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Grid Exploration for svyplan Objects

Source: R/predict.R
predict.svyplan.Rd

Evaluate a svyplan result at new parameter combinations. Returns a data frame with the varied parameters and resulting quantities, suitable for sensitivity analysis or plotting.

Usage

# S3 method for class 'svyplan_n'
predict(object, newdata, ...)

# S3 method for class 'svyplan_cluster'
predict(object, newdata, ...)

# S3 method for class 'svyplan_power'
predict(object, newdata, ...)

# S3 method for class 'svyplan_prec'
predict(object, newdata, ...)

Arguments

object

A svyplan object (svyplan_n, svyplan_cluster, svyplan_power, or svyplan_prec).

newdata

A data frame of parameter combinations to evaluate. Column names must be valid parameters for the object type (see Details). Parameters not in newdata stay at their original values from the object.

...

Ignored.

Value

A data frame with newdata columns followed by result columns. The result columns depend on the object type:

  • svyplan_n: n, se, moe, cv

  • svyplan_cluster: n_psu, psu_size, (opt. ssu_size), total_n, cv, cost

  • svyplan_power: n, power, effect

  • svyplan_prec: se, moe, cv

Details

Valid parameters for newdata by object type:

  • n_prop: p, moe, cv, alpha, N, deff, resp_rate

  • n_mean: var, mu, moe, cv, alpha, N, deff, resp_rate

  • n_cluster: cv, budget, resp_rate, fixed_cost

  • power_prop: p1, p2, n, power, alpha, N, deff, sides, overlap, rho, resp_rate (excluding the solved-for parameter)

  • power_mean: effect, var, n, power, alpha, N, deff, sides, overlap, rho, resp_rate (excluding the solved-for parameter)

  • prec_prop: p, n, alpha, N, deff, resp_rate

  • prec_mean: var, n, mu, alpha, N, deff, resp_rate

For svyplan_n objects, moe and cv are mutually exclusive in newdata. If one appears, that mode is used; if neither, the original mode is preserved.

Similarly, for svyplan_cluster objects, cv and budget are mutually exclusive.

Multi-indicator (n_multi) and multi-indicator cluster results are not supported; use the underlying single-indicator functions instead.

If evaluation fails for a particular row (e.g. invalid parameter combinations), that row's result columns are NA and a warning is issued.

Examples

# Sensitivity of sample size to deff and response rate
x <- n_prop(p = 0.3, moe = 0.05, deff = 1.5)
predict(x, expand.grid(
  deff = seq(1, 3, 0.5),
  resp_rate = c(0.7, 0.8, 0.9)
))
#>    deff resp_rate         n         se  moe         cv
#> 1   1.0       0.7  460.9751 0.02551067 0.05 0.08503558
#> 2   1.5       0.7  691.4626 0.02551067 0.05 0.08503558
#> 3   2.0       0.7  921.9501 0.02551067 0.05 0.08503558
#> 4   2.5       0.7 1152.4376 0.02551067 0.05 0.08503558
#> 5   3.0       0.7 1382.9252 0.02551067 0.05 0.08503558
#> 6   1.0       0.8  403.3532 0.02551067 0.05 0.08503558
#> 7   1.5       0.8  605.0298 0.02551067 0.05 0.08503558
#> 8   2.0       0.8  806.7064 0.02551067 0.05 0.08503558
#> 9   2.5       0.8 1008.3829 0.02551067 0.05 0.08503558
#> 10  3.0       0.8 1210.0595 0.02551067 0.05 0.08503558
#> 11  1.0       0.9  358.5362 0.02551067 0.05 0.08503558
#> 12  1.5       0.9  537.8042 0.02551067 0.05 0.08503558
#> 13  2.0       0.9  717.0723 0.02551067 0.05 0.08503558
#> 14  2.5       0.9  896.3404 0.02551067 0.05 0.08503558
#> 15  3.0       0.9 1075.6085 0.02551067 0.05 0.08503558

# Power curve: how does power vary with sample size?
pw <- power_prop(p1 = 0.30, p2 = 0.35, n = 500, power = NULL)
predict(pw, data.frame(n = seq(100, 1000, 100)))
#>       n     power effect
#> 1   100 0.1175929   0.05
#> 2   200 0.1877131   0.05
#> 3   300 0.2581732   0.05
#> 4   400 0.3272968   0.05
#> 5   500 0.3938436   0.05
#> 6   600 0.4569385   0.05
#> 7   700 0.5160053   0.05
#> 8   800 0.5707088   0.05
#> 9   900 0.6209032   0.05
#> 10 1000 0.6665884   0.05