Data frame

Special methods tailored to these phenomena have been developed for modeling such effects. These methods consist of a collection of experimental data framed in graphs or semiempirical expressions. 

Examples for Selecting Elements from a Matrix, Array, or Data Frame... 

Select column with name VarName (only if x is a data frame)... 

The data cleaning steps may involve the removal from the data frame of columns (of descriptor values) that are constant or nearly constant, imputing missing values and eliminating columns that are redundant due to a strong relationship with other columns. All these steps are easily automated. Approximate algorithms can easily be developed that are more than 100-fold faster than those available in commercial packages. 

The next layer is the Data Link layer. This layer arranges data into chunks called frames. Included in these chunks is control information indicating the beginning and end of the data stream. This layer is very important because it makes transmission easier and more manageable as well as allowing for error checking within the data frames. 

Which OSI layer signals all clear by making sure the data frames are error-free ... 

C.It is the responsibility of the Transport layer to signal an all clear by making sure the data frames are error-free. It also controls the data flow and troubleshoots any problems with transmitting or receiving data frames. 

Type stack < -data.frame(cbind (stack.x,stack, loss) ) (note stack.x and stack.loss are S-Plus built-in data sets) The following is a single command names (stack) < -c( AirFlow , waterTemp , AcidConc , Loss ) System responds with Call gam(formula = Loss s(AirFlow) + s(waterTemp) + s(AcidConc), data = stack, control = gam.control(bf.maxit = 50)) Degrees of Freedom 21 total 8.00097 Residual Residual Deviance 67.79171 ... 

The S-Plus data frame dpi is generated by calling the function sim.dp.mult assuming strict dose proportionahty and no lO variability, as illustrated below. Figure 4.1 shows a trelhs display of the corresponding concentration-time profiles. 

The LME model of Section 4.2.1 and the NLME model of Section 4.2.2 both involved two random components measurement error and subject random effects. In this section we explore a two-level random effect hierarchy by introducing lO variability in the PK parameters Ka, Ke, and V), so that the subject s parameters may vary from period to period. Note that this is not a period effect, but rather an uncontrollable random variation in the subject s pharmacokinetics. The data frame dp2, incorporating lO random effects, is obtained by calling sim.dp.muit as follows ... 

Although the fixed effects have been well estimated, it is also of interest to examine how closely the estimated standard deviations of the random effects reflect the true variability in the simulated data. The dp2 data frame includes values of the generated subject random effects, interoccasion random effects, and measurement errors, from which sample variances can be obtained and compared to the model estimates. The intersubject sample standard deviations of log(ic ), og AUC), and log(T) are 0.33,0.41, and 0.23, respectively. The corresponding model estimates are 0.36, 0.39, and 0.26. For the lO random effects, the sample SDs are 0.17, 0.22, and 0.17, while the corresponding values obtained in the model fit are 0.20, 0.19, and 0.18, respectively. The sample and model SD for measurement error are both equal to 0.1, indicating a good agreement overall between sample and model estimates. 

The data frame pkpdi is generated according to the PK-PD model above, using the following call to the function sim.pkpd.muit. Figure 4.2 shows a trellis display of the corresponding time profiles for the simulated PD response. 

Create PD observation records pdobs.tmp <- data.frame(ID=subj.v,... 

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