Splitting in polynomials for double factorial in DIC and DBC. Note that f1 must always be qualitative and f2 must always be quantitative. This function is an easier way to visualize trends for dual factor schemes with a quantitative and a qualitative factor.

quant.fat2.desd(factors = list(f1, f2, block), response, dec = 3)

## Arguments

factors

Define f1 and f2 and/or block factors in list form. Please note that in the list it is necessary to write f1, f2 and block. See example.

response

response variable

dec

Number of cells

## Value

Returns the coefficients of the linear, quadratic and cubic models, the p-values of the t test for each coefficient (p.value.test) and the p-values for the linear, quadratic, cubic model splits and the regression deviations.

## Author

Gabriel Danilo Shimizu, shimizu@uel.br

## Examples

library(AgroR)
data(cloro)
quant.fat2.desd(factors = list(f1=cloro$f1, f2=rep(c(1:4),e=5,2), block=cloro$bloco),
response=cloro$resp) #> ========================================================== #> Desdobramento #> ========================================================== #> Df Sum Sq Mean Sq F value Pr(>F) #> f1:f2: IN 3 75470 25157 5.676 0.003625 ** #> f1:f2: NI 3 493181 164394 37.092 6.882e-10 *** #> Residuals 28 124098 4432 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> #> ========================================================== #> #> Warning: NaNs produced #> Warning: NaNs produced #>$IN
#> intercept    224.700   305.950  1228.200
#> beta1          5.500   -75.750 -1542.567
#> beta2                   16.250   675.000
#> beta3                            -87.833
#> p.value.test
#> pb0            0.000     0.020     0.000
#> pb1            0.775     0.497     0.002
#> pb2                      0.460     0.002
#> pb3                                0.003
#> p.value.mod
#> linear         0.683     0.683     0.683
#> cubic                              0.000
#> deviation      0.001     0.000       NaN
#>
#> \$NI
#> intercept    367.600   -26.650 -1791.000
#> beta1        -67.740   326.510  3132.667
#> beta2                  -78.850 -1339.100
#> beta3                            168.033
#> p.value.test
#> pb0            0.000     0.874     0.000
#> pb1            0.041     0.046     0.000
#> pb2                      0.017     0.000
#> pb3                                0.000
#> p.value.mod
#> linear         0.000     0.000     0.000