SeqDesignInference: Inference for A Sequential Design
In SeqExpMatch: Sequential Experimental Design via Matching on-the-Fly
Description
Public fields
Methods
Examples
Description
An R6 Class that estimates, tests and provides intervals for a treatment effect in a sequential design.
This class takes a SeqDesign object as an input where this object
contains data for a fully completed sequential experiment (i.e. all treatment
assignments were allocated and all responses were collected). Then the user
specifies the type of estimation (difference-in-means or OLS) and the type
of sampling assumption (i.e. the superpopulation assumption leading to normal-based inference or
the finite population assumption implying randomization-exact-based inference) and then can query the
estimate and pval for the test. If the test is normal-theory based it is
testing the population H_0: beta_T = 0 and if the test is a randomization test,
it is testing the sharp null that H_0: Y_T_i = Y_C_i for all subjects. Confidence
interval construction is available for normal-theory based test type as well.
Public fields
estimate_typeThe type of estimate to compute (either "difference-in-means" or "OLS").
test_typeThe type of test to run (either "normal-based" or "randomization-exact").
num_coresThe number of CPU cores to employr during sampling within randomization inference
verboseA flag that indicates whether messages should be displayed to the user
Methods
Public methods
Method new()
Initialize a sequential experimental design estimation and test object after the sequential design is completed.
Usage
SeqDesignInference$new(
seq_des_obj,
estimate_type = "OLS",
test_type = "randomization-exact",
num_cores = 1,
verbose = TRUE
)
Arguments
seq_des_objA SeqDesign object whose entire n subjects are assigned and response y is recorded within.
estimate_typeThe type of estimate to compute (either "difference-in-means" or "OLS"). Default is "OLS"
as this provided higher power in our simulations.
test_typeThe type of test to run (either "normal-based" implying your subject entrant sampling
assumption is from a superpopulation or "randomization-exact" implying a finite sampling
assumption). The default option is "randomization-exact" as it provided properly-sized
tests in our simulations.
num_coresThe number of CPU cores to use to parallelize the sampling during randomization-based inference
(which is very slow). The default is 1 for serial computation. This parameter is ignored
for test_type = "normal-based".
verboseA flag indicating whether messages should be displayed to the user. Default is TRUE
Returns
A new 'SeqDesignTest' object.
Examples
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des)
Method compute_treatment_estimate()
Computes either the classic different-in-means estimate of the additive treatment effect,
i.e. ybar_T - ybar_C or the OLS estimate of the additive treatment effect linearly i.e.
the treatment different adjusted linearly for the p covariates.
Usage
SeqDesignInference$compute_treatment_estimate()
Returns
The numeric estimate of the treatment effect
Examples
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des)
seq_des_inf$compute_treatment_estimate()
Method compute_pval_for_no_treatment_effect()
Computes either the classic different-in-means estimate of the additive treatment effect,
i.e. ybar_T - ybar_C or the OLS estimate of the additive treatment effect linearly i.e.
the treatment different adjusted linearly for the p covariates.
Usage
SeqDesignInference$compute_pval_for_no_treatment_effect(nsim_exact_test = 501)
Arguments
nsim_exact_testThe number of randomization vectors to use in the randomization test (ignored if test_type
is not "randomization-exact"). The default is 501 providing pvalue resolution to a fifth of a percent.
Returns
The frequentist p-val for the test of nonzero treatment effect
Examples
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des)
seq_des_inf$compute_pval_for_no_treatment_effect()
Method randomization_inference_samples_for_no_treatment_effect()
Computes many randomization samples of either the classic different-in-means estimate of
the additive treatment effect, i.e. ybar_T - ybar_C or the OLS estimate of the additive
treatment effect linearly i.e. the treatment different adjusted linearly for the p covariates.
This function is useful if you wish to run your own, custom hypothesis tests.
Usage
SeqDesignInference$randomization_inference_samples_for_no_treatment_effect(
nsim_exact_test = 501
)
Arguments
nsim_exact_testThe number of randomization vectors.
The default is 501 providing pvalue resolution to a fifth of a percent.
Returns
The nsim_exact_test samples of the treatment effect under the null hypothesis of no treatment effect
where each sample is estimated from a different assignment vector for the prespecified design
Examples
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des)
samps = seq_des_inf$randomization_inference_samples_for_no_treatment_effect()
summary(samps)
Method compute_confidence_interval()
Computes either a:
1. classic frequentist confidence interval (CI) of the additive treatment effect
employing the normal theory approximation for both the
(a) difference in means estimator i.e. [ybar_T - ybar_C +/- t_alpha/2, n_T + n_C - 2 s_ybar_T - ybar_C] or
(b) the OLS estimator i.e. [beta_hat_T +/- t_alpha/2, n + p - 2 s_beta_hat_T]
where the z approximation is employed in lieu of the t is the design is a KK design or
2. a randomization-based CI of an additive shift effect of the potential outcomes under treatment and control
by an inversion of the randomization test at level alpha (this feature is incomplete).
Usage
SeqDesignInference$compute_confidence_interval(
alpha = 0.05,
nsim_exact_test = 501
)
Arguments
alphaThe confidence level in the computed confidence interval is 1 - alpha. The default is 0.05.
nsim_exact_testThe number of randomization vectors.
The default is 1000 providing good resolutions to confidence intervals.
Returns
A 1 - alpha sized frequentist confidence interval for the treatment effect
Examples
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des, test_type = "normal-based")
seq_des_inf$compute_confidence_interval()
Method clone()
The objects of this class are cloneable with this method.
Usage
SeqDesignInference$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
## ------------------------------------------------
## Method `SeqDesignInference$new`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des)
## ------------------------------------------------
## Method `SeqDesignInference$compute_treatment_estimate`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des)
seq_des_inf$compute_treatment_estimate()
## ------------------------------------------------
## Method `SeqDesignInference$compute_pval_for_no_treatment_effect`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des)
seq_des_inf$compute_pval_for_no_treatment_effect()
## ------------------------------------------------
## Method `SeqDesignInference$randomization_inference_samples_for_no_treatment_effect`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des)
samps = seq_des_inf$randomization_inference_samples_for_no_treatment_effect()
summary(samps)
## ------------------------------------------------
## Method `SeqDesignInference$compute_confidence_interval`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des, test_type = "normal-based")
seq_des_inf$compute_confidence_interval()
SeqExpMatch documentation built on June 1, 2021, 9:09 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Public fields Methods Examples
Description
An R6 Class that estimates, tests and provides intervals for a treatment effect in a sequential design.
This class takes a SeqDesign object as an input where this object
contains data for a fully completed sequential experiment (i.e. all treatment
assignments were allocated and all responses were collected). Then the user
specifies the type of estimation (difference-in-means or OLS) and the type
of sampling assumption (i.e. the superpopulation assumption leading to normal-based inference or
the finite population assumption implying randomization-exact-based inference) and then can query the
estimate and pval for the test. If the test is normal-theory based it is
testing the population H_0: beta_T = 0 and if the test is a randomization test,
it is testing the sharp null that H_0: Y_T_i = Y_C_i for all subjects. Confidence
interval construction is available for normal-theory based test type as well.
Public fields
estimate_typeThe type of estimate to compute (either "difference-in-means" or "OLS").
test_typeThe type of test to run (either "normal-based" or "randomization-exact").
num_coresThe number of CPU cores to employr during sampling within randomization inference
verboseA flag that indicates whether messages should be displayed to the user
Methods
Public methods
Method new()
Initialize a sequential experimental design estimation and test object after the sequential design is completed.
Usage
SeqDesignInference$new( seq_des_obj, estimate_type = "OLS", test_type = "randomization-exact", num_cores = 1, verbose = TRUE )
Arguments
seq_des_objA SeqDesign object whose entire n subjects are assigned and response y is recorded within.
estimate_typeThe type of estimate to compute (either "difference-in-means" or "OLS"). Default is "OLS" as this provided higher power in our simulations.
test_typeThe type of test to run (either "normal-based" implying your subject entrant sampling assumption is from a superpopulation or "randomization-exact" implying a finite sampling assumption). The default option is "randomization-exact" as it provided properly-sized tests in our simulations.
num_coresThe number of CPU cores to use to parallelize the sampling during randomization-based inference (which is very slow). The default is 1 for serial computation. This parameter is ignored for
test_type = "normal-based".verboseA flag indicating whether messages should be displayed to the user. Default is
TRUE
Returns
A new 'SeqDesignTest' object.
Examples
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD") seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0)) seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0)) seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1)) seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43)) seq_des_inf = SeqDesignInference$new(seq_des)
Method compute_treatment_estimate()
Computes either the classic different-in-means estimate of the additive treatment effect, i.e. ybar_T - ybar_C or the OLS estimate of the additive treatment effect linearly i.e. the treatment different adjusted linearly for the p covariates.
Usage
SeqDesignInference$compute_treatment_estimate()
Returns
The numeric estimate of the treatment effect
Examples
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD") seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0)) seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0)) seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1)) seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43)) seq_des_inf = SeqDesignInference$new(seq_des) seq_des_inf$compute_treatment_estimate()
Method compute_pval_for_no_treatment_effect()
Computes either the classic different-in-means estimate of the additive treatment effect, i.e. ybar_T - ybar_C or the OLS estimate of the additive treatment effect linearly i.e. the treatment different adjusted linearly for the p covariates.
Usage
SeqDesignInference$compute_pval_for_no_treatment_effect(nsim_exact_test = 501)
Arguments
nsim_exact_testThe number of randomization vectors to use in the randomization test (ignored if
test_typeis not "randomization-exact"). The default is 501 providing pvalue resolution to a fifth of a percent.
Returns
The frequentist p-val for the test of nonzero treatment effect
Examples
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD") seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0)) seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0)) seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1)) seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43)) seq_des_inf = SeqDesignInference$new(seq_des) seq_des_inf$compute_pval_for_no_treatment_effect()
Method randomization_inference_samples_for_no_treatment_effect()
Computes many randomization samples of either the classic different-in-means estimate of the additive treatment effect, i.e. ybar_T - ybar_C or the OLS estimate of the additive treatment effect linearly i.e. the treatment different adjusted linearly for the p covariates. This function is useful if you wish to run your own, custom hypothesis tests.
Usage
SeqDesignInference$randomization_inference_samples_for_no_treatment_effect( nsim_exact_test = 501 )
Arguments
nsim_exact_testThe number of randomization vectors. The default is 501 providing pvalue resolution to a fifth of a percent.
Returns
The nsim_exact_test samples of the treatment effect under the null hypothesis of no treatment effect
where each sample is estimated from a different assignment vector for the prespecified design
Examples
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD") seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0)) seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0)) seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1)) seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43)) seq_des_inf = SeqDesignInference$new(seq_des) samps = seq_des_inf$randomization_inference_samples_for_no_treatment_effect() summary(samps)
Method compute_confidence_interval()
Computes either a:
1. classic frequentist confidence interval (CI) of the additive treatment effect employing the normal theory approximation for both the (a) difference in means estimator i.e. [ybar_T - ybar_C +/- t_alpha/2, n_T + n_C - 2 s_ybar_T - ybar_C] or (b) the OLS estimator i.e. [beta_hat_T +/- t_alpha/2, n + p - 2 s_beta_hat_T] where the z approximation is employed in lieu of the t is the design is a KK design or
2. a randomization-based CI of an additive shift effect of the potential outcomes under treatment and control by an inversion of the randomization test at level alpha (this feature is incomplete).
Usage
SeqDesignInference$compute_confidence_interval( alpha = 0.05, nsim_exact_test = 501 )
Arguments
alphaThe confidence level in the computed confidence interval is 1 -
alpha. The default is 0.05.nsim_exact_testThe number of randomization vectors. The default is 1000 providing good resolutions to confidence intervals.
Returns
A 1 - alpha sized frequentist confidence interval for the treatment effect
Examples
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD") seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0)) seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0)) seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0)) seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1)) seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43)) seq_des_inf = SeqDesignInference$new(seq_des, test_type = "normal-based") seq_des_inf$compute_confidence_interval()
Method clone()
The objects of this class are cloneable with this method.
Usage
SeqDesignInference$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | ## ------------------------------------------------
## Method `SeqDesignInference$new`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des)
## ------------------------------------------------
## Method `SeqDesignInference$compute_treatment_estimate`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des)
seq_des_inf$compute_treatment_estimate()
## ------------------------------------------------
## Method `SeqDesignInference$compute_pval_for_no_treatment_effect`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des)
seq_des_inf$compute_pval_for_no_treatment_effect()
## ------------------------------------------------
## Method `SeqDesignInference$randomization_inference_samples_for_no_treatment_effect`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des)
samps = seq_des_inf$randomization_inference_samples_for_no_treatment_effect()
summary(samps)
## ------------------------------------------------
## Method `SeqDesignInference$compute_confidence_interval`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(0, 27, 127, 60, 5.5, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 42, 169, 74, 5.1, 0, 1, 0, 0, 0))
seq_des$add_subject_to_experiment(c(0, 59, 105, 62, 5.9, 0, 0, 0, 1, 0))
seq_des$add_subject_to_experiment(c(1, 32, 186, 66, 5.6, 1, 0, 0, 0, 0))
seq_des$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des_inf = SeqDesignInference$new(seq_des, test_type = "normal-based")
seq_des_inf$compute_confidence_interval()
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.