without replacement. The default is "LinHB".
- B
The number of bootstrap samples if computing the bootstrap variance estimator. Default is 1000.
- pval
Designated p-value level to reject null hypothesis in permutation test used to fit the regression tree. Default value is 0.05.
- perm_reps
An integer specifying the number of permutations for each permutation test run to fit the regression tree. Default value is 500.
- bin_size
A integer specifying the minimum number of observations in each node.
- fpc
Default to TRUE, logical for whether or not the variance calculation should include a finite population correction when calculating the "LinHTSRS" or the "SRSbootstrap" variance estimator.
- messages
A logical indicating whether to output the messages internal to mase. Default is TRUE.
- parallel
The type of parallel processing to use, if any. Can take values of "multicore", "snow", or "no". Only used if var_method = "bootstrapSRS". See boot::boot() for more details on parallel processing types.
- ncpus
The number of parallel workers to use. Only used if var_method = "bootstrapSRS" and parallel = "snow" or "multicore".
Value
A list of output containing:
* pop_total: Estimate of population total.
* pop_mean: Estimate of the population mean (or proportion).
* weights: Survey weights produced by gregTree.
* pop_total_var: Estimated variance of population total estimate.
* pop_mean_var: Estimated variance of population mean estimate.
References
McConville K~S, Toth D (2018). “Automated selection of post-strata using a model-assisted regression tree estimator.” Scandinavian Journal of Statistics.
Examples
library(dplyr)
data(IdahoPop)
data(IdahoSamp)
xsample <- filter(IdahoSamp, COUNTYFIPS == "16055")
xpop <- filter(IdahoSamp, COUNTYFIPS == "16055")
gregTree(y = xsample$BA_TPA_ADJ,
xsample = xsample[c("tcc", "elev")],
xpop = xpop[c("tcc", "elev")],
var_est = TRUE)
#> Assuming simple random sampling
#> $pop_total
#> [1] 10722.31
#>
#> $pop_mean
#> [1] 107.2231
#>
#> $pop_total_var
#> [1] NaN
#>
#> $pop_mean_var
#> [1] NaN
#>
#> $weights
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#> $tree
#>
#> RPMS Recursive Partitioning Equation
#> y ~ tcc + elev
#>
#> Estimating Equation
#> y ~ 1
#>
#>
#> [1] "unequal probability of selection, sample design"
#> [1] "R-squared of model: 0.189980380370146"
#>
#> ===================== Tree Model ===================
#>
#> Splits
#> sp elev <= 934.5
#>
#> coefficients
#> node 1
#> 2 83.03977
#> 3 143.49798
#>
#>
#>