SeqDesign: A Sequential Design
In SeqExpMatch: Sequential Experimental Design via Matching on-the-Fly
Description
Public fields
Methods
Examples
Description
An R6 Class encapsulating the data and functionality for a sequential experimental design.
This class takes care of data intialization and sequential assignments. The class object
should be saved securely after each assignment e.g. on an encrypted cloud server.
Public fields
tThe current number of subjects in this sequential experiment (begins at zero).
designThe type of sequential experimental design (see constructor's documentation).
XA numeric matrix of subject data with number of rows n (the number of subjects) and number of
columns p (the number of characteristics measured for each subject). This matrix is filled in
sequentially and thus will have data present for rows 1...t (i.e. the number of subjects in the
experiment currently) but otherwise will be missing.
yA numeric vector of subject responses with number of entries n (the number of subjects). During
the KK21 designs this must be filled in sequentially (similar to X) and will have data present for
entries 1...t (i.e. the number of subjects in the experiment currently) but otherwise will be missing.
For non-KK21 designs, this vector can be set at anytime (but must be set before inference is desired).
wA binary vector of subject assignments with number of entries n (the number of subjects).
This vector is filled in sequentially (similar to X) and will have assignments present for
entries 1...t (i.e. the number of subjects in the experiment currently) but otherwise will be missing.
verboseA flag that indicates whether messages should be displayed to the user
Methods
Public methods
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Method new()
Initialize a sequential experimental design
Usage
SeqDesign$new(n, p, design, verbose = TRUE, ...)
Arguments
nNumber of subjects fixed beforehand. A future version of this software will allow
for sequential stopping and thus n will not need to be prespecified.
pNumber of characteristics measured for each subject. If measurement j are
categorical with L_j levels, you must select a reference level and convert this information
to L_j-1 dummies. Thus p := # of numeric variables + sum_j (L_j - 1).
designThe type of sequential experimental design. This must be one of the following
"CRD" for the completely randomized design / Bernoulli design,
"BCRD" for the balanaced completely randomized design with n/2 T's and n/2 C's,
"Efron" for Efron's (1971) Biased Coin Design
"Atkinson" for Atkinson's (1982) Covariate-Adjusted Biased Coin Design
"KK14" for Kapelner and Krieger's (2014) Covariate-Adjusted Matching on the Fly Design
"KK21" for Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights Design
"KK21stepwise" for Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights Stepwise Design
verboseA flag indicating whether messages should be displayed to the user. Default is TRUE.
...Design-specific parameters:
"Efron" requires "weighted_coin_prob" which is the probability of the weighted coin for assignment. If unspecified, default is 2/3.
All "KK" designs require "lambda", the quantile cutoff of the subject distance distribution for determining matches. If unspecified, default is 10
All "KK" designs require "t_0_pct", the percentage of total sample size n where matching begins. If unspecified, default is 35
All "KK21" designs further require "num_boot" which is the number of bootstrap samples taken to approximate the subject-distance distribution. If unspecified, default is 500.
Returns
A new 'SeqDesign' object.
Examples
seq_des = SeqDesign$new(n = 100, p = 10, design = "KK21stepwise")
Method add_subject_to_experiment()
Add subject-specific measurements for the next subject entrant
Usage
SeqDesign$add_subject_to_experiment(x_vec)
Arguments
x_vecA p-length numeric vector
Examples
seq_des = SeqDesign$new(n = 100, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
Method print_current_subject_assignment()
Prints the current assignment to screen. Should be called after add_subject_to_experiment.
Usage
SeqDesign$print_current_subject_assignment()
Examples
seq_des = SeqDesign$new(n = 100, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$print_current_subject_assignment()
Method add_current_subject_response()
For CARA designs, add subject response for the current subject entrant
Usage
SeqDesign$add_current_subject_response(y)
Arguments
yThe response as a numeric scalar
Examples
seq_des = SeqDesign$new(n = 100, p = 10, design = "KK21")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_current_subject_response(4.71)
Method add_all_subject_responses()
For non-CARA designs, add all subject responses
Usage
SeqDesign$add_all_subject_responses(y)
Arguments
yThe responses as a numeric vector of length n
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))
Method matching_statistics()
For KK designs only, this returns a list with useful matching statistics.
Usage
SeqDesign$matching_statistics()
Returns
A list with the following data: num_matches, prop_subjects_matched,
num_subjects_remaining_in_reservoir, prop_subjects_remaining_in_reservoir.
Examples
seq_des = SeqDesign$new(n = 6, p = 10, design = "KK14")
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$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des$matching_statistics()
Method assert_experiment_completed()
Asserts if the experiment is completed (all n assignments are assigned
in the w vector and all n responses in the y vector are recorded), i.e. throws
descriptive error if the experiment is incomplete.
Usage
SeqDesign$assert_experiment_completed()
Examples
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
#if run, it would throw an error since all of the covariate vectors are not yet recorded
#seq_des$assert_experiment_completed()
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$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
#if run, it would throw an error since the responses are not yet recorded
#seq_des$assert_experiment_completed()
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des$assert_experiment_completed() #no response means the assert is true
Method check_experiment_completed()
Checks if the experiment is completed (all n assignments are assigned
in the w vector and all n responses in the y vector are recorded).
Usage
SeqDesign$check_experiment_completed()
Returns
TRUE if experiment is complete, FALSE otherwise.
Examples
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
#returns FALSE since all of the covariate vectors are not yet recorded
seq_des$check_experiment_completed()
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$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
#returns FALSE since the responses are not yet recorded
seq_des$check_experiment_completed()
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des$check_experiment_completed() #returns TRUE
Method clone()
The objects of this class are cloneable with this method.
Usage
SeqDesign$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
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## ------------------------------------------------
## Method `SeqDesign$new`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 100, p = 10, design = "KK21stepwise")
## ------------------------------------------------
## Method `SeqDesign$add_subject_to_experiment`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 100, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
## ------------------------------------------------
## Method `SeqDesign$print_current_subject_assignment`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 100, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$print_current_subject_assignment()
## ------------------------------------------------
## Method `SeqDesign$add_current_subject_response`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 100, p = 10, design = "KK21")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_current_subject_response(4.71)
## ------------------------------------------------
## Method `SeqDesign$add_all_subject_responses`
## ------------------------------------------------
seq_des = SeqDesign$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$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
## ------------------------------------------------
## Method `SeqDesign$matching_statistics`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 6, p = 10, design = "KK14")
seq_des$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$matching_statistics()
## ------------------------------------------------
## Method `SeqDesign$assert_experiment_completed`
## ------------------------------------------------
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))
#if run, it would throw an error since all of the covariate vectors are not yet recorded
#seq_des$assert_experiment_completed()
seq_des$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))
#if run, it would throw an error since the responses are not yet recorded
#seq_des$assert_experiment_completed()
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des$assert_experiment_completed() #no response means the assert is true
## ------------------------------------------------
## Method `SeqDesign$check_experiment_completed`
## ------------------------------------------------
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))
#returns FALSE since all of the covariate vectors are not yet recorded
seq_des$check_experiment_completed()
seq_des$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))
#returns FALSE since the responses are not yet recorded
seq_des$check_experiment_completed()
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des$check_experiment_completed() #returns TRUE
SeqExpMatch documentation built on June 1, 2021, 9:09 a.m.
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Description Public fields Methods Examples
Description
An R6 Class encapsulating the data and functionality for a sequential experimental design. This class takes care of data intialization and sequential assignments. The class object should be saved securely after each assignment e.g. on an encrypted cloud server.
Public fields
tThe current number of subjects in this sequential experiment (begins at zero).
designThe type of sequential experimental design (see constructor's documentation).
XA numeric matrix of subject data with number of rows n (the number of subjects) and number of columns p (the number of characteristics measured for each subject). This matrix is filled in sequentially and thus will have data present for rows 1...t (i.e. the number of subjects in the experiment currently) but otherwise will be missing.
yA numeric vector of subject responses with number of entries n (the number of subjects). During the KK21 designs this must be filled in sequentially (similar to X) and will have data present for entries 1...t (i.e. the number of subjects in the experiment currently) but otherwise will be missing. For non-KK21 designs, this vector can be set at anytime (but must be set before inference is desired).
wA binary vector of subject assignments with number of entries n (the number of subjects). This vector is filled in sequentially (similar to X) and will have assignments present for entries 1...t (i.e. the number of subjects in the experiment currently) but otherwise will be missing.
verboseA flag that indicates whether messages should be displayed to the user
Methods
Public methods
Method new()
Initialize a sequential experimental design
Usage
SeqDesign$new(n, p, design, verbose = TRUE, ...)
Arguments
nNumber of subjects fixed beforehand. A future version of this software will allow for sequential stopping and thus n will not need to be prespecified.
pNumber of characteristics measured for each subject. If measurement j are categorical with L_j levels, you must select a reference level and convert this information to L_j-1 dummies. Thus p := # of numeric variables + sum_j (L_j - 1).
designThe type of sequential experimental design. This must be one of the following "CRD" for the completely randomized design / Bernoulli design, "BCRD" for the balanaced completely randomized design with n/2 T's and n/2 C's, "Efron" for Efron's (1971) Biased Coin Design "Atkinson" for Atkinson's (1982) Covariate-Adjusted Biased Coin Design "KK14" for Kapelner and Krieger's (2014) Covariate-Adjusted Matching on the Fly Design "KK21" for Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights Design "KK21stepwise" for Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights Stepwise Design
verboseA flag indicating whether messages should be displayed to the user. Default is
TRUE....Design-specific parameters: "Efron" requires "weighted_coin_prob" which is the probability of the weighted coin for assignment. If unspecified, default is 2/3. All "KK" designs require "lambda", the quantile cutoff of the subject distance distribution for determining matches. If unspecified, default is 10 All "KK" designs require "t_0_pct", the percentage of total sample size n where matching begins. If unspecified, default is 35 All "KK21" designs further require "num_boot" which is the number of bootstrap samples taken to approximate the subject-distance distribution. If unspecified, default is 500.
Returns
A new 'SeqDesign' object.
Examples
seq_des = SeqDesign$new(n = 100, p = 10, design = "KK21stepwise")
Method add_subject_to_experiment()
Add subject-specific measurements for the next subject entrant
Usage
SeqDesign$add_subject_to_experiment(x_vec)
Arguments
x_vecA p-length numeric vector
Examples
seq_des = SeqDesign$new(n = 100, p = 10, design = "CRD") seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
Method print_current_subject_assignment()
Prints the current assignment to screen. Should be called after add_subject_to_experiment.
Usage
SeqDesign$print_current_subject_assignment()
Examples
seq_des = SeqDesign$new(n = 100, p = 10, design = "CRD") seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0)) seq_des$print_current_subject_assignment()
Method add_current_subject_response()
For CARA designs, add subject response for the current subject entrant
Usage
SeqDesign$add_current_subject_response(y)
Arguments
yThe response as a numeric scalar
Examples
seq_des = SeqDesign$new(n = 100, p = 10, design = "KK21") seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0)) seq_des$add_current_subject_response(4.71)
Method add_all_subject_responses()
For non-CARA designs, add all subject responses
Usage
SeqDesign$add_all_subject_responses(y)
Arguments
yThe responses as a numeric vector of length n
Examples
seq_des = SeqDesign$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$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
Method matching_statistics()
For KK designs only, this returns a list with useful matching statistics.
Usage
SeqDesign$matching_statistics()
Returns
A list with the following data: num_matches, prop_subjects_matched,
num_subjects_remaining_in_reservoir, prop_subjects_remaining_in_reservoir.
Examples
seq_des = SeqDesign$new(n = 6, p = 10, design = "KK14") seq_des$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$matching_statistics()
Method assert_experiment_completed()
Asserts if the experiment is completed (all n assignments are assigned in the w vector and all n responses in the y vector are recorded), i.e. throws descriptive error if the experiment is incomplete.
Usage
SeqDesign$assert_experiment_completed()
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)) #if run, it would throw an error since all of the covariate vectors are not yet recorded #seq_des$assert_experiment_completed() seq_des$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)) #if run, it would throw an error since the responses are not yet recorded #seq_des$assert_experiment_completed() seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43)) seq_des$assert_experiment_completed() #no response means the assert is true
Method check_experiment_completed()
Checks if the experiment is completed (all n assignments are assigned in the w vector and all n responses in the y vector are recorded).
Usage
SeqDesign$check_experiment_completed()
Returns
TRUE if experiment is complete, FALSE otherwise.
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)) #returns FALSE since all of the covariate vectors are not yet recorded seq_des$check_experiment_completed() seq_des$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)) #returns FALSE since the responses are not yet recorded seq_des$check_experiment_completed() seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43)) seq_des$check_experiment_completed() #returns TRUE
Method clone()
The objects of this class are cloneable with this method.
Usage
SeqDesign$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 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | ## ------------------------------------------------
## Method `SeqDesign$new`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 100, p = 10, design = "KK21stepwise")
## ------------------------------------------------
## Method `SeqDesign$add_subject_to_experiment`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 100, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
## ------------------------------------------------
## Method `SeqDesign$print_current_subject_assignment`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 100, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$print_current_subject_assignment()
## ------------------------------------------------
## Method `SeqDesign$add_current_subject_response`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 100, p = 10, design = "KK21")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
seq_des$add_current_subject_response(4.71)
## ------------------------------------------------
## Method `SeqDesign$add_all_subject_responses`
## ------------------------------------------------
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))
## ------------------------------------------------
## Method `SeqDesign$matching_statistics`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 6, p = 10, design = "KK14")
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$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des$matching_statistics()
## ------------------------------------------------
## Method `SeqDesign$assert_experiment_completed`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
#if run, it would throw an error since all of the covariate vectors are not yet recorded
#seq_des$assert_experiment_completed()
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$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
#if run, it would throw an error since the responses are not yet recorded
#seq_des$assert_experiment_completed()
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des$assert_experiment_completed() #no response means the assert is true
## ------------------------------------------------
## Method `SeqDesign$check_experiment_completed`
## ------------------------------------------------
seq_des = SeqDesign$new(n = 6, p = 10, design = "CRD")
seq_des$add_subject_to_experiment(c(1, 38, 142, 71, 5.3, 0, 0, 0, 1, 0))
#returns FALSE since all of the covariate vectors are not yet recorded
seq_des$check_experiment_completed()
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$add_subject_to_experiment(c(1, 37, 178, 75, 6.5, 0, 0, 0, 0, 1))
#returns FALSE since the responses are not yet recorded
seq_des$check_experiment_completed()
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))
seq_des$check_experiment_completed() #returns TRUE
|
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