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(v0.7.1.9030) eucast_rules() fix

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dr. M.S. (Matthijs) Berends
2019-08-08 15:52:07 +02:00
parent f67c739892
commit 22a206ffd8
70 changed files with 470 additions and 420 deletions
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14
vignettes/resistance_predict.Rmd
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@ -42,15 +42,17 @@ 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")
resistance_predict(tbl = septic_patients, col_date = "date", col_ab = "TZP", model = "binomial")
# or:
septic_patients %>%
resistance_predict(col_ab = "TZP")
resistance_predict(col_ab = "TZP",
model "binomial")
# to bind it to object 'predict_TZP' for example:
predict_TZP <- septic_patients %>%
resistance_predict(col_ab = "TZP")
resistance_predict(col_ab = "TZP",
model = "binomial")
```
The function will look for a date column itself if `col_date` is not set.
@ -59,7 +61,7 @@ When running any of these commands, a summary of the regression model will be pr
```{r, echo = FALSE}
predict_TZP <- septic_patients %>%
resistance_predict(col_ab = "TZP")
resistance_predict(col_ab = "TZP", model = "binomial")
```
This text is only a printed summary - the actual result (output) of the function is a `data.frame` containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:
@ -92,13 +94,13 @@ Resistance is not easily predicted; if we look at vancomycin resistance in Gram
```{r}
septic_patients %>%
filter(mo_gramstain(mo, language = NULL) == "Gram-positive") %>%
resistance_predict(col_ab = "VAN", year_min = 2010, info = FALSE) %>%
resistance_predict(col_ab = "VAN", year_min = 2010, info = FALSE, model = "binomial") %>%
ggplot_rsi_predict()
```
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.
You can define the model with the `model` parameter. The model chosen above 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: