glm r tutorial

Ggplot2R's Graphing toolkit, you can use very simple statements to achieve very complex and beautiful results.QplotLoad Qplot=#1. Visualize by basic classification of Color,size,shape#1.1 Simple scatter plot (using color classification, diamonds of different colors are represented by dots of different colors)#1.2. Simple scatter plots (using shape classification, different cutting methods are represented by different shapes of points)#2. Drawing different types of charts: Geom parametersGeom= ""

Decrypt a piece of php code and decrypt a piece of php code. the code is as follows: lt ;? Php nbsp; // nbsp; $ ooo0o00 =__ FILE __; $ OOO000000 = urldecode ('% 74% 68% 36% 73% 62% 65% 68% 71% 6c % 61% 34% 63% 6f % 5f % 7 help decrypt a piece of php code To decrypt a piece of php code, the code is as follows: $ OOO0O0O00 =__ FILE __; $ OOO000000 = urldecode ('% 74% 68% 36% 73% 62% 65% 68% 71% 6c % 61% 34% 63% 6f % 5f % 73% 61% 64% 66% 70% 6e % 72'); $ OO00O0000 = 1400; $ ooo=o0 = $ ooo=00 {4

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Genome.fasta$ Java-xmx10g-jar Picardhome/createsequencedictionary.jar R=genome.fasta o=genome.dict$ samtools Index Sample.dd.bamHere is a need to note that the Genome.fasta prefix genome must be the same as the genome.dict prefix genome, no one next errorNext, compare the Indel (realignment) around the area:Started gatk the software with a knife.Java-xmx10g-jar genomeanalysistk.jar-r genome.fasta-t realignertargetcreator-i sample.dd.bam-o sample.realn.int ErvalsJava-xmx10g-jar $GATKHome/genomea

will be OK.In the usual process we do not need to consider the problem of the main sequence of the column, because we calculate the matrix (such as through LookAt, or GLM related functions obtained by the matrix) and shader in the required format is consistent, so we can use the incoming, There is only one case where we need to consider the column main or row main order relationship of the Matrix, that is, the GLSL matrix into a custom memory block i

)The update function can arbitrarily add or reduce independent variables on the basis of the LM model results, or model the target variables such as logarithm and root, for example: increase x2 squared variable lm.newstepwise regression analysis function step ()Methods for decreasing variables Lm.stepregression analysis of categorical data in self-contained variablesThe value of categorical variable A is I, then the model prediction value is f (a1=0,... Ai=1,ap=0) (3) Logic regression y=1/(1+EXP

testingChisq.test,prop.test,t.test implemented in RIv. Multivariate analysisCor,cov.wt,var: Covariance matrix and correlation matrix calculationBiplot,biplot.princomp: Multivariate data biplot graphCancor: The code is relevantPrincomp: Principal component AnalysisHclust: Genealogy Clusteringkmeans:k-mean-value clusteringCmdscale: Classic Multidimensional scale others have Dist,mahalanobis,cov.robFive, Time seriesTS: Time Series objectsdiff: Calculate differentialTime: Sampling time for a timese

Form: Use the sigmoid function: g(Z)= 1 1+ e? Z Its derivative is g- (Z)=(1?g(Z))g(Z) Assume: That If there is a sample of M, the likelihood function form is: Logarithmic form: Using gradient rise method to find its maximum valueDerivation: The update rules are: It can be found that the rules form and the LMS update rules are the same, however, their demarcation function hθ (x ) is completely different (the H (x) is a nonlinear function in

-linear model.Linear regression modelThe LM () function in the STAT packet can fit the linear regression model using the least squares estimation.# load the librarylibrary(mlbench)# load datadata(BostonHousing)# fit modelfit Rogers regression modelThe GLM () function in the stat package can be used to fit a generalized linear model. It can be used to fit the Rogers regression model to deal with the problem of two-tuple classification.# load the librar

(table (trainsplit$seriousdlqin2yrs))Conclusion: The distribution of data sets achieves a basic balance6.3 Re-modelingModel_full = GLM (seriousdlqin2yrs~.,data=trainsplit,family=binomial,maxit=1000"both ") Summary (step)Conclusion: 8 variables with effect on the result are found, which are different from the variable selection to start modeling6.4 Predictive ModelsPred = Predict (step,cs.test,type="response") FITTED.R=ifelse (pred>0.5,1 == mean (f

= "Rprofmem.out", append =false, threshold = 0)Filenames output File pathAppend append content to existing file or overwrite existing fileThreshold memory allocation is greater than this value will be logged, Unit bytesBoot performance monitoring is rprofmem (filename)Stop performance monitoring when rprofmem () or Rprofmem (NULL)View the results of the run and read the filename directly. The following example is an example of a function description document:Rprofmem ("rprofmem.out", threshold

data mining, so they are included.1. Clustering Commonly used packages: Fpc,cluster,pvclust,mclust Partitioning-based approach: Kmeans, Pam, PAMK, Clara Hierarchy-based approach: Hclust, Pvclust, Agnes, Diana Model-based approach: Mclust Density-based approach: Dbscan Drawing-based method: Plotcluster, Plot.hclust Verification-based method: Cluster.stats 2. Classification Commonly used packages:Rpart,party,randomforest,rpartordinal,tree,margintree,

fit model parameters, and logic regression uses the maximum likelihood method to estimate.Ii. implementation of the R languageGLM () is the core function for logic regression analysis using R language.Parameters:Formula: Setting the form of a linear fit modelFAMILY:GLM's algorithm family. Logic regression analysis, family set to binomial ("logit")Data: SamplesCode:(1) Building a logic regression modelDATA.GLM glm (VIP~., Data=vip.data,family=binomial

, Min, Max, and other functions all have na.rm parameters, which are set to true, and then the NA is removed at the time of calculation. LM, GLM, GAM and other functions have na.action parameters that accept functions as variables, such as Na.omit, Na.fail. Na.pass, Na.exculde and so on. Both Na.omit and complete.cases can return a data.frame that contains only complete rows of data, which means that if one or more Na is in a row, the row is excluded.

,quantile,stem, etc.Third, statistical testingChisq.test,prop.test,t.test implemented in RIv. Multivariate analysisCor,cov.wt,var: Covariance matrix and correlation matrix calculationBiplot,biplot.princomp: Multivariate data biplot graphCancor: The code is relevantPrincomp: Principal component AnalysisHclust: Genealogy Clusteringkmeans:k-mean-value clusteringCmdscale: Classic Multidimensional scale others have Dist,mahalanobis,cov.robFive, Time seriesTS: Time Series objectsdiff: Calculate differ

GBM and GBDT and Xgboost Gradient Boost decision Tree is currently a very popular machine learning algorithm (supervised learning), this article will be from the origin of the GBDT, and introduce the current popular xgboost. In addition, "Adaboost detailed", "GLM (generalized linear model) and LR (logistic regression) detailed" is the basis of this paper. 0. Hello World Here is a list of the simplest and most common GBDT algorithms.For regression pro

. Logistic regression is better than decision tree for analyzing the whole structure of data, and decision tree is better than logistic regression in the analysis of local structure.the accumulation of some fragmentary knowledge points R comes with a stat package in GLM (formula = xx, data = xx, family = binomial) to implement logistic regression (Copyright©http://blog.csdn.net/s_gy_zetrov. All rights Reserved) references [1] Http://52opencourse.com/

recursion forAdaptive integrationscoping An illustration of lexical scoping. DemosinchPackage ' graphics ': Hershey Tables of the charactersinchThe Hershey vector fontsjapanese Tables of the Japanese charactersinchThe Hershey vector fontsgraphics A Show of some of R'S GraphicsCapabilitiesimage the image-Like graphics builtins of RPERSP Extended persp () Examplesplotmath Examples of the use of mathe Matics AnnotationdemosinchPackage ' grdevices ': Colors A Show of R's predefined colors ()hclcolo

, Linux, and MacOS X systems. The following is an example in the RODM package help document. You can first understand the efficient algorithm deployment: ###GLMRegression ##Notrun: x1 noise y1 dataset names(dataset) RODM_create_dbms_table(DB,"dataset") #Pushthetrainingtabletothedatabase glm data_table_name="dataset", target_column_name="Y1", mining_function="regression") glm2 data_table_name="dataset",

Objective:By replacing the traditional conv layer with the MLPCONV layer, we can learn more abstract features. The traditional convolution layer is based on the linear combination of the previous layer and then through the nonlinear activation (GLM), the author thinks that the hypothesis of the traditional convolution layer is a linear integrable feature. The MLPCONV layer uses a multilayer perceptron, which is a deep network structure that can approx