Package 'capm' reference manual

Title:	Companion Animal Population Management
Description:	Quantitative analysis to support companion animal population management. Some functions assist survey sampling tasks (calculate sample size for simple and complex designs, select sampling units and estimate population parameters) while others assist the modelling of population dynamics. For demographic characterizations and population management evaluations see: "Baquero, et al." (2018), <doi:10.1016/j.prevetmed.2018.07.006>. For modelling of population dynamics see: "Baquero et al." (2016), <doi:10.1016/j.prevetmed.2015.11.009>. For sampling methods see: "Levy PS & Lemeshow S" (2013), "ISBN-10: 0470040076"; "Lumley" (2010), "ISBN: 978-0-470-28430-8".
Authors:	Oswaldo Santos Baquero [aut, cre], Marcos Amaku [ctb], Fernando Ferreira [ctb]
Maintainer:	Oswaldo Santos Baquero <[email protected]>
License:	GPL (>= 2)
Version:	0.13.10
Built:	2025-02-20 02:59:03 UTC
Source:	https://github.com/oswaldosantos/capm

The capm Package

Description

Companion Animal Population Management. Provides functions for quantitative Companion Animal Population Management. Further information can be found in the URL given below.

Details

Package:	capm
Type:	Package
Version:	0.13.10
Date:	2019-04-19
Depends:	R (>= 3.4)
Imports:	deSolve, FME, survey, dplyr, tidyr, magrittr, ggplot2, grid, stats, utils, sf
License:	GPL (>= 2)
LazyLoad:	yes
URL:	http://oswaldosantos.github.io/capm
Author:	Oswaldo Santos Baquero [email protected]
Maintainer:	Oswaldo Santos Baquero [email protected]
Contributors:	Marcos Amaku [email protected], Fernando Ferreira [email protected]

Two-stage cluster sampling size and composition (Deprecated)

Description

Calculates sample size and composition to estimate a total from a two-stage cluster sampling design. This function is deprecated, see details.

Usage

Calculate2StageSampleSize(psu.ssu = NULL, psu.x = NULL,
  conf.level = 0.95, error = 0.1, cost = 4, minimum.ssu = 15)
Calculate2StageSampleSize(psu.ssu = NULL, psu.x = NULL,
  conf.level = 0.95, error = 0.1, cost = 4, minimum.ssu = 15)

Arguments

`psu.ssu`	`data.frame` with all primary sampling units (PSU). First column contains PSU unique identifiers. Second column contains `numeric` PSU sizes.
`psu.x`	`data.frame`. Each row corresponds to a secondary sampling unit (SSU) included in a pilot study. First column contains the PSU identifiers to which the ssu belongs to. Second column contains the totals observed in the ssu and must be `numeric`.
`conf.level`	the confidence level required. It must be `numeric` between 0 and 1 inclusive.
`error`	the maximum relative difference between the estimate and the unknown population value. It must be `numeric` between 0 and 1 inclusive.
`cost`	the ratio of the cost of sampling a PSU to the cost of sampling a SSU.
`minimum.ssu`	integer to define the minimum number of SSU to be selected per PSU. If the calculated number of SSU to be selected is lesser than `minimum.ssu`, it is redefined as `minimum.ssu`. To avoid any lower threshold, define `minimum.ssu` as equal to 0.

Details

It is assumed that PSU from the pilot are selected with probability proportional to size (PPS) and with replacement. SSU are assumed to be selected via simple (systematic) random sampling.

PSU must have the same identifiers in psu.ssu and in psu.x. This function is deprecated because a study (Baquero et. al, 2018a) showed that the calculated sample size are too large for practical purposes. The same study found predefined sample compositions that result in estimates with precision equivalent to that achieved with the algorithm implemented in this function. The predefined sample compositions are (PSU * SSU): 65 \* 15, 50 \* 20, and 30 \* 30. If possible, take larger samples.

Value

Matrix with the sample size and composition and with variability estimates.

References

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

Levy P and Lemeshow S (2008). Sampling of populations: methods and applications, Fourth edition. John Wiley and Sons, Inc.

http://oswaldosantos.github.io/capm

Global sensitivity analysis

Description

Wraper for sensRange function, which calculates sensitivities of population sizes to parameters used in one of the following functions: SolveIASA, SolveSI or SolveTC.

Usage

CalculateGlobalSens(model.out = NULL, ranges = NULL, sensv = NULL,
  all = FALSE)
CalculateGlobalSens(model.out = NULL, ranges = NULL, sensv = NULL,
  all = FALSE)

Arguments

`model.out`	output from one of the previous function or a `list` with equivalent structure.
`ranges`	output from the `SetRanges` function applied to the `pars` argument used in the function specified in `model.out`.
`sensv`	string with the name of the output variables for which the sensitivity are to be estimated.
`all`	logical. If `FALSE`, sensitivity ranges are calculated for each parameter. If `TRUE`, sensitivity ranges are calculated for the combination of all aparameters.

Details

When all is equal to TRUE, dist argument in sensRange is defined as "latin" and when equal to FALSE, as "grid". The num argument in sensRange is defined as 100.

Value

A data.frame (extended by summary.sensRange when all == TRUE) containing the parameter set and the corresponding values of the sensitivity output variables.

References

Soetaert K and Petzoldt T (2010). Inverse modelling, sensitivity and monte carlo analysis in R using package FME. Journal of Statistical Software, 33(3), pp. 1-28.

Reichert P and Kfinsch HR (2001). Practical identifiability analysis of large environmental simulation models. Water Resources Research, 37(4), pp.1015-1030.

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

http://oswaldosantos.github.io/capm

Examples

## IASA model

## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                         destination.label = "Pinhais",
                         total.estimate = 50444)

# Solve for point estimates.
solve_iasa_pt <- SolveIASA(pars = dogs_iasa$pars,
                          init = dogs_iasa$init,
                          time = 0:15,
                          alpha.owned = TRUE,
                          method = 'rk4')

## Set ranges 10 % greater and lesser than the
## point estimates.
rg_solve_iasa <- SetRanges(pars = dogs_iasa$pars)

## Calculate golobal sensitivity of combined parameters.
## To calculate global sensitivity to each parameter, set
## all as FALSE.
glob_all_solve_iasa <- CalculateGlobalSens(
  model.out = solve_iasa_pt,
  ranges = rg_solve_iasa, 
  sensv = "n2", all = TRUE)

## IASA model

## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                         destination.label = "Pinhais",
                         total.estimate = 50444)

# Solve for point estimates.
solve_iasa_pt <- SolveIASA(pars = dogs_iasa$pars,
                          init = dogs_iasa$init,
                          time = 0:15,
                          alpha.owned = TRUE,
                          method = 'rk4')

## Set ranges 10 % greater and lesser than the
## point estimates.
rg_solve_iasa <- SetRanges(pars = dogs_iasa$pars)

## Calculate golobal sensitivity of combined parameters.
## To calculate global sensitivity to each parameter, set
## all as FALSE.
glob_all_solve_iasa <- CalculateGlobalSens(
  model.out = solve_iasa_pt,
  ranges = rg_solve_iasa, 
  sensv = "n2", all = TRUE)

Local sensitivity analysis

Description

Wraper for sensFun function, which estimates local effect of all model parameters on population size, applying the so-called sensitivity functions. The set of parameters used in any of the following functions can be assessed: SolveIASA, SolveSI or SolveTC.

Usage

CalculateLocalSens(model.out = NULL, sensv = "n")
CalculateLocalSens(model.out = NULL, sensv = "n")

Arguments

`model.out`	output from one of the previous functions or a `list` with equivalent structure.
`sensv`	string with the name of the output variables for which sensitivity are to be estimated.

Details

For further arguments of sensFun, defaults are used. See the help page of this function for details. Methods for class "sensFun" can be used.

Value

a data.frame of class sensFun containing the sensitivity functions. There is one row for each sensitivity variable at each independent time. The first column x, contains the time value; the second column var, the name of the observed variable; and remaining columns have the sensitivity parameters.

References

Soetaert K and Petzoldt T (2010). Inverse modelling, sensitivity and monte carlo analysis in R using package FME. Journal of Statistical Software, 33(3), pp. 1-28.

Reichert P and Kfinsch HR (2001). Practical identifiability analysis of large environmental simulation models. Water Resources Research, 37(4), pp.1015-1030.

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

http://oswaldosantos.github.io/capm

Examples

## IASA model

## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                         destination.label = "Pinhais",
                         total.estimate = 50444)

# Solve for point estimates.
solve_iasa_pt <- SolveIASA(pars = dogs_iasa$pars,
                          init = dogs_iasa$init,
                          time = 0:15,
                          alpha.owned = TRUE,
                          method = 'rk4')

## Calculate local sensitivities to all parameters.
local_solve_iasa2 <- CalculateLocalSens(
  model.out = solve_iasa_pt, sensv = "n2")
local_solve_iasa1 <- CalculateLocalSens(
  model.out = solve_iasa_pt, sensv = "n1")

## IASA model

## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                         destination.label = "Pinhais",
                         total.estimate = 50444)

# Solve for point estimates.
solve_iasa_pt <- SolveIASA(pars = dogs_iasa$pars,
                          init = dogs_iasa$init,
                          time = 0:15,
                          alpha.owned = TRUE,
                          method = 'rk4')

## Calculate local sensitivities to all parameters.
local_solve_iasa2 <- CalculateLocalSens(
  model.out = solve_iasa_pt, sensv = "n2")
local_solve_iasa1 <- CalculateLocalSens(
  model.out = solve_iasa_pt, sensv = "n1")

Population change.

Description

Calculate the change in population size between two times. When only one time is specified, the population size at that time is returned.

Usage

CalculatePopChange(model.out = NULL, variable = NULL, t1 = NULL,
  t2 = NULL, ratio = TRUE)
CalculatePopChange(model.out = NULL, variable = NULL, t1 = NULL,
  t2 = NULL, ratio = TRUE)

Arguments

`model.out`	output from one of the following functions or a `list` with equivalent structure: `SolveIASA`, `SolveSI`, `SolveTC` or `CalculateGlobalSens`. When the last function is used, its `all` argument must be `TRUE`.
`variable`	string with the name of the the output variable for which the change are to be calculated (see the variable argument for `PlotModels`.
`t1`	value specifying the first time.
`t2`	value specifying the second time.
`ratio`	logical. When `TRUE`, the calculated change is based on poulation size at t2 divided by population size at t1. When `FALSE`, the calculated change is based on poulation size at t2 minus population size at t1.

Value

Value representing the ratio (if ratio is TRUE) or the difference (if ratio is FALSE) between population size at time t2 and t1. If only one time is specified, the value is the population size at that time.

References

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

http://oswaldosantos.github.io/capm

Examples

## IASA model

## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                         destination.label = "Pinhais",
                         total.estimate = 50444)

# Solve for point estimates.
solve_iasa_pt <- SolveIASA(pars = dogs_iasa$pars,
                          init = dogs_iasa$init,
                          time = 0:15,
                          alpha.owned = TRUE,
                          method = 'rk4')

# Calculate the population change (ratio) between times 0 and 15.
CalculatePopChange(solve_iasa_pt, variable = 'N1', t2 = 15, t1 = 0)

# Calculate the population change (difference) between times 0 and 15.
CalculatePopChange(solve_iasa_pt, variable = 'N1', t2 = 15,
                   t1 = 0, ratio = FALSE)

# Calculate the population zises at time 15.
CalculatePopChange(solve_iasa_pt, variable = 'N1', t2 = 15)

## IASA model

## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                         destination.label = "Pinhais",
                         total.estimate = 50444)

# Solve for point estimates.
solve_iasa_pt <- SolveIASA(pars = dogs_iasa$pars,
                          init = dogs_iasa$init,
                          time = 0:15,
                          alpha.owned = TRUE,
                          method = 'rk4')

# Calculate the population change (ratio) between times 0 and 15.
CalculatePopChange(solve_iasa_pt, variable = 'N1', t2 = 15, t1 = 0)

# Calculate the population change (difference) between times 0 and 15.
CalculatePopChange(solve_iasa_pt, variable = 'N1', t2 = 15,
                   t1 = 0, ratio = FALSE)

# Calculate the population zises at time 15.
CalculatePopChange(solve_iasa_pt, variable = 'N1', t2 = 15)

Simple random sample size

Description

Calculates sample size to estimate a total from a simple sampling design.

Usage

CalculateSimpleSampleSize(x = NULL, N = NULL, conf.level = 0.95,
  error = 0.1)
CalculateSimpleSampleSize(x = NULL, N = NULL, conf.level = 0.95,
  error = 0.1)

Arguments

`x`	`vector` with variable collected in a pilot and to be estimated. If x is a scalar, it is used as the relative variance of the variable to be estimated (`((N - 1) / N * sd(x)^2) / mean(x)^2`).
`N`	`numeric` indicating the number of sampling units in the population.
`conf.level`	the confidence level required. It must be `numeric` between 0 and 1 inclusive.
`error`	the maximum relative difference between the estimate and the unknown population value. It must be `numeric` between 0 and 1 inclusive.

Value

numeric sample size rounded up to nearest integer.

References

Levy P and Lemeshow S (2008). Sampling of populations: methods and applications, Fourth edition. John Wiley and Sons, Inc.

http://oswaldosantos.github.io/capm

Examples

# Using a pilot sample from a population with 10000 sampling units.
pilot <- rpois(50, 0.8)
CalculateSimpleSampleSize(x = pilot, N = 10000,
                          conf.level = 0.95, error = 0.1)

# Using expected mean and standard deviation for a population
# with 10000 sampling units.
mean_x <- mean(pilot)
sd_x <- sd(pilot)
N <- 10000
V <- ((N - 1) / N * sd_x^2) / mean_x^2
CalculateSimpleSampleSize(x = V, N = 10000, conf.level = 0.95, error = 0.1)

# Using a pilot sample from a population with 10000 sampling units.
pilot <- rpois(50, 0.8)
CalculateSimpleSampleSize(x = pilot, N = 10000,
                          conf.level = 0.95, error = 0.1)

# Using expected mean and standard deviation for a population
# with 10000 sampling units.
mean_x <- mean(pilot)
sd_x <- sd(pilot)
N <- 10000
V <- ((N - 1) / N * sd_x^2) / mean_x^2
CalculateSimpleSampleSize(x = V, N = 10000, conf.level = 0.95, error = 0.1)

Stratified random sample size

Description

Calculates sample size to estimate a total from a stratified random sampling design.

Usage

CalculateStratifiedSampleSize(strata = NULL, x = NULL,
  conf.level = 0.95, error = 0.1)
CalculateStratifiedSampleSize(strata = NULL, x = NULL,
  conf.level = 0.95, error = 0.1)

Arguments

`strata`	`vector`, `matrix` or `data.frame`. If vector, named elements represent the size of the strata. If matrix or data.frame, first column represent the size of each strata, second column represent the expected mean in each strata and third column represent the expected variance in each strata. Each row is a strata and must be named.
`x`	`data.frame` representing a pilot sample. First column has the variable to be estimated and second column has the strata membership of each observation. Needed when `strata` is a vector.
`conf.level`	the confidence level required. It must be `numeric` between 0 and 1 inclusive.
`error`	the maximum relative difference between the estimate and the unknown population value. It must be `numeric` between 0 and 1 inclusive.

Value

numeric sample size rounded up to nearest integer.

References

Levy P and Lemeshow S (2008). Sampling of populations: methods and applications, Fourth edition. John Wiley and Sons, Inc.

http://oswaldosantos.github.io/capm

Examples

# Using a pilot sample from a population with 10000 sampling units.
strata <- rep(c("rural", "urban"), c(100, 9900))
pilot <- data.frame(c(rpois(5, 1.3), rpois(45, 0.8)),
                           rep(c("rural", "urban"), c(5, 45)))
CalculateStratifiedSampleSize(strata, pilot)
      
# Using expected mean and variance for a population with
# 10000 sampling units.
str_n <- c(rural = 100, urban = 9900)
str_mean <- c(rural = 1.4, urban = 0.98)
str_var <- c(rural = 1.48, urban = 1.02)
CalculateStratifiedSampleSize(cbind(str_n, str_mean, str_var))

# Using a pilot sample from a population with 10000 sampling units.
strata <- rep(c("rural", "urban"), c(100, 9900))
pilot <- data.frame(c(rpois(5, 1.3), rpois(45, 0.8)),
                           rep(c("rural", "urban"), c(5, 45)))
CalculateStratifiedSampleSize(strata, pilot)
      
# Using expected mean and variance for a population with
# 10000 sampling units.
str_n <- c(rural = 100, urban = 9900)
str_mean <- c(rural = 1.4, urban = 0.98)
str_var <- c(rural = 1.48, urban = 1.02)
CalculateStratifiedSampleSize(cbind(str_n, str_mean, str_var))

Cat's sample data from Pinhais, Brazil, 2017

Description

Data described and analyzed by Baquero et al., 2018.

Usage

cats
cats

Format

Data frame with 195 observations (cats) and 22 variables:

interview_id: Interview's ID.
census_tract_id: Census tract's ID.
name: Cat's name.
species: Animal's species
sex: Cat's sex.
age: Cat's age. An age equal to 0 means that the cat had less than 1 year.
sterilized: Cat's reproductive status.
sterilized_ly: For sterilized cats, indicates if the cat was sterilized during the last year.
go_out_on_the_street_alone: Indicates if the cat had access to the street without supervision (free-roaming).
acquisition: Acquisition type.
acquired_ly: Indicates if the cat was acquired during the last year.
acquired_sterilized: Indicates if the cat was sterilized when acquired.
acquisition_city: City of acquisition.
acquisition_state: State of acquisition.
lost_animals: Indicates if the cat was acquired during the year following the lost of another car
births_ly: Litter size if the bitch had the litter during the last year.
name3: Dog's name (cats not present anymore in the household).
species3: Animal's species (animals not present anymore in the household).
sex3: Dog's sex (cats not present anymore in the household).
age3: Dog's age (cats not present anymore in the household). An age equal to 0 means that the dog had less than 1 year.
sterilized3: Dog's reproductive status (cats not present anymore in the household).
fate3: Dog's fate.

References

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

Household data from a two-stage cluster sample

Description

Data described and analyzed by Baquero et al., 2018.

Usage

cluster_sample
cluster_sample

Format

Data frame with 1230 observations (households) and 14 variables:

interview_id: Interview's ID.
census_tract_id: Census tract's ID.
interviewer: Interviewer's name.
date: Interview's date.
address: Household's address.
interview: Interview status.
interviewee: Interviewee's name.
number_of_dogs: Number of dogs.
number_of_cats: Number of cats.
number_of_persons: Number of persons.
cell_phone: Interviewee's cell phone.
e_mail: Interviewee's e-mail.
reasons_for_not_sterilize: Interviewee's reported reason for not sterilizing her/his animal.
reasons_for_not_sterilize_others: Other reasons for not sterilizing her/his animal.

References

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

Survey design

Description

A wraper for svydesign function from the survey package, to define one of the following survey designs: two-stage cluster, simple (systematic) or stratified. In the first case, weights are calculated considering a sample with probability proportional to size and with replacement for the first stage and a simple random sampling for the second stage. Finite population correction is specified as the population size for each level of sampling.

Usage

DesignSurvey(sample = NULL, psu.ssu = NULL, psu.col = NULL,
  ssu.col = NULL, cal.col = NULL, N = NULL, strata = NULL,
  cal.N = NULL, ...)
DesignSurvey(sample = NULL, psu.ssu = NULL, psu.col = NULL,
  ssu.col = NULL, cal.col = NULL, N = NULL, strata = NULL,
  cal.N = NULL, ...)

Arguments

`sample`	`data.frame` with sample observations. for two-stage cluster designs, one of the columns must contain unique identifiers for PSU and another column must contain unique identifiers for Secondary Sampling Units (SSU).
`psu.ssu`	`data.frame` with all Primary Sampling Units (PSU). First column contains PSU unique identifiers. Second column contains `numeric` PSU sizes. It is used only for two-stage cluster designs.
`psu.col`	the column of `sample` containing the psu identifiers (for two-stage cluster designs). It is used only for two-stage cluster designs.
`ssu.col`	the column of `sample` containing the ssu identifiers (for two-stage cluster designs). It is used only for two-stage cluster designs.
`cal.col`	the column of `sample` with the variable to calibrate estimates. It must be used together with `cal.N`.
`N`	for simple designs, a `numeric` value representing the total of sampling units in the population. for a stratified design, it is a column of `sample` indicating, for each observation, the total of sampling units in its respective strata. `N` is ignored in two-stage cluster designs.
`strata`	for stratified designs, a column of `sample` indicating the strata memebership of each observation.
`cal.N`	population total for the variable to calibrate the estimates. It must be used togheter with `cal.col`.
`...`	further arguments passed to `svydesign` function.

Details

For two-stage cluster designs, a PSU appearing in both psu.ssu and in sample must have the same identifier. SSU identifiers must be unique but can appear more than once if there is more than one observation per SSU. sample argument must have just the varibles to be estimated plus the variables required to define the design (two-stage cluster or stratified). cal.col and cal.N are needed only if estimates will be calibrated. The calibration is based on a population total.

Value

An object of class survey.design.

References

Lumley, T. (2011). Complex surveys: A guide to analysis using R (Vol. 565). Wiley.

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

http://oswaldosantos.github.io/capm

Examples

data("cluster_sample")
data("psu_ssu")

## Calibrated two-stage cluster design
design <- DesignSurvey(na.omit(cluster_sample),
                       psu.ssu = psu_ssu,
                       psu.col = "census_tract_id",
                       ssu.col = "interview_id",
                       cal.col = "number_of_persons",
                       cal.N = 129445)

## Simple design
# If data in cluster_sample were from a simple design:
design <- DesignSurvey(na.omit(cluster_sample), 
                       N = sum(psu_ssu$hh),
                       cal.N = 129445)

## Stratified design
# Simulate strata and assume that the data in cluster_design came
# from a stratified design
cluster_sample$strat <- sample(c("urban", "rural"),
                               nrow(cluster_sample),
                               prob = c(.95, .05),
                               replace = TRUE)
cluster_sample$strat_size <- round(sum(psu_ssu$hh) * .95)
cluster_sample$strat_size[cluster_sample$strat == "rural"] <-
  round(sum(psu_ssu$hh) * .05)
design <- DesignSurvey(cluster_sample,
                       N = "strat_size",
                       strata = "strat",
                       cal.N = 129445)

data("cluster_sample")
data("psu_ssu")

## Calibrated two-stage cluster design
design <- DesignSurvey(na.omit(cluster_sample),
                       psu.ssu = psu_ssu,
                       psu.col = "census_tract_id",
                       ssu.col = "interview_id",
                       cal.col = "number_of_persons",
                       cal.N = 129445)

## Simple design
# If data in cluster_sample were from a simple design:
design <- DesignSurvey(na.omit(cluster_sample), 
                       N = sum(psu_ssu$hh),
                       cal.N = 129445)

## Stratified design
# Simulate strata and assume that the data in cluster_design came
# from a stratified design
cluster_sample$strat <- sample(c("urban", "rural"),
                               nrow(cluster_sample),
                               prob = c(.95, .05),
                               replace = TRUE)
cluster_sample$strat_size <- round(sum(psu_ssu$hh) * .95)
cluster_sample$strat_size[cluster_sample$strat == "rural"] <-
  round(sum(psu_ssu$hh) * .05)
design <- DesignSurvey(cluster_sample,
                       N = "strat_size",
                       strata = "strat",
                       cal.N = 129445)

Dog's sample data from Pinhais, Brazil, 2017

Description

Data described and analyzed by Baquero et al., 2018.

Usage

dogs
dogs

Format

Data frame with 1252 observations (dogs) and 22 variables:

interview_id: Interview's ID.
census_tract_id: Census tract's ID.
name: Dog's name.
species: Animal's species
sex: Dog's sex.
age: Dog's age. An age equal to 0 means that the dog had less than 1 year.
sterilized: Dog's reproductive status.
sterilized_ly: For sterilized dogs, indicates if the dog was sterilized during the last year.
go_out_on_the_street_alone: Indicates if the dog had access to the street without supervision (free-roaming).
acquisition: Acquisition type.
acquired_ly: Indicates if the dog was acquired during the last year.
acquired_sterilized: Indicates if the dog was sterilized when acquired.
acquisition_city: City of acquisition.
acquisition_state: State of acquisition.
lost_animals: Indicates if the dog was acquired during the year following the lost of another dog
births_ly: Litter size if the bitch had the litter during the last year.
name3: Dog's name (dogs not present anymore in the household).
species3: Animal's species (animals not present anymore in the household).
sex3: Dog's sex (dogs not present anymore in the household).
age3: Dog's age (dogs not present anymore in the household). An age equal to 0 means that the dog had less than 1 year.
sterilized3: Dog's reproductive status (dogs not present anymore in the household).
fate3: Dog's fate.

References

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

Frequency table of categorical variables

Description

Calculates and sort the count and relative frequency of categories.

Usage

FreqTab(data = NULL, variables = NULL, rnd = 3, decreasing = TRUE,
  use.na = FALSE)
FreqTab(data = NULL, variables = NULL, rnd = 3, decreasing = TRUE,
  use.na = FALSE)

Arguments

`data`	`data.frame` with categorical variables.
`variables`	name or position of categorical variables. If more than one variable is provided, contingency frequencies are calculated.
`rnd`	the number of decimal places (round) or significant digits (signif) to be used.
`decreasing`	`logical`. If `TRUE`, frequencies will be sorted in decreasing order, if `FALSE`, they will be sorted in increasing order.
`use.na`	`logical`. If `FALSE` (default), missing values are omitted.

Value

data.frame.

References

Examples

data(cluster_sample)
FreqTab(cluster_sample$number_of_dogs)

data(dogs)
FreqTab(dogs, c("species", "sex"))

data(cluster_sample)
FreqTab(cluster_sample$number_of_dogs)

data(dogs)
FreqTab(dogs, c("species", "sex"))

Get initial values and paramters for IASA model from survey data

Description

Calculates initial values and paramters for the IASA model, from survey data collectected with the questionnaire described by Bquero at al., 2018.

Usage

GetDataIASA(data = NULL, sex.col = "sex", female.label = "female",
  male.label = "male", sterilized.col = "sterilized",
  sterilized.label = "yes", sterilized.ly.col = "sterilized_ly",
  sterilized.ly.label = "yes", births.ly.col = "births_ly",
  species3.col = "species3", species.label = "dog",
  sex3.col = "sex2", fate.col = "fate", died.label = "died",
  lost.label = "lost", acquisition.col = "acquisition",
  acquired.ly.col = "acquired_ly", acquired.ly.label = "yes",
  adopted.label = "adopted", bought.label = "bought",
  acquisition.source.col = "acquisition_city",
  acquired.sterilized.col = "acquired_sterilized",
  destination.label = NULL, total.estimate = NULL, k1.scale = 5,
  h1 = 1, N2.scale = 0.05, f2.scale = 0.9, fs2.scale = 0.1,
  m2.scale = 0.95, ms2.scale = 0.05, b2.scale = 1.5,
  df2.scale = 1.2, dm2.scale = 1.2, sf2.scale = 0.3,
  sm2.scale = 0.3, k2.scale = 2, h2 = 0.5)
GetDataIASA(data = NULL, sex.col = "sex", female.label = "female",
  male.label = "male", sterilized.col = "sterilized",
  sterilized.label = "yes", sterilized.ly.col = "sterilized_ly",
  sterilized.ly.label = "yes", births.ly.col = "births_ly",
  species3.col = "species3", species.label = "dog",
  sex3.col = "sex2", fate.col = "fate", died.label = "died",
  lost.label = "lost", acquisition.col = "acquisition",
  acquired.ly.col = "acquired_ly", acquired.ly.label = "yes",
  adopted.label = "adopted", bought.label = "bought",
  acquisition.source.col = "acquisition_city",
  acquired.sterilized.col = "acquired_sterilized",
  destination.label = NULL, total.estimate = NULL, k1.scale = 5,
  h1 = 1, N2.scale = 0.05, f2.scale = 0.9, fs2.scale = 0.1,
  m2.scale = 0.95, ms2.scale = 0.05, b2.scale = 1.5,
  df2.scale = 1.2, dm2.scale = 1.2, sf2.scale = 0.3,
  sm2.scale = 0.3, k2.scale = 2, h2 = 0.5)

Arguments

`data`	`data.frame` with survey data.
`sex.col`	name or index of the column with the sex variable.
`female.label`	string with the female category in `sex.col`.
`male.label`	string with the male category in `sex.col`.
`sterilized.col`	name or index of the column with the sterilized variable.
`sterilized.label`	string with the sterilized category (ex. yes) in `sterilized.col`.
`sterilized.ly.col`	name or index of the column with sterilized last year variable.
`sterilized.ly.label`	string with the sterilized during the last year category (ex. yes) in `sterilized.ly.col`.
`births.ly.col`	name or index of the column with the births during the last year variable.
`species3.col`	name or index of the column with the species variable form the third questionnaire's section (animals that left the household).
`species.label`	string with the species category of interest (ex. dog) in `species3.col`.
`sex3.col`	name or index of the column with the sex variable form the third questionnaire's section (animals that left the household).
`fate.col`	name or index of the column with the fate variable.
`died.label`	string with the died category in `fate.col`.
`lost.label`	string with the lost category in `fate.col`.
`acquisition.col`	name or index of the column with the acquisition variable.
`acquired.ly.col`	name or index of the column with the acquisition during the last year variable.
`acquired.ly.label`	string with the acquisition during the last year category (ex. yes) in `acquisition.ly.col`.
`adopted.label`	string with the adopted category in `acquistion.col`.
`bought.label`	string with the bought category in `acquisition.col`.
`acquisition.source.col`	name or index of the column with the source variable (ex. city).
`acquired.sterilized.col`	name or index of the column with the sterilized when acquired variable.
`destination.label`	string with the destination category (ex. city) in `acquisition.source.col`.
`total.estimate`	number representing the estimated total population size.
`k1.scale`	scale to define the carrying capacity of the owned population as k1 = k.scale * total.estimate.
`h1`	number representing the mean harem size in the owned population.
`N2.scale`	scale to define the unowned population size as N2 = N2.sclae * total.estimate.
`f2.scale`	scale to define the female unowned population size as f2 = f2.scale * f1.
`fs2.scale`	scale to define the sterilized female unowned population size as fs2 = fs2.scale * fs1.
`m2.scale`	scale to define the sterimlized male unowned population as m2 = m2.scale * m1.
`ms2.scale`	scale to define the sterilized male unowned population size as ms2 = ms2.scale * ms1.
`b2.scale`	scale to define the birth function of the unowned population as b2 = f2 \* b1 / f1 \* b2.scale.
`df2.scale`	scale to define the death rate of the female unowned population as df2 = df2.scale * df1.
`dm2.scale`	scale to define the death rate of the male unowned population as dm2 = dm2.scale * dm1.
`sf2.scale`	scale to define the sterilized female unowned population size as sf2 = sf2 = sf2.scale * sf1.
`sm2.scale`	scale to define the sterilized male unowned population size as sm2 = sm2.scale * sm1.
`k2.scale`	scale to define the carrying capacity of the unowned population as k2 = k2.scale * N2.
`h2`	number representing the mean harem size in the unowned population.

Details

If column and category names in data match arguments' defaults, the function call is simplified as in the example below.

Value

list with two vectors: init (initial values) and pars (parameters).

References

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

http://oswaldosantos.github.io/capm

Examples

data(dogs)
GetDataIASA(dogs, destination.label = "Pinhais", total.estimate = 50444)

data(dogs)
GetDataIASA(dogs, destination.label = "Pinhais", total.estimate = 50444)

Creates *.kml files of a subset of polygons from a polygon shapefile

Description

Subset the polygons specified in psu and creates *.kml files of theses polygons.

Usage

MapkmlPSU(shape = NULL, psu = NULL, id = NULL,
  path = "./psu_maps/")
MapkmlPSU(shape = NULL, psu = NULL, id = NULL,
  path = "./psu_maps/")

Arguments

`shape`	string with the path of a polygon shapefile or an object of `class` sf (see examples).
`psu`	polygons to subset.
`id`	column of the *.dbf file with the values to be matched against.
`path`	`class` character with the path in which maps will be saved. If the path already exists, it will be overwrited.

Details

If there are *.kml files in the working directory, the new created files will overwrite it in case of name matching.

shape must receive a shapefile with appropriate coordinate reference system, otherwise, MapkmlPSU report an error.

Value

*.kml files of the subsetted polygons.

References

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

http://oswaldosantos.github.io/capm

Examples

data("psu_ssu")

# Take a sample of 5 PSU.
(selected_psu <- SamplePPS(psu.ssu = psu_ssu, psu = 5))
 
## Define shape from shapefile.
shp_path <- system.file("extdata/35SEE250GC_SIR.shp", package="capm")
# The code above used a shapefile avaliable in the
# capm package.
# You might want to write a code like:
# shp.path  <- 'path_to_the_folder_with_the_shapefile'
 
# Create *kml files of 10 polygons.
## Not run: 
MapkmlPSU(shape = shp_path,
         psu = selected_psu[, "selected_psu"],
         id = "CD_GEOCODI")

## Define the shape argument as an object x of class sf.
x <- read_sf(shp_path)
MapkmlPSU(shape = x, psu = selected_psu[, "selected_psu"], id = "CD_GEOCODI")

## End(Not run)

data("psu_ssu")

# Take a sample of 5 PSU.
(selected_psu <- SamplePPS(psu.ssu = psu_ssu, psu = 5))
 
## Define shape from shapefile.
shp_path <- system.file("extdata/35SEE250GC_SIR.shp", package="capm")
# The code above used a shapefile avaliable in the
# capm package.
# You might want to write a code like:
# shp.path  <- 'path_to_the_folder_with_the_shapefile'
 
# Create *kml files of 10 polygons.
## Not run: 
MapkmlPSU(shape = shp_path,
         psu = selected_psu[, "selected_psu"],
         id = "CD_GEOCODI")

## Define the shape argument as an object x of class sf.
x <- read_sf(shp_path)
MapkmlPSU(shape = x, psu = selected_psu[, "selected_psu"], id = "CD_GEOCODI")

## End(Not run)

Plot results of GlobalSens function

Description

Plot results of of CalculateGlobalSens function.

Usage

PlotGlobalSens(global.out = NULL, x.label = "Time",
  y.label = "Population", legend.label = "Sensitivity range",
  qt.label = "Qt 0.05 - 0.95", sd.label = "mean +- sd   ",
  inner.color = "DarkRed", outer.color = "LightBlue")
PlotGlobalSens(global.out = NULL, x.label = "Time",
  y.label = "Population", legend.label = "Sensitivity range",
  qt.label = "Qt 0.05 - 0.95", sd.label = "mean +- sd   ",
  inner.color = "DarkRed", outer.color = "LightBlue")

Arguments

`global.out`	output from `CalculateGlobalSens` function.
`x.label`	string with the name for the x axis.
`y.label`	string with the name for the y axis.
`legend.label`	string with the name for the legend.
`qt.label`	string with the name for the envelope calculated using the quantiles 0.05 and 0.95.
`sd.label`	string with the name for the envelope calculated using the mean +- standard deviation ranges.
`inner.color`	any valid specification of a color for the inner envelope.
`outer.color`	any valid specification of a color for the outer envelope.

Details

Font size of saved plots is usually different to the font size seen in graphic browsers. Before changing font sizes, see the final result in saved (or preview) plots.

Other details of the plot can be modifyed using appropriate functions from ggplot2 package.

References

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

http://oswaldosantos.github.io/capm

Examples

## IASA model

## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                         destination.label = "Pinhais",
                         total.estimate = 50444)

# Solve for point estimates.
solve_iasa_pt <- SolveIASA(pars = dogs_iasa$pars,
                          init = dogs_iasa$init,
                          time = 0:15,
                          alpha.owned = TRUE,
                          method = 'rk4')

## Set ranges 10 % greater and lesser than the
## point estimates.
rg_solve_iasa <- SetRanges(pars = dogs_iasa$pars)

## Calculate golobal sensitivity of combined parameters.
## To calculate global sensitivity to each parameter, set
## all as FALSE.
glob_all_solve_iasa <- CalculateGlobalSens(
  model.out = solve_iasa_pt,
  ranges = rg_solve_iasa, 
  sensv = "n2", all = TRUE)
PlotGlobalSens(glob_all_solve_iasa)

## IASA model

## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                         destination.label = "Pinhais",
                         total.estimate = 50444)

# Solve for point estimates.
solve_iasa_pt <- SolveIASA(pars = dogs_iasa$pars,
                          init = dogs_iasa$init,
                          time = 0:15,
                          alpha.owned = TRUE,
                          method = 'rk4')

## Set ranges 10 % greater and lesser than the
## point estimates.
rg_solve_iasa <- SetRanges(pars = dogs_iasa$pars)

## Calculate golobal sensitivity of combined parameters.
## To calculate global sensitivity to each parameter, set
## all as FALSE.
glob_all_solve_iasa <- CalculateGlobalSens(
  model.out = solve_iasa_pt,
  ranges = rg_solve_iasa, 
  sensv = "n2", all = TRUE)
PlotGlobalSens(glob_all_solve_iasa)

Distribution of households according to the number of inhabitants one or more species

Description

Dodged bar plot of the distribution of households according to the number of inhabitants of one or more species.

Usage

PlotHHxSpecies(dat = NULL, species = NULL, proportion = TRUE,
  x.label = "Individuals per household",
  y.label = "Proportion of households", legend = TRUE)
PlotHHxSpecies(dat = NULL, species = NULL, proportion = TRUE,
  x.label = "Individuals per household",
  y.label = "Proportion of households", legend = TRUE)

Arguments

`dat`	`data.frame` with households as observation unit and columns with the number of individuals of the species of interest.
`species`	names or positions of columns with species data.
`proportion`	`logical`. If `TRUE` (default), the y axis will represent proportions, if `FALSE`, it would represent raw counts.
`x.label`	title for x axis.
`y.label`	title for y axis.
`legend`	`logical`. If `TRUE` (default), the legend will be showed, if `FALSE`, it will be removed.

References

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

Examples

data(cluster_sample)
PlotHHxSpecies(cluster_sample, c("number_of_persons",
                                 "number_of_dogs",
                                 "number_of_cats"))

data(cluster_sample)
PlotHHxSpecies(cluster_sample, c("number_of_persons",
                                 "number_of_dogs",
                                 "number_of_cats"))

Plot immigration flows

Description

Plot rimmigration flows from many sources to one destination.

Usage

PlotImmigrationFlow(data = NULL, source = NULL, destination = NULL,
  n.sources = 5, agg.sources.prefix = "Other ",
  agg.sources.suffix = " sources", cls = NULL, start.degree = 0,
  sources.label.dist = 0.15, sources.label.size = 0.75,
  ticks.label.size = 0.7)
PlotImmigrationFlow(data = NULL, source = NULL, destination = NULL,
  n.sources = 5, agg.sources.prefix = "Other ",
  agg.sources.suffix = " sources", cls = NULL, start.degree = 0,
  sources.label.dist = 0.15, sources.label.size = 0.75,
  ticks.label.size = 0.7)

Arguments

`data`	`data.frame` with sources and destination.
`source`	`data`'s column name or index with places' names. Sources' names and destination's name must be in this column.
`destination`	destination's name.
`n.sources`	number of sources to plot. If smaller than the total number of sources `source`, the less frequent sources are aggregated.
`agg.sources.prefix`	string. If n.sources is smaller than the total number of sources, `agg.sources.prefix` is used to label the aggregated sources.
`agg.sources.suffix`	`character`. If n.sources is smaller than the total number of sources, `agg.sources.prefix` is used to label the aggregated sources.
`cls`	Optional `character` `vector` with `n.sources` + 1 colors.
`start.degree`	The starting degree from which the circle begins to draw. It is passed to the `start.degree` argumento of `circlize::circos.par` function.
`sources.label.dist`	Data point on y-axis to separate the sources' labels from the circle. It is passed to the `y` argument of `circlize::circos.text` function.
`sources.label.size`	Font size for sources' labels. It is passed to the `cex` argument of `circlize::circos.text` function.
`ticks.label.size`	Font size for sources' labels. It is passed to the `labels.cex` argument of `circlize::circos.axis` function.

Details

The numbers arround the circle indicate the number of animals.

References

Gu, Z. (2014) circlize implements and enhances circular visualization in R. Bioinformatics. DOI: 10.1093/bioinformatics/btu393

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

http://oswaldosantos.github.io/capm

Examples

data(dogs)
cls <- c("blue3", "orange", "skyblue", "darkgreen", "yellow3", "black")
PlotImmigrationFlow(dogs, "acquisition_city", "Pinhais",
                    cls = cls, agg.sources.suffix = " cities")

data(dogs)
cls <- c("blue3", "orange", "skyblue", "darkgreen", "yellow3", "black")
PlotImmigrationFlow(dogs, "acquisition_city", "Pinhais",
                    cls = cls, agg.sources.suffix = " cities")

Plot results of CalculateLocalSens function

Description

Plot results of the CalculateLocalSens function.

Usage

PlotLocalSens(local.out = NULL, x.sens = "Time",
  y.sens = "Sensitivity", y.ind = c("L1", "L2", "Mean", "Min", "Max"),
  bar.colors = "DarkRed", label.size = 10, x.axis.angle = 90,
  type = 1)
PlotLocalSens(local.out = NULL, x.sens = "Time",
  y.sens = "Sensitivity", y.ind = c("L1", "L2", "Mean", "Min", "Max"),
  bar.colors = "DarkRed", label.size = 10, x.axis.angle = 90,
  type = 1)

Arguments

`local.out`	output from `CalculateLocalSens` function.
`x.sens`	string with the name for the x axis.
`y.sens`	string with the name for the y axis of the sensitivity functions (when `type = 6`).
`y.ind`	string with the name for the y axis of the parameter importance indices.
`bar.colors`	any valid specification of a color.
`label.size`	a number to specify the size of axes labels and text.
`x.axis.angle`	a number with angle of rotation for x axis text. Passed to `angle` argument of `element_text`.
`type`	a number to define the type of graphical output. `1`: importance index L1; `2`: importance index L2; `3`: mean of sensitivity functions; `5`: minimum of sensitivity functions; and `5`: maximum of sensitivity functions; `6`: sensitivity functions and all importance indices are ploted.

Details

Font size of saved plots is usually different to the font size seen in graphic browsers. Before changing font sizes, see the final result in saved (or preview) plots.

References

Chang W (2012). R Graphics Cookbook. O'Reilly Media, Inc.

Soetaert K, Cash J and Mazzia F (2012). Solving differential equations in R. Springer.

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

http://oswaldosantos.github.io/capm

Examples

## IASA model#' 
## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                        destination.label = "Pinhais",
                        total.estimate = 50444)
# Solve for point estimates.
solve_iasa_pt <- SolveIASA(pars = dogs_iasa$pars,
                         init = dogs_iasa$init,
                         time = 0:15,
                         alpha.owned = TRUE,
                         method = 'rk4')
## Calculate local sensitivities to all parameters.
local_solve_iasa2 <- CalculateLocalSens(
 model.out = solve_iasa_pt, sensv = "n2")
## Plot local sensitivities
PlotLocalSens(local_solve_iasa2)

## IASA model#' 
## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                        destination.label = "Pinhais",
                        total.estimate = 50444)
# Solve for point estimates.
solve_iasa_pt <- SolveIASA(pars = dogs_iasa$pars,
                         init = dogs_iasa$init,
                         time = 0:15,
                         alpha.owned = TRUE,
                         method = 'rk4')
## Calculate local sensitivities to all parameters.
local_solve_iasa2 <- CalculateLocalSens(
 model.out = solve_iasa_pt, sensv = "n2")
## Plot local sensitivities
PlotLocalSens(local_solve_iasa2)

Plot results of capm model functions

Description

Plot results of one of the following functions: SolveIASA, SolveSI or SolveTC.

Usage

PlotModels(model.out = NULL, variable = NULL, col = "red",
  col1 = c("cadetblue1", "yellow", "red"), col2 = c("blue",
  "darkgreen", "darkred"), x.label = "Years", y.label = NULL,
  legend.label = NULL, pop = NULL)
PlotModels(model.out = NULL, variable = NULL, col = "red",
  col1 = c("cadetblue1", "yellow", "red"), col2 = c("blue",
  "darkgreen", "darkred"), x.label = "Years", y.label = NULL,
  legend.label = NULL, pop = NULL)

Arguments

`model.out`	output of one of the function previously mentioned.
`variable`	string to specify the variable to be ploted. For `SolveSI` function: "n" (population size). "q" (proportion of sterilized animals). For `SolveIASA` function using only point estimates: "f1" (owned intact females). "fs1" (owned sterilized females). "m1" (owned intact males). "ms1" (owned sterilized males). "f2" (unowned intact females). "fs2" (unowned sterilized females). "m2" (unowned intact males). "ms2" (unowned sterilized males). "n1" (owned intact animals). "ns1" (owned sterilized animals). "n2" (unowned intact animals). "ns2" (unowned sterilized animals). "N1" (owned animals). "N2" (unowned animals). "N" (total population). For `SolveIASA` function using *.range arguments: "f" (intact females). "fs" (sterilized females). "m" (intact males). "ms" (sterilized males). "n" (intact animals). "ns" (sterilized animals). "N" (Total population stratified by reproductive status). For `SolveTC` function: "n" (fertile animals). "g" (sterilized animals). "u" (cumulative of sterilized animals)
`col`	string indicating the color of ploted line, when `s.range` is `NULL`.
`col1`	`character` `vector` indicating the color of lowest (highest) population sizes (proportion of sterilized animals), when `s.range` is not `NULL`.
`col2`	`character` `vector` indicating the color of highest (lowest) population sizes (proportion of sterilized animals), when `s.range` is not `NULL`.
`x.label`	string with the name for x axis.
`y.label`	string with the name for y axis.
`legend.label`	string with the name of the legend, for plots of `SolveIASA` output.
`pop`	value indicating the output of `SolveIASA` to be ploted. When `NULL` (default), plots for owned and unowned populations under scenarios created by immigartion rate are created. If `1`, the plots of owned population for the minimum immigartion rate are ploted. When `2`, the plots of unowned population for the minimum immigartion rate are ploted. If `3`, the plots of owned population for the maximum immigartion rate are ploted. When `4`, the plots of owned population for the maximum immigartion rate are ploted.

Details

Font size of saved plots is usually different to the font size seen in graphic browsers. Before changing font sizes, see the final result in saved (or preview) plots.

Other details of the plot can be modifyed using appropriate functions from ggplot2 package.

References

Chang W (2012). R Graphics Cookbook. O'Reilly Media, Inc.

http://oswaldosantos.github.io/capm

Examples

## IASA model

## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                         destination.label = "Pinhais",
                         total.estimate = 50444)

# Solve for point estimates.
solve_iasa_pt <- SolveIASA(pars = dogs_iasa$pars,
                           init = dogs_iasa$init,
                           time = 0:15,
                           alpha.owned = TRUE,
                           method = 'rk4')
solve_iasa_rg <- SolveIASA(pars = dogs_iasa$pars,
                           init = dogs_iasa$init, 
                           time = 0:10,
                           alpha.owned = TRUE,
                           s.range = seq(0, .4, l = 15), 
                           a.range = c(0, .2), 
                           alpha.range = c(0, .05),
                           v.range = c(0, .1),
                           method = 'rk4')

## Plot unowned population sizes using point estimates
## Not run: 
PlotModels(solve_iasa_pt, variable = "ns2")

## Plot all scenarios and change the label for the scenarios.
## Not run
PlotModels(solve_iasa_rg, variable = "ns")

## End(Not run)

## IASA model

## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                         destination.label = "Pinhais",
                         total.estimate = 50444)

# Solve for point estimates.
solve_iasa_pt <- SolveIASA(pars = dogs_iasa$pars,
                           init = dogs_iasa$init,
                           time = 0:15,
                           alpha.owned = TRUE,
                           method = 'rk4')
solve_iasa_rg <- SolveIASA(pars = dogs_iasa$pars,
                           init = dogs_iasa$init, 
                           time = 0:10,
                           alpha.owned = TRUE,
                           s.range = seq(0, .4, l = 15), 
                           a.range = c(0, .2), 
                           alpha.range = c(0, .05),
                           v.range = c(0, .1),
                           method = 'rk4')

## Plot unowned population sizes using point estimates
## Not run: 
PlotModels(solve_iasa_pt, variable = "ns2")

## Plot all scenarios and change the label for the scenarios.
## Not run
PlotModels(solve_iasa_rg, variable = "ns")

## End(Not run)

Population PlotPopPyramid

Description

Displays two opposed horizontal barplots (pyramid).

Usage

PlotPopPyramid(dat = NULL, age.col = NULL, sex.col = NULL,
  str.col = NULL, str.tip = NULL, x.label = "Count",
  stage.label = "Years", legend.label = "Sterilized",
  inner.color = "LightBlue", outer.color = "DarkRed",
  label.size = 13)
PlotPopPyramid(dat = NULL, age.col = NULL, sex.col = NULL,
  str.col = NULL, str.tip = NULL, x.label = "Count",
  stage.label = "Years", legend.label = "Sterilized",
  inner.color = "LightBlue", outer.color = "DarkRed",
  label.size = 13)

Arguments

`dat`	`data.frame`.
`age.col`	`dat` column that has a `numeric` `vector` representing ages or stage categories.
`sex.col`	`dat` column that has two unique values representing the sex of individuals (see Details).
`str.col`	`dat` column that has two unique values representing the reproductive status of individuals (see Details).
`str.tip`	string with the category of `str.col` to place at tip of the bars.
`x.label`	string to be used as a label for the x axis. If undefined, `x.label` is equal to "Total" (see Details).
`stage.label`	a string to be used as a label for the ages or stage categories. If undefined, `stage.label` is equal to "Years" (see Details).
`legend.label`	a string to be used as a label for the legend. If undefined, `legend.label` is equal to "Sterilized".
`inner.color`	any valid specification of a color. When `str.col` is not `NULL`, `inner.color` is the color of inner bars.
`outer.color`	any valid way specification of a color. When `str.col` is `NULL`, `outer.color` is the default color. When `str.col` is not `NULL`, `outer.color` is the outer color of bars.
`label.size`	string to define the font size for labels.

Details

PlotPopPyramid is mainly intended for companion animals population pyramids, although it can display other types of opposed bar charts.

The bars to the left of the x axis correspond to sort(unique(dat[, sex.col]))[1]. If str.col is not NULL, bars will be stacked, with sort(unique(dat[, str.col]))[1] as their base.

On the top of the plot, it is displayed the total number of observations of each dat[, sex.col] unique value. This unique values are used as labels.

The legend labels are equal to the dat[, str.col] unique values.

Font size of saved plots is usually different to the font size seen in graphic browsers. Before changing font sizes, see the final result in saved (or preview) plots.

Other details of the plot can be modifyed using appropriate functions from ggplot2 package (see examples).

Value

Two opposed horizontal barplots.

Note

In companion animals population surveys, some age categories might be empty. One difference between PlotPopPyramid and pryramid.plot is that the first does not drop empty age categories.

References

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

http://oswaldosantos.github.io/capm

Examples

data(dogs)

PlotPopPyramid(dogs,
               age.col = "age",
               sex.col = "sex",
               str.col = "sterilized")

PlotPopPyramid(dogs,
               age.col = "age",
               sex.col = "sex")

## Merge age categories
pp_age <- cut(c(dogs$age, dogs$age3),
               breaks = c(0, 1, 3, 5, 7, 9, 11, 13, 15,
                          max(c(dogs$age, dogs$age3), na.rm = TRUE)),
               labels = c("<1", "1-3", "3-5", "5-7", "7-9",
                          "9-11", "11-13", "13-15", ">15"),
               include.lowest = TRUE)
pp_sex <- c(dogs$sex, dogs$sex3)
pp_ster <- c(dogs$sterilized, dogs$sterilized3)
pp <- data.frame(age = pp_age, sex = pp_sex, sterilized = pp_ster)

PlotPopPyramid(pp,
              age.col = "age",
              sex.col = "sex",
              str.col = "sterilized")

PlotPopPyramid(pp,
              age.col = "age",
              sex.col = "sex")

data(dogs)

PlotPopPyramid(dogs,
               age.col = "age",
               sex.col = "sex",
               str.col = "sterilized")

PlotPopPyramid(dogs,
               age.col = "age",
               sex.col = "sex")

## Merge age categories
pp_age <- cut(c(dogs$age, dogs$age3),
               breaks = c(0, 1, 3, 5, 7, 9, 11, 13, 15,
                          max(c(dogs$age, dogs$age3), na.rm = TRUE)),
               labels = c("<1", "1-3", "3-5", "5-7", "7-9",
                          "9-11", "11-13", "13-15", ">15"),
               include.lowest = TRUE)
pp_sex <- c(dogs$sex, dogs$sex3)
pp_ster <- c(dogs$sterilized, dogs$sterilized3)
pp <- data.frame(age = pp_age, sex = pp_sex, sterilized = pp_ster)

PlotPopPyramid(pp,
              age.col = "age",
              sex.col = "sex",
              str.col = "sterilized")

PlotPopPyramid(pp,
              age.col = "age",
              sex.col = "sex")

Census tracts of Pinhais, Brazil.

Description

Census tracts of Pinhais, Brazil, according to the census of 2010. Data described and analyzed by Baquero et al., 2018.

Usage

psu_ssu
psu_ssu

Format

A data frame with 655 observations and 5 variables:

census_tract_id: Census track's ID.
hh: Number of households.

Source

http://www.ibge.gov.br/

References

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

Sampling with probability proportional to size and with replacement

Description

Select Primary Sampling Units (PSU) with probability proportional to size and with replacement.

Usage

SamplePPS(psu.ssu = NULL, psu = NULL, write = FALSE, ...)
SamplePPS(psu.ssu = NULL, psu = NULL, write = FALSE, ...)

Arguments

`psu.ssu`	`data.frame` with all PSU. First column contains PSU unique identifiers. Second column contains `numeric` PSU sizes.
`psu`	the number of PSU to be selected.
`write`	logical. If `TRUE`, a *.csv file containing the PSU and their Secondary Sampling Units (SSU) is writed in the current working directory.
`...`	further arguments passed to `write.table` function.

Value

data.frame. First column contains the selected PSU identifiers, coerced by as.character, to avoid scientific notation in case the identifiers be large numbers of class numeric. Second column contain PSU sizes, a variable needed for second stage sampling with SampleSystematic.

References

Levy P and Lemeshow S (2008). Sampling of populations: methods and applications, Fourth edition. John Wiley and Sons, Inc.

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

http://oswaldosantos.github.io/capm

Examples

data(psu_ssu)

# Take a sample of 10 PSU.
SamplePPS(psu.ssu = psu_ssu, psu = 10, write = FALSE)

data(psu_ssu)

# Take a sample of 10 PSU.
SamplePPS(psu.ssu = psu_ssu, psu = 10, write = FALSE)

Simple and stratified systematic sampling

Description

Select sampling units using simple or stratified systematic samplin. In the context of two-stage cluster sampling, select Secondary Sampling Units (SSU) in one or more Primary Sampling Units (PSU), using systematic sampling.

Usage

SampleSystematic(psu.ssu = NULL, su = NULL, N = NULL,
  write = FALSE, ...)
SampleSystematic(psu.ssu = NULL, su = NULL, N = NULL,
  write = FALSE, ...)

Arguments

`psu.ssu`	`data.frame` with all PSU. First column contains PSU unique identifiers. Second column contains `numeric` PSU sizes. It is used only for the second stage of a two-stage cluster design (see details).
`su`	`numeric` indicating the number of sampling units to be selected. If `su` has more than one element, stratified sampling is applied and `psu.ssu` is ignored (see details).
`N`	`numeric` indicating the number of sampling units in the population. It is intended for simple or stratified sampling designs and when used, `psu.ssu` is ignored (see details).
`write`	logical. If `TRUE`, a *.csv file containing the PSU and their SSU is writed in the current working directory.
`...`	further arguments passed to `write.table` function.

Details

When N is defined, psu.ssu is ignored. If N has one element, su must too and the result is a simple systematic selection. If N has more than one element, su must have the same number of elements and each oredered pair represent an strata. Thus, when N has more than one element, the result is a stratified sampling with systematic selection within each strata (see examples).

Value

A matrix. For the second stage in a two-stage cluster sampling, the names of columns are the identifiers of selected psu, coerced by as.character to avoid scientific notation in case the identifiers be of class numeric. The rows correspond to the selected SSU within each PSU. For simple systematic sampling, the rows correspond to the selected sampling units. For stratified sampling, each column represent an strata and the rows correspond to the selected sampling units in each strata.

References

Levy P and Lemeshow S (2008). Sampling of populations: methods and applications, Fourth edition. John Wiley and Sons, Inc.

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

http://oswaldosantos.github.io/capm

Examples

data(psu_ssu)

## Two-stage cluster sampling
selected_psu <- SamplePPS(psu.ssu = psu_ssu, psu = 10)

# Take a systematic sampling of 5 SSU within each selected PSU.
SampleSystematic(selected_psu, 5, write = FALSE)

## Simple systematic sampling
SampleSystematic(su = 5, N = 100)

## Stratified systematic sampling
SampleSystematic(su = c("urban" = 50, "rural" = 10),
                 N = c("urban" = 4000, "rural" = 150))
data(psu_ssu)

## Two-stage cluster sampling
selected_psu <- SamplePPS(psu.ssu = psu_ssu, psu = 10)

# Take a systematic sampling of 5 SSU within each selected PSU.
SampleSystematic(selected_psu, 5, write = FALSE)

## Simple systematic sampling
SampleSystematic(su = 5, N = 100)

## Stratified systematic sampling
SampleSystematic(su = c("urban" = 50, "rural" = 10),
                 N = c("urban" = 4000, "rural" = 150))

Parameter ranges for global sensitivity analysis

Description

Define the minimum and maximum values for parameters whose global sensitivities are to be assessed with CalculateGlobalSens or sensRange functions.

Usage

SetRanges(pars = NULL, range = 0.1)
SetRanges(pars = NULL, range = 0.1)

Arguments

`pars`	the same `pars` vector used in one of the following functions: `SolveSI` or `SolveIASA`.
`range`	scale factor to define the minimum and maximum for each parameter. The default is 0.1, which set the minimum and maximum as 10 percent lesser and greater than the `pars` values.

Value

data.frame with the complete set of parameter ranges.

References

Soetaert K and Petzoldt T (2010). Inverse modelling, sensitivity and monte carlo analysis in R using package FME. Journal of Statistical Software, 33(3), pp. 1-28.

Reichert P and Kfinsch HR (2001). Practical identifiability analysis of large environmental simulation models. Water Resources Research, 37(4), pp. 1015-1030.

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine.

http://oswaldosantos.github.io/capm

Examples

## IASA model

## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                         destination.label = "Pinhais",
                         total.estimate = 50444)

## Set ranges 10 % greater and lesser than the
## point estimates.
rg_solve_iasa <- SetRanges(pars = dogs_iasa$pars)

## IASA model

## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                         destination.label = "Pinhais",
                         total.estimate = 50444)

## Set ranges 10 % greater and lesser than the
## point estimates.
rg_solve_iasa <- SetRanges(pars = dogs_iasa$pars)

Modelling of immigration, abandonment, sterilization and adoption of companion animals

Description

System of ordinary differential equations to simulate the effect of immigration of owned dogs, abandonment, sterilization of owned and unowned dogs and adoption, on population dynamics.

Usage

SolveIASA(pars = NULL, init = NULL, time = NULL,
  alpha.owned = TRUE, immigration.reference = "N1", s.range = NULL,
  a.range = NULL, alpha.range = NULL, v.range = NULL, s.fm = TRUE,
  ...)
SolveIASA(pars = NULL, init = NULL, time = NULL,
  alpha.owned = TRUE, immigration.reference = "N1", s.range = NULL,
  a.range = NULL, alpha.range = NULL, v.range = NULL, s.fm = TRUE,
  ...)

Arguments

`pars`	a named `vector` of length 21, with point estimates of model parameters (see details).
`init`	a named `vector` of length 8, with point estimates of model parameters (see details).
`time`	time sequence for which output is wanted; the first value of times must be the initial time.
`alpha.owned`	`logical`. If `TRUE` (default), adoption rate is relative to the owned population (proportion of the owned population). If `FALSE`, it is relative to the unowned population.
`immigration.reference`	`character` indicating the value of reference to calculate the immigration rate. If `"N1"` (default), the total of immigrants is the product of the owned population size times the immigration rate (N1 \* v). If `k1`, it is the product of the owned carrying capacity times the immigration rate (k1 \* v).
`s.range`	optional sequence (between 0 and 1) of the sterilization rates to be simulated.
`a.range`	optional `vector` of length 2, with range (ie, confidence interval) of abandonment rates to be assessed. If given, the rates evaluated are those specified by the argument plus the point estimate given in `pars`.
`alpha.range`	optional `vector` of length 2, with range (ie, confidence interval) of adoption rates to be assessed. If given, the rates evaluated are those specified by the argument plus the point estimate given in `pars`.
`v.range`	optional `vector` of length 2, with range of values of immigration rates to be assessed.
`s.fm`	logical. If `TRUE`, s.range is used for females and males and if `FALSE`, it is used for only females (for males, the point estimate given in `pars` is used.)
`...`	further arguments passed to ode function.

Details

The implemented model is described by Baquero, et. al., 2016 and the function is a wrapper around the defaults of ode function, whose help page must be consulted for details.

The pars argument must contain named values, using the following conventions: 1: owned animals; 2: unowned animals; f: females; m: males. Then:

b1 and b2: number of births.

df1, dm1, df2 and dm2: death rate.

sf1, sm1, sf2 and sm2: sterilization rate.

k1 and k2: carrying capacity.

h1 and h2: mean harem size.

a: abandonment rate.

alpha: adoption rate.

v: immigration rate.

z: proportion of sterilized immigrants.

The init argument must contain named values for the inital number of animals, using the following conventions: 1: owned animals; 2: unowned animals; f: females; m: males; and s: sterilized. Then, the names are:

f1, fs1, m1, ms1, f2, fs2, m2 and ms2.

If any range is specified (e.g s.range), the remaining ranges must be specified too (a.range, alpha.range and v.range). The function is a wrapper around the defaults of ode function, whose help page must be consulted for details. An exception is the method argument, which here has "rk4" as a default.

Value

list. The first element, name, is a string with the name of the function, the second element, model, is the model function. The third, fourth and fifth elements are vectors (pars, init, time, respectively) containing the pars, init and time arguments of the function. The sixth element results is a data.frame with up to as many rows as elements in time. The first column contain the time and subsequent columns contain the size of specific subpopulations, named according to conventions above. The group column differentiate between owned and unowned. When *.range arguments are given, the last fourth columsn specify their instances.

Note

Logistic growth models are not intended for scenarios in which population size is greater than carrying capacity and growth rate is negative.

References

Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine. Baquero, O. S., Akamine, L. A., Amaku, M., & Ferreira, F. (2016). Defining priorities for dog population management through mathematical modeling. Preventive veterinary medicine, 123, 121-127.

http://oswaldosantos.github.io/capm

Examples

## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                         destination.label = "Pinhais",
                         total.estimate = 50444)

# Solve for point estimates.
solve_iasa_pt <- SolveIASA(pars = dogs_iasa$pars,
                           init = dogs_iasa$init,
                           time = 0:15,
                           alpha.owned = TRUE,
                           method = 'rk4')
solve_iasa_rg <- SolveIASA(pars = dogs_iasa$pars,
                           init = dogs_iasa$init, 
                           time = 0:10,
                           alpha.owned = TRUE,
                           s.range = seq(0, .4, l = 15), 
                           a.range = c(0, .2), 
                           alpha.range = c(0, .05),
                           v.range = c(0, .1),
                           method = 'rk4')

## Parameters and intial conditions.
data(dogs)
dogs_iasa <- GetDataIASA(dogs,
                         destination.label = "Pinhais",
                         total.estimate = 50444)

# Solve for point estimates.
solve_iasa_pt <- SolveIASA(pars = dogs_iasa$pars,
                           init = dogs_iasa$init,
                           time = 0:15,
                           alpha.owned = TRUE,
                           method = 'rk4')
solve_iasa_rg <- SolveIASA(pars = dogs_iasa$pars,
                           init = dogs_iasa$init, 
                           time = 0:10,
                           alpha.owned = TRUE,
                           s.range = seq(0, .4, l = 15), 
                           a.range = c(0, .2), 
                           alpha.range = c(0, .05),
                           v.range = c(0, .1),
                           method = 'rk4')

Modelling of sterilization and immigration of comapnion animals.

Description

System of ordinary differential equations to simulate the effect of sterilization and immigration on population dynamics.

Usage

SolveSI(pars = NULL, init = NULL, time = NULL, dd = "b", im = 0,
  s.range = NULL, ...)
SolveSI(pars = NULL, init = NULL, time = NULL, dd = "b", im = 0,
  s.range = NULL, ...)

Arguments

`pars`	`vector` of length 4. The values are point estimates of birth rate, death rate, carrying capacity and sterilization rate. The names of this values must be "b", "d", "k" and "s", respectively.
`init`	`vector` of length 2. The values are initial population size and initial proportion of sterilized animals. The names of this values must be "n" and "q", respectively.
`time`	time sequence for which output is wanted; the first value of times must be the initial time.
`dd`	string equal to `b`, `d` or `bd` to define if density-dependece act on birth rate, death rarte or both, respectively.
`im`	a number representing the total of immigrants per time unit.
`s.range`	optional sequence (between 0 and 1) of the sterilization rates to be simulated.
`...`	further arguments passed to ode function.

Details

The implemented model is described by Amaku, et. al., 2009 and the function is a wrapper around the defaults of ode function, whose help page must be consulted for details.

Value

list. The first element, name, is a string with the name of the function, the second element, model, is the model function. The third, fourth and fifth elements are vectors (pars, init, time, respectively) containing the pars, init and time arguments of the function. The sisxth element results is a data.frame with up to as many rows as elements in time. First column contains the time, second column the population size and third column the proportion of sterilized animals. If s.range is specified, fourth column contains its specific instances.

Note

Logistic growth models are not intended for scenarios in which population size is greater than carrying capacity and growth rate is negative.

References

Amaku M, Dias R and Ferreira F (2009). Dinamica populacional canina: potenciais efeitos de campanhas de esterilizacao. Revista Panamericana de Salud Publica, 25(4), pp. 300-304.

Soetaert K, Cash J and Mazzia F (2012). Solving differential equations in R. Springer.

http://oswaldosantos.github.io/capm

Examples

# Parameters and initial conditions.
pars_solve_si = c(b = 0.245, d = 0.101, 
                 k = 98050, s = 0.048)
init_solve_si = c(n = 89137, q = 0.198)

# Solve for a specific sterilization rate.
solve_si_pt = SolveSI(pars = pars_solve_si, 
                     init = init_solve_si, 
                     time = 0:15, dd = "b",
                     im = 100, method = "rk4")

# Solve for a range of sterilization rates.
solve_si_rg = SolveSI(pars = pars_solve_si,
                     init = init_solve_si,
                     time = 0:15, dd = "b", im = 100, 
                     s.range = seq(0, .4, l = 50),
                     method = "rk4")

# Parameters and initial conditions.
pars_solve_si = c(b = 0.245, d = 0.101, 
                 k = 98050, s = 0.048)
init_solve_si = c(n = 89137, q = 0.198)

# Solve for a specific sterilization rate.
solve_si_pt = SolveSI(pars = pars_solve_si, 
                     init = init_solve_si, 
                     time = 0:15, dd = "b",
                     im = 100, method = "rk4")

# Solve for a range of sterilization rates.
solve_si_rg = SolveSI(pars = pars_solve_si,
                     init = init_solve_si,
                     time = 0:15, dd = "b", im = 100, 
                     s.range = seq(0, .4, l = 50),
                     method = "rk4")

Modelling of reversible contraception for companion animals

Description

System of ordinary differential equations to simulate the effect of reversible contraception in a population at equilibrium, where deaths are compensated by births and net immigration.

Usage

SolveTC(pars = NULL, init = NULL, time = NULL, f.range = NULL,
  s.range = NULL, z.range = NULL, ...)
SolveTC(pars = NULL, init = NULL, time = NULL, f.range = NULL,
  s.range = NULL, z.range = NULL, ...)

Arguments

`pars`	a named `vector` of length 5. The values are point estimates of the death rate (d), the fertility recovery rate (f), the sterilization rate (s), the proportion of infertile immigrants (z) and the proportion of the death rate compensated by immigration (r). Abreviations in parentheses indicate the names that must be given to the values.
`init`	a named `vector` of length 2, with the total number of fertile (n) and infertile (g) animals.
`time`	time sequence for which output is wanted; the first value of times must be the initial time.
`f.range`	optional sequence (between 0 and 1) with the fertility recovery rates to be simulated.
`s.range`	optional `vector` of length 2, with a range of sterilization rates to be assessed. If given, the rates evaluated are those specified by the argument plus the point estimate given in `pars`.
`z.range`	optional `vector` of length 2, with a range of the proportion of infertile immigrants. If given, the rates evaluated are those specified by the argument plus the point estimate given in `pars`.
`...`	further arguments passed to ode function.

Value

list. The first element, name, is a string with the name of the function, the second element, model, is the model function. The third, fourth and fifth elements are vectors (pars, init, time, respectively) containing the pars, init and time arguments of the function. The sisxthth element results is a data.frame with up to as many rows as elements in time. The first fourth columns contain the time and the variables: n, g and u. When *.range arguments are given, additional columns contain the variables f, s and z.

References

http://oswaldosantos.github.io/capm

Baquero, O. S., Brandao, A. P. D., Amaku, M., & Ferreira, F. (2016). Effectiveness of reversible contraception in dog population management. Acta Scientiae Veterinariae, 44, 01-06.

Examples

# Parameters and initial conditions.
pars_solvetc <- c(d = 1 / 6, f = 0.5, s = 0.2, 
                   z = 0.2, r = 0.8)

init_solvetc <- c(n = 950, g = 50)

# Solve for point estimates.
solve_tc_pt <- SolveTC(pars = pars_solvetc, 
                      init = init_solvetc, 
                      time = 0:10, method = "rk4")

# Solve for parameter ranges.
solve_tc_rg <- SolveTC(pars = pars_solvetc, 
                      init = init_solvetc, 
                      time = 0:15,
                      f.range = seq(0, 1, 0.1), 
                      s.range = c(0.05, 0.4), 
                      z.range = c(0.05, 0.4),
                      method = "rk4")

# Parameters and initial conditions.
pars_solvetc <- c(d = 1 / 6, f = 0.5, s = 0.2, 
                   z = 0.2, r = 0.8)

init_solvetc <- c(n = 950, g = 50)

# Solve for point estimates.
solve_tc_pt <- SolveTC(pars = pars_solvetc, 
                      init = init_solvetc, 
                      time = 0:10, method = "rk4")

# Solve for parameter ranges.
solve_tc_rg <- SolveTC(pars = pars_solvetc, 
                      init = init_solvetc, 
                      time = 0:15,
                      f.range = seq(0, 1, 0.1), 
                      s.range = c(0.05, 0.4), 
                      z.range = c(0.05, 0.4),
                      method = "rk4")

Summary statistics for sample surveys

Description

Wraps functions for summary statistics from survey package.

Usage

SummarySurvey(design = NULL, variables = NULL, conf.level = 0.95,
  rnd = 3)
SummarySurvey(design = NULL, variables = NULL, conf.level = 0.95,
  rnd = 3)

Arguments

`design`	an output form `DesignSurvey` function.
`variables`	`character` `vector` with the type of estimate for each variable contained in `design` (see details).
`conf.level`	the confidence level required.
`rnd`	the number of decimal places (round) or significant digits (signif) to be used. If `NA`, scientific notation is used.

Details

The length of variables must be equal to the length of names(design$variables) (see examples).

Value

Matrix with survey summaries.

References

Lumley, T. (2011). Complex surveys: A guide to analysis using R (Vol. 565). Wiley.

Baquero, O. S., Akamine, L. A., Amaku, M., & Ferreira, F. (2016). Defining priorities for dog population management through mathematical modeling. Preventive veterinary medicine, 123, 121-127.

http://oswaldosantos.github.io/capm

Examples

data("cluster_sample")
data("psu_ssu")

## Calibrated two-stage cluster design
cs <- cluster_sample[ , c("interview_id",
                          "census_tract_id",
                          "number_of_persons",
                          "number_of_dogs",
                          "number_of_cats")]

design <- DesignSurvey(na.omit(cs),
                       psu.ssu = psu_ssu,
                       psu.col = "census_tract_id",
                       ssu.col = "interview_id",
                       cal.col = "number_of_persons",
                       cal.N = 129445)

SummarySurvey(design, c("total", "total", "total"))

data("cluster_sample")
data("psu_ssu")

## Calibrated two-stage cluster design
cs <- cluster_sample[ , c("interview_id",
                          "census_tract_id",
                          "number_of_persons",
                          "number_of_dogs",
                          "number_of_cats")]

design <- DesignSurvey(na.omit(cs),
                       psu.ssu = psu_ssu,
                       psu.col = "census_tract_id",
                       ssu.col = "interview_id",
                       cal.col = "number_of_persons",
                       cal.N = 129445)

SummarySurvey(design, c("total", "total", "total"))

Package 'capm'

Help Index

The capm Package

Description

Details

Two-stage cluster sampling size and composition (Deprecated)

Description

Usage

Arguments

Details

Value

References

Global sensitivity analysis

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Local sensitivity analysis

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Population change.

Description

Usage

Arguments

Value

References

Examples

Simple random sample size

Description

Usage

Arguments

Value

References

Examples

Stratified random sample size

Description

Usage

Arguments

Value

References

Examples

Cat's sample data from Pinhais, Brazil, 2017

Description

Usage

Format

References

Household data from a two-stage cluster sample

Description

Usage

Format

References

Survey design

Description

Usage

Arguments

Details

Value

References

Examples

Dog's sample data from Pinhais, Brazil, 2017

Description

Usage

Format

References

Frequency table of categorical variables

Description

Usage

Arguments

Value

References