search for: stde

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2012 May 03
1
Proposal: model.data
...tudying termplot code, I saw the "carrier" function approach to deducing the "raw" predictors. However, it does not always work. Here is one problem. termplot mistakes 10 in log(10 + x1) for a variable Example: dat <- data.frame(x1 = rnorm(100), x2 = rpois(100, lambda=7)) STDE <- 10 dat$y <- 1.2 * log(10 + dat$x1) + 2.3 * dat$x2 + rnorm(100, sd = STDE) m1 <- lm( y ~ log(10 + x1) + x2, data=dat) termplot(m1) ## See the trouble? termplot thinks 10 is the term to plot. Another problem is that predict( type="terms") does not behave sensibly sometimes....
2010 Jul 30
4
Programming Statistical Functions
Hello, I'm new in R. I'm meteorological modeller and i will calculate some statistics for my model results. These statistis are the follow: ANB: Average Normalized Absolute BIAS MNB: Mean Normalized BIAS MNE: Mean Normalised Error STDE: Standard Deviation of Error FB: Fractional BIAS MG: Geometric Mean BIAS VG: Geometric Variance SKVAR: Skill Variance RMSE: Root Mean Square Error NMSE: Normalized Mean Square Error r: Correlation Coefficient CV: Coeficient of Variation FAC2: Fractional Predictions within a factor of two of observa...
2003 Nov 15
0
computing a p-value for a one-way ANOVA given only means and std dev of 5 factor levels
...n a p-value indicating whether there is evidence of a difference between these means. Mean Std Dev N Level A mA stdA nA Level B mB stdB nB Level C mC stdC nC Level D mD stdD nD Level E mE stdE nE My current plan is to assume normally distributed responses and use the known mean and std dev to generate data points which can be combined to create a data set similar to the "true" (but unavailable) data set. Then I can use a standard 1-way ANOVA to determine the p-value. D...
2003 Nov 15
0
FW: computing a p-value ...
...that you need in order to do the usual ANOVA. Most elementary statistics texts will tell you how. The ``standard 1-way ANOVA'' assumes that the population standard deviations are equal for the various levels. You thus form SSE by ``pooling'': SSE = (nA-1)*stdA^2 + ... + (nE-1)*stdE^2 You form the sum of squares for the factor as SSF = nA*(mA-mBar)^2 + ... + nE*(mE-mBar)^2 where ``mBar'' is the `grand mean'': mBar = (nA*mA + ... + nE*mE)/n where in turn n = nA + ... + nE Then form MSF = SSF/4 and MSE = SSE/(n - 5) (4 because 4 = 5-1 and the factor has...
2004 Aug 06
2
Hello
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