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bootstrapping chi-squared residuals  (CH Instruments)

 
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    CH Instruments bootstrapping chi-squared residuals
    Host susceptibility distributions for house finches from variable prior exposure treatments: no prior exposure (A,B); low-dose (C); or high-dose (D). Colored lines show estimated susceptibility distributions from either homogeneously (A) or gamma-distributed (B-D) models (note distinct axes for the two models). In (A), host infection probability per 1000 bacterial particles ( p ) is shown as the single best fit parameter p (dotted vertical lines represent 1 standard error) for the homogeneous model, which was the best fit model for the no prior exposure group <t>(see</t> ). In (B-D), the best fit parameters (shape and scale) for gamma distributions (teal lines) are listed for each group, and vertical gray lines indicate mean susceptibility ( x ) for that treatment. Lighter shading represents 95% confidence regions for gamma distributions, obtained by <t>bootstrapping</t> <t>chi-squared</t> <t>residuals</t> to create 1,000 pseudoreplicates of infection data and then refitting the model to pseudoreplicates, as per [ , ]. The gamma model was the best fit for only the low-dose and high-dose groups. Gamma estimates are also shown for the no prior exposure group (B) because this allowed more equivalent comparisons for certain SIR simulations (see ).
    Bootstrapping Chi Squared Residuals, supplied by CH Instruments, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/bootstrapping chi-squared residuals/product/CH Instruments
    Average 90 stars, based on 1 article reviews
    bootstrapping chi-squared residuals - by Bioz Stars, 2026-05
    90/100 stars

    Images

    1) Product Images from "Prior exposure to pathogens augments host heterogeneity in susceptibility and has key epidemiological consequences"

    Article Title: Prior exposure to pathogens augments host heterogeneity in susceptibility and has key epidemiological consequences

    Journal: PLOS Pathogens

    doi: 10.1371/journal.ppat.1012092

    Host susceptibility distributions for house finches from variable prior exposure treatments: no prior exposure (A,B); low-dose (C); or high-dose (D). Colored lines show estimated susceptibility distributions from either homogeneously (A) or gamma-distributed (B-D) models (note distinct axes for the two models). In (A), host infection probability per 1000 bacterial particles ( p ) is shown as the single best fit parameter p (dotted vertical lines represent 1 standard error) for the homogeneous model, which was the best fit model for the no prior exposure group (see ). In (B-D), the best fit parameters (shape and scale) for gamma distributions (teal lines) are listed for each group, and vertical gray lines indicate mean susceptibility ( x ) for that treatment. Lighter shading represents 95% confidence regions for gamma distributions, obtained by bootstrapping chi-squared residuals to create 1,000 pseudoreplicates of infection data and then refitting the model to pseudoreplicates, as per [ , ]. The gamma model was the best fit for only the low-dose and high-dose groups. Gamma estimates are also shown for the no prior exposure group (B) because this allowed more equivalent comparisons for certain SIR simulations (see ).
    Figure Legend Snippet: Host susceptibility distributions for house finches from variable prior exposure treatments: no prior exposure (A,B); low-dose (C); or high-dose (D). Colored lines show estimated susceptibility distributions from either homogeneously (A) or gamma-distributed (B-D) models (note distinct axes for the two models). In (A), host infection probability per 1000 bacterial particles ( p ) is shown as the single best fit parameter p (dotted vertical lines represent 1 standard error) for the homogeneous model, which was the best fit model for the no prior exposure group (see ). In (B-D), the best fit parameters (shape and scale) for gamma distributions (teal lines) are listed for each group, and vertical gray lines indicate mean susceptibility ( x ) for that treatment. Lighter shading represents 95% confidence regions for gamma distributions, obtained by bootstrapping chi-squared residuals to create 1,000 pseudoreplicates of infection data and then refitting the model to pseudoreplicates, as per [ , ]. The gamma model was the best fit for only the low-dose and high-dose groups. Gamma estimates are also shown for the no prior exposure group (B) because this allowed more equivalent comparisons for certain SIR simulations (see ).

    Techniques Used: Infection



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    bootstrapping chi-squared residuals  (CH Instruments)
    90
    CH Instruments bootstrapping chi-squared residuals
    Host susceptibility distributions for house finches from variable prior exposure treatments: no prior exposure (A,B); low-dose (C); or high-dose (D). Colored lines show estimated susceptibility distributions from either homogeneously (A) or gamma-distributed (B-D) models (note distinct axes for the two models). In (A), host infection probability per 1000 bacterial particles ( p ) is shown as the single best fit parameter p (dotted vertical lines represent 1 standard error) for the homogeneous model, which was the best fit model for the no prior exposure group <t>(see</t> ). In (B-D), the best fit parameters (shape and scale) for gamma distributions (teal lines) are listed for each group, and vertical gray lines indicate mean susceptibility ( x ) for that treatment. Lighter shading represents 95% confidence regions for gamma distributions, obtained by <t>bootstrapping</t> <t>chi-squared</t> <t>residuals</t> to create 1,000 pseudoreplicates of infection data and then refitting the model to pseudoreplicates, as per [ , ]. The gamma model was the best fit for only the low-dose and high-dose groups. Gamma estimates are also shown for the no prior exposure group (B) because this allowed more equivalent comparisons for certain SIR simulations (see ).
    Bootstrapping Chi Squared Residuals, supplied by CH Instruments, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/bootstrapping chi-squared residuals/product/CH Instruments
    Average 90 stars, based on 1 article reviews
    bootstrapping chi-squared residuals - by Bioz Stars, 2026-05
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Host susceptibility distributions for house finches from variable prior exposure treatments: no prior exposure (A,B); low-dose (C); or high-dose (D). Colored lines show estimated susceptibility distributions from either homogeneously (A) or gamma-distributed (B-D) models (note distinct axes for the two models). In (A), host infection probability per 1000 bacterial particles ( p ) is shown as the single best fit parameter p (dotted vertical lines represent 1 standard error) for the homogeneous model, which was the best fit model for the no prior exposure group (see ). In (B-D), the best fit parameters (shape and scale) for gamma distributions (teal lines) are listed for each group, and vertical gray lines indicate mean susceptibility ( x ) for that treatment. Lighter shading represents 95% confidence regions for gamma distributions, obtained by bootstrapping chi-squared residuals to create 1,000 pseudoreplicates of infection data and then refitting the model to pseudoreplicates, as per [ , ]. The gamma model was the best fit for only the low-dose and high-dose groups. Gamma estimates are also shown for the no prior exposure group (B) because this allowed more equivalent comparisons for certain SIR simulations (see ).

    Journal: PLOS Pathogens

    Article Title: Prior exposure to pathogens augments host heterogeneity in susceptibility and has key epidemiological consequences

    doi: 10.1371/journal.ppat.1012092

    Figure Lengend Snippet: Host susceptibility distributions for house finches from variable prior exposure treatments: no prior exposure (A,B); low-dose (C); or high-dose (D). Colored lines show estimated susceptibility distributions from either homogeneously (A) or gamma-distributed (B-D) models (note distinct axes for the two models). In (A), host infection probability per 1000 bacterial particles ( p ) is shown as the single best fit parameter p (dotted vertical lines represent 1 standard error) for the homogeneous model, which was the best fit model for the no prior exposure group (see ). In (B-D), the best fit parameters (shape and scale) for gamma distributions (teal lines) are listed for each group, and vertical gray lines indicate mean susceptibility ( x ) for that treatment. Lighter shading represents 95% confidence regions for gamma distributions, obtained by bootstrapping chi-squared residuals to create 1,000 pseudoreplicates of infection data and then refitting the model to pseudoreplicates, as per [ , ]. The gamma model was the best fit for only the low-dose and high-dose groups. Gamma estimates are also shown for the no prior exposure group (B) because this allowed more equivalent comparisons for certain SIR simulations (see ).

    Article Snippet: We simulated the heterogeneous and homogeneous models using the parameter estimates obtained from bootstrapping chi-squared residuals (see ).

    Techniques: Infection