Skip to content
Projects
Groups
Snippets
Help
Loading...
Help
Support
Submit feedback
Contribute to GitLab
Sign in / Register
Toggle navigation
wzCoop
Project overview
Project overview
Details
Activity
Releases
Cycle Analytics
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Charts
Issues
0
Issues
0
List
Boards
Labels
Milestones
Merge Requests
0
Merge Requests
0
CI / CD
CI / CD
Pipelines
Jobs
Schedules
Charts
Wiki
Wiki
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Charts
Create a new issue
Jobs
Commits
Issue Boards
Open sidebar
Walmes Marques Zeviani
wzCoop
Commits
dc619dd3
Commit
dc619dd3
authored
Feb 25, 2017
by
Walmes Marques Zeviani
Browse files
Options
Browse Files
Download
Email Patches
Plain Diff
Correçoes no texto.
parent
f67b8b5d
Pipeline
#8633
passed with stage
in 5 minutes and 41 seconds
Changes
1
Pipelines
1
Show whitespace changes
Inline
Side-by-side
Showing
1 changed file
with
53 additions
and
6 deletions
+53
-6
vignettes/sensitivity_ake_b.Rmd
vignettes/sensitivity_ake_b.Rmd
+53
-6
No files found.
vignettes/sensitivity_ake_b.Rmd
View file @
dc619dd3
...
...
@@ -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
...
...
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment