Perform descriptives and build the bivariate table.
descrTable.RdThis functions builds a bivariate table calling compareGroups and createTable function in one step.
Usage
descrTable(formula, data, subset, na.action = NULL, y = NULL, Xext = NULL,
selec = NA, method = 1, timemax = NA, alpha = 0.05, min.dis = 5, max.ylev = 5,
max.xlev = 10, include.label = TRUE, Q1 = 0.25, Q3 = 0.75, simplify = TRUE,
ref = 1, ref.no = NA, fact.ratio = 1, ref.y = 1, p.corrected = TRUE,
compute.ratio = TRUE, include.miss = FALSE, oddsratio.method = "midp",
chisq.test.perm = FALSE, byrow = FALSE, chisq.test.B = 2000, chisq.test.seed = NULL,
Date.format = "d-mon-Y", var.equal = TRUE, conf.level = 0.95, surv = FALSE,
riskratio = FALSE, riskratio.method = "wald", compute.prop = FALSE,
lab.missing = "'Missing'", p.trend.method = "spearman",
hide = NA, digits = NA, type = NA, show.p.overall = TRUE,
show.all, show.p.trend, show.p.mul = FALSE, show.n, show.ratio =
FALSE, show.descr = TRUE, show.ci = FALSE, hide.no = NA, digits.ratio = NA,
show.p.ratio = show.ratio, digits.p = 3, sd.type = 1, q.type = c(1, 1),
extra.labels = NA, all.last = FALSE, lab.ref="Ref.", stars = FALSE)Arguments
Arguments from compareGroups function:
- formula
an object of class "formula" (or one that can be coerced to that class). Right side of ~ must have the terms in an additive way, and left side of ~ must contain the name of the grouping variable or can be left in blank (in this latter case descriptives for whole sample are calculated and no test is performed).
- data
an optional data frame, list or environment (or object coercible by 'as.data.frame' to a data frame) containing the variables in the model. If they are not found in 'data', the variables are taken from 'environment(formula)'.
- subset
an optional vector specifying a subset of individuals to be used in the computation process. It is applied to all row-variables. 'subset' and 'selec' are added in the sense of '&' to be applied in every row-variable.
- na.action
a function which indicates what should happen when the data contain NAs. The default is NULL, and that is equivalent to
na.pass, which means no action. Valuena.excludecan be useful if it is desired to removed all individuals with some NA in any variable.- y
a vector variable that distinguishes the groups. It must be either a numeric, character, factor or NULL. Default value is NULL which means that descriptives for whole sample are calculated and no test is performed.
- Xext
a data.frame or a matrix with the same rows / individuals contained in
X, and maybe with different variables / columns thanX. This argument is used bycompareGroups.defaultin the sense that the variables specified in the argumentselecare searched inXextand/or in the.GlobalEnv. IfXextisNULL, then Xext is created from variables ofXplusy. Default value isNULL.- selec
a list with as many components as row-variables. If list length is 1 it is recycled for all row-variables. Every component of 'selec' is an expression that will be evaluated to select the individuals to be analyzed for every row-variable. Otherwise, a named list specifying 'selec' row-variables is applied. '.else' is a reserved name that defines the selection for the rest of the variables; if no '.else' variable is defined, default value is applied for the rest of the variables. Default value is NA; all individuals are analyzed (no subsetting).
- method
integer vector with as many components as row-variables. If its length is 1 it is recycled for all row-variables. It only applies for continuous row-variables (for factor row-variables it is ignored). Possible values are: 1 - forces analysis as "normal-distributed"; 2 - forces analysis as "continuous non-normal"; 3 - forces analysis as "categorical"; and 4 - NA, which performs a Shapiro-Wilks test to decide between normal or non-normal. Otherwise, a named vector specifying 'method' row-variables is applied. '.else' is a reserved name that defines the method for the rest of the variables; if no '.else' variable is defined, default value is applied. Default value is 1.
- timemax
double vector with as many components as row-variables. If its length is 1 it is recycled for all row-variables. It only applies for 'Surv' class row-variables (for all other row-variables it is ignored). This value indicates at which time the K-M probability is to be computed. Otherwise, a named vector specifying 'timemax' row-variables is applied. '.else' is a reserved name that defines the 'timemax' for the rest of the variables; if no '.else' variable is defined, default value is applied. Default value is NA; K-M probability is then computed at the median of observed times.
- alpha
double between 0 and 1. Significance threshold for the
shapiro.testnormality test for continuous row-variables. Default value is 0.05.- min.dis
an integer. If a non-factor row-variable contains less than 'min.dis' different values and 'method' argument is set to NA, then it will be converted to a factor. Default value is 5.
- max.ylev
an integer indicating the maximum number of levels of grouping variable ('y'). If 'y' contains more than 'max.ylev' levels, then the function 'compareGroups' produces an error. Default value is 5.
- max.xlev
an integer indicating the maximum number of levels when the row-variable is a factor. If the row-variable is a factor (or converted to a factor if it is a character, for example) and contains more than 'max.xlev' levels, then it is removed from the analysis and a warning is printed. Default value is 10.
- include.label
logical, indicating whether or not variable labels have to be shown in the results. Default value is TRUE
- Q1
double between 0 and 1, indicating the quantile to be displayed as the first number inside the square brackets in the bivariate table. To compute the minimum just type 0. Default value is 0.25 which means the first quartile.
- Q3
double between 0 and 1, indicating the quantile to be displayed as the second number inside the square brackets in the bivariate table. To compute the maximum just type 1. Default value is 0.75 which means the third quartile.
- simplify
logical, indicating whether levels with no values must be removed for grouping variable and for row-variables. Default value is TRUE.
- ref
an integer vector with as many components as row-variables. If its length is 1 it is recycled for all row-variables. It only applies for categorical row-variables. Or a named vector specifying which row-variables 'ref' is applied (a reserved name is '.else' which defines the reference category for the rest of the variables); if no '.else' variable is defined, default value is applied for the rest of the variables. Default value is 1.
- ref.no
character specifying the name of the level to be the reference for Odds Ratio or Hazard Ratio. It is not case-sensitive. This is especially useful for yes/no variables. Default value is NA which means that category specified in 'ref' is the one selected to be the reference.
- fact.ratio
a double vector with as many components as row-variables indicating the units for the HR / OR (note that it does not affect the descriptives). If its length is 1 it is recycled for all row-variables. Otherwise, a named vector specifying 'fact.ratio' row-variables is applied. '.else' is a reserved name that defines the reference category for the rest of the variables; if no '.else' variable is defined, default value is applied. Default value is 1.
- ref.y
an integer indicating the reference category of y variable for computing the OR, when y is a binary factor. Default value is 1.
- p.corrected
logical, indicating whether p-values for pairwise comparisons must be corrected. It only applies when there is a grouping variable with more than 2 categories. Default value is TRUE.
- compute.ratio
logical, indicating whether Odds Ratio (for a binary response) or Hazard Ratio (for a time-to-event response) must be computed. Default value is TRUE.
- include.miss
logical, indicating whether to treat missing values as a new category for categorical variables. Default value is FALSE.
- oddsratio.method
Which method to compute the Odds Ratio. See 'method' argument from
oddsratio(epitoolspackage). Default value is "midp".- byrow
logical or NA. Percentage of categorical variables must be reported by rows (TRUE), by columns (FALSE) or by columns and rows to sum up 1 (NA). Default value is FALSE, which means that percentages are reported by columns (withing groups).
- chisq.test.perm
logical. It applies a permutation chi squared test (
chisq.test) instead of an exact Fisher test (fisher.test). It only applies when expected count in some cells are lower than 5.- chisq.test.B
integer. Number of permutation when computing permuted chi squared test for categorical variables. Default value is 2000.
- chisq.test.seed
integer or NULL. Seed when performing permuted chi squared test for categorical variables. Default value is NULL which sets no seed. It is important to introduce some number different from NULL in order to reproduce the results when permuted chi-squared test is performed.
- Date.format
character indicating how the dates are shown. Default is "d-mon-Y". See
chronfor more details.- var.equal
logical, indicating whether to consider equal variances when comparing means on normal distributed variables on more than two groups. If TRUE
anovafunction is applied andoneway.testotherwise. Default value is TRUE.- conf.level
double. Conficende level of confidence interval for means, medians, proportions or incidence, and hazard, odds and risk ratios. Default value is 0.95.
- surv
logical. Compute survival (TRUE) or incidence (FALSE) for time-to-event row-variables. Default value is FALSE.
- riskratio
logical. Whether to compute Odds Ratio (FALSE) or Risk Ratio (TRUE). Default value is FALSE.
- riskratio.method
Which method to compute the Odds Ratio. See 'method' argument from
riskratio(epitoolspackage). Default value is "wald".- compute.prop
logical. Compute proportions (TRUE) or percentages (FALSE) for cathegorical row-variables. Default value is FALSE.
- lab.missing
character. Label for missing cathegory. Only applied when
include.missing = TRUE. Default value is 'Missing'.- p.trend.method
Character indicating the name of test to use for p-value for trend. It only applies for numerical non-normal variables. Possible values are "spearman", "kendall" or "cuzick". Default value is "spearman".
Arguments from createTable function:
- hide
a vector (or a list) with integers or characters with as many components as row-variables. If its length is 1 it is recycled for all row-variables. Each component specifies which category (the literal name of the category if it is a character, or the position if it is an integer) must be hidden and not shown. This argument only applies to categorical row-variables, and for continuous row-variables it is ignored. If NA, all categories are displayed. Or a named vector (or a named list) specifying which row-variables 'hide' is applied, and for the rest of row-variables default value is applied. Default value is NA.
- digits
an integer vector with as many components as row-variables. If its length is 1 it is recycled for all row-variables. Each component specifies the number of significant decimals to be displayed. Or a named vector specifying which row-variables 'digits' is applied (a reserved name is '.else' which defines 'digits' for the rest of the variables); if no '.else' variable is defined, default value is applied for the rest of the variables. Default value is NA which puts the 'appropriate' number of decimals (see vignette for further details).
- type
an integer that indicates whether absolute and/or relative frequencies are displayed: 1 - only relative frequencies; 2 or NA - absolute and relative frequencies in brackets; 3 - only absolute frequencies.
- show.p.overall
logical indicating whether p-value of overall groups significance ('p.overall' column) is displayed or not. Default value is TRUE.
- show.all
logical indicating whether the '[ALL]' column (all data without stratifying by groups) is displayed or not. Default value is FALSE if grouping variable is defined, and FALSE if there are no groups.
- show.p.trend
logical indicating whether p-trend is displayed or not. It is always FALSE when there are less than 3 groups. If this argument is missing, there are more than 2 groups and the grouping variable is an ordered factor, then p-trend is displayed. By default, p-trend is not displayed, and it is displayed when there are more than 2 groups and the grouping variable is of class ordered-factor.
- show.p.mul
logical indicating whether the pairwise (between groups) comparisons p-values are displayed or not. It is always FALSE when there are less than 3 groups. Default value is FALSE.
- show.n
logical indicating whether number of individuals analyzed for each row-variable is displayed or not in the 'descr' table. Default value is FALSE and it is TRUE when there are no groups.
- show.ratio
logical indicating whether OR / HR is displayed or not. Default value is FALSE.
- show.descr
logical indicating whether descriptives (i.e. mean, proportions, ...) are displayed. Default value is TRUE.
- show.ci
logical indicating whether to show confidence intervals of means, medians, proporcions or incidences are displayed. If so, they are displayed between squared brackets. Default value is FALSE.
- hide.no
character specifying the name of the level to be hidden for all categorical variables with 2 categories. It is not case-sensitive. The result is one row for the variable with only the name displayed and not the category. This is especially useful for yes/no variables. It is ignored for the categorical row-variables with 'hide' argument different from NA. Default value is NA which means that no category is hidden.
- digits.ratio
The same as 'digits' argument but applied for the Hazard Ratio or Odds Ratio.
- show.p.ratio
logical indicating whether p-values corresponding to each Hazard Ratio / Odds Ratio are shown.
- digits.p
integer indicating the number of decimals displayed for all p-values. Default value is 3.
- sd.type
an integer that indicates how standard deviation is shown: 1 - mean (SD), 2 - mean ? SD.
- q.type
a vector with two integer components. The first component refers to the type of brackets to be displayed for non-normal row-variables (1 - squared and 2 - rounded), while the second refers to the percentile separator (1 - ';', 2 - ',' and 3 - '-'. Default value is c(1, 1).
- extra.labels
character vector of 4 components corresponding to key legend to be appended to normal, non-normal, categorical or survival row-variables labels. Default value is NA which appends no extra key. If it is set to
c("","","",""), "Mean (SD)", "Median [25th; 75th]", "N (%)" and "Incidence at time=timemax" are appended (see argumenttimemaxfromcompareGroupsfunction.- all.last
logical. Descriptives of the whole sample is placed after the descriptives by groups. Default value is FALSE which places the descriptives of whole cohort at first.
- lab.ref
character. String shown for reference category. "Ref." as default value.
- stars
logical, indicating whether to append stars beside p-values; '**': p-value < 0.05, '*' 0.05 <= p-value < 0.1; ” p-value >=0.1. Default value is FALSE
Value
An object of class 'createTable' (see createTable).
So, all methods implemented for createTable class objects can be applied (such as plot, '[', etc.).
Note
The use of descrTable function makes easier to build the table (it only needs one line), it may be preferable to build the descriptive table in two steps when computing descriptives and p-values takes some time: first use compareGroups function to store the descriptives and p-values in an object, and then apply createTable to the this object. The two steps strategy saves time since descriptives and p-values are not recomputed every time it is desired to costumize the descriptive table (number of digits, etc.).
References
Isaac Subirana, Hector Sanz, Joan Vila (2014). Building Bivariate Tables: The compareGroups Package for R. Journal of Statistical Software, 57(12), 1-16. URL https://www.jstatsoft.org/v57/i12/.
Examples
require(compareGroups)
# load REGICOR data
data(regicor)
# perform descriptives by year and build the table.
# note the use of arguments from compareGroups (formula and data set) and
# arguments from createTable (hide.no and show.p.mul)
descrTable(year ~ ., regicor, hide.no="no", show.p.mul=TRUE)
#>
#> --------Summary descriptives table by 'Recruitment year'---------
#>
#> _______________________________________________________________________________________________________________________________________________________
#> 1995 2000 2005 p.overall p.1995 vs 2000 p.1995 vs 2005 p.2000 vs 2005
#> N=431 N=786 N=1077
#> ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
#> Individual id 1000002914 (1242) 3000104610 (2379) 1996 (1161) 0.000 <0.001 <0.001 <0.001
#> Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078 0.930 0.143 0.161
#> Sex: 0.506 0.794 0.794 0.792
#> Male 206 (47.8%) 390 (49.6%) 505 (46.9%)
#> Female 225 (52.2%) 396 (50.4%) 572 (53.1%)
#> Smoking status: <0.001 <0.001 <0.001 <0.001
#> Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
#> Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
#> Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
#> Systolic blood pressure 133 (19.2) 133 (21.3) 129 (19.8) <0.001 0.934 0.011 <0.001
#> Diastolic blood pressure 77.0 (10.5) 80.8 (10.3) 79.9 (10.6) <0.001 <0.001 <0.001 0.149
#> History of hypertension 111 (25.8%) 233 (29.6%) 379 (35.5%) <0.001 0.169 0.001 0.015
#> Hypertension treatment 71 (16.5%) 127 (16.2%) 230 (22.2%) 0.002 0.951 0.023 0.004
#> Total cholesterol 225 (43.1) 224 (44.4) 213 (45.9) <0.001 0.826 <0.001 <0.001
#> HDL cholesterol 51.9 (14.5) 52.3 (15.6) 53.2 (14.2) 0.208 0.863 0.252 0.409
#> Triglycerides 114 (74.4) 114 (70.7) 117 (76.0) 0.582 0.999 0.750 0.611
#> LDL cholesterol 152 (38.4) 149 (38.6) 136 (39.7) <0.001 0.521 <0.001 <0.001
#> History of hyperchol. 97 (22.5%) 256 (33.2%) 356 (33.2%) <0.001 <0.001 <0.001 1.000
#> Cholesterol treatment 28 (6.50%) 68 (8.80%) 132 (12.8%) <0.001 0.193 0.002 0.015
#> Height (cm) 163 (9.21) 162 (9.39) 163 (9.05) 0.003 0.021 0.956 0.006
#> Weight (Kg) 72.3 (12.6) 73.8 (14.0) 73.6 (13.9) 0.150 0.146 0.221 0.923
#> Body mass index 27.0 (4.15) 28.1 (4.62) 27.6 (4.63) <0.001 <0.001 0.104 0.032
#> Physical activity (Kcal/week) 491 (419) 422 (377) 351 (378) <0.001 0.013 <0.001 <0.001
#> Physical component 49.3 (8.08) 49.0 (9.63) 50.1 (8.91) 0.032 0.839 0.279 0.032
#> Mental component 49.2 (11.3) 48.9 (11.0) 46.9 (10.8) <0.001 0.872 0.001 0.001
#> Cardiovascular event 10 (2.51%) 35 (4.72%) 47 (4.59%) 0.161 0.151 0.151 0.986
#> Days to cardiovascular event or end of follow-up 1784 (1101) 1686 (1080) 1793 (1072) 0.099 0.310 0.987 0.097
#> Overall death 18 (4.65%) 81 (11.0%) 74 (7.23%) <0.001 0.002 0.103 0.012
#> Days to overall death or end of follow-up 1713 (1042) 1674 (1050) 1758 (1055) 0.252 0.825 0.754 0.224
#> ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯