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scatterplot smoothing  (STATA Corporation)


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    Structured Review

    STATA Corporation scatterplot smoothing
    Co‐plot of odds ratios (ORs, upper row) and Locally Weighted <t>Scatterplot</t> Smoothing curves of stage migration and determination of structural break points with the use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an OR of a specific lymph node examined (LNE, vacant if case number < 10) from logistic regression analysis. ( a ) Overall patients, ( b ) adenocarcinoma patients, and ( c ) esophageal squamous cell carcinoma (ESCC) patients from the Surveillance, Epidemiology, and End Results (SEER) database; and ( d ) ESCC patients from the Thoracic Surgery Department of a single institution (SI). ( ) T1, ( ) T2, ( ) T3, and ( ) T4.
    Scatterplot Smoothing, supplied by STATA Corporation, used in various techniques. Bioz Stars score: 99/100, based on 36840 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/linear+locally+weighted+scatterplot+smoother+(lowess)+method/STATA+12%2E0/pmc06501022-111-32-35
    Average 99 stars, based on 36840 article reviews
    scatterplot smoothing - by Bioz Stars, 2026-10
    99/100 stars

    Images

    1) Product Images from "Effect of lymph node examined count on accurate staging and survival of resected esophageal cancer"

    Article Title: Effect of lymph node examined count on accurate staging and survival of resected esophageal cancer

    Journal: Thoracic Cancer

    doi: 10.1111/1759-7714.13056

    Co‐plot of odds ratios (ORs, upper row) and Locally Weighted Scatterplot Smoothing curves of stage migration and determination of structural break points with the use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an OR of a specific lymph node examined (LNE, vacant if case number < 10) from logistic regression analysis. ( a ) Overall patients, ( b ) adenocarcinoma patients, and ( c ) esophageal squamous cell carcinoma (ESCC) patients from the Surveillance, Epidemiology, and End Results (SEER) database; and ( d ) ESCC patients from the Thoracic Surgery Department of a single institution (SI). ( ) T1, ( ) T2, ( ) T3, and ( ) T4.
    Figure Legend Snippet: Co‐plot of odds ratios (ORs, upper row) and Locally Weighted Scatterplot Smoothing curves of stage migration and determination of structural break points with the use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an OR of a specific lymph node examined (LNE, vacant if case number < 10) from logistic regression analysis. ( a ) Overall patients, ( b ) adenocarcinoma patients, and ( c ) esophageal squamous cell carcinoma (ESCC) patients from the Surveillance, Epidemiology, and End Results (SEER) database; and ( d ) ESCC patients from the Thoracic Surgery Department of a single institution (SI). ( ) T1, ( ) T2, ( ) T3, and ( ) T4.

    Techniques Used: Migration

    Co‐plot of hazard ratios (HRs, upper row) and Locally Weighted Scatterplot Smoothing curves of cancer‐specific survival (CSS) and determination of structural break points with use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an HR of a specific lymph node examined (LNE, vacant if case number < 10) from Cox regression analysis. ( a ) Overall patients, ( b ) adenocarcinoma patients, and ( c ) esophageal squamous cell carcinoma (ESCC) patients from the Surveillance, Epidemiology, and End Results (SEER) database. ( ) T1, ( ) T2, ( ) T3, and ( ) T4.
    Figure Legend Snippet: Co‐plot of hazard ratios (HRs, upper row) and Locally Weighted Scatterplot Smoothing curves of cancer‐specific survival (CSS) and determination of structural break points with use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an HR of a specific lymph node examined (LNE, vacant if case number < 10) from Cox regression analysis. ( a ) Overall patients, ( b ) adenocarcinoma patients, and ( c ) esophageal squamous cell carcinoma (ESCC) patients from the Surveillance, Epidemiology, and End Results (SEER) database. ( ) T1, ( ) T2, ( ) T3, and ( ) T4.

    Techniques Used:

    Co‐plot of hazard ratios (HRs, upper row) and Locally Weighted Scatterplot Smoothing curves of cancer‐specific survival (CSS) and determination of structural break points with the use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an HR of a specific lymph node examined (LNE, vacant if case number < 10) from Cox regression analysis. ( a ) Node‐negative (N negative) and ( b ) node‐positive (N positive) patients. ( ) T1, ( ) T2, ( ) T3, and ( ) T4.
    Figure Legend Snippet: Co‐plot of hazard ratios (HRs, upper row) and Locally Weighted Scatterplot Smoothing curves of cancer‐specific survival (CSS) and determination of structural break points with the use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an HR of a specific lymph node examined (LNE, vacant if case number < 10) from Cox regression analysis. ( a ) Node‐negative (N negative) and ( b ) node‐positive (N positive) patients. ( ) T1, ( ) T2, ( ) T3, and ( ) T4.

    Techniques Used:

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    SAS institute linear locally weighted scatterplot smoother (lowess) method
    Developed methodology to identify and characterize metabolic phases. Experimental data are first cleaned using the methodology presented in Figure 2 and additionally by removing data with a viability below 50% or a depletion of metabolites during a measurement interval. The number of metabolic phases during the cell culture process are determined by differentiating the smoothed <t>(LOWESS)</t> reaction rates of all metabolites with respect to the growth rate (dR/dµ). Recursive partitioning is then applied on those derivatives to get a vector of possible metabolic phase breakpoints. Hierarchical clustering is then applied on this vector of possible breakpoints to define the number of final metabolic phases (clusters). Knowing the number of metabolic phases, the segmented regression can then be calibrated on the calibration dataset for each metabolite and validated on the cross validation dataset of the 2 L bioreactor and also of the 2000 L bioreactor.
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    Developed methodology to identify and characterize metabolic phases. Experimental data are first cleaned using the methodology presented in Figure 2 and additionally by removing data with a viability below 50% or a depletion of metabolites during a measurement interval. The number of metabolic phases during the cell culture process are determined by differentiating the smoothed (LOWESS) reaction rates of all metabolites with respect to the growth rate (dR/dµ). Recursive partitioning is then applied on those derivatives to get a vector of possible metabolic phase breakpoints. Hierarchical clustering is then applied on this vector of possible breakpoints to define the number of final metabolic phases (clusters). Knowing the number of metabolic phases, the segmented regression can then be calibrated on the calibration dataset for each metabolite and validated on the cross validation dataset of the 2 L bioreactor and also of the 2000 L bioreactor.

    Journal: Biotechnology and Bioengineering

    Article Title: Segmented linear modeling of CHO fed‐batch culture and its application to large scale production

    doi: 10.1002/bit.26214

    Figure Lengend Snippet: Developed methodology to identify and characterize metabolic phases. Experimental data are first cleaned using the methodology presented in Figure 2 and additionally by removing data with a viability below 50% or a depletion of metabolites during a measurement interval. The number of metabolic phases during the cell culture process are determined by differentiating the smoothed (LOWESS) reaction rates of all metabolites with respect to the growth rate (dR/dµ). Recursive partitioning is then applied on those derivatives to get a vector of possible metabolic phase breakpoints. Hierarchical clustering is then applied on this vector of possible breakpoints to define the number of final metabolic phases (clusters). Knowing the number of metabolic phases, the segmented regression can then be calibrated on the calibration dataset for each metabolite and validated on the cross validation dataset of the 2 L bioreactor and also of the 2000 L bioreactor.

    Article Snippet: As the derivative can amplify possible biological and analytical errors, the specific production rates were, preliminarily to deriving, smoothed as a function of the specific growth rate with the linear Locally Weighted Scatterplot Smoother (LOWESS) method (Cleveland, ) by using SAS software JMP 11 ©.

    Techniques: Cell Culture, Plasmid Preparation, Biomarker Discovery