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power.anova.test function  (RStudio)

 
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    RStudio power.anova.test function
    Power.Anova.Test Function, 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/function+anovan/power+anova+test+function/pm39814757-182-62-121
    Average 90 stars, based on 1 article reviews
    power.anova.test function - by Bioz Stars, 2026-09
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    Article Title: N-acetylated sugars in clownfish and damselfish skin mucus as messengers involved in chemical recognition by anemone host.
    Article Snippet: To evaluate the proportion of each metabolite in the different fish species, we generated a linear model for each metabolite using the lm function in R. We also used the ANOVA test to compare the four fish species using the aov function in R. We assessed the power of our statistical test to detect a significant difference between the groups using the function power.anova.test in R. To test our hypotheses, we computed the estimated marginal means of the linear model for the contrasts between ‘anemone symbionts’ (grouping A. akindynos and D. trimaculatus) and ‘non symbiotic damsels’ (grouping P. moluccensis and P. pavo) as well as between the clownfish and the 3 damselfish of this study, using the emmeans and contrast functions (http://www.rstudio.com/ version 2023.12.0.369) correcting for multiple tests using the Holm procedure.



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    (Left) Identifying the precise origin of accuracy deficits induced by the <t>attentional</t> blink. T2 identification accuracies at different inter-target ( T1–T2 ) lags, in a conventional attentional blink task. x-axis: inter-target lag in milliseconds; y-axis: T2 identification accuracy (%). Red horizontal line: asymptotic T2 identification accuracy for long inter-target lags; red vertical arrows: accuracy deficit with T2 identification for short inter-target lags (attentional blink). (Right, top) The identification deficit could reflect impaired detection of T2’s presence which, in turn, could arise either from a detection sensitivity deficit (upper row) or a detection bias (criterion) deficit (lower row). Gray and white Gaussians: decision variable distributions corresponding to signal (target present) and noise (target absent), respectively. Black vertical line: criterion for deciding between target present and absent. (Right, bottom) The identification deficit could also reflect impaired discrimination of T2’s features (e.g. orientation), which, again, could arise either from a discrimination sensitivity deficit (upper row) or a discrimination bias (criterion) deficit (lower row). Purple and orange Gaussians: decision variable distributions corresponding to a counterclockwise (CW) and clockwise (CCW) gratings, respectively. Black vertical line: criterion for deciding between target features (clockwise and counterclockwise orientation). Brain schematics (rightmost column ): Distinct neural markers of each subcomponent – detection (top) or discrimination (bottom) -- of attentional blink deficits.
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    (Left) Identifying the precise origin of accuracy deficits induced by the <t>attentional</t> blink. T2 identification accuracies at different inter-target ( T1–T2 ) lags, in a conventional attentional blink task. x-axis: inter-target lag in milliseconds; y-axis: T2 identification accuracy (%). Red horizontal line: asymptotic T2 identification accuracy for long inter-target lags; red vertical arrows: accuracy deficit with T2 identification for short inter-target lags (attentional blink). (Right, top) The identification deficit could reflect impaired detection of T2’s presence which, in turn, could arise either from a detection sensitivity deficit (upper row) or a detection bias (criterion) deficit (lower row). Gray and white Gaussians: decision variable distributions corresponding to signal (target present) and noise (target absent), respectively. Black vertical line: criterion for deciding between target present and absent. (Right, bottom) The identification deficit could also reflect impaired discrimination of T2’s features (e.g. orientation), which, again, could arise either from a discrimination sensitivity deficit (upper row) or a discrimination bias (criterion) deficit (lower row). Purple and orange Gaussians: decision variable distributions corresponding to a counterclockwise (CW) and clockwise (CCW) gratings, respectively. Black vertical line: criterion for deciding between target features (clockwise and counterclockwise orientation). Brain schematics (rightmost column ): Distinct neural markers of each subcomponent – detection (top) or discrimination (bottom) -- of attentional blink deficits.
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    (Left) Identifying the precise origin of accuracy deficits induced by the <t>attentional</t> blink. T2 identification accuracies at different inter-target ( T1–T2 ) lags, in a conventional attentional blink task. x-axis: inter-target lag in milliseconds; y-axis: T2 identification accuracy (%). Red horizontal line: asymptotic T2 identification accuracy for long inter-target lags; red vertical arrows: accuracy deficit with T2 identification for short inter-target lags (attentional blink). (Right, top) The identification deficit could reflect impaired detection of T2’s presence which, in turn, could arise either from a detection sensitivity deficit (upper row) or a detection bias (criterion) deficit (lower row). Gray and white Gaussians: decision variable distributions corresponding to signal (target present) and noise (target absent), respectively. Black vertical line: criterion for deciding between target present and absent. (Right, bottom) The identification deficit could also reflect impaired discrimination of T2’s features (e.g. orientation), which, again, could arise either from a discrimination sensitivity deficit (upper row) or a discrimination bias (criterion) deficit (lower row). Purple and orange Gaussians: decision variable distributions corresponding to a counterclockwise (CW) and clockwise (CCW) gratings, respectively. Black vertical line: criterion for deciding between target features (clockwise and counterclockwise orientation). Brain schematics (rightmost column ): Distinct neural markers of each subcomponent – detection (top) or discrimination (bottom) -- of attentional blink deficits.
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    (Left) Identifying the precise origin of accuracy deficits induced by the attentional blink. T2 identification accuracies at different inter-target ( T1–T2 ) lags, in a conventional attentional blink task. x-axis: inter-target lag in milliseconds; y-axis: T2 identification accuracy (%). Red horizontal line: asymptotic T2 identification accuracy for long inter-target lags; red vertical arrows: accuracy deficit with T2 identification for short inter-target lags (attentional blink). (Right, top) The identification deficit could reflect impaired detection of T2’s presence which, in turn, could arise either from a detection sensitivity deficit (upper row) or a detection bias (criterion) deficit (lower row). Gray and white Gaussians: decision variable distributions corresponding to signal (target present) and noise (target absent), respectively. Black vertical line: criterion for deciding between target present and absent. (Right, bottom) The identification deficit could also reflect impaired discrimination of T2’s features (e.g. orientation), which, again, could arise either from a discrimination sensitivity deficit (upper row) or a discrimination bias (criterion) deficit (lower row). Purple and orange Gaussians: decision variable distributions corresponding to a counterclockwise (CW) and clockwise (CCW) gratings, respectively. Black vertical line: criterion for deciding between target features (clockwise and counterclockwise orientation). Brain schematics (rightmost column ): Distinct neural markers of each subcomponent – detection (top) or discrimination (bottom) -- of attentional blink deficits.

    Journal: eLife

    Article Title: Distinct neural bases of subcomponents of the attentional blink

    doi: 10.7554/eLife.97098

    Figure Lengend Snippet: (Left) Identifying the precise origin of accuracy deficits induced by the attentional blink. T2 identification accuracies at different inter-target ( T1–T2 ) lags, in a conventional attentional blink task. x-axis: inter-target lag in milliseconds; y-axis: T2 identification accuracy (%). Red horizontal line: asymptotic T2 identification accuracy for long inter-target lags; red vertical arrows: accuracy deficit with T2 identification for short inter-target lags (attentional blink). (Right, top) The identification deficit could reflect impaired detection of T2’s presence which, in turn, could arise either from a detection sensitivity deficit (upper row) or a detection bias (criterion) deficit (lower row). Gray and white Gaussians: decision variable distributions corresponding to signal (target present) and noise (target absent), respectively. Black vertical line: criterion for deciding between target present and absent. (Right, bottom) The identification deficit could also reflect impaired discrimination of T2’s features (e.g. orientation), which, again, could arise either from a discrimination sensitivity deficit (upper row) or a discrimination bias (criterion) deficit (lower row). Purple and orange Gaussians: decision variable distributions corresponding to a counterclockwise (CW) and clockwise (CCW) gratings, respectively. Black vertical line: criterion for deciding between target features (clockwise and counterclockwise orientation). Brain schematics (rightmost column ): Distinct neural markers of each subcomponent – detection (top) or discrimination (bottom) -- of attentional blink deficits.

    Article Snippet: Significance testing for the attentional blink effect on sensitivity (d’) and criterion (c) parameters was performed with a two-way ANOVA test ( anovan function in Matlab).

    Techniques:

    ( A ) Schematic of the attentional blink task. Stimuli were presented in a rapid serial visual presentation (RSVP) paradigm at a 10 Hz rate (70ms onset, 30ms offset). Following fixation, plaid gratings appeared for a variable interval (200–1200ms, geometrically distributed), followed by the first target ( T1 ): a low spatial frequency grating (100ms). After this, a series of plaid gratings appeared for variable intervals (100, 300, 500, 700, and 900ms; geometric distribution) followed by the appearance of the second target ( T2 ): a high spatial frequency grating (100ms). Following T2, plaid gratings were presented for a fixed interval (600ms). Finally, in the response epoch, participants reported T1’s orientation as being closer to the cardinal or diagonal axes (two-alternative), and then reported T2’s orientation as being clockwise or counterclockwise of vertical, or absent (three-alternative). All plaids were encircled by a circular placeholder. The fixation dot and the placeholder were present on the screen throughout the trial. ( B ) Psychometric function of accuracy (% correct) for T2 detection with increasing inter-target ( T1–T2 ) lags, for trials in which T1 was reported correctly (n=24 participants). Filled circles and solid lines: average accuracy for high contrast T2 gratings; open circles and dashed lines: average accuracy for low contrast T2 gratings. Error bars: s.e.m. Asterisks: significance levels for comparing accuracies between short (100 and 300ms) and long (700 and 900ms) lag trials; Statistical method: Wilcoxon signed-rank test; solid and dashed brackets: comparisons for high and low contrast gratings respectively. *p<0.05, **p<0.01, ***p<0.001 and n.s.: not significant. ( C ) Same as in panel B, but showing the psychometric function of accuracy for T2 discrimination with increasing inter-target ( T1–T2 ) lags (n=24). Other conventions are the same as in panel B, except that markers and lines are depicted in orange color. ( D ) Stimulus-response contingency table for the 3-alternative T2 decision. Rows represent the three possible T2 stimulus events: clockwise orientation (CW, orange), counterclockwise orientation (CCW, purple) or absent (none, gray). Columns represent three possible choices: clockwise (CW), counterclockwise (CCW) or absent (none). The table depicts the nine stimulus-response contingencies: two each of hit rates (H), misidentification rates (MI), miss rates (M), false alarm rates (FA) – one for each orientation (CW/CCW) – and one correct rejection rate (CR). ( E ) Same as in panel B, but showing psychometric function of average hit rates. ( F ) Same as in panel B, but showing psychometric function of average misidentification rates. ( G ) Same as in panel B, but showing psychometric function of average miss rates. ( E–G ) Other conventions are the same as in panel B except that markers and lines are denoted in black color. ( H ) Same as in panel B, but showing psychometric function correct rejection (filled circles) and false alarm (open circles) rates on T2 absent trials .

    Journal: eLife

    Article Title: Distinct neural bases of subcomponents of the attentional blink

    doi: 10.7554/eLife.97098

    Figure Lengend Snippet: ( A ) Schematic of the attentional blink task. Stimuli were presented in a rapid serial visual presentation (RSVP) paradigm at a 10 Hz rate (70ms onset, 30ms offset). Following fixation, plaid gratings appeared for a variable interval (200–1200ms, geometrically distributed), followed by the first target ( T1 ): a low spatial frequency grating (100ms). After this, a series of plaid gratings appeared for variable intervals (100, 300, 500, 700, and 900ms; geometric distribution) followed by the appearance of the second target ( T2 ): a high spatial frequency grating (100ms). Following T2, plaid gratings were presented for a fixed interval (600ms). Finally, in the response epoch, participants reported T1’s orientation as being closer to the cardinal or diagonal axes (two-alternative), and then reported T2’s orientation as being clockwise or counterclockwise of vertical, or absent (three-alternative). All plaids were encircled by a circular placeholder. The fixation dot and the placeholder were present on the screen throughout the trial. ( B ) Psychometric function of accuracy (% correct) for T2 detection with increasing inter-target ( T1–T2 ) lags, for trials in which T1 was reported correctly (n=24 participants). Filled circles and solid lines: average accuracy for high contrast T2 gratings; open circles and dashed lines: average accuracy for low contrast T2 gratings. Error bars: s.e.m. Asterisks: significance levels for comparing accuracies between short (100 and 300ms) and long (700 and 900ms) lag trials; Statistical method: Wilcoxon signed-rank test; solid and dashed brackets: comparisons for high and low contrast gratings respectively. *p<0.05, **p<0.01, ***p<0.001 and n.s.: not significant. ( C ) Same as in panel B, but showing the psychometric function of accuracy for T2 discrimination with increasing inter-target ( T1–T2 ) lags (n=24). Other conventions are the same as in panel B, except that markers and lines are depicted in orange color. ( D ) Stimulus-response contingency table for the 3-alternative T2 decision. Rows represent the three possible T2 stimulus events: clockwise orientation (CW, orange), counterclockwise orientation (CCW, purple) or absent (none, gray). Columns represent three possible choices: clockwise (CW), counterclockwise (CCW) or absent (none). The table depicts the nine stimulus-response contingencies: two each of hit rates (H), misidentification rates (MI), miss rates (M), false alarm rates (FA) – one for each orientation (CW/CCW) – and one correct rejection rate (CR). ( E ) Same as in panel B, but showing psychometric function of average hit rates. ( F ) Same as in panel B, but showing psychometric function of average misidentification rates. ( G ) Same as in panel B, but showing psychometric function of average miss rates. ( E–G ) Other conventions are the same as in panel B except that markers and lines are denoted in black color. ( H ) Same as in panel B, but showing psychometric function correct rejection (filled circles) and false alarm (open circles) rates on T2 absent trials .

    Article Snippet: Significance testing for the attentional blink effect on sensitivity (d’) and criterion (c) parameters was performed with a two-way ANOVA test ( anovan function in Matlab).

    Techniques:

    The attentional blink selectively impairs a specific component of attention – perceptual sensitivity ( d’ ) – and produces both detection (top, left) and discrimination (top, right) deficits. Detection d’ deficits – deficits with distinguishing the presence versus absence of the second target ( T2 ) – are correlated with reduced amplitudes of N2p and P3 ERPs (gray shading, left top). They are also accompanied by a representational collapse along the detection dimension (gray shading, left bottom). By contrast, discrimination d’ deficits – deficits with discriminating T2’s orientation – is evidenced by reduced left fronto-parietal beta coherence (red shading, left top) and a representational collapse along the discrimination dimension (red shading, left bottom).

    Journal: eLife

    Article Title: Distinct neural bases of subcomponents of the attentional blink

    doi: 10.7554/eLife.97098

    Figure Lengend Snippet: The attentional blink selectively impairs a specific component of attention – perceptual sensitivity ( d’ ) – and produces both detection (top, left) and discrimination (top, right) deficits. Detection d’ deficits – deficits with distinguishing the presence versus absence of the second target ( T2 ) – are correlated with reduced amplitudes of N2p and P3 ERPs (gray shading, left top). They are also accompanied by a representational collapse along the detection dimension (gray shading, left bottom). By contrast, discrimination d’ deficits – deficits with discriminating T2’s orientation – is evidenced by reduced left fronto-parietal beta coherence (red shading, left top) and a representational collapse along the discrimination dimension (red shading, left bottom).

    Article Snippet: Significance testing for the attentional blink effect on sensitivity (d’) and criterion (c) parameters was performed with a two-way ANOVA test ( anovan function in Matlab).

    Techniques: