confint.std_selected {stdmod}

Confidence Intervals for a 'std_selected' Class Object

Description

Return the confidence intervals of estimates in the output of std_selected() or std_selected_boot().

Usage

## S3 method for class 'std_selected'
confint(object, parm, level = 0.95, type, ...)

Arguments

object

The output of std_selected() or std_selected_boot().

parm

The parameters (coefficients) for which confidence intervals should be returned. If missing, the confidence intervals of all parameters will be returned.

level

The level of confidence. For the confidence intervals returned by lm(), default is .95, i.e., 95%. For the bootstrap percentile confidence intervals, default is the level used in calling std_selected_boot(). If a level different from that in the original call is specified, full_output needs to be set in the call to std_selected_boot() such that the original bootstrapping output is stored.

type

The type of the confidence intervals. If est to "lm", returns the confidence interval given by the confint() method of lm(). If set to "boot", the bootstrap percentile confidence intervals are returned. Default is "boot" if bootstrap estimates are stored in object, and "lm" if bootstrap estimates are not stored.

...

Arguments to be passed to summary.lm().

Details

If bootstrapping is used to form the confidence interval by std_selected_boot(), users can request the percentile confidence intervals of the bootstrap estimates. This method does not do the bootstrapping itself.

Value

A matrix of the confidence intervals.

Author(s)

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

Examples

# Load a sample data set

dat <- test_x_1_w_1_v_1_cat1_n_500

# Do a moderated regression by lm
lm_raw <- lm(dv ~ iv*mod + v1 + cat1, dat)
summary(lm_raw)

# Standardize all variables except for categorical variables.
# Interaction terms are formed after standardization.
lm_std <- std_selected(lm_raw, to_center = ~ .,
                               to_scale = ~ .)
# Alternative: use to_standardize as a shortcut
# lm_std <- std_selected(lm_raw, to_standardize = ~ .)
summary(lm_std)

confint(lm_std)

# Use to_standardize as a shortcut
lm_std2 <- std_selected(lm_raw, to_standardize = ~ .)
# The results are the same
confint(lm_std)
confint(lm_std2)
all.equal(confint(lm_std), confint(lm_std2))

# With bootstrapping
# nboot = 100 just for illustration. nboot >= 2000 should be used in read
# research.
set.seed(89572)
lm_std_boot <- std_selected_boot(lm_raw, to_scale = ~ .,
                                         to_center = ~ .,
                                         nboot = 100)
summary(lm_std_boot)

# Bootstrap percentile intervals, default when bootstrap was conduced

confint(lm_std_boot)

# Force OLS confidence intervals

confint(lm_std_boot, type = "lm")

# Use to_standardize as a shortcut
set.seed(89572)
lm_std_boot2 <- std_selected_boot(lm_raw, to_standardize = ~ .,
                                          nboot = 100)
# The results are the same
confint(lm_std_boot)
confint(lm_std_boot2)
all.equal(confint(lm_std_boot), confint(lm_std_boot2))