quant.fat2.desd.RdSplitting 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)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 variable
Number of cells
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.
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
#> Linear Quadratic Cubic
#> 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
#> quadratic 0.284 0.284
#> cubic 0.000
#> deviation 0.001 0.000 NaN
#>
#> $NI
#> Linear Quadratic Cubic
#> 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
#> quadratic 0.000 0.000
#> cubic 0.000
#> deviation 0.000 0.000 NaN
#>