P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2025-07-12 03:02:00 +02:00
Code Releases Activity

(v0.7.1.9063) septic_patients -> example_isolates

Browse Source
This commit is contained in:
dr. M.S. (Matthijs) Berends
2019-08-27 16:45:42 +02:00
parent 7a6fce4eb8
commit 93be16484b
92 changed files with 1143 additions and 818 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
vignettes/resistance_predict.Rmd
View File

@ -42,15 +42,15 @@ Our package contains a function `resistance_predict()`, which takes the same inp
It is basically as easy as:
```{r, eval = FALSE}
# resistance prediction of piperacillin/tazobactam (TZP):
resistance_predict(tbl = septic_patients, col_date = "date", col_ab = "TZP", model = "binomial")
resistance_predict(tbl = example_isolates, col_date = "date", col_ab = "TZP", model = "binomial")
# or:
septic_patients %>%
example_isolates %>%
resistance_predict(col_ab = "TZP",
model "binomial")
# to bind it to object 'predict_TZP' for example:
predict_TZP <- septic_patients %>%
predict_TZP <- example_isolates %>%
resistance_predict(col_ab = "TZP",
model = "binomial")
```
@ -60,7 +60,7 @@ The function will look for a date column itself if `col_date` is not set.
When running any of these commands, a summary of the regression model will be printed unless using `resistance_predict(..., info = FALSE)`.
```{r, echo = FALSE}
predict_TZP <- septic_patients %>%
predict_TZP <- example_isolates %>%
resistance_predict(col_ab = "TZP", model = "binomial")
```
@ -92,7 +92,7 @@ ggplot_rsi_predict(predict_TZP, ribbon = FALSE)
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 %>%
example_isolates %>%
filter(mo_gramstain(mo, language = NULL) == "Gram-positive") %>%
resistance_predict(col_ab = "VAN", year_min = 2010, info = FALSE, model = "binomial") %>%
ggplot_rsi_predict()
@ -113,7 +113,7 @@ Valid values are:
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 the observed years:
```{r}
septic_patients %>%
example_isolates %>%
filter(mo_gramstain(mo, language = NULL) == "Gram-positive") %>%
resistance_predict(col_ab = "VAN", year_min = 2010, info = FALSE, model = "linear") %>%
ggplot_rsi_predict()