update regplot_nuovo

gascoigne/bpower.py

@@ -0,0 +1,23 @@
+import numpy as np
+from scipy.stats import norm
+
+def bpower(p1,p2,n1,n2,n=None,alpha=0.05)
+    """
+    a python version of the r command "bpower" from Hmsc https://github.com/harrelfe/Hmisc/blob/master/R/bpower.s
+
+    """
+    if n!=None:
+        n1=n/2
+        n2=n/2
+
+    z=norm.ppf(1-alpha/2)
+    q1=1-p1
+    q2=1-p2
+    pm = (n1*p1+n2*p2)/(n1+n2)
+    ds = z*np.sqrt((1/n1 + 1/n2)*pm*(1-pm))
+    ex = abs(p1-p2)
+    sd= np.sqrt(p1*q1/n1+p2*q2/n2)
+
+    power=1-norm.cdf((ds-ex)/sd) +norm.cdf((-ds-ex)/sd)
+
+    return power

gascoigne/regplot_nuovo.py

@@ -0,0 +1,250 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.lines import Line2D
+from matplotlib.patches import Rectangle
+
+
+def create_df(list_of_models,model_names="none"):
+    data=pd.DataFrame()
+    if isinstance(list_of_models, list)==False:
+        list_of_models=[list_of_models]
+    if model_names=="none":
+        model_names=["".join(["model_",str(v)]) for v in range(1,len(list_of_models)+2)]
+
+    i=0
+    for mod in list_of_models:
+        # Create dataframe of results summary 
+        coef_df = pd.DataFrame(mod.summary().tables[1].data)
+        # Drop the extra row with column labels
+        coef_df=coef_df.drop(0)
+        coef_df.iloc[:,1:]=coef_df.iloc[:,1:].applymap(lambda x:float(x))
+        # Add column names
+        coef_df.columns=["varname","coef","std_err","t","pvalue","0.25","0.975"]
+        coef_df["err"]=coef_df["coef"].apply(lambda x:float(x))-coef_df["0.25"].apply(lambda x: float(x))
+        coef_df["model"]=model_names[i]
+        data=data.append(coef_df)
+        i+=1
+    return data
+
+
+
+
+def coeff_plot(data,selected_var=None,show_intercept=True,intercept_name="Intercept",fontsize=12,colors="standard",marker_colors="standard",bar_colors="standard",linewidth=2,markersize=8,marker="^",legend=True,legend_position="best",smf=True,orientation="horizontal",
+              axis_title=None,figsize=None,xlabel="variables",ylabel="coefficient",manual_axs_lims=None,xticklabels=None,yticklabels=None,return_figure=False):
+
+    """
+    data: a pandas dataframe with four columns [coef,err,model,varname]
+    show_intercept: show or not intercept coefficient
+    
+    intercept_name: name of the intercept
+    fontsize: fontsize of the text in the figures
+    colors: a unique or a list of colors for bar and markers. if unique, pass it trough a list (e.g. [red])
+    marker_colors: a list of colors for markers
+    bar_colors: a list of colors for bar
+    selected_var:a list-array with the names of the specific variables you want to show or the order
+    
+    linewidth:width of the bar
+    markersize: size of the marker
+    marker: type of marker (triangle, point, etc... see: https://matplotlib.org/3.3.3/api/markers_api.html)
+    color_line: a unqque or a list of colors for bar
+    legend: show legend or not
+    legend_position: legend position, default is "best", available are:
+    axis_title: title of the figure, default is none
+    figsize: a tuple (xsize,ysize) for editing the size of the figure. default is 5,10
+    xlabel: label of x-axis
+    ylabel: label of y-axis
+    manual_axs_lims: list of array limits
+    xticklabels: list of xticklabels
+    yticklabels: list of yticklabels
+    return_figure: wether showing directly the fiure or only returning the object
+    """
+
+
+
+
+    ### shape data
+    if smf==False:
+        if {"coef","model","varname","err"}.issubset(set(data.columns))==False:
+            raise Exception("columns of the dataframe are not correct. please make sure the dataframe has columns named [coef, err, model,varname]") 
+    else:
+        ### shape data
+        data=create_df(data)
+
+    ### order variable and model
+    data.sort_values(["varname","model"],inplace=True,ascending=True)
+
+    ## dimension 
+    if figsize==None:
+        figsize=(10,5)
+
+    ## start plotting
+    fig,axs=plt.subplots(figsize=figsize)
+
+
+    if show_intercept!=False:
+        data.drop((data.loc[data["varname"]==intercept_name]).index,axis=0,inplace=True)
+
+    data.reset_index(inplace=True,drop=True)
+    if selected_var!=None:
+        sorter=selected_var
+        data.drop(data.loc[~data["varname"].isin(sorter),:].index,axis=0,inplace=True)
+        # Create the dictionary that defines the order for sorting
+        sorterIndex = dict(zip(sorter, range(len(sorter))))
+        # Generate a rank column that will be used to sort
+        # the dataframe numerically
+        data['var_rank'] = data['varname'].map(sorterIndex)
+
+        data=data.sort_values(["model","var_rank"],ascending=[True,True]).copy()
+
+    ### this is to have the name of the variable
+    var_to_show=list(data["varname"].unique())
+    ### 
+
+    ## index unique combinations of variables
+    indx=data.loc[data["varname"].isin(var_to_show),"varname"].drop_duplicates().index
+
+    ### the baseline plot
+    if orientation=="horizontal":
+        data.loc[indx,:].plot(y="coef",x="varname",kind="scatter",ax=axs,color="none")
+    else:
+        data.loc[indx,:].plot(x="coef",y="varname",kind="scatter",ax=axs,color="none")
+
+
+    ### give a 10% margin to axis limits of the plot
+
+    ax_min=np.round((data["coef"]-data["err"]).min()*1.10,4)
+    ax_max=np.round((data["coef"]+data["err"]).max()*1.10,4)
+
+    if manual_axs_lims!=None:
+        ax_min=manual_axs_lims[0]
+        ax_max=manual_axs_lims[1]
+
+    ### set label name
+    axs.set_xlabel(xlabel)
+    axs.set_ylabel(ylabel)
+
+    if orientation=="horizontal":
+        axs.set_ylim(ax_min,ax_max)
+        ## in case ax is rotated and label not specified, invert the names
+        if ((xlabel=="variables")&(ylabel=="coefficient"))==True:
+            axs.set_xlabel(ylabel)
+            axs.set_ylabel(xlabel)
+    else:
+        axs.set_xlim(ax_min,ax_max)
+
+
+
+    ## titles and labels
+    if axis_title!=None:
+        axs.set_title(axis_title)
+
+
+    ### collections of model
+    list_of_model=[]
+    for model in data["model"].unique():
+        list_of_model.append(data.loc[data["model"]==model,:])
+
+
+    std_marker_colors=['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928']
+    std_bar_colors=['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928'] 
+
+    if colors!="standard":
+        if len(colors)==1:
+            marker_colors=[colors]*len(list_of_model)
+            bar_colors=[colors]*len(list_of_model)
+        else:
+            marker_colors=colors
+            bar_colors=colors
+
+    if marker_colors!="standard":
+        if len(marker_colors)==1:
+            marker_colors=[marker_colors]*len(list_of_model)
+    else:
+        marker_colors=std_marker_colors
+
+    if bar_colors!="standard":
+        if len(bar_colors)==1:
+            bar_colors=[bar_colors]*len(list_of_model)
+    else:
+        bar_colors=std_bar_colors
+
+    ## name of model
+    list_name_model=data["model"].unique()
+
+    ### positioner
+    variable_count=data.groupby("varname",as_index=False)["coef"].count()
+    ## keep the order of variables as given
+    variable_count=variable_count.set_index("varname").reindex(var_to_show).reset_index(drop=False).copy()
+    variable_count["even"]=variable_count["coef"]%2
+    distance_between=0.1
+    positioner=[]
+    for i,row in variable_count.iterrows():
+        ## if even
+        if row["coef"]%2==0:
+              positioner.append([v for v in np.arange(row.name-(row["coef"]/20)+distance_between/2,row.name+(row["coef"]/20)+0.001, distance_between)])
+        else:
+            positioner.append([v for v in np.arange(row.name-(row["coef"]/20)+distance_between/2,row.name+(row["coef"]/20)+0.001, distance_between)])
+    variable_count["positions"]=positioner
+    variable_count["counter"]=0
+
+
+
+    for bar_color,mrk_color,model in zip(bar_colors,marker_colors,list_of_model):
+        ### markers
+        for i,row in model.iterrows():
+            ## plot lines
+            x_count=variable_count.loc[variable_count["varname"]==row["varname"],"counter"].iloc[0]
+            x_position=variable_count.loc[variable_count["varname"]==row["varname"],"positions"].iloc[0][x_count]
+
+            if orientation=="horizontal":
+                ## plot bar
+                axs.vlines(x=x_position,
+                           ymin=row["coef"]-row["err"],
+                           ymax=row["coef"]+row["err"],linewidth=linewidth,color=bar_color)
+                ###  
+                plt.axhline(y=0, color='grey', linestyle='--',linewidth=linewidth/2)
+                #axs.hlines(y=0,xmin=-1,xmax=len(var_to_show)+0.5,linewidth=1,linestyle="--",color="grey")
+                ## plot markers
+                axs.plot(x_position,row["coef"], 
+                         color=mrk_color, 
+                         marker=marker, 
+                         linestyle='dashed',linewidth=linewidth, markersize=markersize)  
+            else:
+                ## plot bar
+                axs.hlines(y=x_position,
+                           xmin=row["coef"]-row["err"],
+                           xmax=row["coef"]+row["err"],linewidth=linewidth,color=bar_color)
+                ###  
+                plt.axvline(x=0, color='grey', linestyle='--',linewidth=linewidth/2)
+                #axs.hlines(y=0,xmin=-1,xmax=len(var_to_show)+0.5,linewidth=1,linestyle="--",color="grey")
+                ## plot markers
+                axs.plot(row["coef"],x_position, 
+                         color=mrk_color, 
+                         marker=marker, 
+                         linestyle='dashed',linewidth=linewidth, markersize=markersize)  
+
+        ### update the counter
+        variable_count.loc[variable_count["varname"].isin(model["varname"].unique()),"counter"]+=1
+
+    ## create legend
+    if legend==True:        
+        custom_rectangles=[Rectangle((0.2,0.2,0.2),0.2,0.2,color=v) for v in marker_colors]
+        axs.legend(custom_rectangles,list_name_model,loc=legend_position)
+
+    ### set x ticks labels
+    if xticklabels!=None:
+        axs.set_xticklabels(xticklabels)
+
+    ## set yticks labels
+    if yticklabels!=None:
+        axs.set_yticklabels(yticklabels)
+
+    plt.tight_layout()
+
+    if return_figure==True:
+        plt.close(fig)
+        return fig
+    else:
+        return None
+