Compare multiple data frames on a bivariate level and plot them together.
biv_compare(
dfs,
benchmarks,
variables = NULL,
corrtype = "r",
data = TRUE,
id = NULL,
weight = NULL,
strata = NULL,
id_bench = NULL,
weight_bench = NULL,
strata_bench = NULL,
p_value = NULL,
p_adjust = NULL,
varlabels = NULL,
plot_title = NULL,
plots_label = NULL,
diff_perc = TRUE,
diff_perc_size = 4.5,
perc_diff_transparance = 0,
note = FALSE,
order = NULL,
breaks = NULL,
colors = NULL,
mar = c(0, 0, 0, 0),
grid = "white",
gradient = FALSE,
sum_weights = NULL,
missings_x = TRUE,
remove_nas = "pairwise",
ncol_facet = 3,
nboots = 0,
boot_all = FALSE,
parallel = FALSE,
adjustment_weighting = "raking",
adjustment_vars = NULL,
raking_targets = NULL,
post_targets = NULL,
percentile_ci = TRUE
)A object generated with the help of ggplot2::ggplot2() visualizes
the differences between the data frames and benchmarks. If data = TRUE
instead of the plot a list will be returned containing information of the
analyses. This biv_compare object can be used in
plot_biv_compare to build a plot, or in biv_compare_table,
to get a table.
A character vector containing the names of data frames to compare
against the benchmarks.
A character vector containing the names of benchmarks to
compare the dfs against, or the names of a list. If it is a list, it
has to be of the form, as the output of rcorr, with a
Pearson's r matrix in the first position, a n-matrix (matrix of n for every
correlation) in the second position and a p-matrix in the third position.
The vector must either be the same length as dfs, or length 1. If it
has length one every survey will be compared against the same benchmark.
A character vector that containes the names of the variables for
the comparison. If it is NULL, all variables that are named similarly
in both the dfs and the benchmarks will be compared. Variables missing
in one of the dfs or the benchmarks will be neglected for this
comparison.
A character string, indicating the type of the bivariate correlation. It can either be "r" for Pearson's r or "rho" for Spearman's "rho". At the moment, rho is only applicable to unweighted data.
If TRUE, a biv_compare object is returned, containing the results
of the comparison.
A character vector that determines strata variables
that are used to weigh the dfs or benchmarks with the help of
the survey package. It has to be part of the respective data frame.
If fewer characters strings are provided, than in dfs, the first input
is used to weigh every df or benchmark, where no input is provided.
A character vector determining id variables used to weigh
the dfs or benchmarks with the help of the survey package. They have
to be part of the respective data frame. If less characters strings are provided,
than in dfs, the first input is used to weigh every df or
benchmark, where no input is provided.
A character vector that determines variables to weigh
the dfs of benchmarks. They have to be part of the respective
data frame. If fewer characters strings are provided, than in dfs,
the first input is used to weigh every df or benchmark, where no input is
provided. If a weight variable is provided also an id variable is needed.
For weighting, the survey package is used.
A number between zero and one to determine the maximum significance niveau.
Can be either TRUE or a character string indicating an
adjustment method. If p_adjust = TRUE the p_values will be adjusted with the
Bonferroni adjustment method, by default, to account for the problem of
multiple comparisons. All adjustment methods available in
p.adjust can be used here, with the same character strings.
A character string or vector of character strings containing the new names of variables that is used in the plot.
A character string containing the title of the plot.
A character string or vector of character strings containing the new names of the data frames that are used in the plot.
If TRUE a percental difference between surveys and benchmarks is
displayed in the plot.
A number to determine the size of the displayed percental difference between surveys in the plot.
A number to determine the transparency of the displayed percental difference between surveys in the plot.
If note = TRUE, a note will be displayed to describe the plot.
A character vector to determine in which order the variables should be displayed in the plot.
A vector to label the color scheme in the legend.
A vector to determine the colors in the plot.
A vector that determines the margins of the plot.
A color string, that determines the color of the lines between the tiles of the heatmap.
If gradient = TRUE, colors in the heatmap will be more
or less transparent, depending on the difference in Pearson's r of the data
frames of comparison.
A vector containing information for every variable to weigh them in the displayed percental-difference calculation. It can be used if some variables are over- or underrepresented in the analysis.
If TRUE, missing pairs in the plot will be marked with an X.
A character string, that indicates how missing values should be
removed, can either be "all", to remove all cases that contain NA in any
of the variables, or "pairwise", to remove NAs separately for every variable
pair when calculating Pearson's r.
The number of columns used in faced_wrap() for the plots.
A numeric value indicating the number of bootstrap replications.
If nboots = 0 no bootstrapping will be performed. Else nboots
must be >2. Note, that bootstrapping can be very computationally heavy and can
therefore take a while.
If TURE, both, dfs and benchmarks will be bootstrapped. Otherwise the benchmark estimate is assumed to be constant.
Can be either FALSE or a number of cores that should
be used in the function. If it is FALSE, only one core will be used and
otherwise the given number of cores will be used.
A character vector indicating if adjustment
weighting should be used. It can either be "raking" or "post_start".
Variables used to adjust the survey when using raking or post-stratification.
A list of raking targets that can be given to the rake
function of rake, to rake the dfs.
A list of post_stratification targets that can be given to
the postStratify function, to post-stratify the dfs.
If TURE, cofidence intervals will be calculated using the percentile method. If False, they will be calculated using the normal method.
The plot shows a heatmap of a correlation matrix, where the colors are determined by the similarity of the Pearson's r values in both sets of respondents. Leaving default breaks and colors,
Same (green) indicates, that the Pearson's r correlation is not significant > 0 in
the related data frame or benchmark or the Pearson's r correlations are not significantly
different, between data frame and benchmark.
Small Diff (yellow) indicates that the Pearson's r
correlation is significant > 0 in the related data frame or benchmark and the Pearson's r
correlations are significantly different, between data frame and benchmark.
Large Diff (red) indicates, that the same conditions of yellow are fulfilled, and
the correlations are either in opposite directions,or one is double the size of the other.
## Get Data for comparison
data("card")
north <- card[card$south==0,]
white <- card[card$black==0,]
## use the function to plot the data
bivar_comp<-sampcompR::biv_compare(dfs = c("north","white"),
benchmarks = c("card","card"),
variables= c("age","educ","fatheduc","motheduc","wage","IQ"),
data=FALSE)
bivar_comp
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