# Indexes of Agreement Table Indexes of agreement printing function for IA_tab plotting function for a IA_tab, it requires ggplot2 ```r IA_tab(obs, sim, object, null.object) ## S3 method for class 'IA_tab' print(x, ..., digits = 2) ## S3 method for class 'IA_tab' plot(x, y, ..., type = c("OvsS", "RvsS")) ``` ## Arguments - `obs`: vector with observed data - `sim`: vector with simulated data (should be the same length as observed) - `object`: alternative to the previous two arguments. An object of class lm , nls , lme or nlme - `null.object`: optional object which represents the null model. It is an intercept-only model by default. (Not used at the moment). - `x`: object of class IA_tab . - `...`: additional plotting arguments (none use at the moment). - `digits`: number of digits for rounding (default is 2) - `y`: not used at the moment - `type`: either OvsS (observed vs. simulated) or RvsS (residuals vs. simulated). ## Details This function returns several indexes that might be useful for interpretation For objects of class lm and nls bias: mean(obs - sim) intercept: intercept of the model obs ~ beta_0 + beta_1 * sim + error slope: slope of the model obs ~ beta_0 + beta_1 * sim + error RSS (deviance): residual sum of squares of the previous model MSE (RSS / n): mean squared error; where n is the number of observations RMSE: squared root of the previous index R2.1: R-squared extracted from an lm object R2.2: R-squared computed as the correlation between observed and simulated to the power of 2. ME: model efficiency NME: Normalized model efficiency Corr: correlation between observed and simulated ConCorr: concordance correlation For objects of class gls , gnls , lme or nlme there are additional metrics such as: [https://en.wikipedia.org/wiki/Coefficient_of_determination](https://en.wikipedia.org/wiki/Coefficient_of_determination) [https://en.wikipedia.org/wiki/Nash-Sutcliffe_model_efficiency_coefficient](https://en.wikipedia.org/wiki/Nash-Sutcliffe_model_efficiency_coefficient) [https://en.wikipedia.org/wiki/Concordance_correlation_coefficient](https://en.wikipedia.org/wiki/Concordance_correlation_coefficient) ## Examples ```r require(nlme) require(ggplot2) ## Fit a simple model and then compute IAs data(swpg) #' ## Linear model fit0 <- lm(lfgr ~ ftsw + I(ftsw^2), data = swpg) ias0 <- IA_tab(object = fit0) ias0 ## Nonlinear model fit1 <- nls(lfgr ~ SSblin(ftsw, a, b, xs, c), data = swpg) ias1 <- IA_tab(object = fit1) ias1 plot(ias1) ## Linear Mixed Models data(barley, package = "nlraa") fit2 <- lme(yield ~ NF + I(NF^2), random = ~ 1 | year, data = barley) ias2 <- IA_tab(object = fit2) ias2 ## Nonlinear Mixed Model barleyG <- groupedData(yield ~ NF | year, data = barley) fit3L <- nlsLMList(yield ~ SSquadp3(NF, a, b, c), data = barleyG) fit3 <- nlme(fit3L, random = pdDiag(a + b ~ 1)) ias3 <- IA_tab(object = fit3) ias3 plot(ias3) ## Plotting model prds <- predict_nlme(fit3, interval = "conf", plevel = 0) barleyGA <- cbind(barleyG, prds) ggplot(data = barleyGA, aes(x = NF, y = yield)) + geom_point() + geom_line(aes(y = Estimate)) + geom_ribbon(aes(ymin = Q2.5, ymax = Q97.5), fill = "purple", alpha = 0.2) ## R2M for model 2 R2M(fit2) ## R2M for model 3 R2M(fit3) ## Using IA_tab without a model IA_tab(obs = swpg$lfgr, sim = fitted(fit0)) ``` ## See Also `IC_tab`

IA_tab function