##### Powder dictionary (pdCIF) version 1.0.1

# _pd_proc_ls_prof_

**Names:**

`'_pd_proc_ls_prof_R_factor'`

`'_pd_proc_ls_prof_wR_factor'`

`'_pd_proc_ls_prof_wR_expected'`

**Definition:**

Rietveld/profile fit R factors. Note that the R factor computed for Rietveld refinements using the extracted reflection intensity values (often called the Rietveld or Bragg R factor, R~B~) is not properly a profile R factor. This R factor may be specified using _refine_ls_R_I_factor. (Some authors report _refine_ls_R_Fsqd_factor or _refine_ls_R_factor_all as the Rietveld or Bragg R factor. While it is appropriate to compute and report any or all of these R factors, the names "Rietveld or Bragg R factor" refer strictly to _refine_ls_R_I_factor.) _pd_proc_ls_prof_R_factor, often called R~p~, is an unweighted fitness metric for the agreement between the observed and computed diffraction patterns. R~p~ = sum~i~ | I~obs~(i) - I~calc~(i) | / sum~i~ ( I~obs~(i) ) _pd_proc_ls_prof_wR_factor, often called R~wp~, is a weighted fitness metric for the agreement between the observed and computed diffraction patterns. R~wp~ = SQRT { sum~i~ ( w(i) [ I~obs~(i) - I~calc~(i) ]^2^ ) / sum~i~ ( w(i) [I~obs~(i)]^2^ ) } _pd_proc_ls_prof_wR_expected, sometimes called the theoretical R~wp~ or R~exp~, is a weighted fitness metric for the statistical precision of the data set. For an idealized fit, where all deviations between the observed intensities and those computed from the model are due to statistical fluctuations, the observed R~wp~ should match the expected R factor. In reality, R~wp~ will always be higher than R~exp~. R~exp~ = SQRT { (n - p) / sum~i~ ( w(i) [I~obs~(i)]^2^ ) } Note that in the above equations, w(i) is the weight for the ith data point (see _pd_proc_ls_weight). I~obs~(i) is the observed intensity for the ith data point, sometimes referred to as y~i~(obs) or y~oi~. (See _pd_meas_counts_total, _pd_meas_intensity_total or _pd_proc_intensity_total.) I~calc~(i) is the computed intensity for the ith data point with background and other corrections applied to match the scale of the observed data set, sometimes referred to as y~i~(calc) or y~ci~. (See _pd_calc_intensity_total.) n is the total number of data points (see _pd_proc_number_of_points) less the number of data points excluded from the refinement. p is the total number of refined parameters.

The permitted range is 0.0 -> infinity

**Type:** numb

**Category:** pd_proc_ls