Predicted Minimal Important Change (MIC)

Minimal Important Change (MIC) obtained as predicted value from a logistic regression model.

Usage

mic_pred(data = NULL, x, y, tr)

Arguments

data

data.frame that holds the scores at two time points and the transition rate in separate columns.

x

vector with score at time point 1, or column name in the data if !is.null(data)

y

vector with score at time point 1, or column name in the data if !is.null(data)

tr

vector with transition rates (perceived change), or column name in the data if !is.null(data)

Value

vector with the predicted MIC value

Details

mic_pred() is a focused function for estimating the predictive modeling-based MIC. For an all-in-one workflow that returns the predictive MIC, adjusted predictive MIC, and improved adjusted predictive MIC with optional bootstrap confidence intervals, see mic_iapm().

See also

mic_iapm()

Examples

data(example)
nitems <- 10
example$x <- rowSums(example[,1:nitems])                 # sumscore T1
example$y <- rowSums(example[,(nitems+1):(2*nitems)])    # sumscore T2
mic_pred(x = example$x, y = example$y, tr = example$trat)
#> predicted MIC 
#>     0.8484085 
mic_pred(data = example, x = example$x, y = example$y, tr = example$trat)
#> Warning: data object is not used; separate x, y and tr input is used to compute the MIC.
#> predicted MIC 
#>     0.8484085 
mic_pred(data = example, x = "x", y = "y", tr = "trat")
#> predicted MIC 
#>     0.8484085 

# For predictive, adjusted, and improved adjusted MICs in one workflow,
# see mic_iapm().