as.rsi warning, site update

vignettes/Predict.Rmd

@@ -16,8 +16,96 @@ editor_options:
 ```{r setup, include = FALSE, results = 'markup'}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#"
+  comment = "#",
+  fig.width = 7.5,
+  fig.height = 4.5
 )
 ```
 
-*(will be available soon)*
+## Needed R packages
+As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the [tidyverse packages](https://www.tidyverse.org) [`dplyr`](https://dplyr.tidyverse.org/) and [`ggplot2`](https://ggplot2.tidyverse.org) by [Dr Hadley Wickham](https://www.linkedin.com/in/hadleywickham/). The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.
+
+Our `AMR` package depends on these packages and even extends their use and functions.
+
+```{r lib packages, message = FALSE}
+library(dplyr)
+library(ggplot2)
+library(AMR)
+
+# (if not yet installed, install with:)
+# install.packages(c("tidyverse", "AMR"))
+```
+
+## Prediction analysis
+Our package contains a function `resistance_predict()`, which takes the same input as functions for [other AMR analysis](./articles/AMR.html). Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.
+
+It is basically as easy as:
+```{r, eval = FALSE}
+# resistance prediction of piperacillin/tazobactam (pita):
+resistance_predict(tbl = septic_patients, col_date = "date", col_ab = "pita")
+
+# or:
+septic_patients %>% 
+  resistance_predict(col_ab = "pita")
+
+# to bind it to object 'predict_pita' for example:
+predict_pita <- septic_patients %>% 
+  resistance_predict(col_ab = "pita")
+```
+
+```{r, echo = FALSE}
+predict_pita <- septic_patients %>% 
+  resistance_predict(col_ab = "pita")
+```
+
+The function will look for a data column itself if `col_date` is not set. The result is nothing more than a `data.frame`, containing the years, number of observations, actual observed resistance, the estimated resistance and the standard error below and above the estimation:
+```{r}
+predict_pita
+```
+
+The function `plot` is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:
+
+```{r}
+plot(predict_pita)
+```
+
+We also support the `ggplot2` package with the function `ggplot_rsi_predict()`:
+
+```{r}
+library(ggplot2)
+ggplot_rsi_predict(predict_pita)
+```
+
+### Choosing the right model
+
+Resistance is not easily predicted; if we look at vancomycin resistance in Gram positives, the spread (i.e. standard error) is enormous:
+
+```{r}
+septic_patients %>%
+  filter(mo_gramstain(mo) == "Gram positive") %>%
+  resistance_predict(col_ab = "vanc", year_min = 2010, info = FALSE) %>% 
+  plot()
+```
+
+Vancomycin resistance could be 100% in ten years, but might also stay around 0%. 
+
+You can define the model with the `model` parameter. The default model is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.
+
+Valid values are:
+
+| Input values                           | Function used by R            | Type of model                                       |
+|----------------------------------------|-------------------------------|-----------------------------------------------------|
+| `"binomial"` or `"binom"` or `"logit"` | `glm(..., family = binomial)` | Generalised linear model with binomial distribution |
+| `"loglin"` or `"poisson"`              | `glm(..., family = poisson)`  | Generalised linear model with poisson distribution  |
+| `"lin"` or `"linear"`                  | `lm()`                        | Linear model                                        |
+
+For the vancomycin resistance in Gram positive bacteria, a linear model might be more appropriate since no (left half of a) binomial distribution is to be expected based on observed years:
+
+```{r}
+septic_patients %>%
+  filter(mo_gramstain(mo) == "Gram positive") %>%
+  resistance_predict(col_ab = "vanc", year_min = 2010, info = FALSE, model = "linear") %>% 
+  plot()
+```
+
+This seems more likely, doesn't it?