regression_basic_results {clinpubr} | R Documentation |
Basic results of logistic or Cox regression.
Description
Generate the result table of logistic or Cox regression with different settings of the predictor variable and covariates. Also generate KM curves for Cox regression.
Usage
regression_basic_results(
data,
x,
y,
time = NULL,
model_covs = NULL,
pers = c(0.1, 10, 100),
factor_breaks = NULL,
factor_labels = NULL,
quantile_breaks = NULL,
quantile_labels = NULL,
label_with_range = FALSE,
save_output = FALSE,
figure_type = "png",
ref_levels = "lowest",
est_nsmall = 2,
p_nsmall = 3,
pval_eps = 0.001,
median_nsmall = 0,
colors = NULL,
xlab = NULL,
legend_title = x,
legend_pos = c(0.8, 0.8),
pval_pos = NULL,
n_y_pos = 0.9,
height = 6,
width = 6,
...
)
Arguments
data |
A data frame. |
x |
A character string of the predictor variable. |
y |
A character string of the outcome variable. |
time |
A character string of the time variable. If |
model_covs |
A character vector or a named list of covariates for different models.
If |
pers |
A numeric vector of the denominators of variable |
factor_breaks |
A numeric vector of the breaks to factorize the |
factor_labels |
A character vector of the labels for the factor levels. |
quantile_breaks |
A numeric vector of the quantile breaks to factorize the |
quantile_labels |
A character vector of the labels for the quantile levels. |
label_with_range |
A logical value indicating whether to add the range of the levels to the labels. |
save_output |
A logical value indicating whether to save the results. |
figure_type |
A character string of the figure type. Can be |
ref_levels |
A vector of strings of the reference levels of the factor variable. You can use |
est_nsmall |
An integer specifying the precision for the estimates in the plot. |
p_nsmall |
An integer specifying the number of decimal places for the p-values. |
pval_eps |
The threshold for rounding p values to 0. |
median_nsmall |
The minimum number of digits to the right of the decimal point for the median survival time. |
colors |
A vector of colors for the KM curves. |
xlab |
A character string of the x-axis label. |
legend_title |
A character string of the title of the legend. |
legend_pos |
A numeric vector of the position of the legend. |
pval_pos |
A numeric vector of the position of the p-value. |
n_y_pos |
A numerical of range 0 to 1 to assign the y position of total sample count. |
height |
The height of the plot. |
width |
The width of the plot. |
... |
Additional arguments passed to the |
Details
The function regression_basic_results
generates the result table of logistic or Cox regression with
different settings of the predictor variable and covariates. The setting of the predictor variable includes
the original x
, the standardized x
, the log of x
, and x
divided by denominators in pers
as continuous
variables, and the factorization of the variable including split by median, by quartiles, and by factor_breaks
and quantile_breaks
. The setting of the covariates includes different models with different covariates.
Value
A list of results, including the regression table and the KM curve plots.
Note
For factor variables with more than 2 levels, p value for trend is also calculated.
Examples
data(cancer, package = "survival")
# coxph model with time assigned
regression_basic_results(cancer,
x = "age", y = "status", time = "time",
model_covs = list(Crude = c(), Model1 = c("ph.karno"), Model2 = c("ph.karno", "sex")),
save_output = FALSE,
ggtheme = survminer::theme_survminer(font.legend = c(14, "plain", "black")) # theme for KM
)
# logistic model with time not assigned
cancer$dead <- cancer$status == 2
regression_basic_results(cancer,
x = "age", y = "dead", ref_levels = c("Q3", "High"),
model_covs = list(Crude = c(), Model1 = c("ph.karno"), Model2 = c("ph.karno", "sex")),
save_output = FALSE
)