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CH Instruments anova function
Anova Function, 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/function+anovan/anova+function/pmc11487125-216-7-14
Average 90 stars, based on 1 article reviews

anova function - by Bioz Stars, 2026-10

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Article Title: Milking Reactivity in Primiparous Saanen Goats During Early Lactation: Effects on Milk Yield, Milk Quality and Plasma Cortisol Concentration
Article Snippet: The impact of the explanatory variables was calculated using the “anova” function, running Type III Wald chi-square trials.

Article Title: Sites dominated by common fiddleneck (Amsinckia menziesii var. intermedia) support diverse plant-pollinator interactions
Article Snippet: Biodiversity is declining at unprecedented rates worldwide due largely to land use change and abnormal disturbance events.. The high species diversity and endemicity found in California’s coastal sage scrub (CSS) are especially at risk from urban development and ongoing disturbance.. However, several CSS plant species have disturbance adaptations which may allow them to serve as vital resources for insect pollinators when native plant diversity is threatened.

Article Title: The role of intraspecific trait variation in driving post‐metamorphic survival: Implications for recruitment in open populations
Article Snippet: However, (3) if ΔAIC ≤3 but the most complex model showed the lowest AIC, then we used likelihood ratio test with the ‘anova’ function (based on the Chi‐square distribution) to determine if the most complex model contained a significant term (at α = .05).

Article Title: Rapid Increase in Soil Respiration and Reduction in Soil Nitrate Availability Following CO 2 Enrichment in a Mature Oak Forest
Article Snippet: The two models with the lowest AIC score were compared using the Anova function to compute the Chi-squares between the two models for investigating the model fit.

Article Title: Assessing receptive verb knowledge in late talkers and autistic children: advances and cautionary tales
Article Snippet: Regressions were run using the lme4 package (Version 1.1–12; [ ]) in R [ ] with model comparisons made using the drop1() function with chi-square tests.

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Article Title: Eye size does not change with artificial selection on relative telencephalon size in guppies (Poecilia reticulata).
Article Snippet: .. Statistical significance of the explanatory variables was computed by using the ANOVA function, specifying Type III Wald chi-square tests, in the car package [48]. (i) Absolute and relative eye size We asked if eye size in absolute and relative terms are dependent on telencephalon size selection regime, while also testing for sex-specific effects of artificial selection for telencephalon size on eye size. ..

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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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