Add plot

doc/drc.Rmd

@@ -32,7 +32,7 @@ str(ryegrass)
 ?ryegrass
 ```
 
-The workhorse of the `drc` is function `drm` (Dose-Response Model) and it works pretty similarly to `lm` which we used to fit linear model. The only new thing is `fct` argument. `fct` defines the exact function to be used and some sane initial values for parameters. For four parameter logistic regression, we need to set `fct = LL.4` (log-logistic with 4 parameters, the extra "log" is just to denote that x-axis is in log scale; there is alos LL.3 for instance, this sets slope to be 1).
+The workhorse of the `drc` is function `drm` (Dose-Response Model) and it works pretty similarly to `lm` which we used to fit linear model. The only new thing is `fct` argument. `fct` defines the exact function to be used and some sane initial values for parameters. For four parameter logistic regression, we need to set `fct = LL.4` (log-logistic with 4 parameters, the extra "log" is just to denote that x-axis is in log scale; there is also LL.3 for instance, this sets slope to be 1).
 
 ```{r}
 mod = drm(rootl ~ conc, data = ryegrass, fct = LL.4())
@@ -47,14 +47,17 @@ mod = drm(rootl ~ conc, data = ryegrass,
 plot(mod, type = 'all')
 ```
 
-We can get a summary of the model parameters using the `summary` function and
-calculate the IC~10~ (dose that gives 10% of effect), IC~20~, IC~50~, IC~90~
-etc with the `ED` function.
+![](tasks/drm_ryegrass.png)
+
+We can get a summary of the model parameters using the `summary` function
 
 ```{r}
 summary(mod)
 ```
 
+We can calculate the IC~10~ (dose that gives 10% of effect), IC~20~, IC~50~,
+IC~90~ etc with the `ED` function.
+
 ```{r}
 #interval = "delta" gives confidence intervals at a default 95% level.  
 ED(mod, c(10,20,50, 90), interval="delta")

doc/drc.md

@@ -29,7 +29,7 @@ only new thing is `fct` argument. `fct` defines the exact function to be
 used and some sane initial values for parameters. For four parameter
 logistic regression, we need to set `fct = LL.4` (log-logistic with 4
 parameters, the extra “log” is just to denote that x-axis is in log
-scale; there is alos LL.3 for instance, this sets slope to be 1).
+scale; there is also LL.3 for instance, this sets slope to be 1).
 
     mod = drm(rootl ~ conc, data = ryegrass, fct = LL.4())
 
@@ -44,10 +44,10 @@ explain four-parameter logistic regression:
 
 ![](drc_files/figure-markdown_strict/unnamed-chunk-4-1.png)
 
+![](tasks/drm_ryegrass.png)
+
 We can get a summary of the model parameters using the `summary`
-function and calculate the IC<sub>10</sub> (dose that gives 10% of
-effect), IC<sub>20</sub>, IC<sub>50</sub>, IC<sub>90</sub> etc with the
-`ED` function.
+function
 
     summary(mod)
 
@@ -68,6 +68,10 @@ effect), IC<sub>20</sub>, IC<sub>50</sub>, IC<sub>90</sub> etc with the
     ## 
     ##  0.5196256 (20 degrees of freedom)
 
+We can calculate the IC<sub>10</sub> (dose that gives 10% of effect),
+IC<sub>20</sub>, IC<sub>50</sub>, IC<sub>90</sub> etc with the `ED`
+function.
+
     #interval = "delta" gives confidence intervals at a default 95% level.  
     ED(mod, c(10,20,50, 90), interval="delta")