Find agent skills by outcome

- If data fits too exactly (no residual noise)

convergence check fails — use `control = list(scaleOffset = 1)` or jitter data.

- Self-starting models (`SSlogis`

`SSasymp`

- For Type III SS

use `car::Anova()` or set contrasts to `contr.sum`/`contr.helmert`.

- `aov` is a wrapper around `lm` that stores extra info for balanced ANOVA. For unbalanced designs

Type I SS (sequential) are computed — order of terms matters.

- Quasi-families (`quasibinomial`

`quasipoisson`) allow overdispersion — no AIC is computed.

- `binomial` accepts response as: 0/1 vector

logical

- `anova(glm_obj

test = Chisq)` for deviance-based tests; `F` is invalid for non-Gaussian families.

- `weights` in `glm` means prior weights (not frequency weights) — for frequency weights

use the cbind trick or offset.

- Common families: `binomial(link = logit)`

`poisson(link = log)`

- Default `family = gaussian(link = identity)` — `glm()` with no `family` silently fits OLS (same as `lm`

but slower and with deviance-based output).

- Column names from `model.matrix` can be surprising: e.g.

`factorLevelName` concatenation.

- `terms` object attributes: `order` (interaction order per term)

`intercept`

- `model.matrix` creates the design matrix including dummy coding. Default contrasts: `contr.treatment` for unordered factors

`contr.poly` for ordered.

- `I()` is required to use arithmetic operators literally: `y ~ x + I(x^2)`. Without `I()`

`^` means interaction crossing.

Coding & Debugging

- `URLencode(url

reserved = FALSE)` by default does NOT encode reserved chars (`/`

- Captures output from `cat`

`print`

> Non-obvious behaviors

gotchas

- `formatC(x

format = f

- Converts character vectors to appropriate types (logical

integer

- `as.is = TRUE` (recommended): keeps characters as character

not factor.

- `format(x

digits

- `which = both` (default)

`left`

- Assignment form: `substr(x

- `substr(x

start