| Type: | Package |
| Title: | Forest Growth Model Utilities |
| Version: | 0.9.5 |
| Date: | 2018-10-11 |
| Author: | Clayton Vieira Fraga Filho, Ana Paula Simiqueli, Gilson Fernandes da Silva, Miqueias Fernandes, Wagner Amorim da Silva Altoe |
| Maintainer: | Clayton Vieira Fraga Filho <forestgrowthsoftware@gmail.com> |
| Description: | Growth models and forest production require existing data manipulation and the creation of new data, structured from basic forest inventory data. The purpose of this package is provide functions to support these activities. |
| License: | GPL-2 |
| Depends: | sqldf, stringr, plyr, R (≥ 3.0) |
| Imports: | data.table, tcltk, utils, stats, graphics, devEMF, png, grDevices, methods, ggplot2, gridExtra |
| LazyData: | TRUE |
| Suggests: | testthat |
| RoxygenNote: | 6.1.0 |
| NeedsCompilation: | no |
| Packaged: | 2018-11-17 22:29:47 UTC; clayt |
| Repository: | CRAN |
| Date/Publication: | 2018-11-18 00:50:22 UTC |
R21a
Description
To avoid any problems and confudion on the part of the data analyst, it seems a safe recommendation to use R21a consistently for any type of model with the appropeiate a value, rather than ajusting any of the other
Usage
R21a(observados, estimados, k)
Arguments
observados |
vector of values observed. |
estimados |
vector of values estimated. |
k |
is the number of model parameters |
Details
R21a <- 1-a*(1 - R21)
R29a
Description
To avoid any problems and confusion on the part of the data analyst, it seems a safe recommendation to use R21a consistently for any type of model with the appropeiate a value, rather than adjusting any of the other.
Usage
R29a(observados, estimados, k)
Arguments
observados |
vector of values observed. |
estimados |
vector of values estimated. |
k |
is the number of model parameters |
Details
R29a <- 1-a*(1 - R29)
add column
Description
take a data-frame and a vector and combine by columns, respectively.
Usage
add.col(dataf, vec, namevec)
Arguments
dataf |
dataframe |
vec |
vector |
namevec |
the names of the columns of vector |
Value
dataf dataframe combined with the vector
updated base field
Description
this function update certain fields in a dataframe, based on the provided key
Usage
atualizaCampoBase(camposAtualizar, baseAgrupada, baseAtualizar, keys,
verbose = FALSE)
Arguments
camposAtualizar |
is the vector you want to update |
baseAgrupada |
It is the database that contains the data you want to update on dataframe |
baseAtualizar |
It is dataframe that you want to change fields |
keys |
are the keys of the table that will be used in the compare |
verbose |
default false |
Value
baseAtualizar with the updated fields according to baseAgrupada
avalia Ajuste
Description
this function evaluates the quality of the adjustment of the statistical model, rom observed data and those estimated by the model, observed
Usage
avaliaAjuste(dataFrame, variavelObservados, variavelEstimados,
linear = TRUE, nParametros = NA, intercepto = TRUE, plot = NA,
modelo = NA, resumo = FALSE, emf = TRUE)
Arguments
dataFrame |
dataFrane with information observed, estimated |
variavelObservados |
vector of values observed. |
variavelEstimados |
vector of values estimated. |
linear |
boolean is linear model |
nParametros |
number of parameters used in the adjusted model |
intercepto |
if you model is no-intercepto use FALSE |
plot |
Vector graphic information |
modelo |
the name of the adjusted model |
resumo |
if you want summary information, use TRUE |
emf |
to save the graphic in the format emf use TRUE |
calculate Estimates
Description
given a list of observations and an estimated list of these observations this function evaluates how close it is the estimated value of observed and saves the differences
Usage
avaliaEstimativas(observado, estimado, estatisticas, ajuste = NULL,
graficos = NULL, salvarEm = NULL, nome = "observadoXestimado")
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
estatisticas |
list of arg to calc estatistics |
ajuste |
is ajust obtained a function like lm or nlsLM |
graficos |
list of arg to plot graphics |
salvarEm |
directory to save files |
nome |
name of files will be save |
Value
will be returned
avalia Volume Age Based
Description
this function evaluate volume based on ages
Usage
avaliaVolumeAgeBased(base, firstAge, lastAge, models, mapper = list(age1
= "idade1", age2 = "idade2", dap1 = "dap1", dap2 = "dap2", dap2est =
"dap2est", ht1 = "ht1", ht2 = "ht2", ht2est = "ht2est", volume1 =
"volume1", volume2 = "volume2", volume2est = "volume2est"),
groupBy = "parcela", save = NULL, percTraining = 0.7,
paramEstatisticsDAP, paramEstatisticsHT, paramEstatisticsVolume,
plot = "parcela", ageER = "^.*_", ageRound = NaN, ageInYears = F,
forcePredict = F)
Arguments
base |
the data base |
firstAge |
the first age to eval |
lastAge |
the last age to eval |
models |
list of exclusive for base models |
mapper |
mapper from labels of fields volume, dap, ht |
groupBy |
name field of base is group of individuals |
save |
list of param to save the files |
percTraining |
percentage that will be reserved for training (default 0.70) |
paramEstatisticsDAP |
parameters to pass to function 'fnAvaliaEstimativas' |
paramEstatisticsHT |
analogous to paramEstatisticsDAP |
paramEstatisticsVolume |
analogous to paramEstatisticsDAP |
plot |
is list of plots to function roundAges |
ageER |
regex used to discover age in names from dataframe in listOfdata |
ageRound |
synchronize begin of ages with an age? what age? |
ageInYears |
ages are in year? |
forcePredict |
force the calculation without using predict? |
Value
will be returned a list of round ages
evaluates Volume Advanced
Description
this function performs an assessment of estimates of a variable as the forcefulness with expected
Usage
avaliaVolumeAvancado(base, mapeamento = list(dap1 = "dap1", dap2 =
"dap2", ht1 = "ht1", ht2 = "ht2"), modelos = NULL, salvar = NULL,
graficos = NULL, estatisticas = NULL, forcePredict = F,
dividirEm = "parcela", percentualDeTreino = 0.7,
agruparPor = "parcela", fnCalculaVolume = calculaVolumeDefault)
Arguments
base |
data.frame with data |
mapeamento |
name of field eight and diameter |
modelos |
list of exclusive for base models |
salvar |
list of param to save the files |
graficos |
list of param to plot graphics |
estatisticas |
list of param to caclc estatistics |
forcePredict |
force the calculation without using predict? |
dividirEm |
how divide the base in training and validation |
percentualDeTreino |
how many percent will stay in the training group? |
agruparPor |
name field of base is group of individuals |
fnCalculaVolume |
list of estatistics results |
Value
will be returned a result of statistics and ranking of volume
Bias
Description
In statistics, the bias (or bias function) of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased. Otherwise the estimator is said to be biased.
Usage
bias(observados, estimados)
Arguments
observados |
vector of values observed. |
estimados |
vector of values estimated. |
Details
bias = (sum(estimados-observados))/length(observados)
References
see https://en.wikipedia.org/wiki/Bias_of_an_estimator for more details.
Fator A
Description
The linear intercept model,
Usage
calculaA(n, k)
Arguments
n |
the size of the vector of regression model data |
k |
is the number of model parameters |
Details
a = (n-1)/(n-k-1)
calculates percentage
Description
With this function, you can calculate the ratio of one quantity or magnitude relative to another evaluated in percentage.
Usage
calculaPerc(valor, observados)
Arguments
valor |
number amount you to know the percentage |
observados |
number relationship to which you want to calculate the percentage, if it is a vector of integers is calculated its average. |
Details
calculaPerc = ((valor)/mean(observados))*100
calculates Volume Default
Description
this function calculates the volume based on the height and volume of literature of the equation
Usage
calculaVolumeDefault(ht, dap, ...)
Arguments
ht |
is list of height of individuals |
dap |
is list of diameter of individuals |
... |
only for compatibility with other functions |
Value
will be returned a list of volume calc
coefficient of efficiency
Description
Nash Sutcliffe 1970 model efficiency coefficient is used to assess the predictive power of hydrological models.
Usage
ce(observados, estimados)
Arguments
observados |
vector of values observed. |
estimados |
vector of regression model data. |
References
( Nash and Sutcliffe, 1970) https://en.wikipedia.org/wiki/Nash-Sutcliffe_model_efficiency_coefficient for more details.
Ckeck Integer
Description
checks if a variable is integer
Usage
check.integer(x)
Arguments
x |
any variable |
Value
TRUE if "x" is integer, FALSE if "x" not is interger
Examples
x = 5
check.integer(x)
classifica Classe DAP
Description
the center of the class that the DAP belongs.
Usage
classificaClasseDAP(dfClassesDAP, dap, getNhaClasse = FALSE,
getNCLASSES = FALSE)
Arguments
dfClassesDAP |
a frequency distribution with the attributes $classe and $centro |
dap |
integer Diameter at breast height |
getNhaClasse |
get NhaClasse field of dfClassesDAP, default false |
getNCLASSES |
get NCLASSES field of dfClassesDAP, default false |
Examples
dados = defineClasses(1, 10, 2, getDataFrame = TRUE)
classificaClasseDAP(dados,7)
classify field dap
Description
classify field dap as specified amplitude and includes a few fields
Usage
classificarDAP(inventario, amplitude = 1, verbose = FALSE)
Arguments
inventario |
the database to update |
amplitude |
it is amplitude of dap class |
verbose |
use TRUE to show status of process |
Value
data.frame with classeDAP field and other
which parameters are missing?
Description
this function checks whether the labels of the parameters list to move to the functions is sufficient
Usage
contemParametros(funcoes, parametro, addParametro = c(), addArgs = c(),
exclui3pontos = T)
Arguments
funcoes |
is a or set of functions whose param will be verify |
parametro |
is list whose labels is name of param in funcoes, list of args to funcoes ex list(a="1", b="2") |
addParametro |
list of param included |
addArgs |
more param required |
exclui3pontos |
verify por ... ? in f<-function(a, ...) |
Value
will be returned the parameters that have not been reported in parametro and addParametro
Field Converts To Character
Description
converts a column of a dataframe to String
Usage
converteCampoParaCharacter(nomeCampo, base)
Arguments
nomeCampo |
the column name you want to convert |
base |
the column having dataFrame, that you want to convert to String |
Value
base dataFrame with a column converted to String
Examples
measurement_date <- c(02/2009,02/2010,02/2011,02/2011)
plot <- c(1,2,3,4)
test <- data.frame(measurement_date,plot)
converteCampoParaCharacter("measurement_date",test)
Create Date Paired
Description
paired a dataframe
Usage
criaDadosPareados(dataFrame, campoChave, campoComparacao, camposPareados,
camposNaoPareados, progress = TRUE)
Arguments
dataFrame |
dataframe that you want to pair dataFrame must contain columns cod_id, ANO_MEDICAO1, ANO_MEDICAO2, DAP1, DAP2, HT1, HT2, ID_PROJETO |
campoChave |
character the column that will be paired |
campoComparacao |
character the field used to compare the period of change |
camposPareados |
vector the fields that will be paired exemple CampoesPareados=c(dap,ht) |
camposNaoPareados |
the fields he wants to be present without the paired |
progress |
if TRUE show a progress bar |
Value
will be returned a dataframe containing columns cod_id, ANO_MEDICAO1, ANO_MEDICAO2, DAP1, DAP2, HT1, HT2, ID_PROJETO
Create Exclusive Model for a database
Description
this function returns a unique model is variable receive each mapeda variable ex .: criaModeloExclusivo (modeloCamposLeite, c ("age1", "age2", "bai1", "s"))
Usage
criaModeloExclusivo(modeloGenerico, variaveis, palpite = NULL)
Arguments
modeloGenerico |
model of pattern criaModeloGenerico |
variaveis |
list of name fields (strings) in database and model, the order of variables matter |
palpite |
string containing start values of function of regression |
Value
will be returned a function with exclusive model
Create function with generic model
Description
This function creates a generic model that will be a funcao that has parameters for the variables that can be mapped to each different base. her return will be a generic model that should be mapped to be used by the function avaliaEstimativas
Usage
criaModeloGenerico(nome, formula, funcaoRegressao, variaveis,
palpite = NULL, maisParametros = NULL, requires = NULL)
Arguments
nome |
is the name of model |
formula |
is the string formula begin with y2~y1 |
funcaoRegressao |
is the function that will make the regression, ex.: 'nlsLM' |
variaveis |
list variables that are present in the model that are field database |
palpite |
param start of funcaoRegressao |
maisParametros |
string add in funcaoRegressao, ex lm(y2~y1, data=base, maisParametros) |
requires |
list of string of packges used to work with funcaoRegressao |
Value
will be returned function with generic model to map to a base
define Classes
Description
creates a list with the class interval of a frequency distribution
Usage
defineClasses(limiteMin, limiteMax, amplitude, decrescente = TRUE,
getDataFrame = FALSE, verbose = FALSE)
Arguments
limiteMin |
the lowest list number |
limiteMax |
the largest number in the list |
amplitude |
List amplitude |
decrescente |
order by true decreasing , false increasing |
getDataFrame |
return a data.frame default false because old uses |
verbose |
show status default false |
define Classes 2
Description
creates a list with the class interval of a frequency distribution
Usage
defineClasses2(dados, amplitude)
Arguments
dados |
a vector of numbers |
amplitude |
integer Class amplitude range |
Examples
dados <- c(1,2,3,4)
defineClasses2(dados,2)
Estatistics
Description
this function returns a data.frame containing fields observado and estimado
Usage
estatisticas(observado, estimado, dfEstatisticas = NULL, ...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
dfEstatisticas |
a data.frame |
... |
only for compatibility with other functions |
Value
will be returned a list with data.frame with observado and estimado fields and other with statictcs of model add
BIAS Estatistics
Description
this function returns a data.frame containing fields bias
Usage
estatisticasBIAS(observado, estimado, dfEstatisticas = NULL, ...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
dfEstatisticas |
a data.frame |
... |
only for compatibility with other functions |
Value
will be returned data.frame with bias
percent BIAS Estatistics
Description
this function returns a data.frame containing fields biasPERCENTUAL
Usage
estatisticasBiasPERCENTUAL(observado, estimado, dfEstatisticas, ...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
dfEstatisticas |
a data.frame with field bias |
... |
only for compatibility with other functions |
Value
will be returned data.frame with biasPERCENTUAL
CE Estatistics
Description
this function returns a data.frame containing fields
Usage
estatisticasCE(observado, estimado, dfEstatisticas = NULL, ...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
dfEstatisticas |
a data.frame |
... |
only for compatibility with other functions |
Value
will be returned data.frame with CE
Correlacion Estatistics
Description
this function returns a data.frame containing fields corr
Usage
estatisticasCORR(observado, estimado, dfEstatisticas = NULL, ...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
dfEstatisticas |
a data.frame |
... |
only for compatibility with other functions |
Value
will be returned data.frame with corr field
Co variance Estatistics
Description
this function returns a data.frame containing fields cv
Usage
estatisticasCV(observado, estimado, ajuste = NULL,
dfEstatisticas = NULL, baseDoAjuste = NULL, formulaDoAjuste = NULL,
...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
ajuste |
is ajust obtained a function like lm or nlsLM |
dfEstatisticas |
a data.frame |
baseDoAjuste |
data.frame optional |
formulaDoAjuste |
formula used in ajust |
... |
only for compatibility with other functions |
Value
will be returned data.frame with cv
Percent Correlacion Estatistics
Description
this function returns a data.frame containing fields corr_PERCENTUAL
Usage
estatisticasCorrPERCENTUAL(observado, estimado, dfEstatisticas, ...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
dfEstatisticas |
a data.frame with corr field |
... |
only for compatibility with other functions |
Value
will be returned data.frame with corr_PERCENTUAL field
Percent Co variance Estatistics
Description
this function returns a data.frame containing fields cvPERCENTUAL
Usage
estatisticasCvPERCENTUAL(observado, estimado, dfEstatisticas, ...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
dfEstatisticas |
a data.frame with cv field |
... |
only for compatibility with other functions |
Value
will be returned data.frame with cvPERCENTUAL
MAE Estatistics
Description
this function returns a data.frame containing fields mae
Usage
estatisticasMAE(observado, estimado, dfEstatisticas = NULL, ...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
dfEstatisticas |
a data.frame |
... |
only for compatibility with other functions |
Value
will be returned data.frame with mae
R2 Estatistics for linear models
Description
this function returns a data.frame containing fields r2
Usage
estatisticasR2(observado, estimado, dfEstatisticas = NULL,
ajuste = NULL, intercepto = TRUE, formulaDoAjuste = NULL,
baseDoAjuste = NULL, ...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
dfEstatisticas |
a data.frame |
ajuste |
is ajust obtained a function like lm or nlsLM |
intercepto |
intercepts? |
formulaDoAjuste |
formula used in ajust |
baseDoAjuste |
data.frame optional |
... |
only for compatibility with other functions |
Value
will be returned data.frame with r2
RMSE Estatistics
Description
this function returns a data.frame containing fields rmse
Usage
estatisticasRMSE(observado, estimado, dfEstatisticas = NULL, ...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
dfEstatisticas |
a data.frame |
... |
only for compatibility with other functions |
Value
will be returned data.frame with RMSE calc
RRMSE Estatistics
Description
this function returns a data.frame containing fields RRMSE
Usage
estatisticasRRMSE(observado, estimado, dfEstatisticas = NULL, ...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
dfEstatisticas |
a data.frame |
... |
only for compatibility with other functions |
Value
will be returned data.frame with rrmse
Residuals Estatistics
Description
this function returns a data.frame containing field residuoPERCENTUAL
Usage
estatisticasResiduoPERCENTUAL(observado, estimado, dfEstatisticas = NULL,
...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
dfEstatisticas |
a data.frame containing field residuo |
... |
only for compatibility with other functions |
Value
will be returned data.frame with percent Residuals field
Residuals Estatistics
Description
this function returns a data.frame containing field residuo
Usage
estatisticasResiduos(observado, estimado, dfEstatisticas = NULL, ...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
dfEstatisticas |
a data.frame |
... |
only for compatibility with other functions |
Value
will be returned data.frame with Residuals field
percent RMSE Estatistics
Description
this function returns a data.frame containing fields rmsePERCENTUAL
Usage
estatisticasRmsePERCENTUAL(observado, estimado, dfEstatisticas, ...)
Arguments
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
dfEstatisticas |
a data.frame containing field rmse |
... |
only for compatibility with other functions |
Value
will be returned data.frame with rmse PERCENTUAL calc
Evaluate Age Based
Description
This function evaluates the volume of past data frames based on the parameter 'listOfdata'
Usage
evalAgeBased(listOfdata, mapper = list(volume2 = "volume2", volume2est =
"volume2est", dap2 = "dap2", dap2est = "dap2est", ht2 = "ht2", ht2est =
"ht2est"), fnAvaliaEstimativas = avaliaEstimativas,
paramEstatisticsDAP, paramEstatisticsHT, paramEstatisticsVolume,
titulos = "paste(\"Idade\", idade)", ageER = "^.*_",
nameModel = NULL)
Arguments
listOfdata |
the list that contains the data frames predicts |
mapper |
mapper from labels of fields volume, dap, ht |
fnAvaliaEstimativas |
funcion to evaluate dataframes of listOfdata |
paramEstatisticsDAP |
parameters to pass to function 'fnAvaliaEstimativas' |
paramEstatisticsHT |
analogous to paramEstatisticsDAP |
paramEstatisticsVolume |
analogous to paramEstatisticsDAP |
titulos |
customize titles of grafics |
ageER |
regex used to discover age in names from dataframe in listOfdata |
nameModel |
name of model used to predict to generate listOfdata optional |
Value
will be returned a list of round ages
Fator Bias
Description
The bias factor indicates the average of the observed values is above or below the equity line.
Usage
fator_bias(observados, estimados, n)
Arguments
observados |
vector of values observed. |
estimados |
vector of values estimated. |
n |
the size of the vector of regression model data |
Details
fator_bias = 10^(sum(log(estimados/observados)/n)) #' @references see https://www.sciencedirect.com/science/article/pii/S0165176599001949 for more details.
Generates function to work with a model
Description
this function generates unique model given: A formula and a guess (optional: name, funcaoRegressao, maisParametros, requires - proidido: custom)] or[A string saying how the return will be obtained eg custom = "lm (dap2 dap1 ~ * b 0)" (if the formula can not be passed just go empty, ex .: formula = "")]
Usage
geraModelo(nome = "modelo sem nome", formula,
funcaoRegressao = "nlsLM", palpite = NULL, maisParametros = NULL,
requires = NULL, customizado = NULL)
Arguments
nome |
is the name of model |
formula |
is the string formula begin with y2~y1 |
funcaoRegressao |
is the function that will make the regression, ex.: 'nlsLM' |
palpite |
param start of funcaoRegressao |
maisParametros |
string add in funcaoRegressao, ex lm(y2~y1, data=base, maisParametros) |
requires |
list of string of packges used to work with funcaoRegressao |
customizado |
if you want to write as the return will be obtained report as a string |
Value
will be returned a function with exclusive model
Get Year Measurement
Description
using column_name_measurement_date column in the form MM/YYYY creates a new column with the name "ANO_MEDICAO" in YYYY format
Usage
getAnoMedicao(dataFrame, column_name_measurement_date, column_name_plot)
Arguments
dataFrame |
that has the column DATE(MM/YYYY) and a ID column_name_plot |
column_name_measurement_date |
column with a date format |
column_name_plot |
a column of dataFrame, identification of plot (ID_plot) |
Value
dataFrame dataframe that has columns column_name_measurement_date, column_name_plot, ANO_MEDICAO
Examples
column_name_measurement_date <- c("02/2009","02/2010","02/2011","02/2012")
column_name_plot <- c(1,2,3,4)
test <- data.frame(column_name_measurement_date,column_name_plot)
getAnoMedicao(test,"column_name_measurement_date","column_name_plot")
get database Of Ajust
Description
this function returns the database used in the setting
Usage
getBaseOfAjust(ajuste)
Arguments
ajuste |
is ajust obtained a function like lm or nlsLM |
Value
will be returned a string which is the database of ajust
Get List of DAP Classes
Description
this function return a list of data.frame where each contains a number of dap classes according to reported basis
Usage
getClasses(base, amplitude, verbose = FALSE)
Arguments
base |
the data.frame containing fields limiteMin, limiteMax of parcela and idadearred |
amplitude |
it is amplitude of dap class |
verbose |
use TRUE to show status of process |
Value
list of data.frame
get Columns used in Ajust
Description
this function returns an array with the column names that are on the model and reported basis or basis used in ajust
Usage
getColumnsOfAjust(ajuste, dfDados = NULL, excludeY1andY2 = T)
Arguments
ajuste |
is ajust obtained a function like lm or nlsLM |
dfDados |
data.frame optional |
excludeY1andY2 |
delete Y1 and Y2 fields? del formula(y1~y2...) |
Value
will be returned list of columns used in ajust
get Columns Of Base present in the string
Description
this function returns the columns of a base whose names are present in the string strColumns
Usage
getColumnsOfBase(base, strColumns)
Arguments
base |
data.frame |
strColumns |
string containing name fields of the base |
Value
will be returned list with fields whose name are present in the string
get Formula Exclusive Of Ajust
Description
this function returns the formula of the model used in ajust
Usage
getFormulaExclusivaOfAjust(ajuste)
Arguments
ajuste |
is ajust obtained a function like lm or nlsLM |
Value
will be returned a string which is the formula of ajust
Get Histogram of Residuals absolute
Description
this function displays/saves a histogram graph illustrating the frequency of waste in classes
Usage
getGraphicHistogram(titulo = "residuos", nome = "observadoXestimado",
estatisticas, save = NULL, vetorial = T, ...)
Arguments
titulo |
is the title graphic |
nome |
name of file case save |
estatisticas |
data.frame containing field 'residuo' |
save |
If you want to save enter the directory as a string |
vetorial |
save picture in vector type? (Default TRUE) |
... |
only for compatibility with other functions |
Get Graphic Observed X Estimated
Description
this function display/save a graphic scatter.smooth illustrating the difference between the observed and estimated
Usage
getGraphicObservadoXEstimado(titulo = "observadoXestimado",
nome = "observadoXestimado", observado, estimado, showTestF = TRUE,
save = NULL, labsX = "observado", labsy = "estimado",
vetorial = T, ...)
Arguments
titulo |
is the title graphic |
nome |
name of file case save |
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
showTestF |
draw results of test F in graphic? |
save |
If you want to save enter the directory as a string |
labsX |
label x |
labsy |
label y |
vetorial |
save picture in vector type? (Default TRUE) |
... |
only for compatibility with other functions |
Get Graphic Residuals absolute
Description
this function displays/saves a graph illustrating the distribution scatter.smooth of residues
Usage
getGraphicResiduoAbs(titulo = "residuo absoluto",
nome = "observadoXestimado", strVariavelXResiduo = NULL,
estatisticas, save = NULL, labsX = "observacao",
labsy = "residuos", vetorial = T, ...)
Arguments
titulo |
is the title graphic |
nome |
name of file case save |
strVariavelXResiduo |
list containing variable for compare with residuals |
estatisticas |
data.frame containing field 'residuo' |
save |
If you want to save enter the directory as a string |
labsX |
label x |
labsy |
label y |
vetorial |
save picture in vector type? (Default TRUE) |
... |
only for compatibility with other functions |
Get Graphic Residuals percent
Description
this function displays/saves a graph illustrating the distribution scatter.smooth of residues
Usage
getGraphicResiduoPerc(titulo = "Residuo Percentual (%)",
nome = "observadoXestimado", strVariavelXResiduo = NULL,
estatisticas, save = NULL, labsX = "observacao",
labsy = "residuos", vetorial = T, ...)
Arguments
titulo |
is the title graphic |
nome |
name of file case save |
strVariavelXResiduo |
list containing variable for compare with residuals |
estatisticas |
data.frame containing field 'residuoPERCENTUAL' |
save |
If you want to save enter the directory as a string |
labsX |
label x |
labsy |
label y |
vetorial |
save picture in vector type? (Default TRUE) |
... |
only for compatibility with other functions |
get Parametros Of Model
Description
this function retona columns the base of the parameter or setting present in the model
Usage
getParametrosOfModel(ajuste, base = NULL, formula = NULL)
Arguments
ajuste |
is ajust obtained a function like lm or nlsLM |
base |
optional data.frame whose fields name is present in formula |
formula |
string containing name fields of the base |
Value
will be returned list of columns used in ajust or in formula
Get ggplot2 Grapic observed versus estimated
Description
this function displays/saves/returns a Graphical ggplot2 illustrating the difference between the observed and estimated
Usage
getggplot2GraphicObservadoXEstimado(titulo = "observadoXestimado",
nome = "observadoXestimado", observado, estimado,
identificadorIndividual = NULL, identificadorGrupal = NULL,
showTestF = TRUE, TestFposition = 4,
titleIdentificadorGrupal = NULL, save = NULL, labsX = "observado",
labsy = "estimado", nomeParaExibir = NULL, environ = 1,
extensao = ".png", ...)
Arguments
titulo |
is the title graphic |
nome |
name of file case save |
observado |
list containing the observations of variable |
estimado |
list containing estimates of variable |
identificadorIndividual |
list containing 'id' of individuals |
identificadorGrupal |
list containing group of individuals |
showTestF |
draw results of test F in graphic? |
TestFposition |
show one of the four corners of the graph clockwise |
titleIdentificadorGrupal |
title of Legend of the groups |
save |
If you want to save enter the directory as a string |
labsX |
label x |
labsy |
label y |
nomeParaExibir |
This is the name to display the graph as a function after the completion of this |
environ |
environment in which the function to display the ggplot2 must be saved |
extensao |
type of image that will be saved |
... |
only for compatibility with other functions |
Value
will be returned the graphical generated by ggplot2
ifrm
Description
if the object does not exist an error will not happen.
Usage
ifrm(obj, env = globalenv())
Arguments
obj |
the object that you want to remove |
env |
The global environment |
Examples
a = 5
ifrm(a)
ifrm(b)
is finite data frame
Description
check if a data.frame has any non-finite elements
Usage
isfinitedataframe(obj)
Arguments
obj |
any object |
Value
TRUE if "x" is finite, FALSE if "x" is not finite
Examples
date <- c("02/2009","02/2010","02/2011","02/2012")
x <- c(1,2,3,4)
test <- data.frame(x,date)
isfinitedataframe(test)
isfinitedataframe(x)
List to DataFrame
Description
converts a list in a dataframe
Usage
listToDataFrame(dlist)
Arguments
dlist |
a list |
Examples
a <- 1:5
listToDataFrame(a)
b = listToDataFrame(a)
mean absolute error (mae)
Description
is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. The mean absolute error is given by.
Usage
mae(observados, estimados)
Arguments
observados |
vector of values observed. |
estimados |
vector of regression model data. |
Details
mae = mean(abs(observados-estimados))
Value
Function that returns Mean Absolute Error
References
see https://en.wikipedia.org/wiki/Mean_absolute_error for more details.
Mean squared error
Description
the MSE is the mean of the square of the errors, corresponding to the expected value of the squared error loss or quadratic loss. The difference occurs because of randomness or because the estimator doesn't account for information that could produce a more accurate estimate.
Usage
mse(observados, estimados, k)
Arguments
observados |
vector of values observed. |
estimados |
vector of regression model data. |
k |
the number of model parameters |
Details
mse = (sum(estimados-observados)^2)/(length(observados)-k)
References
See https://en.wikipedia.org/wiki/Mean_squared_error for more details.
mspr
Description
average square of the prediction errors .
Usage
mspr(observados, estimados, nValidacao)
Arguments
observados |
vector of values observed. |
estimados |
vector of regression model data. |
nValidacao |
number of cases in the validation data set. |
References
JESUS, S. C.; MIURA, A. K. Analise de regressao linear multipla para estimativa do indice de vegetacao melhorado (EVI) a partir das bandas 3 4 e 5 do sensor TM/Landsat 5. In: SIMPOSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 14. (SBSR), 2009, Natal. Anais... Sao Jose dos Campos: INPE, 2009. p. 1103-1110. DVD, On-line. ISBN 978-85-17-00044-7. (INPE-15901-PRE/10511)
Predict
Description
this function is the replacement predict, she tries to predict if the return zero predict it calculates the prediction with the coefficients reported in the parameter setting
Usage
predizer(ajuste, newdata, force = FALSE, ...)
Arguments
ajuste |
is ajust obtained a function like lm or nlsLM |
newdata |
dataframe where fields will be update |
force |
force the calculation without using predict? |
... |
only for compatibility with other functions |
Value
will be returned list of values predicts
Project Base Oriented
Description
this function build a list of dataframe with projects of ages between 'firstAge' and 'lastAge' params
Usage
projectBaseOriented(firstAge = NaN, lastAge = NaN, fitDAP, fitHT, base,
mapper = list(age1 = "idadearred1", dap1 = "dap1", dap2 = "dap2", ht1 =
"ht1", ht2 = "ht2"), calcVolume = calculaVolumeDefault,
forcePredict = F)
Arguments
firstAge |
the first age to predict |
lastAge |
the last age to predict |
fitDAP |
a fit get function inherit lm to DAP |
fitHT |
a fit get function inherit lm to HT |
base |
data base |
mapper |
the label used in fields to age, dap and ht |
calcVolume |
function to calc volume |
forcePredict |
force calc base coefficients or se predict()? |
Value
will be returned a list of volume predict to ages in dataframe and/or param
calculates residue percentage
Description
this function calculates the vector residue percentage.
Usage
residuoPerc(observados, estimados)
Arguments
observados |
vector of values observed. |
estimados |
vector of values estimated. |
Details
calculaPerc = ((valor)/mean(observados))*100
return value
Description
this feature is designed to fix variables that its content was a command
Usage
retornaValor(valor)
Arguments
valor |
any variable |
Value
the variable converted to its value
Examples
a = 5
retornaValor(a)
Root Mean Square Error
Description
The root-mean-square error (RMSE) is a frequently used measure of the differences between values (sample and population values) predicted by a model or an estimator and the values actually observed.
Usage
rmse(observados, estimados)
Arguments
observados |
vector of values observed. |
estimados |
vector of regression model data. |
Details
rmse = sqrt(mean((observados - estimados)^2))
References
See https://en.wikipedia.org/wiki/Root-mean-square_deviation for more details.
Round Ages
Description
this function approaching the age to the nearest age as an integer
Usage
roundAge(plots, ages, inYears = F, firstAge = NaN)
Arguments
plots |
is list of plots |
ages |
is list of age |
inYears |
ages are in year? |
firstAge |
synchronize begin of ages with an age? what age? |
Value
will be returned a list of round ages
relative root mean square error
Description
relative root mean square error (RRMSE) is calculated by dividing the RMSE by the mean observed data
Usage
rrmse(observados, estimados)
Arguments
observados |
vector of values observed. |
estimados |
vector of regression model data. |
save function with Model
Description
save function with Model of type criaModeloGenerico or criaModeloExclusivo
Usage
salvaModelo(modelo, diretorio = "")
Arguments
modelo |
function with Model the save |
diretorio |
directory to save the file, if not informed saved in the work directory |
Data Separates
Description
divides the dataFrame as the percentage defined in percTraining enabling apply and measure the performance of the regression equation.
Usage
separaDados(dataFrame, fieldName, percTraining = 0.7, seed = NULL)
Arguments
dataFrame |
source of data |
fieldName |
column of dataFrame that will be applied regression |
percTraining |
percentage that will be reserved for training (default 0.70) |
seed |
integer that determines how the sample is randomly chosen (default NULL) |
Standard Error of Estimate
Description
Measures the variability, or scatter of the observed values around the regression line
Usage
syx(observados, estimados, n, p)
Arguments
observados |
vector of values observed. |
estimados |
vector of values estimated. |
n |
the amount of values observed |
p |
the size of the vector of regression model data |
Standard Error of Estimate Percentage
Description
Measures the variability, or scatter of the observed values around the regression line
Usage
syxPerc(syx, observados)
Arguments
syx |
result of the function syx(Standard Error of Estimate). |
observados |
vector of values observed. |
Check de type of Column
Description
this function returns the type of a column of a dataFrame, if it is numeric or character.
Usage
verificaTipoColuna(coluna)
Arguments
coluna |
column of dataframe |
Examples
ID_REGIAO <- c(1,2,3,4)
CD_PLANTIO <- c("ACD","CDB","CDC","CDD")
test <- data.frame(ID_REGIAO,CD_PLANTIO)
verificaTipoColuna(test$ID_REGIAO)
whichmedian
Description
vector position that has its closest median value
Usage
whichmedian(x)
Arguments
x |
a vector of numbers |
Value
vector position that has its closest median value
Examples
dados <- c(1,2,3,4,9,5,6)
whichmedian(dados)