[R] Sensitivity and Specificity

[Michael Dewey]

So predict is a one-dimensional vector of predictions but you are 
treating it as a two-dimensional matrix (presumably you think those are 
the totals).

Michael

On 24/10/2022 16:50, greg holly wrote:
> Hi Michael,
> 
> I appreciate your writing. Here are what I have after;
> 
>  > predict_testing <- ifelse(predict > 0.5,1,0)
>  >
>  > head(predict)
>           1          2          3          5          7          8
> 0.29006984 0.28370507 0.10761993 0.02204224 0.12873872 0.08127920
>  >
>  > # Sensitivity and Specificity
>  >
>  > 
> sensitivity<-(predict_testing[2,2]/(predict_testing[2,2]+predict_testing[2,1]))*100
> Error in predict_testing[2, 2] : incorrect number of dimensions
>  > sensitivity
> function (data, ...)
> {
>      UseMethod("sensitivity")
> }
> <bytecode: 0x000002082a2f01d8>
> <environment: namespace:caret>
>  >
>  > 
> specificity<-(predict_testing[1,1]/(predict_testing[1,1]+predict_testing[1,2]))*100
> Error in predict_testing[1, 1] : incorrect number of dimensions
>  > specificity
> function (data, ...)
> {
>      UseMethod("specificity")
> }
> <bytecode: 0x000002082a2fa600>
> <environment: namespace:caret>
> 
> On Mon, Oct 24, 2022 at 10:45 AM Michael Dewey <lists using dewey.myzen.co.uk 
> <mailto:lists using dewey.myzen.co.uk>> wrote:
> 
>     Rather hard to know without seeing what output you expected and what
>     error message you got if any but did you mean to summarise your
>     variable
>     predict before doing anything with it?
> 
>     Michael
> 
>     On 24/10/2022 16:17, greg holly wrote:
>      > Hi all R-Help ,
>      >
>      > After partitioning my data to testing and training (please see
>     below), I
>      > need to estimate the Sensitivity and Specificity. I failed. It
>     would be
>      > appropriate to get your help.
>      >
>      > Best regards,
>      > Greg
>      >
>      >
>      > inTrain <- createDataPartition(y=data$case,
>      >                                 p=0.7,
>      >                                 list=FALSE)
>      > training <- data[ inTrain,]
>      > testing  <- data[-inTrain,]
>      >
>      > attach(training)
>      > #model training and prediction
>      > data_training <- glm(case ~ age+BMI+Calcium+Albumin+meno_1, data =
>      > training, family = binomial(link="logit"))
>      >
>      > predict <- predict(data_training, data_predict = testing, type =
>     "response")
>      >
>      > predict_testing <- ifelse(predict > 0.5,1,0)
>      >
>      > # Sensitivity and Specificity
>      >
>      > 
>       sensitivity<-(predict_testing[2,2]/(predict_testing[2,2]+predict_testing[2,1]))*100
>      >   sensitivity
>      >
>      > 
>       specificity<-(predict_testing[1,1]/(predict_testing[1,1]+predict_testing[1,2]))*100
>      >   specificity
>      >
>      >       [[alternative HTML version deleted]]
>      >
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> 
>     -- 
>     Michael
>     http://www.dewey.myzen.co.uk/home.html
>     <http://www.dewey.myzen.co.uk/home.html>
> 
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Michael
http://www.dewey.myzen.co.uk/home.html