General
PromptBeginner5 minmarkdown
- For tables larger than 2x2
uses simulation (`simulate.p.value = TRUE`) or network algorithm.
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Showing 24 of 113,501Categories: Data & Insights, Cursor-rules, General, Coding & Debugging
General
PromptBeginner5 minmarkdown
- Paired test: `paired = TRUE` uses signed-rank test (Wilcoxon)
not rank-sum (Mann-Whitney).
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General
PromptBeginner5 minmarkdown
- `exact = TRUE` by default for small samples with no ties. With ties
normal approximation used.
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General
PromptBeginner5 minmarkdown
- Return object includes `$expected`
`$residuals` (Pearson)
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General
PromptBeginner5 minmarkdown
- `simulate.p.value = TRUE`: Monte Carlo with `B = 2000` replicates (min p ~ 0.0005). Simulation assumes **fixed marginals** (Fisher-style sampling
not the chi-sq assumption).
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General
PromptBeginner5 minmarkdown
- For goodness-of-fit: pass a vector
not a matrix. `p` must sum to 1 (or set `rescale.p = TRUE`).
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General
PromptBeginner5 minmarkdown
> Non-obvious behaviors
gotchas
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Coding & Debugging
PromptBeginner5 minmarkdown
- `SANN` (simulated annealing): convergence code is **always 0** — it never fails. `maxit` = total function evals (default 10000)
no other stopping criterion.
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General
PromptBeginner5 minmarkdown
- `uniroot`: finds a root of `f` in `[lower
upper]`. **Requires** `f(lower)` and `f(upper)` to have opposite signs.
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General
PromptBeginner5 minmarkdown
- `anova(model1
model2)`: F-test comparing nested models.
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General
PromptBeginner5 minmarkdown
- Returns object with `predict`
`print`
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General
PromptBeginner5 minmarkdown
- Default method is Nelder-Mead (no gradients
robust but slow). Poor for 1D — use `Brent` or `optimize()`.
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General
PromptBeginner5 minmarkdown
- `lowess` parameter is `f` (span
default 2/3); `loess` parameter is `span` (default 0.75).
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General
PromptBeginner5 minmarkdown
- Direction: `direction = both` (default)
`forward`
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General
PromptBeginner5 minmarkdown
- `lowess` is the older function; returns `list(x
y)` — cannot predict at new points.
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General
PromptBeginner5 minmarkdown
- `predict.glm` with `type = response` gives predictions on the response scale (e.g.
probabilities for logistic); `type = link` (default) gives on the link scale.
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General
PromptBeginner5 minmarkdown
- For Type III SS
use `car::Anova()` or set contrasts to `contr.sum`/`contr.helmert`.
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General
PromptBeginner5 minmarkdown
- `anova(glm_obj
test = Chisq)` for deviance-based tests; `F` is invalid for non-Gaussian families.
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General
PromptBeginner5 minmarkdown
- Quasi-families (`quasibinomial`
`quasipoisson`) allow overdispersion — no AIC is computed.
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General
PromptBeginner5 minmarkdown
- `weights` in `glm` means **prior weights** (not frequency weights) — for frequency weights
use the cbind trick or offset.
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General
PromptBeginner5 minmarkdown
- `binomial` accepts response as: 0/1 vector
logical
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General
PromptBeginner5 minmarkdown
- Common families: `binomial(link = logit)`
`poisson(link = log)`
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General
PromptBeginner5 minmarkdown
- Column names from `model.matrix` can be surprising: e.g.
`factorLevelName` concatenation.
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General
PromptBeginner5 minmarkdown
- `terms` object attributes: `order` (interaction order per term)
`intercept`
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