You can check the permissions on all files and folders in the current directory by using the command:. This will show you the permissions for every file and folder, in the same way as your FTP program does. This post is wonderful. Probably best for me to explain my issue…. Yet, I am unable to view the folder at my domain now?! How much do I need to set in order for you and other to be able to goto mywebsite. Depending on your web server setup this might be or Very good post. Making me sign up even says how good and interesting to read your article is.
Wonderful article. I have inherited a web business and am trying so hard to learn so I can continue to help customers as I try to shut parts of the business down.
Thank you for the clear and concise tutorial. In here the owner permission contains 4 characters, read permission 2 times here. That 4th character r is part of the group permission, not the owner. There are 9 characters in total 3 sets of 3, for owner, group, and other respectively. Not a permission for one of the three groups.
This is a great post. But no where on the internet does it say who is in this Group. The middle set. I have done all the things you wrote there. I still cant access the files. Oh… Hi again! Your email address will not be published. Improve this question. Programmatically or do you just need to know how to translate?
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Sign up using Facebook. Thus the implicit parameterization is to contrast the response at each level with that at the first. For ordered factors the k - 1 columns are the orthogonal polynomials on 1, …, k , omitting the constant term. Although the answer is already complicated, it is not the whole story. First, if the intercept is omitted in a model that contains a factor term, the first such term is encoded into k columns giving the indicators for all the levels.
Second, the whole behavior can be changed by the options setting for contrasts. The default setting in R is. The main reason for mentioning this is that R and S have different defaults for unordered factors, S using Helmert contrasts. So if you need to compare your results to those of a textbook or paper which used S-PLUS , you will need to set. We have still not finished, as the contrast scheme to be used can be set for each term in the model using the functions contrasts and C.
We have not yet considered interaction terms: these generate the products of the columns introduced for their component terms. Although the details are complicated, model formulae in R will normally generate the models that an expert statistician would expect, provided that marginality is preserved. Fitting, for example, a model with an interaction but not the corresponding main effects will in general lead to surprising results, and is for experts only.
Next: Generic functions for extracting model information , Previous: Defining statistical models; formulae , Up: Statistical models in R [ Contents ][ Index ]. The basic function for fitting ordinary multiple models is lm , and a streamlined version of the call is as follows:. This is the case regardless of whether data frame production has been attached on the search path or not. The value of lm is a fitted model object; technically a list of results of class "lm". Information about the fitted model can then be displayed, extracted, plotted and so on by using generic functions that orient themselves to objects of class "lm".
These include. The data frame supplied must have variables specified with the same labels as the original. The value is a vector or matrix of predicted values corresponding to the determining variable values in data. Select a suitable model by adding or dropping terms and preserving hierarchies.
It should be noted that in addition aov allows an analysis of models with multiple error strata such as split plot experiments, or balanced incomplete block designs with recovery of inter-block information. The model formula. In the simplest case, strata. Previous: Analysis of variance and model comparison , Up: Analysis of variance and model comparison [ Contents ][ Index ]. Note also that the analysis of variance table or tables are for a sequence of fitted models. The sums of squares shown are the decrease in the residual sums of squares resulting from an inclusion of that term in the model at that place in the sequence.
Hence only for orthogonal experiments will the order of inclusion be inconsequential. For multistratum experiments the procedure is first to project the response onto the error strata, again in sequence, and to fit the mean model to each projection. A more flexible alternative to the default full ANOVA table is to compare two or more models directly using the anova function.
The display is then an ANOVA table showing the differences between the fitted models when fitted in sequence. The fitted models being compared would usually be an hierarchical sequence, of course.
This does not give different information to the default, but rather makes it easier to comprehend and control. The update function is largely a convenience function that allows a model to be fitted that differs from one previously fitted usually by just a few additional or removed terms. Its form is. In the new. Other functions for exploring incremental sequences of models are add1 , drop1 and step.
The names of these give a good clue to their purpose, but for full details see the on-line help. Generalized linear modeling is a development of linear models to accommodate both non-normal response distributions and transformations to linearity in a clean and straightforward way.
A generalized linear model may be described in terms of the following sequence of assumptions:. So it is assumed that the distribution of y is determined by its mean and possibly a scale parameter as well. These assumptions are loose enough to encompass a wide class of models useful in statistical practice, but tight enough to allow the development of a unified methodology of estimation and inference, at least approximately.
The class of generalized linear models handled by facilities supplied in R includes gaussian , binomial , poisson , inverse gaussian and gamma response distributions and also quasi-likelihood models where the response distribution is not explicitly specified. In the latter case the variance function must be specified as a function of the mean, but in other cases this function is implied by the response distribution. Each response distribution admits a variety of link functions to connect the mean with the linear predictor.
Those automatically available are shown in the following table:. The combination of a response distribution, a link function and various other pieces of information that are needed to carry out the modeling exercise is called the family of the generalized linear model. Since the distribution of the response depends on the stimulus variables through a single linear function only , the same mechanism as was used for linear models can still be used to specify the linear part of a generalized model.
The family has to be specified in a different way. The only new feature is the family. It is the name of a function that generates a list of functions and expressions that together define and control the model and estimation process. Although this may seem a little complicated at first sight, its use is quite simple. Where there is a choice of links, the name of the link may also be supplied with the family name, in parentheses as a parameter. In the case of the quasi family, the variance function may also be specified in this way.
Note how the gaussian family is not automatically provided with a choice of links, so no parameter is allowed. If a problem requires a gaussian family with a nonstandard link, this can usually be achieved through the quasi family, as we shall see later. On the Aegean island of Kalythos the male inhabitants suffer from a congenital eye disease, the effects of which become more marked with increasing age.
Samples of islander males of various ages were tested for blindness and the results recorded. The data is shown below:. Since the logit link is the default the parameter may be omitted on the second call. To see the results of each fit we could use. With the Poisson family the default link is the log , and in practice the major use of this family is to fit surrogate Poisson log-linear models to frequency data, whose actual distribution is often multinomial.
This is a large and important subject we will not discuss further here. It even forms a major part of the use of non-gaussian generalized models overall. Occasionally genuinely Poisson data arises in practice and in the past it was often analyzed as gaussian data after either a log or a square-root transformation. As a graceful alternative to the latter, a Poisson generalized linear model may be fitted as in the following example:.
For all families the variance of the response will depend on the mean and will have the scale parameter as a multiplier. For quasi-likelihood estimation and inference the precise response distribution is not specified, but rather only a link function and the form of the variance function as it depends on the mean.
Since quasi-likelihood estimation uses formally identical techniques to those for the gaussian distribution, this family provides a way of fitting gaussian models with non-standard link functions or variance functions, incidentally. Supposing a suitable data frame to be set up we could fit this non-linear regression as. Certain forms of nonlinear model can be fitted by Generalized Linear Models glm.
But in the majority of cases we have to approach the nonlinear curve fitting problem as one of nonlinear optimization.
We seek the parameter values that minimize some index of lack-of-fit, and they do this by trying out various parameter values iteratively.
Unlike linear regression for example, there is no guarantee that the procedure will converge on satisfactory estimates. All the methods require initial guesses about what parameter values to try, and convergence may depend critically upon the quality of the starting values. Next: Maximum likelihood , Previous: Nonlinear least squares and maximum likelihood models , Up: Nonlinear least squares and maximum likelihood models [ Contents ][ Index ].
One way to fit a nonlinear model is by minimizing the sum of the squared errors SSE or residuals. This method makes sense if the observed errors could have plausibly arisen from a normal distribution. In order to do the fit we need initial estimates of the parameters. One way to find sensible starting values is to plot the data, guess some parameter values, and superimpose the model curve using those values.
To obtain the approximate standard errors SE of the estimates we do:. The 2 which is subtracted in the line above represents the number of parameters.
We can superimpose the least squares fit on a new plot:. The standard package stats provides much more extensive facilities for fitting non-linear models by least squares. The model we have just fitted is the Michaelis-Menten model, so we can use. Previous: Least squares , Up: Nonlinear least squares and maximum likelihood models [ Contents ][ Index ].
Maximum likelihood is a method of nonlinear model fitting that applies even if the errors are not normal. The method finds the parameter values which maximize the log likelihood, or equivalently which minimize the negative log-likelihood.
Here is an example from Dobson , pp. This example fits a logistic model to dose-response data, which clearly could also be fit by glm. The data are:. To obtain the approximate SEs of the estimates we do:. We conclude this chapter with just a brief mention of some of the other facilities available in R for special regression and data analysis problems.
Function loess is in the standard package stats , together with code for projection pursuit regression. Models are again specified in the ordinary linear model form. The model fitting function is tree , but many other generic functions such as plot and text are well adapted to displaying the results of a tree-based model fit in a graphical way.
Tree models are available in R via the user-contributed packages rpart and tree. Graphical facilities are an important and extremely versatile component of the R environment. It is possible to use the facilities to display a wide variety of statistical graphs and also to build entirely new types of graph. The graphics facilities can be used in both interactive and batch modes, but in most cases, interactive use is more productive.
Interactive use is also easy because at startup time R initiates a graphics device driver which opens a special graphics window for the display of interactive graphics. A new device can always be opened by dev. Once the device driver is running, R plotting commands can be used to produce a variety of graphical displays and to create entirely new kinds of display.
In addition, R maintains a list of graphical parameters which can be manipulated to customize your plots. A separate graphics sub-system in package grid coexists with base — it is more powerful but harder to use. There is a recommended package lattice which builds on grid and provides ways to produce multi-panel plots akin to those in the Trellis system in S. High-level plotting functions are designed to generate a complete plot of the data passed as arguments to the function.
Where appropriate, axes, labels and titles are automatically generated unless you request otherwise. High-level plotting commands always start a new plot, erasing the current plot if necessary. One of the most frequently used plotting functions in R is the plot function. This is a generic function: the type of plot produced is dependent on the type or class of the first argument.
If x and y are vectors, plot x , y produces a scatterplot of y against x. The same effect can be produced by supplying one argument second form as either a list containing two elements x and y or a two-column matrix. If x is a time series, this produces a time-series plot. If x is a numeric vector, it produces a plot of the values in the vector against their index in the vector.
If x is a complex vector, it produces a plot of imaginary versus real parts of the vector elements. The first form generates a bar plot of f ; the second form produces boxplots of y for each level of f.
The first two forms produce distributional plots of the variables in a data frame first form or of a number of named objects second form. The third form plots y against every object named in expr. R provides two very useful functions for representing multivariate data.
If X is a numeric matrix or data frame, the command. When three or four variables are involved a coplot may be more enlightening. If a and b are numeric vectors and c is a numeric vector or factor object all of the same length , then the command. If c is a factor, this simply means that a is plotted against b for every level of c. When c is numeric, it is divided into a number of conditioning intervals and for each interval a is plotted against b for values of c within the interval.
The number and position of intervals can be controlled with given. You can also use two given variables with a command like.
An example panel function useful for coplots is panel. Next: Arguments to high-level plotting functions , Previous: Displaying multivariate data , Up: High-level plotting commands [ Contents ][ Index ]. Distribution-comparison plots. The first form plots the numeric vector x against the expected Normal order scores a normal scores plot and the second adds a straight line to such a plot by drawing a line through the distribution and data quartiles.
The third form plots the quantiles of x against those of y to compare their respective distributions. Produces a histogram of the numeric vector x. Constructs a dotchart of the data in x. In a dotchart the y -axis gives a labelling of the data in x and the x -axis gives its value. For example it allows easy visual selection of all data entries with values lying in specified ranges. Plots of three variables. The image plot draws a grid of rectangles using different colours to represent the value of z , the contour plot draws contour lines to represent the value of z , and the persp plot draws a 3D surface.
There are a number of arguments which may be passed to high-level graphics functions, as follows:. Forces the function to act as a low-level graphics function, superimposing the plot on the current plot some functions only.
Suppresses generation of axes—useful for adding your own custom axes with the axis function. Causes the x , y or both axes to be logarithmic. This will work for many, but not all, types of plot. Step-function plots. In the first form, the top of the vertical defines the point; in the second, the bottom. No plotting at all. However axes are still drawn by default and the coordinate system is set up according to the data.
Ideal for creating plots with subsequent low-level graphics functions. Axis labels for the x and y axes. Use these arguments to change the default labels, usually the names of the objects used in the call to the high-level plotting function.
In this case, low-level plotting commands can be used to add extra information such as points, lines or text to the current plot. Adds points or connected lines to the current plot. Add text to a plot at points given by x, y.
Normally labels is an integer or character vector in which case labels[i] is plotted at point x[i], y[i]. The default is 1:length x. Adds a line of slope b and intercept a to the current plot. Also lm. Draws a polygon defined by the ordered vertices in x , y and optionally shade it in with hatch lines, or fill it if the graphics device allows the filling of figures.
Adds a legend to the current plot at the specified position. Plotting characters, line styles, colors etc. At least one other argument v a vector the same length as legend with the corresponding values of the plotting unit must also be given, as follows:.
Adds a title main to the top of the current plot in a large font and optionally a sub-title sub at the bottom in a smaller font. Adds an axis to the current plot on the side given by the first argument 1 to 4, counting clockwise from the bottom.
Other arguments control the positioning of the axis within or beside the plot, and tick positions and labels. Low-level plotting functions usually require some positioning information e. Coordinates are given in terms of user coordinates which are defined by the previous high-level graphics command and are chosen based on the supplied data.
Where x and y arguments are required, it is also sufficient to supply a single argument being a list with elements named x and y. Similarly a matrix with two columns is also valid input. In this way functions such as locator see below may be used to specify positions on a plot interactively.
In some cases, it is useful to add mathematical symbols and formulae to a plot. This can be achieved in R by specifying an expression rather than a character string in any one of text , mtext , axis , or title.
For example, the following code draws the formula for the Binomial probability function:. More information, including a full listing of the features available can obtained from within R using the commands:.
It is possible to specify Hershey vector fonts for rendering text when using the text and contour functions. There are three reasons for using the Hershey fonts:. More information, including tables of Hershey characters can be obtained from within R using the commands:.
R also provides functions which allow users to extract or add information to a plot using a mouse. The simplest of these is the locator function:. Waits for the user to select locations on the current plot using the left mouse button.
This continues until n default points have been selected, or another mouse button is pressed. The type argument allows for plotting at the selected points and has the same effect as for high-level graphics commands; the default is no plotting. It is particularly useful for interactively selecting positions for graphic elements such as legends or labels when it is difficult to calculate in advance where the graphic should be placed.
For example, to place some informative text near an outlying point, the command. Allow the user to highlight any of the points defined by x and y using the left mouse button by plotting the corresponding component of labels nearby or the index number of the point if labels is absent. Returns the indices of the selected points when another button is pressed.
Sometimes we want to identify particular points on a plot, rather than their positions. For example, we may wish the user to select some observation of interest from a graphical display and then manipulate that observation in some way. Given a number of x, y coordinates in two numeric vectors x and y , we could use the identify function as follows:.
The identify functions performs no plotting itself, but simply allows the user to move the mouse pointer and click the left mouse button near a point. When the process is terminated see above , identify returns the indices of the selected points; you can use these indices to extract the selected points from the original vectors x and y.
You can, however, customize almost every aspect of the display using graphics parameters. R maintains a list of a large number of graphics parameters which control things such as line style, colors, figure arrangement and text justification among many others. A separate list of graphics parameters is maintained for each active device, and each device has a default set of parameters when initialized.
Graphics parameters can be set in two ways: either permanently, affecting all graphics functions which access the current device; or temporarily, affecting only a single graphics function call. The par function is used to access and modify the list of graphics parameters for the current graphics device.
Without arguments, returns a list of all graphics parameters and their values for the current device. With named arguments or a single list argument , sets the values of the named graphics parameters, and returns the original values of the parameters as a list.
Setting graphics parameters with the par function changes the value of the parameters permanently , in the sense that all future calls to graphics functions on the current device will be affected by the new value.
Note that calls to par always affect the global values of graphics parameters, even when par is called from within a function. You can restore the initial values by saving the result of par when making changes, and restoring the initial values when plotting is complete. To save and restore all settable 25 graphical parameters use. Graphics parameters may also be passed to almost any graphics function as named arguments.
This has the same effect as passing the arguments to the par function, except that the changes only last for the duration of the function call. For example:. Unfortunately, this is not implemented entirely consistently and it is sometimes necessary to set and reset graphics parameters using par.
The following sections detail many of the commonly-used graphical parameters. The R help documentation for the par function provides a more concise summary; this is provided as a somewhat more detailed alternative. Note that axes is not a graphics parameter but an argument to a few plot methods: see xaxt and yaxt. R plots are made up of points, lines, text and polygons filled regions.
Graphical parameters exist which control how these graphical elements are drawn, as follows:. Character to be used for plotting points. The default varies with graphics drivers, but it is usually a circle.
Plotted points tend to appear slightly above or below the appropriate position unless you use ". When pch is given as an integer between 0 and 25 inclusive, a specialized plotting symbol is produced. To see what the symbols are, use the command. Those from 21 to 25 may appear to duplicate earlier symbols, but can be coloured in different ways: see the help on points and its examples.
In addition, pch can be a character or a number in the range representing a character in the current font. Line types. Alternative line styles are not supported on all graphics devices and vary on those that do but line type 1 is always a solid line, line type 0 is always invisible, and line types 2 and onwards are dotted or dashed lines, or some combination of both.
Line widths. Affects axis lines as well as lines drawn with lines , etc. Not all devices support this, and some have restrictions on the widths that can be used. Colors to be used for points, lines, text, filled regions and images. A number from the current palette see? The color to be used for axis annotation, x and y labels, main and sub-titles, respectively.
An integer which specifies which font to use for text. If possible, device drivers arrange so that 1 corresponds to plain text, 2 to bold face, 3 to italic, 4 to bold italic and 5 to a symbol font which include Greek letters. The font to be used for axis annotation, x and y labels, main and sub-titles, respectively.
Justification of text relative to the plotting position. The actual value is the proportion of text that appears to the left of the plotting position, so a value of Character expansion. The value is the desired size of text characters including plotting characters relative to the default text size. The character expansion to be used for axis annotation, x and y labels, main and sub-titles, respectively.
Axes have three main components: the axis line line style controlled by the lty graphics parameter , the tick marks which mark off unit divisions along the axis line and the tick labels which mark the units. These components can be customized with the following graphics parameters. The first two numbers are the desired number of tick intervals on the x and y axes respectively. The third number is the desired length of axis labels, in characters including the decimal point.
Choosing a too-small value for this parameter may result in all tick labels being rounded to the same number! Orientation of axis labels. Positions of axis components. The first component is the distance from the axis label to the axis position, in text lines. The second component is the distance to the tick labels, and the final component is the distance from the axis position to the axis line usually zero. Positive numbers measure outside the plot region, negative numbers inside.
Length of tick marks, as a fraction of the size of the plotting region. When tck is small less than 0. A value of 1 gives grid lines.
Negative values give tick marks outside the plotting region. Axis styles for the x and y axes, respectively. With styles "i" internal and "r" the default tick marks always fall within the range of the data, however style "r" leaves a small amount of space at the edges.
S has other styles not implemented in R. A single plot in R is known as a figure and comprises a plot region surrounded by margins possibly containing axis labels, titles, etc. The default values chosen for this parameter are often too large; the right-hand margin is rarely needed, and neither is the top margin if no title is being used. The bottom and left margins must be large enough to accommodate the axis and tick labels.
When multiple figures are in use see below the margins are reduced, however this may not be enough when many figures share the same page. R allows you to create an n by m array of figures on a single page. Each figure has its own margins, and the array of figures is optionally surrounded by an outer margin , as shown in the following figure.
Set the size of a multiple figure array. The first value is the number of rows; the second is the number of columns. The only difference between these two parameters is that setting mfcol causes figures to be filled by column; mfrow fills by rows. Setting either of these can reduce the base size of symbols and text controlled by par "cex" and the pointsize of the device. In a layout with exactly two rows and columns the base size is reduced by a factor of 0.
Position of the current figure in a multiple figure environment. The first two numbers are the row and column of the current figure; the last two are the number of rows and columns in the multiple figure array. Set this parameter to jump between figures in the array.
You can even use different values for the last two numbers than the true values for unequally-sized figures on the same page. Position of the current figure on the page. Values are the positions of the left, right, bottom and top edges respectively, as a percentage of the page measured from the bottom left corner. The example value would be for a figure in the bottom right of the page. Set this parameter for arbitrary positioning of figures within a page.
Size of outer margins. Like mar and mai , the first measures in text lines and the second in inches, starting with the bottom margin and working clockwise. Outer margins are particularly useful for page-wise titles, etc. There are no outer margins by default, however, so you must create them explicitly using oma or omi. More complicated arrangements of multiple figures can be produced by the split. R can generate graphics of varying levels of quality on almost any type of display or printing device.
Before this can begin, however, R needs to be informed what type of device it is dealing with. This is done by starting a device driver. Device drivers are started by calling a device driver function. There is one such function for every device driver: type help Devices for a list of them all.
For example, issuing the command. Some commonly-used device drivers are:. Produces a bitmap JPEG file, best used for image plots. Not always available: see its help page. When you have finished with a device, be sure to terminate the device driver by issuing the command. This ensures that the device finishes cleanly; for example in the case of hardcopy devices this ensures that every page is completed and has been sent to the printer.
This will happen automatically at the normal end of a session. By passing the file argument to the postscript device driver function, you may store the graphics in PostScript format in a file of your choice. For example, the command. It is important to note that if the file named in the command already exists, it will be overwritten. This is the case even if the file was only created earlier in the same R session. Many usages of PostScript output will be to incorporate the figure in another document.
This unusual notation stems from S-compatibility: it really means that the output will be a single page which is part of the EPSF specification. Thus to produce a plot for inclusion use something like.
In advanced use of R it is often useful to have several graphics devices in use at the same time. Of course only one graphics device can accept graphics commands at any one time, and this is known as the current device. When multiple devices are open, they form a numbered sequence with names giving the kind of device at any position.
Each new call to a device driver function opens a new graphics device, thus extending by one the device list. This device becomes the current device, to which graphics output will be sent.
Returns the number and name of all active devices. The device at position 1 on the list is always the null device which does not accept graphics commands at all. Returns the number and name of the graphics device next to, or previous to the current device, respectively. Can be used to change the current graphics device to the one at position k of the device list.
Returns the number and label of the device. Terminate the graphics device at point k of the device list. For some devices, such as postscript devices, this will either print the file immediately or correctly complete the file for later printing, depending on how the device was initiated.
Make a copy of the device k. R does not have builtin capabilities for dynamic or interactive graphics, e. However, extensive dynamic graphics facilities are available in the system GGobi by Swayne, Cook and Buja available from. Also, package rgl provides ways to interact with 3D plots, for example of surfaces. All R functions and datasets are stored in packages. Only when a package is loaded are its contents available. This is done both for efficiency the full list would take more memory and would take longer to search than a subset , and to aid package developers, who are protected from name clashes with other code.
To load a particular package e. Users connected to the Internet can use the install. Some packages may be loaded but not available on the search list see Namespaces : these will be included in the list given by. The standard or base packages are considered part of the R source code. They contain the basic functions that allow R to work, and the datasets and standard statistical and graphical functions that are described in this manual.
They should be automatically available in any R installation. There are thousands of contributed packages for R, written by many different authors. Some of these packages implement specialized statistical methods, others give access to data or hardware, and others are designed to complement textbooks.
Some the recommended packages are distributed with every binary distribution of R. Packages have namespaces , which do three things: they allow the package writer to hide functions and data that are meant only for internal use, they prevent functions from breaking when a user or other package writer picks a name that clashes with one in the package, and they provide a way to refer to an object within a particular package.
For example, t is the transpose function in R, but users might define their own function named t. There are two operators that work with namespaces. The double-colon operator :: selects definitions from a particular namespace. In the example above, the transpose function will always be available as base::t , because it is defined in the base package.
Only functions that are exported from the package can be retrieved in this way. The triple-colon operator may be seen in a few places in R code: it acts like the double-colon operator but also allows access to hidden objects. Users are more likely to use the getAnywhere function, which searches multiple packages.
Packages are often inter-dependent, and loading one may cause others to be automatically loaded. The colon operators described above will also cause automatic loading of the associated package. When packages with namespaces are loaded automatically they are not added to the search list. R has quite extensive facilities to access the OS under which it is running: this allows it to be used as a scripting language and that ability is much used by R itself, for example to install packages.
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