library(tidyr) library(dplyr) library(ggplot2) library(ggpubr) library(ggsci) res %>% select(1:11) %>% bind_cols(sample_group = sample_group) %>% pivot_longer(2:11,names_to = "drugs",values_to = "ic50") %>% ggplot(., aes(sample_group,ic50))+ geom_boxplot(aes(fill=...
rx_fast_trees(airFormula, method="regression", data=data_train) # Put score and model variables in data frame score_df = rx_predict(ff_reg, data=data_test, write_model_vars=True) print(score_df.head()) # Plot actual versus predicted values with smoothed line # Supported in the next ...
# Example 2 (continued again) # plotresidualsby fitted values #plot(fit$residuals, fit$fitted.values) # predict mpg from wt in a new set of data mynewdata = mtcars[c(1,2,3,4,5)]$wt predict(fit, mynewdata) # get and save influence statistics cook <- cooks.distance(fit) cook #...
rx_fast_trees(airFormula, method="regression", data=data_train) # Put score and model variables in data frame score_df = rx_predict(ff_reg, data=data_test, write_model_vars=True) print(score_df.head()) # Plot actual versus predicted values with smoothed line # Supported in the next ...
输入文件有问题吧；你看你的输入文件格式和例子是否一致
We first investigated whether the mutation load in genes belonging to distinct biological processes can accurately predict ICI responses. For each GO biological process, we counted the number of mutations in that process per sample in the training datasets and used these values to predict anti-PD1 ...
Before R2021a, use commas to separate each name and value, and enclose Name in quotes. Example: [ypred,yci] = predict(Mdl,Xnew,'Alpha',0.01,'Simultaneous',true) returns the confidence interval yci with a 99% confidence level, computed simultaneously for all predictor values. Alpha— Sign...
vbaliga/gaussplotRPublic NotificationsYou must be signed in to change notification settings Fork0 Star4 master 1Branch15Tags Code README GPL-3.0 license gaussplotR gaussplotRprovides functions to fit two-dimensional Gaussian functions, predict values from such functions, and produce plots of predicte...
Behind each door was a reward that drifted through the rounds in values ranging from 1 to 98, denoted \({r}_{t}\) (or reward at time t). The rewards behind the four doors were predefined, and three such payoff structures were used, in line with previous work15. Figure 1 ...
oncoPredict包提供了两种当前经典的药物预测训练集-Cancer Therapeutics Response Portal (CTRP-V2) 和 Genomics of Drug Sensitivity in Cancer (GDSC-V2) 。可以直接使用R包oncoPredict整理好的这两个数据库的rdata文件,下载链接https://osf.io/c6tfx/files/osfstorage。