Breen, Jeffrey
Margin=1 means that R calculates the proportions across rows, while margin=2 is down columns. I show a table of Sex vs Marital status below with two types of proportion tables.table2<-table(mydata$Sex, mydata$Married)table2prop.table(table2, margin=1)prop.table(table2, margin=2) And...
With a data set with NA's, use na.rm=TRUEsummarySE(dataNA,measurevar="change",groupvars=c("sex","condition"),na.rm=TRUE)#> sex condition N change sd se ci#> 1 F aspirin 4 -3.425000 0.9979145 0.4989572 1.5879046#> 2 F placebo 12 -2.058333 0.5247655 0.1514867 0.3334201#> 3 M ...
Big Data Analytics - Summarizing Data - Reporting is very important in big data analytics. Every organization must have a regular provision of information to support its decision making process. This task is normally handled by data analysts with SQL and
Use the remaining 80% of data to train and test the models. Exercise 4 Find the dimensions of the “iris” dataset.HINT: Usedim(). < aside class='stb-icon'> Learn moreabout machine learning in the online courseBeginner to Advanced Guide on Machine Learning with R Tool. In this course...
It is recommended for use when a census or good-quality data are not available. Multiplier The multiplier method is always integrated with other methods, such as the respondent-driven sampling method to estimate the size of the key populations. There were three different types of multipliers ...
cc> #include <iostream> using namespace streamingcc; int main() { // create an object which will maintain // 10 samples (with replacement) dynamically ReservoirSampler<int> rsmp(10); // sample from a data stream with length 1,000,000 for (int i = 0; i < 1000000; i++) rsmp....
Based on the compressed sampling theorem to compress and expand small sample data, we use CNN to directly classify the compressed sampling data features. Compared with the original image input, compressing the input can greatly reduce the network's demand for samples. In addition, the surface ...
Data Frame Summarizing Available Probability Distributions and Estimation MethodsThe package
[5] R. Bayardo, Efficiently mining long patterns from databases, In Proc. of 1998 Int. Conf. on Management of Data (SIGMOD’98), pages 85:93, 1998. [6] T. Calders and B. Goethals, Mining all non-derivable frequent itemsets, In Proc. of 2002 European Conf. on Principles of Dat...