Utility Functions in TAM — TAM-utilities • TAM

Utility functions in TAM.

## RISE item fit statistic of two models
IRT.RISE( mod_p, mod_np, use_probs=TRUE )
## model-implied means
tam_model_implied_means(mod)

## information about used package version
tam_packageinfo(pack)
## call statement in a string format
tam_print_call(CALL)
## information about R session
tam_rsessinfo()
## grep list of arguments for a specific variable
tam_args_CALL_search(args_CALL, variable, default_value)
## requireNamespace with message of needed installation
require_namespace_msg(pkg)
## add leading zeroes
add.lead(x, width=max(nchar(x)))
## round some columns in a data frame
tam_round_data_frame(obji, from=1, to=ncol(obji), digits=3, rownames_null=FALSE)
## round some columns in a data frame and print this data frame
tam_round_data_frame_print(obji, from=1, to=ncol(obji), digits=3, rownames_null=FALSE)
## copy of CDM::osink
tam_osink(file, suffix=".Rout")
## copy of CDM::csink
tam_csink(file)

## base::matrix function with argument value byrow=TRUE
tam_matrix2(x, nrow=NULL, ncol=NULL)
## more efficient base::outer functions for operations "*", "+" and "-"
tam_outer(x, y, op="*")
## row normalization of a matrix
tam_normalize_matrix_rows(x)
## row normalization of a vector
tam_normalize_vector(x)
## aggregate function for mean and sum based on base::rowsum
tam_aggregate(x, group, mean=FALSE, na.rm=TRUE)
## column index when a value in a matrix is exceeded (used in TAM::tam.pv)
tam_interval_index(matr, rn)
## cumulative sum of row entries in a matrix
tam_rowCumsums(matr)
## extension of mvtnorm::dmvnorm to matrix entries of mean
tam_dmvnorm(x, mean, sigma, log=FALSE )
## Bayesian bootstrap in TAM (used in tam.pv.mcmc)
tam_bayesian_bootstrap(N, sample_integers=FALSE, do_boot=TRUE)
## weighted covariance matrix
tam_cov_wt(x, wt=NULL, method="ML")
## weighted correlation matrix
tam_cor_wt(x, wt=NULL, method="ML")
## generalized inverse
tam_ginv(x, eps=.05)
## generalized inverse with scaled matrix using MASS::ginv
tam_ginv_scaled(x, use_MASS=TRUE)

## remove items or persons with complete missing entries
tam_remove_missings( dat, items, elim_items=TRUE, elim_persons=TRUE )
## compute AXsi given A and xsi
tam_AXsi_compute(A, xsi)
## fit xsi given A and AXsi
tam_AXsi_fit(A, AXsi)

## maximum absolute difference between objects
tam_max_abs( list1, list2, label )
tam_max_abs_list( list1, list2)

## trimming increments in iterations
tam_trim_increment(increment, max.increment, trim_increment="cut",
     trim_incr_factor=2, eps=1E-10, avoid_na=FALSE)
## numerical differentiation by central difference
tam_difference_quotient(d0, d0p, d0m, h)
## assign elements of a list in an environment
tam_assign_list_elements(x, envir)

Arguments

mod_p: Fitted model
mod_np: Fitted model
mod: Fitted model
use_probs: Logical
pack: An R package
CALL: An R call
args_CALL: Arguments obtained from as.list( sys.call() )
variable: Name of a variable
default_value: Default value of a variable
pkg: String
x: Vector or matrix or list
width: Number of zeroes before decimal
obji: Data frame or vector
from: Integer
to: Integer
digits: Integer
rownames_null: Logical
file: File name
suffix: Suffix for file name of summary output
nrow: Number of rows
ncol: Number of columns
y: Vector
op: An operation "*", "+" or "-"
group: Vector of grouping identifiers
mean: Logical indicating whether mean should be calculated or the sum or vector or matrix
na.rm: Logical indicating whether missing values should be removed
matr: Matrix
sigma: Matrix
log: Logical
N: Integer
sample_integers: Logical indicating whether weights for complete cases should be sampled in bootstrap
do_boot: Logical
wt: Optional vector containing weights
method: Method, see stats::cov.wt
rn: Vector
dat: Data frame
items: Vector
elim_items: Logical
elim_persons: Logical
A: Array
xsi: Vector
AXsi: Matrix
increment: Vector
max.increment: Numeric
trim_increment: One of the methods "half" or "cut"
trim_incr_factor: Factor of trimming in method "half"
eps: Small number preventing from division by zero
use_MASS: Logical indicating whether MASS package should be used.
avoid_na: Logical indicating whether missing values should be set to zero.
d0: Vector
d0p: Vector
d0m: Vector
h: Vector
envir: Environment variable
list1: List
list2: List
label: Element of a list