Package 'aaltobda'

algae

Description

Algae data set. The data contains the status of algae in 274 measurement sites in Finnish lakes and rivers.

Usage

algae

Format

A vector with 274 elements:

1

Algae present

0

No algae present

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bioassay

Description

Bioassay data set. Taken from Table 3.1 in the BDA book

Usage

bioassay

Format

A data frame with 4 rows and 3 variables:

x

dose [log g/ml]

n

number of animals

y

number of deaths

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bioassay_posterior

Description

Sample from the posterior of the bioassay model.

Usage

bioassay_posterior

Format

A data frame with 1000 rows and 2 variables:

alpha

Parameter of the bioassay model

beta

Parameter of the bioassay model

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Unnormalized log-posterior for bioassay, assuming uniform prior

Description

Unnormalized log-posterior for bioassay, assuming uniform prior

Usage

bioassaylp(alpha, beta, x, y, n)

Arguments

alpha	intercept parameter in the dose-response model (vector or single number)
beta	slope parameter in the dose-response model (vector or single number)
x	vector of doses for each observation
y	vector of number of deaths for each observation
n	vector of number of animals for each observation

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Computes the density of a multivariate normal distribution

Description

Computes the density of a multivariate normal distribution

Usage

dmvnorm(x, mean, sigma, log = FALSE)

Arguments

x	vector or matrix of values for which the density is computed. If x is a vector, it represents a single draw. If x is a matrix, each row is taken to be a single draw.
mean	mean vector
sigma	covariance matrix
log	logical indicating whether to return on the log scale or not. False by default.

Value

Densities of the draws x.

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drowning

Description

drowning data set. The data contains the number of people who have drowned per year in Finland between 1980 and 2016.

Usage

drowning

Format

A data frame with 37 rows and 2 variables:

year

year

drownings

number of drownings in Finland

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factory

Description

factory data set. The data contains quality control measurements from 6 machines in a factory.

Usage

factory

Format

A data frame with 5 rows and 6 variables:

V1

Machine 1

V2

Machine 2

V3

Machine 3

V4

Machine 4

V5

Machine 5

V6

Machine 6

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kilpisjarvi

Description

Kilpisjärvi data set. The data contains the Kilpisjärvi summer month temperatures 1952–2013. Kilpisjärvi is in very northern part of Finland. Data by Finnish Meteorological Institute (CC-BY 4.0). Name of the observation station in FMI database is "Enontekiö Kilpisjärvi kyläkeskus"

Usage

kilpisjarvi

Format

A data frame with 62 rows and 5 columns:

year

Year

temp.june

Average temperature in June

temp.july

Average temperature in July

temp.august

Average temperature in August

temp.summer

Average temperature in July–August

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kilpisjarvi2022

Description

Kilpisjärvi data set. The data contains the Kilpisjärvi summer month temperatures 1952–2022. Kilpisjärvi is in very northern part of Finland. Data by Finnish Meteorological Institute (CC-BY 4.0). Name of the observation station in FMI database is "Enontekiö Kilpisjärvi kyläkeskus"

Usage

kilpisjarvi2022

Format

A data frame with 71 rows and 5 columns:

year

Year

temp.june

Average temperature in June

temp.july

Average temperature in July

temp.august

Average temperature in August

temp.summer

Average temperature in July–August

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Implementation of log(1 / (1 + exp(-x))) robust to over- and under-flow

Description

Implementation of log(1 / (1 + exp(-x))) robust to over- and under-flow

Usage

log_inv_logit(x)

Arguments

x	numeric input

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Implementation of log(1 - 1 / (1 + exp(-x))) robust to over- and under-flow

Description

Implementation of log(1 - 1 / (1 + exp(-x))) robust to over- and under-flow

Usage

log1m_inv_logit(x)

Arguments

x	numeric input

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Computes MCSE for quantile estimates based on independent draws

Description

Computes MCSE for quantile estimates based on independent draws

Usage

mcse_quantile(draws, prob)

Arguments

draws	Monte Carlo draws
prob	probability for which quantiles is computed

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Produces random draws from a multivariate normal distribution

Description

Produces random draws from a multivariate normal distribution

Usage

rmvnorm(n, mean, sigma)

Arguments

n	number of draws
mean	mean vector
sigma	covariance matrix

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The Student-t Distribution

Description

Density, distribution function, quantile function and random generation for the Student-t distribution with location/mean mean, scale scale, and degrees of freedom df. See https://en.wikipedia.org/wiki/Location%E2%80%93scale_family for more details on how to get from the standard t-distribution to the t-distribution with additional location and scale parameters.

Usage

dtnew(x, df, mean = 0, scale = 1, log = FALSE)

ptnew(q, df, mean = 0, scale = 1, lower.tail = TRUE, log.p = FALSE)

qtnew(p, df, mean = 0, scale = 1)

rtnew(n, df, mean = 0, scale = 1)

Arguments

x, q	Vector of quantiles.
df	Vector of degrees of freedom.
mean	Vector of location/mean values.
scale	Vector of scale values.
log, log.p	Logical; If TRUE, values are returned on the log scale.
lower.tail	Logical; If TRUE (default), return P(X <= x). Else, return P(X > x) .
p	Vector of probabilities.
n	Number of samples to draw from the distribution.

See Also

TDist

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windshieldy1

Description

windshieldy1 data set. The data represents a sample of windshields whose hardness has been measured.

Usage

windshieldy1

Format

A vector with 9 elements

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windshieldy2

Description

windshieldy2 data set. The data represents a sample of windshields whose hardness has been measured.

Usage

windshieldy2

Format

A vector with 13 elements

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