# r cards at TU München

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## Study with flashcards and summaries for the course r cards at the TU München

### Exemplary flashcards for r cards at the TU München on StudySmarter:

False discovery rate:

### Exemplary flashcards for r cards at the TU München on StudySmarter:

Family-wise error rate: Bonferroni correction

Layers:

Aesthetics:

### Exemplary flashcards for r cards at the TU München on StudySmarter:

Family-wise error rate:

### Exemplary flashcards for r cards at the TU München on StudySmarter:

Tidy data conclusions

### Exemplary flashcards for r cards at the TU München on StudySmarter:

Separating and uniting (1 <-> more variables)

### Exemplary flashcards for r cards at the TU München on StudySmarter:

Transform data form long to wide?

### Exemplary flashcards for r cards at the TU München on StudySmarter:

Transform data from wide to long?

### Exemplary flashcards for r cards at the TU München on StudySmarter:

Perform the conditional analysis by testing it with a full model that depends on the genotype (marker 5091) and a reduced model that does not depend on the genotype. full <- lm(growth_rate ~ genotype + condition_on_mk_geno, data=dt) reduced <- lm(growth_rate ~ condition_on_mk_geno, data=dt) anova(reduced, full) ## Analysis of Variance Table ## ## Model 1: growth_rate ~ condition_on_mk_geno ## Model 2: growth_rate ~ genotype + condition_on_mk_geno ## Res.Df RSS Df Sum of Sq F Pr(>F) ## 1 152 208.28 ## 2 151 207.23 1 1.0414 0.7588 0.3851

### Exemplary flashcards for r cards at the TU München on StudySmarter:

Grammar of Graphics

### Exemplary flashcards for r cards at the TU München on StudySmarter:

m <- lm(y ~ x1 * x2)

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## Exemplary flashcards for r cards at the TU München on StudySmarter:

r cards

False discovery rate:

E[V/ max(R, 1) ] , the expected fraction of false positives among all discoveries.

r cards

Family-wise error rate: Bonferroni correction

Family-wise error rate: P(V > 0) , the probability of one or more false positives. For large m0 , this is
di cult to keep small.
m
· Suppose we conduct hypothesis tests for each g = 1, . . . , m , producing each a p-value pg
p = min{m , 1}
˜g
pg
Selecting all tests with p ≤ α˜g
Proof: Boole’s inequality implies:

i=1
pi
controls the FWER at level , ie.,α Pr(V > 0) ≤ α .
FWER = P{ ( ≤ ) } ≤ {P( ≤ ) } = m0
m0
α
m
m0

i=1
pi
α
m
α
m
≤ m = α.
α
m
This control does not require any assumptions about dependence among the p-values or about how
many of the null hypotheses are true.
In R: p.adjust(p_values, method = “bonferroni”)

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Layers:

made up of geometric objects that represent data (geom_)

points, lines, boxplots, …

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Aesthetics:

describes visual characteristics that represent data (aes)
· position (x,y), color, size, shape, transparency

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Family-wise error rate:

P(V > 0) , the probability of one or more false positives. For large m0 , this is
di cult to keep small.

r cards

Tidy data conclusions

For data to be considered tidy, it has to pass certain criteria

Each column must represent one and only one variable indicated in its name.
Columns musn’t be values (eg. 1999, \$10-20k).
Must be aggregated in the right amount of tables – avoid lists.

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Separating and uniting (1 <-> more variables)

1 variable -> multiple variables
multiple variables -> 1 variable
other useful functions:
– tidyr::separate()
– tidyr::unite()
– data.table::tstrsplit, strsplit, paste, substr

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Transform data form long to wide?

data.table::dcast()

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Transform data from wide to long?

data.table::melt()
tidyr::gather()

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Perform the conditional analysis by testing it with a full model that depends on the genotype (marker 5091) and a reduced model that does not depend on the genotype. full <- lm(growth_rate ~ genotype + condition_on_mk_geno, data=dt) reduced <- lm(growth_rate ~ condition_on_mk_geno, data=dt) anova(reduced, full) ## Analysis of Variance Table ## ## Model 1: growth_rate ~ condition_on_mk_geno ## Model 2: growth_rate ~ genotype + condition_on_mk_geno ## Res.Df RSS Df Sum of Sq F Pr(>F) ## 1 152 208.28 ## 2 151 207.23 1 1.0414 0.7588 0.3851
## note: observe the probability under the null hypothesis of an F statistic as
## extreme as the one observed here is rather high (> 0.05) so we would not
## reject the null hypothesis

r cards

Grammar of Graphics

The Grammar of Graphics is a visualization theory developed by Leland Wilkinson in 1999.

Separation of data from aesthetics (e.g. x and y axis, color-coding)
Denition of common plot/chart elements (e.g. dot plots, boxplots, etc.)
Composition of these common elements (one can combine elements as layers)

r cards

m <- lm(y ~ x1 * x2)
lm(response ~ terms):
where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response.

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