Type: | Package |
Title: | Easy Polynomial Regression |
Version: | 3.0 |
Date: | 2017-11-14 |
Author: | Emmanuel Arnhold |
Maintainer: | Emmanuel Arnhold <emmanuelarnhold@yahoo.com.br> |
Description: | Performs analysis of polynomial regression in simple designs with quantitative treatments. |
Depends: | R (≥ 3.0.0) |
Imports: | car, lme4 |
License: | GPL-2 |
NeedsCompilation: | no |
Packaged: | 2017-11-15 09:33:10 UTC; emmanuel |
Repository: | CRAN |
Date/Publication: | 2017-11-16 22:15:46 UTC |
Easy Polynomial Regression
Description
Performs analysis of polynomial regression in simple designs with quantitative treatments.
Details
Package: | epr |
Type: | Package |
Version: | 3.0 |
Date: | 2017-11-14 |
License: | GPL-2 |
Author(s)
Emmanuel Arnhold <emmanuelarnhold@yahoo.com.br>
References
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.
Examples
# analysis in completely randomized design
data(data1)
r1=pr2(data1)
names(r1)
r1
r1[1]
pr1(data1)
# analysis in randomized block design
data(data2)
r2=pr2(data2, design=2)
r2
# analysis in latin square design
data(data3)
r3=pr2(data3, design=3)
r3
# analysis in several latin squares
data(data4)
r4=pr2(data4, design=4)
r4
Analysis of bronken line regression
Description
The function performs analysis of broken line regression.
Usage
bl(data, xlab="Explanatory Variable", ylab="Response Variable", position=1)
Arguments
data |
data is a data.frame The first column should contain the treatments (explanatory variable) and the second column the response variable |
xlab |
name of explanatory variable |
ylab |
name of response variable |
position |
position of equation in the graph top=1 bottomright=2 bottom=3 bottomleft=4 left=5 topleft=6 (default) topright=7 right=8 center=9 |
Value
Returns coefficients of the models, t test for coefficients, R squared, adjusted R squared, AIC and BIC, normality test and residuals.
Author(s)
Emmanuel Arnhold <emmanuelarnhold@yahoo.com.br>
See Also
lm, ea1(easyanova package), pr2, regplot
Examples
x=c(0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08,0.09,0.10)
y=c(5.5,4,3.2,2.1,1,0.1,1.6,2.2,3,5)
y=y/100
data=data.frame(x,y)
### bl(data)
data1: Sampaio (2010): page 134
Description
Quantitative treatments in completely randomized design.
Usage
data(data1)
Format
A data frame with 24 observations on the following 2 variables.
treatment
a numeric vector
gain
a numeric vector
References
SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.
Examples
data(data1)
summary(data1)
data2: Kaps and Lamberson (2009): page 434
Description
Quantitative treatments in randomizad block design.
Usage
data(data2)
Format
A data frame with 25 observations on the following 3 variables.
protein_level
a numeric vector
litter
a factor with levels
l1
l2
l3
l4
l5
feed_conversion
a numeric vector
References
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
Examples
data(data2)
summary(data2)
data3: fictional example
Description
Quantitative treatments in latin square design.
Usage
data(data3)
Format
A data frame with 25 observations on the following 4 variables.
treatment
a numeric vector
animal
a factor with levels
a1
a2
a3
a4
a5
period
a factor with levels
p1
p2
p3
p4
p5
milk_fat
a numeric vector
Examples
data(data3)
summary(data3)
data4: fictional example
Description
Quantitative treatments in several latin squares design.
Usage
data(data4)
Format
A data frame with 50 observations on the following 5 variables.
treatment
a numeric vector
square
a numeric vector
animal
a factor with levels
a1
a2
a3
a4
a5
period
a factor with levels
p1
p2
p3
p4
p5
milk_fat
a numeric vector
Examples
data(data4)
summary(data4)
data5: fictional example
Description
Quantitative treatments and three response variable.
Usage
data(data5)
Format
A data frame with 24 observations on the following 4 variables.
treatments
a numeric vector
variable1
a numeric vector
variable2
a numeric vector
variable3
a numeric vector
Examples
data(data5)
summary(data5)
Analysis of polynomial regression
Description
The function performs analysis of polynomial regression in simple designs with quantitative treatments. The function also performs with randon factor in mixed models.
Usage
pr1(data, mixed = FALSE, digits = 6)
Arguments
data |
data is a data.frame The first column should contain the treatments (explanatory variable) and the remaining columns the response variables (fixed model). The first column should contain the treatments (explanatory variable), second colunm should contais de random variable and the remaining columns the response variables (mixed model). |
mixed |
FALSE = fixed model TRUE = mixed model |
digits |
6 = defalt (number of digits) |
Value
Returns coefficients of the models, t test for coefficients, R squared, adjusted R squared, AIC, BIC and the maximum (or minimum) values of y and critical point of x, residuals and normality test.
Author(s)
Emmanuel Arnhold <emmanuelarnhold@yahoo.com.br>
See Also
lm, ea1(easyanova package), pr2, regplot
Examples
# data
data(data5)
# linear and quadratic models
results1=pr1(data5)
results1
# analysis in completely randomized design
data(data1)
r1=pr2(data1)
names(r1)
r1
r1[1]
pr1(data1)
# analysis in randomized block design
data(data2)
r2=pr2(data2, design=2)
r2
pr1(data2, mixed=TRUE)
Analysis of polynomial regression
Description
The function performs analysis of polynomial regression in simple designs with quantitative treatments. This function performs analysis the lack of fit .
Usage
pr2(data, design = 1, list = FALSE, type = 2)
Arguments
data |
data is a data.frame data frame with two columns, treatments and response (completely randomized design) data frame with three columns, treatments, blocks and response (randomized block design) data frame with four columns, treatments, rows, cols and response (latin square design) data frame with five columns, treatments, square, rows, cols and response (several latin squares) |
design |
1 = completely randomized design 2 = randomized block design 3 = latin square design 4 = several latin squares |
list |
FALSE = a single response variable TRUE = multivariable response |
type |
type is form of obtain sum of squares 1 = a sequential sum of squares 2 = a partial sum of squares |
Details
The response and the treatments must be numeric. Other variables can be numeric or factors.
Value
Returns analysis of variance, models, t test for coefficients and R squared and adjusted R squared.
Author(s)
Emmanuel Arnhold <emmanuelarnhold@yahoo.com.br>
References
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.
See Also
lm, lme(package nlme), ea1(package easyanova), pr1, regplot
Examples
# analysis in completely randomized design
data(data1)
r1=pr2(data1)
names(r1)
r1
r1[1]
# analysis in randomized block design
data(data2)
r2=pr2(data2, design=2)
r2
# analysis in latin square design
data(data3)
r3=pr2(data3, design=3)
r3
# analysis in several latin squares
data(data4)
r4=pr2(data4, design=4)
r4
# data
treatments=rep(c(0.5,1,1.5,2,2.5,3), c(3,3,3,3,3,3))
r1=rnorm(18,60,3)
r2=r1*1:18
r3=r1*18:1
r4=r1*c(c(1:10),10,10,10,10,10,10,10,10)
data6=data.frame(treatments,r1,r2,r3, r4)
# use the argument list = TRUE
pr2(data6, design=1, list=TRUE)
Tests for model identity and parameter
Description
The function performs tests of parameters and models.
Usage
r.test(data, digits=6)
Arguments
data |
data is a data.frame The first column should contain the x (explanatory variable) second treatments and the remaining columns the response variables. |
digits |
number of digits (defalt = 6) |
Value
Returns coefficients of the models, t test for coefficients and tests for parameters and models.
Author(s)
Emmanuel Arnhold <emmanuelarnhold@yahoo.com.br>
See Also
lm, ea1(easyanova package), pr2, regplot
Examples
x=c(1,1,1,2,2,2,3,3,3,4,4,4)
y=c(5,5.3,6,8,8.9,12,14,18,25,25,29,32)
t=c("a1","a2","a3","a1","a2","a3","a1","a2","a3","a1","a2","a3")
data=data.frame(x,t,y)
r.test(data)
Graphics of the regression
Description
The function generates the scatter plot with the regression equation.
Usage
regplot(data, xlab="Explanatory Variable", ylab="Response Variable",
position=6, mean=TRUE, digits=4)
Arguments
data |
data is a data.frame the first column contain the explanatory variable the others columns contain the responses variables |
xlab |
name of variable x |
ylab |
name of variable y |
position |
position of equation in the graph top=1 bottomright=2 bottom=3 bottomleft=4 left=5 topleft=6 (default) topright=7 right=8 center=9 |
mean |
TRUE = scatter plots with averages (default) FALSE = scatter plots with all data |
digits |
number of digits |
Value
The function generates the scatter plot with the regression equation.
Author(s)
Emmanuel Arnhold <emmanuelarnhold@yahoo.com.br>
See Also
lm, lme, ea1(easyanova package), pr2, pr2, dplot(ds package)
Examples
# data
data(data5)
d1=data5[,c(1,2)]
regplot(d1, position=8)
d2=data5[,c(1,3)]
regplot(d2, position=8)
d3=data5[,c(1,4)]
regplot(d3, position=8)