dataquieR.GAM_for_LOESS {dataquieR}R Documentation

Enable to switch to a general additive model instead of LOESS

Description

If this option is set to TRUE, time course plots will use general additive models (GAM) instead of LOESS when the number of observations exceeds a specified threshold. LOESS computations for large datasets have a high memory consumption.

See Also

Other options: dataquieR, dataquieR.CONDITIONS_LEVEL_TRHESHOLD, dataquieR.CONDITIONS_WITH_STACKTRACE, dataquieR.ELEMENT_MISSMATCH_CHECKTYPE, dataquieR.ERRORS_WITH_CALLER, dataquieR.MAX_LABEL_LEN, dataquieR.MAX_VALUE_LABEL_LEN, dataquieR.MESSAGES_WITH_CALLER, dataquieR.MULTIVARIATE_OUTLIER_CHECK, dataquieR.VALUE_LABELS_htmlescaped, dataquieR.WARNINGS_WITH_CALLER, dataquieR.acc_loess.exclude_constant_subgroups, dataquieR.acc_loess.mark_time_points, dataquieR.acc_loess.min_bw, dataquieR.acc_loess.min_proportion, dataquieR.acc_loess.plot_format, dataquieR.acc_loess.plot_observations, dataquieR.acc_margins_num, dataquieR.acc_margins_sort, dataquieR.acc_multivariate_outlier.scale, dataquieR.col_con_con_empirical, dataquieR.col_con_con_logical, dataquieR.debug, dataquieR.des_summary_hard_lim_remove, dataquieR.dontwrapresults, dataquieR.fix_column_type_on_read, dataquieR.flip_mode, dataquieR.force_item_specific_missing_codes, dataquieR.force_label_col, dataquieR.grading_formats, dataquieR.grading_rulesets, dataquieR.guess_missing_codes, dataquieR.lang, dataquieR.max_group_var_levels_in_plot, dataquieR.max_group_var_levels_with_violins, dataquieR.min_obs_per_group_var_in_plot, dataquieR.non_disclosure, dataquieR.progress_fkt, dataquieR.progress_msg_fkt, dataquieR.scale_level_heuristics_control_binaryrecodelimit, dataquieR.scale_level_heuristics_control_metriclevels, dataquieR.testdebug


[Package dataquieR version 2.5.1 Index]