REmargins: Calculate the predicted value for each observation across the...

In merTools: Tools for Analyzing Mixed Effect Regression Models

Calculate the predicted value for each observation across the distribution of the random effect terms.

Description

REmargins 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

REmargins(
  merMod,
  newdata = NULL,
  groupFctr = NULL,
  term = NULL,
  breaks = 4,
  .parallel = FALSE,
  ...
)

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. If the length is > 1 then the combined effect of all listed groups will calculated and marginalized over co-occurences of those groups if desired.
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.
.parallel,	logical should parallel computation be used, default is TRUE
...	additional arguments to pass to predictInterval

Details

The function simulates the

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.

Value

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

...

The columns of the original data taken from newdata

case

The row number of the observation from newdata. Each row in newdata will be repeated for all unique levels of the grouping_var, term, and breaks.

grouping_var

The grouping variable the random effect is being marginalized over.

term

The term for the grouping variable the random effect is being marginalized over.

breaks

The ntile of the effect size for grouping_var and term

original_group_level

The original grouping value for this case

fit_combined

The predicted value from predictInterval for this case simulated at the Nth ntile of the expected rank distribution of grouping_var and term

upr_combined

The upper bound of the predicted value.

lwr_combined

The lower bound of the predicted value.

fit_XX

For each grouping term in newdata the predicted value is decomposed into its fit components via predictInterval and these are all returned here

upr_XX

The upper bound for the effect of each grouping term

lwr_XX

The lower bound for the effect of each grouping term

fit_fixed

The predicted fit with all the grouping terms set to 0 (average)

upr_fixed

The upper bound fit with all the grouping terms set to 0 (average)

lwr_fixed

The lower bound fit with all the grouping terms set to 0 (average)

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

fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
mfx <- REmargins(merMod = fm1, newdata = sleepstudy[1:10,])

# You can also pass additional arguments to predictInterval through REimpact
 g1 <- lmer(y ~ lectage + studage + (1|d) + (1|s), data=InstEval)
 margin_df <- REmargins(g1, newdata = InstEval[20:25, ], groupFctr = c("s"),
                        breaks = 4)
 margin_df <- REmargins(g1, newdata = InstEval[20:25, ], groupFctr = c("d"),
                         breaks = 3)

merTools documentation built on March 31, 2023, 8:43 p.m.