Linear regression analysis for significant interaction of an experiment with two factors, one quantitative and one qualitative

polynomial2(
fator1,
resp,
fator2,
color = NA,
grau = NA,
ylab = "Response",
xlab = "Independent",
theme = theme_classic(),
se = FALSE,
point = "mean_sd",
legend.title = "Treatments",
posi = "top",
textsize = 12,
ylim = NA,
family = "sans",
width.bar = NA,
pointsize = 3,
linesize = 0.8,
separate = c("(\"", "\")"),
n = NA,
DFres = NA,
SSq = NA
)

## Arguments

fator1

Numeric or complex vector with factor 1 levels

resp

Numerical vector containing the response of the experiment.

fator2

Numeric or complex vector with factor 2 levels

color

Graph color (default is NA)

grau

Degree of the polynomial (1,2 or 3)

ylab

Dependent variable name (Accepts the expression() function)

xlab

Independent variable name (Accepts the expression() function)

theme

ggplot2 theme (default is theme_classic())

se

Adds confidence interval (default is FALSE)

point

Defines whether to plot all points ("all"), mean ("mean"), mean with standard deviation (default - "mean_sd") or mean with standard error ("mean_se").

legend.title

Title legend

posi

Legend position

textsize

Font size (default is 12)

ylim

y-axis scale

family

Font family (default is sans)

width.bar

width of the error bars of a regression graph.

pointsize

Point size (default is 4)

linesize

line size (Trendline and Error Bar)

separate

Separation between treatment and equation (default is c("(\"","\")"))

n

Number of decimal places for regression equations

DFres

Residue freedom degrees

SSq

Sum of squares of the residue

## Value

Returns two or more linear, quadratic or cubic regression analyzes.

## Author

Gabriel Danilo Shimizu, shimizu@uel.br

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

## Examples

dose=rep(c(0,0,0,2,2,2,4,4,4,6,6,6),3)
resp=c(8,7,5,23,24,25,30,34,36,80,90,80,
12,14,15,23,24,25,50,54,56,80,90,40,
12,14,15,3,4,5,50,54,56,80,90,40)
trat=rep(c("A","B","C"),e=12)
polynomial2(dose, resp, trat, grau=c(1,2,3))
#> Warning: NaNs produced
#>
#> ----------------------------------------------------
#> Regression Models
#> ----------------------------------------------------
#> $A #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 0.9333333 5.395430 0.1729859 8.661137e-01 #> x 11.9666667 1.441989 8.2987205 8.528803e-06 #> #>$B
#>               Estimate Std. Error   t value  Pr(>|t|)
#> (Intercept) 12.0833333  7.4460094 1.6227932 0.1390822
#> x            7.5416667  5.9788110 1.2613991 0.2388777
#> I(x^2)       0.3958333  0.9549306 0.4145153 0.6882023
#>
#> $C #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 13.666667 7.7064187 1.773413 0.11409087 #> x -34.861111 14.7845901 -2.357936 0.04610656 #> I(x^2) 18.833333 6.5334343 2.882609 0.02042957 #> I(x^3) -1.909722 0.7180032 -2.659769 0.02881582 #> #> #> ---------------------------------------------------- #> Anova #> ---------------------------------------------------- #>$Anova A
#>           Df      SSq       MSQ        F      p-value
#> Linear     1 8592.067 8592.0667 70.07576 1.401237e-08
#> Deviation  2 1155.600  577.8000  4.71246 1.877977e-02
#> Residual  24 2942.667  122.6111
#>
#> $Anova B #> Df SSq MSQ F p-value #> Linear 1 5900.41667 5900.41667 48.1230177 3.571320e-07 #> Quadratic 1 30.08333 30.08333 0.2453557 6.248694e-01 #> Deviation 1 150.41667 150.41667 1.2267784 2.790120e-01 #> Residual 24 2942.66667 122.61111 #> #>$Anova C
#>           Df          SSq       MSQ         F      p-value
#> Linear     1 7.150417e+03 7150.4167 58.317852 7.114165e-08
#> Quadratic  1 5.200833e+02  520.0833  4.241731 5.044762e-02
#> Cubic      1 1.260417e+03 1260.4167 10.279792 3.783350e-03
#> Deviation  0 4.547474e-13      -Inf      -Inf          NaN
#> Residual  24 2.942667e+03  122.6111
#>