Data
PromptBeginner5 minmarkdownQuality: 24
boxplot(%s ~ %s
data = df
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Data
PromptBeginner5 minmarkdownQuality: 24
fit_aov <- aov(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 24
t.test(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 24
fit <- lm(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 24
if (is.null(datafile)) datafile <- paste0(project_name
.csv)
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Data
PromptBeginner5 minmarkdownQuality: 24
check_data <- function(df
topnlevels = 8) {
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Data
PromptBeginner5 minmarkdownQuality: 24
saveRDS(df
data_clean.rds)
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Data
PromptBeginner5 minmarkdownQuality: 24
fitaov <- aov(outcomevar ~ group_var
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 24
t.test(outcomevar ~ groupvar
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 24
abline(lm(outcome_var ~ predictor
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 24
df <- read.csv(your_data.csv
stringsAsFactors = FALSE)
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Coding & Debugging
PromptBeginner5 minmarkdownQuality: 24
- arrows: code = 1 (head at start)
code = 2 (head at end
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Data
PromptBeginner5 minmarkdownQuality: 24
- Formula interface: `pairs(~ var1 + var2 + var3
data = df)`.
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Data
PromptBeginner5 minmarkdownQuality: 24
- Formula interface: `cor.test(~ x + y
data = df) — note the ~` with no LHS.
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Data
PromptBeginner5 minmarkdownQuality: 28
- Default kernel: gaussian. Range of density extends beyond data range (controlled by cut
default 3 bandwidths).
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Data
PromptBeginner5 minmarkdownQuality: 28
- Default bandwidth: bw = nrd0 (Silverman's rule of thumb). For multimodal data
consider SJ or bcv.
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Creative
PromptBeginner5 minmarkdownQuality: 24
- nstart > 1 recommended (e.g.
nstart = 25): runs algorithm from multiple random starts
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Data
PromptBeginner5 minmarkdownQuality: 24
- Returns a matrix of p-values
not test statistics.
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Data
PromptBeginner5 minmarkdownQuality: 28
- 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 minmarkdownQuality: 28
- SANN (simulated annealing): convergence code is always 0 — it never fails. maxit = total function...
no other stopping criterion.
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Data
PromptBeginner5 minmarkdownQuality: 28
- 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 minmarkdownQuality: 28
- 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 minmarkdownQuality: 24
- Self-starting models (SSlogis
SSasymp
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Creative
PromptBeginner5 minmarkdownQuality: 28
- model.matrix creates the design matrix including dummy coding. Default contrasts: contr.treatment...
contr.poly for ordered.
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