search for: dffit

R-users E-mail: r-help@r-project.org Hi! R-users. I am just wondering what the definition of "dffits" in R language is. Let me show you an simple example. function() { library(MASS) xx <- c(1,2,3,4,5) yy <- c(1,3,4,2,4) data1 <- data.frame(x=xx, y=yy) lm.out <- lm(y~., data=data1, x=T) lev1 <- lm.influence(lm.out)$hat sig1 <- lm.influence(lm.out)$sigma re...

...2681 81 2.176091259 1.414973348 81 2.176091259 1.447158031 81 2.181843588 1.414973348 81 2.181843588 1.447158031 81 2.184691431 1.462397998 81 2.187520721 1.447158031 81 2.187520721 1.477121255 81 2.187520721 1.505149978 ... [truncated] I'm trying to: 1) fit a simple lm(LW~LL) 2) calculate the dffits for those data points 3) remove those data points that are 2*sqrt(p/n) (where p=the number of parameters and n=number of data points; p=3 in a linear model, correct? Intercept, slope, and error term?) 4) rerun the model MINUS those data points 5) compare the two lm() Now, each of these steps I...

Hi all I'm getting a NaN returned on using dffits, as explained below. To me, there seems no obvious (or non-obvious reason for that matter) reason why a NaN appears. Before I start digging further, can anyone see why dffits might be failing? Is there a problem with the data? Consider: # Load data dep <- read.table("http://www.s...

influence.measures gives several measures of influence for each observation (Cook's Distance, etc) and actually flags observations that it determines are influential by any of the measures. Looks good! But how does it discriminate between the influential and non- influential observations by each of the measures? Like does it do a Bonferroni-corrected t on the residuals identified by

...w to look particularly for any points of high influence. In order to do this I need to extract values for delta-beta. The function dfbeta gives a value for change in each coefficient but I am looking for a composite value that gives an overall measure of change to the coefficients. It may be that dffits is this function but I cannot gather what this is or what it is calculated from? If anyone has any advice re plotting H&L logistic regression diagnostics in R that would be greatly appreciated. Also, can any of these functions be applied to glmmPQL mixed models? Thanks very much Jo Halliday

...-0.02 0.00 -87 -0.03 0.04 -0.01 -0.04 -0.04 -0.03 -0.02 0.04 -89 -0.11 0.11 -0.05 0.28 -0.06 -0.04 -0.06 -0.01 -93 -0.11 0.11 -0.05 -0.45 -0.06 -0.04 -0.06 -0.01 - dfb.TypM dfb.TypSm dfb.TypSp dfb.TypV dfb.EngS dfb.DrTF dfb.DrTR dffit -8 0.00 0.21 0.06 -0.04 0.47 -0.04 0.03 0.73 -19 0.01 0.00 -0.04 -0.01 -0.15 0.01 0.02 -0.24 -28 0.08 -0.08 -0.09 0.16 0.06 0.09 0.12 -0.26 -39 -0.01 0.04 0.01 -0.06 0.01 -0.08 -0.08 -0.44...

...nicaros que había un fallo en esta función. Como os prometí os comento la respuesta por si alguno está interesado en utilizar el paquete LMERconvenientsfucntions Dear Javier, The package has been updated and should work for you fine now. Note that function mcp.fnc does not return the fourth plot (dffits) anymore. We still have to figure out a way to compute the dffits for the new merMod objects. Also note that mcposthoc.fnc doesn't work with pvals.fnc / MCMC anymore. Please see Note in help page. Finally, the package gained function plotLMER.fnc from archived package languageR. It's the...

...initely not my study ID numbers. > Myoutput<-aov(sib~newgroup1, data=Study1) > influencePlot(Myoutput) [1] 7 18 26 105 > influence.measures(Myoutput) Influence measures of aov(formula = sib ~ newgroup1, data = Study1) : dfb.1_ dfb.nw12 dfb.nw13 dfb.nw14 dfb.nw15 dffit cov.r cook.d hat inf 33 1.70e-01 -1.33e-01 -1.53e-01 -1.56e-01 -1.52e-01 0.170405 1.124 5.83e-03 0.0909 * 34 7.79e-02 -6.07e-02 -7.00e-02 -7.14e-02 -6.94e-02 0.077934 1.131 1.22e-03 0.0909 * 35 1.47e-01 -1.15e-01 -1.32e-01 -1.35e-01 -1.31e-01 0.147268 1.126 4.36e-03 0.0909 * 36...

Hi dear all, I'm wondering about the question that; Does the influence.measures(model) for linear models valid for general linear models such as logistic regression models? That is; If I fit the model like model <- glm( y~X1+X2, family=binomial) Then, if i apply the function "influence.measures(model), i will get the result of influence measures. These result are valid for

...fluence function, and it seems that it returns elements sigma and coefficients that are only based on the first variable (first column of the residual matrix wt.res) and give wrong results for other variables. This will influence functions dfbeta.lm (coefficients), dfbetas.lm (coefficients, sigma), dffits (sigma), rstudent.lm (sigma) and covratio (sigma). lm.influence finds these elements in compiled code and this is harder to fix. MASS (the book & the package) avoid using compiled code in their (univariate) studentized residuals, and instead use a clever short-cut. In addition to these, there...

...with (or without) adjustments for multiple testing. i. Custom linear hypothesis tests j. Effect mean summaries (with confidence intervals), and plots k. Exports: Residuals, Standardized residuals, Studentized residuals, Predicted Values (linear and link), Cooks distance, DFBETA, DFFITS, hat values, and Cov Ratio l. Observation weights and subseting 9. Logistic Regression a. All GLM features b. ROC Plot 10. Linear Model a. All GLM features b. Heteroskedastic robust tests _______________________________________________ R-packages mailing list R-packages at r-pro...

...with (or without) adjustments for multiple testing. i. Custom linear hypothesis tests j. Effect mean summaries (with confidence intervals), and plots k. Exports: Residuals, Standardized residuals, Studentized residuals, Predicted Values (linear and link), Cooks distance, DFBETA, DFFITS, hat values, and Cov Ratio l. Observation weights and subseting 9. Logistic Regression a. All GLM features b. ROC Plot 10. Linear Model a. All GLM features b. Heteroskedastic robust tests _______________________________________________ R-packages mailing list R-packages at r-pro...

I am using rw0641 with Windows 98. To list just the influential repetitiones that result from "influence.measures", I am using the input result <- lm(y~x) and the code from the example in the help for "influence.measures" INFLM <- function(result){ inflm <- influence.measures(result) which(apply(inflm$is.inf,1,any)) } It works fine up to now with the

dear:teacher i have a problem which about the polr()(package "MASS"), if the response must have 3 or more levels? and how to fit the polr() to 2 levels? thank you. turly yours [[alternative HTML version deleted]]

...w happens column by column as documented, so character or logical matrices generate factor columns. o Formulae containing interactions with the response were handled incorrectly by model.matrix, giving wrong answers or crashes. o anova(lm(.)) with 0 weights now works. o plot.lm(), dffits(), covratio() now do the right thing for weighted lm regression. o formatC(as.integer(c(1,0,NA))) now works. PR#394, thanks to Jens O.-A. o format.char(.) is much faster for long vector argument [Jens O.-A.] o \enumerate now enumerates in text conversion of .Rd files. o glm()...

...w happens column by column as documented, so character or logical matrices generate factor columns. o Formulae containing interactions with the response were handled incorrectly by model.matrix, giving wrong answers or crashes. o anova(lm(.)) with 0 weights now works. o plot.lm(), dffits(), covratio() now do the right thing for weighted lm regression. o formatC(as.integer(c(1,0,NA))) now works. PR#394, thanks to Jens O.-A. o format.char(.) is much faster for long vector argument [Jens O.-A.] o \enumerate now enumerates in text conversion of .Rd files. o glm()...

Javier, Creo que aquí aplica la ley de Linus que dice: "Dado un número suficientemente elevado de ojos, todos los errores se convierten en obvios". La persona que revisa y encuentra un error no necesariamente tiene que ser la misma que la que lo escribe. Una motivación muy importante al compartir un código es la de recibir los beneficios del control de calidad por parte de tus pares.