Data
PromptBeginner5 minmarkdown
# fit_aov <- aov(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdown
# t.test(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdown
if (is.null(data_file)) data_file <- paste0(project_name
.csv)
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Data
PromptBeginner5 minmarkdown
check_data <- function(df
top_n_levels = 8) {
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Data
PromptBeginner5 minmarkdown
# saveRDS(df
data_clean.rds)
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Data
PromptBeginner5 minmarkdown
# fit_aov <- aov(outcome_var ~ group_var
data = df)
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Data
PromptBeginner5 minmarkdown
t.test(outcome_var ~ group_var
data = df)
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Data
PromptBeginner5 minmarkdown
abline(lm(outcome_var ~ predictor
data = df)
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Data
PromptBeginner5 minmarkdown
df <- read.csv(your_data.csv
stringsAsFactors = FALSE)
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Coding & Debugging
PromptBeginner5 minmarkdown
- `arrows`: `code = 1` (head at start)
`code = 2` (head at end
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Data
PromptBeginner5 minmarkdown
- Formula interface: `pairs(~ var1 + var2 + var3
data = df)`.
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Data
PromptBeginner5 minmarkdown
- Formula interface: `cor.test(~ x + y
data = df)` — note the `~` with no LHS.
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Data
PromptBeginner5 minmarkdown
- Default kernel: `gaussian`. Range of density extends beyond data range (controlled by `cut`
default 3 bandwidths).
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Data
PromptBeginner5 minmarkdown
- Default bandwidth: `bw = nrd0` (Silverman's rule of thumb). For multimodal data
consider `SJ` or `bcv`.
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Creative
PromptBeginner5 minmarkdown
- `nstart > 1` recommended (e.g.
`nstart = 25`): runs algorithm from multiple random starts
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Data
PromptBeginner5 minmarkdown
- Returns a matrix of p-values
not test statistics.
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Data
PromptBeginner5 minmarkdown
- For composite hypotheses (parameters estimated from data)
p-values are **conservative** (too large). Use `dgof` or `ks.test` with `exact = NULL` for discrete distributions.
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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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Data
PromptBeginner5 minmarkdown
- If data fits too exactly (no residual noise)
convergence check fails — use `control = list(scaleOffset = 1)` or jitter data.
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Creative
PromptBeginner5 minmarkdown
- Self-starting models (`SSlogis`
`SSasymp`
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Creative
PromptBeginner5 minmarkdown
- `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.
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Creative
PromptBeginner5 minmarkdown
- `model.matrix` creates the design matrix including dummy coding. Default contrasts: `contr.treatment` for unordered factors
`contr.poly` for ordered.
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Coding & Debugging
PromptBeginner5 minmarkdown
- `URLencode(url
reserved = FALSE)` by default does NOT encode reserved chars (`/`
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Creative
PromptBeginner5 minmarkdown
- `substr(x
start
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