Replace challenger_R_org_Windows_64bits.org

src/R/challenger_R_org_Windows_64bits.org

@@ -170,9 +170,6 @@ Let's visually inspect how temperature affects malfunction:
 plot(data=data, Malfunction/Count ~ Temperature, ylim = c(0,1))
 #+end_src
 
-#+RESULTS:
-[[file:c:/Users/dondon/AppData/Local/Temp/babel-mb830V/figuremlknwM.png]]
-
 * Logistic regression
 
 Let's assume O-rings independently fail with the same probability
@@ -232,9 +229,6 @@ plot(tempv,rmv,type="l",ylim=c(0,1))
 points(data=data, Malfunction/Count ~ Temperature)
 #+end_src
 
-#+RESULTS:
-[[file:c:/Users/dondon/AppData/Local/Temp/babel-mb830V/figurePDN133.png]]
-
 This figure is very similar to the Figure 4 of Dalal /et al./ *I have
 managed to replicate the Figure 4 of the Dalal /et al./ article.* 
 
@@ -249,9 +243,6 @@ ggplot(data, aes(y=Malfunction/Count, x=Temperature)) +
       xlim(30,90) + ylim(0,1) + theme_bw()
 #+end_src
 
-#+RESULTS:
-[[file:c:/Users/dondon/AppData/Local/Temp/babel-mb830V/figureUmDY5S.png]]
-
 I don't get any warning from ~ggplot2~ indicating /"non-integer
 #successes in a binomial glm!"/ but this confidence region seems
 huge... It seems strange to me that the uncertainty grows so large for 
@@ -335,9 +326,6 @@ ggplot(data=data_flat, aes(y=Malfunction, x=Temperature)) +
        geom_point(alpha=.5, size=.5) + xlim(30,90) + ylim(0,1) + theme_bw()
 #+end_src
 
-#+RESULTS:
-[[file:c:/Users/dondon/AppData/Local/Temp/babel-mb830V/figureEStjqI.png]]
-
 This confidence interval seems much more reasonable (in accordance
 with the data) than the previous one. Let's check whether it
 corresponds to the prediction obtained when calling directly
@@ -375,7 +363,15 @@ pred_link
 #+end_src
 
 #+RESULTS:
-: Erreur : objet 'pred.link' introuvable
+: $fit
+:        1 
+: 1.616942 
+: 
+: $se.fit
+: [1] 1.659473
+: 
+: $residual.scale
+: [1] 1
 
 #+begin_src R :results output :session *R* :exports both
 logistic_reg$family$linkinv(pred_link$fit)