util_acc_loess_bin {dataquieR} | R Documentation |
Utility function for smoothed longitudinal trends from logistic regression models
Description
This function is under development. It computes a logistic regression for
binary variables and visualizes smoothed time trends of the residuals by
LOESS or GAM. The function can also be called for non-binary outcome
variables. These will be transformed to binary variables, either using
user-specified groups in the metadata columns RECODE_CASES
and/or
RECODE_CONTROL
(see util_dichotomize
), or it will attempt to recode the
variables automatically. For nominal variables, it will consider the most
frequent category as 'cases' and every other category as 'control', if there
are more than two categories. Nominal variables with only two distinct values
will be transformed by assigning the less frequent category to 'cases' and
the more frequent category to 'control'. For variables of other statistical
data types, values inside the interquartile range are considered as
'control', values outside this range as 'cases'. Variables with few
different values are transformed in a simplified way to obtain two groups.
Usage
util_acc_loess_bin(
resp_vars,
label_col = NULL,
study_data,
item_level = "item_level",
group_vars = NULL,
time_vars,
co_vars = NULL,
min_obs_in_subgroup = 30,
resolution = 80,
plot_format = getOption("dataquieR.acc_loess.plot_format",
dataquieR.acc_loess.plot_format_default),
meta_data = item_level,
n_group_max = getOption("dataquieR.max_group_var_levels_in_plot",
dataquieR.max_group_var_levels_in_plot_default),
enable_GAM = getOption("dataquieR.GAM_for_LOESS", dataquieR.GAM_for_LOESS.default),
exclude_constant_subgroups =
getOption("dataquieR.acc_loess.exclude_constant_subgroups",
dataquieR.acc_loess.exclude_constant_subgroups.default),
min_bandwidth = getOption("dataquieR.acc_loess.min_bw",
dataquieR.acc_loess.min_bw.default),
min_proportion = getOption("dataquieR.acc_loess.min_proportion",
dataquieR.acc_loess.min_proportion.default)
)
Arguments
resp_vars |
variable the name of the (binary) measurement variable |
label_col |
variable attribute the name of the column in the metadata with labels of variables |
study_data |
data.frame the data frame that contains the measurements |
group_vars |
variable the name of the observer, device or reader variable |
time_vars |
variable the name of the variable giving the time of measurement |
co_vars |
variable list a vector of co-variables, e.g. age and sex for adjustment |
min_obs_in_subgroup |
integer from=0. This optional argument specifies
the minimum number of observations that is required to
include a subgroup (level) of the |
resolution |
integer the maximum number of time points used for plotting the trend lines |
plot_format |
enum AUTO | COMBINED | FACETS | BOTH. Return the plot
as one combined plot for all groups or as
facet plots (one figure per group). |
meta_data |
data.frame the data frame that contains metadata attributes of study data |
n_group_max |
integer maximum number of categories to be displayed
individually for the grouping variable ( |
enable_GAM |
logical Can LOESS computations be replaced by general additive models to reduce memory consumption for large datasets? |
exclude_constant_subgroups |
logical Should subgroups with constant values be excluded? |
min_bandwidth |
numeric lower limit for the LOESS bandwidth, should be greater than 0 and less than or equal to 1. In general, increasing the bandwidth leads to a smoother trend line. |
min_proportion |
numeric lower limit for the proportion of the smaller group (cases or controls) for creating a LOESS figure, should be greater than 0 and less than 0.4. |
Details
Value
a list with:
-
SummaryPlotList
: a plot.