The pumpresult object is an S3 class that holds the results from `pump_power()`, `pump_sample()`, and `pump_mdes()`.
It has several methods that pull different information from this object, and some printing methods for getting nicely formatted results.
Pump result objects are also data.frames, so they can be easily manipulated and combined. The return values from the `grid` functions will just return data frames in general.
Returns whether call was power, mdes, or sample.
Calls the print_context method with results and control both set to TRUE.
Usage
params(x, ...)
d_m(x, ...)
design(x, ...)
search_path(x, ...)
pump_type(x)
is.pumpresult(x)
# S3 method for class 'pumpresult'
x[...]
# S3 method for class 'pumpresult'
x[[...]]
# S3 method for class 'pumpresult'
dim(x, ...)
# S3 method for class 'pumpresult'
summary(object, ...)
# S3 method for class 'pumpresult'
print(x, n = 10, header = TRUE, search = FALSE, include_SE = TRUE, ...)
# S3 method for class 'pumpresult'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)Arguments
- x
a pumpresult object (except for is.pumpresult, where it is a generic object to check).
- ...
additional arguments to be passed to the as.data.frame.list methods.
- object
Object to summarize.
- n
Number of lines of search path to print, max.
- header
FALSE means skip some header info on the result, just print the data.frame of actual results.
- search
FALSE means don't print the search path for a result for mdes or sample.
- include_SE
TRUE means include standard errors given design (if any) in the printout. Default to TRUE.
- row.names
NULL or a character vector giving the row names for the data frame.
- optional
logical. If TRUE, setting row names and converting column names is optional.
Value
params: List of design parameters used.
d_m: Context (d_m) used (as string).
design (the randomization and levels) as string.
search_path: Dataframe describing search path, if it was saved in the pumpresult object.
pump_type: power, mdes, or sample, as a string.
is.pumpresult: TRUE if object is a pumpresult object.
`[`: pull out rows and columns of the dataframe.
`[[`: pull out single element of dataframe.
dim: Dimension of pumpresult (as matrix)
summary: No return value; prints results.
print: No return value; prints results.
as.data.frame: pumpresult object as a clean dataframe (no more attributes from pumpresult).
Examples
pp <- pump_power(d_m = "d3.2_m3ff2rc",
MTP = 'HO', nbar = 50, J = 30, K = 10,
M = 5, MDES = 0.125, Tbar = 0.5, alpha = 0.05,
numCovar.1 = 1, numCovar.2 = 1,
R2.1 = 0.1, R2.2 = 0.1, ICC.2 = 0.2, ICC.3 = 0.2,
omega.2 = 0, omega.3 = 0.1, rho = 0.5, tnum = 1000)
print(pp)
#> power result: d3.2_m3ff2rc d_m with 5 outcomes
#> MTP D1indiv D2indiv D3indiv D4indiv D5indiv indiv.mean min1
#> None 0.694 0.702 0.738 0.713 0.715 0.712
#> SE ( 0.050 ) ( 0.050 ) ( 0.050 ) ( 0.050 ) ( 0.050 )
#> HO 0.550 0.551 0.555 0.567 0.552 0.555 0.807
#> min2 min3 min4 complete df1
#> 279
#>
#> 0.657 0.542 0.436 0.379
#> 0.005 <= MCSE <= 0.008
params(pp)
#> $MTP
#> [1] "HO"
#>
#> $MDES
#> [1] 0.125 0.125 0.125 0.125 0.125
#>
#> $numZero
#> NULL
#>
#> $M
#> [1] 5
#>
#> $J
#> [1] 30
#>
#> $K
#> [1] 10
#>
#> $nbar
#> [1] 50
#>
#> $Tbar
#> [1] 0.5
#>
#> $alpha
#> [1] 0.05
#>
#> $two.tailed
#> [1] TRUE
#>
#> $numCovar.1
#> [1] 1
#>
#> $numCovar.2
#> [1] 1
#>
#> $numCovar.3
#> [1] 0
#>
#> $R2.1
#> [1] 0.1 0.1 0.1 0.1 0.1
#>
#> $R2.2
#> [1] 0.1 0.1 0.1 0.1 0.1
#>
#> $R2.3
#> [1] 0 0 0 0 0
#>
#> $ICC.2
#> [1] 0.2 0.2 0.2 0.2 0.2
#>
#> $ICC.3
#> [1] 0.2 0.2 0.2 0.2 0.2
#>
#> $omega.2
#> [1] 0 0 0 0 0
#>
#> $omega.3
#> [1] 0.1 0.1 0.1 0.1 0.1
#>
#> $rho
#> [1] 0.5
#>
#> $rho.matrix
#> NULL
#>
#> $B
#> [1] 1000
#>
#> $tnum
#> [1] 1000
#>
#> $d_m
#> [1] "d3.2_m3ff2rc"
#>
print_context(pp)
#> power result: d3.2_m3ff2rc d_m with 5 outcomes
#>
#> MDES vector: 0.125, 0.125, 0.125, 0.125, 0.125
#> nbar: 50 J: 30 K: 10 Tbar: 0.5
#> alpha: 0.05
#> Level:
#> 1: R2: 0.1 (1 covariate)
#> 2: R2: 0.1 (1 covariate) ICC: 0.2 omega: 0
#> 3: fixed effects ICC: 0.2 omega: 0.1
#> rho = 0.5
d_m(pp)
#> [1] "d3.2_m3ff2rc"
pump_type(pp)
#> [1] "power"
is.pumpresult(pp)
#> [1] TRUE
as.data.frame(pp)
#> MTP D1indiv D2indiv D3indiv D4indiv D5indiv indiv.mean min1 min2 min3
#> 1 None 0.694 0.702 0.738 0.713 0.715 0.7124 NA NA NA
#> 2 HO 0.550 0.551 0.555 0.567 0.552 0.5550 0.807 0.657 0.542
#> min4 complete SE1 SE2 SE3 SE4 SE5 df1
#> 1 NA NA 0.05043808 0.05043808 0.05043808 0.05043808 0.05043808 279
#> 2 0.436 0.379 NA NA NA NA NA NA
dim(pp)
#> [1] 2 18
summary(pp)
#> power result: d3.2_m3ff2rc d_m with 5 outcomes
#>
#> MDES vector: 0.125, 0.125, 0.125, 0.125, 0.125
#> nbar: 50 J: 30 K: 10 Tbar: 0.5
#> alpha: 0.05
#> Level:
#> 1: R2: 0.1 (1 covariate)
#> 2: R2: 0.1 (1 covariate) ICC: 0.2 omega: 0
#> 3: fixed effects ICC: 0.2 omega: 0.1
#> rho = 0.5
#> MTP D1indiv D2indiv D3indiv D4indiv D5indiv indiv.mean min1
#> None 0.694 0.702 0.738 0.713 0.715 0.712
#> SE ( 0.050 ) ( 0.050 ) ( 0.050 ) ( 0.050 ) ( 0.050 )
#> HO 0.550 0.551 0.555 0.567 0.552 0.555 0.807
#> min2 min3 min4 complete df1
#> 279
#>
#> 0.657 0.542 0.436 0.379
#> 0.005 <= MCSE <= 0.008
#> (tnum = 1000)
transpose_power_table(pp)
#> power result: d3.2_m3ff2rc d_m with 5 outcomes
#> power None HO
#> individual outcome 1 0.694 0.550
#> SE ( )
#> individual outcome 2 0.702 0.551
#> individual outcome 3 0.738 0.555
#> individual outcome 4 0.713 0.567
#> individual outcome 5 0.715 0.552
#> mean individual 0.712 0.555
#> 1-minimum 0.807
#> 2-minimum 0.657
#> 3-minimum 0.542
#> 4-minimum 0.436
#> complete 0.379
#> 0.005 <= MCSE <= 0.008
J <- pump_sample(d_m = "d2.1_m2fc",
MTP = 'HO', power.definition = 'D1indiv',
typesample = 'J', target.power = 0.7,
nbar = 50, M = 3, MDES = 0.125,
Tbar = 0.5, alpha = 0.05, numCovar.1 = 1,
R2.1 = 0.1, ICC.2 = 0.05, rho = 0.2, tnum = 1000)
search_path(J)
#> step MTP target.power pt dx w power delta
#> 1 0 HO 0.7 27.00000 NA 100 0.6000000 -0.10000000
#> 2 0 HO 0.7 29.35261 NA 100 0.6100000 -0.09000000
#> 3 0 HO 0.7 31.80348 NA 100 0.7200000 0.02000000
#> 4 0 HO 0.7 34.35261 NA 100 0.7600000 0.06000000
#> 5 0 HO 0.7 37.00000 NA 100 0.8500000 0.15000000
#> 6 1 HO 0.7 31.96015 0.02845129 110 0.6000000 -0.10000000
#> 7 2 HO 0.7 33.10667 0.04118361 121 0.7272727 0.02727273
#> 8 3 HO 0.7 32.84062 0.04189974 133 0.6842105 -0.01578947
#> 9 4 HO 0.7 32.93989 0.04237974 146 0.7328767 0.03287671
#> 10 5 HO 0.7 32.77115 0.04273019 161 0.6645963 -0.03540373
#> 11 6 HO 0.7 32.92223 0.04430372 177 0.7401130 0.04011299
#> 12 7 HO 0.7 32.77356 0.04493267 195 0.7589744 0.05897436
#> 13 8 HO 0.7 32.57876 0.04375818 1000 0.7100000 0.01000000
#> 14 9 HO 0.7 32.51694 0.04231497 1000 0.7160000 0.01600000
#> 15 10 HO 0.7 32.43206 0.04015613 1000 0.6900000 -0.01000000
#> 16 11 HO 0.7 32.47802 0.04176746 287 0.7317073 0.03170732
#> 17 12 HO 0.7 32.39186 0.03508176 316 0.7120253 0.01202532
#> 18 13 HO 0.7 32.36125 0.03441224 348 0.7155172 0.01551724
#> 19 14 HO 0.7 32.13187 0.02219274 383 0.7650131 0.06501305
#> 20 15 HO 0.7 32.08622 0.02766443 421 0.7173397 0.01733967
#> 21 16 HO 0.7 31.88810 0.02205884 1000 0.6890000 -0.01100000
#> 22 17 HO 0.7 32.06172 0.02737484 509 0.7308448 0.03084479
#> 23 18 HO 0.7 31.95504 0.02555561 560 0.7107143 0.01071429
#> 24 19 HO 0.7 31.91377 0.02484937 1000 0.7190000 0.01900000
#> 25 20 HO 0.7 31.81727 0.02333786 1000 0.6960000 -0.00400000
#> 26 20 HO 0.7 31.81727 0.02333786 4000 0.7050000 0.00500000
power_curve(J)
#> step pt w MTP target.power power
#> 1 0 27.00000 2000 HO 0.7 0.6265
#> 2 0 30.94638 2000 HO 0.7 0.6915
#> 3 0 35.16185 2000 HO 0.7 0.7395
#> 4 0 39.64638 2000 HO 0.7 0.8035
#> 5 0 44.40000 2000 HO 0.7 0.8655