Agreement and dose-response
stat bland_altman <json_a> <json_b>
Description
Bland-Altman analysis of two methods: bias with its 95 % interval, limits of agreement (bias +/- 1.96 sd) with their intervals, proportional bias (slope of the differences against the means), points outside the limits
Bland-Altman analysis of two methods: bias with its 95 % interval, limits of agreement (bias +/- 1.96 sd) with their intervals, proportional bias (slope of the differences against the means), points outside the limits
Parameters
json_a: The measures of method A - String - required
json_b: The measures of method B - String - required
json_a: The measures of method A - String - required
json_b: The measures of method B - String - required
admin
stat bland_altman "[10.2, 12.1, 9.8, 15.3, 11.0, 13.4]" "[10.6, 12.0, 10.5, 15.9, 11.2, 14.1]"
mentdb
{"n": 6, "bias": -0.41666666666666635, "bias_lower": -0.7512814324778379, "bias_upper": -0.0820519008554948, "sd": 0.31885210782848283, "loa_lower": -1.0416167980104927, "loa_upper": 0.20828346467716002, "loa_lower_ci": [-1.621186573358203, -0.4620470226627823], "loa_upper_ci": [-0.37128631067055035, 0.7878532400248703], "proportional_slope": -0.036560446236262166, "proportional_p_value": 0.641322335697295, "outside_limits": 0}
stat icc <json_ratings>
Description
Intraclass correlations (Shrout and Fleiss): ICC1, ICC2 (absolute agreement), ICC3 (consistency), single and average raters, with F test and 95 % intervals; rows = subjects, columns = raters
Intraclass correlations (Shrout and Fleiss): ICC1, ICC2 (absolute agreement), ICC3 (consistency), single and average raters, with F test and 95 % intervals; rows = subjects, columns = raters
Parameters
json_ratings: The ratings - String - required
json_ratings: The ratings - String - required
admin
stat icc "[[9, 2, 5, 8], [6, 1, 3, 2], [8, 4, 6, 8], [7, 1, 2, 6], [10, 5, 6, 9], [6, 2, 4, 7]]"
mentdb
{"subjects": 6, "raters": 4, "icc": [{"type": "ICC1", "description": "single rater, one-way random", "icc": 0.16574176840547541, "f": 1.7946784922394678, "df1": 5, "df2": 18, "p_value": 0.16476880834463936, "lower": -0.13293232489230816, "upper": 0.7225600623390379}, {"type": "ICC2", "description": "single rater, absolute agreement, two-way random", "icc": 0.289763779527559, "f": 11.027247956403249, "df1": 5, "df2": 15, "p_value": 1.345665164843579E-4, "lower": 0.018786513374711388, "upper": 0.7610843696489529}, {"type": "ICC3", "description": "single rater, consistency, two-way mixed", "icc": 0.7148407148407144, "f": 11.027247956403249, "df1": 5, "df2": 15, "p_value": 1.345665164843579E-4, "lower": 0.34246476503033424, "upper": 0.9458582599586243}, {"type": "ICC1k", "description": "average of the raters, one-way random", "icc": 0.4427971336792686, "f": 1.7946784922394678, "df1": 5, "df2": 18, "p_value": 0.16476880834463936, "lower": -0.8844421554324198, "upper": 0.9124154203451275}, {"type": "ICC2k", "description": "average of the raters, absolute agreement", "icc": 0.6200505475989889, "f": 11.027247956403249, "df1": 5, "df2": 15, "p_value": 1.345665164843579E-4, "lower": 0.07113681530250113, "upper": 0.9272320401677219}, {"type": "ICC3k", "description": "average of the raters, consistency", "icc": 0.9093155423770691, "f": 11.027247956403249, "df1": 5, "df2": 15, "p_value": 1.345665164843579E-4, "lower": 0.6756747138128101, "upper": 0.985891678169949}]}
stat ccc <json_a> <json_b>
Description
Lin's concordance correlation coefficient: agreement on the identity line (precision x accuracy), with its 95 % interval
Lin's concordance correlation coefficient: agreement on the identity line (precision x accuracy), with its 95 % interval
Parameters
json_a: The measures of method A - String - required
json_b: The measures of method B - String - required
json_a: The measures of method A - String - required
json_b: The measures of method B - String - required
admin
stat ccc "[10.2, 12.1, 9.8, 15.3, 11.0, 13.4]" "[10.6, 12.0, 10.5, 15.9, 11.2, 14.1]"
mentdb
{"n": 6, "ccc": 0.96671200515427, "lower": 0.8332362930574677, "upper": 0.9937212228154881, "pearson": 0.9894873156498109, "bias_correction": 0.9769827160638397}
stat dose_response <json_doses> <json_responses> <model>
Description
Dose-response curve: 4PL (bottom, top, ec50, hill) or 5PL (+ asymmetry) by Levenberg-Marquardt, parameters with their intervals, EC10, EC50, EC90 with their intervals, R2, AIC
Dose-response curve: 4PL (bottom, top, ec50, hill) or 5PL (+ asymmetry) by Levenberg-Marquardt, parameters with their intervals, EC10, EC50, EC90 with their intervals, R2, AIC
Parameters
json_doses: The doses (>= 0) - String - required
json_responses: The responses - String - required
model: 4pl or 5pl - String - required
json_doses: The doses (>= 0) - String - required
json_responses: The responses - String - required
model: 4pl or 5pl - String - required
admin
stat dose_response "[0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100]" "[2.1, 3.0, 5.2, 12.8, 31.0, 62.5, 85.1, 94.0, 97.2]" "4pl"
mentdb
{"model": "4pl", "n": 9, "parameters": [{"parameter": "bottom", "estimate": 2.3087795090374192, "se": 0.5347681800206683, "lower": 0.9341141391999812, "upper": 3.683444878874857}, {"parameter": "top", "estimate": 97.94060837231903, "se": 0.7311077099044914, "lower": 96.06123617334487, "upper": 99.8199805712932}, {"parameter": "ec50", "estimate": 1.9697463758381668, "lower": 1.82733126546761, "upper": 2.1232607674639303}, {"parameter": "hill", "estimate": 1.1698400099382216, "se": 0.03742991364725996, "lower": 1.073623353807138, "upper": 1.2660566660693051}], "ec": [{"percent": 10, "dose": 0.3010973126844263, "lower": 0.2564328917484541, "upper": 0.35354119780669574}, {"percent": 50, "dose": 1.9697463758381668, "lower": 1.8273312654636666, "upper": 2.1232607674685124}, {"percent": 90, "dose": 12.88586985561719, "lower": 10.73911885595928, "upper": 15.461756608063128}], "r2": 0.9997585656785379, "rmse": 0.600565840658861, "aic": 0.8221059926156151, "df": 5}
Asymmetric curve
admin
stat dose_response "[0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100]" "[2.1, 3.0, 5.2, 12.8, 31.0, 62.5, 85.1, 94.0, 97.2]" "5pl"
mentdb
{"model": "5pl", "n": 9, "parameters": [{"parameter": "bottom", "estimate": 2.05899759577612, "se": 0.7694765047340151, "lower": -0.07741167921935066, "upper": 4.195406870771591}, {"parameter": "top", "estimate": 97.62307989081319, "se": 0.9677921005571195, "lower": 94.93605825054584, "upper": 100.31010153108053}, {"parameter": "xmid", "estimate": 2.2668151840443085, "lower": 1.0898831749093758, "upper": 4.71468061615053}, {"parameter": "hill", "estimate": 1.224050940442931, "se": 0.11710300400495788, "lower": 0.8989208781904083, "upper": 1.5491810026954536}, {"parameter": "asymmetry", "estimate": 0.8830035683989568, "lower": 0.4574277402093517, "upper": 1.7045212462375954}], "ec": [{"percent": 10, "dose": 0.2866656892635966, "lower": 0.20481225826330118, "upper": 0.4012319286833314}, {"percent": 50, "dose": 1.9633071345008544, "lower": 1.7984476368147801, "upper": 2.1432789175940483}, {"percent": 90, "dose": 12.255307029210039, "lower": 8.79597872999147, "upper": 17.0751379682282}], "r2": 0.9997749851271636, "rmse": 0.579784679102985, "aic": 2.188227228685056, "df": 4}
stat dose_response <json_doses> <json_responses> <model> <json_params>
Description
Dose-response curve with parameters: ec (the percentages to compute, ex: [20, 50, 80]), level, max_iter
Dose-response curve with parameters: ec (the percentages to compute, ex: [20, 50, 80]), level, max_iter
Parameters
json_doses: The doses - String - required
json_responses: The responses - String - required
model: 4pl or 5pl - String - required
json_params: The parameters - String - required
json_doses: The doses - String - required
json_responses: The responses - String - required
model: 4pl or 5pl - String - required
json_params: The parameters - String - required
admin
stat dose_response "[0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100]" "[2.1, 3.0, 5.2, 12.8, 31.0, 62.5, 85.1, 94.0, 97.2]" "4pl" "{\"ec\": [20, 50, 80]}"
mentdb
{"model": "4pl", "n": 9, "parameters": [{"parameter": "bottom", "estimate": 2.3087795090374192, "se": 0.5347681800206683, "lower": 0.9341141391999812, "upper": 3.683444878874857}, {"parameter": "top", "estimate": 97.94060837231903, "se": 0.7311077099044914, "lower": 96.06123617334487, "upper": 99.8199805712932}, {"parameter": "ec50", "estimate": 1.9697463758381668, "lower": 1.82733126546761, "upper": 2.1232607674639303}, {"parameter": "hill", "estimate": 1.1698400099382216, "se": 0.03742991364725996, "lower": 1.073623353807138, "upper": 1.2660566660693051}], "ec": [{"percent": 20, "dose": 0.6022249518705802, "lower": 0.5378320084354573, "upper": 0.6743274609306664}, {"percent": 50, "dose": 1.9697463758381668, "lower": 1.8273312654636666, "upper": 2.1232607674685124}, {"percent": 80, "dose": 6.442610478154672, "lower": 5.644823832319323, "upper": 7.353148832666766}], "r2": 0.9997585656785379, "rmse": 0.600565840658861, "aic": 0.8221059926156151, "df": 5}