Commit dc619dd3 by Walmes Marques Zeviani

### Correçoes no texto.

parent f67b8b5d
Pipeline #8633 passed with stage
in 5 minutes and 41 seconds
 ... ... @@ -32,6 +32,10 @@ library(wzCoop)  {r setup, include = FALSE} source("config/setup.R") tbn_ <- captioner(prefix = "Table") fgn_ <- captioner(prefix = "Figure") tbl_ <- function(label) tbn_(label, display = "cite") fgl_ <- function(label) fgn_(label, display = "cite")  # Experiment and Data Description ... ... @@ -141,11 +145,11 @@ xyplot(dm ~ dos^0.2 | iso, ylab = "Second diameter (mm)")  Figure r fgl_("dm-x-dos") (top) shows that doses are very skewed. Figure r fgl_("dm-x-dos") (bottom) shows that in the log-log scale r fgl_("dm-x-dos") (top) shows that doses are very skewed. r fgl_("dm-x-dos") (bottom) shows that in the log-log scale there isn't a linear relation between mean diameter and fungicide dose. Figure r fgl_("dm-x-dos0.2") shows that, under the transformed r fgl_("dm-x-dos0.2") shows that, under the transformed 5th-root scale, doses levels are close to equally spaced. In fact, 0.2 was found by minimization of the variance of distance between doses ($\sigma^2$) a power transformation ($p$) of dose rescaled to a unit ... ... @@ -187,12 +191,33 @@ plot(log(v) ~ p, type = "o") abline(v = op$minimum)  So$x^0.2$is the most equally spaced set obtained with a power So$x^{0.2}$is the most equally spaced set obtained with a power transformation. Equally spaced levels are beneficial beacause reduce problems related to leverage. # Half Effective Concentration (EC~50~) Estimation A cubic spline is function constructed of piecewise third-order polynomials which pass through a set of$m + 1$knots. These knots spans the observed domain of the continous factor$x$, so the set of knots is $$K = \{\xi_0, \xi_1, \ldots, \xi_m\}.$$ A function$s(x)$is a cubic spline if it is made of cubic polynomials$s_{i}(x)$in each interval$[x_{i-1}, x_{m}]$,$i = 1, \ldots, m$. Those adjacent cubic pylinomials pieces must bind and be smooth at the internal knots , so additional constrais are made to result in a composite continuous smooth function. Requering continous derivatives, we ensure that the resulting function is as smooth as possible. For natural splines, two aditional boundary conditions are made $$s^{''}_{1}(x) = 0, \quad s^{''}_{m}(x) = 0,$$ that is, the pieces at borders aren't cubic but instead linear. Natural cubic splines were used to estimate the half effective concentration (EC~50~). A non linear model is usually applied in this context but wasn't found a non linear model flexible enough to give a ... ... @@ -201,7 +226,7 @@ haven't a model equation, they are vey flexible and numerical root-finding algorithms can e used to compute EG~50~ based on a linear interpolated function on a predicted grid. Also, area under the sensibility curve (AUSC) were computed by numerical integration under dose studied domain. studied dose domain. {r} sen$iso <- factor(sen$iso, levels = sort(unique(sen$iso))) ... ... @@ -210,7 +235,7 @@ sen$doz <- sen$dos^0.2 # A data frame without dose. senu <- unique(subset(sen, select = c(ue, iso, fun, tra, hed, pop, yr))) select = c(ue, iso, fun, tra, hed, pop, yr, plot))) # Splines. library(splines) ... ... @@ -350,6 +375,28 @@ p <- xyplot(auc ~ pop | tra + fun, useOuterStrips(p)  {r} #----------------------------------------------------------------------- # Creates block, treatment cell and plants. ec$blk <- as.integer(as.integer(substr(ec$plot, 0, 1)) > 2) ec$cell <- with(ec, interaction(yr, blk, hed, tra, drop = TRUE)) # TODO FIXME: Falta o Paulo passar a coluna que indentifica as plantas # (1, 2, e 3) em cada cela experimental. # ec <- arrange(df = ec, yr, blk, hed, tra, iso, fun) # head(ec) # # Experimental units (plants) # ec$plnt <- with(ec, interaction(blk, pop, hed, tra, drop = TRUE)) # nlevels(ec\$plnt) # # xtabs(~plnt, ec) # # m0 <- lm() ` **** # Session information ... ...
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