Description Usage Arguments Value Examples
Most statistical techniques need to specify some settings for them to run. This function sets those settings in the blueprint, before the statistical method is used at the construction phase.
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 | add_settings(data, ...)
## S3 method for class 'gee_bp'
add_settings(
data,
cluster.id,
family,
corstr = c("independence", "exchangeable", "ar1"),
conf.int = TRUE,
conf.level = 0.95,
...
)
## S3 method for class 'cor_bp'
add_settings(
data,
method = c("pearson", "kendall", "spearman"),
use = c("complete.obs", "all.obs", "pairwise.complete.obs", "everything",
"na.or.complete"),
hclust.order = FALSE,
...
)
## S3 method for class 'glm_bp'
add_settings(data, family, conf.int = TRUE, conf.level = 0.95, ...)
## S3 method for class 'pls_bp'
add_settings(
data,
ncomp = NULL,
scale = TRUE,
validation = c("none", "CV", "LOO"),
cv.data = TRUE,
cv.seed = 1234,
...
)
## S3 method for class 't.test_bp'
add_settings(data, paired = FALSE, ...)
|
data |
The blueprint data object. |
... |
Additional args. |
cluster.id |
Variable that represents the cluster for GEE. |
family |
a description of the error distribution and link
function to be used in the model. For |
corstr |
The correlation structure. See |
conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level |
The confidence level to use for the confidence interval
if |
method |
the method to be used in fitting the model. The default
method User-supplied fitting functions can be supplied either as a function
or a character string naming a function, with a function which takes
the same arguments as |
use |
an optional character string giving a
method for computing covariances in the presence
of missing values. This must be (an abbreviation of) one of the strings
|
hclust.order |
Whether to order the correlation data based on the
|
ncomp |
the number of components to include in the model (see below). |
scale |
numeric vector, or logical. If numeric vector, X
is scaled by dividing each variable with the corresponding element
of |
validation |
character. What kind of (internal) validation to use. See below. |
cv.data |
Whether to cross-validate the dataset into training and testing sets. |
cv.seed |
Seed to set for cv.data. |
paired |
a logical indicating whether you want a paired t-test. |
Settings for the analysis are added to the blueprint
1 2 3 4 5 6 7 8 9 10 11 12 | ## Not run:
design(iris, 'gee') %>%
add_settings('Species', family = binomial('logit'), conf.int = FALSE)
ds <- design(iris, 'cor')
ds <- add_settings(ds, method = 'spearman')
ds <- design(iris, 't.test')
add_settings(ds, paired = TRUE)
add_settings(ds)
## End(Not run)
|
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