Variables which are not formal parameters or local variables are called free variables. Free variables become local variables if they are assigned to. Consider the following function definition.
In this function, x is a formal parameter, y is a local variable and z is a free variable. In R the free variable bindings are resolved by first looking in the environment in which the function was created. This is called lexical scope. First we define a function called cube.
The variable n in the function sq is not an argument to that function. Therefore it is a free variable and the scoping rules must be used to ascertain the value that is to be associated with it. Under lexical scope R it is the parameter to the function cube since that is the active binding for the variable n at the time the function sq was defined.
Lexical scope can also be used to give functions mutable state. In the following example we show how R can be used to mimic a bank account. A functioning bank account needs to have a balance or total, a function for making withdrawals, a function for making deposits and a function for stating the current balance. We achieve this by creating the three functions within account and then returning a list containing them.
When account is invoked it takes a numerical argument total and returns a list containing the three functions. Because these functions are defined in an environment which contains total , they will have access to its value. This operator looks back in enclosing environments for an environment that contains the symbol total and when it finds such an environment it replaces the value, in that environment, with the value of right hand side.
If the global or top-level environment is reached without finding the symbol total then that variable is created and assigned to there. Users can customize their environment in several different ways. There is a site initialization file and every directory can have its own special initialization file. Finally, the special functions. First and. Last can be used. If that variable is unset, the file Rprofile. This file should contain the commands that you want to execute every time R is started under your system.
A second, personal, profile file named. Rprofile 24 can be placed in any directory. If R is invoked in that directory then that file will be sourced. This file gives individual users control over their workspace and allows for different startup procedures in different working directories. If no. Rprofile file is found in the startup directory, then R looks for a.
Rprofile files. Any function named. First in either of the two profile files or in the. RData image has a special status. It is automatically performed at the beginning of an R session and may be used to initialize the environment. Thus, the sequence in which files are executed is, Rprofile. RData and then. A definition in later files will mask definitions in earlier files.
Similarly a function. Last , if defined, is normally executed at the very end of the session. An example is given below. The class of an object determines how it will be treated by what are known as generic functions.
Put the other way round, a generic function performs a task or action on its arguments specific to the class of the argument itself. If the argument lacks any class attribute, or has a class not catered for specifically by the generic function in question, there is always a default action provided.
An example makes things clearer. The class mechanism offers the user the facility of designing and writing generic functions for special purposes. Among the other generic functions are plot for displaying objects graphically, summary for summarizing analyses of various types, and anova for comparing statistical models.
The number of generic functions that can treat a class in a specific way can be quite large. For example, the functions that can accommodate in some fashion objects of class "data. Conversely the number of classes a generic function can handle can also be quite large. For example the plot function has a default method and variants for objects of classes "data.
A complete list can be got again by using the methods function:. The presence of UseMethod indicates this is a generic function. To see what methods are available we can use methods. In this example there are six methods, none of which can be seen by typing its name. We can read these by either of. A function named gen. The reader is referred to the R Language Definition for a more complete discussion of this mechanism.
This section presumes the reader has some familiarity with statistical methodology, in particular with regression analysis and the analysis of variance. Later we make some rather more ambitious presumptions, namely that something is known about generalized linear models and nonlinear regression. The requirements for fitting statistical models are sufficiently well defined to make it possible to construct general tools that apply in a broad spectrum of problems.
R provides an interlocking suite of facilities that make fitting statistical models very simple. As we mention in the introduction, the basic output is minimal, and one needs to ask for the details by calling extractor functions. The template for a statistical model is a linear regression model with independent, homoscedastic errors.
Suppose y , x , x0 , x1 , x2 , … are numeric variables, X is a matrix and A , B , C , … are factors. The following formulae on the left side below specify statistical models as described on the right. Both imply the same simple linear regression model of y on x. The first has an implicit intercept term, and the second an explicit one. Simple linear regression of y on x through the origin that is, without an intercept term.
Multiple regression of the transformed variable, log y , on x1 and x2 with an implicit intercept term. Polynomial regression of y on x of degree 2. The first form uses orthogonal polynomials, and the second uses explicit powers, as basis. Multiple regression y with model matrix consisting of the matrix X as well as polynomial terms in x to degree 2.
Single classification analysis of variance model of y , with classes determined by A. Single classification analysis of covariance model of y , with classes determined by A , and with covariate x. Two factor non-additive model of y on A and B. The first two specify the same crossed classification and the second two specify the same nested classification.
In abstract terms all four specify the same model subspace. Three factor experiment but with a model containing main effects and two factor interactions only. Both formulae specify the same model. Separate simple linear regression models of y on x within the levels of A , with different codings. The last form produces explicit estimates of as many different intercepts and slopes as there are levels in A.
An experiment with two treatment factors, A and B , and error strata determined by factor C. For example a split plot experiment, with whole plots and hence also subplots , determined by factor C.
The form, for an ordinary linear model, is. In all cases each term defines a collection of columns either to be added to or removed from the model matrix.
A 1 stands for an intercept column and is by default included in the model matrix unless explicitly removed. The formula operators are similar in effect to the Wilkinson and Rogers notation used by such programs as Glim and Genstat. Insulate M. Inside M all operators have their normal arithmetic meaning, and that term appears in the model matrix. Note that inside the parentheses that usually enclose function arguments all operators have their normal arithmetic meaning.
The function I is an identity function used to allow terms in model formulae to be defined using arithmetic operators.
Note particularly that the model formulae specify the columns of the model matrix , the specification of the parameters being implicit. This is not the case in other contexts, for example in specifying nonlinear models. Previous: Defining statistical models; formulae , Up: Defining statistical models; formulae [ Contents ][ Index ]. We need at least some idea how the model formulae specify the columns of the model matrix. This is easy if we have continuous variables, as each provides one column of the model matrix and the intercept will provide a column of ones if included in the model.
What about a k -level factor A? The answer differs for unordered and ordered factors. For unordered factors k - 1 columns are generated for the indicators of the second, …, k th levels of the factor. 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. By using our service you accept our Terms of Service and Privacy Policy. Online Video Downloader How to use?
Use short domain names: ssyoutube. Install the browser addon and download in 1 click. Install Helper. Install the mobile application and download in 1 click. Its a bit difficult to explain, but from what I saw all cutscenes that are rare require your character to have some kind of status effect or deffect such as "Queen" or "Virgin" to have happen.
What I found on the male goblin POV is that you have to have something in your characters rear. Other than that, it's just getting lucky to encounter a male goblin and then get defeated by them by either losing all of your HP or succumbing to Lust.
The option to get it is either hands and knees or the other one. This was also the area where the last cutscene I was missing was at LOL. You can get all 3 of the bottom hiro cutscenes in the town square of the main town basically.
I looked into it for a while and found that, unless the common person has access to a "rooted" android device, modifying files like how this windows tutorial does is impossible. I'm actually brand new to this game and have only messed around with the current build so any previous builds may be different.
Unseen events on the other hand may be pretty hard since you would probably unlock more items more quickly by flat out playing the game instead of searching the code like I have been doing. Finally, I wasn't even aware of a mechanic for the ironman points haha. If they unlock based on achievements, I can definitely hook you up with all achievements, but if not it may be too difficult to find out alone without knowing the trigger events.
Ironman achievements give 1 point, regular achievements give 0,5 points. Also, there are guides for the achievements. This guide was meant for version 0. I redownloaded the game just now to check if the devs posted a hot fix, but I didn't see any changes to how the new download looked compared to my current one. I did go through and add all of the achievements on the left side of the Progress menu, but that is probably where I will stop at since the main reason for this post was to supply the thighs with the added bonus of a maxed starting character for newer players to get a feel for combat without it seeming too difficult at first.
I've managed to change the apk to a zip and extract it. Where do I go from there? If you let me know how you changed the apk to a zip file I'd be able to make the method public most likely. I made a copy file of the apk, renamed the. It let me view some of the source code and text. I can't find profile. Updated for 0.
Well when I paste it I have all puca scenes unlocked however as far as I remember one of the scenes is bugged and sometimes disappears. I've checked it works fine for me but Puca is bugged and one of her arts sometimes disappear. Dose this work for the newest for version just found out about the game today and I'm not really good with editing the games files.
Can you tell me how to do it because last time I messed up and when I went into the game I pressed load and a error appeared. Hello, for those that want to do it on android, you need root access. Backup all your data before rooting because you can have a potential wipe on your phone.
Although those are generic steps. Look up your specific phone. Every single phone, to even same brand but different model number, all preforms different for rooting and flashing. It is a literal puzzle piece for every phone.
We are only interested in the savedatas off course. Get an app that lets you view root internal access like SD Maid. Just create the same files on your pc and then transfer over them. Innkeep: First row second and third column - Second row all columns - Third row first column - Still missing, seems to be a male version of the innkeep, but I don't know how you'd get these. Oddly there's an extra image and game over version in the game itself, but not listed in the gallery.
Finally solved, just copied gangbang scene and removed 'bloody' - It's a non bloody version of the same. Minotauress: Third row, second column - It's some 'Oral' variant, but just clicking through options doesn't unlock it.
Nothing unlockable yet, as far as I can tell, you just sorta have to go there. There aren't any scenes either, just the paperdoll of the character itself. August Version Scenes 0. Scenes added - Dark Knight scenes just variations of earlier ones, unlocked on first combat meeting Orc scenes Face scenes, unlocked as 'slave' while choosing the 'Tease' option in camp.
NOTE that this profile does not have a number of the achievements or events, but all the CG's apart from the ones mentioned above, which I assume most are looking for anyway. I got them by combining my own playthrough with the CG's of yours, plus some guesswork of the Hiro 2 page CG's.
If you want the cheeves and so on, I assume combining them should be fairly easy as you just copy over everything below 'cgSeen' from my link. I have updated my main file to reflect all of the cut-scenes of the most recent 0.
Sadly, Hiro's images are harder to find because there are so dang many of them scattered all through-out the code and the actual story so our best bet is to possibly wait for someone to respond with that scene. If it even is one. I appreciate his work a lot and it really works but on Android it doesn't,from what I saw in the comments you need root to access the save and it is really okay if it is not in parallel space , however I have root access and would like to share my profile or all the saves if necessary for your editing since they do not respond the same way as PC so I asked for your help to understand this problem.
IF you want to call me. Sorry for the late response, but from the looks of the save file you have, there shouldn't be any issue with copying the "complete" save over top of the one you currently have.
If you describe your issue a bit more, I'm sure anyone that has the same issue in the future would benefit!
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