ARDs for LS Mean Difference and LS Means
Source:R/ard_emmeans_contrast.R, R/ard_emmeans_emmeans.R
ard_emmeans.RdThe ard_emmeans_contrast() function calculates least-squares mean differences using the 'emmeans'
package using the following
emmeans::emmeans(object = <regression model>, specs = ~ <primary covariate>) |>
emmeans::contrast(method = "pairwise") |>
summary(infer = TRUE, level = <confidence level>)
The ard_emmeans_emmeans() function calculates least-squares means using the 'emmeans'
package using the following
emmeans::emmeans(object = <regression model>, specs = ~ <primary covariate>) |>
summary(emmeans, calc = c(n = ".wgt."))
The arguments data, formula, method, method.args, package are used
to construct the regression model via cardx::construct_model().
Usage
ard_emmeans_contrast(
data,
formula,
method,
method.args = list(),
package = "base",
response_type = c("continuous", "dichotomous"),
conf.level = 0.95,
primary_covariate = getElement(attr(stats::terms(formula), "term.labels"), 1L)
)
ard_emmeans_emmeans(
data,
formula,
method,
method.args = list(),
package = "base",
response_type = c("continuous", "dichotomous"),
conf.level = 0.95,
primary_covariate = getElement(attr(stats::terms(formula), "term.labels"), 1L)
)Arguments
- data
(
data.frame/survey.design)
a data frame or survey design object- formula
(
formula)
a formula- method
(
string)
string of function naming the function to be called, e.g."glm". If function belongs to a library that is not attached, the package name must be specified in thepackageargument.- method.args
-
(named
list)
named list of arguments that will be passed tomethod.Note that this list may contain non-standard evaluation components. If you are wrapping this function in other functions, the argument must be passed in a way that does not evaluate the list, e.g. using rlang's embrace operator
{{ . }}. - package
(
string)
a package name that will be temporarily loaded when function specified inmethodis executed.- response_type
(
string) string indicating whether the model outcome is'continuous'or'dichotomous'. When'dichotomous', the call toemmeans::emmeans()is supplemented with argumentregrid="response".- conf.level
(scalar
numeric)
confidence level for confidence interval. Default is0.95.- primary_covariate
(
string)
string indicating the primary covariate (typically the dichotomous treatment variable). Default is the first covariate listed in the formula.
Examples
# LS Mean Difference
ard_emmeans_contrast(
data = mtcars,
formula = mpg ~ am + cyl,
method = "lm"
)
#> {cards} data frame: 8 x 10
#> group1 variable variable_level stat_name stat_label stat
#> 1 am contrast am0 - am1 estimate Mean Dif… -2.567
#> 2 am contrast am0 - am1 std.error Standard… 1.291
#> 3 am contrast am0 - am1 df Degrees … 29
#> 4 am contrast am0 - am1 conf.low CI Lower… -5.208
#> 5 am contrast am0 - am1 conf.high CI Upper… 0.074
#> 6 am contrast am0 - am1 p.value p-value 0.056
#> 7 am contrast am0 - am1 conf.level CI Confi… 0.95
#> 8 am contrast am0 - am1 method method Least-sq…
#> ℹ 4 more variables: context, fmt_fun, warning, error
ard_emmeans_contrast(
data = mtcars,
formula = vs ~ am + mpg,
method = "glm",
method.args = list(family = binomial),
response_type = "dichotomous"
)
#> {cards} data frame: 8 x 10
#> group1 variable variable_level stat_name stat_label stat
#> 1 am contrast am0 - am1 estimate Mean Dif… 0.61
#> 2 am contrast am0 - am1 std.error Standard… 0.229
#> 3 am contrast am0 - am1 df Degrees … Inf
#> 4 am contrast am0 - am1 conf.low CI Lower… 0.162
#> 5 am contrast am0 - am1 conf.high CI Upper… 1.059
#> 6 am contrast am0 - am1 p.value p-value 0.008
#> 7 am contrast am0 - am1 conf.level CI Confi… 0.95
#> 8 am contrast am0 - am1 method method Least-sq…
#> ℹ 4 more variables: context, fmt_fun, warning, error
# LS Means
ard_emmeans_emmeans(
data = mtcars,
formula = mpg ~ am + cyl,
method = "lm"
)
#> {cards} data frame: 16 x 10
#> group1 variable variable_level stat_name stat_label stat
#> 1 am contrast 0 estimate Mean 19.048
#> 2 am contrast 0 std.error Standard… 0.753
#> 3 am contrast 0 df Degrees … 29
#> 4 am contrast 0 n n 19
#> 5 am contrast 0 conf.low CI Lower… 17.507
#> 6 am contrast 0 conf.high CI Upper… 20.589
#> 7 am contrast 0 conf.level CI Confi… 0.95
#> 8 am contrast 0 method method Least-sq…
#> 9 am contrast 1 estimate Mean 21.615
#> 10 am contrast 1 std.error Standard… 0.938
#> 11 am contrast 1 df Degrees … 29
#> 12 am contrast 1 n n 13
#> 13 am contrast 1 conf.low CI Lower… 19.696
#> 14 am contrast 1 conf.high CI Upper… 23.534
#> 15 am contrast 1 conf.level CI Confi… 0.95
#> 16 am contrast 1 method method Least-sq…
#> ℹ 4 more variables: context, fmt_fun, warning, error
ard_emmeans_emmeans(
data = mtcars,
formula = vs ~ am + mpg,
method = "glm",
method.args = list(family = binomial),
response_type = "dichotomous"
)
#> {cards} data frame: 16 x 10
#> group1 variable variable_level stat_name stat_label stat
#> 1 am contrast 0 estimate Mean 0.726
#> 2 am contrast 0 std.error Standard… 0.165
#> 3 am contrast 0 df Degrees … Inf
#> 4 am contrast 0 n n 19
#> 5 am contrast 0 conf.low CI Lower… 0.402
#> 6 am contrast 0 conf.high CI Upper… 1.05
#> 7 am contrast 0 conf.level CI Confi… 0.95
#> 8 am contrast 0 method method Least-sq…
#> 9 am contrast 1 estimate Mean 0.116
#> 10 am contrast 1 std.error Standard… 0.117
#> 11 am contrast 1 df Degrees … Inf
#> 12 am contrast 1 n n 13
#> 13 am contrast 1 conf.low CI Lower… -0.114
#> 14 am contrast 1 conf.high CI Upper… 0.346
#> 15 am contrast 1 conf.level CI Confi… 0.95
#> 16 am contrast 1 method method Least-sq…
#> ℹ 4 more variables: context, fmt_fun, warning, error