A filter whose frequencies are between given upper and lower cutoff values, while substantially by low densities (< 1.0x109/cc and high temperatures (> 1.0x106 K). 2. One Dobson unit is the equivalent of 2.69/ x 10/16 molecules of ozone/cm2. G) - A rapid fluctuation of wind speed with variations of 10 knots or more 

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mod <- gam (y ~ s (x, bs = "cr", k = 2), family = betar (link = "logit"), data = d) where x can take only two unique values. However, when I run the model I get the following error. Error in smooth.construct.cr.smooth.spec (object, dk$data, dk$knots) : x has insufficient unique values to support 3 knots: reduce k.

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X has insufficient unique values to support 10 knots  reduce k.

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in the wood. 10 k = number of the board. is involuntary exposure lo radiofrequency (RF)-EMF from different sources. to 300 GHZ, the recommended reference value Is 10 W/m'.

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed. orientation: The orientation of the layer. The default (NA) automatically determines the orientation from the aesthetic mapping. In the rare event that this fails it can be given explicitly by setting orientation to either "x" or "y".

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X has insufficient unique values to support 10 knots  reduce k.

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X has insufficient unique values to support 10 knots  reduce k.

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X has insufficient unique values to support 10 knots  reduce k.

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## Warning: Computation failed in `stat_smooth()`: ## x has insufficient unique values to support 10 knots: reduce k. The SentimentAnalysis package works very cleverly and neatly here in order to remove the effort for the user: it recognizes that the user has inserted a vector of strings and thus automatically performs a set of default preprocessing operations from text mining.

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In general, a large k-neighborhood value is more precise as it reduces the overall noise but there is no guarantee. Cross-validation is another way to determine a good k-neighborhood value by using an independent data set to validate the value of k-neighborhood.

,. (4) where d(p,X) = min x∈X x − p 2 . That is, the operator d(p,X) is the strongly deviating data values are effectually reduced.

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