search for: patze003

...if (grep("4",init.dat1$Slide1_RESP)) {2 == 1, 4 == 2}; else init.dat1$Slide1_RESP } -- Edward H. Patzelt Research Assistant – TRiCAM Lab University of Minnesota – Psychology/Psychiatry VA Medical Center Office: S355 Elliot Hall - Twin Cities Campus Phone: 612-626-0072 Email: patze003@umn.edu Please consider the environment before printing this email www.psych.umn.edu/research/tricam [[alternative HTML version deleted]]

...nerate the most probable path and then compare that to the actual path to see if they match up. Best, -- Edward H. Patzelt Research Assistant – TRiCAM Lab University of Minnesota – Psychology/Psychiatry VA Medical Center Office: S355 Elliot Hall - Twin Cities Campus Phone: 612-626-0072 Email: patze003@umn.edu Please consider the environment before printing this email www.psych.umn.edu/research/tricam [[alternative HTML version deleted]]

...;Rev", "FF", "bin" ), row.names = c(NA, -40L), class = "data.frame") -- Edward H. Patzelt Research Assistant – TRiCAM Lab University of Minnesota – Psychology/Psychiatry VA Medical Center Office: S355 Elliot Hall - Twin Cities Campus Phone: 612-626-0072 Email: patze003@umn.edu Please consider the environment before printing this email www.psych.umn.edu/research/tricam [[alternative HTML version deleted]]

I'm am trying to create a vector that has a running total that adds each time a 1 occurs. here's the code and data c(1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,

...= "\t", append = "red.txt"), col.names = FALSE, row.names = FALSE) } } -- Edward H. Patzelt Research Assistant – TRiCAM Lab University of Minnesota – Psychology/Psychiatry VA Medical Center Office: S355 Elliot Hall - Twin Cities Campus Phone: 612-626-0072 Email: patze003@umn.edu Please consider the environment before printing this email www.psych.umn.edu/research/tricam [[alternative HTML version deleted]]

Hello Everyone - I want to print a number of results from lme function objects out to a txt file. How could I do this more efficiently than what you see here: out2 <- capture.output(summary(mod2a)) out3 <- capture.output(summary(mod3)) out4 <- capture.output(summary(mod5)) out5 <- capture.output(summary(mod6)) out6 <- capture.output(summary(mod7))

R - Does anyone know of a cubic gradient descent package? I found grad.desc() but that only allows for a 2d function. I have 3 free parameters and thus am looking for a 3d function. Thank you, -- Edward H. Patzelt Research Assistant – TRiCAM Lab University of Minnesota – Psychology/Psychiatry VA Medical Center S355 Elliot Hall: 612-626-0072 www.psych.umn.edu/research/tricam [[alternative

...(which(thsum==minSum))*inc + min) betterThanChance[s] = sum(pbinom(0:minSum, length(dat2$choice), 0.5)) -- Edward H. Patzelt Research Assistant – TRiCAM Lab University of Minnesota – Psychology/Psychiatry VA Medical Center Office: S355 Elliot Hall - Twin Cities Campus Phone: 612-626-0072 Email: patze003@umn.edu Please consider the environment before printing this email www.psych.umn.edu/research/tricam [[alternative HTML version deleted]]

R-help - I'm trying to create axis breaks similar to this : http://www.r-bloggers.com/wp-content/uploads/2010/08/bar-chart-natural-axis-split1.png . Is there a way to do this in R? Here's my code thus far: structure(list(condition = structure(c(2L, 1L, 3L), .Label = c("con", "exp", "unedit"), class = "factor"), trial.avg = c(4.04583333333333,

R-help - I have this set of aggregated tables (sample data below via dput()). And I would like to have delayValue as the column variables with the "temp" (temp1, temp2, temp3) values as the row variables. However I would like to have the temp variables *aggregated into single rows* so that I have the frequency ("Freq" | counts) of each time each "delayValue" occurs

1. I'm attempting to test for Random Effects. I've grouped the data on subject (grid) but want to use lme to build the model without subject as a RE then add it and do anova between the 2 models. This is the result I get and it appears it's adding Random Effects. tmp.dat4 <- groupedData(Trials ~ 1 | grid, data = tmp.dat4) mod2a <- lme(Trials ~ factor(group_id) + reversal,

Everyone - What do the NaN's mean here? Is this analysis a problem? Linear mixed-effects model fit by maximum likelihood Data: tmp.dat AIC BIC logLik 1611.251 1638.363 -797.6253 Random effects: Formula: ~1 | group_id (Intercept) Residual StdDev: 0.0003077668 9.236715 Fixed effects: AvgTrials ~ time + factor(group_id) + time * factor(group_id)