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NEWS.md
1
NEWS.md
@ -30,6 +30,7 @@
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* Changed default settings for `age_groups()`, to let groups of fives and tens end with 100+ instead of 120+
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* Fix for `freq()` for when all values are `NA`
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* Fix for `first_isolate()` for when dates are missing
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* Improved speed of `guess_ab_col()`
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#### Other
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* Support for R 3.6.0
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@ -69,13 +69,18 @@ guess_ab_col <- function(x = NULL, search_string = NULL, verbose = FALSE) {
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if (search_string %in% colnames(x)) {
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ab_result <- search_string
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} else {
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# sort colnames on length - longest first
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cols <- colnames(x[, x %>% colnames() %>% nchar() %>% order() %>% rev()])
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df_trans <- data.frame(cols = cols,
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abs = suppressWarnings(as.ab(cols)),
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stringsAsFactors = FALSE)
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ab_result <- df_trans[which(df_trans$abs == as.ab(search_string)), "cols"]
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ab_result <- ab_result[!is.na(ab_result)][1L]
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search_string.ab <- suppressWarnings(as.ab(search_string))
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if (search_string.ab %in% colnames(x)) {
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ab_result <- colnames(x)[colnames(x) == search_string.ab][1L]
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} else {
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# sort colnames on length - longest first
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cols <- colnames(x[, x %>% colnames() %>% nchar() %>% order() %>% rev()])
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df_trans <- data.frame(cols = cols,
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abs = suppressWarnings(as.ab(cols)),
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stringsAsFactors = FALSE)
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ab_result <- df_trans[which(df_trans$abs == search_string.ab), "cols"]
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ab_result <- ab_result[!is.na(ab_result)][1L]
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}
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}
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if (length(ab_result) == 0) {
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@ -320,7 +320,9 @@ rsi_predict <- resistance_predict
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#' @importFrom dplyr filter
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#' @importFrom graphics plot axis arrows points
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#' @rdname resistance_predict
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plot.resistance_predict <- function(x, main = paste("Resistance prediction of", attributes(x)$ab), ...) {
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plot.resistance_predict <- function(x, main = paste("Resistance Prediction of", x_name), ...) {
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x_name <- paste0(ab_name(attributes(x)$ab), " (", attributes(x)$ab, ")")
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if (attributes(x)$I_as_S == TRUE) {
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ylab <- "%R"
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} else {
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@ -359,14 +361,15 @@ plot.resistance_predict <- function(x, main = paste("Resistance prediction of",
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#' @importFrom dplyr filter
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#' @export
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ggplot_rsi_predict <- function(x,
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main = paste("Resistance prediction of", attributes(x)$ab),
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main = paste("Resistance Prediction of", x_name),
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ribbon = TRUE,
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...) {
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if (!"resistance_predict" %in% class(x)) {
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stop("`x` must be a resistance prediction model created with resistance_predict().")
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}
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x_name <- paste0(ab_name(attributes(x)$ab), " (", attributes(x)$ab, ")")
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if (attributes(x)$I_as_S == TRUE) {
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ylab <- "%R"
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} else {
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@ -192,7 +192,7 @@
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<h1>How to predict antimicrobial resistance</h1>
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<h4 class="author">Matthijs S. Berends</h4>
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<h4 class="date">12 May 2019</h4>
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<h4 class="date">13 May 2019</h4>
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<div class="hidden name"><code>resistance_predict.Rmd</code></div>
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@ -240,52 +240,52 @@
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#>
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#> Deviance Residuals:
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#> Min 1Q Median 3Q Max
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#> -2.9203 -1.3066 0.0166 0.7641 3.1984
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#> -2.6817 -1.4087 -0.5657 0.9672 3.5728
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#>
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#> Coefficients:
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#> Estimate Std. Error z value Pr(>|z|)
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#> (Intercept) -222.51053 45.94675 -4.843 1.28e-06 ***
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#> year 0.10973 0.02284 4.805 1.55e-06 ***
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#> (Intercept) -224.39872 48.03354 -4.672 2.99e-06 ***
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#> year 0.11061 0.02388 4.633 3.61e-06 ***
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#> ---
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#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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#>
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#> (Dispersion parameter for binomial family taken to be 1)
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#>
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#> Null deviance: 59.763 on 14 degrees of freedom
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#> Residual deviance: 35.261 on 13 degrees of freedom
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#> AIC: 93.537
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#> Null deviance: 61.512 on 14 degrees of freedom
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#> Residual deviance: 38.692 on 13 degrees of freedom
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#> AIC: 95.212
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#>
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#> Number of Fisher Scoring iterations: 4</code></pre>
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<p>This text is only a printed summary - the actual result (output) of the function is a <code>data.frame</code> containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:</p>
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<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1">predict_TZP</a>
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<a class="sourceLine" id="cb4-2" title="2"><span class="co">#> year value se_min se_max observations observed estimated</span></a>
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<a class="sourceLine" id="cb4-3" title="3"><span class="co">#> 1 2003 0.06250000 NA NA 32 0.06250000 0.06179057</span></a>
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<a class="sourceLine" id="cb4-4" title="4"><span class="co">#> 2 2004 0.08536585 NA NA 82 0.08536585 0.06846623</span></a>
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<a class="sourceLine" id="cb4-5" title="5"><span class="co">#> 3 2005 0.10000000 NA NA 60 0.10000000 0.07580483</span></a>
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<a class="sourceLine" id="cb4-6" title="6"><span class="co">#> 4 2006 0.05084746 NA NA 59 0.05084746 0.08385921</span></a>
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<a class="sourceLine" id="cb4-7" title="7"><span class="co">#> 5 2007 0.12121212 NA NA 66 0.12121212 0.09268356</span></a>
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<a class="sourceLine" id="cb4-8" title="8"><span class="co">#> 6 2008 0.04166667 NA NA 72 0.04166667 0.10233276</span></a>
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<a class="sourceLine" id="cb4-9" title="9"><span class="co">#> 7 2009 0.01639344 NA NA 61 0.01639344 0.11286156</span></a>
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<a class="sourceLine" id="cb4-10" title="10"><span class="co">#> 8 2010 0.09433962 NA NA 53 0.09433962 0.12432363</span></a>
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<a class="sourceLine" id="cb4-11" title="11"><span class="co">#> 9 2011 0.18279570 NA NA 93 0.18279570 0.13677030</span></a>
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<a class="sourceLine" id="cb4-12" title="12"><span class="co">#> 10 2012 0.30769231 NA NA 65 0.30769231 0.15024926</span></a>
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<a class="sourceLine" id="cb4-13" title="13"><span class="co">#> 11 2013 0.08620690 NA NA 58 0.08620690 0.16480299</span></a>
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<a class="sourceLine" id="cb4-14" title="14"><span class="co">#> 12 2014 0.15000000 NA NA 60 0.15000000 0.18046706</span></a>
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<a class="sourceLine" id="cb4-15" title="15"><span class="co">#> 13 2015 0.27272727 NA NA 55 0.27272727 0.19726831</span></a>
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<a class="sourceLine" id="cb4-16" title="16"><span class="co">#> 14 2016 0.25000000 NA NA 84 0.25000000 0.21522295</span></a>
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<a class="sourceLine" id="cb4-17" title="17"><span class="co">#> 15 2017 0.16279070 NA NA 86 0.16279070 0.23433471</span></a>
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<a class="sourceLine" id="cb4-18" title="18"><span class="co">#> 16 2018 0.25459302 0.2223385 0.2868476 NA NA 0.25459302</span></a>
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<a class="sourceLine" id="cb4-19" title="19"><span class="co">#> 17 2019 0.27597143 0.2381174 0.3138255 NA NA 0.27597143</span></a>
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<a class="sourceLine" id="cb4-20" title="20"><span class="co">#> 18 2020 0.29842630 0.2545398 0.3423128 NA NA 0.29842630</span></a>
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<a class="sourceLine" id="cb4-21" title="21"><span class="co">#> 19 2021 0.32189595 0.2716308 0.3721611 NA NA 0.32189595</span></a>
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<a class="sourceLine" id="cb4-22" title="22"><span class="co">#> 20 2022 0.34630028 0.2894072 0.4031934 NA NA 0.34630028</span></a>
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<a class="sourceLine" id="cb4-23" title="23"><span class="co">#> 21 2023 0.37154107 0.3078773 0.4352048 NA NA 0.37154107</span></a>
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<a class="sourceLine" id="cb4-24" title="24"><span class="co">#> 22 2024 0.39750288 0.3270414 0.4679643 NA NA 0.39750288</span></a>
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<a class="sourceLine" id="cb4-25" title="25"><span class="co">#> 23 2025 0.42405472 0.3468903 0.5012191 NA NA 0.42405472</span></a>
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<a class="sourceLine" id="cb4-26" title="26"><span class="co">#> 24 2026 0.45105237 0.3674044 0.5347004 NA NA 0.45105237</span></a>
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<a class="sourceLine" id="cb4-27" title="27"><span class="co">#> 25 2027 0.47834130 0.3885523 0.5681303 NA NA 0.47834130</span></a>
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<a class="sourceLine" id="cb4-28" title="28"><span class="co">#> 26 2028 0.50576012 0.4102900 0.6012302 NA NA 0.50576012</span></a>
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<a class="sourceLine" id="cb4-29" title="29"><span class="co">#> 27 2029 0.53314434 0.4325600 0.6337287 NA NA 0.53314434</span></a></code></pre></div>
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<a class="sourceLine" id="cb4-3" title="3"><span class="co">#> 1 2003 0.06250000 NA NA 32 0.06250000 0.05486389</span></a>
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<a class="sourceLine" id="cb4-4" title="4"><span class="co">#> 2 2004 0.08536585 NA NA 82 0.08536585 0.06089002</span></a>
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<a class="sourceLine" id="cb4-5" title="5"><span class="co">#> 3 2005 0.05000000 NA NA 60 0.05000000 0.06753075</span></a>
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<a class="sourceLine" id="cb4-6" title="6"><span class="co">#> 4 2006 0.05084746 NA NA 59 0.05084746 0.07483801</span></a>
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<a class="sourceLine" id="cb4-7" title="7"><span class="co">#> 5 2007 0.12121212 NA NA 66 0.12121212 0.08286570</span></a>
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<a class="sourceLine" id="cb4-8" title="8"><span class="co">#> 6 2008 0.04166667 NA NA 72 0.04166667 0.09166918</span></a>
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<a class="sourceLine" id="cb4-9" title="9"><span class="co">#> 7 2009 0.01639344 NA NA 61 0.01639344 0.10130461</span></a>
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<a class="sourceLine" id="cb4-10" title="10"><span class="co">#> 8 2010 0.05660377 NA NA 53 0.05660377 0.11182814</span></a>
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<a class="sourceLine" id="cb4-11" title="11"><span class="co">#> 9 2011 0.18279570 NA NA 93 0.18279570 0.12329488</span></a>
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<a class="sourceLine" id="cb4-12" title="12"><span class="co">#> 10 2012 0.30769231 NA NA 65 0.30769231 0.13575768</span></a>
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<a class="sourceLine" id="cb4-13" title="13"><span class="co">#> 11 2013 0.06896552 NA NA 58 0.06896552 0.14926576</span></a>
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<a class="sourceLine" id="cb4-14" title="14"><span class="co">#> 12 2014 0.10000000 NA NA 60 0.10000000 0.16386307</span></a>
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<a class="sourceLine" id="cb4-15" title="15"><span class="co">#> 13 2015 0.23636364 NA NA 55 0.23636364 0.17958657</span></a>
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<a class="sourceLine" id="cb4-16" title="16"><span class="co">#> 14 2016 0.22619048 NA NA 84 0.22619048 0.19646431</span></a>
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<a class="sourceLine" id="cb4-17" title="17"><span class="co">#> 15 2017 0.16279070 NA NA 86 0.16279070 0.21451350</span></a>
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<a class="sourceLine" id="cb4-18" title="18"><span class="co">#> 16 2018 0.23373852 0.2021578 0.2653193 NA NA 0.23373852</span></a>
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<a class="sourceLine" id="cb4-19" title="19"><span class="co">#> 17 2019 0.25412909 0.2168525 0.2914057 NA NA 0.25412909</span></a>
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<a class="sourceLine" id="cb4-20" title="20"><span class="co">#> 18 2020 0.27565854 0.2321869 0.3191302 NA NA 0.27565854</span></a>
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<a class="sourceLine" id="cb4-21" title="21"><span class="co">#> 19 2021 0.29828252 0.2481942 0.3483709 NA NA 0.29828252</span></a>
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<a class="sourceLine" id="cb4-22" title="22"><span class="co">#> 20 2022 0.32193804 0.2649008 0.3789753 NA NA 0.32193804</span></a>
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<a class="sourceLine" id="cb4-23" title="23"><span class="co">#> 21 2023 0.34654311 0.2823269 0.4107593 NA NA 0.34654311</span></a>
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<a class="sourceLine" id="cb4-24" title="24"><span class="co">#> 22 2024 0.37199700 0.3004860 0.4435080 NA NA 0.37199700</span></a>
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<a class="sourceLine" id="cb4-25" title="25"><span class="co">#> 23 2025 0.39818127 0.3193839 0.4769787 NA NA 0.39818127</span></a>
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<a class="sourceLine" id="cb4-26" title="26"><span class="co">#> 24 2026 0.42496142 0.3390173 0.5109056 NA NA 0.42496142</span></a>
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||||
<a class="sourceLine" id="cb4-27" title="27"><span class="co">#> 25 2027 0.45218939 0.3593720 0.5450068 NA NA 0.45218939</span></a>
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||||
<a class="sourceLine" id="cb4-28" title="28"><span class="co">#> 26 2028 0.47970658 0.3804212 0.5789920 NA NA 0.47970658</span></a>
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<a class="sourceLine" id="cb4-29" title="29"><span class="co">#> 27 2029 0.50734745 0.4021241 0.6125708 NA NA 0.50734745</span></a></code></pre></div>
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<p>The function <code>plot</code> 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:</p>
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<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/plot">plot</a></span>(predict_TZP)</a></code></pre></div>
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<p><img src="resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
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@ -362,9 +362,9 @@
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<a class="sourceLine" id="cb10-6" title="6"><span class="co">#> Link function: logit</span></a>
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<a class="sourceLine" id="cb10-7" title="7"></a>
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<a class="sourceLine" id="cb10-8" title="8"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">summary</a></span>(model)<span class="op">$</span>coefficients</a>
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<a class="sourceLine" id="cb10-9" title="9"><span class="co">#> Estimate Std. Error z value Pr(>|z|)</span></a>
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<a class="sourceLine" id="cb10-10" title="10"><span class="co">#> (Intercept) -222.5105288 45.94675125 -4.842791 1.280277e-06</span></a>
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<a class="sourceLine" id="cb10-11" title="11"><span class="co">#> year 0.1097306 0.02283874 4.804581 1.550761e-06</span></a></code></pre></div>
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<a class="sourceLine" id="cb10-9" title="9"><span class="co">#> Estimate Std. Error z value Pr(>|z|)</span></a>
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<a class="sourceLine" id="cb10-10" title="10"><span class="co">#> (Intercept) -224.3987194 48.0335384 -4.671709 2.987038e-06</span></a>
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<a class="sourceLine" id="cb10-11" title="11"><span class="co">#> year 0.1106102 0.0238753 4.632831 3.606990e-06</span></a></code></pre></div>
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</div>
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||||
</div>
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||||
</div>
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||||
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@ -284,6 +284,8 @@ Please create an issue in one of our repositories if you want additions in this
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||||
<li>Fix for <code><a href="../reference/freq.html">freq()</a></code> for when all values are <code>NA</code>
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</li>
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<li>Fix for <code><a href="../reference/first_isolate.html">first_isolate()</a></code> for when dates are missing</li>
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<li>Improved speed of <code><a href="../reference/guess_ab_col.html">guess_ab_col()</a></code>
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||||
</li>
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||||
</ul>
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</div>
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<div id="other" class="section level4">
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@ -253,10 +253,10 @@
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<span class='co'># S3 method for resistance_predict</span>
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||||
<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>x</span>,
|
||||
<span class='kw'>main</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Resistance prediction of"</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/attributes'>attributes</a></span>(<span class='no'>x</span>)$<span class='no'>ab</span>), <span class='no'>...</span>)
|
||||
<span class='kw'>main</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Resistance Prediction of"</span>, <span class='no'>x_name</span>), <span class='no'>...</span>)
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|
||||
<span class='fu'>ggplot_rsi_predict</span>(<span class='no'>x</span>, <span class='kw'>main</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Resistance prediction of"</span>,
|
||||
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/attributes'>attributes</a></span>(<span class='no'>x</span>)$<span class='no'>ab</span>), <span class='kw'>ribbon</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='no'>...</span>)</pre>
|
||||
<span class='fu'>ggplot_rsi_predict</span>(<span class='no'>x</span>, <span class='kw'>main</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Resistance Prediction of"</span>, <span class='no'>x_name</span>),
|
||||
<span class='kw'>ribbon</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='no'>...</span>)</pre>
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||||
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
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<table class="ref-arguments">
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||||
@ -18,10 +18,10 @@ rsi_predict(x, col_ab, col_date = NULL, year_min = NULL,
|
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info = TRUE, ...)
|
||||
|
||||
\method{plot}{resistance_predict}(x,
|
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main = paste("Resistance prediction of", attributes(x)$ab), ...)
|
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main = paste("Resistance Prediction of", x_name), ...)
|
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|
||||
ggplot_rsi_predict(x, main = paste("Resistance prediction of",
|
||||
attributes(x)$ab), ribbon = TRUE, ...)
|
||||
ggplot_rsi_predict(x, main = paste("Resistance Prediction of", x_name),
|
||||
ribbon = TRUE, ...)
|
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}
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\arguments{
|
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\item{x}{a \code{data.frame} containing isolates.}
|
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@ -39,37 +39,37 @@ test_that("prediction of rsi works", {
|
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|
||||
library(dplyr)
|
||||
|
||||
expect_output(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
expect_output(rsi_predict(x = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
model = "binomial",
|
||||
col_ab = "AMX",
|
||||
col_date = "date",
|
||||
info = TRUE))
|
||||
expect_output(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
expect_output(rsi_predict(x = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
model = "loglin",
|
||||
col_ab = "AMX",
|
||||
col_date = "date",
|
||||
info = TRUE))
|
||||
expect_output(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
expect_output(rsi_predict(x = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
model = "lin",
|
||||
col_ab = "AMX",
|
||||
col_date = "date",
|
||||
info = TRUE))
|
||||
|
||||
expect_error(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
expect_error(rsi_predict(x = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
model = "INVALID MODEL",
|
||||
col_ab = "AMX",
|
||||
col_date = "date",
|
||||
info = TRUE))
|
||||
expect_error(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
expect_error(rsi_predict(x = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
col_ab = "NOT EXISTING COLUMN",
|
||||
col_date = "date",
|
||||
info = TRUE))
|
||||
expect_error(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
expect_error(rsi_predict(x = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
col_ab = "AMX",
|
||||
col_date = "NOT EXISTING COLUMN",
|
||||
info = TRUE))
|
||||
# almost all E. coli are MEM S in the Netherlands :)
|
||||
expect_error(resistance_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
expect_error(resistance_predict(x = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
col_ab = "MEM",
|
||||
col_date = "date",
|
||||
info = TRUE))
|
||||
|
||||
Loading…
Reference in New Issue
Block a user