Description Usage Arguments Details Value Author(s) See Also Examples

Density, distribution function, quantile function, and random generation for
a univariate (one-dimensional) distribution composed of a mixture of normal
distributions with means equal to `mean`

, standard deviations equal to
`sd`

, and mixing proportion of the components equal to `pro`

.

1 2 3 4 5 6 7 |

`x` |
Vector of quantiles. |

`mean` |
Vector of means, one for each component. |

`sd` |
Vector of standard deviations, one for each component. If a single value is provided, an equal-variance mixture model is implemented. Must be non-negative. |

`pro` |
Vector of mixing proportions, one for each component. If missing, an equal-proportion model is implemented, with a warning. If proportions do not sum to unity, they are rescaled to do so. Must be non-negative. |

`q` |
Vector of quantiles. |

`p` |
Vector of probabilities. |

`expand` |
Value to expand the range of probabilities for quantile
approximation. |

`n` |
Number of observations. |

These functions use, modify, and wrap around those from the
`mclust`

package, especially `dens`

, and
`sim`

. Functions are slightly faster than the
corresponding `mclust`

functions when used with univariate
distributions.

Unlike `mclust`

, which primarily focuses on parameter estimation based
on mixture samples, the functions here are modified to calculate PDFs,
CDFs, approximate quantiles, and random numbers for mixture distributions
with user-specified parameters. The functions are written to emulate the
syntax of other R distribution functions (e.g.,
`Normal`

).

The number of mixture components (argument `G`

in `mclust`

) is
specified from the length of the `mean`

vector. If a single `sd`

value is provided, an equal-variance mixture model (`modelNames="E"`

in `mclust`

) is implemented; if multiple values are provided, a
variable-variance model (`modelNames="V"`

in `mclust`

) is
implemented. If mixing proportion `pro`

is missing, all components are
assigned equal mixing proportions, with a warning. Mixing proportions are
rescaled to sum to unity. If the lengths of supplied means, standard
deviations, and mixing proportions conflict, an error is called.

Analytical solutions are not available to calculate a quantile function for
all combinations of mixture parameters. `qmixnorm`

approximates the
quantile function using a spline function calculated from cumulative
density functions for the specified mixture distribution. Quantile values
for probabilities near zero and one are approximated by taking a randomly
generated sample (with sample size equal to the product of 1000 and the
number of mixture components), and expanding that range positively and
negatively by a multiple (specified by `(default) expand = 1`

) of the
observed range in the random sample. In cases where the distribution range
is large (such as when mixture components are discrete or there are large
distances between components), resulting extreme probability values will be
very close to zero or one and can result in non-calculable (`NaN`

)
quantiles (and a warning). Use of other `expand`

values (especially
`expand < 1.0`

that expand the ranges by smaller multiples) often will
yield improved approximations. Note that `expand`

values equal to or
close to 0 may result in inaccurate approximation of extreme quantiles. In
situations requiring extreme quantile values, it is recommended that the
largest `expand`

value that does not result in a non-calculable
quantile (i.e., no warning called) be used. See `examples`

for
confirmation that approximations are accurate, comparing the approximate
quantiles from a single 'mixture' distribution to those calculated for the
same distribution using `qnorm`

, and demonstrating cases in which
using non-default `expand`

values will allow correct approximation of
quantiles.

`dmixnorm`

gives the density, `pmixnorm`

gives the
distribution function, `qmixnorm`

approximates the quantile function,
and `rmixnorm`

generates random numbers.

Phil Novack-Gottshall pnovack-gottshall@ben.edu and Steve Wang scwang@swarthmore.edu, based on functions written by Luca Scrucca.

`Distributions`

for other standard distributions,
and `mclust::dens`

, `sim`

, and
`cdfMclust`

for alternative density, quantile, and
random number functions for multivariate mixture distributions.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | ```
# Mixture of two normal distributions
mean <- c(3, 6)
pro <- c(.25, .75)
sd <- c(.5, 1)
x <- rmixnorm(n=5000, mean=mean, pro=pro, sd=sd)
hist(x, n=20, main="random bimodal sample")
## Not run:
# Requires functions from the 'mclust' package
require(mclust)
# Confirm 'rmixnorm' above produced specified model
mod <- mclust::Mclust(x)
mod # Best model (correctly) has two-components with unequal variances
mod$parameters # and approximately same parameters as specified above
sd^2 # Note reports var (sigma-squared) instead of sd used above
## End(Not run)
# Density, distribution, and quantile functions
plot(seq(0, 10, .1), dmixnorm(seq(0, 10, .1), mean=mean, sd=sd, pro=pro),
type="l", main="Normal mixture density")
plot(seq(0, 10, .1), pmixnorm(seq(0, 10, .1), mean=mean, sd=sd, pro=pro),
type="l", main="Normal mixture cumulative")
plot(stats::ppoints(100), qmixnorm(stats::ppoints(100), mean=mean, sd=sd, pro=pro),
type="l", main="Normal mixture quantile")
# Any number of mixture components are allowed
plot(seq(0, 50, .01), pmixnorm(seq(0, 50, .01), mean=1:50, sd=.05, pro=rep(1, 50)),
type="l", main="50-component normal mixture cumulative")
# 'expand' can be specified to prevent non-calculable quantiles:
q1 <- qmixnorm(stats::ppoints(30), mean=c(1, 20), sd=c(1, 1), pro=c(1, 1))
q1 # Calls a warning because of NaNs
# Reduce 'expand'. (Values < 0.8 allow correct approximation)
q2 <- qmixnorm(stats::ppoints(30), mean=c(1, 20), sd=c(1, 1), pro=c(1, 1), expand=.5)
plot(stats::ppoints(30), q2, type="l", main="Quantile with reduced range")
## Not run:
# Requires functions from the 'mclust' package
# Confirmation that qmixnorm approximates correct solution
# (single component 'mixture' should mimic qnorm):
x <- rmixnorm(n=5000, mean=0, pro=1, sd=1)
mpar <- mclust::Mclust(x)$param
approx <- qmixnorm(p=ppoints(100), mean=mpar$mean, pro=mpar$pro,
sd=sqrt(mpar$variance$sigmasq))
known <- qnorm(p=ppoints(100), mean=mpar$mean, sd=sqrt(mpar$variance$sigmasq))
cor(approx, known) # Approximately the same
plot(approx, main="Quantiles for (unimodal) normal")
lines(known)
legend("topleft", legend=c("known", "approximation"), pch=c(NA,1),
lty=c(1, NA), bty="n")
## End(Not run)
``` |

```
Loading required package: mclust
Package 'mclust' version 5.3
Type 'citation("mclust")' for citing this R package in publications.
'Mclust' model object:
best model: univariate, unequal variance (V) with 2 components
$pro
[1] 0.2440519 0.7559481
$mean
1 2
2.982775 5.995598
$variance
$variance$modelName
[1] "V"
$variance$d
[1] 1
$variance$G
[1] 2
$variance$sigmasq
[1] 0.241333 1.026897
$variance$scale
[1] 0.241333 1.026897
[1] 0.25 1.00
Warning message:
In pmixnorm(seq(0, 50, 0.01), mean = 1:50, sd = 0.05, pro = rep(1, :
'equal variance model' implemented. If want 'variable-variance model', specify remaining 'sd's.
Warning message:
In qmixnorm(stats::ppoints(30), mean = c(1, 20), sd = c(1, 1), pro = c(1, :
Some quantile values could not be calculated. If all 'p's are within [0,1], try reducing the value of 'expand' and try again.
[1] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
[20] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
[1] 1
```

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