bootstrap test Search Results


sieve-bootstrap student’s t-test for linear trends in ‘funtimes’ package  (RStudio)
90
RStudio sieve-bootstrap student’s t-test for linear trends in ‘funtimes’ package
Sieve Bootstrap Student’s T Test For Linear Trends In ‘Funtimes’ Package, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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sieve-bootstrap student’s t-test for linear trends in ‘funtimes’ package - by Bioz Stars, 2026-09
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tests two proportions [differences  (ncss llc)
90
ncss llc tests two proportions [differences
Tests Two Proportions [Differences, supplied by ncss llc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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tests two proportions [differences - by Bioz Stars, 2026-09
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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
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bootstrapping chi-squared residuals - by Bioz Stars, 2026-09
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bootstrap-based test  (MedCalc Software Ltd)
90
MedCalc Software Ltd bootstrap-based test
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 ).
Bootstrap Based Test, supplied by MedCalc Software Ltd, 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/product/bootstrap+test/bootstrap+based+test/pmc09340884-155-18-21
Average 90 stars, based on 1 article reviews
bootstrap-based test - by Bioz Stars, 2026-09
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sieve-bootstrap student’s t-test for linear trends  (RStudio)
90
RStudio sieve-bootstrap student’s t-test for linear trends
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 ).
Sieve Bootstrap Student’s T Test For Linear Trends, supplied by RStudio, 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/product/bootstrap+test/sieve+bootstrap+student%E2%80%99s+t+test+for+linear+trends/pmc11163650-72-22-29
Average 90 stars, based on 1 article reviews
sieve-bootstrap student’s t-test for linear trends - by Bioz Stars, 2026-09
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bootstrap version of the univariate kolmogorov-smirnov test  (RStudio)
90
RStudio bootstrap version of the univariate kolmogorov-smirnov test
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 ).
Bootstrap Version Of The Univariate Kolmogorov Smirnov Test, supplied by RStudio, 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/product/bootstrap+test/bootstrap+version+of+the+univariate+kolmogorov+smirnov+test/pmc08456781-445-6-16
Average 90 stars, based on 1 article reviews
bootstrap version of the univariate kolmogorov-smirnov test - by Bioz Stars, 2026-09
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bootstrap test  (CH Instruments)
90
CH Instruments bootstrap test
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 ).
Bootstrap Test, 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/product/bootstrap+test/bootstrap+test/pmc03416865-110-33-15
Average 90 stars, based on 1 article reviews
bootstrap test - by Bioz Stars, 2026-09
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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