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I have a dataset of real data, for example looking like this: # Dataset 1 with known data known <- data.frame( x = c(0:6), y = c(0, 10, 20, 23, 41, 39, 61) ) plot (known$x, known$y, type="o") Now I want to get an aswer to the question "What would the Y value for 0.3 be, if all intermediate datapoints of the original dataset, are on a straight line between the surrounding measured values?" # X values of points to interpolate from known data aim <- c(0.3, 0.7, 2.3, 3.3, 4.3, 5.6, 5.9) If you look at the graph: I want to get the Y-Values, where the ablines intersect with the linear interpolation of the known data abline(v = aim, col = "#ff0000") So, in the ideal case I would create a "linearInterpolationModel" with my known data, e.g. model <- linearInterpol(known) ... which I can then ask for the Y values, e.g. model$getEstimation(0.3) (which should in this case give "3") abline(h = 3, col = "#00ff00") How can I realize this? Manually I would for each value do something like this: What is the closest X-value smaller Xsmall and the closest X-value larger Xlarge than the current X-value X . Calculate the relative position to the smaller X-Value relPos = (X - Xsmall) / (Xlarge - Xsmall) Calculate the expected Y-value Yexp = Ysmall + (relPos * (Ylarge - Ysmall)) At least for the software Matlab I heard that there is a built-in function for such problems. Thanks for your help, Sven

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