Returns a table based on the information of a multi_compare_object that indicates the proportion of biased variables. It can be outputted as HTML or LaTex Table, for example with the help of the stargazer function.
multi_per_variable.RdReturns a table based on the information of a multi_compare_object that
indicates the proportion of biased variables. It can be outputted as HTML or
LaTex Table, for example with the help of the stargazer
function.
Usage
multi_per_variable(
multi_compare_objects,
type = "coefs",
label_df = NULL,
lables_coefs = NULL,
lables_models = NULL,
ndigits = 1
)Arguments
- multi_compare_objects
A object returned by the
multi_comparefunction. Object can either be inserted as single object or as a character string containing the names of multiple objects.- type
The
typeof table, can either be"coefs","models", or"complete". When coefs is chosen, the average bias of the coefficients is outputted, when models is chosen, the average bias for the models is outputted, and when complete is chosen, both are outputted.- label_df
A character vector containing labels for the data frames.
- lables_coefs
A character vector containing labels for the coefficients.
- lables_models
A character vector containing labels for the models.
- ndigits
Number of digits that is shown in the table.
Value
A matrix, that indicates the proportion of bias for every individual coefficient or model for multivariate comparisons. This is given separately for every comparison, as well as averaged over comparisons.
Examples
data("card")
north <- card[card$south==0,]
white <- card[card$black==0,]
## use the function to plot the data
multi_data1 <- sampcompR::multi_compare(df = north,
bench = card,
independent = c("age","fatheduc","motheduc","IQ"),
dependent = c("educ","wage"),
family = "ols")
#>
#> Difference in coeficients between sets of respondents
#>
#> educ wage
#> age -2.43e-02 -8.55e-01
#> fatheduc -2.37e-02 -2.93e-01
#> motheduc 1.23e-02 2.35e+00
#> IQ -7.25e-03 4.80e-01
#>
#> Overall difference between north & card: 0% of coeficients are significant different
#> (*p<0.05 ; **p<0.005 ; ***p<0.001; for t-test robust standard errors are used)
#>
multi_data2 <- sampcompR::multi_compare(df = white,
bench = card,
independent = c("age","fatheduc","motheduc","IQ"),
dependent = c("educ","wage"),
family = "ols")
#>
#> Difference in coeficients between sets of respondents
#>
#> educ wage
#> age -1.20e-02 -9.51e-01
#> fatheduc -1.45e-02 1.28e-01
#> motheduc 9.99e-03 2.03e-01
#> IQ -9.28e-03 3.61e-01
#>
#> Overall difference between white & card: 0% of coeficients are significant different
#> (*p<0.05 ; **p<0.005 ; ***p<0.001; for t-test robust standard errors are used)
#>
table<-sampcompR::multi_per_variable(multi_compare_objects = c("multi_data1","multi_data2"))
#> Error in purrr::map(multi_compare_objects, ~multi_same_func(multi_compare_object = .x, p_adjust = p_adjust)): ℹ In index: 1.
#> Caused by error in `get()`:
#> ! object 'multi_data1' not found
noquote(table)
#> function (..., exclude = if (useNA == "no") c(NA, NaN), useNA = c("no",
#> "ifany", "always"), dnn = list.names(...), deparse.level = 1)
#> {
#> list.names <- function(...) {
#> l <- as.list(substitute(list(...)))[-1L]
#> if (length(l) == 1L && is.list(..1) && !is.null(nm <- names(..1)))
#> return(nm)
#> nm <- names(l)
#> fixup <- if (is.null(nm))
#> seq_along(l)
#> else nm == ""
#> dep <- vapply(l[fixup], function(x) switch(deparse.level +
#> 1, "", if (is.symbol(x)) as.character(x) else "",
#> deparse(x, nlines = 1)[1L]), "")
#> if (is.null(nm))
#> dep
#> else {
#> nm[fixup] <- dep
#> nm
#> }
#> }
#> miss.use <- missing(useNA)
#> miss.exc <- missing(exclude)
#> useNA <- if (miss.use && !miss.exc && !match(NA, exclude,
#> nomatch = 0L))
#> "ifany"
#> else match.arg(useNA)
#> doNA <- useNA != "no"
#> if (!miss.use && !miss.exc && doNA && match(NA, exclude,
#> nomatch = 0L))
#> warning("'exclude' containing NA and 'useNA' != \"no\"' are a bit contradicting")
#> args <- list(...)
#> if (length(args) == 1L && is.list(args[[1L]])) {
#> args <- args[[1L]]
#> if (length(dnn) != length(args))
#> dnn <- paste(dnn[1L], seq_along(args), sep = ".")
#> }
#> if (!length(args))
#> stop("nothing to tabulate")
#> bin <- 0L
#> lens <- NULL
#> dims <- integer()
#> pd <- 1L
#> dn <- NULL
#> for (a in args) {
#> if (is.null(lens))
#> lens <- length(a)
#> else if (length(a) != lens)
#> stop("all arguments must have the same length")
#> fact.a <- is.factor(a)
#> if (doNA)
#> aNA <- anyNA(a)
#> if (!fact.a) {
#> a0 <- a
#> op <- options(warn = 2)
#> on.exit(options(op))
#> a <- factor(a, exclude = exclude)
#> options(op)
#> }
#> add.na <- doNA
#> if (add.na) {
#> ifany <- (useNA == "ifany")
#> anNAc <- anyNA(a)
#> add.na <- if (!ifany || anNAc) {
#> ll <- levels(a)
#> if (add.ll <- !anyNA(ll)) {
#> ll <- c(ll, NA)
#> TRUE
#> }
#> else if (!ifany && !anNAc)
#> FALSE
#> else TRUE
#> }
#> else FALSE
#> }
#> if (add.na)
#> a <- factor(a, levels = ll, exclude = NULL)
#> else ll <- levels(a)
#> a <- as.integer(a)
#> if (fact.a && !miss.exc) {
#> ll <- ll[keep <- which(match(ll, exclude, nomatch = 0L) ==
#> 0L)]
#> a <- match(a, keep)
#> }
#> else if (!fact.a && add.na) {
#> if (ifany && !aNA && add.ll) {
#> ll <- ll[!is.na(ll)]
#> is.na(a) <- match(a0, c(exclude, NA), nomatch = 0L) >
#> 0L
#> }
#> else {
#> is.na(a) <- match(a0, exclude, nomatch = 0L) >
#> 0L
#> }
#> }
#> nl <- length(ll)
#> dims <- c(dims, nl)
#> if (prod(dims) > .Machine$integer.max)
#> stop("attempt to make a table with >= 2^31 elements")
#> dn <- c(dn, list(ll))
#> bin <- bin + pd * (a - 1L)
#> pd <- pd * nl
#> }
#> names(dn) <- dnn
#> bin <- bin[!is.na(bin)]
#> if (length(bin))
#> bin <- bin + 1L
#> y <- array(tabulate(bin, pd), dims, dimnames = dn)
#> class(y) <- "table"
#> y
#> }
#> <bytecode: 0x564278513310>
#> <environment: namespace:base>