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Package caret. July 17, 2015
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1 Version Date Title Classification and Regression Training Package caret July 17, 2015 Author Max Kuhn. Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, Brenton Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, Andrew Ziem, and Luca Scrucca. Description Misc functions for training and plotting classification and regression models. Maintainer Max Kuhn Depends R (>= 2.10), lattice (>= 0.20), ggplot2 URL BugReports Imports car, reshape2, foreach, methods, plyr, nlme, BradleyTerry2, stats, stats4, utils, grdevices Suggests e1071, earth (>= 2.2-3), fastica, gam, ipred, kernlab, klar, MASS, ellipse, mda, mgcv, mlbench, nnet, party (>= ), pls, proc (>= 1.8), proxy, randomforest, RANN, spls, subselect, pamr, superpc, Cubist, testthat (>= 0.9.1) License GPL (>= 2) NeedsCompilation yes Repository CRAN Date/Publication :31:52 R topics documented: as.table.confusionmatrix avnnet.default bag.default bagearth
2 2 R topics documented: bagfda BloodBrain BoxCoxTrans.default calibration caretfuncs caretsbf cars classdist confusionmatrix confusionmatrix.train cox createdatapartition dhfr diff.resamples dotplot dotplot.diff.resamples downsample dummyvars featureplot filtervarimp findcorrelation findlinearcombos format.bagearth gafs.default gafs_initial GermanCredit getsamplinginfo histogram.train icr.formula index2vec knn knnreg lattice.rfe lift maxdissim mdrr modellookup nearzerovar nullmodel oil onese panel.lift panel.needle pcannet.default plot.gafs plot.rfe plot.train plot.varimp.train
3 R topics documented: 3 plotclassprobs plotobsvspred plsda postresample pottery prcomp.resamples predict.bagearth predict.gafs predict.knn predict.knnreg predict.train predictors preprocess print.confusionmatrix print.train resamplehist resamples resamplesummary rfe rfecontrol safs.default safscontrol safs_initial sbf sbfcontrol segmentationdata sensitivity spatialsign summary.bagearth tecator train traincontrol train_model_list twoclasssim update.safs update.train varimp varimp.gafs var_seq xyplot.resamples Index 187
4 4 as.table.confusionmatrix as.table.confusionmatrix Save Confusion Table Results Description Usage Conversion functions for class confusionmatrix ## S3 method for class confusionmatrix as.matrix(x, what = "xtabs",...) ## S3 method for class confusionmatrix as.table(x,...) Arguments x what Details Value... not currently used an object of class confusionmatrix data to conver to matrix. Either "xtabs", "overall" or "classes" For as.table, the cross-tabulations are saved. For as.matrix, the three object types are saved in matrix format. A matrix or table Author(s) See Also Max Kuhn confusionmatrix Examples ################### ## 2 class example lvs <- c("normal", "abnormal") truth <- factor(rep(lvs, times = c(86, 258)), levels = rev(lvs)) pred <- factor(
5 avnnet.default 5 xtab <- table(pred, truth) c( rep(lvs, times = c(54, 32)), rep(lvs, times = c(27, 231))), levels = rev(lvs)) results <- confusionmatrix(xtab) as.table(results) as.matrix(results) as.matrix(results, what = "overall") as.matrix(results, what = "classes") ################### ## 3 class example xtab <- confusionmatrix(iris$species, sample(iris$species)) as.matrix(xtab) avnnet.default Neural Networks Using Model Averaging Description Usage Aggregate several neural network models ## Default S3 method: avnnet(x, y, repeats = 5, bag = FALSE, allowparallel = TRUE,...) ## S3 method for class formula avnnet(formula, data, weights,..., repeats = 5, bag = FALSE, allowparallel = TRUE, subset, na.action, contrasts = NULL) ## S3 method for class avnnet predict(object, newdata, type = c("raw", "class", "prob"),...) Arguments formula A formula of the form class ~ x1 + x x y matrix or data frame of x values for examples. matrix or data frame of target values for examples. weights (case) weights for each example if missing defaults to 1. repeats bag allowparallel the number of neural networks with different random number seeds a logical for bagging for each repeat if a parallel backend is loaded and available, should the function use it?
6 6 avnnet.default data subset na.action contrasts object newdata type Data frame from which variables specified in formula are preferentially to be taken. An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) A function to specify the action to be taken if NAs are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.) a list of contrasts to be used for some or all of the factors appearing as variables in the model formula. an object of class avnnet as returned by avnnet. matrix or data frame of test examples. A vector is considered to be a row vector comprising a single case. Type of output, either: raw for the raw outputs, code for the predicted class or prob for the class probabilities.... arguments passed to nnet Details Following Ripley (1996), the same neural network model is fit using different random number seeds. All the resulting models are used for prediction. For regression, the output from each network are averaged. For classification, the model scores are first averaged, then translated to predicted classes. Bagging can also be used to create the models. If a parallel backend is registered, the foreach package is used to train the networks in parallel. Value For avnnet, an object of "avnnet" or "avnnet.formula". Items of interest in the output are: model repeats names a list of the models generated from nnet an echo of the model input if any predictors had only one distinct value, this is a character string of the remaining columns. Otherwise a value of NULL Author(s) These are heavily based on the nnet code from Brian Ripley. References Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge. See Also nnet, preprocess
7 bag.default 7 Examples data(bloodbrain) ## Not run: modelfit <- avnnet(bbbdescr, logbbb, size = 5, linout = TRUE, trace = FALSE) modelfit predict(modelfit, bbbdescr) ## End(Not run) bag.default A General Framework For Bagging Description Usage bag provides a framework for bagging classification or regression models. The user can provide their own functions for model building, prediction and aggregation of predictions (see Details below). bag(x,...) ## Default S3 method: bag(x, y, B = 10, vars = ncol(x), bagcontrol = NULL,...) bagcontrol(fit = NULL, predict = NULL, aggregate = NULL, downsample = FALSE, oob = TRUE, allowparallel = TRUE) ldabag plsbag nbbag ctreebag svmbag nnetbag ## S3 method for class bag predict(object, newdata = NULL,...) Arguments x y a matrix or data frame of predictors a vector of outcomes
8 8 bag.default B bagcontrol the number of bootstrap samples to train over. a list of options.... arguments to pass to the model function fit predict aggregate downsample oob allowparallel vars Details Value object newdata a function that has arguments x, y and... and produces a model object that can later be used for prediction. Example functions are found in ldabag, plsbag, nbbag, svmbag and nnetbag. a function that generates predictions for each sub-model. The function should have arguments object and x. The output of the function can be any type of object (see the example below where posterior probabilities are generated. Example functions are found in ldabag, plsbag, nbbag, svmbag and nnetbag.) a function with arguments x and type. The function that takes the output of the predict function and reduces the bagged predictions to a single prediction per sample. the type argument can be used to switch between predicting classes or class probabilities for classification models. Example functions are found in ldabag, plsbag, nbbag, svmbag and nnetbag. a logical: for classification, should the data set be randomly sampled so that each class has the same number of samples as the smallest class? a logical: should out-of-bag statistics be computed and the predictions retained? if a parallel backend is loaded and available, should the function use it? an integer. If this argument is not NULL, a random sample of size vars is taken of the predictors in each bagging iteration. If NULL, all predictors are used. an object of class bag. a matrix or data frame of samples for prediction. Note that this argument must have a non-null value The function is basically a framework where users can plug in any model in to assess the effect of bagging. Examples functions can be found in ldabag, plsbag, nbbag, svmbag and nnetbag. Each has elements fit, pred and aggregate. One note: when vars is not NULL, the sub-setting occurs prior to the fit and predict functions are called. In this way, the user probably does not need to account for the change in predictors in their functions. When using bag with train, classification models should use type = "prob" inside of the predict function so that predict.train(object, newdata, type = "prob") will work. If a parallel backend is registered, the foreach package is used to train the models in parallel. bag produces an object of class bag with elements fits control a list with two sub-objects: the fit object has the actual model fit for that bagged samples and the vars object is either NULL or a vector of integers corresponding to which predictors were sampled for that model a mirror of the arguments passed into bagcontrol
9 bagearth 9 call B dims the call the number of bagging iterations the dimensions of the training set Author(s) Max Kuhn Examples ## A simple example of bagging conditional inference regression trees: data(bloodbrain) ## treebag <- bag(bbbdescr, logbbb, B = 10, ## bagcontrol = bagcontrol(fit = ctreebag$fit, ## predict = ctreebag$pred, ## aggregate = ctreebag$aggregate)) ## An example of pooling posterior probabilities to generate class predictions data(mdrr) ## remove some zero variance predictors and linear dependencies mdrrdescr <- mdrrdescr[, -nearzerovar(mdrrdescr)] mdrrdescr <- mdrrdescr[, -findcorrelation(cor(mdrrdescr),.95)] ## basiclda <- train(mdrrdescr, mdrrclass, "lda") ## baglda2 <- train(mdrrdescr, mdrrclass, ## "bag", ## B = 10, ## bagcontrol = bagcontrol(fit = ldabag$fit, ## predict = ldabag$pred, ## aggregate = ldabag$aggregate), ## tunegrid = data.frame(vars = c((1:10)*10, ncol(mdrrdescr)))) bagearth Bagged Earth Description A bagging wrapper for multivariate adaptive regression splines (MARS) via the earth function
10 10 bagearth Usage ## S3 method for class formula bagearth(formula, data = NULL, B = 50, summary = mean, keepx = TRUE,..., subset, weights, na.action = na.omit) ## Default S3 method: bagearth(x, y, weights = NULL, B = 50, summary = mean, keepx = TRUE,...) Arguments formula A formula of the form y ~ x1 + x x y matrix or data frame of x values for examples. matrix or data frame of numeric values outcomes. weights (case) weights for each example - if missing defaults to 1. data subset na.action B Details summary keepx Data frame from which variables specified in formula are preferentially to be taken. An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) A function to specify the action to be taken if NA s are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.) the number of bootstrap samples a function with a single argument specifying how the bagged predictions should be summarized a logical: should the original training data be kept?... arguments passed to the earth function The function computes a Earth model for each bootstap sample. Value A list with elements fit B call x oob a list of B Earth fits the number of bootstrap samples the function call either NULL or the value of x, depending on the value of keepx a matrix of performance estimates for each bootstrap sample Author(s) Max Kuhn (bagearth.formula is based on Ripley s nnet.formula)
11 bagfda 11 References J. Friedman, Multivariate Adaptive Regression Splines (with discussion) (1991). Annals of Statistics, 19/1, See Also earth, predict.bagearth Examples ## Not run: library(mda) library(earth) data(trees) fit1 <- earth(trees[,-3], trees[,3]) fit2 <- bagearth(trees[,-3], trees[,3], B = 10) ## End(Not run) bagfda Bagged FDA Description Usage A bagging wrapper for flexible discriminant analysis (FDA) using multivariate adaptive regression splines (MARS) basis functions bagfda(x,...) ## S3 method for class formula bagfda(formula, data = NULL, B = 50, keepx = TRUE,..., subset, weights, na.action = na.omit) ## Default S3 method: bagfda(x, y, weights = NULL, B = 50, keepx = TRUE,...) Arguments formula A formula of the form y ~ x1 + x x y matrix or data frame of x values for examples. matrix or data frame of numeric values outcomes. weights (case) weights for each example - if missing defaults to 1. data subset Data frame from which variables specified in formula are preferentially to be taken. An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
12 12 bagfda na.action B Details Value keepx A function to specify the action to be taken if NA s are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.) the number of bootstrap samples a logical: should the original training data be kept?... arguments passed to the mars function The function computes a FDA model for each bootstap sample. A list with elements fit B call x oob a list of B FDA fits the number of bootstrap samples the function call either NULL or the value of x, depending on the value of keepx a matrix of performance estimates for each bootstrap sample Author(s) Max Kuhn (bagfda.formula is based on Ripley s nnet.formula) References J. Friedman, Multivariate Adaptive Regression Splines (with discussion) (1991). Annals of Statistics, 19/1, See Also fda, predict.bagfda Examples library(mlbench) library(earth) data(glass) set.seed(36) intrain <- sample(1:dim(glass)[1], 150) traindata <- Glass[ intrain, ] testdata <- Glass[-inTrain, ] baggedfit <- bagfda(type ~., traindata)
13 BloodBrain 13 confusionmatrix(predict(baggedfit, testdata[, -10]), testdata[, 10]) BloodBrain Blood Brain Barrier Data Description Mente and Lombardo (2005) develop models to predict the log of the ratio of the concentration of a compound in the brain and the concentration in blood. For each compound, they computed three sets of molecular descriptors: MOE 2D, rule-of-five and Charge Polar Surface Area (CPSA). In all, 134 descriptors were calculated. Included in this package are 208 non-proprietary literature compounds. The vector logbbb contains the concentration ratio and the data fame bbbdescr contains the descriptor values. Usage data(bloodbrain) Value bbbdescr logbbb data frame of chemical descriptors vector of assay results Source Mente, S.R. and Lombardo, F. (2005). A recursive-partitioning model for blood-brain barrier permeation, Journal of Computer-Aided Molecular Design, Vol. 19, pg BoxCoxTrans.default Box-Cox and Exponential Transformations Description These classes can be used to estimate transformations and apply them to existing and future data
14 14 BoxCoxTrans.default Usage BoxCoxTrans(y,...) expotrans(y,...) ## Default S3 method: BoxCoxTrans(y, x = rep(1, length(y)), fudge = 0.2, numunique = 3, na.rm = FALSE,...) ## Default S3 method: expotrans(y, na.rm = TRUE, init = 0, lim = c(-4, 4), method = "Brent", numunique = 3,...) ## S3 method for class BoxCoxTrans predict(object, newdata,...) ## S3 method for class expotrans predict(object, newdata,...) Arguments y x fudge numunique na.rm a numeric vector of data to be transformed. For BoxCoxTrans, the data must be strictly positive. an optional dependent variable to be used in a linear model. a tolerance value: lambda values within +/-fudge will be coerced to 0 and within 1+/-fudge will be coerced to 1. how many unique values should y have to estimate the transformation? a logical value indicating whether NA values should be stripped from y and x before the computation proceeds. init, lim, method initial values, limits and optimization method for optim.... for BoxCoxTrans: options to pass to boxcox. plotit should not be passed through. For predict.boxcoxtrans, additional arguments are ignored. object newdata an object of class BoxCoxTrans or expotrans. a numeric vector of values to transform. Details BoxCoxTrans function is basically a wrapper for the boxcox function in the MASS library. It can be used to estimate the transformation and apply it to new data. expotrans estimates the exponential transformation of Manly (1976) but assumes a common mean for the data. The transformation parameter is estimated by directly maximizing the likelihood. If any(y <= 0) or if length(unique(y)) < numunique, lambda is not estimated and no transformation is applied.
15 BoxCoxTrans.default 15 Value Both functions returns a list of class of either BoxCoxTrans or expotrans with elements lambda fudge n summary ratio skewness estimated transformation value value of fudge number of data points used to estimate lambda the results of summary(y) max(y)/min(y) sample skewness statistic BoxCoxTrans also returns: fudge value of fudge The predict functions returns numeric vectors of transformed values Author(s) Max Kuhn References Box, G. E. P. and Cox, D. R. (1964) An analysis of transformations (with discussion). Journal of the Royal Statistical Society B, 26, See Also Manly, B. L. (1976) Exponential data transformations. The Statistician, 25, boxcox, preprocess, optim Examples data(bloodbrain) ratio <- exp(logbbb) bc <- BoxCoxTrans(ratio) bc predict(bc, ratio[1:5]) ratio[5] <- NA bc2 <- BoxCoxTrans(ratio, bbbdescr$tpsa, na.rm = TRUE) bc2 manly <- expotrans(ratio) manly
16 16 calibration calibration Probability Calibration Plot Description Usage For classification models, this function creates a calibration plot that describes how consistent model probabilities are with observed event rates. calibration(x,...) ## S3 method for class formula calibration(x, data = NULL, class = NULL, cuts = 11, subset = TRUE, lattice.options = NULL,...) ## S3 method for class calibration xyplot(x, data,...) panel.calibration(...) Arguments x data class cuts a lattice formula (see xyplot for syntax) where the left-hand side of the formula is a factor class variable of the observed outcome and the right-hand side specifies one or model columns corresponding to a numeric ranking variable for a model (e.g. class probabilities). The classification variable should have two levels. For calibration.formula, a data frame (or more precisely, anything that is a valid envir argument in eval, e.g., a list or an environment) containing values for any variables in the formula, as well as groups and subset if applicable. If not found in data, or if data is unspecified, the variables are looked for in the environment of the formula. This argument is not used for xyplot.calibration. a character string for the class of interest If a single number this indicates the number of splits of the data are used to create the plot. By default, it uses as many cuts as there are rows in data. If a vector, these are the actual cuts that will be used. subset An expression that evaluates to a logical or integer indexing vector. It is evaluated in data. Only the resulting rows of data are used for the plot. lattice.options A list that could be supplied to lattice.options... options to pass through to xyplot or the panel function (not used in calibration.formula).
17 calibration 17 Details Value calibration.formula is used to process the data and xyplot.calibration is used to create the plot. To construct the calibration plot, the following steps are used for each model: 1. The data are split into cuts - 1 roughly equal groups by their class probabilities 2. the number of samples with true results equal to class are determined 3. the event rate is determined for each bin xyplot.calibration produces a plot of the observed event rate by the mid-point of the bins. This implementation uses the lattice function xyplot, so plot elements can be changed via panel functions, trellis.par.set or other means. calibration uses the panel function panel.calibration by default, but it can be changed by passing that argument into xyplot.calibration. The following elements are set by default in the plot but can be changed by passing new values into xyplot.calibration: xlab = "Bin Midpoint", ylab = "Observed Event Percentage", type = "o", ylim = extendrange(c(0, 100)),xlim = extendrange(c(0, 100)) and panel = panel.calibration calibration.formula returns a list with elements: data cuts class probnames the data used for plotting the number of cuts the event class the names of the model probabilities xyplot.calibration returns a lattice object Author(s) See Also Max Kuhn, some lattice code and documentation by Deepayan Sarkar xyplot, trellis.par.set Examples ## Not run: data(mdrr) mdrrdescr <- mdrrdescr[, -nearzerovar(mdrrdescr)] mdrrdescr <- mdrrdescr[, -findcorrelation(cor(mdrrdescr),.5)] intrain <- createdatapartition(mdrrclass) trainx <- mdrrdescr[intrain[[1]], ] trainy <- mdrrclass[intrain[[1]]]
18 18 caretfuncs testx <- mdrrdescr[-intrain[[1]], ] testy <- mdrrclass[-intrain[[1]]] library(mass) ldafit <- lda(trainx, trainy) qdafit <- qda(trainx, trainy) testprobs <- data.frame(obs = testy, lda = predict(ldafit, testx)$posterior[,1], qda = predict(qdafit, testx)$posterior[,1]) calibration(obs ~ lda + qda, data = testprobs) calplotdata <- calibration(obs ~ lda + qda, data = testprobs) calplotdata xyplot(calplotdata, auto.key = list(columns = 2)) ## End(Not run) caretfuncs Backwards Feature Selection Helper Functions Description Usage Ancillary functions for backwards selection picksizetolerance(x, metric, tol = 1.5, maximize) picksizebest(x, metric, maximize) pickvars(y, size) caretfuncs lmfuncs rffuncs treebagfuncs ldafuncs nbfuncs gamfuncs lrfuncs Arguments x metric a matrix or data frame with the performance metric of interest a character string with the name of the performance metric that should be used to choose the appropriate number of variables
19 caretfuncs 19 maximize tol y size a logical; should the metric be maximized? a scalar to denote the acceptable difference in optimal performance (see Details below) a list of data frames with variables Overall and var an integer for the number of variables to retain Details This page describes the functions that are used in backwards selection (aka recursive feature elimination). The functions described here are passed to the algorithm via the functions argument of rfecontrol. See rfecontrol for details on how these functions should be defined. The pick functions are used to find the appropriate subset size for different situations. pickbest will find the position associated with the numerically best value (see the maximize argument to help define this). picksizetolerance picks the lowest position (i.e. the smallest subset size) that has no more of an X percent loss in performances. When maximizing, it calculates (O-X)/O*100, where X is the set of performance values and O is max(x). This is the percent loss. When X is to be minimized, it uses (X-O)/O*100 (so that values greater than X have a positive "loss"). The function finds the smallest subset size that has a percent loss less than tol. Both of the pick functions assume that the data are sorted from smallest subset size to largest. Author(s) See Also Max Kuhn rfecontrol, rfe Examples ## For picking subset sizes: ## Minimize the RMSE example <- data.frame(rmse = c(1.2, 1.1, 1.05, 1.01, 1.01, 1.03, 1.00), Variables = 1:7) ## Percent Loss in performance (positive) example$pctloss <- (example$rmse - min(example$rmse))/min(example$rmse)*100 xyplot(rmse ~ Variables, data= example) xyplot(pctloss ~ Variables, data= example) absolutebest <- picksizebest(example, metric = "RMSE", maximize = FALSE) within5pct <- picksizetolerance(example, metric = "RMSE", maximize = FALSE) cat("numerically optimal:", example$rmse[absolutebest], "RMSE in position", absolutebest, "\n")
20 20 caretsbf cat("accepting a 1.5 pct loss:", example$rmse[within5pct], "RMSE in position", within5pct, "\n") ## Example where we would like to maximize example2 <- data.frame(rsquared = c(0.4, 0.6, 0.94, 0.95, 0.95, 0.95, 0.95), Variables = 1:7) ## Percent Loss in performance (positive) example2$pctloss <- (max(example2$rsquared) - example2$rsquared)/max(example2$rsquared)*100 xyplot(rsquared ~ Variables, data= example2) xyplot(pctloss ~ Variables, data= example2) absolutebest2 <- picksizebest(example2, metric = "Rsquared", maximize = TRUE) within5pct2 <- picksizetolerance(example2, metric = "Rsquared", maximize = TRUE) cat("numerically optimal:", example2$rsquared[absolutebest2], "R^2 in position", absolutebest2, "\n") cat("accepting a 1.5 pct loss:", example2$rsquared[within5pct2], "R^2 in position", within5pct2, "\n") caretsbf Selection By Filtering (SBF) Helper Functions Description Usage Ancillary functions for univariate feature selection anovascores(x, y) gamscores(x, y) caretsbf lmsbf rfsbf treebagsbf ldasbf nbsbf Arguments x y a matrix or data frame of numeric predictors a numeric or factor vector of outcomes
21 cars 21 Details More details on these functions can be found at html#filter. This page documents the functions that are used in selection by filtering (SBF). The functions described here are passed to the algorithm via the functions argument of sbfcontrol. See sbfcontrol for details on how these functions should be defined. anovascores and gamscores are two examples of univariate filtering functions. anovascores fits a simple linear model between a single feature and the outcome, then the p-value for the whole model F-test is returned. gamscores fits a generalized additive model between a single predictor and the outcome using a smoothing spline basis function. A p-value is generated using the whole model test from summary.gam and is returned. If a particular model fails for lm or gam, a p-value of 1 is returned. Author(s) See Also Max Kuhn sbfcontrol, sbf, summary.gam cars Kelly Blue Book resale data for 2005 model year GM cars Description Kuiper (2008) collected data on Kelly Blue Book resale data for 804 GM cars (2005 model year). Usage data(cars) Value cars data frame of the suggested retail price (column Price) and various characteristics of each car (columns Mileage, Cylinder, Doors, Cruise, Sound, Leather, Buick, Cadillac, Chevy, Pontiac, Saab, Saturn, convertible, coupe, hatchback, sedan and wagon) Source Kuiper, S. (2008). Introduction to Multiple Regression: How Much Is Your Car Worth?, Journal of Statistics Education, Vol. 16, html
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text:
Israel News - Stay Informed, Pray Informed
While Israel is focusing its attention on mounting Iranian and Syrian threats, Egypt may be the country most actively setting the stage for a fourth pan-Arab attempt to annihilate the Jewish state, warned a prominent Israeli lawmaker on Wednesday.
In an interview with Arutz 7, Likud Member of Knesset Yuval Steinitz accused Egypt of very cleverly making visible diplomatic efforts to broker a ceasefire between Israel and the Palestinians, while behind the scenes Cairo is doing everything to make sure Palestinian terror groups are able to continue draining the energy and resources of the Israel Defense Forces (IDF).
Steinitz, a former chairman of the Knesset Foreign Affairs and Defense Committee, said that Egypt is facilitating ongoing conflict between Israel and the Palestinian Authority-controlled Gaza Strip by allowing terrorists to smuggle enormous quantities of explosives and other weapons into the area.
The lawmaker has long charged that Egypt could easily put a halt to the arms smuggling at any moment, but prefers not to because Cairo believes the Gaza-based terrorists are succeeding in weakening the IDF.
What's more, noted Steinitz, is that Egypt is actively cooperating with Syria and Iran, raising the possibility that if war breaks out this summer, as some defense officials believe, Israel's southern neighbor and ostensible peace partner may join in the fray.
Three years ago, Tom Lantos, then the ranking Democrat in the US House of Representatives International Relations Committee, tried to curtail American military aid to Egypt over what he called preparations by the latter to make war on Israel.
Lantos noted that Egypt continues to bolster its massive military machine, despite having no serious outside threats to its sovereignty. The congressman also called attention to the fact that "Egyptian military exercises are ominously geared toward an Israeli enemy that doesn't obviously exist."
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subset: commoncrawl


text:
Welcome to TempleMPI!
TempleMPI is a student professional organization at Temple University for the School of Tourism and Hospitality Management.
We combine education, networking, and students in creating an organization to help individuals succeed academically and professionally. Through fundraising, networking and various other opportunities, we will create a positive name for ourselves as well as our organization. We at TempleMPI feel the need to create opportunities, instead of wait for them. Join us and let us help you shape your future.
Our mission is to provide an engaging and informational experience by offering access to the industry's professionals and networking with other students.
TempleMPI is geared to build relationships with industry professionals and network with the affiliates of MPI International and PAMPI (Philadelphia's Chapter of MPI), but also attend and participate in the organization's events, meetings, and learning activities


text: I'm trying to do something super simple. Put a personal picture on my wallpaper. However, whenever I select a photo (regardless of if it is in portrait or landscape) to put as my wallpaper, it #1 forces me to crop to landscape even though it should be portrait to fit the wallpaper and #2 when added to my wallpaper is ultra-zoomed in, way more than the crop it requires me to select in #1. What is going on? This should be super simple to do! Also, is there a way to save a picture I see online to my albums? Thank you!


text:
Lima ( L ibrary for Im age A cquisition) is a project for the unified control of 2D detectors. The aim is to clearly separate hardware specific code from common software configuration and features, like setting standard acquisition parameters (exposure time, external trigger), file saving and image processing.
A PDF version of this documentation is available: pdf documentation
If you want to be in touch with LIMA community, please send email to email@example.com. You can also subscribe to the mailing-list by sending a message to firstname.lastname@example.com with as subject subscribe lima.
There is no binary package for lima today, Lima is a very active project and many developments are ongoing and available from github.com at github.com/esrf-bliss/Lima .
However you can find stable versions track through git branches and tags on github.com.
The latest stable branch is core-1.7 and the latest release tag is core-1.7.2.
Please, feel free to fork/clone or download Lima at http://github.com/esrf-bliss/Lima .
For latest changes please consult the release notes: ReleaseNotes.txt.
(Also available under the Git repository master branch as ReleaseNotes.txt text file).


text:
The Heifer Ukraine team (all 16 of them) are hard working people. And they dont mind working under less-than ideal circumstances. Like their offices.
Pierre Ferrari, Pietro Turilli, Clinton School of Public Service student (and Heifer translator) Anatoly Shatkovskyy and myself were all warmly welcomed into Heifer Ukraines offices last Tuesday; we experienced a high degree of togetherness as we joined Golden Talent nominee Iryna Paviluk and the Heifer staff in a space better suited for about three or four bodies. Fortunately, everyone who works at Heifer is someone you dont mind being very, very close to (see photos). We watched presentations from Heifer project managers in a small office dominated by a table overflowing with the products of the projectshoney, cheeses, sour cream, milk, hand-knit sheeps wool socks, butter, literature.
|Big presentations in a small room (old office).
|Tour of the new Heifer Ukraine office building.
Immediately after the presentations (and sampling the delicious products, of course), we drove a few minutes to Heifers new offices. Situated on the second floor of a modern 23 story building in Kiev, Heifer Ukraines new offices will be ready early in 2012. The staff is ready NOW. Theyve begun the arduous task of packing everything that goes into creating, administering and documenting Heifer projects you can see a few binders on the brand spanking new shelves already.
Join Heifer HQ in wishing the Ukraine team continued success in their new, bigger and efficient offices.
|(Left to right) Svitlana Fedchyshena, Pietro Turilli, Viktor Teres, Pierre Ferrari, Natalia Zaharkiv
|Pietro Turilli, Pierre Ferrari, Viktor Teres, Tetiana Nychyporenko, Iryna Paviluk, Natalia Zaharkiv, Anna Pidgorna and Svitlana Fedchysena toast the new office.
|A Ukrainian tradition: tossing coins on the floor of a new dwelling for good luck.


text: Another of the samples I cut and polished: This flowstone originates from an exposed karst cavity. The sample is taken from the floor of the former cave that is now infilled with flowstone and other sediments. These deposits form where water flows over rock walls or floors. Looks pretty cool, no?


text:
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- Our daily walk. The temperature is much milder today. Makes our walk so much more pleasant. I was fascinated by fences and gates as you can see. #ourdailywalk #fences #gates #photochallenge #taleswithfriendsInstagram has memories now. Well, this is a nice one- my almost first geisha. Ta - dah! #memories #geisha #taleswithfriendsOne minute of puppy joy - Rudy and Sheba. A video from Sheba's puppy hood. I think she is 3 years old here. #video #puppyjoy #sheba #rudy #taleswithfriendsA little one minute roadtrip. Down memory lane without Sheba. #video #roadtrip #memorylane #taleswithfriendsOur daily walk. It's a minus 30 C with the windchill. The back alleys has the garbage bins and throwaway food and wrappers on the ground. Sheba can smell them under snow. Too cold to wrestle with her for them. I try to capture interesting features along the way. Today it's fences. #ourdailywalk #sheba #backalleys #garbagebins #throwawayfood #fences #taleswithfriends55 seconds of Sheba-nanigans. #video #sheba #shenanigans #taleswithfriendsOur daily walk. Struggling with dog on a rope, walking stick and camera. We have different ideas of where to go. I want the park but she won. Not much snow on our solar panels but slant of sun not optimal for production. Another month or so to perk up. #ourdailywalk #struggling #park #solarpanels #taleswithfriendsPieces of my forgotten self. Funny how bits of my life as Wonder Woman keeps showing up even after years of hanging up my duty shoes. Last night the new drama on CBC, Unspeakable, brought back all the memories. It's based on the tainted blood scandal in Canada in the 1980s. Many people was infected with HIV and Hepatitis C. I remembered our first AIDS patient. I remembered our first code of an AIDS patient, the chaos, the uncertainties, the anxieties. No need for worrying about Alzheimer's yet for me. This time, the past memories gave me good feelings of 'I was a good nurse.' I cared for people. I am over my PTSD. #piecesofmyself #memories #mypast #ptsd #nurse #taleswithfriendsGiving some of my plants much neglected TLC. I'm thinking oh Mary Oliver. She died today at age 83. I'm thinking of her words. What is it that I plan to do with my one wild and precious life? The Summer Day Who made the world? Who made the swan, and the black bear? Who made the grasshopper? This grasshopper, I mean- the one who has flung herself out of the grass, the one who is eating sugar out of my hand, who is moving her jaws back and forth instead of up and down- who is gazing around with her enormous and complicated eyes. Now she lifts her pale forearms and thoroughly washes her face. Now she snaps her wings open, and floats away. I don't know exactly what a prayer is. I do know how to pay attention, how to fall down into the grass, how to kneel down in the grass, how to be idle and blessed, how to stroll through the fields, which is what I have been doing all day. Tell me, what else should I have done? Doesn't everything die at last, and too soon? Tell me, what is it you plan to do with your one wild and precious life? —Mary Oliver #tlc #repotting #maryoliver #words #myonewildandpreciouslife #taleswithfriendsIt's hard to make bread in 1 minute. 😤 Hope it brings a minute of giggles. Hope I didn't leave out any steps. #video #makingbread #oneminute #taleswithfriends
Tag Archives: cooking
How we spend our days is, of course, how we spend our lives. – Annie Dillard I wish I could write such insights as Annie Dillard. But then I'm not a Pulitizer winning authour. She has written many books. I … Continue reading
Posted in Uncategorized Tagged baking bread, cleaning and washing, contemplating, cooking, gardening, hobbies, how I spend my days, life, muse, painting, Reading, sewing, walking the dog, writing Leave a comment
The sun is pouring into my space. I loathe to leave its warmth and brightness. I shall linger here for a bit with my decaf. The pasta for beef stroganoff and Sheba's biscuit can wait awhile. It's easy when you … Continue reading
Posted in Uncategorized Tagged beef stroganoff, cleaning up, cooking, first time, gizmo, hold the egg, life, making pasta, pasta maker, writing 3 Comments
LEARNING & NOVELTY – Day 62 in a year of…
Day 62, September 22, 2016 @6:05 pm Day 62 finds me here a little earlier but I am depleted. I am fortifying myself with a few chocolates. Hope they can give me a little boost. I am a bit of … Continue reading
Posted in Uncategorized Tagged a year of change, cooking, day 62, learning, life, novelty, stir fry, writing Leave a comment
Hurray, it is 6:30 am and the sun is up and so am I. The house is suffused with the quiet and soft light of early morning. I love this time of the day when the city is still asleep. … Continue reading
Posted in Uncategorized Tagged baking, building, cooking, life, morning, musings, passions, whims Leave a comment


text: Watch this delicate hummingbird feast upon the nectar of these brightly colored flowers. It flies right above a deep orange bloom, sticking its beak into the pollen. The other flowers are just as vibrant in tones of red, aqua, and yellow. They are all surrounded by healthy green leaves for a vigorous appearance.


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Hide Forgot
jakarta-commons-discovery-0.4-5.3.fc12.src.rpm Failed To Build From Source against the rawhide tree. See http://fedoraproject.org/wiki/FTBFS for more information.
If you believe this is actually a bug in another package, do NOT change the component in this bug or close this bug. Instead, add the appropriate bug number from the other package to the "Depends on" line in this bug. If the other package does not yet have a bug created that you think matches, please create one. Doing so helps us properly track bugs and their dependencies, just as we track package dependencies. (If you close this bug, and the other package is not fixed before the next FTBFS run, a new bug will get created. Please follow the above advice to avoid such duplication.)
Created attachment 419904 [details]
root.log for i386
Created attachment 419905 [details]
build.log for i386
Created attachment 419906 [details]
mock.log for i386
Created attachment 419907 [details]
root.log for x86_64
Created attachment 419908 [details]
build.log for x86_64
Created attachment 419909 [details]
mock.log for x86_64
This package is orphaned in rawhide. It's obsoleted by apache-commons-discovery.
gives steps that need to be taken then. It's not listed on the RetiredPackages page, and it's not blocked from the Rawhide package collection by rel-eng yet.
(In reply to comment #8)
> http://fedoraproject.org/wiki/PackageMaintainers/RetiredPackages
> gives steps that need to be taken then.
I believe you meant:
> It's not listed on the RetiredPackages
> page, and it's not blocked from the Rawhide package collection by rel-eng yet.
Putting info on RetiredPackages wiki is now considered optional. PKGDB is main source of information for this. But you are right that it's not blocked from package collection. We were doing a mass rename of several jakarta-commons packages and planned to do the clean-up after all packages were renamed. This rename is not yet finished. I would like to avoid creating 20+ bugs/rel-eng tickets. Any ideas?
This bug appears to have been reported against 'rawhide' during the Fedora 14 development cycle.
Changing version to '14'.
More information and reason for this action is here:
discovery has been properly killed.


text:
The SimCity launch was a well publicised disaster. Between server failures, missing or broken features, and general outcry from fans of the series, EA took an internet beating. But now that the dust has settled, and Maxis have released their first major patches to repair some of the damage, what are we left with?
Good to see another balanced review of the game that doesn't pander to the rage.
The reviewer sounded as if he was paid to write a good review for the game.


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great service i'd say
Every once in a while there are some posts about renting a 4x4. Here is something that happened to us a few weeks back. We were in the CKR on our way to piper pans. About 20K before the pans we noticed our defender was losing power. gauges were all ok though and no warning light on. We opened the hood and discovered oil purging out of the coolent bottle. Most Lilkely cause a blow head gasket. we called Bushlore our rental company that I have used 4 times before on our sat phone. We came up with a plan to camp where we were because driving further could do more damage to the vehicle. Right away they dispatched two vehicles on there way to us. One from Windhoek and one from Kasane. We camped that night along the road hearing lion all around through out the night. The breakdown happened about 4:00 pm and by 5:00 pm the next day we had two vehicles at our camp. We took one of the vehicles for ourself and they used the other to tow the other one out. Our down time was not bad considering the distance they had to drive to get us another vehicle
great service i'd say
Work harder, as there are a lot on wellfare that depend on you!
Good advertising - thanks.
It helps to further business.
Kalahari SafariORRA Call: WB58 | ICASA ZRF430
Nissan Patrol GU TB45 | Nissan Safari GU TD42 | B'rakah 4x4 Trailer
Agree great service.
Well done bushlore!!!
Thats the back up you need!
and well done JohnU - you are the kind of customer everyone needs - accept that pawpaw textured stuff happens - and looks after the equipment too. not everyone would stop until it ground to a halt...
Sounds like a fantastic place to have broken down though


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|Dress - Evans , Leggings - Evans , Shoes - Primark
I know i'm a bit late on the whole Clements Ribeiro for Evans collection ! But i had wanted this dress from when it first came out but with a £55 price tag i just could afford it ! But the other week after trawling shops all day and finding nothing i headed into Evans as it was the last shop i passed on the way to the bus and low and behold there was a gigantic sale ! I immediately thought to this dress but didn't think the collection would have made the sale, Luckily it did ! And for more than half price at £25 so i couldn't resist ! When i got to the till the woman serving me also told me that they had amazing high waisted leggings just gone into the sale for £10 as well so i thought why not with my big saving and got a pair of those too ! I have to say i really love this dress, i wasn't too sure whether it looked a bit too long but after wearing it out i love the length and its super comfy aswell. Not only that its so easy to wear i could dress it up or down. I have to say it's my new favourite dress.
P.s. Sorry for the lack of ootd's lately my photographer ( aka my boyfriend ) has been working non stop so i haven't seen him really.


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Hood #formations and Ho shoes
I debated about writing this at all, but I think there is some conversation that needs to be had with the black community about Beyoncé.
I know we've spent the last few days discussing her activist cred, whether or not she is truly woke or truly radical. We pass judgments on people that are part of the 'beyhive' and those that are labeled as Stans.
I am not and probably never will never be a major bey fan. Her music just isn't my preferred genre, and stylistically I am not in love with what she does.
But Formation?
That song is my shit.
I've been called a stan, whacktavist, tumberlina, beyhive head, and on and on. What is interesting is that I am none of these things but I am called such because I like one Beyoncé song.
So I got to thinking *why* am I so obsessed with this song? Why do I love it so much?
And then I realized that my love of the song is based on a much larger discussion we need to have in the black community.
There's cries that Bey isn't really woke. She celebrates capitalism, she's a thot, she needs to get rid of the blonde hair, she isn't the only saying that she proud of her negro nose with Jackson 5 nostrils. That truly woke revolutionaries have been rocking afros before Blue was even conceived. That bey thinks she is on a pedestal above us. She is multi-millionaire, show stopper, first name basis celeb. The average black person cannot compete with this, so she's not here for us.
And you know what?
All that shit is 100% true. (Expect the thot part, because f**k slut shaming).
So if Bey isn't the messiah, isn't the ultimate activist, why are people obsessed with this song?
I cannot speak for others, But I can speak about what I see.
The video visually was great. The lyrics were good even if they weren't terribly complex. The dancing was on point. But aside from all of that…
The song is ratchet as f**k.
Hot sauce in my bag, swag ratchet.
Wig shop ratchet
Talmbout how hard I slay ratchet.
Talmbout getting good d**k and rewarding him with red lobster ratchet.
Basically… my life kinda ratchet.
I am black. But I am sure as sh*t not respectable. I am sex worker. I am poor. Single parent. I spend money on ho shoes and fake nails. I'm bisexual. I drink too much, curse more than I should, and try to live life to the fullest.
I am the kind of black chick who will go to red lobster with my own hot sauce. I drink cuervo, no chaser, f**k training wheels.
I am not the girl you bring home to your mother. I am not a "good" girl. I will not go to church with you.
I don't speak academics, I speak hood.
I do not act like a proper queen, I will never call my man kang.
And this leaves me on the outside of the black movement most of the time.
yeah, I went to Ferguson to deliver supplies. Yes, I have a trip to Flint in my future. Yes, I go to rallies and marches for #blacklivesmatter. I talk online about white supremacy, colonialism, patriarchy, and misogynoir. I blog, write think pieces, and podcast and talk about black shit. A LOT.
But I am always on the outside, because at the end of the day, my life is not compatible enough with the respectability that is required to officially be *in* the movement. I stay on the outside pushing for change because I think white supremacy is a threat, but I also know deep in my heart a lot of black people who are fighting for blackness aren't fighting for my blackness.
My blackness is tainted with poverty, queerness, and too much sex. My blackness is judged because it involves anti-depressants and a latinx identity. My blackness is not middle class or college educated. My blackness is authentic, but often it's not seen as good enough.
There's a lot of black people who fight for blackademics, but not for high school dropouts.
They fight for nation building and black love, as long it isn't queer.
They fight for black people who have a good backstory and were murdered by cops, but not for black addicts who get killed by cops
They fight for clean water, but not for those who need access to mental health medications.
They fight for respectable women who are done wrong by white supremacy, but won't raise a finger to help rape victims and domestic violence victims.
They say BLACK LIVES MATTER at rallies, but not to the homeless, the sex workers, black people in prison.
I love blackness in general, but not everyone loves mine back.
In many ways, my inability to stay respectable has left me on the outside of the movement.
That is why this song speaks to me.
Even though I am aware that Beyoncé probably doesn't live her life the way this particular video portrays, I appreciate the lyrics. Best of all because of the sound of the song + Beyoncé's reach, this song will blow up.
I can go to a gay club and hear this song, and watch everyone flip their sh*t. There will be other queer girls and gay boys dancing to this song. We will feel authentically black while it plays. We can be black and queer and slay without being told we're "destroying the black family unit" or "unnatural perversions"
This song will be in the background when black women get weaves and get their nails done. This song will be playing in a store where I get my ho… I mean… work shoes. And we will sing along and be black, and be happy for 4 minutes without being told we are the problem with the black community.
This song will be my theme, even if it on some levels it is basic as hell. But this not a problem with Beyoncé. This is a problem with the black community at large.
Instead of chastising us for feasting on basic scraps, you should be asking you never let us at your table.
You can complain that the song isn't revolutionary enough, but then who else is mainstream and black and making dance anthems for the lgtbqia community and women in general? Woke male artists who mock weaves and only want "good" women? Respectable woke queens who call the lgtbqia community a perversion and are too polite to twerk?
Ratchet, ghetto, hood ass black women and femmes matter too. And we've done enough to be deserving of a theme song.
You wanna talk about how the song is not radical? How Beyonce is not a "real" activist? Address yourself first. Why did a simple statement about afros and negro noses over a beat resonate with so many of us? How did she manage to give us something the black movement at large has denied us?
Maybe Beyoncé isn't a revolutionary. but she won this round because nobody within the black movement even fucking competed with her to include us.
Let that marinate while I get these shoes on. and remember.


text: Fall is here and Thanksgiving holiday right around the corner. The end of the year is packed with opportunities to entertain, socialize and travel. In the next 45 days, there will be celebrations for over 24 different religious and culturally significant events. If you observe them all, that's an event every three to four days. Entertaining during the holidays can be easy with a little pre-planning. So, get out the red pen and mark your calendars, or set digital reminders on your smartphone or tablet to help you complete your holiday planning checklist tasks. Holiday Countdown Checklist Four to Six Weeks Before the Holiday 1.) Mail invitations to your guests. If guests are expected to contribute to the celebration by bringing food or drink, it's ideal to touch base before the invite is sent. The invitation should confirm your expectations. 2.) Will guests be spending the night at your home? If so, it's important to inventory your linens to make sure you'll have extras for guests. If you want to upgrade your collection, you may consider Belgian Huis or F&B Linens. 3.) Is this a gift-giving holiday? If so, make a list of who you'll be buying gifts for now, and start to shop for them over the next few weeks. Shopping online could make things simpler for you this season. Consider visiting PointShop for online comparison shopping of over 300 merchants. Two to Three Weeks Before the Holiday 1.) Confirm arrival times and any food allergies with guests. 2.) Decide on a menu. Will it be casual buffet or formal sit-down dining? Will it be catered or home made food? Is your current set of cookware in need of an update? If so, consider checking out La Belle Cuisine for great deals on Le Creuset, Copper Cookware and French Porcelain. Prepare any freezer safe foods ahead time to spend less time in the kitchen during the holiday week. 3.) Housekeeping. Divide the major chores into smaller chunks to make them manageable. Enlist the help of your family and share the work. 4.) Complete your gift shopping to allow time for wrapping. The Week of the Holiday 1.) Touch base with guests before they depart their home to see if there are any last minute items you will need for them. 2.) Ensure all items are complete on your housekeeping list. Place a few welcome gifts in your guests rooms. The Candy Shop is a great place to get snacks to make your guests feel welcome. 3.) Complete final grocery shopping trip, and prepare what you can ahead of time. 4.) Relax. If you didn't get everything done on your checklist, it will be OK. Remember you're guests are coming to see you. Resource: Upcoming U.S. Holidays from TimeAndDate.com NOVEMBER Holidays 2012 Thursday, Nov 1 - All Saint's Day - Christian Friday, Nov 2 - All Soul's Day - Christian Sunday, Nov 4 - Daylight Saving Time Ends - Time Falls Back Tuesday, Nov 6 - Election Day - Observance Sunday, Nov 11 - Veterans Day - National holiday Tuesday, Nov 13 - Diwali / Deepavali - Observance Thursday, Nov 15 - Muharram / Islamic New Year - Muslim Thursday, Nov 22 - Thanksgiving Day - National holiday Friday, Nov 23 - Black Friday - State holiday 17 states DECEMBER Holidays 2012 Sunday, Dec 2 - First Sunday Advent - Christian Friday, Dec 7 - Pearl Harbor Rememberance Day - Observance Saturday, Dec 8 - Feast of the Immaculate Conception - Christian Sunday, Dec 9 - Dec 16 - Chanuka- Jewish holiday Friday, Dec 21 - December Solstice - Season Monday, Dec 24 - Christmas Eve - Christian Tuesday, Dec 25 - Christmas Day - National holiday, Christian Wednesday, Dec 26 - Jan 1 - Kwanzaa - Observance Monday, Dec 31 - New Year's Eve - Observance Posted: Monday, November 05, 2012


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