Utility functions in TAM.
## 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="__SUMMARY.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)
## 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)
An R package
An R call
Arguments obtained from as.list( sys.call() )
Name of a variable
Default value of a variable
String
Vector or matrix or list
Number of zeroes before decimal
Data frame or vector
Integer
Integer
Integer
Logical
File name
Suffix for file name of summary output
Number of rows
Number of columns
Vector
An operation "*", "+" or "-"
Vector of grouping identifiers
Logical indicating whether mean should be calculated or the sum or vector or matrix
Logical indicating whether missing values should be removed
Matrix
Matrix
Logical
Integer
Logical indicating whether weights for complete cases should be sampled in bootstrap
Logical
Optional vector containing weights
Method, see stats::cov.wt
Vector
Data frame
Vector
Logical
Logical
Array
Vector
Matrix
Vector
Numeric
One of the methods "half" or "cut"
Factor of trimming in method "half"
Small number preventing from division by zero
Logical indicating whether missing values should be set to zero.
Vector
Vector
Vector
Vector
Environment variable
List
List
Element of a list