Predicted Minimal Important Change (MIC)

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

Usage

mic_adjust(data = NULL, x, y, tr, reliability)

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)

reliability

the reliability for the transition score. Can be computed with the tr_reliability() function.

Value

vector with the adjusted MIC value

Details

mic_adjust() is a focused function for estimating the adjusted 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(), tr_reliability()

Examples

data(example)
nitems <- 10
example$x <- rowSums(example[,1:nitems])                 # sumscore T1
example$y <- rowSums(example[,(nitems+1):(2*nitems)])    # sumscore T2
mic_adjust(x = example$x, y = example$y, tr = example$trat, reliability = 0.5)
#> adjusted MIC 
#>     2.757705 
mic_adjust(data = example, x = "x", y = "y", tr = "trat", reliability = 0.5)
#> adjusted MIC 
#>     2.757705 


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