merTools (version 0.6.2)

REimpact: Calculate the weighted mean of fitted values for various levels of random effect terms.

Description

REimpact calculates the average predicted value for each row of a new data frame across the distribution of expectedRank for a merMod object. This allows the user to make meaningful comparisons about the influence of random effect terms on the scale of the response variable, for user-defined inputs, and accounting for the variability in grouping terms.

Usage

REimpact(merMod, newdata, groupFctr = NULL, term = NULL, breaks = 3, ...)

Value

A data.frame with all unique combinations of the number of cases, rows in the newdata element, and number of bins:

case

The row number of the observation from newdata.

bin

The ranking bin for the expected rank, the higher the bin number, the greater the expected rank of the groups in that bin.

AvgFitWght

The weighted mean of the fitted values for case i in bin k

AvgFitWghtSE

The standard deviation of the mean of the fitted values for case i in bin k.

nobs

The number of group effects contained in that bin.

Arguments

merMod

An object of class merMod

newdata

a data frame of observations to calculate group-level differences for

groupFctr

The name of the grouping factor over which the random coefficient of interest varies. This is the variable to the right of the pipe, |, in the [g]lmer formula. This parameter is optional, if not specified, it will perform the calculation for the first effect listed by ranef.

term

The name of the random coefficient of interest. This is the variable to the left of the pipe, |, in the [g]lmer formula. Partial matching is attempted on the intercept term so the following character strings will all return rankings based on the intercept (provided that they do not match the name of another random coefficient for that factor): c("(Intercept)", "Int", "intercep", ...).

breaks

an integer representing the number of bins to divide the group effects into, the default is 3; alternatively it can specify breaks from 0-100 for how to cut the expected rank distribution

...

additional arguments to pass to predictInterval

Details

The function predicts the response at every level in the random effect term specified by the user. Then, the expected rank of each group level is binned to the number of bins specified by the user. Finally, a weighted mean of the fitted value for all observations in each bin of the expected ranks is calculated using the inverse of the variance as the weight -- so that less precise estimates are downweighted in the calculation of the mean for the bin. Finally, a standard error for the bin mean is calculated.

This function uses the formula for variance of a weighted mean recommended by Cochran (1977).

References

Gatz, DF and Smith, L. The Standard Error of a Weighted Mean Concentration. I. Bootstrapping vs other methods. Atmospheric Environment. 1995;11(2)1185-1193. Available at https://www.sciencedirect.com/science/article/pii/135223109400210C

Cochran, WG. 1977. Sampling Techniques (3rd Edition). Wiley, New York.

See Also

expectedRank, predictInterval

Examples

# \donttest{
#For a one-level random intercept model
m1 <- lmer(Reaction ~ Days + (1 | Subject), sleepstudy)
m1.er <- REimpact(m1, newdata = sleepstudy[1, ], breaks = 2)
#For a one-level random intercept model with multiple random terms
m2 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
#ranked by the random slope on Days
m2.er1 <- REimpact(m2,  newdata = sleepstudy[1, ],
           groupFctr = "Subject", term="Days")
#ranked by the random intercept
m2.er2 <- REimpact(m2, newdata = sleepstudy[1, ],
             groupFctr = "Subject", term="int")

# You can also pass additional arguments to predictInterval through REimpact
g1 <- lmer(y ~ lectage + studage + (1|d) + (1|s), data=InstEval)
zed <- REimpact(g1, newdata = InstEval[9:12, ], groupFctr = "d", n.sims = 50,
                include.resid.var = TRUE)
zed2 <- REimpact(g1, newdata = InstEval[9:12, ], groupFctr = "s", n.sims = 50,
                 include.resid.var = TRUE)
zed3 <- REimpact(g1, newdata = InstEval[9:12, ], groupFctr = "d", breaks = 5,
                n.sims = 50, include.resid.var = TRUE)
# }