dca | Decision Curve Analysis | Machine Learning library

 by   ddsjoberg R Version: Current License: Non-SPDX

kandi X-RAY | dca Summary

kandi X-RAY | dca Summary

dca is a R library typically used in Artificial Intelligence, Machine Learning applications. dca has no bugs, it has no vulnerabilities and it has low support. However dca has a Non-SPDX License. You can download it from GitHub.

Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information and may be cumbersome to apply to models that yield a continuous result. Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results.
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              dca has a low active ecosystem.
              It has 7 star(s) with 1 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              dca has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of dca is current.

            kandi-Quality Quality

              dca has 0 bugs and 0 code smells.

            kandi-Security Security

              dca has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              dca code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              dca has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              dca releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.

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            dca Key Features

            No Key Features are available at this moment for dca.

            dca Examples and Code Snippets

            No Code Snippets are available at this moment for dca.

            Community Discussions

            QUESTION

            softlockup with dpdk 19 mellanox connectx5
            Asked 2022-Apr-03 at 08:42

            I have server with centos 7.9 3.10.0-1160.53.1.el7.x86_64

            When running my dpdk 19 muliple process application i have softlockup

            The server i run on have 2 ixgbe 10G, and one 100G connectx-5

            ...

            ANSWER

            Answered 2022-Apr-03 at 08:42

            Adding iommu=pt intel_iommu=on to the grub solve the softlockup and the 10 G Rx

            Source https://stackoverflow.com/questions/71018338

            QUESTION

            How to define the difference between id's nodes of graph?
            Asked 2022-Apr-01 at 21:05

            I have a graph g

            ...

            ANSWER

            Answered 2022-Apr-01 at 10:31

            You can use subgraph.edges:

            Source https://stackoverflow.com/questions/71705273

            QUESTION

            R plotly sankey - how to make the column annonation aligned with each column
            Asked 2022-Mar-01 at 10:26

            I have below code, which is to add annonation for each column in the sankey chart.

            ...

            ANSWER

            Answered 2022-Feb-25 at 14:04

            It appears that your definePosition() function is putting your node labels in the expected places. It is doing its job. However, it is the nodes that are not appearing where they should.

            You are attempting to fix the node positions using calculated node_x and node_y vectors. However, plotly's handling of manual node positions is finicky and a bit opaque. In some cases it appears to override manual positions. I have found that in your situation, omitting one or both of the arguments x or y in node will align your nodes where expected. For example:

            Source https://stackoverflow.com/questions/71217049

            QUESTION

            R networkD3 sankey - add value to node label
            Asked 2022-Feb-25 at 12:09

            example code to create a Sankey chart with networkD3::sankeyNetwork()...

            ...

            ANSWER

            Answered 2022-Feb-25 at 12:06

            The jsCode argument of htmlwidgets::onRender() is a string/character vector that contains valid JavaScript.

            Source https://stackoverflow.com/questions/71264918

            QUESTION

            R networkD3 sankey - change the colors for all links to blue
            Asked 2022-Feb-25 at 08:33

            Here is example code that I use to make a Sankey chart using networkD3::sankeyNetwork()

            ...

            ANSWER

            Answered 2022-Feb-25 at 08:29

            Add a column to your links data.frame that will specify the group for each link (in your case, they will all be in the same group). Then add a custom color scale command that will specify "blue" for that group. Then tell the sankeyNetwork() function the name of the column in your links data.frame that specifies the link group and pass it you custom colorScale.

            Source https://stackoverflow.com/questions/71262001

            QUESTION

            R plotly - sankey chart not returned
            Asked 2022-Feb-14 at 02:34

            I am running below code in RStudio, and want to create sankey chart with plotly. code runs without error. but the sankey chart is not displayed. what's wrong here?

            ...

            ANSWER

            Answered 2022-Feb-10 at 08:03

            You'll need to define the node position by setting the arrangement argument to stop plotly from automatically justifying the position.

            This requires some finessing as you need to specify the coordinates of the nodes. You can find more details here: https://plotly.com/python/sankey-diagram/#define-node-position

            Code

            Source https://stackoverflow.com/questions/71049211

            QUESTION

            Error 'An object was provided as `children` instead of a component....' appears on dashboard
            Asked 2022-Feb-07 at 08:24

            sample csv data:

            date,Data Center,Customer,companyID,source,target,value 6/1/2021,dcA,customer1,companyID1,step1:open_list_view,exit,1 6/1/2021,dcB,customer2,companyID2,step1:open_list_view,exit,1 6/1/2021,dcC,customer3,companyID3,step1:open_list_view,exit,1 6/2/2021,dcD,customer4,companyID4,step1:open_list_view,exit,2 6/2/2021,dcE,customer5,companyID5,step1:open_list_view,step2:switch_display_option,1

            I run below code for put the sankey chart in dashboard, and the chart can be updated accordingly by applying filters. but an error appears on dashboard. What is the issue?

            An object was provided as children instead of a component, string, or number (or list of those). Check the children property that looks something like:

            ...

            ANSWER

            Answered 2022-Feb-06 at 12:22

            Your callback function returns a figure and is trying to assign it to the children property of a html.Div. Can't do that as far as I know. A div's child should be other components, strings or numbers as stated in the error message.

            Did you mean to write

            Source https://stackoverflow.com/questions/70985391

            QUESTION

            Same compute intensive function running on two different cores resulting in different latency
            Asked 2022-Jan-13 at 08:40
            #include 
            #include 
            #include 
            
            #include 
            #include 
            
            using namespace std;
            
            static inline void stick_this_thread_to_core(int core_id);
            static inline void* incrementLoop(void* arg);
            
            struct BenchmarkData {
                long long iteration_count;
                int core_id;
            };
            
            pthread_barrier_t g_barrier;
            
            int main(int argc, char** argv)
            {
                if(argc != 3) {
                    cout << "Usage: ./a.out  " << endl;
                    return EXIT_FAILURE;
                }
            
                cout << "================================================ STARTING ================================================" << endl;
            
                int core1 = std::stoi(argv[1]);
                int core2 = std::stoi(argv[2]);
            
                pthread_barrier_init(&g_barrier, nullptr, 2);
            
                const long long iteration_count = 100'000'000'000;
            
                BenchmarkData benchmark_data1{iteration_count, core1};
                BenchmarkData benchmark_data2{iteration_count, core2};
            
                pthread_t worker1, worker2;
                pthread_create(&worker1, nullptr, incrementLoop, static_cast(&benchmark_data1));
                cout << "Created worker1" << endl;
                pthread_create(&worker2, nullptr, incrementLoop, static_cast(&benchmark_data2));
                cout << "Created worker2" << endl;
            
                pthread_join(worker1, nullptr);
                cout << "Joined worker1" << endl;
                pthread_join(worker2, nullptr);
                cout << "Joined worker2" << endl;
            
                return EXIT_SUCCESS;
            }
            
            static inline void stick_this_thread_to_core(int core_id) {
                int num_cores = sysconf(_SC_NPROCESSORS_ONLN);
                if (core_id < 0 || core_id >= num_cores) {
                    cerr << "Core " << core_id << " is out of assignable range.\n";
                    return;
                }
            
                cpu_set_t cpuset;
                CPU_ZERO(&cpuset);
                CPU_SET(core_id, &cpuset);
            
                pthread_t current_thread = pthread_self();
            
                int res = pthread_setaffinity_np(current_thread, sizeof(cpu_set_t), &cpuset);
            
                if(res == 0) {
                    cout << "Thread bound to core " << core_id << " successfully." << endl;
                } else {
                    cerr << "Error in binding this thread to core " << core_id << '\n';
                }
            }
            
            static inline void* incrementLoop(void* arg)
            {
                BenchmarkData* arg_ = static_cast(arg);
                int core_id = arg_->core_id;
                long long iteration_count = arg_->iteration_count;
            
                stick_this_thread_to_core(core_id);
            
                cout << "Thread bound to core " << core_id << " will now wait for the barrier." << endl;
                pthread_barrier_wait(&g_barrier);
                cout << "Thread bound to core " << core_id << " is done waiting for the barrier." << endl;
            
                long long data = 0; 
                long long i;
            
                cout << "Thread bound to core " << core_id << " will now increment private data " << iteration_count / 1'000'000'000.0 << " billion times." << endl;
                std::chrono::steady_clock::time_point begin = std::chrono::steady_clock::now();
                for(i = 0; i < iteration_count; ++i) {
                    ++data;
                    __asm__ volatile("": : :"memory");
                }
            
                std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now();
                unsigned long long elapsed_time = std::chrono::duration_cast(end - begin).count();
            
                cout << "Elapsed time: " << elapsed_time << " ms, core: " << core_id << ", iteration_count: " << iteration_count << ", data value: " << data << ", i: " << i << endl;
            
                return nullptr;
            }
            
            
            ...

            ANSWER

            Answered 2022-Jan-13 at 08:40

            It turns out that cores 0, 16, 17 were running at much higher frequency on my Skylake server.

            Source https://stackoverflow.com/questions/70668229

            QUESTION

            How to perform a multi-conditional replace in dplyr?
            Asked 2022-Jan-11 at 10:56

            I have a dataset data.csv with around 180 variables (words) and 3000 samples (cases), and it looks like this (excerpt):

            I am running decorana and plotting a cluster using kmeans and fviz_cluster:

            ...

            ANSWER

            Answered 2022-Jan-09 at 16:19

            A possible solution, where Calculate is determined in the first mutate (therefore, outside if_else), which can correspond to a very complicated calculation, as you declare you are needing:

            Source https://stackoverflow.com/questions/70642720

            QUESTION

            How to calculate the value count in multi-value columns and visualize in bar chart?
            Asked 2022-Jan-07 at 11:04

            I have below csv file where values in C_DS and C_RT could be multi-value.

            ...

            ANSWER

            Answered 2022-Jan-07 at 11:04

            You can try this way. First, calculate the unique list of labels from your column as new table.

            'SampleData'

            Source https://stackoverflow.com/questions/70615427

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install dca

            You can install dca from [GitHub](https://github.com/) with:.

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            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            gh repo clone ddsjoberg/dca

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