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# R lm formula

Aktuelle Buch-Tipps und Rezensionen. Alle Bücher natürlich versandkostenfre Formula beim führenden Marktplatz für Gebrauchtmaschinen kaufen. Jetzt eine riesige Auswahl an Gebrauchtmaschinen von zertifizierten Händlern entdecke lm(formula, data, subset, weights, na.action, method = qr, model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE, contrasts = NULL, offset, ) Arguments formula. an object of class formula (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'. data. an optional data frame. lm function in R provides us the linear regression equation which helps us to predict the data. It is one of the most important functions which is widely used in statistics and mathematics. The only limitation with the lm function is that we require historical data set to predict the value in this function

### Linear Regression Example in R using lm() Function - Learn

1. R Source Code. Contribute to SurajGupta/r-source development by creating an account on GitHub
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3. g - lm() Function Last Updated: 24-06-2020 lm() function in R Language is a linear model function, used for linear regression analysis

### R Linear Model (lm) Function -- EndMem

• es the value of the coefficients using the input data. Next we can predict the value of the response variable for a given set of predictor variables using these coefficients. lm() Function. This function creates the relationship model between the predictor and the response variable. Syntax. The basic syntax for lm(
• Here is simple modeling problem in R. We want to fit a linear model where the names of the data columns carrying the outcome to predict (y), the explanatory variables (x1, x2), and per-example row weights (wt) are given to us as strings. Lets start with our example data and.
• R's lm() function is fast, easy, and succinct. However, when you're getting started, that brevity can be a bit of a curse. I'm going to explain some of the key components to the summary() function in R for linear regression models. In addition, I'll also show you how to calculate these figures for yourself so you have a better intuition of what they mean. Getting Started: Build a Model.
1. lm(formula,data) Following is the description of the parameters used − formula is a symbol presenting the relation between x and y. data is the vector on which the formula will be applied. Create Relationship Model & get the Coefficients. Live Demo. x <- c(151, 174, 138, 186, 128, 136, 179, 163, 152, 131) y <- c(63, 81, 56, 91, 47, 57, 76, 72, 62, 48) # Apply the lm() function. relation.
2. I'm quite familiar with R, lm and linear models more generally, but it's not at all clear what, exactly, you want. Can you give an example or something to clarify? Is this for some subject? $\endgroup$ - Glen_b Jul 8 '13 at 3:27. 2 $\begingroup$ I guess you want the coefficients of the linear regression formula. Try calling coef() on the fitted lm object, as in: mod <- lm(y ~ x); coef(mod.
3. Multiple R-Squared: 0.9993, Adjusted R-squared: 0.9989 F-statistic: 2220 on 2 and 3 DF, p-value: 1.755e-05 Nach Call wird die eingegebene Funktion und unter Residuals der Abstand zwischen beobachtetem y und geschätztem y ausgegeben
4. model.matrix(lm1 <- lm(y ~ r*s, data=d)) model.matrix(lm2 <- lm(y ~ r + s + rs, data=d)) When you look at these matrices, you can compare the constellations of s2=1 with the other variables (i.e. when s2=1, which values do the other variables take?). You will see that these constellations differ slightly, which just means that the base category.

Mischbar mit allen HD-Motorenölen • Für Benzin- und Dieselmotoren Liqui Moly Formula Super 15W-40 1 l Öle & Additive bei OBI kaufen und bestelle R provides comprehensive support for multiple linear regression. The topics below are provided in order of increasing complexity. Fitting the Model # Multiple Linear Regression Example fit <- lm(y ~ x1 + x2 + x3, data=mydata) summary(fit) # show results # Other useful functions coefficients(fit) # model coefficients confint(fit, level=0.95) # CIs for model parameters fitted(fit) # predicted. The R Layer. Our point or origin is lm, the interface exposed to the R programmer. It offers a friendly way to specify models using the core R formula and data.frame datatypes. A prototypical call to lm looks something like this. m <-lm (y ~ x 1 + x 2, data = df) The first argument is a model formula, and the second is a dataframe Version info: Code for this page was tested in R Under development (unstable) (2012-07-05 r59734) On: 2012-08-08 With: knitr 0.6.3 It is not uncommon to wish to run an analysis in R in which one analysis step is repeated with a different variable each time. Often, the easiest way to list these variable names is as strings. The code below gives. > Thank you all very much for replying. Of course you are absolutely > right > but unfortunately I really deal with the case of a 4-d matrix so > what you > said does not apply. I should have specified but being a new R user > I > hadn't realized the difference between a matrix and an array. > > So please tell me if you know a fast way (not using a loop) to > perform a > linear fit on all the. 3.4 S-Funktionen 75 R-Funktionen zur linearen Regression a Im package stat (immer vorhanden): lm > r.lm <− lm(log10(ersch) ∼ log10(dist), data = d.spreng) b Funktion summary produziert Resultate, die man üblicherweise will. > summary(r.lm) Genauer: print zeigt die Resultate. (generic function, method print.summary.lm) > r.lms <− summary(r.lm Creating a formula from a string Problem. You want to create a formula from a string. Solution. It can be useful to create a formula from a string. This often occurs in functions where the formula arguments are passed in as strings. In the most basic case, use as.formula(): # This returns a string: y ~ x1 + x2 #>  y ~ x1 + x2 # This returns a formula: as.formula (y ~ x1 + x2) #> y. In this post we describe how to interpret the summary of a linear regression model in R given by summary(lm). We discuss interpretation of the residual quantiles and summary statistics, the standard errors and t statistics , along with the p-values of the latter, the residual standard error, and the F-test. Let's first load the Boston housing dataset and fit a naive model. We won't worry. In statistics, the coefficient of determination, denoted R 2 or r 2 and pronounced R squared, is the proportion of the variance in the dependent variable that is predictable from the independent variable(s).. It is a statistic used in the context of statistical models whose main purpose is either the prediction of future outcomes or the testing of hypotheses, on the basis of other related.

### R: Fitting Linear Model

• The lm() function allows you to specify anything from the most simple linear model to complex interaction models. To model the mileage in function of the weight of a car, you use the lm() function, like this: > Model <- lm(mpg ~ wt, data=mtcars) You supply two arguments: A formula that describes the model: Here, you model the variable mpg as a function of the variable wt. A data frame that.
• R has a tool specifically designed for fitting linear models called lm(). lm() has a special way to specify the model family: formulas. Formulas look like y ~ x, which lm() will translate to a function like y = a_1 + a_2 * x. We can fit the model and look at the output
• The first argument to lm() is a model formula, which has the response on the left of the tilde ~ (read is modeled as), and a Wilkinson-Rogers model specification formula on the right. R uses + to combine elementary terms, as in A + B: for interactions, as in A:B; * for both main effects and interactions, so A * B = A + B + A:B. A nice feature of R is that it lets you create interactions.
• Lineares Modell, Moderationsanalyse (Interaktion
• utes Hier eine Liste einiger meiner Lieblin
• ation or R². This measure is defined by the proportion of the.
• e it explicitly: model.matrix(fit) model.matrix can be used without first calling lm. model.matrix(~ 1+age, data=people

### r - Pass a vector of variables into lm() formula - Stack

The 1 in the formula models the intercept , 0 would be a model without intercept. paste(c(y ~ 1, regressors[vec]), collapse= + ) Now let's make a formula out of it. as.formula(paste(c(y ~ 1, regressors[vec]), collapse= + )) So we can construct a formula from each row of a TRUE /FALSE matrix which determines if a regressor is used or. Regression model is fitted using the function lm. stat_regline_equation ( mapping = NULL, data = NULL A function can be created from a formula (e.g. ~ head(.x, 10)). formula: a formula object. label.x.npc, label.y.npc: can be numeric or character vector of the same length as the number of groups and/or panels. If too short they will be recycled. If numeric, value should be between 0 and 1.

### formula function R Documentatio

• Unfortunately, the R version of the nlme package does not provide this functionality. Update: The R version of the nlme package does allow the use of the lmeControl(sigma = 1) control argument (this was added in version 3.1-123, which was released 2016-01-17). However, using this does not yield the same results as obtained above (the results.
• Man erkennt an den im R-Code eingeblendeten Significance-Codes (ganz unten im Output), dass die drei Sterne für einen p-Wert von p < 0.001 stehen. Ein p-Wert der kleiner ist als 0.001 bedeutet, dass zwischen den drei Gruppen ein hochsignifikanter Unterschied besteht
• LM magic begins, thanks to R. It is like yi = b0 + b1xi1 + b2xi2 + bpxip + ei for i = 1,2, n. here y = BSAAM and x1xn is all other variable
• Formulas: Fitting models using R-style formulas¶. Since version 0.5.0, statsmodels allows users to fit statistical models using R-style formulas. Internally, statsmodels uses the patsy package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this tutorial only scratches the surface
• # Use span to control the wiggliness of the default loess smoother. # The span is the fraction of points used to fit each local regression: # small numbers make a wigglier curve, larger numbers make a smoother curve. ggplot (mpg, aes (displ, hwy)) + geom_point + geom_smooth (span = 0.3) #> geom_smooth()` using method = 'loess' and formula 'y ~ x

### Regression mit R - Jan Teichman

Formula specification. Regression models are specified as an R formula. The basic form of a formula is $response \sim term_1 + \cdots + term_p.$ The $$\sim$$ is used to separate the response variable, on the left, from the terms of the model, which are on the right. A term is one of the followin Formula in lm inside lapply. I am trying to run separate regressions for different groups of observations using the lapply function. It works fine when I write the formula inside the lm() function.... R › R help. Search everywhere only in this topic Advanced Search. Formula in lm inside lapply ‹ Previous Topic Next Topic › Classic List: Threaded ♦ ♦ 5 messages Li, Yan (IED) Reply. 4 posts were merged into an existing topic: lm(y~x )model, R only displays first 10 rows, how to get remaining results see below system closed January 23, 2020, 1:33am #

Formula. The R-squared formula is calculated by dividing the sum of the first errors by the sum of the second errors and subtracting the derivation from 1. Here's what the r-squared equation looks like. R-squared = 1 - (First Sum of Errors / Second Sum of Errors) Keep in mind that this is the very last step in calculating the r-squared for a set of data point. There are several steps that. In this post I discuss how to construct the formula that can be passed to model fitting functions like lm(). I then demonstrate how to use this within a user-created function in order to streamline the process of fitting many similar models diab_lm = r_lm(formula=simple_formula) # the formula object is storing all the needed variables. Instead of specifying each of the individual float vectors related to the robjects.Formula object, we can reference the dataset in the formula itself (as long as this has been made into an R object itself). simple_formula = robjects.Formula(y~age) # reset the formula diab_lm = r_lm(formula=simple. R's generalized linear regression function, glm(), suffers the same usability problems as lm(): its name is an acronym, and its formula and data arguments are in the wrong order. To solve this exercise, you need to know two things about generalized linear regression: glm() formulas are specified like lm() formulas: response is on the left, and explanatory variables are added on the right How about another example. Let's calculate the R-squared values for the linear relationship between Weight and Miles per Gallon, according to the number of Cylinders.. I have written code below that does this for 4 cylinder cars from the mtcars dataset. This is a worst case scenario, you know some dplyr code (dplyr::filter), but are not comfortable with the pipe

### The R Formula Method: The Good Parts · R View

formula {stats} R Documentation: Model Formulae Description. The generic function formula and its specific methods provide a way of extracting formulae which have been included in other objects. as.formula is almost identical, additionally preserving attributes when object already inherits from formula. The default value of the env argument is used only when the formula would otherwise lack. Version info: Code for this page was tested in R version 3.0.2 (2013-09-25) On: 2013-11-19 With: lattice .20-24; foreign 0.8-57; knitr 1.5 In R there are at least three different functions that can be used to obtain contrast variables for use in regression or ANOVA Spline regression. Polynomial regression only captures a certain amount of curvature in a nonlinear relationship. An alternative, and often superior, approach to modeling nonlinear relationships is to use splines (P. Bruce and Bruce 2017).. Splines provide a way to smoothly interpolate between fixed points, called knots > resid(lm.r) # gives the residual errors in Y 1 2 3 4 5 0.4 -1.0 1.6 -1.8 0.8 > fitted(lm.r) # gives the predicted values for The first is related to the Adjusted R-squared (which is simply the R-squared corrected for the number of predictors so that it is less affected by overfitting), which in this case is around 0.3. If we look back at the summary table of the model with only nitrogen, the R-squared was only 0.01. This means that by adding the continuous variable. Anova(lm(time ~ topic * sys, data=search, contrasts=list(topic=contr.sum, sys=contr.sum)), type=3)) NOTE: Again, due to the way in which the SS are calculated when incorporating the interaction effect, for type III you must specify the contrasts option to obtain sensible results (an explanation is given here). here refers to the references that follow in the original article Demand for economics journals Data set from Stock & Watson (2007), originally collected by T. Bergstrom, on subscriptions to 180 economics journals at U Multiple R-squared: 0.8449, Adjusted R-squared: 0.8384 F-statistic: 129.4 on 4 and 95 DF, p-value: < 2.2e-16. One of the great features of R for data analysis is that most results of functions like lm() contain all the details we can see in the summary above, which makes them accessible programmatically. In the case above, the typical approach. F Test summary: (R 2 =0.9999, F(2,7)=4.347e+04, p=4.681e-15).. Intentionally forget to inform wrapFTest of the true number of parameters

### GNU R: Regression - Wikibooks, Sammlung freier Lehr-, Sach

By model-fitting functions we mean functions like lm() which take a formula, create a model frame and perhaps a model matrix, and have methods (or use the default methods) for many of the standard accessor functions such as coef(), residuals() and predict(). A fairly complete list of such functions in the standard and recommended packages is . stats: aov (via lm), lm, glm, ppr. MASS: glm.nb. lm: Fitting Linear Models Description Usage Arguments Details Value Using time series Note Author(s) References See Also Examples Description. lm is used to fit linear models. It can be used to carry out regression, single stratum analysis of variance and analysis of covariance (although aov may provide a more convenient interface for these).. Usag

It is here, the adjusted R-Squared value comes to help. Adjusted R-Squared is formulated such that it penalises the number of terms (read predictors) in your model. So unlike R-sq, as the number of predictors in the model increases, the adj-R-sq may not always increase Copy and paste the following code to the R command line to create this variable. height <- c(176, 154, 138, 196, 132, 176, 181, 169, 150, 175) Now let's take bodymass to be a variable that describes the masses (in kg) of the same ten people. Copy and paste the following code to the R command line to create the bodymass variable. bodymass <- c(82, 49, 53, 112, 47, 69, 77, 71, 62, 78) Both.

predict.lm produces a vector of predictions or a matrix of predictions and bounds with column names fit, lwr, and upr if interval is set. whereas those specified by an offset term in the formula will be. See Also. The model fitting function lm, predict. Examples ## Predictions x <- rnorm(15) y <- x + rnorm(15) predict(lm(y ~ x)) new <- data.frame(x = seq(-3, 3, 0.5)) predict(lm(y ~ x), new. Here's the basic issue of the post. Suppose we have two formula objects with the same response, form1 <- y ~ -1 + a + sin(b) form2 <- y ~ c + d and we want to have a quick intuitive way to get the formula that adds together all of the terms in eac Das Bestimmtheitsmaß, auch Determinationskoeffizient (von lateinisch determinatio Abgrenzung, Bestimmung bzw. determinare eingrenzen, festlegen, bestimmen und coefficere mitwirken), bezeichnet mit , ist in der Statistik eine Kennzahl zur Beurteilung der Anpassungsgüte einer Regression - beispielsweise, um zu bewerten, wie gut Messwerte zu einem Modell passen History. The IS-LM model was first introduced at a conference of the Econometric Society held in Oxford during September 1936. Roy Harrod, John R. Hicks, and James Meade all presented papers describing mathematical models attempting to summarize John Maynard Keynes' General Theory of Employment, Interest, and Money. Hicks, who had seen a draft of Harrod's paper, invented the IS-LM model.

### A quick and easy function to plot lm() results with

An R tutorial on the confidence interval for a simple linear regression model When you make the call to lm it returns a variable with a lot of information in it. If you are just learning about least squares regression you are probably only interested in two things at this point, the slope and the y-intercept. If you just type the name of the variable returned by lm it will print out this minimal information to the screen. An R tutorial on estimated regression equation for a simple linear regression model

### R Formula Tutorial For Beginners - DataCam

Strictly speaking, the formula used for prediction limits assumes that the degrees of freedom for the fit are the same as those for the residual variance. This may not be the case if res.var is not obtained from the fit. See Also. The model fitting function lm, predict. SafePrediction for prediction from (univariable) polynomial and spline fits. References Introduction to econometrics, James H. Stock, Mark W. Watson. 2nd ed., Boston: Pearson Addison Wesley, 2007. Difference‐in‐Differences Estimation. Crashkurs Datenanalyse mit R ifes and friends (Sebastian Sauer) 2017-09-27 Contents 1 WillkommenzumR-Crashkurs3 2 Software 3 2.1 Programme. Details. The interface and internals of dynlm are very similar to lm, but currently dynlm offers two advantages over the direct use of lm: 1. extended formula processing, 2. preservation of time-series attributes.. For specifying the formula of the model to be fitted, there are additional functions available which facilitate the specification of dynamic models Datenanalyse mit R mosaic und ggformula KarstenLübke 2019-09-03 Vorbemerkungen • RunterscheidetzwischenGroß-undKleinbuchstaben • RverwendetdenPunkt. alsDezimaltrennzeiche

+ geom_smooth(method=lm, formula=y~I(x^3)+I(x^2)) Polynomial Regression Fitting in Python. By DataTechNotes at 2/18/2018. Email This BlogThis! Share to Twitter Share to Facebook Share to Pinterest. 1 comment: Unknown July 14, 2018 at 2:56 AM. Drawing trend lines is one of the few easy techniques that really WORK. Prices respect a trend line, or break through it resulting in a massive move. While the formulas are a little more complex, it boils down to the basic economic analysis of supply versus demand. LM Equation. The LM equation calculates the demand for money, and the equation. ### R Tip: How to Pass a formula to lm - Win Vector LL

The R data.frame ontime_s is this same data pulled to the client R engine using ore.pull and is ~39.4MB. Note: The results reported below use R 2.15.2 on Windows. Serialization of some components in the lm model has been improved in R 3.0.0, but the implications are the same Multiple R-squared: 0.651, Adjusted R-squared: 0.644 F-statistic: 89.6 on 1 and 48 DF, p-value: 1.49e-12 The estimates of the regression coeﬃcients β and their covariance matrix ca The VIFs of all the X's are below 2 now. So, the condition of multicollinearity is satisfied. But the variable wind_speed in the model with p value > .1 is not statistically significant. For this specific case, we could just re-build the model without wind_speed and check all variables are statistically significant. But, what if you had a different data that selected a model with 2 or more. This is probably more a statistical question rather than an R question, however I want to know how this lm() anaysis comes out with a significant adjusted p-value (p=0.008) when the St Err on the change in IGF2 (-.04ng/ml) for every Kg increase in weight is huge (0.45ng/ml). The confidence interval of the effect size is therefore massive (-0.9-0.8). I think I must be reading the output wrong. Intro. Panel data (also known as longitudinal or cross -sectional time-series data) is a dataset in which the behavior of entities are observed across time ### Einfache Lineare Regression in R berechnen R Codin

Bootstrapping a Single Statistic (k=1) The following example generates the bootstrapped 95% confidence interval for R-squared in the linear regression of miles per gallon (mpg) on car weight (wt) and displacement (disp) For linear models with unknown scale (i.e., for lm and aov), -2log L is computed from the deviance and uses a different additive constant to logLik and hence AIC. If RSS denotes the (weighted) residual sum of squares then extractAIC uses for - 2log L the formulae RSS/s - n (corresponding to Mallows' Cp ) in the case of known scale s and n log (RSS/n) for unknown scale Asia <tibble [1 <lm> 1952 28.8 8.43e6 779. -1.11 ## 2 Afghanis Asia <tibble [1 <lm> 1957 30.3 9.24e6 821. -0.952 ## 3 Afghanis Asia <tibble [1 <lm> 1962 32.0 1.03e7 853. -0.664 ## 4 Afghanis Asia <tibble [1 <lm> 1967 34.0 1.15e7 836. -0.0172 ## 5 Afghanis Asia <tibble [1 <lm> 1972 36.1 1.31e7 740. 0.674 ## 6 Afghanis. Pearson correlation (r), which measures a linear dependence between two variables Kendall correlation formula. The Kendall correlation method measures the correspondence between the ranking of x and y variables. The total number of possible pairings of x with y observations is $$n (n-1)/2$$, where n is the size of x and y. The procedure is as follow: Begin by ordering the pairs by the x.

### Linear Regression in R using lm() Function - TechVidva

formula: a symbolic description for the model to be tested (or a fitted lm object). order: integer. maximal order of serial correlation to be tested. order.by: Either a vector z or a formula with a single explanatory variable like ~ z. The observations in the model are ordered by the size of z. If set to NULL (the default) the observations are assumed to be ordered (e.g., a time series. The Fujifilm XF 90mm F2 R LM WR is a fast short telephoto prime lens in Fujifilm's XF line-up. The weather-proof Fujifilm XF 90mm F2 R LM WR lens offers an angle-of-view similar to that of a 137mm lens in a 35mm system, ideal for portrait and sports photography, and a bright f/2 maximum aperture for low-light shooting and throwing the background completely out-of-focus Have a look at this page where I introduce and plot the Iris data before diving into this topic. To summarise, the data set consists of four measurements (length and width of the petals and sepals) of one hundred and fifty Iris flowers from three species Load in the data. library(ggplot2) theme_set(theme_bw(base_size = 18)) library(scatterplot3d) library(effects) ## Loading required package: lattice ## Loading. lm(formula = CRIME ~ INC + HOVAL, data = columbus) Coefficients: (Intercept) INC HOVAL 68.6190 -1.5973 -0.2739. 6 In contrast, if you use lm with an assignment, you create a new object, but there is no printout. For example, assign the result to the object columbus.lm, as in: >columbus.lm <- lm( CRIME ~ INC + HOVAL, data=columbus) Nothing happens However, you did create a new object. To.

Please pay attention to the formula format, dependant variance Expression is in front of the independant variance Subtype. Report the means and the number of subjects: >print(model.tables(a,means),digits=2) Tables of means Grand mean -0.3053381 Subtype A B C -0.18 -0.39 -0.49 rep 143.00 75.00 63.00 Two way ANOVA analysis ! ! 6! 8. R!follows!the!popular!customof!flagging!significant!coefficients!with!one,!two!or!three! starsdependingontheirpBvalues.Try>plot(lrfit).!You!get!the!same. The Fujifilm XF 18-55mm F2.8-4 R LM OIS is a fast standard zoom lens for Fujifilm X-series compact system cameras. Offering a focal range of 27-84mm in 35mm terms, 4 stops of built-in image stabilisation, a compact and lightweight design, 0.1 second auto-focusing and an aperture ring, is the Fujifilm XF 18-55mm F2.8-4 R LM OIS a viable alternative to the XF prime lenses that have been released. Warum gibt lm Werte zurück, wenn der vorhergesagte Wert nicht variiert? Übergeben Sie einen Vektor von Variablen in die Formel lm() Predict()-Vielleicht verstehe ich es nicht ; R: numerisches 'envir' arg nicht von der Länge eins in predict(      FUJINON XF50-140mmF2.8 R LM OIS WR, Hinterer Objektivdeckel, Gegenlichtblende, Objektiveinschlagtuch, Converter, Stoffbeutel, Vorderer Objektivdeckel, Gegenlichtblendendeckel, XF1.4x TC WR, Bedienungsanleitung Max Focal Length 196 Min Focal Length 50 Model Year 2015 Mounting Type Fujifilm X Objective Lens Diameter 82.9 Millimeter Part Number X Series Warranty Description 1 Jahr Lens Design. lm (formula = y ~ x1 + x2) coef.est coef.se (Intercept) 0.89 0.05 x1 1.05 0.04 x2 1.02 0.03---n = 1000, k = 3 residual sd = 0.96, R-Squared = 0.86. Weights . This section is a stub. You can help Wikibooks by expanding it. Tests . This section is a stub. You can help Wikibooks by expanding it. Confidence intervals . This section is a stub. You can help Wikibooks by expanding it. Delta Method. Base above information from R(regression of sales and income): Campaign dummies and the interactions between Income and Campaign dummies. When you create the Campaign dummies, Campaign 1 should be the reference category Das FG120mmF4 R LM OIS WR ist ein Makro-Objektiv mit einer mittleren Telebrennweite, das speziell für den G Mount und den 43,8 x 32,9mm großen Sensor konstruiert wurde. Die GF Objektive sind in der Lage, bis zu 100MP aufzulösen und kombinieren die aktuellste Technologie mit dem Wissen und der Erfahrung aus der XF-Objektivproduktion, um so bestmögliche Bildqualität zu garantieren und das.

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