- datL
Data frame in the long format (i.e. each line represents one ID unit in one imputation of one nest) containing all
variables for analysis.
- ID
Variable name or column number of student identifier (ID) variable. ID variable must not contain any missing values.
- wgt
Optional: Variable name or column number of weighting variable. If no weighting variable is specified,
all cases will be equally weighted.
- type
Defines the replication method for cluster replicates which is to be applied. Depending on type, additional
arguments must be specified (e.g., PSU and/or repInd or repWgt).
- PSU
Variable name or column number of variable indicating the primary sampling unit (PSU). When a jackknife procedure is applied,
the PSU is the jackknife zone variable. If NULL, no cluster structure is assumed and
standard errors are computed according to a random sample.
- repInd
Variable name or column number of variable indicating replicate ID. In a jackknife procedure, this is the jackknife replicate
variable. If NULL, no cluster structure is assumed and standard errors are computed according to a random sample.
- repWgt
Normally, replicate weights are created by repGlm directly from PSU and repInd variables. Alternatively,
if replicate weights are included in the data.frame, specify the variable names or column number in the repWgt argument.
- nest
Optional: name or column number of the nesting variable. Only applies in nested multiple imputed data sets.
- imp
Optional: name or column number of the imputation variable. Only applies in multiple imputed data sets.
- groups
Optional: vector of names or column numbers of one or more grouping variables.
- group.splits
Optional: If groups are defined, group.splits optionally specifies whether analysis should be done also
in the whole group or overlying groups. See examples for more details.
- group.delimiter
Character string which separates the group names in the output frame.
- cross.differences
Either a list of vectors, specifying the pairs of levels for which cross-level differences should be computed.
Alternatively, if TRUE, cross-level differences for all pairs of levels are computed. If FALSE, no cross-level
differences are computed. (see examples 2a, 3, and 4 in the help file of the repMean function)
- trend
Optional: name or column number of the trend variable which contains the measurement time of the survey.
Note: Levels of all grouping variables and predictors must be equal in all 'sub populations' partitioned by the discrete trend variable.
repGlm computes differences for all pairwise contrasts defined by trend variable levels. or three measurement
occasions, i.e. 2010, 2015, and 2020, contrasts (i.e. trends) are computed for 2010 vs. 2015, 2010 vs. 2020, and
2015 vs. 2020.
- linkErr
Optional: name or column number of the linking error variable. If NULL, a linking error of 0 will be assumed in trend estimation.
- formula
Model formula, see help page of glm for details.
- family
A description of the error distribution and link function to be used in the model. See help page of glm for details.
- forceSingularityTreatment
Logical: Forces the function to use the workaround to handle singularities in regression models.
- glmTransformation
Optional: Allows for transformation of parameters from linear regression and logistic regression before pooling.
Useful to compare parameters from different glm models, see Mood (2010). Note: This argument applies only if
forceSingularityTreatment is set to 'TRUE'.
- doCheck
Logical: Check the data for consistency before analysis? If TRUE groups with insufficient data are excluded from
analysis to prevent subsequent functions from crashing.
- na.rm
Logical: Should cases with missing values be dropped?
- poolMethod
Which pooling method should be used? The ``mice'' method is recommended.
- useWec
Logical: use weighted effect coding?
- scale
scaling constant for variance, for details, see help page of svrepdesign from the survey package
- rscales
scaling constant for variance, for details, see help page of svrepdesign from the survey package
- mse
Logical: If TRUE, compute variances based on sum of squares around the point estimate, rather than the mean of the replicates.
See help page of svrepdesign from the survey package for further details.
- rho
Shrinkage factor for weights in Fay's method. See help page of svrepdesign from the survey package for further details.
- hetero
Logical: Assume heteroscedastic variance for weighted effect coding? Only applies for random samples, i.e. if no replication analyses are executed.
- se_type
The sort of standard error sought for cross level differences. Only applies if crossDiffSE == "wec" and hetero == TRUE
and crossDiffSE.engine == "lm". See the help page of lm_robust from the estimatr package for further details.
- clusters
Optional: Variable name or column number of cluster variable. Only necessary if weighted effecting coding
should be performed using heteroscedastic variances. See the help page of lm_robust
from the estimatr package for further details.
- crossDiffSE.engine
Optional: Sort of estimator which should be used for standard error estimation in weighted effect coding regression.
Only applies if useWec == TRUE. To date, only lavaan allows for stochastic group sizes.
- stochasticGroupSizes
Logical: Assume stochastic group sizes for using weighted effect coding regression with categorical predictors? Note: To date,
only lavaan allows for stochastic group sizes. Stochastic group sizes cannot be assumed if any replication method
(jackknife, BRR) is applied.
- verbose
Logical: Show analysis information on console?
- progress
Logical: Show progress bar on console?
- nCores
integer (default: NULL), number of cores to use for parallel processing, if engine = "survey". If NULL,
single core processing is used.