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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). For svyplan_prec, only types "prop" and "mean" are supported (not "cluster" or "multi").

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, rel_var, resp_rate, fixed_cost, stage deltas (delta or delta_psu; plus delta_ssu for 3-stage), stage ratios (k or k_psu; plus k_ssu for 3-stage), stage costs (cost_psu, cost_ssu, cost_tsu; for 2-stage, cost_tsu aliases cost_ssu)

  • power_prop: p1, p2, n, power, alpha, N, deff, alternative, overlap, rho, resp_rate (excluding the solved-for parameter). Not supported for objects with vector n.

  • power_mean: effect, var, n, power, alpha, N, deff, alternative, overlap, rho, resp_rate (excluding the solved-for parameter). Not supported for objects with vector n.

  • 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

# Cluster design: sensitivity to delta (homogeneity)
cl <- n_cluster(stage_cost = c(500, 50), delta = 0.05, budget = 100000)
predict(cl, data.frame(delta = c(0.01, 0.03, 0.05, 0.10, 0.15)))
#>   delta     n_psu  psu_size   total_n         cv  cost
#> 1  0.01  48.23430 31.464265 1517.6570 0.02931966 1e+05
#> 2  0.03  71.47587 17.981472 1285.2413 0.03427016 1e+05
#> 3  0.05  84.08997 13.784049 1159.1003 0.03760588 1e+05
#> 4  0.10 102.63340  9.486833  973.6660 0.04357388 1e+05
#> 5  0.15 114.10493  7.527727  858.9507 0.04800166 1e+05

# Allocation: how does the CV change with sample size?
frame <- data.frame(
  N    = c(4000, 3000, 3000),
  sd   = c(10, 15, 8),
  mean = c(50, 60, 55)
)
alloc <- n_alloc(frame, n = 600)
predict(alloc, data.frame(n = seq(200, 1000, 200)))
#>      n        se       moe          cv cost
#> 1  200 0.7624828 1.4944388 0.013990510  200
#> 2  400 0.5332495 1.0451498 0.009784394  400
#> 3  600 0.4305191 0.8438019 0.007899432  600
#> 4  800 0.3685682 0.7223803 0.006762719  800
#> 5 1000 0.3257913 0.6385393 0.005977823 1000