pheatmap 报错: Error in seq.int(rx[1L], rx[2L], length.out = nb) : 'from' must be a finite number 一般是由于文件ID 不匹配,一般是gene_exp 中ID 的-自动转化为. 而clinical data中ID 是-; 将所有ID 中的-替换为即可。 提取奇数和偶数列是报错:Error in seq.default(1, ncol(dat), 2) ...
错误于seq.default(from = bottom, to = roof, by = 10^floor(log10(resolution))/4): 'to' must be a finite number这个报错是为什么啊本来是说列数不同,弄了个新列数现在又有新的报错了,但现在没原数据…..求救!! 贴吧用户_... 11-22 4 兄弟们,我问一个不专业的问题 行百裡 用R语言做...
错误于seq.default(from = bottom, to = roof, by = 10^floor(log10(resolution))/4): 'to' must be a finite number这个报错是为什么啊本来是说列数不同,弄了个新列数现在又有新的报错了,但现在没原数据…..求救!! 贴吧用户_... 11-22 0 BAPC模型预测 forlove铭记 nordpred下载不了,有没...
Error: JAVA_HOME cannot be determined from the Registry Error: Mapping should be created with `aes()` or `aes_()`. Error: object X not found Error: stat_count() must not be used with a y aesthetic. Error: StatBin requires a continuous x variable: the x variable is discrete.Perhaps ...
(a) Write an R function that uses the EM algorithm to find parameters which maximise the likelihood (or minimise the negative log-likelihood) for a sample of size n from p(x), for a given choice of K. The function prototype should be ...
(a) Write an R function that uses the EM algorithm to find parameters which maximise the likelihood (or minimise the negative log-likelihood) for a sample of size n from p(x), for a given choice of K. The function prototype should be ...
(a) Write an R function that uses the EM algorithm to find parameters which maximise the likelihood (or minimise the negative log-likelihood) for a sample of size n from p(x), for a given choice of K. The function prototype should be ...
xlim = range(x, finite = TRUE), ylim = range(y, finite = TRUE), zlim = range(z, finite = TRUE), labcex = 0.6, drawlabels = TRUE, method = "flattest", vfont, axes = TRUE, frame.plot = axes, col = par("fg"), lty = par("lty"), lwd = par("lwd"), ...
(a) Devise and implement two efficient algorithms for simulating from f(x). (b) Estimate the normalizing constant using Monte Carlo integration. (c) Devise and implement a Metropolis-Hastings sampler for generating variates from f(x). In particular: i) You should tune the Metropolis-Hastings ...
#K=2时根据上图,当将样本点分成两个簇的时候,可以预估均值迭代初始值为c(-0.5,0.3),c(0.4,0.5) # Create starting values mustart = rbind(c(-0.5,0.3),c(0.4,0.5)) # must be at least slightly different covstart = list(cov(Y), cov(Y)) probs = c(.01, .99) 代码语言:javascript 复制...