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RStudio function anova
Function Anova, 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/anova+function/pmc07079572-201-9-12
Average 90 stars, based on 1 article reviews

function anova - by Bioz Stars, 2026-10

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Article Title: Effects of past mating behavior versus past ejaculation on male mate choice and male attractiveness
Article Snippet: Wald chi-square tests were performed to calculate the P value using the Anova function in the car package (R studio v1.3.1093 with R v4.0.5).

Article Title: Actions and feelings in sync: exploring the relationship between synchrony and empathy in children’s dyadic musical interactions
Article Snippet: Where variance was not zero, each model was compared with a reduced one by assessing the Akaike Information Criterion (AIC), chi-square estimates, and associated p -values via the ANOVA function in RStudio (using Restricted Maximum Likelihood - REML).

Article Title: Structure of ant assemblages and effect of environmental conditions in gallery forests with different conservation status in the Humid Chaco ecoregion of Argentina
Article Snippet: Environmental modification resulting from human activities has negative consequences, including biodiversity loss and a decline in insect populations, which threaten crucial ecosystem functions.. A comparative study was conducted in gallery forests with different conservation statuses (Good, Intermediate, and Unfavorable) in the Humid Chaco ecoregion, Chaco Province, Argentina.. We hypothesize that the conservation status of gallery forests influences the diversity parameters, composition, and structure of ant assemblages because it provides better conditions, thereby increasing diversity parameters and resulting in distinct patterns of ant species composition.

Article Title: The development of prosodic focus marking in French
Article Snippet: Model comparisons were conducted using the ANOVA function in RStudio, in order to derive the model with the best fit.

Article Title: Examining the relation between emotional experiences and emotional expressions in competitive tennis matches
Article Snippet: To compare the different models, using ANOVA function within RStudio, the χ2-difference test was applied.

Article Title: Simulating atmospheric drought: Silica gel packets dehumidify mesocosm microclimates
Article Snippet: Finally, we analyzed the best‐fit model from each dataset (daytime, hourly, and 24‐h) using the anova function from the stats library (v4.2.2; R Core Team, ; RStudio Team, ).

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Article Title: Autonomy support encourages use of more-affected arm post-stroke.
Article Snippet: .. The anova function in R studio was applied for model comparison and to define the best-fit model with the lowest Akaike information criterion. ..

Generated:

Article Title: Artificial Light Increases Nighttime Prevalence of Predatory Fishes, Altering Community Composition on Coral Reefs.
Article Snippet: .. We generated summary values for fixed terms by comparing a model with and without the relevant term using the anova() function in R studio. .. If the interaction term or the main effect of exposure duration was significant, we used post hoc Tukey's multiple comparisons tests with the package emmeans (Lenth 2022) in R studio to explore the effect further.

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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).

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